Sample records for accurate molecular classification

  1. Discovering interesting molecular substructures for molecular classification.

    PubMed

    Lam, Winnie W M; Chan, Keith C C

    2010-06-01

    Given a set of molecular structure data preclassified into a number of classes, the molecular classification problem is concerned with the discovering of interesting structural patterns in the data so that "unseen" molecules not originally in the dataset can be accurately classified. To tackle the problem, interesting molecular substructures have to be discovered and this is done typically by first representing molecular structures in molecular graphs, and then, using graph-mining algorithms to discover frequently occurring subgraphs in them. These subgraphs are then used to characterize different classes for molecular classification. While such an approach can be very effective, it should be noted that a substructure that occurs frequently in one class may also does occur in another. The discovering of frequent subgraphs for molecular classification may, therefore, not always be the most effective. In this paper, we propose a novel technique called mining interesting substructures in molecular data for classification (MISMOC) that can discover interesting frequent subgraphs not just for the characterization of a molecular class but also for the distinguishing of it from the others. Using a test statistic, MISMOC screens each frequent subgraph to determine if they are interesting. For those that are interesting, their degrees of interestingness are determined using an information-theoretic measure. When classifying an unseen molecule, its structure is then matched against the interesting subgraphs in each class and a total interestingness measure for the unseen molecule to be classified into a particular class is determined, which is based on the interestingness of each matched subgraphs. The performance of MISMOC is evaluated using both artificial and real datasets, and the results show that it can be an effective approach for molecular classification.

  2. Accurate Arabic Script Language/Dialect Classification

    DTIC Science & Technology

    2014-01-01

    Army Research Laboratory Accurate Arabic Script Language/Dialect Classification by Stephen C. Tratz ARL-TR-6761 January 2014 Approved for public...1197 ARL-TR-6761 January 2014 Accurate Arabic Script Language/Dialect Classification Stephen C. Tratz Computational and Information Sciences...Include area code) Standard Form 298 (Rev. 8/98) Prescribed by ANSI Std. Z39.18 January 2014 Final Accurate Arabic Script Language/Dialect Classification

  3. [Research progress in molecular classification of gastric cancer].

    PubMed

    Zhou, Menglong; Li, Guichao; Zhang, Zhen

    2016-09-25

    Gastric cancer(GC) is a highly heterogeneous malignancy. The present widely used histopathological classifications have gradually failed to meet the needs of individualized diagnosis and treatment. Development of technologies such as microarray and next-generation sequencing (NGS) has allowed GC to be studied at the molecular level. Mechanisms about tumorigenesis and progression of GC can be elucidated in the aspects of gene mutations, chromosomal alterations, transcriptional and epigenetic changes, on the basis of which GC can be divided into several subtypes. The classifications of Tan's, Lei's, TCGA and ACRG are relatively comprehensive. Especially the TCGA and ACRG classifications have large sample size and abundant molecular profiling data, thus, the genomic characteristics of GC can be depicted more accurately. However, significant differences between both classifications still exist so that they cannot be substituted for each other. So far there is no widely accepted molecular classification of GC. Compared with TCGA classification, ACRG system may have more clinical significance in Chinese GC patients since the samples are mostly from Asian population and show better association with prognosis. The molecular classification of GC may provide the theoretical and experimental basis for early diagnosis, therapeutic efficacy prediction and treatment stratification while their clinical application is still limited. Future work should involve the application of molecular classifications in the clinical settings for improving the medical management of GC.

  4. Non-linear molecular pattern classification using molecular beacons with multiple targets.

    PubMed

    Lee, In-Hee; Lee, Seung Hwan; Park, Tai Hyun; Zhang, Byoung-Tak

    2013-12-01

    In vitro pattern classification has been highlighted as an important future application of DNA computing. Previous work has demonstrated the feasibility of linear classifiers using DNA-based molecular computing. However, complex tasks require non-linear classification capability. Here we design a molecular beacon that can interact with multiple targets and experimentally shows that its fluorescent signals form a complex radial-basis function, enabling it to be used as a building block for non-linear molecular classification in vitro. The proposed method was successfully applied to solving artificial and real-world classification problems: XOR and microRNA expression patterns. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

  5. Molecular Classification and Correlates in Colorectal Cancer

    PubMed Central

    Ogino, Shuji; Goel, Ajay

    2008-01-01

    Molecular classification of colorectal cancer is evolving. As our understanding of colorectal carcinogenesis improves, we are incorporating new knowledge into the classification system. In particular, global genomic status [microsatellite instability (MSI) status and chromosomal instability (CIN) status] and epigenomic status [CpG island methylator phenotype (CIMP) status] play a significant role in determining clinical, pathological and biological characteristics of colorectal cancer. In this review, we discuss molecular classification and molecular correlates based on MSI status and CIMP status in colorectal cancer. Studying molecular correlates is important in cancer research because it can 1) provide clues to pathogenesis, 2) propose or support the existence of a new molecular subtype, 3) alert investigators to be aware of potential confounding factors in association studies, and 4) suggest surrogate markers in clinical or research settings. PMID:18165277

  6. [Evaluation of traditional pathological classification at molecular classification era for gastric cancer].

    PubMed

    Yu, Yingyan

    2014-01-01

    Histopathological classification is in a pivotal position in both basic research and clinical diagnosis and treatment of gastric cancer. Currently, there are different classification systems in basic science and clinical application. In medical literatures, different classifications are used including Lauren and WHO systems, which have confused many researchers. Lauren classification has been proposed for half a century, but is still used worldwide. It shows many advantages of simple, easy handling with prognostic significance. The WHO classification scheme is better than Lauren classification in that it is continuously being revised according to the progress of gastric cancer, and is always used in the clinical and pathological diagnosis of common scenarios. Along with the progression of genomics, transcriptomics, proteomics, metabolomics researches, molecular classification of gastric cancer becomes the current hot topics. The traditional therapeutic approach based on phenotypic characteristics of gastric cancer will most likely be replaced with a gene variation mode. The gene-targeted therapy against the same molecular variation seems more reasonable than traditional chemical treatment based on the same morphological change.

  7. dropEst: pipeline for accurate estimation of molecular counts in droplet-based single-cell RNA-seq experiments.

    PubMed

    Petukhov, Viktor; Guo, Jimin; Baryawno, Ninib; Severe, Nicolas; Scadden, David T; Samsonova, Maria G; Kharchenko, Peter V

    2018-06-19

    Recent single-cell RNA-seq protocols based on droplet microfluidics use massively multiplexed barcoding to enable simultaneous measurements of transcriptomes for thousands of individual cells. The increasing complexity of such data creates challenges for subsequent computational processing and troubleshooting of these experiments, with few software options currently available. Here, we describe a flexible pipeline for processing droplet-based transcriptome data that implements barcode corrections, classification of cell quality, and diagnostic information about the droplet libraries. We introduce advanced methods for correcting composition bias and sequencing errors affecting cellular and molecular barcodes to provide more accurate estimates of molecular counts in individual cells.

  8. Accurate crop classification using hierarchical genetic fuzzy rule-based systems

    NASA Astrophysics Data System (ADS)

    Topaloglou, Charalampos A.; Mylonas, Stelios K.; Stavrakoudis, Dimitris G.; Mastorocostas, Paris A.; Theocharis, John B.

    2014-10-01

    This paper investigates the effectiveness of an advanced classification system for accurate crop classification using very high resolution (VHR) satellite imagery. Specifically, a recently proposed genetic fuzzy rule-based classification system (GFRBCS) is employed, namely, the Hierarchical Rule-based Linguistic Classifier (HiRLiC). HiRLiC's model comprises a small set of simple IF-THEN fuzzy rules, easily interpretable by humans. One of its most important attributes is that its learning algorithm requires minimum user interaction, since the most important learning parameters affecting the classification accuracy are determined by the learning algorithm automatically. HiRLiC is applied in a challenging crop classification task, using a SPOT5 satellite image over an intensively cultivated area in a lake-wetland ecosystem in northern Greece. A rich set of higher-order spectral and textural features is derived from the initial bands of the (pan-sharpened) image, resulting in an input space comprising 119 features. The experimental analysis proves that HiRLiC compares favorably to other interpretable classifiers of the literature, both in terms of structural complexity and classification accuracy. Its testing accuracy was very close to that obtained by complex state-of-the-art classification systems, such as the support vector machines (SVM) and random forest (RF) classifiers. Nevertheless, visual inspection of the derived classification maps shows that HiRLiC is characterized by higher generalization properties, providing more homogeneous classifications that the competitors. Moreover, the runtime requirements for producing the thematic map was orders of magnitude lower than the respective for the competitors.

  9. Molecular classification of gastric cancer.

    PubMed

    Röcken, Christoph

    2017-03-01

    Gastric cancer is among the most common cancers worldwide. Despite declining incidences, the prognosis remains dismal in Western countries and is better in Asian countries with national cancer screening programs. Complete endoscopic or surgical resection of the primary tumor with or without lymphadenectomy offers the only chance of cure in the early stage of the disease. Survival of more locally advanced gastric cancers was improved by the introduction of perioperative, adjuvant and palliative chemotherapy. However, the identification and usage of novel predictive and diagnostic targets is urgently needed. Areas covered: Recent comprehensive molecular profiling of gastric cancer proposed four molecular subtypes, i.e. Epstein-Barr virus-associated, microsatellite instable, chromosomal instable and genomically stable carcinomas. The new molecular classification will spur clinical trials exploring novel targeted therapeutics. This review summarizes recent advancements of the molecular classification, and based on that, putative pitfalls for the development of tissue-based companion diagnostics, i.e. prevalence of actionable targets and therapeutic efficacy, tumor heterogeneity and tumor evolution, impact of ethnicity on gastric cancer biology, and standards of care in the East and West. Expert commentary: The overall low prevalence of actionable targets and tumor heterogeneity are the two main obstacles of precision medicine for gastric cancer.

  10. Prostatic cancers: understanding their molecular pathology and the 2016 WHO classification

    PubMed Central

    Inamura, Kentaro

    2018-01-01

    Accumulating evidence suggests that prostatic cancers represent a group of histologically and molecularly heterogeneous diseases with variable clinical courses. In accordance with the increased knowledge of their clinicopathologies and genetics, the World Health Organization (WHO) classification of prostatic cancers has been revised. Additionally, recent data on their comprehensive molecular characterization have increased our understanding of the genomic basis of prostatic cancers and enabled us to classify them into subtypes with distinct molecular pathologies and clinical features. Our increased understanding of the molecular pathologies of prostatic cancers has permitted their evolution from a poorly understood, heterogeneous group of diseases with variable clinical courses to characteristic molecular subtypes that allow the implementation of personalized therapies and better patient management. This review provides perspectives on the new 2016 WHO classification of prostatic cancers as well as recent knowledge of their molecular pathologies. The WHO classification of prostatic cancers will require additional revisions to allow for reliable and clinically meaningful cancer diagnoses as a better understanding of their molecular characteristics is obtained. PMID:29581876

  11. Empirical evaluation of data normalization methods for molecular classification.

    PubMed

    Huang, Huei-Chung; Qin, Li-Xuan

    2018-01-01

    Data artifacts due to variations in experimental handling are ubiquitous in microarray studies, and they can lead to biased and irreproducible findings. A popular approach to correct for such artifacts is through post hoc data adjustment such as data normalization. Statistical methods for data normalization have been developed and evaluated primarily for the discovery of individual molecular biomarkers. Their performance has rarely been studied for the development of multi-marker molecular classifiers-an increasingly important application of microarrays in the era of personalized medicine. In this study, we set out to evaluate the performance of three commonly used methods for data normalization in the context of molecular classification, using extensive simulations based on re-sampling from a unique pair of microRNA microarray datasets for the same set of samples. The data and code for our simulations are freely available as R packages at GitHub. In the presence of confounding handling effects, all three normalization methods tended to improve the accuracy of the classifier when evaluated in an independent test data. The level of improvement and the relative performance among the normalization methods depended on the relative level of molecular signal, the distributional pattern of handling effects (e.g., location shift vs scale change), and the statistical method used for building the classifier. In addition, cross-validation was associated with biased estimation of classification accuracy in the over-optimistic direction for all three normalization methods. Normalization may improve the accuracy of molecular classification for data with confounding handling effects; however, it cannot circumvent the over-optimistic findings associated with cross-validation for assessing classification accuracy.

  12. Empirical evaluation of data normalization methods for molecular classification

    PubMed Central

    Huang, Huei-Chung

    2018-01-01

    Background Data artifacts due to variations in experimental handling are ubiquitous in microarray studies, and they can lead to biased and irreproducible findings. A popular approach to correct for such artifacts is through post hoc data adjustment such as data normalization. Statistical methods for data normalization have been developed and evaluated primarily for the discovery of individual molecular biomarkers. Their performance has rarely been studied for the development of multi-marker molecular classifiers—an increasingly important application of microarrays in the era of personalized medicine. Methods In this study, we set out to evaluate the performance of three commonly used methods for data normalization in the context of molecular classification, using extensive simulations based on re-sampling from a unique pair of microRNA microarray datasets for the same set of samples. The data and code for our simulations are freely available as R packages at GitHub. Results In the presence of confounding handling effects, all three normalization methods tended to improve the accuracy of the classifier when evaluated in an independent test data. The level of improvement and the relative performance among the normalization methods depended on the relative level of molecular signal, the distributional pattern of handling effects (e.g., location shift vs scale change), and the statistical method used for building the classifier. In addition, cross-validation was associated with biased estimation of classification accuracy in the over-optimistic direction for all three normalization methods. Conclusion Normalization may improve the accuracy of molecular classification for data with confounding handling effects; however, it cannot circumvent the over-optimistic findings associated with cross-validation for assessing classification accuracy. PMID:29666754

  13. A combination of molecular markers and clinical features improve the classification of pancreatic cysts.

    PubMed

    Springer, Simeon; Wang, Yuxuan; Dal Molin, Marco; Masica, David L; Jiao, Yuchen; Kinde, Isaac; Blackford, Amanda; Raman, Siva P; Wolfgang, Christopher L; Tomita, Tyler; Niknafs, Noushin; Douville, Christopher; Ptak, Janine; Dobbyn, Lisa; Allen, Peter J; Klimstra, David S; Schattner, Mark A; Schmidt, C Max; Yip-Schneider, Michele; Cummings, Oscar W; Brand, Randall E; Zeh, Herbert J; Singhi, Aatur D; Scarpa, Aldo; Salvia, Roberto; Malleo, Giuseppe; Zamboni, Giuseppe; Falconi, Massimo; Jang, Jin-Young; Kim, Sun-Whe; Kwon, Wooil; Hong, Seung-Mo; Song, Ki-Byung; Kim, Song Cheol; Swan, Niall; Murphy, Jean; Geoghegan, Justin; Brugge, William; Fernandez-Del Castillo, Carlos; Mino-Kenudson, Mari; Schulick, Richard; Edil, Barish H; Adsay, Volkan; Paulino, Jorge; van Hooft, Jeanin; Yachida, Shinichi; Nara, Satoshi; Hiraoka, Nobuyoshi; Yamao, Kenji; Hijioka, Susuma; van der Merwe, Schalk; Goggins, Michael; Canto, Marcia Irene; Ahuja, Nita; Hirose, Kenzo; Makary, Martin; Weiss, Matthew J; Cameron, John; Pittman, Meredith; Eshleman, James R; Diaz, Luis A; Papadopoulos, Nickolas; Kinzler, Kenneth W; Karchin, Rachel; Hruban, Ralph H; Vogelstein, Bert; Lennon, Anne Marie

    2015-11-01

    The management of pancreatic cysts poses challenges to both patients and their physicians. We investigated whether a combination of molecular markers and clinical information could improve the classification of pancreatic cysts and management of patients. We performed a multi-center, retrospective study of 130 patients with resected pancreatic cystic neoplasms (12 serous cystadenomas, 10 solid pseudopapillary neoplasms, 12 mucinous cystic neoplasms, and 96 intraductal papillary mucinous neoplasms). Cyst fluid was analyzed to identify subtle mutations in genes known to be mutated in pancreatic cysts (BRAF, CDKN2A, CTNNB1, GNAS, KRAS, NRAS, PIK3CA, RNF43, SMAD4, TP53, and VHL); to identify loss of heterozygozity at CDKN2A, RNF43, SMAD4, TP53, and VHL tumor suppressor loci; and to identify aneuploidy. The analyses were performed using specialized technologies for implementing and interpreting massively parallel sequencing data acquisition. An algorithm was used to select markers that could classify cyst type and grade. The accuracy of the molecular markers was compared with that of clinical markers and a combination of molecular and clinical markers. We identified molecular markers and clinical features that classified cyst type with 90%-100% sensitivity and 92%-98% specificity. The molecular marker panel correctly identified 67 of the 74 patients who did not require surgery and could, therefore, reduce the number of unnecessary operations by 91%. We identified a panel of molecular markers and clinical features that show promise for the accurate classification of cystic neoplasms of the pancreas and identification of cysts that require surgery. Copyright © 2015 AGA Institute. Published by Elsevier Inc. All rights reserved.

  14. A Combination of Molecular Markers and Clinical Features Improve the Classification of Pancreatic Cysts

    PubMed Central

    Springer, Simeon; Wang, Yuxuan; Molin, Marco Dal; Masica, David L.; Jiao, Yuchen; Kinde, Isaac; Blackford, Amanda; Raman, Siva P.; Wolfgang, Christopher L.; Tomita, Tyler; Niknafs, Noushin; Douville, Christopher; Ptak, Janine; Dobbyn, Lisa; Allen, Peter J.; Klimstra, David S.; Schattner, Mark A.; Schmidt, C. Max; Yip-Schneider, Michele; Cummings, Oscar W.; Brand, Randall E.; Zeh, Herbert J.; Singhi, Aatur D.; Scarpa, Aldo; Salvia, Roberto; Malleo, Giuseppe; Zamboni, Giuseppe; Falconi, Massimo; Jang, Jin-Young; Kim, Sun-Whe; Kwon, Wooil; Hong, Seung-Mo; Song, Ki-Byung; Kim, Song Cheol; Swan, Niall; Murphy, Jean; Geoghegan, Justin; Brugge, William; Fernandez-Del Castillo, Carlos; Mino-Kenudson, Mari; Schulick, Richard; Edil, Barish H.; Adsay, Volkan; Paulino, Jorge; van Hooft, Jeanin; Yachida, Shinichi; Nara, Satoshi; Hiraoka, Nobuyoshi; Yamao, Kenji; Hijioka, Susuma; van der Merwe, Schalk; Goggins, Michael; Canto, Marcia Irene; Ahuja, Nita; Hirose, Kenzo; Makary, Martin; Weiss, Matthew J.; Cameron, John; Pittman, Meredith; Eshleman, James R.; Diaz, Luis A.; Papadopoulos, Nickolas; Kinzler, Kenneth W.; Karchin, Rachel; Hruban, Ralph H.; Vogelstein, Bert; Lennon, Anne Marie

    2016-01-01

    Background & Aims The management of pancreatic cysts poses challenges to both patients and their physicians. We investigated whether a combination of molecular markers and clinical information could improve the classification of pancreatic cysts and management of patients. Methods We performed a multi-center, retrospective study of 130 patients with resected pancreatic cystic neoplasms (12 serous cystadenomas, 10 solid-pseudopapillary neoplasms, 12 mucinous cystic neoplasms, and 96 intraductal papillary mucinous neoplasms). Cyst fluid was analyzed to identify subtle mutations in genes known to be mutated in pancreatic cysts (BRAF, CDKN2A, CTNNB1, GNAS, KRAS, NRAS, PIK3CA, RNF43, SMAD4, TP53 and VHL); to identify loss of heterozygozity at CDKN2A, RNF43, SMAD4, TP53, and VHL tumor suppressor loci; and to identify aneuploidy. The analyses were performed using specialized technologies for implementing and interpreting massively parallel sequencing data acquisition. An algorithm was used to select markers that could classify cyst type and grade. The accuracy of the molecular markers were compared with that of clinical markers, and a combination of molecular and clinical markers. Results We identified molecular markers and clinical features that classified cyst type with 90%–100% sensitivity and 92%–98% specificity. The molecular marker panel correctly identified 67 of the 74 patients who did not require surgery, and could therefore reduce the number of unnecessary operations by 91%. Conclusions We identified a panel of molecular markers and clinical features that show promise for the accurate classification of cystic neoplasms of the pancreas and identification of cysts that require surgery. PMID:26253305

  15. Molecular classification of gastric cancer.

    PubMed

    Chia, N-Y; Tan, P

    2016-05-01

    Gastric cancer (GC), a heterogeneous disease characterized by epidemiologic and histopathologic differences across countries, is a leading cause of cancer-related death. Treatment of GC patients is currently suboptimal due to patients being commonly treated in a uniform fashion irrespective of disease subtype. With the advent of next-generation sequencing and other genomic technologies, GCs are now being investigated in great detail at the molecular level. High-throughput technologies now allow a comprehensive study of genomic and epigenomic alterations associated with GC. Gene mutations, chromosomal aberrations, differential gene expression and epigenetic alterations are some of the genetic/epigenetic influences on GC pathogenesis. In addition, integrative analyses of molecular profiling data have led to the identification of key dysregulated pathways and importantly, the establishment of GC molecular classifiers. Recently, The Cancer Genome Atlas (TCGA) network proposed a four subtype classification scheme for GC based on the underlying tumor molecular biology of each subtype. This landmark study, together with other studies, has expanded our understanding on the characteristics of GC at the molecular level. Such knowledge may improve the medical management of GC in the future. © The Author 2016. Published by Oxford University Press on behalf of the European Society for Medical Oncology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

  16. Molecular classification of endometrial carcinoma on diagnostic specimens is highly concordant with final hysterectomy: Earlier prognostic information to guide treatment.

    PubMed

    Talhouk, Aline; Hoang, Lien N; McConechy, Melissa K; Nakonechny, Quentin; Leo, Joyce; Cheng, Angela; Leung, Samuel; Yang, Winnie; Lum, Amy; Köbel, Martin; Lee, Cheng-Han; Soslow, Robert A; Huntsman, David G; Gilks, C Blake; McAlpine, Jessica N

    2016-10-01

    Categorization and risk stratification of endometrial carcinomas is inadequate; histomorphologic assessment shows considerable interobserver variability, and risk of metastases and recurrence can only be derived after surgical staging. We have developed a Proactive Molecular Risk classification tool for Endometrial cancers (ProMisE) that identifies four distinct prognostic subgroups. Our objective was to assess whether molecular classification could be performed on diagnostic endometrial specimens obtained prior to surgical staging and its concordance with molecular classification performed on the subsequent hysterectomy specimen. Sequencing of tumors for exonuclease domain mutations (EDMs) in POLE and immunohistochemistry for mismatch repair (MMR) proteins and p53 were applied to both pre- and post-staging archival specimens from 60 individuals to identify four molecular subgroups: MMR-D, POLE EDM, p53 wild type, p53 abn (abnormal). Three gynecologic subspecialty pathologists assigned histotype and grade to a subset of samples. Concordance of molecular and clinicopathologic subgroup assignments were determined, comparing biopsy/curetting to hysterectomy specimens. Complete molecular and pathologic categorization was achieved in 57 cases. Concordance metrics for pre- vs. post-staging endometrial samples categorized by ProMisE were highly favorable; average per ProMisE class sensitivity(0.9), specificity(0.96), PPV(0.9), NPV(0.96) and kappa statistic 0.86(95%CI, 0.72-0.93), indicating excellent agreement. We observed the highest level of concordance for 'p53 abn' tumors, the group associated with the worst prognosis. In contrast, grade and histotype assignment from original pathology reports pre- vs. post-staging showed only moderate levels of agreement (kappa=0.55 and 0.44 respectively); even with subspecialty pathology review only moderate levels of agreement were observed. Molecular classification can be achieved on diagnostic endometrial samples and accurately

  17. Advances in the molecular genetics of gliomas - implications for classification and therapy.

    PubMed

    Reifenberger, Guido; Wirsching, Hans-Georg; Knobbe-Thomsen, Christiane B; Weller, Michael

    2017-07-01

    Genome-wide molecular-profiling studies have revealed the characteristic genetic alterations and epigenetic profiles associated with different types of gliomas. These molecular characteristics can be used to refine glioma classification, to improve prediction of patient outcomes, and to guide individualized treatment. Thus, the WHO Classification of Tumours of the Central Nervous System was revised in 2016 to incorporate molecular biomarkers - together with classic histological features - in an integrated diagnosis, in order to define distinct glioma entities as precisely as possible. This paradigm shift is markedly changing how glioma is diagnosed, and has important implications for future clinical trials and patient management in daily practice. Herein, we highlight the developments in our understanding of the molecular genetics of gliomas, and review the current landscape of clinically relevant molecular biomarkers for use in classification of the disease subtypes. Novel approaches to the genetic characterization of gliomas based on large-scale DNA-methylation profiling and next-generation sequencing are also discussed. In addition, we illustrate how advances in the molecular genetics of gliomas can promote the development and clinical translation of novel pathogenesis-based therapeutic approaches, thereby paving the way towards precision medicine in neuro-oncology.

  18. MULTI-K: accurate classification of microarray subtypes using ensemble k-means clustering

    PubMed Central

    Kim, Eun-Youn; Kim, Seon-Young; Ashlock, Daniel; Nam, Dougu

    2009-01-01

    Background Uncovering subtypes of disease from microarray samples has important clinical implications such as survival time and sensitivity of individual patients to specific therapies. Unsupervised clustering methods have been used to classify this type of data. However, most existing methods focus on clusters with compact shapes and do not reflect the geometric complexity of the high dimensional microarray clusters, which limits their performance. Results We present a cluster-number-based ensemble clustering algorithm, called MULTI-K, for microarray sample classification, which demonstrates remarkable accuracy. The method amalgamates multiple k-means runs by varying the number of clusters and identifies clusters that manifest the most robust co-memberships of elements. In addition to the original algorithm, we newly devised the entropy-plot to control the separation of singletons or small clusters. MULTI-K, unlike the simple k-means or other widely used methods, was able to capture clusters with complex and high-dimensional structures accurately. MULTI-K outperformed other methods including a recently developed ensemble clustering algorithm in tests with five simulated and eight real gene-expression data sets. Conclusion The geometric complexity of clusters should be taken into account for accurate classification of microarray data, and ensemble clustering applied to the number of clusters tackles the problem very well. The C++ code and the data sets tested are available from the authors. PMID:19698124

  19. Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry

    NASA Astrophysics Data System (ADS)

    Alves, Gelio; Wang, Guanghui; Ogurtsov, Aleksey Y.; Drake, Steven K.; Gucek, Marjan; Sacks, David B.; Yu, Yi-Kuo

    2018-06-01

    Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is complicating correct microbial identification even in a simple sample due to the large number of candidates present. To properly untwine candidate microbes in samples containing one or more microbes, one needs to go beyond apparent morphology or simple "fingerprinting"; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptide-centric representations of microbes to better separate them and by augmenting our earlier analysis method that yields accurate statistical significance. Here, we present an updated analysis workflow that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using 226 MS/MS publicly available data files (each containing from 2500 to nearly 100,000 MS/MS spectra) and 4000 additional MS/MS data files, that the updated workflow can correctly identify multiple microbes at the genus and often the species level for samples containing more than one microbe. We have also shown that the proposed workflow computes accurate statistical significances, i.e., E values for identified peptides and unified E values for identified microbes. Our updated analysis workflow MiCId, a freely available software for Microorganism Classification and Identification, is available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.

  20. Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry.

    PubMed

    Alves, Gelio; Wang, Guanghui; Ogurtsov, Aleksey Y; Drake, Steven K; Gucek, Marjan; Sacks, David B; Yu, Yi-Kuo

    2018-06-05

    Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is complicating correct microbial identification even in a simple sample due to the large number of candidates present. To properly untwine candidate microbes in samples containing one or more microbes, one needs to go beyond apparent morphology or simple "fingerprinting"; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptide-centric representations of microbes to better separate them and by augmenting our earlier analysis method that yields accurate statistical significance. Here, we present an updated analysis workflow that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using 226 MS/MS publicly available data files (each containing from 2500 to nearly 100,000 MS/MS spectra) and 4000 additional MS/MS data files, that the updated workflow can correctly identify multiple microbes at the genus and often the species level for samples containing more than one microbe. We have also shown that the proposed workflow computes accurate statistical significances, i.e., E values for identified peptides and unified E values for identified microbes. Our updated analysis workflow MiCId, a freely available software for Microorganism Classification and Identification, is available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html . Graphical Abstract ᅟ.

  1. Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification.

    PubMed

    Verschuuren, Marlies; De Vylder, Jonas; Catrysse, Hannes; Robijns, Joke; Philips, Wilfried; De Vos, Winnok H

    2017-01-01

    A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows.

  2. Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification

    PubMed Central

    Verschuuren, Marlies; De Vylder, Jonas; Catrysse, Hannes; Robijns, Joke; Philips, Wilfried

    2017-01-01

    A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows. PMID:28125723

  3. Photometric brown-dwarf classification. I. A method to identify and accurately classify large samples of brown dwarfs without spectroscopy

    NASA Astrophysics Data System (ADS)

    Skrzypek, N.; Warren, S. J.; Faherty, J. K.; Mortlock, D. J.; Burgasser, A. J.; Hewett, P. C.

    2015-02-01

    Aims: We present a method, named photo-type, to identify and accurately classify L and T dwarfs onto the standard spectral classification system using photometry alone. This enables the creation of large and deep homogeneous samples of these objects efficiently, without the need for spectroscopy. Methods: We created a catalogue of point sources with photometry in 8 bands, ranging from 0.75 to 4.6 μm, selected from an area of 3344 deg2, by combining SDSS, UKIDSS LAS, and WISE data. Sources with 13.0 0.8, were then classified by comparison against template colours of quasars, stars, and brown dwarfs. The L and T templates, spectral types L0 to T8, were created by identifying previously known sources with spectroscopic classifications, and fitting polynomial relations between colour and spectral type. Results: Of the 192 known L and T dwarfs with reliable photometry in the surveyed area and magnitude range, 189 are recovered by our selection and classification method. We have quantified the accuracy of the classification method both externally, with spectroscopy, and internally, by creating synthetic catalogues and accounting for the uncertainties. We find that, brighter than J = 17.5, photo-type classifications are accurate to one spectral sub-type, and are therefore competitive with spectroscopic classifications. The resultant catalogue of 1157 L and T dwarfs will be presented in a companion paper.

  4. Accurate, Rapid Taxonomic Classification of Fungal Large-Subunit rRNA Genes

    PubMed Central

    Liu, Kuan-Liang; Porras-Alfaro, Andrea; Eichorst, Stephanie A.

    2012-01-01

    Taxonomic and phylogenetic fingerprinting based on sequence analysis of gene fragments from the large-subunit rRNA (LSU) gene or the internal transcribed spacer (ITS) region is becoming an integral part of fungal classification. The lack of an accurate and robust classification tool trained by a validated sequence database for taxonomic placement of fungal LSU genes is a severe limitation in taxonomic analysis of fungal isolates or large data sets obtained from environmental surveys. Using a hand-curated set of 8,506 fungal LSU gene fragments, we determined the performance characteristics of a naïve Bayesian classifier across multiple taxonomic levels and compared the classifier performance to that of a sequence similarity-based (BLASTN) approach. The naïve Bayesian classifier was computationally more rapid (>460-fold with our system) than the BLASTN approach, and it provided equal or superior classification accuracy. Classifier accuracies were compared using sequence fragments of 100 bp and 400 bp and two different PCR primer anchor points to mimic sequence read lengths commonly obtained using current high-throughput sequencing technologies. Accuracy was higher with 400-bp sequence reads than with 100-bp reads. It was also significantly affected by sequence location across the 1,400-bp test region. The highest accuracy was obtained across either the D1 or D2 variable region. The naïve Bayesian classifier provides an effective and rapid means to classify fungal LSU sequences from large environmental surveys. The training set and tool are publicly available through the Ribosomal Database Project (http://rdp.cme.msu.edu/classifier/classifier.jsp). PMID:22194300

  5. Novel high/low solubility classification methods for new molecular entities.

    PubMed

    Dave, Rutwij A; Morris, Marilyn E

    2016-09-10

    This research describes a rapid solubility classification approach that could be used in the discovery and development of new molecular entities. Compounds (N=635) were divided into two groups based on information available in the literature: high solubility (BDDCS/BCS 1/3) and low solubility (BDDCS/BCS 2/4). We established decision rules for determining solubility classes using measured log solubility in molar units (MLogSM) or measured solubility (MSol) in mg/ml units. ROC curve analysis was applied to determine statistically significant threshold values of MSol and MLogSM. Results indicated that NMEs with MLogSM>-3.05 or MSol>0.30mg/mL will have ≥85% probability of being highly soluble and new molecular entities with MLogSM≤-3.05 or MSol≤0.30mg/mL will have ≥85% probability of being poorly soluble. When comparing solubility classification using the threshold values of MLogSM or MSol with BDDCS, we were able to correctly classify 85% of compounds. We also evaluated solubility classification of an independent set of 108 orally administered drugs using MSol (0.3mg/mL) and our method correctly classified 81% and 95% of compounds into high and low solubility classes, respectively. The high/low solubility classification using MLogSM or MSol is novel and independent of traditionally used dose number criteria. Copyright © 2016 Elsevier B.V. All rights reserved.

  6. Machine Learning of Accurate Energy-Conserving Molecular Force Fields

    NASA Astrophysics Data System (ADS)

    Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel; Poltavsky, Igor; Schütt, Kristof; Müller, Klaus-Robert; GDML Collaboration

    Efficient and accurate access to the Born-Oppenheimer potential energy surface (PES) is essential for long time scale molecular dynamics (MD) simulations. Using conservation of energy - a fundamental property of closed classical and quantum mechanical systems - we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio MD trajectories (AIMD). The GDML implementation is able to reproduce global potential-energy surfaces of intermediate-size molecules with an accuracy of 0.3 kcal/mol for energies and 1 kcal/mol/Å for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, malonaldehyde, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative MD simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.

  7. Molecular classification of soft tissue sarcomas and its clinical applications

    PubMed Central

    Jain, Shilpa; Xu, Ruliang; Prieto, Victor G; Lee, Peng

    2010-01-01

    Sarcomas are a heterogeneous group of tumors that are traditionally classified according to the morphology and type of tissue that they resemble, such as rhabdomyosarcoma, which resembles skeletal muscle. However, the cell of origin is unclear in numerous sarcomas. Molecular genetics analyses have not only assisted in understanding the molecular mechanism in sarcoma pathogenesis but also demonstrated new relationships within different types of sarcomas leading to a more proper classification of sarcomas. Molecular classification based on the genetic alteration divides sarcomas into two main categories: (i) sarcomas with specific genetic alterations; which can further be subclassified based on a) reciprocal translocations resulting in oncogenic fusion transcripts (e.g. EWSR1-FLI1 in Ewing sarcoma) and b) specific oncogenic mutations (e.g. KIT and PDGFRA mutations in gastrointestinal stromal tumors) and (ii) sarcomas displaying multiple, complex karyotypic abnormalities with no specific pattern, including leiomyo-sarcoma, and pleomorphic liposarcoma. These specific genetic alterations are an important adjunct to standard morphological and immunohistochemical diagnoses, and in some cases have a prognostic value, e. g., Ewing family tumors, synovial sarcoma, and alveolar rhabdomyosarcoma. In addition, these studies may also serve as markers to detect minimal residual disease and can aid in staging or monitor the efficacy of therapy. Furthermore, sarcoma-specific fusion genes and other emerging molecular events may also represent potential targets for novel therapeutic approaches such as Gleevec for dermatofibrosarcoma protuberans. Therefore, increased understanding of the molecular biology of sarcomas is leading towards development of newer and more effective treatment regimens. The review focuses on recent advances in molecular genetic alterations having an impact on diagnostics, prognostication and clinical management of selected sarcomas. PMID:20490332

  8. Serrated colorectal cancer: Molecular classification, prognosis, and response to chemotherapy

    PubMed Central

    Murcia, Oscar; Juárez, Miriam; Hernández-Illán, Eva; Egoavil, Cecilia; Giner-Calabuig, Mar; Rodríguez-Soler, María; Jover, Rodrigo

    2016-01-01

    Molecular advances support the existence of an alternative pathway of colorectal carcinogenesis that is based on the hypermethylation of specific DNA regions that silences tumor suppressor genes. This alternative pathway has been called the serrated pathway due to the serrated appearance of tumors in histological analysis. New classifications for colorectal cancer (CRC) were proposed recently based on genetic profiles that show four types of molecular alterations: BRAF gene mutations, KRAS gene mutations, microsatellite instability, and hypermethylation of CpG islands. This review summarizes what is known about the serrated pathway of CRC, including CRC molecular and clinical features, prognosis, and response to chemotherapy. PMID:27053844

  9. Integration of Chinese medicine with Western medicine could lead to future medicine: molecular module medicine.

    PubMed

    Zhang, Chi; Zhang, Ge; Chen, Ke-ji; Lu, Ai-ping

    2016-04-01

    The development of an effective classification method for human health conditions is essential for precise diagnosis and delivery of tailored therapy to individuals. Contemporary classification of disease systems has properties that limit its information content and usability. Chinese medicine pattern classification has been incorporated with disease classification, and this integrated classification method became more precise because of the increased understanding of the molecular mechanisms. However, we are still facing the complexity of diseases and patterns in the classification of health conditions. With continuing advances in omics methodologies and instrumentation, we are proposing a new classification approach: molecular module classification, which is applying molecular modules to classifying human health status. The initiative would be precisely defining the health status, providing accurate diagnoses, optimizing the therapeutics and improving new drug discovery strategy. Therefore, there would be no current disease diagnosis, no disease pattern classification, and in the future, a new medicine based on this classification, molecular module medicine, could redefine health statuses and reshape the clinical practice.

  10. Accurate Evaluation Method of Molecular Binding Affinity from Fluctuation Frequency

    NASA Astrophysics Data System (ADS)

    Hoshino, Tyuji; Iwamoto, Koji; Ode, Hirotaka; Ohdomari, Iwao

    2008-05-01

    Exact estimation of the molecular binding affinity is significantly important for drug discovery. The energy calculation is a direct method to compute the strength of the interaction between two molecules. This energetic approach is, however, not accurate enough to evaluate a slight difference in binding affinity when distinguishing a prospective substance from dozens of candidates for medicine. Hence more accurate estimation of drug efficacy in a computer is currently demanded. Previously we proposed a concept of estimating molecular binding affinity, focusing on the fluctuation at an interface between two molecules. The aim of this paper is to demonstrate the compatibility between the proposed computational technique and experimental measurements, through several examples for computer simulations of an association of human immunodeficiency virus type-1 (HIV-1) protease and its inhibitor (an example for a drug-enzyme binding), a complexation of an antigen and its antibody (an example for a protein-protein binding), and a combination of estrogen receptor and its ligand chemicals (an example for a ligand-receptor binding). The proposed affinity estimation has proven to be a promising technique in the advanced stage of the discovery and the design of drugs.

  11. An NRG Oncology/GOG study of molecular classification for risk prediction in endometrioid endometrial cancer

    PubMed Central

    Cosgrove, Casey M; Tritchler, David L; Cohn, David E; Mutch, David G; Rush, Craig M; Lankes, Heather A; Creasman, William T.; Miller, David S; Ramirez, Nilsa C; Geller, Melissa A; Powell, Matthew A; Backes, Floor J; Landrum, Lisa M; Timmers, Cynthia; Suarez, Adrian A; Zaino, Richard J; Pearl, Michael L; DiSilvestro, Paul A; Lele, Shashikant B; Goodfellow, Paul J

    2017-01-01

    Objectives The purpose of this study was to assess the prognostic significance of a simplified, clinically accessible classification system for endometrioid endometrial cancers combining Lynch syndrome screening and molecular risk stratification. Methods Tumors from NRG/GOG GOG210 were evaluated for mismatch repair defects (MSI, MMR IHC, and MLH1 methylation), POLE mutations, and loss of heterozygosity. TP53 was evaluated in a subset of cases. Tumors were assigned to four molecular classes. Relationships between molecular classes and clinicopathologic variables were assessed using contingency tests and Cox proportional methods. Results Molecular classification was successful for 982 tumors. Based on the NCI consensus MSI panel assessing MSI and loss of heterozygosity combined with POLE testing, 49% of tumors were classified copy number stable (CNS), 39% MMR deficient, 8% copy number altered (CNA) and 4% POLE mutant. Cancer-specific mortality occurred in 5% of patients with CNS tumors; 2.6% with POLE tumors; 7.6% with MMR deficient tumors and 19% with CNA tumors. The CNA group had worse progression-free (HR 2.31, 95%CI 1.53–3.49) and cancer-specific survival (HR 3.95; 95%CI 2.10–7.44). The POLE group had improved outcomes, but the differences were not statistically significant. CNA class remained significant for cancer-specific survival (HR 2.11; 95%CI 1.04–4.26) in multivariable analysis. The CNA molecular class was associated with TP53 mutation and expression status. Conclusions A simple molecular classification for endometrioid endometrial cancers that can be easily combined with Lynch syndrome screening provides important prognostic information. These findings support prospective clinical validation and further studies on the predictive value of a simplified molecular classification system. PMID:29132872

  12. Machine learning of accurate energy-conserving molecular force fields.

    PubMed

    Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E; Poltavsky, Igor; Schütt, Kristof T; Müller, Klaus-Robert

    2017-05-01

    Using conservation of energy-a fundamental property of closed classical and quantum mechanical systems-we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol -1 for energies and 1 kcal mol -1 Å̊ -1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.

  13. Machine learning of accurate energy-conserving molecular force fields

    PubMed Central

    Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E.; Poltavsky, Igor; Schütt, Kristof T.; Müller, Klaus-Robert

    2017-01-01

    Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems—we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol−1 for energies and 1 kcal mol−1 Å̊−1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods. PMID:28508076

  14. Molecular Pathological Classification of Neurodegenerative Diseases: Turning towards Precision Medicine.

    PubMed

    Kovacs, Gabor G

    2016-02-02

    Neurodegenerative diseases (NDDs) are characterized by selective dysfunction and loss of neurons associated with pathologically altered proteins that deposit in the human brain but also in peripheral organs. These proteins and their biochemical modifications can be potentially targeted for therapy or used as biomarkers. Despite a plethora of modifications demonstrated for different neurodegeneration-related proteins, such as amyloid-β, prion protein, tau, α-synuclein, TAR DNA-binding protein 43 (TDP-43), or fused in sarcoma protein (FUS), molecular classification of NDDs relies on detailed morphological evaluation of protein deposits, their distribution in the brain, and their correlation to clinical symptoms together with specific genetic alterations. A further facet of the neuropathology-based classification is the fact that many protein deposits show a hierarchical involvement of brain regions. This has been shown for Alzheimer and Parkinson disease and some forms of tauopathies and TDP-43 proteinopathies. The present paper aims to summarize current molecular classification of NDDs, focusing on the most relevant biochemical and morphological aspects. Since the combination of proteinopathies is frequent, definition of novel clusters of patients with NDDs needs to be considered in the era of precision medicine. Optimally, neuropathological categorizing of NDDs should be translated into in vivo detectable biomarkers to support better prediction of prognosis and stratification of patients for therapy trials.

  15. Molecular Pathological Classification of Neurodegenerative Diseases: Turning towards Precision Medicine

    PubMed Central

    Kovacs, Gabor G.

    2016-01-01

    Neurodegenerative diseases (NDDs) are characterized by selective dysfunction and loss of neurons associated with pathologically altered proteins that deposit in the human brain but also in peripheral organs. These proteins and their biochemical modifications can be potentially targeted for therapy or used as biomarkers. Despite a plethora of modifications demonstrated for different neurodegeneration-related proteins, such as amyloid-β, prion protein, tau, α-synuclein, TAR DNA-binding protein 43 (TDP-43), or fused in sarcoma protein (FUS), molecular classification of NDDs relies on detailed morphological evaluation of protein deposits, their distribution in the brain, and their correlation to clinical symptoms together with specific genetic alterations. A further facet of the neuropathology-based classification is the fact that many protein deposits show a hierarchical involvement of brain regions. This has been shown for Alzheimer and Parkinson disease and some forms of tauopathies and TDP-43 proteinopathies. The present paper aims to summarize current molecular classification of NDDs, focusing on the most relevant biochemical and morphological aspects. Since the combination of proteinopathies is frequent, definition of novel clusters of patients with NDDs needs to be considered in the era of precision medicine. Optimally, neuropathological categorizing of NDDs should be translated into in vivo detectable biomarkers to support better prediction of prognosis and stratification of patients for therapy trials. PMID:26848654

  16. Obtaining Accurate Probabilities Using Classifier Calibration

    ERIC Educational Resources Information Center

    Pakdaman Naeini, Mahdi

    2016-01-01

    Learning probabilistic classification and prediction models that generate accurate probabilities is essential in many prediction and decision-making tasks in machine learning and data mining. One way to achieve this goal is to post-process the output of classification models to obtain more accurate probabilities. These post-processing methods are…

  17. Pathological Bases for a Robust Application of Cancer Molecular Classification

    PubMed Central

    Diaz-Cano, Salvador J.

    2015-01-01

    Any robust classification system depends on its purpose and must refer to accepted standards, its strength relying on predictive values and a careful consideration of known factors that can affect its reliability. In this context, a molecular classification of human cancer must refer to the current gold standard (histological classification) and try to improve it with key prognosticators for metastatic potential, staging and grading. Although organ-specific examples have been published based on proteomics, transcriptomics and genomics evaluations, the most popular approach uses gene expression analysis as a direct correlate of cellular differentiation, which represents the key feature of the histological classification. RNA is a labile molecule that varies significantly according with the preservation protocol, its transcription reflect the adaptation of the tumor cells to the microenvironment, it can be passed through mechanisms of intercellular transference of genetic information (exosomes), and it is exposed to epigenetic modifications. More robust classifications should be based on stable molecules, at the genetic level represented by DNA to improve reliability, and its analysis must deal with the concept of intratumoral heterogeneity, which is at the origin of tumor progression and is the byproduct of the selection process during the clonal expansion and progression of neoplasms. The simultaneous analysis of multiple DNA targets and next generation sequencing offer the best practical approach for an analytical genomic classification of tumors. PMID:25898411

  18. Molecular classification and molecular forecasting of breast cancer: ready for clinical application?

    PubMed

    Brenton, James D; Carey, Lisa A; Ahmed, Ahmed Ashour; Caldas, Carlos

    2005-10-10

    Profiling breast cancer with expression arrays has become common, and it has been suggested that the results from early studies will lead to understanding of the molecular differences between clinical cases and allow individualization of care. We critically review two main applications of expression profiling; studies unraveling novel breast cancer classifications and those that aim to identify novel markers for prediction of clinical outcome. Breast cancer may now be subclassified into luminal, basal, and HER2 subtypes with distinct differences in prognosis and response to therapy. However, profiling studies to identify predictive markers have suffered from methodologic problems that prevent general application of their results. Future work will need to reanalyze existing microarray data sets to identify more representative sets of candidate genes for use as prognostic signatures and will need to take into account the new knowledge of molecular subtypes of breast cancer when assessing predictive effects.

  19. Integrating molecular markers into the World Health Organization classification of CNS tumors: a survey of the neuro-oncology community.

    PubMed

    Aldape, Kenneth; Nejad, Romina; Louis, David N; Zadeh, Gelareh

    2017-03-01

    Molecular markers provide important biological and clinical information related to the classification of brain tumors, and the integration of relevant molecular parameters into brain tumor classification systems has been a widely discussed topic in neuro-oncology over the past decade. With recent advances in the development of clinically relevant molecular signatures and the 2016 World Health Organization (WHO) update, the views of the neuro-oncology community on such changes would be informative for implementing this process. A survey with 8 questions regarding molecular markers in tumor classification was sent to an email list of Society for Neuro-Oncology members and attendees of prior meetings (n=5065). There were 403 respondents. Analysis was performed using whole group response, based on self-reported subspecialty. The survey results show overall strong support for incorporating molecular knowledge into the classification and clinical management of brain tumors. Across all 7 subspecialty groups, ≥70% of respondents agreed to this integration. Interestingly, some variability is seen among subspecialties, notably with lowest support from neuropathologists, which may reflect their roles in implementing such diagnostic technologies. Based on a survey provided to the neuro-oncology community, we report strong support for the integration of molecular markers into the WHO classification of brain tumors, as well as for using an integrated "layered" diagnostic format. While membership from each specialty showed support, there was variation by specialty in enthusiasm regarding proposed changes. The initial results of this survey influenced the deliberations underlying the 2016 WHO classification of tumors of the central nervous system. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.

  20. Molecular classification of gastric cancer: Towards a pathway-driven targeted therapy

    PubMed Central

    Espinoza, Jaime A.; Weber, Helga; García, Patricia; Nervi, Bruno; Garrido, Marcelo; Corvalán, Alejandro H.; Roa, Juan Carlos; Bizama, Carolina

    2015-01-01

    Gastric cancer (GC) is the third leading cause of cancer mortality worldwide. Although surgical resection is a potentially curative approach for localized cases of GC, most cases of GC are diagnosed in an advanced, non-curable stage and the response to traditional chemotherapy is limited. Fortunately, recent advances in our understanding of the molecular mechanisms that mediate GC hold great promise for the development of more effective treatment strategies. In this review, an overview of the morphological classification, current treatment approaches, and molecular alterations that have been characterized for GC are provided. In particular, the most recent molecular classification of GC and alterations identified in relevant signaling pathways, including ErbB, VEGF, PI3K/AKT/mTOR, and HGF/MET signaling pathways, are described, as well as inhibitors of these pathways. An overview of the completed and active clinical trials related to these signaling pathways are also summarized. Finally, insights regarding emerging stem cell pathways are described, and may provide additional novel markers for the development of therapeutic agents against GC. The development of more effective agents and the identification of biomarkers that can be used for the diagnosis, prognosis, and individualized therapy for GC patients, have the potential to improve the efficacy, safety, and cost-effectiveness for GC treatments. PMID:26267324

  1. [New molecular classification of colorectal cancer, pancreatic cancer and stomach cancer: Towards "à la carte" treatment?].

    PubMed

    Dreyer, Chantal; Afchain, Pauline; Trouilloud, Isabelle; André, Thierry

    2016-01-01

    This review reports 3 of recently published molecular classifications of the 3 main gastro-intestinal cancers: gastric, pancreatic and colorectal adenocarcinoma. In colorectal adenocarcinoma, 6 independent classifications were combined to finally hold 4 molecular sub-groups, Consensus Molecular Subtypes (CMS 1-4), linked to various clinical, molecular and survival data. CMS1 (14% MSI with immune activation); CMS2 (37%: canonical with epithelial differentiation and activation of the WNT/MYC pathway); CMS3 (13% metabolic with epithelial differentiation and RAS mutation); CMS4 (23%: mesenchymal with activation of TGFβ pathway and angiogenesis with stromal invasion). In gastric adenocarcinoma, 4 groups were established: subtype "EBV" (9%, high frequency of PIK3CA mutations, hypermetylation and amplification of JAK2, PD-L1 and PD-L2), subtype "MSI" (22%, high rate of mutation), subtype "genomically stable tumor" (20%, diffuse histology type and mutations of RAS and genes encoding integrins and adhesion proteins including CDH1) and subtype "tumors with chromosomal instability" (50%, intestinal type, aneuploidy and receptor tyrosine kinase amplification). In pancreatic adenocarcinomas, a classification in four sub-groups has been proposed, stable subtype (20%, aneuploidy), locally rearranged subtype (30%, focal event on one or two chromosoms), scattered subtype (36%,<200 structural variation events), and unstable subtype (14%,>200 structural variation events, defects in DNA maintenance). Although currently away from the care of patients, these classifications open the way to "à la carte" treatment depending on molecular biology. Copyright © 2016 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

  2. Molecular classification of breast cancer: what the pathologist needs to know.

    PubMed

    Rakha, Emad A; Green, Andrew R

    2017-02-01

    Breast cancer is a heterogeneous disease featuring distinct histological, molecular and clinical phenotypes. Although traditional classification systems utilising clinicopathological and few molecular markers are well established and validated, they remain insufficient to reflect the diverse biological and clinical heterogeneity of breast cancer. Advancements in high-throughput molecular techniques and bioinformatics have contributed to the improved understanding of breast cancer biology, refinement of molecular taxonomies and the development of novel prognostic and predictive molecular assays. Application of such technologies is already underway, and is expected to change the way we manage breast cancer. Despite the enormous amount of work that has been carried out to develop and refine breast cancer molecular prognostic and predictive assays, molecular testing is still in evolution. Pathologists should be aware of the new technology and be ready for the challenge. In this review, we provide an update on the application of molecular techniques with regard to breast cancer diagnosis, prognosis and outcome prediction. The current contribution of emerging technology to our understanding of breast cancer is also highlighted. Copyright © 2016 Royal College of Pathologists of Australasia. Published by Elsevier B.V. All rights reserved.

  3. Molecular Classification of Low-Grade Diffuse Gliomas

    PubMed Central

    Kim, Young-Ho; Nobusawa, Sumihito; Mittelbronn, Michel; Paulus, Werner; Brokinkel, Benjamin; Keyvani, Kathy; Sure, Ulrich; Wrede, Karsten; Nakazato, Yoichi; Tanaka, Yuko; Vital, Anne; Mariani, Luigi; Stawski, Robert; Watanabe, Takuya; De Girolami, Umberto; Kleihues, Paul; Ohgaki, Hiroko

    2010-01-01

    The current World Health Organization classification recognizes three histological types of grade II low-grade diffuse glioma (diffuse astrocytoma, oligoastrocytoma, and oligodendroglioma). However, the diagnostic criteria, in particular for oligoastrocytoma, are highly subjective. The aim of our study was to establish genetic profiles for diffuse gliomas and to estimate their predictive impact. In this study, we screened 360 World Health Organization grade II gliomas for mutations in the IDH1, IDH2, and TP53 genes and for 1p/19q loss and correlated these with clinical outcome. Most tumors (86%) were characterized genetically by TP53 mutation plus IDH1/2 mutation (32%), 1p/19q loss plus IDH1/2 mutation (37%), or IDH1/2 mutation only (17%). TP53 mutations only or 1p/19q loss only was rare (2 and 3%, respectively). The median survival of patients with TP53 mutation ± IDH1/2 mutation was significantly shorter than that of patients with 1p/19q loss ± IDH1/2 mutation (51.8 months vs. 58.7 months, respectively; P = 0.0037). Multivariate analysis with adjustment for age and treatment confirmed these results (P = 0.0087) and also revealed that TP53 mutation is a significant prognostic marker for shorter survival (P = 0.0005) and 1p/19q loss for longer survival (P = 0.0002), while IDH1/2 mutations are not prognostic (P = 0.8737). The molecular classification on the basis of IDH1/2 mutation, TP53 mutation, and 1p/19q loss has power similar to histological classification and avoids the ambiguity inherent to the diagnosis of oligoastrocytoma. PMID:21075857

  4. Molecular acidity: An accurate description with information-theoretic approach in density functional reactivity theory.

    PubMed

    Cao, Xiaofang; Rong, Chunying; Zhong, Aiguo; Lu, Tian; Liu, Shubin

    2018-01-15

    Molecular acidity is one of the important physiochemical properties of a molecular system, yet its accurate calculation and prediction are still an unresolved problem in the literature. In this work, we propose to make use of the quantities from the information-theoretic (IT) approach in density functional reactivity theory and provide an accurate description of molecular acidity from a completely new perspective. To illustrate our point, five different categories of acidic series, singly and doubly substituted benzoic acids, singly substituted benzenesulfinic acids, benzeneseleninic acids, phenols, and alkyl carboxylic acids, have been thoroughly examined. We show that using IT quantities such as Shannon entropy, Fisher information, Ghosh-Berkowitz-Parr entropy, information gain, Onicescu information energy, and relative Rényi entropy, one is able to simultaneously predict experimental pKa values of these different categories of compounds. Because of the universality of the quantities employed in this work, which are all density dependent, our approach should be general and be applicable to other systems as well. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  5. The Transporter Classification Database: recent advances.

    PubMed

    Saier, Milton H; Yen, Ming Ren; Noto, Keith; Tamang, Dorjee G; Elkan, Charles

    2009-01-01

    The Transporter Classification Database (TCDB), freely accessible at http://www.tcdb.org, is a relational database containing sequence, structural, functional and evolutionary information about transport systems from a variety of living organisms, based on the International Union of Biochemistry and Molecular Biology-approved transporter classification (TC) system. It is a curated repository for factual information compiled largely from published references. It uses a functional/phylogenetic system of classification, and currently encompasses about 5000 representative transporters and putative transporters in more than 500 families. We here describe novel software designed to support and extend the usefulness of TCDB. Our recent efforts render it more user friendly, incorporate machine learning to input novel data in a semiautomatic fashion, and allow analyses that are more accurate and less time consuming. The availability of these tools has resulted in recognition of distant phylogenetic relationships and tremendous expansion of the information available to TCDB users.

  6. Use of multivariate analysis to suggest a new molecular classification of colorectal cancer

    PubMed Central

    Domingo, Enric; Ramamoorthy, Rajarajan; Oukrif, Dahmane; Rosmarin, Daniel; Presz, Michal; Wang, Haitao; Pulker, Hannah; Lockstone, Helen; Hveem, Tarjei; Cranston, Treena; Danielsen, Havard; Novelli, Marco; Davidson, Brian; Xu, Zheng-Zhou; Molloy, Peter; Johnstone, Elaine; Holmes, Christopher; Midgley, Rachel; Kerr, David; Sieber, Oliver; Tomlinson, Ian

    2013-01-01

    Abstract Molecular classification of colorectal cancer (CRC) is currently based on microsatellite instability (MSI), KRAS or BRAF mutation and, occasionally, chromosomal instability (CIN). Whilst useful, these categories may not fully represent the underlying molecular subgroups. We screened 906 stage II/III CRCs from the VICTOR clinical trial for somatic mutations. Multivariate analyses (logistic regression, clustering, Bayesian networks) identified the primary molecular associations. Positive associations occurred between: CIN and TP53 mutation; MSI and BRAF mutation; and KRAS and PIK3CA mutations. Negative associations occurred between: MSI and CIN; MSI and NRAS mutation; and KRAS mutation, and each of NRAS, TP53 and BRAF mutations. Some complex relationships were elucidated: KRAS and TP53 mutations had both a direct negative association and a weaker, confounding, positive association via TP53–CIN–MSI–BRAF–KRAS. Our results suggested a new molecular classification of CRCs: (1) MSI+ and/or BRAF-mutant; (2) CIN+ and/or TP53– mutant, with wild-type KRAS and PIK3CA; (3) KRAS- and/or PIK3CA-mutant, CIN+, TP53-wild-type; (4) KRAS– and/or PIK3CA-mutant, CIN–, TP53-wild-type; (5) NRAS-mutant; (6) no mutations; (7) others. As expected, group 1 cancers were mostly proximal and poorly differentiated, usually occurring in women. Unexpectedly, two different types of CIN+ CRC were found: group 2 cancers were usually distal and occurred in men, whereas group 3 showed neither of these associations but were of higher stage. CIN+ cancers have conventionally been associated with all three of these variables, because they have been tested en masse. Our classification also showed potentially improved prognostic capabilities, with group 3, and possibly group 1, independently predicting disease-free survival. Copyright © 2012 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd. PMID:23165447

  7. Conversion of calibration curves for accurate estimation of molecular weight averages and distributions of polyether polyols by conventional size exclusion chromatography.

    PubMed

    Xu, Xiuqing; Yang, Xiuhan; Martin, Steven J; Mes, Edwin; Chen, Junlan; Meunier, David M

    2018-08-17

    Accurate measurement of molecular weight averages (M¯ n, M¯ w, M¯ z ) and molecular weight distributions (MWD) of polyether polyols by conventional SEC (size exclusion chromatography) is not as straightforward as it would appear. Conventional calibration with polystyrene (PS) standards can only provide PS apparent molecular weights which do not provide accurate estimates of polyol molecular weights. Using polyethylene oxide/polyethylene glycol (PEO/PEG) for molecular weight calibration could improve the accuracy, but the retention behavior of PEO/PEG is not stable in THF-based (tetrahydrofuran) SEC systems. In this work, two approaches for calibration curve conversion with narrow PS and polyol molecular weight standards were developed. Equations to convert PS-apparent molecular weight to polyol-apparent molecular weight were developed using both a rigorous mathematical analysis and graphical plot regression method. The conversion equations obtained by the two approaches were in good agreement. Factors influencing the conversion equation were investigated. It was concluded that the separation conditions such as column batch and operating temperature did not have significant impact on the conversion coefficients and a universal conversion equation could be obtained. With this conversion equation, more accurate estimates of molecular weight averages and MWDs for polyether polyols can be achieved from conventional PS-THF SEC calibration. Moreover, no additional experimentation is required to convert historical PS equivalent data to reasonably accurate molecular weight results. Copyright © 2018. Published by Elsevier B.V.

  8. Molecular cancer classification using a meta-sample-based regularized robust coding method.

    PubMed

    Wang, Shu-Lin; Sun, Liuchao; Fang, Jianwen

    2014-01-01

    Previous studies have demonstrated that machine learning based molecular cancer classification using gene expression profiling (GEP) data is promising for the clinic diagnosis and treatment of cancer. Novel classification methods with high efficiency and prediction accuracy are still needed to deal with high dimensionality and small sample size of typical GEP data. Recently the sparse representation (SR) method has been successfully applied to the cancer classification. Nevertheless, its efficiency needs to be improved when analyzing large-scale GEP data. In this paper we present the meta-sample-based regularized robust coding classification (MRRCC), a novel effective cancer classification technique that combines the idea of meta-sample-based cluster method with regularized robust coding (RRC) method. It assumes that the coding residual and the coding coefficient are respectively independent and identically distributed. Similar to meta-sample-based SR classification (MSRC), MRRCC extracts a set of meta-samples from the training samples, and then encodes a testing sample as the sparse linear combination of these meta-samples. The representation fidelity is measured by the l2-norm or l1-norm of the coding residual. Extensive experiments on publicly available GEP datasets demonstrate that the proposed method is more efficient while its prediction accuracy is equivalent to existing MSRC-based methods and better than other state-of-the-art dimension reduction based methods.

  9. Analysis of A Drug Target-based Classification System using Molecular Descriptors.

    PubMed

    Lu, Jing; Zhang, Pin; Bi, Yi; Luo, Xiaomin

    2016-01-01

    Drug-target interaction is an important topic in drug discovery and drug repositioning. KEGG database offers a drug annotation and classification using a target-based classification system. In this study, we gave an investigation on five target-based classes: (I) G protein-coupled receptors; (II) Nuclear receptors; (III) Ion channels; (IV) Enzymes; (V) Pathogens, using molecular descriptors to represent each drug compound. Two popular feature selection methods, maximum relevance minimum redundancy and incremental feature selection, were adopted to extract the important descriptors. Meanwhile, an optimal prediction model based on nearest neighbor algorithm was constructed, which got the best result in identifying drug target-based classes. Finally, some key descriptors were discussed to uncover their important roles in the identification of drug-target classes.

  10. A machine learning approach for viral genome classification.

    PubMed

    Remita, Mohamed Amine; Halioui, Ahmed; Malick Diouara, Abou Abdallah; Daigle, Bruno; Kiani, Golrokh; Diallo, Abdoulaye Baniré

    2017-04-11

    Advances in cloning and sequencing technology are yielding a massive number of viral genomes. The classification and annotation of these genomes constitute important assets in the discovery of genomic variability, taxonomic characteristics and disease mechanisms. Existing classification methods are often designed for specific well-studied family of viruses. Thus, the viral comparative genomic studies could benefit from more generic, fast and accurate tools for classifying and typing newly sequenced strains of diverse virus families. Here, we introduce a virus classification platform, CASTOR, based on machine learning methods. CASTOR is inspired by a well-known technique in molecular biology: restriction fragment length polymorphism (RFLP). It simulates, in silico, the restriction digestion of genomic material by different enzymes into fragments. It uses two metrics to construct feature vectors for machine learning algorithms in the classification step. We benchmark CASTOR for the classification of distinct datasets of human papillomaviruses (HPV), hepatitis B viruses (HBV) and human immunodeficiency viruses type 1 (HIV-1). Results reveal true positive rates of 99%, 99% and 98% for HPV Alpha species, HBV genotyping and HIV-1 M subtyping, respectively. Furthermore, CASTOR shows a competitive performance compared to well-known HIV-1 specific classifiers (REGA and COMET) on whole genomes and pol fragments. The performance of CASTOR, its genericity and robustness could permit to perform novel and accurate large scale virus studies. The CASTOR web platform provides an open access, collaborative and reproducible machine learning classifiers. CASTOR can be accessed at http://castor.bioinfo.uqam.ca .

  11. Accurate mobile malware detection and classification in the cloud.

    PubMed

    Wang, Xiaolei; Yang, Yuexiang; Zeng, Yingzhi

    2015-01-01

    As the dominator of the Smartphone operating system market, consequently android has attracted the attention of s malware authors and researcher alike. The number of types of android malware is increasing rapidly regardless of the considerable number of proposed malware analysis systems. In this paper, by taking advantages of low false-positive rate of misuse detection and the ability of anomaly detection to detect zero-day malware, we propose a novel hybrid detection system based on a new open-source framework CuckooDroid, which enables the use of Cuckoo Sandbox's features to analyze Android malware through dynamic and static analysis. Our proposed system mainly consists of two parts: anomaly detection engine performing abnormal apps detection through dynamic analysis; signature detection engine performing known malware detection and classification with the combination of static and dynamic analysis. We evaluate our system using 5560 malware samples and 6000 benign samples. Experiments show that our anomaly detection engine with dynamic analysis is capable of detecting zero-day malware with a low false negative rate (1.16 %) and acceptable false positive rate (1.30 %); it is worth noting that our signature detection engine with hybrid analysis can accurately classify malware samples with an average positive rate 98.94 %. Considering the intensive computing resources required by the static and dynamic analysis, our proposed detection system should be deployed off-device, such as in the Cloud. The app store markets and the ordinary users can access our detection system for malware detection through cloud service.

  12. Classification of lymphoid neoplasms: the microscope as a tool for disease discovery

    PubMed Central

    Harris, Nancy Lee; Stein, Harald; Isaacson, Peter G.

    2008-01-01

    In the past 50 years, we have witnessed explosive growth in the understanding of normal and neoplastic lymphoid cells. B-cell, T-cell, and natural killer (NK)–cell neoplasms in many respects recapitulate normal stages of lymphoid cell differentiation and function, so that they can be to some extent classified according to the corresponding normal stage. Likewise, the molecular mechanisms involved the pathogenesis of lymphomas and lymphoid leukemias are often based on the physiology of the lymphoid cells, capitalizing on deregulated normal physiology by harnessing the promoters of genes essential for lymphocyte function. The clinical manifestations of lymphomas likewise reflect the normal function of lymphoid cells in vivo. The multiparameter approach to classification adopted by the World Health Organization (WHO) classification has been validated in international studies as being highly reproducible, and enhancing the interpretation of clinical and translational studies. In addition, accurate and precise classification of disease entities facilitates the discovery of the molecular basis of lymphoid neoplasms in the basic science laboratory. PMID:19029456

  13. Molecular classification of gastric adenocarcinoma: translating new insights from the cancer genome atlas research network.

    PubMed

    Sunakawa, Yu; Lenz, Heinz-Josef

    2015-04-01

    Gastric cancer is a heterogenous cancer, which may be classified into several distinct subtypes based on pathology and epidemiology, each with different initiating pathological processes and each possibly having different tumor biology. A classification of gastric cancer should be important to select patients who can benefit from the targeted therapies or to precisely predict prognosis. The Cancer Genome Atlas (TCGA) study collaborated with previous reports regarding subtyping gastric cancer but also proposed a refined classification based on molecular characteristics. The addition of the new molecular classification strategy to a current classical subtyping may be a promising option, particularly stratification by Epstein-Barr virus (EBV) and microsatellite instability (MSI) statuses. According to TCGA study, EBV gastric cancer patients may benefit the programmed cell death protein 1 (PD-1)/programmed death-ligand 1 (PD-L1) antibodies or phosphoinositide 3-kinase (PI3K) inhibitors which are now being developed. The discoveries of predictive biomarkers should improve patient care and individualized medicine in the management since the targeted therapies may have the potential to change the landscape of gastric cancer treatment, moreover leading to both better understanding of the heterogeneity and better outcomes. Patient enrichment by predictive biomarkers for new treatment strategies will be critical to improve clinical outcomes. Additionally, liquid biopsies will be able to enable us to monitor in real-time molecular escape mechanism, resulting in better treatment strategies.

  14. Searching for an Accurate Marker-Based Prediction of an Individual Quantitative Trait in Molecular Plant Breeding

    PubMed Central

    Fu, Yong-Bi; Yang, Mo-Hua; Zeng, Fangqin; Biligetu, Bill

    2017-01-01

    Molecular plant breeding with the aid of molecular markers has played an important role in modern plant breeding over the last two decades. Many marker-based predictions for quantitative traits have been made to enhance parental selection, but the trait prediction accuracy remains generally low, even with the aid of dense, genome-wide SNP markers. To search for more accurate trait-specific prediction with informative SNP markers, we conducted a literature review on the prediction issues in molecular plant breeding and on the applicability of an RNA-Seq technique for developing function-associated specific trait (FAST) SNP markers. To understand whether and how FAST SNP markers could enhance trait prediction, we also performed a theoretical reasoning on the effectiveness of these markers in a trait-specific prediction, and verified the reasoning through computer simulation. To the end, the search yielded an alternative to regular genomic selection with FAST SNP markers that could be explored to achieve more accurate trait-specific prediction. Continuous search for better alternatives is encouraged to enhance marker-based predictions for an individual quantitative trait in molecular plant breeding. PMID:28729875

  15. Radiomics biomarkers for accurate tumor progression prediction of oropharyngeal cancer

    NASA Astrophysics Data System (ADS)

    Hadjiiski, Lubomir; Chan, Heang-Ping; Cha, Kenny H.; Srinivasan, Ashok; Wei, Jun; Zhou, Chuan; Prince, Mark; Papagerakis, Silvana

    2017-03-01

    Accurate tumor progression prediction for oropharyngeal cancers is crucial for identifying patients who would best be treated with optimized treatment and therefore minimize the risk of under- or over-treatment. An objective decision support system that can merge the available radiomics, histopathologic and molecular biomarkers in a predictive model based on statistical outcomes of previous cases and machine learning may assist clinicians in making more accurate assessment of oropharyngeal tumor progression. In this study, we evaluated the feasibility of developing individual and combined predictive models based on quantitative image analysis from radiomics, histopathology and molecular biomarkers for oropharyngeal tumor progression prediction. With IRB approval, 31, 84, and 127 patients with head and neck CT (CT-HN), tumor tissue microarrays (TMAs) and molecular biomarker expressions, respectively, were collected. For 8 of the patients all 3 types of biomarkers were available and they were sequestered in a test set. The CT-HN lesions were automatically segmented using our level sets based method. Morphological, texture and molecular based features were extracted from CT-HN and TMA images, and selected features were merged by a neural network. The classification accuracy was quantified using the area under the ROC curve (AUC). Test AUCs of 0.87, 0.74, and 0.71 were obtained with the individual predictive models based on radiomics, histopathologic, and molecular features, respectively. Combining the radiomics and molecular models increased the test AUC to 0.90. Combining all 3 models increased the test AUC further to 0.94. This preliminary study demonstrates that the individual domains of biomarkers are useful and the integrated multi-domain approach is most promising for tumor progression prediction.

  16. Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

    DOE PAGES

    Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; ...

    2015-06-04

    Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstratemore » prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.« less

  17. Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space

    PubMed Central

    2015-01-01

    Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the “holy grail” of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. In addition, the same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies. PMID:26113956

  18. A clinical perspective on the 2016 WHO brain tumor classification and routine molecular diagnostics.

    PubMed

    van den Bent, Martin J; Weller, Michael; Wen, Patrick Y; Kros, Johan M; Aldape, Ken; Chang, Susan

    2017-05-01

    The 2007 World Health Organization (WHO) classification of brain tumors did not use molecular abnormalities as diagnostic criteria. Studies have shown that genotyping allows a better prognostic classification of diffuse glioma with improved treatment selection. This has resulted in a major revision of the WHO classification, which is now for adult diffuse glioma centered around isocitrate dehydrogenase (IDH) and 1p/19q diagnostics. This revised classification is reviewed with a focus on adult brain tumors, and includes a recommendation of genes of which routine testing is clinically useful. Apart from assessment of IDH mutational status including sequencing of R132H-immunohistochemistry negative cases and testing for 1p/19q, several other markers can be considered for routine testing, including assessment of copy number alterations of chromosome 7 and 10 and of TERT promoter, BRAF, and H3F3A mutations. For "glioblastoma, IDH mutated" the term "astrocytoma grade IV" could be considered. It should be considered to treat IDH wild-type grades II and III diffuse glioma with polysomy of chromosome 7 and loss of 10q as glioblastoma. New developments must be more quickly translated into further revised diagnostic categories. Quality control and rapid integration of molecular findings into the final diagnosis and the communication of the final diagnosis to clinicians require systematic attention. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  19. Molecular-genetic analysis is essential for accurate classification of renal carcinoma resembling Xp11.2 translocation carcinoma.

    PubMed

    Hayes, Malcolm; Peckova, Kvetoslava; Martinek, Petr; Hora, Milan; Kalusova, Kristyna; Straka, Lubomir; Daum, Ondrej; Kokoskova, Bohuslava; Rotterova, Pavla; Pivovarčikova, Kristyna; Branzovsky, Jindrich; Dubova, Magdalena; Vesela, Pavla; Michal, Michal; Hes, Ondrej

    2015-03-01

    tumours can only be sub-classified accurately by multi-parameter molecular-genetic analysis.

  20. Genomic landscape of gastric cancer: molecular classification and potential targets.

    PubMed

    Guo, Jiawei; Yu, Weiwei; Su, Hui; Pang, Xiufeng

    2017-02-01

    Gastric cancer imposes a considerable health burden worldwide, and its mortality ranks as the second highest for all types of cancers. The limited knowledge of the molecular mechanisms underlying gastric cancer tumorigenesis hinders the development of therapeutic strategies. However, ongoing collaborative sequencing efforts facilitate molecular classification and unveil the genomic landscape of gastric cancer. Several new drivers and tumorigenic pathways in gastric cancer, including chromatin remodeling genes, RhoA-related pathways, TP53 dysregulation, activation of receptor tyrosine kinases, stem cell pathways and abnormal DNA methylation, have been revealed. These newly identified genomic alterations await translation into clinical diagnosis and targeted therapies. Considering that loss-of-function mutations are intractable, synthetic lethality could be employed when discussing feasible therapeutic strategies. Although many challenges remain to be tackled, we are optimistic regarding improvements in the prognosis and treatment of gastric cancer in the near future.

  1. Molecular Simulation of the Free Energy for the Accurate Determination of Phase Transition Properties of Molecular Solids

    NASA Astrophysics Data System (ADS)

    Sellers, Michael; Lisal, Martin; Brennan, John

    2015-06-01

    Investigating the ability of a molecular model to accurately represent a real material is crucial to model development and use. When the model simulates materials in extreme conditions, one such property worth evaluating is the phase transition point. However, phase transitions are often overlooked or approximated because of difficulty or inaccuracy when simulating them. Techniques such as super-heating or super-squeezing a material to induce a phase change suffer from inherent timescale limitations leading to ``over-driving,'' and dual-phase simulations require many long-time runs to seek out what frequently results in an inexact location of phase-coexistence. We present a compilation of methods for the determination of solid-solid and solid-liquid phase transition points through the accurate calculation of the chemical potential. The methods are applied to the Smith-Bharadwaj atomistic potential's representation of cyclotrimethylene trinitramine (RDX) to accurately determine its melting point (Tm) and the alpha to gamma solid phase transition pressure. We also determine Tm for a coarse-grain model of RDX, and compare its value to experiment and atomistic counterpart. All methods are employed via the LAMMPS simulator, resulting in 60-70 simulations that total 30-50 ns. Approved for public release. Distribution is unlimited.

  2. Bilateral weighted radiographs are required for accurate classification of acromioclavicular separation: an observational study of 59 cases.

    PubMed

    Ibrahim, E F; Forrest, N P; Forester, A

    2015-10-01

    Misinterpretation of the Rockwood classification system for acromioclavicular joint (ACJ) separations has resulted in a trend towards using unilateral radiographs for grading. Further, the use of weighted views to 'unmask' a grade III injury has fallen out of favour. Recent evidence suggests that many radiographic grade III injuries represent only a partial injury to the stabilising ligaments. This study aimed to determine (1) whether accurate classification is possible on unilateral radiographs and (2) the efficacy of weighted bilateral radiographs in unmasking higher-grade injuries. Complete bilateral non-weighted and weighted sets of radiographs for patients presenting with an acromioclavicular separation over a 10-year period were analysed retrospectively, and they were graded I-VI according to Rockwood's criteria. Comparison was made between grading based on (1) a single antero-posterior (AP) view of the injured side, (2) bilateral non-weighted views and (3) bilateral weighted views. Radiographic measurements for cases that changed grade after weighted views were statistically compared to see if this could have been predicted beforehand. Fifty-nine sets of radiographs on 59 patients (48 male, mean age of 33 years) were included. Compared with unilateral radiographs, non-weighted bilateral comparison films resulted in a grade change for 44 patients (74.5%). Twenty-eight of 56 patients initially graded as I, II or III were upgraded to grade V and two of three initial grade V patients were downgraded to grade III. The addition of a weighted view further upgraded 10 patients to grade V. No grade II injury was changed to grade III and no injury of any severity was downgraded by a weighted view. Grade III injuries upgraded on weighted views had a significantly greater baseline median percentage coracoclavicular distance increase than those that were not upgraded (80.7% vs. 55.4%, p=0.015). However, no cut-off point for this value could be identified to predict an

  3. Molecular classifications of breast carcinoma with similar terminology and different definitions: are they the same?

    PubMed

    Tang, Ping; Wang, Jianmin; Bourne, Patria

    2008-04-01

    There are 4 major molecular classifications in the literature that divide breast carcinoma into basal and nonbasal subtypes, with basal subtypes associated with poor prognosis. Basal subtype is defined as positive for cytokeratin (CK) 5/6, CK14, and/or CK17 in CK classification; negative for ER, PR, and HER2 in triple negative (TN) classification; negative for ER and negative or positive for HER2 in ER/HER2 classification; and positive for CK5/6, CK14, CK17, and/or EGFR; and negative for ER, PR, and HER2 in CK/TN classification. These classifications use similar terminology but different definitions; it is critical to understand the precise relationship between them. We compared these 4 classifications in 195 breast carcinomas and found that (1) the rates of basal subtypes varied from 5% to 36% for ductal carcinoma in situ and 14% to 40% for invasive ductal carcinoma. (2) The rates of basal subtypes varied from 19% to 76% for HG carcinoma and 1% to 7% for NHG carcinoma. (3) The rates of basal subtypes were strongly associated with tumor grades (P < .001) in all classifications and associated with tumor types (in situ versus invasive ductal carcinomas) in TN (P < .001) and CK/TN classifications (P = .035). (4) These classifications were related but not interchangeable (kappa ranges from 0.140 to 0.658 for HG carcinoma and from 0.098 to 0.654 for NHG carcinoma). In conclusion, although these classifications all divide breast carcinoma into basal and nonbasal subtypes, they are not interchangeable. More studies are needed to evaluate to their values in predicting prognosis and guiding individualized therapy.

  4. The Molecular Classification of Medulloblastoma: Driving the next generation clinical trials

    PubMed Central

    Leary, Sarah E. S.; Olson, James M.

    2012-01-01

    Purpose of Review Most children diagnosed with cancer today are expected to be cured. Medulloblastoma, the most common pediatric malignant brain tumor, is an example of a disease that has benefitted from advances in diagnostic imaging, surgical techniques, radiation therapy and combination chemotherapy over the past decades. An incurable disease 50 years ago, approximately 70% of children with medulloblastoma are now cured of their disease. However, the pace of increasing the cure rate has slowed over the past two decades, and we have likely reached the maximal benefit that can be achieved with cytotoxic therapy and clinical risk stratification. Long-term toxicity of therapy also remains significant. To increase cure rates and decrease long-term toxicity, there is great interest in incorporating biologic “targeted” therapy into treatment of medulloblastoma, but this will require a paradigm shift in how we classify and study disease. Recent Findings Using genome-based high-throughput analytic techniques, several groups have independently reported methods of molecular classification of medulloblastoma within the past year. This has resulted in a working consensus to view medulloblastoma as four molecular subtypes including WNT pathway subtype, SHH pathway subtype, and two less well-defined subtypes, Group C and Group D. Summary Novel classification and risk stratification based on biologic subtypes of disease will form the basis of further study in medulloblastoma, and identify specific subtypes which warrant greater research focus. PMID:22189395

  5. A Comparative Study of YSO Classification Techniques using WISE Observations of the KR 120 Molecular Cloud

    NASA Astrophysics Data System (ADS)

    Kang, Sung-Ju; Kerton, C. R.

    2014-01-01

    KR 120 (Sh2-187) is a small Galactic HII region located at a distance of 1.4 kpc that shows evidence for triggered star formation in the surrounding molecular cloud. We present an analysis of the young stellar object (YSO) population of the molecular cloud as determined using a variety of classification techniques. YSO candidates are selected from the WISE all sky catalog and classified as Class I, Class II and Flat based on 1) spectral index, 2) color-color or color-magnitude plots, and 3) spectral energy distribution (SED) fits to radiative transfer models. We examine the discrepancies in YSO classification between the various techniques and explore how these discrepancies lead to uncertainty in such scientifically interesting quantities such as the ratio of Class I/Class II sources and the surface density of YSOs at various stages of evolution.

  6. Matching mice to malignancy: molecular subgroups and models of medulloblastoma

    PubMed Central

    Lau, Jasmine; Schmidt, Christin; Markant, Shirley L.; Taylor, Michael D.; Wechsler-Reya, Robert J.

    2012-01-01

    Introduction Medulloblastoma, the largest group of embryonal brain tumors, has historically been classified into five variants based on histopathology. More recently, epigenetic and transcriptional analyses of primary tumors have sub-classified medulloblastoma into four to six subgroups, most of which are incongruous with histopathological classification. Discussion Improved stratification is required for prognosis and development of targeted treatment strategies, to maximize cure and minimize adverse effects. Several mouse models of medulloblastoma have contributed both to an improved understanding of progression and to developmental therapeutics. In this review, we summarize the classification of human medulloblastoma subtypes based on histopathology and molecular features. We describe existing genetically engineered mouse models, compare these to human disease, and discuss the utility of mouse models for developmental therapeutics. Just as accurate knowledge of the correct molecular subtype of medulloblastoma is critical to the development of targeted therapy in patients, we propose that accurate modeling of each subtype of medulloblastoma in mice will be necessary for preclinical evaluation and optimization of those targeted therapies. PMID:22315164

  7. [Haemolytic uremic syndrome and thrombotic thrombocytopenic purpura: classification based on molecular etiology and review of recent developments in diagnostics].

    PubMed

    Prohászka, Zoltán

    2008-07-06

    Haemolytic uremic syndrome and thrombotic thrombocytopenic purpura are overlapping clinical entities based on historical classification. Recent developments in the unfolding of the pathomechanisms of these diseases resulted in the creation of a molecular etiology-based classification. Understanding of some causative relationships yielded detailed diagnostic approaches, novel therapeutic options and thorough prognostic assortment of the patients. Although haemolytic uremic syndrome and thrombotic thrombocytopenic purpura are rare diseases with poor prognosis, the precise molecular etiology-based diagnosis might properly direct the therapy of the affected patients. The current review focuses on the theoretical background and detailed description of the available diagnostic possibilities, and some practical information necessary for the interpretation of their results.

  8. Can glenoid wear be accurately assessed using x-ray imaging? Evaluating agreement of x-ray and magnetic resonance imaging (MRI) Walch classification.

    PubMed

    Kopka, Michaela; Fourman, Mitchell; Soni, Ashish; Cordle, Andrew C; Lin, Albert

    2017-09-01

    The Walch classification is the most recognized means of assessing glenoid wear in preoperative planning for shoulder arthroplasty. This classification relies on advanced imaging, which is more expensive and less practical than plain radiographs. The purpose of this study was to determine whether the Walch classification could be accurately applied to x-ray images compared with magnetic resonance imaging (MRI) as the gold standard. We hypothesized that x-ray images cannot adequately replace advanced imaging in the evaluation of glenoid wear. Preoperative axillary x-ray images and MRI scans of 50 patients assessed for shoulder arthroplasty were independently reviewed by 5 raters. Glenoid wear was individually classified according to the Walch classification using each imaging modality. The raters then collectively reviewed the MRI scans and assigned a consensus classification to serve as the gold standard. The κ coefficient was used to determine interobserver agreement for x-ray images and independent MRI reads, as well as the agreement between x-ray images and consensus MRI. The inter-rater agreement for x-ray images and MRIs was "moderate" (κ = 0.42 and κ = 0.47, respectively) for the 5-category Walch classification (A1, A2, B1, B2, C) and "moderate" (κ = 0.54 and κ = 0.59, respectively) for the 3-category Walch classification (A, B, C). The agreement between x-ray images and consensus MRI was much lower: "fair-to-moderate" (κ = 0.21-0.51) for the 5-category and "moderate" (κ = 0.36-0.60) for the 3-category Walch classification. The inter-rater agreement between x-ray images and consensus MRI is "fair-to-moderate." This is lower than the previously reported reliability of the Walch classification using computed tomography scans. Accordingly, x-ray images are inferior to advanced imaging when assessing glenoid wear. Copyright © 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights

  9. Accurate vehicle classification including motorcycles using piezoelectric sensors.

    DOT National Transportation Integrated Search

    2013-03-01

    State and federal departments of transportation are charged with classifying vehicles and monitoring mileage traveled. Accurate data reporting enables suitable roadway design for safety and capacity. Vehicle classifiers currently employ inductive loo...

  10. Accurate calibration of a molecular beam time-of-flight mass spectrometer for on-line analysis of high molecular weight species.

    PubMed

    Apicella, B; Wang, X; Passaro, M; Ciajolo, A; Russo, C

    2016-10-15

    Time-of-Flight (TOF) Mass Spectrometry is a powerful analytical technique, provided that an accurate calibration by standard molecules in the same m/z range of the analytes is performed. Calibration in a very large m/z range is a difficult task, particularly in studies focusing on the detection of high molecular weight clusters of different molecules or high molecular weight species. External calibration is the most common procedure used for TOF mass spectrometric analysis in the gas phase and, generally, the only available standards are made up of mixtures of noble gases, covering a small mass range for calibration, up to m/z 136 (higher mass isotope of xenon). In this work, an accurate calibration of a Molecular Beam Time-of Flight Mass Spectrometer (MB-TOFMS) is presented, based on the use of water clusters up to m/z 3000. The advantages of calibrating a MB-TOFMS with water clusters for the detection of analytes with masses above those of the traditional calibrants such as noble gases were quantitatively shown by statistical calculations. A comparison of the water cluster and noble gases calibration procedures in attributing the masses to a test mixture extending up to m/z 800 is also reported. In the case of the analysis of combustion products, another important feature of water cluster calibration was shown, that is the possibility of using them as "internal standard" directly formed from the combustion water, under suitable experimental conditions. The water clusters calibration of a MB-TOFMS gives rise to a ten-fold reduction in error compared to the traditional calibration with noble gases. The consequent improvement in mass accuracy in the calibration of a MB-TOFMS has important implications in various fields where detection of high molecular mass species is required. In combustion products analysis, it is also possible to obtain a new calibration spectrum before the acquisition of each spectrum, only modifying some operative conditions. Copyright © 2016

  11. FSR: feature set reduction for scalable and accurate multi-class cancer subtype classification based on copy number.

    PubMed

    Wong, Gerard; Leckie, Christopher; Kowalczyk, Adam

    2012-01-15

    Feature selection is a key concept in machine learning for microarray datasets, where features represented by probesets are typically several orders of magnitude larger than the available sample size. Computational tractability is a key challenge for feature selection algorithms in handling very high-dimensional datasets beyond a hundred thousand features, such as in datasets produced on single nucleotide polymorphism microarrays. In this article, we present a novel feature set reduction approach that enables scalable feature selection on datasets with hundreds of thousands of features and beyond. Our approach enables more efficient handling of higher resolution datasets to achieve better disease subtype classification of samples for potentially more accurate diagnosis and prognosis, which allows clinicians to make more informed decisions in regards to patient treatment options. We applied our feature set reduction approach to several publicly available cancer single nucleotide polymorphism (SNP) array datasets and evaluated its performance in terms of its multiclass predictive classification accuracy over different cancer subtypes, its speedup in execution as well as its scalability with respect to sample size and array resolution. Feature Set Reduction (FSR) was able to reduce the dimensions of an SNP array dataset by more than two orders of magnitude while achieving at least equal, and in most cases superior predictive classification performance over that achieved on features selected by existing feature selection methods alone. An examination of the biological relevance of frequently selected features from FSR-reduced feature sets revealed strong enrichment in association with cancer. FSR was implemented in MATLAB R2010b and is available at http://ww2.cs.mu.oz.au/~gwong/FSR.

  12. PyVCI: A flexible open-source code for calculating accurate molecular infrared spectra

    NASA Astrophysics Data System (ADS)

    Sibaev, Marat; Crittenden, Deborah L.

    2016-06-01

    The PyVCI program package is a general purpose open-source code for simulating accurate molecular spectra, based upon force field expansions of the potential energy surface in normal mode coordinates. It includes harmonic normal coordinate analysis and vibrational configuration interaction (VCI) algorithms, implemented primarily in Python for accessibility but with time-consuming routines written in C. Coriolis coupling terms may be optionally included in the vibrational Hamiltonian. Non-negligible VCI matrix elements are stored in sparse matrix format to alleviate the diagonalization problem. CPU and memory requirements may be further controlled by algorithmic choices and/or numerical screening procedures, and recommended values are established by benchmarking using a test set of 44 molecules for which accurate analytical potential energy surfaces are available. Force fields in normal mode coordinates are obtained from the PyPES library of high quality analytical potential energy surfaces (to 6th order) or by numerical differentiation of analytic second derivatives generated using the GAMESS quantum chemical program package (to 4th order).

  13. A molecular phylogeny and classification of Leptochloa (Poaceae: Chloridoideae: Chlorideae) sensu lato and related genera

    PubMed Central

    Peterson, Paul M.; Romaschenko, Konstantin; Snow, Neil; Johnson, Gabriel

    2012-01-01

    Background and Aims Leptochloa (including Diplachne) sensu lato (s.l.) comprises a diverse assemblage of C4 (NAD-ME and PCK) grasses with approx. 32 annual or perennial species. Evolutionary relationships and a modern classification of Leptochloa spp. based on the study of molecular characters have only been superficially investigated in four species. The goals of this study were to reconstruct the evolutionary history of Leptochloa s.l. with molecular data and broad taxon sampling. Methods A phylogenetic analysis was conducted of 130 species (mostly Chloridoideae), of which 22 are placed in Leptochloa, using five plastid (rpL32-trn-L, ndhA intron, rps16 intron, rps16-trnK and ccsA) and the nuclear ITS 1 and 2 (ribosomal internal transcribed spacer regions) to infer evolutionary relationships and revise the classification. Key results Leptochloa s.l. is polyphyletic and strong support was found for five lineages. Embedded within the Leptochloa sensu stricto (s.s.) clade are two Trichloris spp. and embedded in Dinebra are Drake-brockmania and 19 Leptochloa spp. Conclusions The molecular results support the dissolution of Leptochloa s.l. into the following five genera: Dinebra with 23 species, Diplachne with two species, Disakisperma with three species, Leptochloa s.s. with five species and a new genus, Trigonochloa, with two species. PMID:22628365

  14. Taxonomic update on proposed nomenclature and classification changes for bacteria of medical importance, 2015.

    PubMed

    Janda, J Michael

    2016-10-01

    A key aspect of medical, public health, and diagnostic microbiology laboratories is the accurate and rapid reporting and communication regarding infectious agents of clinical significance. Microbial taxonomy in the age of molecular diagnostics and phylogenetics creates changes in taxonomy at a rapid rate further complicating this process. This update focuses on the description of new species and classification changes proposed in 2015. Copyright © 2016 Elsevier Inc. All rights reserved.

  15. Calcium ions in aqueous solutions: Accurate force field description aided by ab initio molecular dynamics and neutron scattering

    NASA Astrophysics Data System (ADS)

    Martinek, Tomas; Duboué-Dijon, Elise; Timr, Štěpán; Mason, Philip E.; Baxová, Katarina; Fischer, Henry E.; Schmidt, Burkhard; Pluhařová, Eva; Jungwirth, Pavel

    2018-06-01

    We present a combination of force field and ab initio molecular dynamics simulations together with neutron scattering experiments with isotopic substitution that aim at characterizing ion hydration and pairing in aqueous calcium chloride and formate/acetate solutions. Benchmarking against neutron scattering data on concentrated solutions together with ion pairing free energy profiles from ab initio molecular dynamics allows us to develop an accurate calcium force field which accounts in a mean-field way for electronic polarization effects via charge rescaling. This refined calcium parameterization is directly usable for standard molecular dynamics simulations of processes involving this key biological signaling ion.

  16. Molecular Classification of Grade 3 Endometrioid Endometrial Cancers Identifies Distinct Prognostic Subgroups.

    PubMed

    Bosse, Tjalling; Nout, Remi A; McAlpine, Jessica N; McConechy, Melissa K; Britton, Heidi; Hussein, Yaser R; Gonzalez, Carlene; Ganesan, Raji; Steele, Jane C; Harrison, Beth T; Oliva, Esther; Vidal, August; Matias-Guiu, Xavier; Abu-Rustum, Nadeem R; Levine, Douglas A; Gilks, C Blake; Soslow, Robert A

    2018-05-01

    Our aim was to investigate whether molecular classification can be used to refine prognosis in grade 3 endometrial endometrioid carcinomas (EECs). Grade 3 EECs were classified into 4 subgroups: p53 abnormal, based on mutant-like immunostaining (p53abn); MMR deficient, based on loss of mismatch repair protein expression (MMRd); presence of POLE exonuclease domain hotspot mutation (POLE); no specific molecular profile (NSMP), in which none of these aberrations were present. Overall survival (OS) and recurrence-free survival (RFS) rates were compared using the Kaplan-Meier method (Log-rank test) and univariable and multivariable Cox proportional hazard models. In total, 381 patients were included. The median age was 66 years (range, 33 to 96 y). Federation Internationale de Gynecologie et d'Obstetrique stages (2009) were as follows: IA, 171 (44.9%); IB, 120 (31.5%); II, 24 (6.3%); III, 50 (13.1%); IV, 11 (2.9%). There were 49 (12.9%) POLE, 79 (20.7%) p53abn, 115 (30.2%) NSMP, and 138 (36.2%) MMRd tumors. Median follow-up of patients was 6.1 years (range, 0.2 to 17.0 y). Compared to patients with NSMP, patients with POLE mutant grade 3 EEC (OS: hazard ratio [HR], 0.36 [95% confidence interval, 0.18-0.70]; P=0.003; RFS: HR, 0.17 [0.05-0.54]; P=0.003) had a significantly better prognosis; patients with p53abn tumors had a significantly worse RFS (HR, 1.73 [1.09-2.74]; P=0.021); patients with MMRd tumors showed a trend toward better RFS. Estimated 5-year OS rates were as follows: POLE 89%, MMRd 75%, NSMP 69%, p53abn 55% (Log rank P=0.001). Five-year RFS rates were as follows: POLE 96%, MMRd 77%, NSMP 64%, p53abn 47% (P=0.000001), respectively. In a multivariable Cox model that included age and Federation Internationale de Gynecologie et d'Obstetrique stage, POLE and MMRd status remained independent prognostic factors for better RFS; p53 status was an independent prognostic factor for worse RFS. Molecular classification of grade 3 EECs reveals that these tumors are a

  17. A non-contact method based on multiple signal classification algorithm to reduce the measurement time for accurately heart rate detection

    NASA Astrophysics Data System (ADS)

    Bechet, P.; Mitran, R.; Munteanu, M.

    2013-08-01

    Non-contact methods for the assessment of vital signs are of great interest for specialists due to the benefits obtained in both medical and special applications, such as those for surveillance, monitoring, and search and rescue. This paper investigates the possibility of implementing a digital processing algorithm based on the MUSIC (Multiple Signal Classification) parametric spectral estimation in order to reduce the observation time needed to accurately measure the heart rate. It demonstrates that, by proper dimensioning the signal subspace, the MUSIC algorithm can be optimized in order to accurately assess the heart rate during an 8-28 s time interval. The validation of the processing algorithm performance was achieved by minimizing the mean error of the heart rate after performing simultaneous comparative measurements on several subjects. In order to calculate the error the reference value of heart rate was measured using a classic measurement system through direct contact.

  18. Medulloblastoma in the Molecular Era

    PubMed Central

    Miranda Kuzan-Fischer, Claudia; Juraschka, Kyle; Taylor, Michael D.

    2018-01-01

    Medulloblastoma is the most common malignant brain tumor of childhood and remains a major cause of cancer related mortality in children. Significant scientific advancements have transformed the understanding of medulloblastoma, leading to the recognition of four distinct clinical and molecular subgroups, namely wingless (WNT), sonic hedgehog, group 3, and group 4. Subgroup classification combined with the recognition of subgroup specific molecular alterations has also led to major changes in risk stratification of medulloblastoma patients and these changes have begun to alter clinical trial design, in which the newly recognized subgroups are being incorporated as individualized treatment arms. Despite these recent advancements, identification of effective targeted therapies remains a challenge for several reasons. First, significant molecular heterogeneity exists within the four subgroups, meaning this classification system alone may not be sufficient to predict response to a particular therapy. Second, the majority of novel agents are currently tested at the time of recurrence, after which significant selective pressures have been exerted by radiation and chemotherapy. Recent studies demonstrate selection of tumor sub-clones that exhibit genetic divergence from the primary tumor, exist within metastatic and recurrent tumor populations. Therefore, tumor resampling at the time of recurrence may become necessary to accurately select patients for personalized therapy. PMID:29742881

  19. Medulloblastoma in the Molecular Era.

    PubMed

    Miranda Kuzan-Fischer, Claudia; Juraschka, Kyle; Taylor, Michael D

    2018-05-01

    Medulloblastoma is the most common malignant brain tumor of childhood and remains a major cause of cancer related mortality in children. Significant scientific advancements have transformed the understanding of medulloblastoma, leading to the recognition of four distinct clinical and molecular subgroups, namely wingless (WNT), sonic hedgehog, group 3, and group 4. Subgroup classification combined with the recognition of subgroup specific molecular alterations has also led to major changes in risk stratification of medulloblastoma patients and these changes have begun to alter clinical trial design, in which the newly recognized subgroups are being incorporated as individualized treatment arms. Despite these recent advancements, identification of effective targeted therapies remains a challenge for several reasons. First, significant molecular heterogeneity exists within the four subgroups, meaning this classification system alone may not be sufficient to predict response to a particular therapy. Second, the majority of novel agents are currently tested at the time of recurrence, after which significant selective pressures have been exerted by radiation and chemotherapy. Recent studies demonstrate selection of tumor sub-clones that exhibit genetic divergence from the primary tumor, exist within metastatic and recurrent tumor populations. Therefore, tumor resampling at the time of recurrence may become necessary to accurately select patients for personalized therapy.

  20. Self Organizing Map-Based Classification of Cathepsin k and S Inhibitors with Different Selectivity Profiles Using Different Structural Molecular Fingerprints: Design and Application for Discovery of Novel Hits.

    PubMed

    Ihmaid, Saleh K; Ahmed, Hany E A; Zayed, Mohamed F; Abadleh, Mohammed M

    2016-01-30

    The main step in a successful drug discovery pipeline is the identification of small potent compounds that selectively bind to the target of interest with high affinity. However, there is still a shortage of efficient and accurate computational methods with powerful capability to study and hence predict compound selectivity properties. In this work, we propose an affordable machine learning method to perform compound selectivity classification and prediction. For this purpose, we have collected compounds with reported activity and built a selectivity database formed of 153 cathepsin K and S inhibitors that are considered of medicinal interest. This database has three compound sets, two K/S and S/K selective ones and one non-selective KS one. We have subjected this database to the selectivity classification tool 'Emergent Self-Organizing Maps' for exploring its capability to differentiate selective cathepsin inhibitors for one target over the other. The method exhibited good clustering performance for selective ligands with high accuracy (up to 100 %). Among the possibilites, BAPs and MACCS molecular structural fingerprints were used for such a classification. The results exhibited the ability of the method for structure-selectivity relationship interpretation and selectivity markers were identified for the design of further novel inhibitors with high activity and target selectivity.

  1. Accurate classification of brain gliomas by discriminate dictionary learning based on projective dictionary pair learning of proton magnetic resonance spectra.

    PubMed

    Adebileje, Sikiru Afolabi; Ghasemi, Keyvan; Aiyelabegan, Hammed Tanimowo; Saligheh Rad, Hamidreza

    2017-04-01

    Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of cMRI, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites, and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task, and the result were compared with the sub-dictionary learning methods. The proton magnetic resonance spectroscopy data contain a total of 150 spectra (74 healthy, 23 grade II, 23 grade III, and 30 grade IV) from two databases. The datasets from both databases were first coupled together, followed by column normalization. The Kennard-Stone algorithm was used to split the datasets into its training and test sets. Performance comparison based on the overall accuracy, sensitivity, specificity, and precision was conducted. Based on the overall accuracy of our classification scheme, the dictionary pair learning method was found to outperform the sub-dictionary learning methods 97.78% compared with 68.89%, respectively. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  2. Fuzzy-C-Means Clustering Based Segmentation and CNN-Classification for Accurate Segmentation of Lung Nodules

    PubMed

    K, Jalal Deen; R, Ganesan; A, Merline

    2017-07-27

    Objective: Accurate segmentation of abnormal and healthy lungs is very crucial for a steadfast computer-aided disease diagnostics. Methods: For this purpose a stack of chest CT scans are processed. In this paper, novel methods are proposed for segmentation of the multimodal grayscale lung CT scan. In the conventional methods using Markov–Gibbs Random Field (MGRF) model the required regions of interest (ROI) are identified. Result: The results of proposed FCM and CNN based process are compared with the results obtained from the conventional method using MGRF model. The results illustrate that the proposed method can able to segment the various kinds of complex multimodal medical images precisely. Conclusion: However, in this paper, to obtain an exact boundary of the regions, every empirical dispersion of the image is computed by Fuzzy C-Means Clustering segmentation. A classification process based on the Convolutional Neural Network (CNN) classifier is accomplished to distinguish the normal tissue and the abnormal tissue. The experimental evaluation is done using the Interstitial Lung Disease (ILD) database. Creative Commons Attribution License

  3. Fuzzy-C-Means Clustering Based Segmentation and CNN-Classification for Accurate Segmentation of Lung Nodules

    PubMed Central

    K, Jalal Deen; R, Ganesan; A, Merline

    2017-01-01

    Objective: Accurate segmentation of abnormal and healthy lungs is very crucial for a steadfast computer-aided disease diagnostics. Methods: For this purpose a stack of chest CT scans are processed. In this paper, novel methods are proposed for segmentation of the multimodal grayscale lung CT scan. In the conventional methods using Markov–Gibbs Random Field (MGRF) model the required regions of interest (ROI) are identified. Result: The results of proposed FCM and CNN based process are compared with the results obtained from the conventional method using MGRF model. The results illustrate that the proposed method can able to segment the various kinds of complex multimodal medical images precisely. Conclusion: However, in this paper, to obtain an exact boundary of the regions, every empirical dispersion of the image is computed by Fuzzy C-Means Clustering segmentation. A classification process based on the Convolutional Neural Network (CNN) classifier is accomplished to distinguish the normal tissue and the abnormal tissue. The experimental evaluation is done using the Interstitial Lung Disease (ILD) database. PMID:28749127

  4. Breast cancer molecular subtype classification using deep features: preliminary results

    NASA Astrophysics Data System (ADS)

    Zhu, Zhe; Albadawy, Ehab; Saha, Ashirbani; Zhang, Jun; Harowicz, Michael R.; Mazurowski, Maciej A.

    2018-02-01

    Radiogenomics is a field of investigation that attempts to examine the relationship between imaging characteris- tics of cancerous lesions and their genomic composition. This could offer a noninvasive alternative to establishing genomic characteristics of tumors and aid cancer treatment planning. While deep learning has shown its supe- riority in many detection and classification tasks, breast cancer radiogenomic data suffers from a very limited number of training examples, which renders the training of the neural network for this problem directly and with no pretraining a very difficult task. In this study, we investigated an alternative deep learning approach referred to as deep features or off-the-shelf network approach to classify breast cancer molecular subtypes using breast dynamic contrast enhanced MRIs. We used the feature maps of different convolution layers and fully connected layers as features and trained support vector machines using these features for prediction. For the feature maps that have multiple layers, max-pooling was performed along each channel. We focused on distinguishing the Luminal A subtype from other subtypes. To evaluate the models, 10 fold cross-validation was performed and the final AUC was obtained by averaging the performance of all the folds. The highest average AUC obtained was 0.64 (0.95 CI: 0.57-0.71), using the feature maps of the last fully connected layer. This indicates the promise of using this approach to predict the breast cancer molecular subtypes. Since the best performance appears in the last fully connected layer, it also implies that breast cancer molecular subtypes may relate to high level image features

  5. On the nature of global classification

    NASA Technical Reports Server (NTRS)

    Wheelis, M. L.; Kandler, O.; Woese, C. R.

    1992-01-01

    Molecular sequencing technology has brought biology into the era of global (universal) classification. Methodologically and philosophically, global classification differs significantly from traditional, local classification. The need for uniformity requires that higher level taxa be defined on the molecular level in terms of universally homologous functions. A global classification should reflect both principal dimensions of the evolutionary process: genealogical relationship and quality and extent of divergence within a group. The ultimate purpose of a global classification is not simply information storage and retrieval; such a system should also function as an heuristic representation of the evolutionary paradigm that exerts a directing influence on the course of biology. The global system envisioned allows paraphyletic taxa. To retain maximal phylogenetic information in these cases, minor notational amendments in existing taxonomic conventions should be adopted.

  6. A new multidimensional stoichiometric classification of compounds: moving beyond the van Krevelen diagram.

    NASA Astrophysics Data System (ADS)

    Rivas-Ubach, A.; Liu, Y.; Bianchi, T. S.; Tolic, N.; Jansson, C.; Paša-Tolić, L.

    2017-12-01

    The role of nutrients in organisms, especially primary producers, has been a topic of special interest in ecosystem research for understanding the ecosystem structure and function. The majority of macro-elements in organisms, such as C, H, O, N and P, do not act as single elements but are components of organic compounds (lipids, peptides, carbohydrates, etc), which are more directly related to the physiology of organisms and thus to the ecosystem function. However, accurately deciphering the overall content of the main compound classes (lipids, proteins, carbohydrates,…) in organisms is still a major challenge. van Krevelen (vK) diagrams have been widely used as an estimation of the main compound categories present in environmental samples based on O:C vs H:C molecular ratios, but a stoichiometric classification based exclusively on O:C and H:C ratios is feeble. Different compound classes show large O:C and H:C ratio overlapping and other heteroatoms, such as N and P, should be considered to robustly distinguish the different classes. We propose a new compound classification for biological/environmental samples based on the C:H:O:N:P stoichiometric ratios of thousands of molecular formulas of characterized compounds from 6 different main categories: lipids, peptides, amino-sugars, carbohydrates, nucleotides and phytochemical compounds (oxy-aromatic compounds). This new multidimensional stoichiometric compound constraints classification (MSCC) can be applied to data obtained with high resolution mass spectrometry (HRMS), allowing an accurate overview of the relative abundances of the main compound categories present in organismal samples. The MSCC has been optimized for plants, but it could be also applied to different organisms and serve as a strong starting point to further investigate other environmental complex matrices (soils, aerosols, etc). The proposed MSCC advances environmental research, especially eco-metabolomics, ecophysiology and ecological

  7. Molecular classification based on apomorphic amino acids (Arthropoda, Hexapoda): Integrative taxonomy in the era of phylogenomics.

    PubMed

    Wu, Hao-Yang; Wang, Yan-Hui; Xie, Qiang; Ke, Yun-Ling; Bu, Wen-Jun

    2016-06-17

    With the great development of sequencing technologies and systematic methods, our understanding of evolutionary relationships at deeper levels within the tree of life has greatly improved over the last decade. However, the current taxonomic methodology is insufficient to describe the growing levels of diversity in both a standardised and general way due to the limitations of using only morphological traits to describe clades. Herein, we propose the idea of a molecular classification based on hierarchical and discrete amino acid characters. Clades are classified based on the results of phylogenetic analyses and described using amino acids with group specificity in phylograms. Practices based on the recently published phylogenomic datasets of insects together with 15 de novo sequenced transcriptomes in this study demonstrate that such a methodology can accommodate various higher ranks of taxonomy. Such an approach has the advantage of describing organisms in a standard and discrete way within a phylogenetic framework, thereby facilitating the recognition of clades from the view of the whole lineage, as indicated by PhyloCode. By combining identification keys and phylogenies, the molecular classification based on hierarchical and discrete characters may greatly boost the progress of integrative taxonomy.

  8. Molecular classification based on apomorphic amino acids (Arthropoda, Hexapoda): Integrative taxonomy in the era of phylogenomics

    PubMed Central

    Wu, Hao-Yang; Wang, Yan-Hui; Xie, Qiang; Ke, Yun-Ling; Bu, Wen-Jun

    2016-01-01

    With the great development of sequencing technologies and systematic methods, our understanding of evolutionary relationships at deeper levels within the tree of life has greatly improved over the last decade. However, the current taxonomic methodology is insufficient to describe the growing levels of diversity in both a standardised and general way due to the limitations of using only morphological traits to describe clades. Herein, we propose the idea of a molecular classification based on hierarchical and discrete amino acid characters. Clades are classified based on the results of phylogenetic analyses and described using amino acids with group specificity in phylograms. Practices based on the recently published phylogenomic datasets of insects together with 15 de novo sequenced transcriptomes in this study demonstrate that such a methodology can accommodate various higher ranks of taxonomy. Such an approach has the advantage of describing organisms in a standard and discrete way within a phylogenetic framework, thereby facilitating the recognition of clades from the view of the whole lineage, as indicated by PhyloCode. By combining identification keys and phylogenies, the molecular classification based on hierarchical and discrete characters may greatly boost the progress of integrative taxonomy. PMID:27312960

  9. Fast and accurate quantum molecular dynamics of dense plasmas across temperature regimes

    DOE PAGES

    Sjostrom, Travis; Daligault, Jerome

    2014-10-10

    Here, we develop and implement a new quantum molecular dynamics approximation that allows fast and accurate simulations of dense plasmas from cold to hot conditions. The method is based on a carefully designed orbital-free implementation of density functional theory. The results for hydrogen and aluminum are in very good agreement with Kohn-Sham (orbital-based) density functional theory and path integral Monte Carlo calculations for microscopic features such as the electron density as well as the equation of state. The present approach does not scale with temperature and hence extends to higher temperatures than is accessible in the Kohn-Sham method and lowermore » temperatures than is accessible by path integral Monte Carlo calculations, while being significantly less computationally expensive than either of those two methods.« less

  10. Molecular approaches for classifying endometrial carcinoma.

    PubMed

    Piulats, Josep M; Guerra, Esther; Gil-Martín, Marta; Roman-Canal, Berta; Gatius, Sonia; Sanz-Pamplona, Rebeca; Velasco, Ana; Vidal, August; Matias-Guiu, Xavier

    2017-04-01

    Endometrial carcinoma is the most common cancer of the female genital tract. This review article discusses the usefulness of molecular techniques to classify endometrial carcinoma. Any proposal for molecular classification of neoplasms should integrate morphological features of the tumors. For that reason, we start with the current histological classification of endometrial carcinoma, by discussing the correlation between genotype and phenotype, and the most significant recent improvements. Then, we comment on some of the possible flaws of this classification, by discussing also the value of molecular pathology in improving them, including interobserver variation in pathologic interpretation of high grade tumors. Third, we discuss the importance of applying TCGA molecular approach to clinical practice. We also comment on the impact of intratumor heterogeneity in classification, and finally, we will discuss briefly, the usefulness of TCGA classification in tailoring immunotherapy in endometrial cancer patients. We suggest combining pathologic classification and the surrogate TCGA molecular classification for high-grade endometrial carcinomas, as an option to improve assessment of prognosis. Copyright © 2016 Elsevier Inc. All rights reserved.

  11. Contextual classification of multispectral image data: Approximate algorithm

    NASA Technical Reports Server (NTRS)

    Tilton, J. C. (Principal Investigator)

    1980-01-01

    An approximation to a classification algorithm incorporating spatial context information in a general, statistical manner is presented which is computationally less intensive. Classifications that are nearly as accurate are produced.

  12. Classification of proteins: available structural space for molecular modeling.

    PubMed

    Andreeva, Antonina

    2012-01-01

    The wealth of available protein structural data provides unprecedented opportunity to study and better understand the underlying principles of protein folding and protein structure evolution. A key to achieving this lies in the ability to analyse these data and to organize them in a coherent classification scheme. Over the past years several protein classifications have been developed that aim to group proteins based on their structural relationships. Some of these classification schemes explore the concept of structural neighbourhood (structural continuum), whereas other utilize the notion of protein evolution and thus provide a discrete rather than continuum view of protein structure space. This chapter presents a strategy for classification of proteins with known three-dimensional structure. Steps in the classification process along with basic definitions are introduced. Examples illustrating some fundamental concepts of protein folding and evolution with a special focus on the exceptions to them are presented.

  13. CAST: a new program package for the accurate characterization of large and flexible molecular systems.

    PubMed

    Grebner, Christoph; Becker, Johannes; Weber, Daniel; Bellinger, Daniel; Tafipolski, Maxim; Brückner, Charlotte; Engels, Bernd

    2014-09-15

    The presented program package, Conformational Analysis and Search Tool (CAST) allows the accurate treatment of large and flexible (macro) molecular systems. For the determination of thermally accessible minima CAST offers the newly developed TabuSearch algorithm, but algorithms such as Monte Carlo (MC), MC with minimization, and molecular dynamics are implemented as well. For the determination of reaction paths, CAST provides the PathOpt, the Nudge Elastic band, and the umbrella sampling approach. Access to free energies is possible through the free energy perturbation approach. Along with a number of standard force fields, a newly developed symmetry-adapted perturbation theory-based force field is included. Semiempirical computations are possible through DFTB+ and MOPAC interfaces. For calculations based on density functional theory, a Message Passing Interface (MPI) interface to the Graphics Processing Unit (GPU)-accelerated TeraChem program is available. The program is available on request. Copyright © 2014 Wiley Periodicals, Inc.

  14. Genome-Wide Comparative Gene Family Classification

    PubMed Central

    Frech, Christian; Chen, Nansheng

    2010-01-01

    Correct classification of genes into gene families is important for understanding gene function and evolution. Although gene families of many species have been resolved both computationally and experimentally with high accuracy, gene family classification in most newly sequenced genomes has not been done with the same high standard. This project has been designed to develop a strategy to effectively and accurately classify gene families across genomes. We first examine and compare the performance of computer programs developed for automated gene family classification. We demonstrate that some programs, including the hierarchical average-linkage clustering algorithm MC-UPGMA and the popular Markov clustering algorithm TRIBE-MCL, can reconstruct manual curation of gene families accurately. However, their performance is highly sensitive to parameter setting, i.e. different gene families require different program parameters for correct resolution. To circumvent the problem of parameterization, we have developed a comparative strategy for gene family classification. This strategy takes advantage of existing curated gene families of reference species to find suitable parameters for classifying genes in related genomes. To demonstrate the effectiveness of this novel strategy, we use TRIBE-MCL to classify chemosensory and ABC transporter gene families in C. elegans and its four sister species. We conclude that fully automated programs can establish biologically accurate gene families if parameterized accordingly. Comparative gene family classification finds optimal parameters automatically, thus allowing rapid insights into gene families of newly sequenced species. PMID:20976221

  15. Classification of Instructional Programs: 2000 Edition.

    ERIC Educational Resources Information Center

    Morgan, Robert L.; Hunt, E. Stephen

    This third revision of the Classification of Instructional Programs (CIP) updates and modifies education program classifications, providing a taxonomic scheme that supports the accurate tracking, assessment, and reporting of field of study and program completions activity. This edition has also been adopted as the standard field of study taxonomy…

  16. Retrieving the Molecular Composition of Planet-Forming Material: An Accurate Non-LTE Radiative Transfer Code for JWST

    NASA Astrophysics Data System (ADS)

    Pontoppidan, Klaus

    Based on the observed distributions of exoplanets and dynamical models of their evolution, the primary planet-forming regions of protoplanetary disks are thought to span distances of 1-20 AU from typical stars. A key observational challenge of the next decade will be to understand the links between the formation of planets in protoplanetary disks and the chemical composition of exoplanets. Potentially habitable planets in particular are likely formed by solids growing within radii of a few AU, augmented by unknown contributions from volatiles formed at larger radii of 10-50 AU. The basic chemical composition of these inner disk regions is characterized by near- to far-infrared (2-200 micron) emission lines from molecular gas at temperatures of 50-1500 K. A critical step toward measuring the chemical composition of planet-forming regions is therefore to convert observed infrared molecular line fluxes, profiles and images to gas temperatures, densities and molecular abundances. However, current techniques typically employ approximate radiative transfer methods and assumptions of local thermodynamic equilibrium (LTE) to retrieve abundances, leading to uncertainties of orders of magnitude and inconclusive comparisons to chemical models. Ultimately, the scientific impact of the high quality spectroscopic data expected from the James Webb Space Telescope (JWST) will be limited by the availability of radiative transfer tools for infrared molecular lines. We propose to develop a numerically accurate, non-LTE 3D line radiative transfer code, needed to interpret mid-infrared molecular line observations of protoplanetary and debris disks in preparation for the James Webb Space Telescope (JWST). This will be accomplished by adding critical functionality to the existing Monte Carlo code LIME, which was originally developed to support (sub)millimeter interferometric observations. In contrast to existing infrared codes, LIME calculates the exact statistical balance of arbitrary

  17. Validation of the prognostic gene portfolio, ClinicoMolecular Triad Classification, using an independent prospective breast cancer cohort and external patient populations.

    PubMed

    Wang, Dong-Yu; Done, Susan J; Mc Cready, David R; Leong, Wey L

    2014-07-04

    Using genome-wide expression profiles of a prospective training cohort of breast cancer patients, ClinicoMolecular Triad Classification (CMTC) was recently developed to classify breast cancers into three clinically relevant groups to aid treatment decisions. CMTC was found to be both prognostic and predictive in a large external breast cancer cohort in that study. This study serves to validate the reproducibility of CMTC and its prognostic value using independent patient cohorts. An independent internal cohort (n = 284) and a new external cohort (n = 2,181) were used to validate the association of CMTC between clinicopathological factors, 12 known gene signatures, two molecular subtype classifiers, and 19 oncogenic signalling pathway activities, and to reproduce the abilities of CMTC to predict clinical outcomes of breast cancer. In addition, we also updated the outcome data of the original training cohort (n = 147). The original training cohort reached a statistically significant difference (p < 0.05) in disease-free survivals between the three CMTC groups after an additional two years of follow-up (median = 55 months). The prognostic value of the triad classification was reproduced in the second independent internal cohort and the new external validation cohort. CMTC achieved even higher prognostic significance when all available patients were analyzed (n = 4,851). Oncogenic pathways Myc, E2F1, Ras and β-catenin were again implicated in the high-risk groups. Both prospective internal cohorts and the independent external cohorts reproduced the triad classification of CMTC and its prognostic significance. CMTC is an independent prognostic predictor, and it outperformed 12 other known prognostic gene signatures, molecular subtype classifications, and all other standard prognostic clinicopathological factors. Our results support further development of CMTC portfolio into a guide for personalized breast cancer treatments.

  18. Validation of the prognostic gene portfolio, ClinicoMolecular Triad Classification, using an independent prospective breast cancer cohort and external patient populations

    PubMed Central

    2014-01-01

    Introduction Using genome-wide expression profiles of a prospective training cohort of breast cancer patients, ClinicoMolecular Triad Classification (CMTC) was recently developed to classify breast cancers into three clinically relevant groups to aid treatment decisions. CMTC was found to be both prognostic and predictive in a large external breast cancer cohort in that study. This study serves to validate the reproducibility of CMTC and its prognostic value using independent patient cohorts. Methods An independent internal cohort (n = 284) and a new external cohort (n = 2,181) were used to validate the association of CMTC between clinicopathological factors, 12 known gene signatures, two molecular subtype classifiers, and 19 oncogenic signalling pathway activities, and to reproduce the abilities of CMTC to predict clinical outcomes of breast cancer. In addition, we also updated the outcome data of the original training cohort (n = 147). Results The original training cohort reached a statistically significant difference (p < 0.05) in disease-free survivals between the three CMTC groups after an additional two years of follow-up (median = 55 months). The prognostic value of the triad classification was reproduced in the second independent internal cohort and the new external validation cohort. CMTC achieved even higher prognostic significance when all available patients were analyzed (n = 4,851). Oncogenic pathways Myc, E2F1, Ras and β-catenin were again implicated in the high-risk groups. Conclusions Both prospective internal cohorts and the independent external cohorts reproduced the triad classification of CMTC and its prognostic significance. CMTC is an independent prognostic predictor, and it outperformed 12 other known prognostic gene signatures, molecular subtype classifications, and all other standard prognostic clinicopathological factors. Our results support further development of CMTC portfolio into a guide for personalized breast cancer treatments. PMID

  19. Exchange-Hole Dipole Dispersion Model for Accurate Energy Ranking in Molecular Crystal Structure Prediction.

    PubMed

    Whittleton, Sarah R; Otero-de-la-Roza, A; Johnson, Erin R

    2017-02-14

    Accurate energy ranking is a key facet to the problem of first-principles crystal-structure prediction (CSP) of molecular crystals. This work presents a systematic assessment of B86bPBE-XDM, a semilocal density functional combined with the exchange-hole dipole moment (XDM) dispersion model, for energy ranking using 14 compounds from the first five CSP blind tests. Specifically, the set of crystals studied comprises 11 rigid, planar compounds and 3 co-crystals. The experimental structure was correctly identified as the lowest in lattice energy for 12 of the 14 total crystals. One of the exceptions is 4-hydroxythiophene-2-carbonitrile, for which the experimental structure was correctly identified once a quasi-harmonic estimate of the vibrational free-energy contribution was included, evidencing the occasional importance of thermal corrections for accurate energy ranking. The other exception is an organic salt, where charge-transfer error (also called delocalization error) is expected to cause the base density functional to be unreliable. Provided the choice of base density functional is appropriate and an estimate of temperature effects is used, XDM-corrected density-functional theory is highly reliable for the energetic ranking of competing crystal structures.

  20. Quantitative LC-MS of polymers: determining accurate molecular weight distributions by combined size exclusion chromatography and electrospray mass spectrometry with maximum entropy data processing.

    PubMed

    Gruendling, Till; Guilhaus, Michael; Barner-Kowollik, Christopher

    2008-09-15

    We report on the successful application of size exclusion chromatography (SEC) combined with electrospray ionization mass spectrometry (ESI-MS) and refractive index (RI) detection for the determination of accurate molecular weight distributions of synthetic polymers, corrected for chromatographic band broadening. The presented method makes use of the ability of ESI-MS to accurately depict the peak profiles and retention volumes of individual oligomers eluting from the SEC column, whereas quantitative information on the absolute concentration of oligomers is obtained from the RI-detector only. A sophisticated computational algorithm based on the maximum entropy principle is used to process the data gained by both detectors, yielding an accurate molecular weight distribution, corrected for chromatographic band broadening. Poly(methyl methacrylate) standards with molecular weights up to 10 kDa serve as model compounds. Molecular weight distributions (MWDs) obtained by the maximum entropy procedure are compared to MWDs, which were calculated by a conventional calibration of the SEC-retention time axis with peak retention data obtained from the mass spectrometer. Comparison showed that for the employed chromatographic system, distributions below 7 kDa were only weakly influenced by chromatographic band broadening. However, the maximum entropy algorithm could successfully correct the MWD of a 10 kDa standard for band broadening effects. Molecular weight averages were between 5 and 14% lower than the manufacturer stated data obtained by classical means of calibration. The presented method demonstrates a consistent approach for analyzing data obtained by coupling mass spectrometric detectors and concentration sensitive detectors to polymer liquid chromatography.

  1. Phylogenetic classification of bony fishes.

    PubMed

    Betancur-R, Ricardo; Wiley, Edward O; Arratia, Gloria; Acero, Arturo; Bailly, Nicolas; Miya, Masaki; Lecointre, Guillaume; Ortí, Guillermo

    2017-07-06

    Fish classifications, as those of most other taxonomic groups, are being transformed drastically as new molecular phylogenies provide support for natural groups that were unanticipated by previous studies. A brief review of the main criteria used by ichthyologists to define their classifications during the last 50 years, however, reveals slow progress towards using an explicit phylogenetic framework. Instead, the trend has been to rely, in varying degrees, on deep-rooted anatomical concepts and authority, often mixing taxa with explicit phylogenetic support with arbitrary groupings. Two leading sources in ichthyology frequently used for fish classifications (JS Nelson's volumes of Fishes of the World and W. Eschmeyer's Catalog of Fishes) fail to adopt a global phylogenetic framework despite much recent progress made towards the resolution of the fish Tree of Life. The first explicit phylogenetic classification of bony fishes was published in 2013, based on a comprehensive molecular phylogeny ( www.deepfin.org ). We here update the first version of that classification by incorporating the most recent phylogenetic results. The updated classification presented here is based on phylogenies inferred using molecular and genomic data for nearly 2000 fishes. A total of 72 orders (and 79 suborders) are recognized in this version, compared with 66 orders in version 1. The phylogeny resolves placement of 410 families, or ~80% of the total of 514 families of bony fishes currently recognized. The ordinal status of 30 percomorph families included in this study, however, remains uncertain (incertae sedis in the series Carangaria, Ovalentaria, or Eupercaria). Comments to support taxonomic decisions and comparisons with conflicting taxonomic groups proposed by others are presented. We also highlight cases were morphological support exist for the groups being classified. This version of the phylogenetic classification of bony fishes is substantially improved, providing resolution

  2. Highly Accurate Classification of Watson-Crick Basepairs on Termini of Single DNA Molecules

    PubMed Central

    Winters-Hilt, Stephen; Vercoutere, Wenonah; DeGuzman, Veronica S.; Deamer, David; Akeson, Mark; Haussler, David

    2003-01-01

    We introduce a computational method for classification of individual DNA molecules measured by an α-hemolysin channel detector. We show classification with better than 99% accuracy for DNA hairpin molecules that differ only in their terminal Watson-Crick basepairs. Signal classification was done in silico to establish performance metrics (i.e., where train and test data were of known type, via single-species data files). It was then performed in solution to assay real mixtures of DNA hairpins. Hidden Markov Models (HMMs) were used with Expectation/Maximization for denoising and for associating a feature vector with the ionic current blockade of the DNA molecule. Support Vector Machines (SVMs) were used as discriminators, and were the focus of off-line training. A multiclass SVM architecture was designed to place less discriminatory load on weaker discriminators, and novel SVM kernels were used to boost discrimination strength. The tuning on HMMs and SVMs enabled biophysical analysis of the captured molecule states and state transitions; structure revealed in the biophysical analysis was used for better feature selection. PMID:12547778

  3. Molecular classification of fatty liver by high-throughput profiling of protein post-translational modifications.

    PubMed

    Urasaki, Yasuyo; Fiscus, Ronald R; Le, Thuc T

    2016-04-01

    We describe an alternative approach to classifying fatty liver by profiling protein post-translational modifications (PTMs) with high-throughput capillary isoelectric focusing (cIEF) immunoassays. Four strains of mice were studied, with fatty livers induced by different causes, such as ageing, genetic mutation, acute drug usage, and high-fat diet. Nutrient-sensitive PTMs of a panel of 12 liver metabolic and signalling proteins were simultaneously evaluated with cIEF immunoassays, using nanograms of total cellular protein per assay. Changes to liver protein acetylation, phosphorylation, and O-N-acetylglucosamine glycosylation were quantified and compared between normal and diseased states. Fatty liver tissues could be distinguished from one another by distinctive protein PTM profiles. Fatty liver is currently classified by morphological assessment of lipid droplets, without identifying the underlying molecular causes. In contrast, high-throughput profiling of protein PTMs has the potential to provide molecular classification of fatty liver. Copyright © 2016 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

  4. Molecular Testing of Brain Tumor

    PubMed Central

    Park, Sung-Hye; Won, Jaekyung; Kim, Seong-Ik; Lee, Yujin; Park, Chul-Kee; Kim, Seung-Ki; Choi, Seung-Hong

    2017-01-01

    The World Health Organization (WHO) classification of central nervous system (CNS) tumors was revised in 2016 with a basis on the integrated diagnosis of molecular genetics. We herein provide the guidelines for using molecular genetic tests in routine pathological practice for an accurate diagnosis and appropriate management. While astrocytomas and IDH-mutant (secondary) glioblastomas are characterized by the mutational status of IDH, TP53, and ATRX, oligodendrogliomas have a 1p/19q codeletion and mutations in IDH, CIC, FUBP1, and the promoter region of telomerase reverse transcriptase (TERTp). IDH-wildtype (primary) glioblastomas typically lack mutations in IDH, but are characterized by copy number variations of EGFR, PTEN, CDKN2A/B, PDGFRA, and NF1 as well as mutations of TERTp. High-grade pediatric gliomas differ from those of adult gliomas, consisting of mutations in H3F3A, ATRX, and DAXX, but not in IDH genes. In contrast, well-circumscribed low-grade neuroepithelial tumors in children, such as pilocytic astrocytoma, pleomorphic xanthoastrocytoma, and ganglioglioma, often have mutations or activating rearrangements in the BRAF, FGFR1, and MYB genes. Other CNS tumors, such as ependymomas, neuronal and glioneuronal tumors, embryonal tumors, meningothelial, and other mesenchymal tumors have important genetic alterations, many of which are diagnostic, prognostic, and predictive markers and therapeutic targets. Therefore, the neuropathological evaluation of brain tumors is increasingly dependent on molecular genetic tests for proper classification, prediction of biological behavior and patient management. Identifying these gene abnormalities requires cost-effective and high-throughput testing, such as next-generation sequencing. Overall, this paper reviews the global guidelines and diagnostic algorithms for molecular genetic testing of brain tumors. PMID:28535583

  5. Update on diabetes classification.

    PubMed

    Thomas, Celeste C; Philipson, Louis H

    2015-01-01

    This article highlights the difficulties in creating a definitive classification of diabetes mellitus in the absence of a complete understanding of the pathogenesis of the major forms. This brief review shows the evolving nature of the classification of diabetes mellitus. No classification scheme is ideal, and all have some overlap and inconsistencies. The only diabetes in which it is possible to accurately diagnose by DNA sequencing, monogenic diabetes, remains undiagnosed in more than 90% of the individuals who have diabetes caused by one of the known gene mutations. The point of classification, or taxonomy, of disease, should be to give insight into both pathogenesis and treatment. It remains a source of frustration that all schemes of diabetes mellitus continue to fall short of this goal. Copyright © 2015 Elsevier Inc. All rights reserved.

  6. Accurate phylogenetic classification of DNA fragments based onsequence composition

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    McHardy, Alice C.; Garcia Martin, Hector; Tsirigos, Aristotelis

    2006-05-01

    Metagenome studies have retrieved vast amounts of sequenceout of a variety of environments, leading to novel discoveries and greatinsights into the uncultured microbial world. Except for very simplecommunities, diversity makes sequence assembly and analysis a verychallenging problem. To understand the structure a 5 nd function ofmicrobial communities, a taxonomic characterization of the obtainedsequence fragments is highly desirable, yet currently limited mostly tothose sequences that contain phylogenetic marker genes. We show that forclades at the rank of domain down to genus, sequence composition allowsthe very accurate phylogenetic 10 characterization of genomic sequence.We developed a composition-based classifier, PhyloPythia, for de novophylogenetic sequencemore » characterization and have trained it on adata setof 340 genomes. By extensive evaluation experiments we show that themethodis accurate across all taxonomic ranks considered, even forsequences that originate fromnovel organisms and are as short as 1kb.Application to two metagenome datasets 15 obtained from samples ofphosphorus-removing sludge showed that the method allows the accurateclassification at genus level of most sequence fragments from thedominant populations, while at the same time correctly characterizingeven larger parts of the samples at higher taxonomic levels.« less

  7. Prostate segmentation by sparse representation based classification

    PubMed Central

    Gao, Yaozong; Liao, Shu; Shen, Dinggang

    2012-01-01

    Purpose: The segmentation of prostate in CT images is of essential importance to external beam radiotherapy, which is one of the major treatments for prostate cancer nowadays. During the radiotherapy, the prostate is radiated by high-energy x rays from different directions. In order to maximize the dose to the cancer and minimize the dose to the surrounding healthy tissues (e.g., bladder and rectum), the prostate in the new treatment image needs to be accurately localized. Therefore, the effectiveness and efficiency of external beam radiotherapy highly depend on the accurate localization of the prostate. However, due to the low contrast of the prostate with its surrounding tissues (e.g., bladder), the unpredicted prostate motion, and the large appearance variations across different treatment days, it is challenging to segment the prostate in CT images. In this paper, the authors present a novel classification based segmentation method to address these problems. Methods: To segment the prostate, the proposed method first uses sparse representation based classification (SRC) to enhance the prostate in CT images by pixel-wise classification, in order to overcome the limitation of poor contrast of the prostate images. Then, based on the classification results, previous segmented prostates of the same patient are used as patient-specific atlases to align onto the current treatment image and the majority voting strategy is finally adopted to segment the prostate. In order to address the limitations of the traditional SRC in pixel-wise classification, especially for the purpose of segmentation, the authors extend SRC from the following four aspects: (1) A discriminant subdictionary learning method is proposed to learn a discriminant and compact representation of training samples for each class so that the discriminant power of SRC can be increased and also SRC can be applied to the large-scale pixel-wise classification. (2) The L1 regularized sparse coding is replaced by

  8. Prostate segmentation by sparse representation based classification.

    PubMed

    Gao, Yaozong; Liao, Shu; Shen, Dinggang

    2012-10-01

    The segmentation of prostate in CT images is of essential importance to external beam radiotherapy, which is one of the major treatments for prostate cancer nowadays. During the radiotherapy, the prostate is radiated by high-energy x rays from different directions. In order to maximize the dose to the cancer and minimize the dose to the surrounding healthy tissues (e.g., bladder and rectum), the prostate in the new treatment image needs to be accurately localized. Therefore, the effectiveness and efficiency of external beam radiotherapy highly depend on the accurate localization of the prostate. However, due to the low contrast of the prostate with its surrounding tissues (e.g., bladder), the unpredicted prostate motion, and the large appearance variations across different treatment days, it is challenging to segment the prostate in CT images. In this paper, the authors present a novel classification based segmentation method to address these problems. To segment the prostate, the proposed method first uses sparse representation based classification (SRC) to enhance the prostate in CT images by pixel-wise classification, in order to overcome the limitation of poor contrast of the prostate images. Then, based on the classification results, previous segmented prostates of the same patient are used as patient-specific atlases to align onto the current treatment image and the majority voting strategy is finally adopted to segment the prostate. In order to address the limitations of the traditional SRC in pixel-wise classification, especially for the purpose of segmentation, the authors extend SRC from the following four aspects: (1) A discriminant subdictionary learning method is proposed to learn a discriminant and compact representation of training samples for each class so that the discriminant power of SRC can be increased and also SRC can be applied to the large-scale pixel-wise classification. (2) The L1 regularized sparse coding is replaced by the elastic net in

  9. Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds.

    PubMed

    Sur, Maitreyi; Suffredini, Tony; Wessells, Stephen M; Bloom, Peter H; Lanzone, Michael; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd

    2017-01-01

    Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.

  10. Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

    PubMed Central

    Suffredini, Tony; Wessells, Stephen M.; Bloom, Peter H.; Lanzone, Michael; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd

    2017-01-01

    Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data. PMID:28403159

  11. Improved supervised classification of accelerometry data to distinguish behaviors of soaring birds

    USGS Publications Warehouse

    Sur, Maitreyi; Suffredini, Tony; Wessells, Stephen M.; Bloom, Peter H.; Lanzone, Michael J.; Blackshire, Sheldon; Sridhar, Srisarguru; Katzner, Todd

    2017-01-01

    Soaring birds can balance the energetic costs of movement by switching between flapping, soaring and gliding flight. Accelerometers can allow quantification of flight behavior and thus a context to interpret these energetic costs. However, models to interpret accelerometry data are still being developed, rarely trained with supervised datasets, and difficult to apply. We collected accelerometry data at 140Hz from a trained golden eagle (Aquila chrysaetos) whose flight we recorded with video that we used to characterize behavior. We applied two forms of supervised classifications, random forest (RF) models and K-nearest neighbor (KNN) models. The KNN model was substantially easier to implement than the RF approach but both were highly accurate in classifying basic behaviors such as flapping (85.5% and 83.6% accurate, respectively), soaring (92.8% and 87.6%) and sitting (84.1% and 88.9%) with overall accuracies of 86.6% and 92.3% respectively. More detailed classification schemes, with specific behaviors such as banking and straight flights were well classified only by the KNN model (91.24% accurate; RF = 61.64% accurate). The RF model maintained its accuracy of classifying basic behavior classification accuracy of basic behaviors at sampling frequencies as low as 10Hz, the KNN at sampling frequencies as low as 20Hz. Classification of accelerometer data collected from free ranging birds demonstrated a strong dependence of predicted behavior on the type of classification model used. Our analyses demonstrate the consequence of different approaches to classification of accelerometry data, the potential to optimize classification algorithms with validated flight behaviors to improve classification accuracy, ideal sampling frequencies for different classification algorithms, and a number of ways to improve commonly used analytical techniques and best practices for classification of accelerometry data.

  12. The biobank for the molecular classification of kidney disease: research translation and precision medicine in nephrology.

    PubMed

    Muruve, Daniel A; Mann, Michelle C; Chapman, Kevin; Wong, Josee F; Ravani, Pietro; Page, Stacey A; Benediktsson, Hallgrimur

    2017-07-26

    Advances in technology and the ability to interrogate disease pathogenesis using systems biology approaches are exploding. As exemplified by the substantial progress in the personalized diagnosis and treatment of cancer, the application of systems biology to enable precision medicine in other disciplines such as Nephrology is well underway. Infrastructure that permits the integration of clinical data, patient biospecimens and advanced technologies is required for institutions to contribute to, and benefit from research in molecular disease classification and to devise specific and patient-oriented treatments. We describe the establishment of the Biobank for the Molecular Classification of Kidney Disease (BMCKD) at the University of Calgary, Alberta, Canada. The BMCKD consists of a fully equipped wet laboratory, an information technology infrastructure, and a formal operational, ethical and legal framework for banking human biospecimens and storing clinical data. The BMCKD first consolidated a large retrospective cohort of kidney biopsy specimens to create a population-based renal pathology database and tissue inventory of glomerular and other kidney diseases. The BMCKD will continue to prospectively bank all kidney biopsies performed in Southern Alberta. The BMCKD is equipped to perform molecular, clinical and epidemiologic studies in renal pathology. The BMCKD also developed formal biobanking procedures for human specimens such as blood, urine and nucleic acids collected for basic and clinical research studies or for advanced diagnostic technologies in clinical care. The BMCKD is guided by standard operating procedures, an ethics framework and legal agreements with stakeholders that include researchers, data custodians and patients. The design and structure of the BMCKD permits its inclusion in a wide variety of research and clinical activities. The BMCKD is a core multidisciplinary facility that will bridge basic and clinical research and integrate precision

  13. Automatic classification of blank substrate defects

    NASA Astrophysics Data System (ADS)

    Boettiger, Tom; Buck, Peter; Paninjath, Sankaranarayanan; Pereira, Mark; Ronald, Rob; Rost, Dan; Samir, Bhamidipati

    2014-10-01

    Mask preparation stages are crucial in mask manufacturing, since this mask is to later act as a template for considerable number of dies on wafer. Defects on the initial blank substrate, and subsequent cleaned and coated substrates, can have a profound impact on the usability of the finished mask. This emphasizes the need for early and accurate identification of blank substrate defects and the risk they pose to the patterned reticle. While Automatic Defect Classification (ADC) is a well-developed technology for inspection and analysis of defects on patterned wafers and masks in the semiconductors industry, ADC for mask blanks is still in the early stages of adoption and development. Calibre ADC is a powerful analysis tool for fast, accurate, consistent and automatic classification of defects on mask blanks. Accurate, automated classification of mask blanks leads to better usability of blanks by enabling defect avoidance technologies during mask writing. Detailed information on blank defects can help to select appropriate job-decks to be written on the mask by defect avoidance tools [1][4][5]. Smart algorithms separate critical defects from the potentially large number of non-critical defects or false defects detected at various stages during mask blank preparation. Mechanisms used by Calibre ADC to identify and characterize defects include defect location and size, signal polarity (dark, bright) in both transmitted and reflected review images, distinguishing defect signals from background noise in defect images. The Calibre ADC engine then uses a decision tree to translate this information into a defect classification code. Using this automated process improves classification accuracy, repeatability and speed, while avoiding the subjectivity of human judgment compared to the alternative of manual defect classification by trained personnel [2]. This paper focuses on the results from the evaluation of Automatic Defect Classification (ADC) product at MP Mask

  14. Clinical application of a microfluidic chip for immunocapture and quantification of circulating exosomes to assist breast cancer diagnosis and molecular classification.

    PubMed

    Fang, Shimeng; Tian, Hongzhu; Li, Xiancheng; Jin, Dong; Li, Xiaojie; Kong, Jing; Yang, Chun; Yang, Xuesong; Lu, Yao; Luo, Yong; Lin, Bingcheng; Niu, Weidong; Liu, Tingjiao

    2017-01-01

    Increasing attention has been attracted by exosomes in blood-based diagnosis because cancer cells release more exosomes in serum than normal cells and these exosomes overexpress a certain number of cancer-related biomarkers. However, capture and biomarker analysis of exosomes for clinical application are technically challenging. In this study, we developed a microfluidic chip for immunocapture and quantification of circulating exosomes from small sample volume and applied this device in clinical study. Circulating EpCAM-positive exosomes were measured in 6 cases breast cancer patients and 3 healthy controls to assist diagnosis. A significant increase in the EpCAM-positive exosome level in these patients was detected, compared to healthy controls. Furthermore, we quantified circulating HER2-positive exosomes in 19 cases of breast cancer patients for molecular classification. We demonstrated that the exosomal HER2 expression levels were almost consistent with that in tumor tissues assessed by immunohistochemical staining. The microfluidic chip might provide a new platform to assist breast cancer diagnosis and molecular classification.

  15. Protein classification based on text document classification techniques.

    PubMed

    Cheng, Betty Yee Man; Carbonell, Jaime G; Klein-Seetharaman, Judith

    2005-03-01

    The need for accurate, automated protein classification methods continues to increase as advances in biotechnology uncover new proteins. G-protein coupled receptors (GPCRs) are a particularly difficult superfamily of proteins to classify due to extreme diversity among its members. Previous comparisons of BLAST, k-nearest neighbor (k-NN), hidden markov model (HMM) and support vector machine (SVM) using alignment-based features have suggested that classifiers at the complexity of SVM are needed to attain high accuracy. Here, analogous to document classification, we applied Decision Tree and Naive Bayes classifiers with chi-square feature selection on counts of n-grams (i.e. short peptide sequences of length n) to this classification task. Using the GPCR dataset and evaluation protocol from the previous study, the Naive Bayes classifier attained an accuracy of 93.0 and 92.4% in level I and level II subfamily classification respectively, while SVM has a reported accuracy of 88.4 and 86.3%. This is a 39.7 and 44.5% reduction in residual error for level I and level II subfamily classification, respectively. The Decision Tree, while inferior to SVM, outperforms HMM in both level I and level II subfamily classification. For those GPCR families whose profiles are stored in the Protein FAMilies database of alignments and HMMs (PFAM), our method performs comparably to a search against those profiles. Finally, our method can be generalized to other protein families by applying it to the superfamily of nuclear receptors with 94.5, 97.8 and 93.6% accuracy in family, level I and level II subfamily classification respectively. Copyright 2005 Wiley-Liss, Inc.

  16. Simultaneous fecal microbial and metabolite profiling enables accurate classification of pediatric irritable bowel syndrome.

    PubMed

    Shankar, Vijay; Reo, Nicholas V; Paliy, Oleg

    2015-12-09

    We previously showed that stool samples of pre-adolescent and adolescent US children diagnosed with diarrhea-predominant IBS (IBS-D) had different compositions of microbiota and metabolites compared to healthy age-matched controls. Here we explored whether observed fecal microbiota and metabolite differences between these two adolescent populations can be used to discriminate between IBS and health. We constructed individual microbiota- and metabolite-based sample classification models based on the partial least squares multivariate analysis and then applied a Bayesian approach to integrate individual models into a single classifier. The resulting combined classification achieved 84 % accuracy of correct sample group assignment and 86 % prediction for IBS-D in cross-validation tests. The performance of the cumulative classification model was further validated by the de novo analysis of stool samples from a small independent IBS-D cohort. High-throughput microbial and metabolite profiling of subject stool samples can be used to facilitate IBS diagnosis.

  17. Analysis and application of classification methods of complex carbonate reservoirs

    NASA Astrophysics Data System (ADS)

    Li, Xiongyan; Qin, Ruibao; Ping, Haitao; Wei, Dan; Liu, Xiaomei

    2018-06-01

    There are abundant carbonate reservoirs from the Cenozoic to Mesozoic era in the Middle East. Due to variation in sedimentary environment and diagenetic process of carbonate reservoirs, several porosity types coexist in carbonate reservoirs. As a result, because of the complex lithologies and pore types as well as the impact of microfractures, the pore structure is very complicated. Therefore, it is difficult to accurately calculate the reservoir parameters. In order to accurately evaluate carbonate reservoirs, based on the pore structure evaluation of carbonate reservoirs, the classification methods of carbonate reservoirs are analyzed based on capillary pressure curves and flow units. Based on the capillary pressure curves, although the carbonate reservoirs can be classified, the relationship between porosity and permeability after classification is not ideal. On the basis of the flow units, the high-precision functional relationship between porosity and permeability after classification can be established. Therefore, the carbonate reservoirs can be quantitatively evaluated based on the classification of flow units. In the dolomite reservoirs, the average absolute error of calculated permeability decreases from 15.13 to 7.44 mD. Similarly, the average absolute error of calculated permeability of limestone reservoirs is reduced from 20.33 to 7.37 mD. Only by accurately characterizing pore structures and classifying reservoir types, reservoir parameters could be calculated accurately. Therefore, characterizing pore structures and classifying reservoir types are very important to accurate evaluation of complex carbonate reservoirs in the Middle East.

  18. The new WHO 2016 classification of brain tumors-what neurosurgeons need to know.

    PubMed

    Banan, Rouzbeh; Hartmann, Christian

    2017-03-01

    The understanding of molecular alterations of tumors has severely changed the concept of classification in all fields of pathology. The availability of high-throughput technologies such as next-generation sequencing allows for a much more precise definition of tumor entities. Also in the field of brain tumors a dramatic increase of knowledge has occurred over the last years partially calling into question the purely morphologically based concepts that were used as exclusive defining criteria in the WHO 2007 classification. Review of the WHO 2016 classification of brain tumors as well as a search and review of publications in the literature relevant for brain tumor classification from 2007 up to now. The idea of incorporating the molecular features in classifying tumors of the central nervous system led the authors of the new WHO 2016 classification to encounter inevitable conceptual problems, particularly with respect to linking morphology to molecular alterations. As a solution they introduced the concept of a "layered diagnosis" to the classification of brain tumors that still allows at a lower level a purely morphologically based diagnosis while partially forcing the incorporation of molecular characteristics for an "integrated diagnosis" at the highest diagnostic level. In this context the broad availability of molecular assays was debated. On the one hand molecular antibodies specifically targeting mutated proteins should be available in nearly all neuropathological laboratories. On the other hand, different high-throughput assays are accessible only in few first-world neuropathological institutions. As examples oligodendrogliomas are now primarily defined by molecular characteristics since the required assays are generally established, whereas molecular grouping of ependymomas, found to clearly outperform morphologically based tumor interpretation, was rejected from inclusion in the WHO 2016 classification because the required assays are currently only

  19. Scalable metagenomic taxonomy classification using a reference genome database

    PubMed Central

    Ames, Sasha K.; Hysom, David A.; Gardner, Shea N.; Lloyd, G. Scott; Gokhale, Maya B.; Allen, Jonathan E.

    2013-01-01

    Motivation: Deep metagenomic sequencing of biological samples has the potential to recover otherwise difficult-to-detect microorganisms and accurately characterize biological samples with limited prior knowledge of sample contents. Existing metagenomic taxonomic classification algorithms, however, do not scale well to analyze large metagenomic datasets, and balancing classification accuracy with computational efficiency presents a fundamental challenge. Results: A method is presented to shift computational costs to an off-line computation by creating a taxonomy/genome index that supports scalable metagenomic classification. Scalable performance is demonstrated on real and simulated data to show accurate classification in the presence of novel organisms on samples that include viruses, prokaryotes, fungi and protists. Taxonomic classification of the previously published 150 giga-base Tyrolean Iceman dataset was found to take <20 h on a single node 40 core large memory machine and provide new insights on the metagenomic contents of the sample. Availability: Software was implemented in C++ and is freely available at http://sourceforge.net/projects/lmat Contact: allen99@llnl.gov Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23828782

  20. Molecular phylogeny of the Bothriocephalidea (Cestoda): molecular data challenge morphological classification.

    PubMed

    Brabec, Jan; Waeschenbach, Andrea; Scholz, Tomáš; Littlewood, D Timothy J; Kuchta, Roman

    2015-10-01

    In this study, the relationships of the cestode order Bothriocephalidea, parasites of marine and freshwater bony fish, were assessed using multi-gene molecular phylogenetic analyses. The dataset included 59 species, covering approximately 70% of currently recognised genera, a sample of bothriocephalidean biodiversity gathered through an intense 15year effort. The order as currently circumscribed, while monophyletic, includes three non-monophyletic and one monophyletic families. Bothriocephalidae is monophyletic and forms the most derived lineage of the order, comprised of a single freshwater and several marine clades. Biogeographic patterns within the freshwater clade are indicative of past radiations having occurred in Africa and North America. The earliest diverging lineages of the order comprise a paraphyletic Triaenophoridae. The Echinophallidae, consisting nearly exclusively of parasites of pelagic fish, was also resolved as paraphyletic with respect to the Bothriocephalidae. Philobythoides sp., the only representative included from the Philobythiidae, a unique family of parasites of bathypelagic fish, was sister to the genus Eubothrium, the latter constituting one of the lineages of the paraphyletic Triaenophoridae. Due to the weak statistical support for most of the basal nodes of the Triaenophoridae and Echinophallidae, as well as the lack of obvious morphological synapomorphies shared by taxa belonging to the statistically well-supported lineages, the current family-level classification, although mostly non-monophyletic, is provisionally retained, with the exception of the family Philobythiidae, which is recognised as a synonym of the Triaenophoridae. In addition, Schyzocotyle is resurrected to accommodate the invasive Asian fish tapeworm, Schyzocotyle acheilognathi (Yamaguti, 1934) n. comb. (syn. Bothriocephalus acheilognathi Yamaguti, 1934), which is of veterinary importance, and Schyzocotyle nayarensis (Malhotra, 1983) n. comb. (syn. Ptychobothrium

  1. Molecular Pathology: Predictive, Prognostic, and Diagnostic Markers in Uterine Tumors.

    PubMed

    Ritterhouse, Lauren L; Howitt, Brooke E

    2016-09-01

    This article focuses on the diagnostic, prognostic, and predictive molecular biomarkers in uterine malignancies, in the context of morphologic diagnoses. The histologic classification of endometrial carcinomas is reviewed first, followed by the description and molecular classification of endometrial epithelial malignancies in the context of histologic classification. Taken together, the molecular and histologic classifications help clinicians to approach troublesome areas encountered in clinical practice and evaluate the utility of molecular alterations in the diagnosis and subclassification of endometrial carcinomas. Putative prognostic markers are reviewed. The use of molecular alterations and surrogate immunohistochemistry as prognostic and predictive markers is also discussed. Copyright © 2016 Elsevier Inc. All rights reserved.

  2. Free classification of American English dialects by native and non-native listeners

    PubMed Central

    Clopper, Cynthia G.; Bradlow, Ann R.

    2009-01-01

    Most second language acquisition research focuses on linguistic structures, and less research has examined the acquisition of sociolinguistic patterns. The current study explored the perceptual classification of regional dialects of American English by native and non-native listeners using a free classification task. Results revealed similar classification strategies for the native and non-native listeners. However, the native listeners were more accurate overall than the non-native listeners. In addition, the non-native listeners were less able to make use of constellations of cues to accurately classify the talkers by dialect. However, the non-native listeners were able to attend to cues that were either phonologically or sociolinguistically relevant in their native language. These results suggest that non-native listeners can use information in the speech signal to classify talkers by regional dialect, but that their lack of signal-independent cultural knowledge about variation in the second language leads to less accurate classification performance. PMID:20161400

  3. Multiple Sparse Representations Classification

    PubMed Central

    Plenge, Esben; Klein, Stefan S.; Niessen, Wiro J.; Meijering, Erik

    2015-01-01

    Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this empirical study we propose to further leverage the redundancy of the learned dictionaries to achieve a more accurate classifier. In conventional SRC, each image pixel is associated with a small patch surrounding it. Using these patches, a dictionary is trained for each class in a supervised fashion. Commonly, redundant/overcomplete dictionaries are trained and image patches are sparsely represented by a linear combination of only a few of the dictionary elements. Given a set of trained dictionaries, a new patch is sparse coded using each of them, and subsequently assigned to the class whose dictionary yields the minimum residual energy. We propose a generalization of this scheme. The method, which we call multiple sparse representations classification (mSRC), is based on the observation that an overcomplete, class specific dictionary is capable of generating multiple accurate and independent estimates of a patch belonging to the class. So instead of finding a single sparse representation of a patch for each dictionary, we find multiple, and the corresponding residual energies provides an enhanced statistic which is used to improve classification. We demonstrate the efficacy of mSRC for three example applications: pixelwise classification of texture images, lumen segmentation in carotid artery magnetic resonance imaging (MRI), and bifurcation point detection in carotid artery MRI. We compare our method with conventional SRC, K-nearest neighbor, and support vector machine classifiers. The results show that mSRC outperforms SRC and the other reference methods. In addition, we present an extensive evaluation of the effect of the main mSRC parameters: patch size, dictionary size, and

  4. Effects of stress typicality during speeded grammatical classification.

    PubMed

    Arciuli, Joanne; Cupples, Linda

    2003-01-01

    The experiments reported here were designed to investigate the influence of stress typicality during speeded grammatical classification of disyllabic English words by native and non-native speakers. Trochaic nouns and iambic gram verbs were considered to be typically stressed, whereas iambic nouns and trochaic verbs were considered to be atypically stressed. Experiments 1a and 2a showed that while native speakers classified typically stressed words individual more quickly and more accurately than atypically stressed words during differences reading, there were no overall effects during classification of spoken stimuli. However, a subgroup of native speakers with high error rates did show a significant effect during classification of spoken stimuli. Experiments 1b and 2b showed that non-native speakers classified typically stressed words more quickly and more accurately than atypically stressed words during reading. Typically stressed words were classified more accurately than atypically stressed words when the stimuli were spoken. Importantly, there was a significant relationship between error rates, vocabulary size and the size of the stress typicality effect in each experiment. We conclude that participants use information about lexical stress to help them distinguish between disyllabic nouns and verbs during speeded grammatical classification. This is especially so for individuals with a limited vocabulary who lack other knowledge (e.g., semantic knowledge) about the differences between these grammatical categories.

  5. GAB2 amplifications refine molecular classification of melanoma.

    PubMed

    Chernoff, Karen A; Bordone, Lindsey; Horst, Basil; Simon, Katherine; Twadell, William; Lee, Keagan; Cohen, Jason A; Wang, Shuang; Silvers, David N; Brunner, Georg; Celebi, Julide Tok

    2009-07-01

    Gain-of-function mutations in BRAF, NRAS, or KIT are associated with distinct melanoma subtypes with KIT mutations and/or copy number changes frequently observed among melanomas arising from sun-protected sites, such as acral skin (palms, soles, and nail bed) and mucous membranes. GAB2 has recently been implicated in melanoma pathogenesis, and increased copy numbers are found in a subset of melanomas. We sought to determine the association of increased copy numbers of GAB2 among melanoma subtypes in the context of genetic alterations in BRAF, NRAS, and KIT. A total of 85 melanomas arising from sun-protected (n = 23) and sun-exposed sites (n = 62) were analyzed for copy number changes using array-based comparative genomic hybridization and for gain-of-function mutations in BRAF, NRAS, and KIT. GAB2 amplifications were found in 9% of the cases and were associated with melanomas arising from acral and mucosal sites (P = 0.005). Increased copy numbers of the KIT locus were observed in 6% of the cases. The overall mutation frequencies for BRAF and NRAS were 43.5% and 14%, respectively, and were mutually exclusive. Among the acral and mucosal melanomas studied, the genetic alteration frequency was 26% for GAB2, 13% for KIT, 30% for BRAF, and 4% for NRAS. Importantly, the majority of GAB2 amplifications occurred independent from genetic events in BRAF, NRAS, and KIT. GAB2 amplification is critical for melanomas arising from sun-protected sites. Genetic alterations in GAB2 will help refine the molecular classification of melanomas.

  6. Accurate Gaussian basis sets for atomic and molecular calculations obtained from the generator coordinate method with polynomial discretization.

    PubMed

    Celeste, Ricardo; Maringolo, Milena P; Comar, Moacyr; Viana, Rommel B; Guimarães, Amanda R; Haiduke, Roberto L A; da Silva, Albérico B F

    2015-10-01

    Accurate Gaussian basis sets for atoms from H to Ba were obtained by means of the generator coordinate Hartree-Fock (GCHF) method based on a polynomial expansion to discretize the Griffin-Wheeler-Hartree-Fock equations (GWHF). The discretization of the GWHF equations in this procedure is based on a mesh of points not equally distributed in contrast with the original GCHF method. The results of atomic Hartree-Fock energies demonstrate the capability of these polynomial expansions in designing compact and accurate basis sets to be used in molecular calculations and the maximum error found when compared to numerical values is only 0.788 mHartree for indium. Some test calculations with the B3LYP exchange-correlation functional for N2, F2, CO, NO, HF, and HCN show that total energies within 1.0 to 2.4 mHartree compared to the cc-pV5Z basis sets are attained with our contracted bases with a much smaller number of polarization functions (2p1d and 2d1f for hydrogen and heavier atoms, respectively). Other molecular calculations performed here are also in very good accordance with experimental and cc-pV5Z results. The most important point to be mentioned here is that our generator coordinate basis sets required only a tiny fraction of the computational time when compared to B3LYP/cc-pV5Z calculations.

  7. Progressive Classification Using Support Vector Machines

    NASA Technical Reports Server (NTRS)

    Wagstaff, Kiri; Kocurek, Michael

    2009-01-01

    An algorithm for progressive classification of data, analogous to progressive rendering of images, makes it possible to compromise between speed and accuracy. This algorithm uses support vector machines (SVMs) to classify data. An SVM is a machine learning algorithm that builds a mathematical model of the desired classification concept by identifying the critical data points, called support vectors. Coarse approximations to the concept require only a few support vectors, while precise, highly accurate models require far more support vectors. Once the model has been constructed, the SVM can be applied to new observations. The cost of classifying a new observation is proportional to the number of support vectors in the model. When computational resources are limited, an SVM of the appropriate complexity can be produced. However, if the constraints are not known when the model is constructed, or if they can change over time, a method for adaptively responding to the current resource constraints is required. This capability is particularly relevant for spacecraft (or any other real-time systems) that perform onboard data analysis. The new algorithm enables the fast, interactive application of an SVM classifier to a new set of data. The classification process achieved by this algorithm is characterized as progressive because a coarse approximation to the true classification is generated rapidly and thereafter iteratively refined. The algorithm uses two SVMs: (1) a fast, approximate one and (2) slow, highly accurate one. New data are initially classified by the fast SVM, producing a baseline approximate classification. For each classified data point, the algorithm calculates a confidence index that indicates the likelihood that it was classified correctly in the first pass. Next, the data points are sorted by their confidence indices and progressively reclassified by the slower, more accurate SVM, starting with the items most likely to be incorrectly classified. The user

  8. Automated cell-type classification in intact tissues by single-cell molecular profiling

    PubMed Central

    2018-01-01

    A major challenge in biology is identifying distinct cell classes and mapping their interactions in vivo. Tissue-dissociative technologies enable deep single cell molecular profiling but do not provide spatial information. We developed a proximity ligation in situ hybridization technology (PLISH) with exceptional signal strength, specificity, and sensitivity in tissue. Multiplexed data sets can be acquired using barcoded probes and rapid label-image-erase cycles, with automated calculation of single cell profiles, enabling clustering and anatomical re-mapping of cells. We apply PLISH to expression profile ~2900 cells in intact mouse lung, which identifies and localizes known cell types, including rare ones. Unsupervised classification of the cells indicates differential expression of ‘housekeeping’ genes between cell types, and re-mapping of two sub-classes of Club cells highlights their segregated spatial domains in terminal airways. By enabling single cell profiling of various RNA species in situ, PLISH can impact many areas of basic and medical research. PMID:29319504

  9. Addition of Histology to the Paris Classification of Pediatric Crohn Disease Alters Classification of Disease Location.

    PubMed

    Fernandes, Melissa A; Verstraete, Sofia G; Garnett, Elizabeth A; Heyman, Melvin B

    2016-02-01

    The aim of the study was to investigate the value of microscopic findings in the classification of pediatric Crohn disease (CD) by determining whether classification of disease changes significantly with inclusion of histologic findings. Sixty patients were randomly selected from a cohort of patients studied at the Pediatric Inflammatory Bowel Disease Clinic at the University of California, San Francisco Benioff Children's Hospital. Two physicians independently reviewed the electronic health records of the included patients to determine the Paris classification for each patient by adhering to present guidelines and then by including microscopic findings. Macroscopic and combined disease location classifications were discordant in 34 (56.6%), with no statistically significant differences between groups. Interobserver agreement was higher in the combined classification (κ = 0.73, 95% confidence interval 0.65-0.82) as opposed to when classification was limited to macroscopic findings (κ = 0.53, 95% confidence interval 0.40-0.58). When evaluating the proximal upper gastrointestinal tract (Paris L4a), the interobserver agreement was better in macroscopic compared with the combined classification. Disease extent classifications differed significantly when comparing isolated macroscopic findings (Paris classification) with the combined scheme that included microscopy. Further studies are needed to determine which scheme provides more accurate representation of disease extent.

  10. Land use/cover classification in the Brazilian Amazon using satellite images.

    PubMed

    Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira

    2012-09-01

    Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

  11. Land use/cover classification in the Brazilian Amazon using satellite images

    PubMed Central

    Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant’Anna, Sidnei João Siqueira

    2013-01-01

    Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data. PMID:24353353

  12. Free radicals, reactive oxygen species, oxidative stress and its classification.

    PubMed

    Lushchak, Volodymyr I

    2014-12-05

    Reactive oxygen species (ROS) initially considered as only damaging agents in living organisms further were found to play positive roles also. This paper describes ROS homeostasis, principles of their investigation and technical approaches to investigate ROS-related processes. Especial attention is paid to complications related to experimental documentation of these processes, their diversity, spatiotemporal distribution, relationships with physiological state of the organisms. Imbalance between ROS generation and elimination in favor of the first with certain consequences for cell physiology has been called "oxidative stress". Although almost 30years passed since the first definition of oxidative stress was introduced by Helmut Sies, to date we have no accepted classification of oxidative stress. In order to fill up this gape here classification of oxidative stress based on its intensity is proposed. Due to that oxidative stress may be classified as basal oxidative stress (BOS), low intensity oxidative stress (LOS), intermediate intensity oxidative stress (IOS), and high intensity oxidative stress (HOS). Another classification of potential interest may differentiate three categories such as mild oxidative stress (MOS), temperate oxidative stress (TOS), and finally severe (strong) oxidative stress (SOS). Perspective directions of investigations in the field include development of sophisticated classification of oxidative stresses, accurate identification of cellular ROS targets and their arranged responses to ROS influence, real in situ functions and operation of so-called "antioxidants", intracellular spatiotemporal distribution and effects of ROS, deciphering of molecular mechanisms responsible for cellular response to ROS attacks, and ROS involvement in realization of normal cellular functions in cellular homeostasis. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  13. Toward Biological Subtyping of Papillary Renal Cell Carcinoma With Clinical Implications Through Histologic, Immunohistochemical, and Molecular Analysis.

    PubMed

    Saleeb, Rola M; Brimo, Fadi; Farag, Mina; Rompré-Brodeur, Alexis; Rotondo, Fabio; Beharry, Vidya; Wala, Samantha; Plant, Pamela; Downes, Michelle R; Pace, Kenneth; Evans, Andrew; Bjarnason, Georg; Bartlett, John M S; Yousef, George M

    2017-12-01

    Papillary renal cell carcinoma (PRCC) has 2 histologic subtypes. Almost half of the cases fail to meet all morphologic criteria for either type, hence are characterized as PRCC not otherwise specified (NOS). There are yet no markers to resolve the PRCC NOS category. Accurate classification can better guide the management of these patients. In our previous PRCC study we identified markers that can distinguish between the subtypes. A PRCC patient cohort of 108 cases was selected for the current study. A panel of potentially distinguishing markers was chosen from our previous genomic analysis, and assessed by immunohistochemistry. The panel exhibited distinct staining patterns between the 2 classic PRCC subtypes; and successfully reclassified the NOS (45%) cases. Moreover, these immunomarkers revealed a third subtype, PRCC3 (35% of the cohort). Molecular testing using miRNA expression and copy number variation analysis confirmed the presence of 3 distinct molecular signatures corresponding to the 3 subtypes. Disease-free survival was significantly enhanced in PRCC1 versus 2 and 3 (P=0.047) on univariate analysis. The subtypes stratification was also significant on multivariate analysis (P=0.025; hazard ratio, 6; 95% confidence interval, 1.25-32.2). We propose a new classification system of PRCC integrating morphologic, immunophenotypical, and molecular analysis. The newly described PRCC3 has overlapping morphology between PRCC1 and PRCC2, hence would be subtyped as NOS in the current classification. Molecularly PRCC3 has a distinct signature and clinically it behaves similar to PRCC2. The new classification stratifies PRCC patients into clinically relevant subgroups and has significant implications on the management of PRCC.

  14. Developments toward more accurate molecular modeling of liquids

    NASA Astrophysics Data System (ADS)

    Evans, Tom J.

    2000-12-01

    The general goal of this research has been to improve upon existing combined quantum mechanics/molecular mechanics (QM/MM) methodologies. Error weighting functions have been introduced into the perturbative Monte Carlo (PMC) method for use with QM/MM. The PMC approach, introduced earlier, provides a means to reduce the number of full self-consistent field (SCF) calculations in simulations using the QM/MM potential by evoking perturbation theory to calculate energy changes due to displacements of a MM molecule. This will allow the ab initio QM/MM approach to be applied to systems that require more advanced, computationally demanding treatments of the QM and/or MM regions. Efforts have also been made to improve the accuracy of the representation of the solvent molecules usually represented by MM force fields. Results from an investigation of the applicability of the embedded density functional theory (EDFT) for studying physical properties of solutions will be presented. In this approach, the solute wavefunction is solved self- consistently in the field of individually frozen electron-density solvent molecules. To test its accuracy, the potential curves for interactions between Li+, Cl- and H2O with a single frozen-density H 2O molecule in different orientations have been calculated. With the development of the more sophisticated effective fragment potential (EFP) representation of solvent molecules, a QM/EFP technique was created. This hybrid QM/EFP approach was used to investigate the solvation of Li + by small clusters of water, as a test case for larger ionic dusters. The EFP appears to provide an accurate representation of the strong interactions that exist between Li+ and H2O. With the QM/EFP methodology comes an increased computational expense, resulting in an even greater need to rely on the PMC approach. However, while including the PMC into the hybrid QM/EFP technique, it was discovered that the previous implementation of the PMC was done incorrectly

  15. Molecular classification of pesticides including persistent organic pollutants, phenylurea and sulphonylurea herbicides.

    PubMed

    Torrens, Francisco; Castellano, Gloria

    2014-06-05

    Pesticide residues in wine were analyzed by liquid chromatography-tandem mass spectrometry. Retentions are modelled by structure-property relationships. Bioplastic evolution is an evolutionary perspective conjugating effect of acquired characters and evolutionary indeterminacy-morphological determination-natural selection principles; its application to design co-ordination index barely improves correlations. Fractal dimensions and partition coefficient differentiate pesticides. Classification algorithms are based on information entropy and its production. Pesticides allow a structural classification by nonplanarity, and number of O, S, N and Cl atoms and cycles; different behaviours depend on number of cycles. The novelty of the approach is that the structural parameters are related to retentions. Classification algorithms are based on information entropy. When applying procedures to moderate-sized sets, excessive results appear compatible with data suffering a combinatorial explosion. However, equipartition conjecture selects criterion resulting from classification between hierarchical trees. Information entropy permits classifying compounds agreeing with principal component analyses. Periodic classification shows that pesticides in the same group present similar properties; those also in equal period, maximum resemblance. The advantage of the classification is to predict the retentions for molecules not included in the categorization. Classification extends to phenyl/sulphonylureas and the application will be to predict their retentions.

  16. Bottom-up coarse-grained models that accurately describe the structure, pressure, and compressibility of molecular liquids

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Dunn, Nicholas J. H.; Noid, W. G., E-mail: wnoid@chem.psu.edu

    2015-12-28

    The present work investigates the capability of bottom-up coarse-graining (CG) methods for accurately modeling both structural and thermodynamic properties of all-atom (AA) models for molecular liquids. In particular, we consider 1, 2, and 3-site CG models for heptane, as well as 1 and 3-site CG models for toluene. For each model, we employ the multiscale coarse-graining method to determine interaction potentials that optimally approximate the configuration dependence of the many-body potential of mean force (PMF). We employ a previously developed “pressure-matching” variational principle to determine a volume-dependent contribution to the potential, U{sub V}(V), that approximates the volume-dependence of the PMF.more » We demonstrate that the resulting CG models describe AA density fluctuations with qualitative, but not quantitative, accuracy. Accordingly, we develop a self-consistent approach for further optimizing U{sub V}, such that the CG models accurately reproduce the equilibrium density, compressibility, and average pressure of the AA models, although the CG models still significantly underestimate the atomic pressure fluctuations. Additionally, by comparing this array of models that accurately describe the structure and thermodynamic pressure of heptane and toluene at a range of different resolutions, we investigate the impact of bottom-up coarse-graining upon thermodynamic properties. In particular, we demonstrate that U{sub V} accounts for the reduced cohesion in the CG models. Finally, we observe that bottom-up coarse-graining introduces subtle correlations between the resolution, the cohesive energy density, and the “simplicity” of the model.« less

  17. Lissencephaly: expanded imaging and clinical classification

    PubMed Central

    Di Donato, Nataliya; Chiari, Sara; Mirzaa, Ghayda M.; Aldinger, Kimberly; Parrini, Elena; Olds, Carissa; Barkovich, A. James; Guerrini, Renzo; Dobyns, William B.

    2017-01-01

    Lissencephaly (“smooth brain”, LIS) is a malformation of cortical development associated with deficient neuronal migration and abnormal formation of cerebral convolutions or gyri. The LIS spectrum includes agyria, pachygyria, and subcortical band heterotopia. Our first classification of LIS and subcortical band heterotopia (SBH) was developed to distinguish between the first two genetic causes of LIS – LIS1 (PAFAH1B1) and DCX. However, progress in molecular genetics has led to identification of 19 LIS-associated genes, leaving the existing classification system insufficient to distinguish the increasingly diverse patterns of LIS. To address this challenge, we reviewed clinical, imaging and molecular data on 188 patients with LIS-SBH ascertained during the last five years, and reviewed selected archival data on another ~1,400 patients. Using these data plus published reports, we constructed a new imaging based classification system with 21 recognizable patterns that reliably predict the most likely causative genes. These patterns do not correlate consistently with the clinical outcome, leading us to also develop a new scale useful for predicting clinical severity and outcome. Taken together, our work provides new tools that should prove useful for clinical management and genetic counselling of patients with LIS-SBH (imaging and severity based classifications), and guidance for prioritizing and interpreting genetic testing results (imaging based classification). PMID:28440899

  18. Molecular classification of gastric cancer: a new paradigm.

    PubMed

    Shah, Manish A; Khanin, Raya; Tang, Laura; Janjigian, Yelena Y; Klimstra, David S; Gerdes, Hans; Kelsen, David P

    2011-05-01

    Gastric cancer may be subdivided into 3 distinct subtypes--proximal, diffuse, and distal gastric cancer--based on histopathologic and anatomic criteria. Each subtype is associated with unique epidemiology. Our aim is to test the hypothesis that these distinct gastric cancer subtypes may also be distinguished by gene expression analysis. Patients with localized gastric adenocarcinoma being screened for a phase II preoperative clinical trial (National Cancer Institute, NCI #5917) underwent endoscopic biopsy for fresh tumor procurement. Four to 6 targeted biopsies of the primary tumor were obtained. Macrodissection was carried out to ensure more than 80% carcinoma in the sample. HG-U133A GeneChip (Affymetrix) was used for cDNA expression analysis, and all arrays were processed and analyzed using the Bioconductor R-package. Between November 2003 and January 2006, 57 patients were screened to identify 36 patients with localized gastric cancer who had adequate RNA for expression analysis. Using supervised analysis, we built a classifier to distinguish the 3 gastric cancer subtypes, successfully classifying each into tightly grouped clusters. Leave-one-out cross-validation error was 0.14, suggesting that more than 85% of samples were classified correctly. Gene set analysis with the false discovery rate set at 0.25 identified several pathways that were differentially regulated when comparing each gastric cancer subtype to adjacent normal stomach. Subtypes of gastric cancer that have epidemiologic and histologic distinctions are also distinguished by gene expression data. These preliminary data suggest a new classification of gastric cancer with implications for improving our understanding of disease biology and identification of unique molecular drivers for each gastric cancer subtype. ©2011 AACR.

  19. Molecular Classification of Gastric Cancer: A new paradigm

    PubMed Central

    Shah, Manish A.; Khanin, Raya; Tang, Laura; Janjigian, Yelena Y.; Klimstra, David S.; Gerdes, Hans; Kelsen, David P.

    2011-01-01

    Purpose Gastric cancer may be subdivided into three distinct subtypes –proximal, diffuse, and distal gastric cancer– based on histopathologic and anatomic criteria. Each subtype is associated with unique epidemiology. Our aim is to test the hypothesis that these distinct gastric cancer subtypes may also be distinguished by gene expression analysis. Experimental Design Patients with localized gastric adenocarcinoma being screened for a phase II preoperative clinical trial (NCI 5917) underwent endoscopic biopsy for fresh tumor procurement. 4–6 targeted biopsies of the primary tumor were obtained. Macrodissection was performed to ensure >80% carcinoma in the sample. HG-U133A GeneChip (Affymetrix) was used for cDNA expression analysis, and all arrays were processed and analyzed using the Bioconductor R-package. Results Between November 2003 and January 2006, 57 patients were screened to identify 36 patients with localized gastric cancer who had adequate RNA for expression analysis. Using supervised analysis, we built a classifier to distinguish the three gastric cancer subtypes, successfully classifying each into tightly grouped clusters. Leave-one-out cross validation error was 0.14, suggesting that >85% of samples were classified correctly. Gene set analysis with the False Discovery Rate set at 0.25 identified several pathways that were differentially regulated when comparing each gastric cancer subtype to adjacent normal stomach. Conclusions Subtypes of gastric cancer that have epidemiologic and histologic distinction are also distinguished by gene expression data. These preliminary data suggest a new classification of gastric cancer with implications for improving our understanding of disease biology and identification of unique molecular drivers for each gastric cancer subtype. PMID:21430069

  20. Pathological and Molecular Evaluation of Pancreatic Neoplasms

    PubMed Central

    Rishi, Arvind; Goggins, Michael; Wood, Laura D.; Hruban, Ralph H.

    2015-01-01

    Pancreatic neoplasms are morphologically and genetically heterogeneous and include wide variety of neoplasms ranging from benign to malignant with an extremely poor clinical outcome. Our understanding of these pancreatic neoplasms has improved significantly with recent advances in cancer sequencing. Awareness of molecular pathogenesis brings in new opportunities for early detection, improved prognostication, and personalized gene-specific therapies. Here we review the pathological classification of pancreatic neoplasms from their molecular and genetic perspective. All of the major tumor types that arise in the pancreas have been sequenced, and a new classification that incorporates molecular findings together with pathological findings is now possible (Table 1). This classification has significant implications for our understanding of why tumors aggregate in some families, for the development of early detection tests, and for the development of personalized therapies for patients with established cancers. Here we describe this new classification using the framework of the standard histological classification. PMID:25726050

  1. Accurate description of charged excitations in molecular solids from embedded many-body perturbation theory

    NASA Astrophysics Data System (ADS)

    Li, Jing; D'Avino, Gabriele; Duchemin, Ivan; Beljonne, David; Blase, Xavier

    2018-01-01

    We present a novel hybrid quantum/classical approach to the calculation of charged excitations in molecular solids based on the many-body Green's function G W formalism. Molecules described at the G W level are embedded into the crystalline environment modeled with an accurate classical polarizable scheme. This allows the calculation of electron addition and removal energies in the bulk and at crystal surfaces where charged excitations are probed in photoelectron experiments. By considering the paradigmatic case of pentacene and perfluoropentacene crystals, we discuss the different contributions from intermolecular interactions to electronic energy levels, distinguishing between polarization, which is accounted for combining quantum and classical polarizabilities, and crystal field effects, that can impact energy levels by up to ±0.6 eV. After introducing band dispersion, we achieve quantitative agreement (within 0.2 eV) on the ionization potential and electron affinity measured at pentacene and perfluoropentacene crystal surfaces characterized by standing molecules.

  2. Classification of male lower torso for underwear design

    NASA Astrophysics Data System (ADS)

    Cheng, Z.; Kuzmichev, V. E.

    2017-10-01

    By means of scanning technology we have got new information about the morphology of male bodies and have redistricted the classification of men’s underwear by adopting one to consumer demands. To build the new classification in accordance with male body characteristic factors of lower torso, we make the method of underwear designing which allow to get the accurate and convenience for consumers products.

  3. Trends and concepts in fern classification.

    PubMed

    Christenhusz, Maarten J M; Chase, Mark W

    2014-03-01

    Throughout the history of fern classification, familial and generic concepts have been highly labile. Many classifications and evolutionary schemes have been proposed during the last two centuries, reflecting different interpretations of the available evidence. Knowledge of fern structure and life histories has increased through time, providing more evidence on which to base ideas of possible relationships, and classification has changed accordingly. This paper reviews previous classifications of ferns and presents ideas on how to achieve a more stable consensus. An historical overview is provided from the first to the most recent fern classifications, from which conclusions are drawn on past changes and future trends. The problematic concept of family in ferns is discussed, with a particular focus on how this has changed over time. The history of molecular studies and the most recent findings are also presented. Fern classification generally shows a trend from highly artificial, based on an interpretation of a few extrinsic characters, via natural classifications derived from a multitude of intrinsic characters, towards more evolutionary circumscriptions of groups that do not in general align well with the distribution of these previously used characters. It also shows a progression from a few broad family concepts to systems that recognized many more narrowly and highly controversially circumscribed families; currently, the number of families recognized is stabilizing somewhere between these extremes. Placement of many genera was uncertain until the arrival of molecular phylogenetics, which has rapidly been improving our understanding of fern relationships. As a collective category, the so-called 'fern allies' (e.g. Lycopodiales, Psilotaceae, Equisetaceae) were unsurprisingly found to be polyphyletic, and the term should be abandoned. Lycopodiaceae, Selaginellaceae and Isoëtaceae form a clade (the lycopods) that is sister to all other vascular plants, whereas

  4. Trends and concepts in fern classification

    PubMed Central

    Christenhusz, Maarten J. M.; Chase, Mark W.

    2014-01-01

    Background and Aims Throughout the history of fern classification, familial and generic concepts have been highly labile. Many classifications and evolutionary schemes have been proposed during the last two centuries, reflecting different interpretations of the available evidence. Knowledge of fern structure and life histories has increased through time, providing more evidence on which to base ideas of possible relationships, and classification has changed accordingly. This paper reviews previous classifications of ferns and presents ideas on how to achieve a more stable consensus. Scope An historical overview is provided from the first to the most recent fern classifications, from which conclusions are drawn on past changes and future trends. The problematic concept of family in ferns is discussed, with a particular focus on how this has changed over time. The history of molecular studies and the most recent findings are also presented. Key Results Fern classification generally shows a trend from highly artificial, based on an interpretation of a few extrinsic characters, via natural classifications derived from a multitude of intrinsic characters, towards more evolutionary circumscriptions of groups that do not in general align well with the distribution of these previously used characters. It also shows a progression from a few broad family concepts to systems that recognized many more narrowly and highly controversially circumscribed families; currently, the number of families recognized is stabilizing somewhere between these extremes. Placement of many genera was uncertain until the arrival of molecular phylogenetics, which has rapidly been improving our understanding of fern relationships. As a collective category, the so-called ‘fern allies’ (e.g. Lycopodiales, Psilotaceae, Equisetaceae) were unsurprisingly found to be polyphyletic, and the term should be abandoned. Lycopodiaceae, Selaginellaceae and Isoëtaceae form a clade (the lycopods) that is

  5. Classification of spatially unresolved objects

    NASA Technical Reports Server (NTRS)

    Nalepka, R. F.; Horwitz, H. M.; Hyde, P. D.; Morgenstern, J. P.

    1972-01-01

    A proportion estimation technique for classification of multispectral scanner images is reported that uses data point averaging to extract and compute estimated proportions for a single average data point to classify spatial unresolved areas. Example extraction calculations of spectral signatures for bare soil, weeds, alfalfa, and barley prove quite accurate.

  6. Avoiding fractional electrons in subsystem DFT based ab-initio molecular dynamics yields accurate models for liquid water and solvated OH radical.

    PubMed

    Genova, Alessandro; Ceresoli, Davide; Pavanello, Michele

    2016-06-21

    In this work we achieve three milestones: (1) we present a subsystem DFT method capable of running ab-initio molecular dynamics simulations accurately and efficiently. (2) In order to rid the simulations of inter-molecular self-interaction error, we exploit the ability of semilocal frozen density embedding formulation of subsystem DFT to represent the total electron density as a sum of localized subsystem electron densities that are constrained to integrate to a preset, constant number of electrons; the success of the method relies on the fact that employed semilocal nonadditive kinetic energy functionals effectively cancel out errors in semilocal exchange-correlation potentials that are linked to static correlation effects and self-interaction. (3) We demonstrate this concept by simulating liquid water and solvated OH(•) radical. While the bulk of our simulations have been performed on a periodic box containing 64 independent water molecules for 52 ps, we also simulated a box containing 256 water molecules for 22 ps. The results show that, provided one employs an accurate nonadditive kinetic energy functional, the dynamics of liquid water and OH(•) radical are in semiquantitative agreement with experimental results or higher-level electronic structure calculations. Our assessments are based upon comparisons of radial and angular distribution functions as well as the diffusion coefficient of the liquid.

  7. Learning accurate very fast decision trees from uncertain data streams

    NASA Astrophysics Data System (ADS)

    Liang, Chunquan; Zhang, Yang; Shi, Peng; Hu, Zhengguo

    2015-12-01

    Most existing works on data stream classification assume the streaming data is precise and definite. Such assumption, however, does not always hold in practice, since data uncertainty is ubiquitous in data stream applications due to imprecise measurement, missing values, privacy protection, etc. The goal of this paper is to learn accurate decision tree models from uncertain data streams for classification analysis. On the basis of very fast decision tree (VFDT) algorithms, we proposed an algorithm for constructing an uncertain VFDT tree with classifiers at tree leaves (uVFDTc). The uVFDTc algorithm can exploit uncertain information effectively and efficiently in both the learning and the classification phases. In the learning phase, it uses Hoeffding bound theory to learn from uncertain data streams and yield fast and reasonable decision trees. In the classification phase, at tree leaves it uses uncertain naive Bayes (UNB) classifiers to improve the classification performance. Experimental results on both synthetic and real-life datasets demonstrate the strong ability of uVFDTc to classify uncertain data streams. The use of UNB at tree leaves has improved the performance of uVFDTc, especially the any-time property, the benefit of exploiting uncertain information, and the robustness against uncertainty.

  8. An ensemble predictive modeling framework for breast cancer classification.

    PubMed

    Nagarajan, Radhakrishnan; Upreti, Meenakshi

    2017-12-01

    Molecular changes often precede clinical presentation of diseases and can be useful surrogates with potential to assist in informed clinical decision making. Recent studies have demonstrated the usefulness of modeling approaches such as classification that can predict the clinical outcomes from molecular expression profiles. While useful, a majority of these approaches implicitly use all molecular markers as features in the classification process often resulting in sparse high-dimensional projection of the samples often comparable to that of the sample size. In this study, a variant of the recently proposed ensemble classification approach is used for predicting good and poor-prognosis breast cancer samples from their molecular expression profiles. In contrast to traditional single and ensemble classifiers, the proposed approach uses multiple base classifiers with varying feature sets obtained from two-dimensional projection of the samples in conjunction with a majority voting strategy for predicting the class labels. In contrast to our earlier implementation, base classifiers in the ensembles are chosen based on maximal sensitivity and minimal redundancy by choosing only those with low average cosine distance. The resulting ensemble sets are subsequently modeled as undirected graphs. Performance of four different classification algorithms is shown to be better within the proposed ensemble framework in contrast to using them as traditional single classifier systems. Significance of a subset of genes with high-degree centrality in the network abstractions across the poor-prognosis samples is also discussed. Copyright © 2017 Elsevier Inc. All rights reserved.

  9. Classification of Radiological Changes in Burst Fractures

    PubMed Central

    Şentürk, Salim; Öğrenci, Ahmet; Gürçay, Ahmet Gürhan; Abdioğlu, Ahmet Atilla; Yaman, Onur; Özer, Ali Fahir

    2018-01-01

    AIM: Burst fractures can occur with different radiological images after high energy. We aimed to simplify radiological staging of burst fractures. METHODS: Eighty patients whom exposed spinal trauma and had burst fracture were evaluated concerning age, sex, fracture segment, neurological deficit, secondary organ injury and radiological changes that occurred. RESULTS: We performed a new classification in burst fractures at radiological images. CONCLUSIONS: According to this classification system, secondary organ injury and neurological deficit can be an indicator of energy exposure. If energy is high, the clinical status will be worse. Thus, we can get an idea about the likelihood of neurological deficit and secondary organ injuries. This classification has simplified the radiological staging of burst fractures and is a classification that gives a very accurate idea about the neurological condition. PMID:29531604

  10. Algorithms for Hyperspectral Endmember Extraction and Signature Classification with Morphological Dendritic Networks

    NASA Astrophysics Data System (ADS)

    Schmalz, M.; Ritter, G.

    Accurate multispectral or hyperspectral signature classification is key to the nonimaging detection and recognition of space objects. Additionally, signature classification accuracy depends on accurate spectral endmember determination [1]. Previous approaches to endmember computation and signature classification were based on linear operators or neural networks (NNs) expressed in terms of the algebra (R, +, x) [1,2]. Unfortunately, class separation in these methods tends to be suboptimal, and the number of signatures that can be accurately classified often depends linearly on the number of NN inputs. This can lead to poor endmember distinction, as well as potentially significant classification errors in the presence of noise or densely interleaved signatures. In contrast to traditional CNNs, autoassociative morphological memories (AMM) are a construct similar to Hopfield autoassociatived memories defined on the (R, +, ?,?) lattice algebra [3]. Unlimited storage and perfect recall of noiseless real valued patterns has been proven for AMMs [4]. However, AMMs suffer from sensitivity to specific noise models, that can be characterized as erosive and dilative noise. On the other hand, the prior definition of a set of endmembers corresponds to material spectra lying on vertices of the minimum convex region covering the image data. These vertices can be characterized as morphologically independent patterns. It has further been shown that AMMs can be based on dendritic computation [3,6]. These techniques yield improved accuracy and class segmentation/separation ability in the presence of highly interleaved signature data. In this paper, we present a procedure for endmember determination based on AMM noise sensitivity, which employs morphological dendritic computation. We show that detected endmembers can be exploited by AMM based classification techniques, to achieve accurate signature classification in the presence of noise, closely spaced or interleaved signatures, and

  11. Machine learning for the structure-energy-property landscapes of molecular crystals.

    PubMed

    Musil, Félix; De, Sandip; Yang, Jack; Campbell, Joshua E; Day, Graeme M; Ceriotti, Michele

    2018-02-07

    Molecular crystals play an important role in several fields of science and technology. They frequently crystallize in different polymorphs with substantially different physical properties. To help guide the synthesis of candidate materials, atomic-scale modelling can be used to enumerate the stable polymorphs and to predict their properties, as well as to propose heuristic rules to rationalize the correlations between crystal structure and materials properties. Here we show how a recently-developed machine-learning (ML) framework can be used to achieve inexpensive and accurate predictions of the stability and properties of polymorphs, and a data-driven classification that is less biased and more flexible than typical heuristic rules. We discuss, as examples, the lattice energy and property landscapes of pentacene and two azapentacene isomers that are of interest as organic semiconductor materials. We show that we can estimate force field or DFT lattice energies with sub-kJ mol -1 accuracy, using only a few hundred reference configurations, and reduce by a factor of ten the computational effort needed to predict charge mobility in the crystal structures. The automatic structural classification of the polymorphs reveals a more detailed picture of molecular packing than that provided by conventional heuristics, and helps disentangle the role of hydrogen bonded and π-stacking interactions in determining molecular self-assembly. This observation demonstrates that ML is not just a black-box scheme to interpolate between reference calculations, but can also be used as a tool to gain intuitive insights into structure-property relations in molecular crystal engineering.

  12. Biomarker selection and classification of "-omics" data using a two-step bayes classification framework.

    PubMed

    Assawamakin, Anunchai; Prueksaaroon, Supakit; Kulawonganunchai, Supasak; Shaw, Philip James; Varavithya, Vara; Ruangrajitpakorn, Taneth; Tongsima, Sissades

    2013-01-01

    Identification of suitable biomarkers for accurate prediction of phenotypic outcomes is a goal for personalized medicine. However, current machine learning approaches are either too complex or perform poorly. Here, a novel two-step machine-learning framework is presented to address this need. First, a Naïve Bayes estimator is used to rank features from which the top-ranked will most likely contain the most informative features for prediction of the underlying biological classes. The top-ranked features are then used in a Hidden Naïve Bayes classifier to construct a classification prediction model from these filtered attributes. In order to obtain the minimum set of the most informative biomarkers, the bottom-ranked features are successively removed from the Naïve Bayes-filtered feature list one at a time, and the classification accuracy of the Hidden Naïve Bayes classifier is checked for each pruned feature set. The performance of the proposed two-step Bayes classification framework was tested on different types of -omics datasets including gene expression microarray, single nucleotide polymorphism microarray (SNParray), and surface-enhanced laser desorption/ionization time-of-flight (SELDI-TOF) proteomic data. The proposed two-step Bayes classification framework was equal to and, in some cases, outperformed other classification methods in terms of prediction accuracy, minimum number of classification markers, and computational time.

  13. Algorithmic Classification of Five Characteristic Types of Paraphasias.

    PubMed

    Fergadiotis, Gerasimos; Gorman, Kyle; Bedrick, Steven

    2016-12-01

    This study was intended to evaluate a series of algorithms developed to perform automatic classification of paraphasic errors (formal, semantic, mixed, neologistic, and unrelated errors). We analyzed 7,111 paraphasias from the Moss Aphasia Psycholinguistics Project Database (Mirman et al., 2010) and evaluated the classification accuracy of 3 automated tools. First, we used frequency norms from the SUBTLEXus database (Brysbaert & New, 2009) to differentiate nonword errors and real-word productions. Then we implemented a phonological-similarity algorithm to identify phonologically related real-word errors. Last, we assessed the performance of a semantic-similarity criterion that was based on word2vec (Mikolov, Yih, & Zweig, 2013). Overall, the algorithmic classification replicated human scoring for the major categories of paraphasias studied with high accuracy. The tool that was based on the SUBTLEXus frequency norms was more than 97% accurate in making lexicality judgments. The phonological-similarity criterion was approximately 91% accurate, and the overall classification accuracy of the semantic classifier ranged from 86% to 90%. Overall, the results highlight the potential of tools from the field of natural language processing for the development of highly reliable, cost-effective diagnostic tools suitable for collecting high-quality measurement data for research and clinical purposes.

  14. An efficient and accurate molecular alignment and docking technique using ab initio quality scoring

    PubMed Central

    Füsti-Molnár, László; Merz, Kenneth M.

    2008-01-01

    An accurate and efficient molecular alignment technique is presented based on first principle electronic structure calculations. This new scheme maximizes quantum similarity matrices in the relative orientation of the molecules and uses Fourier transform techniques for two purposes. First, building up the numerical representation of true ab initio electronic densities and their Coulomb potentials is accelerated by the previously described Fourier transform Coulomb method. Second, the Fourier convolution technique is applied for accelerating optimizations in the translational coordinates. In order to avoid any interpolation error, the necessary analytical formulas are derived for the transformation of the ab initio wavefunctions in rotational coordinates. The results of our first implementation for a small test set are analyzed in detail and compared with published results of the literature. A new way of refinement of existing shape based alignments is also proposed by using Fourier convolutions of ab initio or other approximate electron densities. This new alignment technique is generally applicable for overlap, Coulomb, kinetic energy, etc., quantum similarity measures and can be extended to a genuine docking solution with ab initio scoring. PMID:18624561

  15. Improved Hierarchical Optimization-Based Classification of Hyperspectral Images Using Shape Analysis

    NASA Technical Reports Server (NTRS)

    Tarabalka, Yuliya; Tilton, James C.

    2012-01-01

    A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two neighboring regions with the smallest Dissimilarity Criterion (DC) are merged, and classification probabilities are recomputed. The important contribution of this work consists in estimating a DC between regions as a function of statistical, classification and geometrical (area and rectangularity) features. Experimental results are presented on a 102-band ROSIS image of the Center of Pavia, Italy. The developed approach yields more accurate classification results when compared to previously proposed methods.

  16. Molecular profiling of sarcomas: new vistas for precision medicine.

    PubMed

    Al-Zaid, Tariq; Wang, Wei-Lien; Somaiah, Neeta; Lazar, Alexander J

    2017-08-01

    Sarcoma is a large and heterogeneous group of malignant mesenchymal neoplasms with significant histological overlap. Accurate diagnosis can be challenging yet important for selecting the appropriate treatment approach and prognosis. The currently torrid pace of new genomic discoveries aids our classification and diagnosis of sarcomas, understanding of pathogenesis, development of new medications, and identification of alterations that predict prognosis and response to therapy. Unfortunately, demonstrating effective targets for precision oncology has been elusive in most sarcoma types. The list of potential targets greatly outnumbers the list of available inhibitors at the present time. This review will discuss the role of molecular profiling in sarcomas in general with emphasis on selected entities with particular clinical relevance.

  17. Highly efficient classification and identification of human pathogenic bacteria by MALDI-TOF MS.

    PubMed

    Hsieh, Sen-Yung; Tseng, Chiao-Li; Lee, Yun-Shien; Kuo, An-Jing; Sun, Chien-Feng; Lin, Yen-Hsiu; Chen, Jen-Kun

    2008-02-01

    Accurate and rapid identification of pathogenic microorganisms is of critical importance in disease treatment and public health. Conventional work flows are time-consuming, and procedures are multifaceted. MS can be an alternative but is limited by low efficiency for amino acid sequencing as well as low reproducibility for spectrum fingerprinting. We systematically analyzed the feasibility of applying MS for rapid and accurate bacterial identification. Directly applying bacterial colonies without further protein extraction to MALDI-TOF MS analysis revealed rich peak contents and high reproducibility. The MS spectra derived from 57 isolates comprising six human pathogenic bacterial species were analyzed using both unsupervised hierarchical clustering and supervised model construction via the Genetic Algorithm. Hierarchical clustering analysis categorized the spectra into six groups precisely corresponding to the six bacterial species. Precise classification was also maintained in an independently prepared set of bacteria even when the numbers of m/z values were reduced to six. In parallel, classification models were constructed via Genetic Algorithm analysis. A model containing 18 m/z values accurately classified independently prepared bacteria and identified those species originally not used for model construction. Moreover bacteria fewer than 10(4) cells and different species in bacterial mixtures were identified using the classification model approach. In conclusion, the application of MALDI-TOF MS in combination with a suitable model construction provides a highly accurate method for bacterial classification and identification. The approach can identify bacteria with low abundance even in mixed flora, suggesting that a rapid and accurate bacterial identification using MS techniques even before culture can be attained in the near future.

  18. A drone detection with aircraft classification based on a camera array

    NASA Astrophysics Data System (ADS)

    Liu, Hao; Qu, Fangchao; Liu, Yingjian; Zhao, Wei; Chen, Yitong

    2018-03-01

    In recent years, because of the rapid popularity of drones, many people have begun to operate drones, bringing a range of security issues to sensitive areas such as airports and military locus. It is one of the important ways to solve these problems by realizing fine-grained classification and providing the fast and accurate detection of different models of drone. The main challenges of fine-grained classification are that: (1) there are various types of drones, and the models are more complex and diverse. (2) the recognition test is fast and accurate, in addition, the existing methods are not efficient. In this paper, we propose a fine-grained drone detection system based on the high resolution camera array. The system can quickly and accurately recognize the detection of fine grained drone based on hd camera.

  19. Phylogenetic classification and the universal tree.

    PubMed

    Doolittle, W F

    1999-06-25

    From comparative analyses of the nucleotide sequences of genes encoding ribosomal RNAs and several proteins, molecular phylogeneticists have constructed a "universal tree of life," taking it as the basis for a "natural" hierarchical classification of all living things. Although confidence in some of the tree's early branches has recently been shaken, new approaches could still resolve many methodological uncertainties. More challenging is evidence that most archaeal and bacterial genomes (and the inferred ancestral eukaryotic nuclear genome) contain genes from multiple sources. If "chimerism" or "lateral gene transfer" cannot be dismissed as trivial in extent or limited to special categories of genes, then no hierarchical universal classification can be taken as natural. Molecular phylogeneticists will have failed to find the "true tree," not because their methods are inadequate or because they have chosen the wrong genes, but because the history of life cannot properly be represented as a tree. However, taxonomies based on molecular sequences will remain indispensable, and understanding of the evolutionary process will ultimately be enriched, not impoverished.

  20. Avoiding fractional electrons in subsystem DFT based ab-initio molecular dynamics yields accurate models for liquid water and solvated OH radical

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Genova, Alessandro, E-mail: alessandro.genova@rutgers.edu; Pavanello, Michele, E-mail: m.pavanello@rutgers.edu; Ceresoli, Davide, E-mail: davide.ceresoli@cnr.it

    2016-06-21

    In this work we achieve three milestones: (1) we present a subsystem DFT method capable of running ab-initio molecular dynamics simulations accurately and efficiently. (2) In order to rid the simulations of inter-molecular self-interaction error, we exploit the ability of semilocal frozen density embedding formulation of subsystem DFT to represent the total electron density as a sum of localized subsystem electron densities that are constrained to integrate to a preset, constant number of electrons; the success of the method relies on the fact that employed semilocal nonadditive kinetic energy functionals effectively cancel out errors in semilocal exchange–correlation potentials that aremore » linked to static correlation effects and self-interaction. (3) We demonstrate this concept by simulating liquid water and solvated OH{sup •} radical. While the bulk of our simulations have been performed on a periodic box containing 64 independent water molecules for 52 ps, we also simulated a box containing 256 water molecules for 22 ps. The results show that, provided one employs an accurate nonadditive kinetic energy functional, the dynamics of liquid water and OH{sup •} radical are in semiquantitative agreement with experimental results or higher-level electronic structure calculations. Our assessments are based upon comparisons of radial and angular distribution functions as well as the diffusion coefficient of the liquid.« less

  1. Formaldehyde in Absorption: Tracing Molecular Gas in Early-Type Galaxies

    NASA Astrophysics Data System (ADS)

    Dollhopf, Niklaus M.; Donovan Meyer, Jennifer

    2016-01-01

    Early-Type Galaxies (ETGs) have been long-classified as the red, ellipsoidal branch of the classic Hubble tuning fork diagram of galactic structure. In part with this classification, ETGs are thought to be molecular and atomic gas-poor with little to no recent star formation. However, recent efforts have questioned this ingrained classification. Most notably, the ATLAS3D survey of 260 ETGs within ~40 Mpc found 22% contain CO, a common tracer for molecular gas. The presence of cold molecular gas also implies the possibility for current star formation within these galaxies. Simulations do not accurately predict the recent observations and further studies are necessary to understand the mechanisms of ETGs.CO traces molecular gas starting at densities of ~102 cm-3, which makes it a good tracer of bulk molecular gas, but does little to constrain the possible locations of star formation within the cores of dense molecular gas clouds. Formaldehyde (H2CO) traces molecular gas on the order of ~104 cm-3, providing a further constraint on the location of star-forming gas, while being simple enough to possibly be abundant in gas-poor ETGs. In cold molecular clouds at or above ~104 cm-3 densities, the structure of formaldehyde enables a phenomenon in which rotational transitions have excitation temperatures driven below the temperature of the cosmic microwave background (CMB), ~2.7 K. Because the CMB radiates isotropically, formaldehyde can be observed in absorption, independent of distance, as a tracer of moderately-dense molecular clouds and star formation.This novel observation technique of formaldehyde was incorporated for observations of twelve CO-detected ETGs from the ATLAS3D sample, including NGC 4710 and PGC 8815, to investigate the presence of cold molecular gas, and possible star formation, in ETGs. We present images from the Very Large Array, used in its C-array configuration, of the J = 11,0 - 11,1 transition of formaldehyde towards these sources. We report our

  2. Refining Landsat classification results using digital terrain data

    USGS Publications Warehouse

    Miller, Wayne A.; Shasby, Mark

    1982-01-01

     Scientists at the U.S. Geological Survey's Earth Resources Observation systems (EROS) Data Center have recently completed two land-cover mapping projects in which digital terrain data were used to refine Landsat classification results. Digital ter rain data were incorporated into the Landsat classification process using two different procedures that required developing decision criteria either subjectively or quantitatively. The subjective procedure was used in a vegetation mapping project in Arizona, and the quantitative procedure was used in a forest-fuels mapping project in Montana. By incorporating digital terrain data into the Landsat classification process, more spatially accurate landcover maps were produced for both projects.

  3. Automatic grade classification of Barretts Esophagus through feature enhancement

    NASA Astrophysics Data System (ADS)

    Ghatwary, Noha; Ahmed, Amr; Ye, Xujiong; Jalab, Hamid

    2017-03-01

    Barretts Esophagus (BE) is a precancerous condition that affects the esophagus tube and has the risk of developing esophageal adenocarcinoma. BE is the process of developing metaplastic intestinal epithelium and replacing the normal cells in the esophageal area. The detection of BE is considered difficult due to its appearance and properties. The diagnosis is usually done through both endoscopy and biopsy. Recently, Computer Aided Diagnosis systems have been developed to support physicians opinion when facing difficulty in detection/classification in different types of diseases. In this paper, an automatic classification of Barretts Esophagus condition is introduced. The presented method enhances the internal features of a Confocal Laser Endomicroscopy (CLE) image by utilizing a proposed enhancement filter. This filter depends on fractional differentiation and integration that improve the features in the discrete wavelet transform of an image. Later on, various features are extracted from each enhanced image on different levels for the multi-classification process. Our approach is validated on a dataset that consists of a group of 32 patients with 262 images with different histology grades. The experimental results demonstrated the efficiency of the proposed technique. Our method helps clinicians for more accurate classification. This potentially helps to reduce the need for biopsies needed for diagnosis, facilitate the regular monitoring of treatment/development of the patients case and can help train doctors with the new endoscopy technology. The accurate automatic classification is particularly important for the Intestinal Metaplasia (IM) type, which could turn into deadly cancerous. Hence, this work contributes to automatic classification that facilitates early intervention/treatment and decreasing biopsy samples needed.

  4. Influence of pansharpening techniques in obtaining accurate vegetation thematic maps

    NASA Astrophysics Data System (ADS)

    Ibarrola-Ulzurrun, Edurne; Gonzalo-Martin, Consuelo; Marcello-Ruiz, Javier

    2016-10-01

    In last decades, there have been a decline in natural resources, becoming important to develop reliable methodologies for their management. The appearance of very high resolution sensors has offered a practical and cost-effective means for a good environmental management. In this context, improvements are needed for obtaining higher quality of the information available in order to get reliable classified images. Thus, pansharpening enhances the spatial resolution of the multispectral band by incorporating information from the panchromatic image. The main goal in the study is to implement pixel and object-based classification techniques applied to the fused imagery using different pansharpening algorithms and the evaluation of thematic maps generated that serve to obtain accurate information for the conservation of natural resources. A vulnerable heterogenic ecosystem from Canary Islands (Spain) was chosen, Teide National Park, and Worldview-2 high resolution imagery was employed. The classes considered of interest were set by the National Park conservation managers. 7 pansharpening techniques (GS, FIHS, HCS, MTF based, Wavelet `à trous' and Weighted Wavelet `à trous' through Fractal Dimension Maps) were chosen in order to improve the data quality with the goal to analyze the vegetation classes. Next, different classification algorithms were applied at pixel-based and object-based approach, moreover, an accuracy assessment of the different thematic maps obtained were performed. The highest classification accuracy was obtained applying Support Vector Machine classifier at object-based approach in the Weighted Wavelet `à trous' through Fractal Dimension Maps fused image. Finally, highlight the difficulty of the classification in Teide ecosystem due to the heterogeneity and the small size of the species. Thus, it is important to obtain accurate thematic maps for further studies in the management and conservation of natural resources.

  5. The Evolving Classification of Pulmonary Hypertension.

    PubMed

    Foshat, Michelle; Boroumand, Nahal

    2017-05-01

    - An explosion of information on pulmonary hypertension has occurred during the past few decades. The perception of this disease has shifted from purely clinical to incorporate new knowledge of the underlying pathology. This transfer has occurred in light of advancements in pathophysiology, histology, and molecular medical diagnostics. - To update readers about the evolving understanding of the etiology and pathogenesis of pulmonary hypertension and to demonstrate how pathology has shaped the current classification. - Information presented at the 5 World Symposia on pulmonary hypertension held since 1973, with the last meeting occurring in 2013, was used in this review. - Pulmonary hypertension represents a heterogeneous group of disorders that are differentiated based on differences in clinical, hemodynamic, and histopathologic features. Early concepts of pulmonary hypertension were largely influenced by pharmacotherapy, hemodynamic function, and clinical presentation of the disease. The initial nomenclature for pulmonary hypertension segregated the clinical classifications from pathologic subtypes. Major restructuring of this disease classification occurred between the first and second symposia, which was the first to unite clinical and pathologic information in the categorization scheme. Additional changes were introduced in subsequent meetings, particularly between the third and fourth World Symposia meetings, when additional pathophysiologic information was gained. Discoveries in molecular diagnostics significantly progressed the understanding of idiopathic pulmonary arterial hypertension. Continued advancements in imaging modalities, mechanistic pathogenicity, and molecular biomarkers will enable physicians to define pulmonary hypertension phenotypes based on the pathobiology and allow for treatment customization.

  6. Accurate ensemble molecular dynamics binding free energy ranking of multidrug-resistant HIV-1 proteases.

    PubMed

    Sadiq, S Kashif; Wright, David W; Kenway, Owain A; Coveney, Peter V

    2010-05-24

    Accurate calculation of important thermodynamic properties, such as macromolecular binding free energies, is one of the principal goals of molecular dynamics simulations. However, single long simulation frequently produces incorrectly converged quantitative results due to inadequate sampling of conformational space in a feasible wall-clock time. Multiple short (ensemble) simulations have been shown to explore conformational space more effectively than single long simulations, but the two methods have not yet been thermodynamically compared. Here we show that, for end-state binding free energy determination methods, ensemble simulations exhibit significantly enhanced thermodynamic sampling over single long simulations and result in accurate and converged relative binding free energies that are reproducible to within 0.5 kcal/mol. Completely correct ranking is obtained for six HIV-1 protease variants bound to lopinavir with a correlation coefficient of 0.89 and a mean relative deviation from experiment of 0.9 kcal/mol. Multidrug resistance to lopinavir is enthalpically driven and increases through a decrease in the protein-ligand van der Waals interaction, principally due to the V82A/I84V mutation, and an increase in net electrostatic repulsion due to water-mediated disruption of protein-ligand interactions in the catalytic region. Furthermore, we correctly rank, to within 1 kcal/mol of experiment, the substantially increased chemical potency of lopinavir binding to the wild-type protease compared to saquinavir and show that lopinavir takes advantage of a decreased net electrostatic repulsion to confer enhanced binding. Our approach is dependent on the combined use of petascale computing resources and on an automated simulation workflow to attain the required level of sampling and turn around time to obtain the results, which can be as little as three days. This level of performance promotes integration of such methodology with clinical decision support systems for

  7. DNA methylation-based classification and grading system for meningioma: a multicentre, retrospective analysis.

    PubMed

    Sahm, Felix; Schrimpf, Daniel; Stichel, Damian; Jones, David T W; Hielscher, Thomas; Schefzyk, Sebastian; Okonechnikov, Konstantin; Koelsche, Christian; Reuss, David E; Capper, David; Sturm, Dominik; Wirsching, Hans-Georg; Berghoff, Anna Sophie; Baumgarten, Peter; Kratz, Annekathrin; Huang, Kristin; Wefers, Annika K; Hovestadt, Volker; Sill, Martin; Ellis, Hayley P; Kurian, Kathreena M; Okuducu, Ali Fuat; Jungk, Christine; Drueschler, Katharina; Schick, Matthias; Bewerunge-Hudler, Melanie; Mawrin, Christian; Seiz-Rosenhagen, Marcel; Ketter, Ralf; Simon, Matthias; Westphal, Manfred; Lamszus, Katrin; Becker, Albert; Koch, Arend; Schittenhelm, Jens; Rushing, Elisabeth J; Collins, V Peter; Brehmer, Stefanie; Chavez, Lukas; Platten, Michael; Hänggi, Daniel; Unterberg, Andreas; Paulus, Werner; Wick, Wolfgang; Pfister, Stefan M; Mittelbronn, Michel; Preusser, Matthias; Herold-Mende, Christel; Weller, Michael; von Deimling, Andreas

    2017-05-01

    The WHO classification of brain tumours describes 15 subtypes of meningioma. Nine of these subtypes are allotted to WHO grade I, and three each to grade II and grade III. Grading is based solely on histology, with an absence of molecular markers. Although the existing classification and grading approach is of prognostic value, it harbours shortcomings such as ill-defined parameters for subtypes and grading criteria prone to arbitrary judgment. In this study, we aimed for a comprehensive characterisation of the entire molecular genetic landscape of meningioma to identify biologically and clinically relevant subgroups. In this multicentre, retrospective analysis, we investigated genome-wide DNA methylation patterns of meningiomas from ten European academic neuro-oncology centres to identify distinct methylation classes of meningiomas. The methylation classes were further characterised by DNA copy number analysis, mutational profiling, and RNA sequencing. Methylation classes were analysed for progression-free survival outcomes by the Kaplan-Meier method. The DNA methylation-based and WHO classification schema were compared using the Brier prediction score, analysed in an independent cohort with WHO grading, progression-free survival, and disease-specific survival data available, collected at the Medical University Vienna (Vienna, Austria), assessing methylation patterns with an alternative methylation chip. We retrospectively collected 497 meningiomas along with 309 samples of other extra-axial skull tumours that might histologically mimic meningioma variants. Unsupervised clustering of DNA methylation data clearly segregated all meningiomas from other skull tumours. We generated genome-wide DNA methylation profiles from all 497 meningioma samples. DNA methylation profiling distinguished six distinct clinically relevant methylation classes associated with typical mutational, cytogenetic, and gene expression patterns. Compared with WHO grading, classification by

  8. Adipose Tissue Quantification by Imaging Methods: A Proposed Classification

    PubMed Central

    Shen, Wei; Wang, ZiMian; Punyanita, Mark; Lei, Jianbo; Sinav, Ahmet; Kral, John G.; Imielinska, Celina; Ross, Robert; Heymsfield, Steven B.

    2007-01-01

    Recent advances in imaging techniques and understanding of differences in the molecular biology of adipose tissue has rendered classical anatomy obsolete, requiring a new classification of the topography of adipose tissue. Adipose tissue is one of the largest body compartments, yet a classification that defines specific adipose tissue depots based on their anatomic location and related functions is lacking. The absence of an accepted taxonomy poses problems for investigators studying adipose tissue topography and its functional correlates. The aim of this review was to critically examine the literature on imaging of whole body and regional adipose tissue and to create the first systematic classification of adipose tissue topography. Adipose tissue terminology was examined in over 100 original publications. Our analysis revealed inconsistencies in the use of specific definitions, especially for the compartment termed “visceral” adipose tissue. This analysis leads us to propose an updated classification of total body and regional adipose tissue, providing a well-defined basis for correlating imaging studies of specific adipose tissue depots with molecular processes. PMID:12529479

  9. Classification of earth terrain using polarimetric synthetic aperture radar images

    NASA Technical Reports Server (NTRS)

    Lim, H. H.; Swartz, A. A.; Yueh, H. A.; Kong, J. A.; Shin, R. T.; Van Zyl, J. J.

    1989-01-01

    Supervised and unsupervised classification techniques are developed and used to classify the earth terrain components from SAR polarimetric images of San Francisco Bay and Traverse City, Michigan. The supervised techniques include the Bayes classifiers, normalized polarimetric classification, and simple feature classification using discriminates such as the absolute and normalized magnitude response of individual receiver channel returns and the phase difference between receiver channels. An algorithm is developed as an unsupervised technique which classifies terrain elements based on the relationship between the orientation angle and the handedness of the transmitting and receiving polariation states. It is found that supervised classification produces the best results when accurate classifier training data are used, while unsupervised classification may be applied when training data are not available.

  10. Nutritional status in sick children and adolescents is not accurately reflected by BMI-SDS.

    PubMed

    Fusch, Gerhard; Raja, Preeya; Dung, Nguyen Quang; Karaolis-Danckert, Nadina; Barr, Ronald; Fusch, Christoph

    2013-01-01

    Nutritional status provides helpful information of disease severity and treatment effectiveness. Body mass index standard deviation scores (BMI-SDS) provide an approximation of body composition and thus are frequently used to classify nutritional status of sick children and adolescents. However, the accuracy of estimating body composition in this population using BMI-SDS has not been assessed. Thus, this study aims to evaluate the accuracy of nutritional status classification in sick infants and adolescents using BMI-SDS, upon comparison to classification using percentage body fat (%BF) reference charts. BMI-SDS was calculated from anthropometric measurements and %BF was measured using dual-energy x-ray absorptiometry (DXA) for 393 sick children and adolescents (5 months-18 years). Subjects were classified by nutritional status (underweight, normal weight, overweight, and obese), using 2 methods: (1) BMI-SDS, based on age- and gender-specific percentiles, and (2) %BF reference charts (standard). Linear regression and a correlation analysis were conducted to compare agreement between both methods of nutritional status classification. %BF reference value comparisons were also made between 3 independent sources based on German, Canadian, and American study populations. Correlation between nutritional status classification by BMI-SDS and %BF agreed moderately (r (2) = 0.75, 0.76 in boys and girls, respectively). The misclassification of nutritional status in sick children and adolescents using BMI-SDS was 27% when using German %BF references. Similar rates observed when using Canadian and American %BF references (24% and 23%, respectively). Using BMI-SDS to determine nutritional status in a sick population is not considered an appropriate clinical tool for identifying individual underweight or overweight children or adolescents. However, BMI-SDS may be appropriate for longitudinal measurements or for screening purposes in large field studies. When accurate nutritional

  11. Multiclass classification of microarray data samples with a reduced number of genes

    PubMed Central

    2011-01-01

    Background Multiclass classification of microarray data samples with a reduced number of genes is a rich and challenging problem in Bioinformatics research. The problem gets harder as the number of classes is increased. In addition, the performance of most classifiers is tightly linked to the effectiveness of mandatory gene selection methods. Critical to gene selection is the availability of estimates about the maximum number of genes that can be handled by any classification algorithm. Lack of such estimates may lead to either computationally demanding explorations of a search space with thousands of dimensions or classification models based on gene sets of unrestricted size. In the former case, unbiased but possibly overfitted classification models may arise. In the latter case, biased classification models unable to support statistically significant findings may be obtained. Results A novel bound on the maximum number of genes that can be handled by binary classifiers in binary mediated multiclass classification algorithms of microarray data samples is presented. The bound suggests that high-dimensional binary output domains might favor the existence of accurate and sparse binary mediated multiclass classifiers for microarray data samples. Conclusions A comprehensive experimental work shows that the bound is indeed useful to induce accurate and sparse multiclass classifiers for microarray data samples. PMID:21342522

  12. Branch classification: A new mechanism for improving branch predictor performance

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Chang, P.Y.; Hao, E.; Patt, Y.

    There is wide agreement that one of the most significant impediments to the performance of current and future pipelined superscalar processors is the presence of conditional branches in the instruction stream. Speculative execution is one solution to the branch problem, but speculative work is discarded if a branch is mispredicted. For it to be effective, speculative work is discarded if a branch is mispredicted. For it to be effective, speculative execution requires a very accurate branch predictor; 95% accuracy is not good enough. This paper proposes branch classification, a methodology for building more accurate branch predictors. Branch classification allows anmore » individual branch instruction to be associated with the branch predictor best suited to predict its direction. Using this approach, a hybrid branch predictor can be constructed such that each component branch predictor predicts those branches for which it is best suited. To demonstrate the usefulness of branch classification, an example classification scheme is given and a new hybrid predictor is built based on this scheme which achieves a higher prediction accuracy than any branch predictor previously reported in the literature.« less

  13. [Definition and classification of pulmonary arterial hypertension].

    PubMed

    Nakanishi, Norifumi

    2008-11-01

    Pulmonary hypertension(PH) is a disorder that may occur either in the setting of a variety of underlying medical conditions or as a disease that uniquely affects the pulmonary vasculature. Because an accurate diagnosis of PH in a patient is essential to establish an effective treatment, a classification of PH has been helpful. The first classification, established at WHO Symposium in 1973, classified PH into groups based on the known cause and defined primary pulmonary hypertension (PPH) as a separate entity of unknown cause. In 1998, the second World Symposium on PPH was held in Evian. Evian classification introduced the concept of conditions that directly affected the pulmonary vasculature (i.e., PAH), which included PPH. In 2003, the third World Symposium on PAH convened in Venice. In Venice classification, the term 'PPH' was abandoned in favor of 'idiopathic' within the group of disease known as 'PAH'.

  14. Spatial-spectral blood cell classification with microscopic hyperspectral imagery

    NASA Astrophysics Data System (ADS)

    Ran, Qiong; Chang, Lan; Li, Wei; Xu, Xiaofeng

    2017-10-01

    Microscopic hyperspectral images provide a new way for blood cell examination. The hyperspectral imagery can greatly facilitate the classification of different blood cells. In this paper, the microscopic hyperspectral images are acquired by connecting the microscope and the hyperspectral imager, and then tested for blood cell classification. For combined use of the spectral and spatial information provided by hyperspectral images, a spatial-spectral classification method is improved from the classical extreme learning machine (ELM) by integrating spatial context into the image classification task with Markov random field (MRF) model. Comparisons are done among ELM, ELM-MRF, support vector machines(SVM) and SVMMRF methods. Results show the spatial-spectral classification methods(ELM-MRF, SVM-MRF) perform better than pixel-based methods(ELM, SVM), and the proposed ELM-MRF has higher precision and show more accurate location of cells.

  15. Behavior Based Social Dimensions Extraction for Multi-Label Classification

    PubMed Central

    Li, Le; Xu, Junyi; Xiao, Weidong; Ge, Bin

    2016-01-01

    Classification based on social dimensions is commonly used to handle the multi-label classification task in heterogeneous networks. However, traditional methods, which mostly rely on the community detection algorithms to extract the latent social dimensions, produce unsatisfactory performance when community detection algorithms fail. In this paper, we propose a novel behavior based social dimensions extraction method to improve the classification performance in multi-label heterogeneous networks. In our method, nodes’ behavior features, instead of community memberships, are used to extract social dimensions. By introducing Latent Dirichlet Allocation (LDA) to model the network generation process, nodes’ connection behaviors with different communities can be extracted accurately, which are applied as latent social dimensions for classification. Experiments on various public datasets reveal that the proposed method can obtain satisfactory classification results in comparison to other state-of-the-art methods on smaller social dimensions. PMID:27049849

  16. A "TNM" classification system for cancer pain: the Edmonton Classification System for Cancer Pain (ECS-CP).

    PubMed

    Fainsinger, Robin L; Nekolaichuk, Cheryl L

    2008-06-01

    The purpose of this paper is to provide an overview of the development of a "TNM" cancer pain classification system for advanced cancer patients, the Edmonton Classification System for Cancer Pain (ECS-CP). Until we have a common international language to discuss cancer pain, understanding differences in clinical and research experience in opioid rotation and use remains problematic. The complexity of the cancer pain experience presents unique challenges for the classification of pain. To date, no universally accepted pain classification measure can accurately predict the complexity of pain management, particularly for patients with cancer pain that is difficult to treat. In response to this gap in clinical assessment, the Edmonton Staging System (ESS), a classification system for cancer pain, was developed. Difficulties in definitions and interpretation of some aspects of the ESS restricted acceptance and widespread use. Construct, inter-rater reliability, and predictive validity evidence have contributed to the development of the ECS-CP. The five features of the ECS-CP--Pain Mechanism, Incident Pain, Psychological Distress, Addictive Behavior and Cognitive Function--have demonstrated value in predicting pain management complexity. The development of a standardized classification system that is comprehensive, prognostic and simple to use could provide a common language for clinical management and research of cancer pain. An international study to assess the inter-rater reliability and predictive value of the ECS-CP is currently in progress.

  17. Consensus Classification Using Non-Optimized Classifiers.

    PubMed

    Brownfield, Brett; Lemos, Tony; Kalivas, John H

    2018-04-03

    Classifying samples into categories is a common problem in analytical chemistry and other fields. Classification is usually based on only one method, but numerous classifiers are available with some being complex, such as neural networks, and others are simple, such as k nearest neighbors. Regardless, most classification schemes require optimization of one or more tuning parameters for best classification accuracy, sensitivity, and specificity. A process not requiring exact selection of tuning parameter values would be useful. To improve classification, several ensemble approaches have been used in past work to combine classification results from multiple optimized single classifiers. The collection of classifications for a particular sample are then combined by a fusion process such as majority vote to form the final classification. Presented in this Article is a method to classify a sample by combining multiple classification methods without specifically classifying the sample by each method, that is, the classification methods are not optimized. The approach is demonstrated on three analytical data sets. The first is a beer authentication set with samples measured on five instruments, allowing fusion of multiple instruments by three ways. The second data set is composed of textile samples from three classes based on Raman spectra. This data set is used to demonstrate the ability to classify simultaneously with different data preprocessing strategies, thereby reducing the need to determine the ideal preprocessing method, a common prerequisite for accurate classification. The third data set contains three wine cultivars for three classes measured at 13 unique chemical and physical variables. In all cases, fusion of nonoptimized classifiers improves classification. Also presented are atypical uses of Procrustes analysis and extended inverted signal correction (EISC) for distinguishing sample similarities to respective classes.

  18. Molecular phylogeny of Pasiphaeidae (Crustacea, Decapoda, Caridea) reveals systematic incongruence of the current classification.

    PubMed

    Liao, Yunshi; De Grave, Sammy; Ho, Tsz Wai; Ip, Brian H Y; Tsang, Ling Ming; Chan, Tin-Yam; Chu, Ka Hou

    2017-10-01

    Caridean shrimps constitute one of the most diverse groups of decapod crustaceans, notwithstanding their poorly resolved infraordinal relationships. One of the systematically controversial families in Caridea is the predominantly pelagic Pasiphaeidae, comprises 101 species in seven genera. Pasiphaeidae species exhibit high morphological disparity, as well as ecological niche width, inhabiting shallow to very deep waters (>4000m). The present work presents the first molecular phylogeny of the family, based on a combined dataset of six mitochondrial and nuclear gene markers (12S rDNA, 16S rDNA, histone 3, sodium-potassium ATPase α-subunit, enolase and ATP synthase β-subunit) from 33 species belonged to six genera of Pasiphaeidae with 19 species from 12 other caridean families as outgroup taxa. Maximum likelihood and Bayesian inference analyses conducted on the concatenated dataset of 2265bp suggest the family Pasiphaeidae is not monophyletic, with Psathyrocaris more closely related to other carideans than to the other five pasiphaeid genera included in this analysis. Leptochela occupies a sister position to the remaining genera and is genetically quite distant from them. At the generic level, the analysis supports the monophyly of Pasiphaea, Leptochela and Psathyrocaris, while Eupasiphae is shown to be paraphyletic, closely related to Parapasiphae and Glyphus. The present molecular result strongly implies that certain morphological characters used in the present systematic delineation within Pasiphaeidae may not be synapomorphies and the classification within the family needs to be urgently revised. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. Neuropsychological Test Selection for Cognitive Impairment Classification: A Machine Learning Approach

    PubMed Central

    Williams, Jennifer A.; Schmitter-Edgecombe, Maureen; Cook, Diane J.

    2016-01-01

    Introduction Reducing the amount of testing required to accurately detect cognitive impairment is clinically relevant. The aim of this research was to determine the fewest number of clinical measures required to accurately classify participants as healthy older adult, mild cognitive impairment (MCI) or dementia using a suite of classification techniques. Methods Two variable selection machine learning models (i.e., naive Bayes, decision tree), a logistic regression, and two participant datasets (i.e., clinical diagnosis, clinical dementia rating; CDR) were explored. Participants classified using clinical diagnosis criteria included 52 individuals with dementia, 97 with MCI, and 161 cognitively healthy older adults. Participants classified using CDR included 154 individuals CDR = 0, 93 individuals with CDR = 0.5, and 25 individuals with CDR = 1.0+. Twenty-seven demographic, psychological, and neuropsychological variables were available for variable selection. Results No significant difference was observed between naive Bayes, decision tree, and logistic regression models for classification of both clinical diagnosis and CDR datasets. Participant classification (70.0 – 99.1%), geometric mean (60.9 – 98.1%), sensitivity (44.2 – 100%), and specificity (52.7 – 100%) were generally satisfactory. Unsurprisingly, the MCI/CDR = 0.5 participant group was the most challenging to classify. Through variable selection only 2 – 9 variables were required for classification and varied between datasets in a clinically meaningful way. Conclusions The current study results reveal that machine learning techniques can accurately classifying cognitive impairment and reduce the number of measures required for diagnosis. PMID:26332171

  20. MOLECULAR CLASSIFICATION OF OUTCOMES FROM DENGUE VIRUS -3 INFECTIONS

    PubMed Central

    Brasier, Allan R.; Zhao, Yingxin; Wiktorowicz, John E.; Spratt, Heidi M.; Nascimento, Eduardo J. M.; Cordeiro, Marli T.; Soman, Kizhake V.; Ju, Hyunsu; Recinos, Adrian; Stafford, Susan; Wu, Zheng; Marques, Ernesto T.A.; Vasilakis, Nikos

    2015-01-01

    Objectives Dengue virus (DENV) infection is a significant risk to over a third of the human population that causes a wide spectrum of illness, ranging from sub-clinical disease to intermediate syndrome of vascular complications called Dengue Fever Complicated (DFC) and severe, dengue hemorrhagic fever (DHF). Methods for discriminating outcomes will impact clinical trials and understanding disease pathophysiology. Study Design We integrated a proteomics discovery pipeline with a heuristics to develop a molecular classifier to identify an intermediate phenotype of DENV-3 infectious outcome. Results 121 differentially expressed proteins were identified in plasma from DHF vs dengue fever (DF), and informative candidates were selected using nonparametric statistics. These were combined with markers that measure complement activation, acute phase response, cellular leak, granulocyte differentiation and viral load. From this, we applied quantitative proteomics to select a 15 member panel of proteins that accurately predicted DF, DHF, and DFC using a Random Forest Classifier. The classifier primarily relied on acute phase (A2M), complement (CFD), platelet counts and cellular leak (TPM4) to produce an 86% accuracy of prediction with an area under the receiver operating curve of >0.9 for DHF and DFC vs DF. Conclusions Integrating discovery and heuristic approaches to sample distinct pathophysiological processes is a powerful approach in infectious disease. Early detection of intermediate outcomes of DENV-3 will speed clinical trials evaluating vaccines or drug interventions. PMID:25728087

  1. Molecular classification of outcomes from dengue virus -3 infections.

    PubMed

    Brasier, Allan R; Zhao, Yingxin; Wiktorowicz, John E; Spratt, Heidi M; Nascimento, Eduardo J M; Cordeiro, Marli T; Soman, Kizhake V; Ju, Hyunsu; Recinos, Adrian; Stafford, Susan; Wu, Zheng; Marques, Ernesto T A; Vasilakis, Nikos

    2015-03-01

    Dengue virus (DENV) infection is a significant risk to over a third of the human population that causes a wide spectrum of illness, ranging from sub-clinical disease to intermediate syndrome of vascular complications called dengue fever complicated (DFC) and severe, dengue hemorrhagic fever (DHF). Methods for discriminating outcomes will impact clinical trials and understanding disease pathophysiology. We integrated a proteomics discovery pipeline with a heuristics approach to develop a molecular classifier to identify an intermediate phenotype of DENV-3 infectious outcome. 121 differentially expressed proteins were identified in plasma from DHF vs dengue fever (DF), and informative candidates were selected using nonparametric statistics. These were combined with markers that measure complement activation, acute phase response, cellular leak, granulocyte differentiation and viral load. From this, we applied quantitative proteomics to select a 15 member panel of proteins that accurately predicted DF, DHF, and DFC using a random forest classifier. The classifier primarily relied on acute phase (A2M), complement (CFD), platelet counts and cellular leak (TPM4) to produce an 86% accuracy of prediction with an area under the receiver operating curve of >0.9 for DHF and DFC vs DF. Integrating discovery and heuristic approaches to sample distinct pathophysiological processes is a powerful approach in infectious disease. Early detection of intermediate outcomes of DENV-3 will speed clinical trials evaluating vaccines or drug interventions. Copyright © 2015 Elsevier B.V. All rights reserved.

  2. Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis.

    PubMed

    Chen, Peng-Jen; Lin, Meng-Chiung; Lai, Mei-Ju; Lin, Jung-Chun; Lu, Henry Horng-Shing; Tseng, Vincent S

    2018-02-01

    Narrow-band imaging is an image-enhanced form of endoscopy used to observed microstructures and capillaries of the mucosal epithelium which allows for real-time prediction of histologic features of colorectal polyps. However, narrow-band imaging expertise is required to differentiate hyperplastic from neoplastic polyps with high levels of accuracy. We developed and tested a system of computer-aided diagnosis with a deep neural network (DNN-CAD) to analyze narrow-band images of diminutive colorectal polyps. We collected 1476 images of neoplastic polyps and 681 images of hyperplastic polyps, obtained from the picture archiving and communications system database in a tertiary hospital in Taiwan. Histologic findings from the polyps were also collected and used as the reference standard. The images and data were used to train the DNN. A test set of images (96 hyperplastic and 188 neoplastic polyps, smaller than 5 mm), obtained from patients who underwent colonoscopies from March 2017 through August 2017, was then used to test the diagnostic ability of the DNN-CAD vs endoscopists (2 expert and 4 novice), who were asked to classify the images of the test set as neoplastic or hyperplastic. Their classifications were compared with findings from histologic analysis. The primary outcome measures were diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic time. The accuracy, sensitivity, specificity, PPV, NPV, and diagnostic time were compared among DNN-CAD, the novice endoscopists, and the expert endoscopists. The study was designed to detect a difference of 10% in accuracy by a 2-sided McNemar test. In the test set, the DNN-CAD identified neoplastic or hyperplastic polyps with 96.3% sensitivity, 78.1% specificity, a PPV of 89.6%, and a NPV of 91.5%. Fewer than half of the novice endoscopists classified polyps with a NPV of 90% (their NPVs ranged from 73.9% to 84.0%). DNN-CAD classified polyps as

  3. Accurate Classification of RNA Structures Using Topological Fingerprints

    PubMed Central

    Li, Kejie; Gribskov, Michael

    2016-01-01

    While RNAs are well known to possess complex structures, functionally similar RNAs often have little sequence similarity. While the exact size and spacing of base-paired regions vary, functionally similar RNAs have pronounced similarity in the arrangement, or topology, of base-paired stems. Furthermore, predicted RNA structures often lack pseudoknots (a crucial aspect of biological activity), and are only partially correct, or incomplete. A topological approach addresses all of these difficulties. In this work we describe each RNA structure as a graph that can be converted to a topological spectrum (RNA fingerprint). The set of subgraphs in an RNA structure, its RNA fingerprint, can be compared with the fingerprints of other RNA structures to identify and correctly classify functionally related RNAs. Topologically similar RNAs can be identified even when a large fraction, up to 30%, of the stems are omitted, indicating that highly accurate structures are not necessary. We investigate the performance of the RNA fingerprint approach on a set of eight highly curated RNA families, with diverse sizes and functions, containing pseudoknots, and with little sequence similarity–an especially difficult test set. In spite of the difficult test set, the RNA fingerprint approach is very successful (ROC AUC > 0.95). Due to the inclusion of pseudoknots, the RNA fingerprint approach both covers a wider range of possible structures than methods based only on secondary structure, and its tolerance for incomplete structures suggests that it can be applied even to predicted structures. Source code is freely available at https://github.rcac.purdue.edu/mgribsko/XIOS_RNA_fingerprint. PMID:27755571

  4. Improved Sparse Multi-Class SVM and Its Application for Gene Selection in Cancer Classification

    PubMed Central

    Huang, Lingkang; Zhang, Hao Helen; Zeng, Zhao-Bang; Bushel, Pierre R.

    2013-01-01

    Background Microarray techniques provide promising tools for cancer diagnosis using gene expression profiles. However, molecular diagnosis based on high-throughput platforms presents great challenges due to the overwhelming number of variables versus the small sample size and the complex nature of multi-type tumors. Support vector machines (SVMs) have shown superior performance in cancer classification due to their ability to handle high dimensional low sample size data. The multi-class SVM algorithm of Crammer and Singer provides a natural framework for multi-class learning. Despite its effective performance, the procedure utilizes all variables without selection. In this paper, we propose to improve the procedure by imposing shrinkage penalties in learning to enforce solution sparsity. Results The original multi-class SVM of Crammer and Singer is effective for multi-class classification but does not conduct variable selection. We improved the method by introducing soft-thresholding type penalties to incorporate variable selection into multi-class classification for high dimensional data. The new methods were applied to simulated data and two cancer gene expression data sets. The results demonstrate that the new methods can select a small number of genes for building accurate multi-class classification rules. Furthermore, the important genes selected by the methods overlap significantly, suggesting general agreement among different variable selection schemes. Conclusions High accuracy and sparsity make the new methods attractive for cancer diagnostics with gene expression data and defining targets of therapeutic intervention. Availability: The source MATLAB code are available from http://math.arizona.edu/~hzhang/software.html. PMID:23966761

  5. Evaluating data mining algorithms using molecular dynamics trajectories.

    PubMed

    Tatsis, Vasileios A; Tjortjis, Christos; Tzirakis, Panagiotis

    2013-01-01

    Molecular dynamics simulations provide a sample of a molecule's conformational space. Experiments on the mus time scale, resulting in large amounts of data, are nowadays routine. Data mining techniques such as classification provide a way to analyse such data. In this work, we evaluate and compare several classification algorithms using three data sets which resulted from computer simulations, of a potential enzyme mimetic biomolecule. We evaluated 65 classifiers available in the well-known data mining toolkit Weka, using 'classification' errors to assess algorithmic performance. Results suggest that: (i) 'meta' classifiers perform better than the other groups, when applied to molecular dynamics data sets; (ii) Random Forest and Rotation Forest are the best classifiers for all three data sets; and (iii) classification via clustering yields the highest classification error. Our findings are consistent with bibliographic evidence, suggesting a 'roadmap' for dealing with such data.

  6. GENE-07. MOLECULAR NEUROPATHOLOGY 2.0 - INCREASING DIAGNOSTIC ACCURACY IN PEDIATRIC NEUROONCOLOGY

    PubMed Central

    Sturm, Dominik; Jones, David T.W.; Capper, David; Sahm, Felix; von Deimling, Andreas; Rutkoswki, Stefan; Warmuth-Metz, Monika; Bison, Brigitte; Gessi, Marco; Pietsch, Torsten; Pfister, Stefan M.

    2017-01-01

    Abstract The classification of central nervous system (CNS) tumors into clinically and biologically distinct entities and subgroups is challenging. Children and adolescents can be affected by >100 histological variants with very variable outcomes, some of which are exceedingly rare. The current WHO classification has introduced a number of novel molecular markers to aid routine neuropathological diagnostics, and DNA methylation profiling is emerging as a powerful tool to distinguish CNS tumor classes. The Molecular Neuropathology 2.0 study aims to integrate genome wide (epi-)genetic diagnostics with reference neuropathological assessment for all newly-diagnosed pediatric brain tumors in Germany. To date, >350 patients have been enrolled. A molecular diagnosis is established by epigenetic tumor classification through DNA methylation profiling and targeted panel sequencing of >130 genes to detect diagnostically and/or therapeutically useful DNA mutations, structural alterations, and fusion events. Results are aligned with the reference neuropathological diagnosis, and discrepant findings are discussed in a multi-disciplinary tumor board including reference neuroradiological evaluation. Ten FFPE sections as input material are sufficient to establish a molecular diagnosis in >95% of tumors. Alignment with reference pathology results in four broad categories: a) concordant classification (~77%), b) discrepant classification resolvable by tumor board discussion and/or additional data (~5%), c) discrepant classification without currently available options to resolve (~8%), and d) cases currently unclassifiable by molecular diagnostics (~10%). Discrepancies are enriched in certain histopathological entities, such as histological high grade gliomas with a molecularly low grade profile. Gene panel sequencing reveals predisposing germline events in ~10% of patients. Genome wide (epi-)genetic analyses add a valuable layer of information to routine neuropathological

  7. Automated classification of Acid Rock Drainage potential from Corescan drill core imagery

    NASA Astrophysics Data System (ADS)

    Cracknell, M. J.; Jackson, L.; Parbhakar-Fox, A.; Savinova, K.

    2017-12-01

    Classification of the acid forming potential of waste rock is important for managing environmental hazards associated with mining operations. Current methods for the classification of acid rock drainage (ARD) potential usually involve labour intensive and subjective assessment of drill core and/or hand specimens. Manual methods are subject to operator bias, human error and the amount of material that can be assessed within a given time frame is limited. The automated classification of ARD potential documented here is based on the ARD Index developed by Parbhakar-Fox et al. (2011). This ARD Index involves the combination of five indicators: A - sulphide content; B - sulphide alteration; C - sulphide morphology; D - primary neutraliser content; and E - sulphide mineral association. Several components of the ARD Index require accurate identification of sulphide minerals. This is achieved by classifying Corescan Red-Green-Blue true colour images into the presence or absence of sulphide minerals using supervised classification. Subsequently, sulphide classification images are processed and combined with Corescan SWIR-based mineral classifications to obtain information on sulphide content, indices representing sulphide textures (disseminated versus massive and degree of veining), and spatially associated minerals. This information is combined to calculate ARD Index indicator values that feed into the classification of ARD potential. Automated ARD potential classifications of drill core samples associated with a porphyry Cu-Au deposit are compared to manually derived classifications and those obtained by standard static geochemical testing and X-ray diffractometry analyses. Results indicate a high degree of similarity between automated and manual ARD potential classifications. Major differences between approaches are observed in sulphide and neutraliser mineral percentages, likely due to the subjective nature of manual estimates of mineral content. The automated approach

  8. Rapid classification of heavy metal-exposed freshwater bacteria by infrared spectroscopy coupled with chemometrics using supervised method

    NASA Astrophysics Data System (ADS)

    Gurbanov, Rafig; Gozen, Ayse Gul; Severcan, Feride

    2018-01-01

    Rapid, cost-effective, sensitive and accurate methodologies to classify bacteria are still in the process of development. The major drawbacks of standard microbiological, molecular and immunological techniques call for the possible usage of infrared (IR) spectroscopy based supervised chemometric techniques. Previous applications of IR based chemometric methods have demonstrated outstanding findings in the classification of bacteria. Therefore, we have exploited an IR spectroscopy based chemometrics using supervised method namely Soft Independent Modeling of Class Analogy (SIMCA) technique for the first time to classify heavy metal-exposed bacteria to be used in the selection of suitable bacteria to evaluate their potential for environmental cleanup applications. Herein, we present the powerful differentiation and classification of laboratory strains (Escherichia coli and Staphylococcus aureus) and environmental isolates (Gordonia sp. and Microbacterium oxydans) of bacteria exposed to growth inhibitory concentrations of silver (Ag), cadmium (Cd) and lead (Pb). Our results demonstrated that SIMCA was able to differentiate all heavy metal-exposed and control groups from each other with 95% confidence level. Correct identification of randomly chosen test samples in their corresponding groups and high model distances between the classes were also achieved. We report, for the first time, the success of IR spectroscopy coupled with supervised chemometric technique SIMCA in classification of different bacteria under a given treatment.

  9. Evaluating Support for the Current Classification of Eukaryotic Diversity

    PubMed Central

    Parfrey, Laura Wegener; Barbero, Erika; Lasser, Elyse; Dunthorn, Micah; Bhattacharya, Debashish; Patterson, David J; Katz, Laura A

    2006-01-01

    Perspectives on the classification of eukaryotic diversity have changed rapidly in recent years, as the four eukaryotic groups within the five-kingdom classification—plants, animals, fungi, and protists—have been transformed through numerous permutations into the current system of six “supergroups.” The intent of the supergroup classification system is to unite microbial and macroscopic eukaryotes based on phylogenetic inference. This supergroup approach is increasing in popularity in the literature and is appearing in introductory biology textbooks. We evaluate the stability and support for the current six-supergroup classification of eukaryotes based on molecular genealogies. We assess three aspects of each supergroup: (1) the stability of its taxonomy, (2) the support for monophyly (single evolutionary origin) in molecular analyses targeting a supergroup, and (3) the support for monophyly when a supergroup is included as an out-group in phylogenetic studies targeting other taxa. Our analysis demonstrates that supergroup taxonomies are unstable and that support for groups varies tremendously, indicating that the current classification scheme of eukaryotes is likely premature. We highlight several trends contributing to the instability and discuss the requirements for establishing robust clades within the eukaryotic tree of life. PMID:17194223

  10. Use of mutation profiles to refine the classification of endometrial carcinomas

    PubMed Central

    Cheang, Maggie CU; Wiegand, Kimberly; Senz, Janine; Tone, Alicia; Yang, Winnie; Prentice, Leah; Tse, Kane; Zeng, Thomas; McDonald, Helen; Schmidt, Amy P.; Mutch, David G.; McAlpine, Jessica N; Hirst, Martin; Shah, Sohrab P; Lee, Cheng-Han; Goodfellow, Paul J; Gilks, C. Blake; Huntsman, David G

    2014-01-01

    The classification of endometrial carcinomas is based on pathological assessment of tumour cell type; the different cell types (endometrioid, serous, carcinosarcoma, mixed, and clear cell) are associated with distinct molecular alterations. This current classification system for high-grade subtypes, in particular the distinction between high-grade endometrioid (EEC-3) and serous carcinomas (ESC), is limited in its reproducibility and prognostic abilities. Therefore, a search for specific molecular classifiers to improve endometrial carcinoma subclassification is warranted. We performed target enrichment sequencing on 393 endometrial carcinomas from two large cohorts, sequencing exons from the following 9 genes; ARID1A, PPP2R1A, PTEN, PIK3CA, KRAS, CTNNB1, TP53, BRAF and PPP2R5C. Based on this gene panel each endometrial carcinoma subtype shows a distinct mutation profile. EEC-3s have significantly different frequencies of PTEN and TP53 mutations when compared to low-grade endometrioid carcinomas. ESCs and EEC-3s are distinct subtypes with significantly different frequencies of mutations in PTEN, ARID1A, PPP2R1A, TP53, and CTNNB1. From the mutation profiles we were able to identify subtype outliers, i.e. cases diagnosed morphologically as one subtype but with a mutation profile suggestive of a different subtype. Careful review of these diagnostically challenging cases suggested that the original morphological classification was incorrect in most instances. The molecular profile of carcinosarcomas suggests two distinct mutation profiles for these tumours; endometrioid-type (PTEN, PIK3CA, ARID1A, KRAS mutations), and serous-type (TP53 and PPP2R1A mutations). While this nine gene panel does not allow for a purely molecularly based classification of endometrial carcinoma, it may prove useful as an adjunct to morphological classification and serve as an aid in the classification of problematic cases. If used in practice, it may lead to improved diagnostic reproducibility

  11. Use of mutation profiles to refine the classification of endometrial carcinomas.

    PubMed

    McConechy, Melissa K; Ding, Jiarui; Cheang, Maggie Cu; Wiegand, Kimberly; Senz, Janine; Tone, Alicia; Yang, Winnie; Prentice, Leah; Tse, Kane; Zeng, Thomas; McDonald, Helen; Schmidt, Amy P; Mutch, David G; McAlpine, Jessica N; Hirst, Martin; Shah, Sohrab P; Lee, Cheng-Han; Goodfellow, Paul J; Gilks, C Blake; Huntsman, David G

    2012-09-01

    The classification of endometrial carcinomas is based on pathological assessment of tumour cell type; the different cell types (endometrioid, serous, carcinosarcoma, mixed, undifferentiated, and clear cell) are associated with distinct molecular alterations. This current classification system for high-grade subtypes, in particular the distinction between high-grade endometrioid (EEC-3) and serous carcinomas (ESC), is limited in its reproducibility and prognostic abilities. Therefore, a search for specific molecular classifiers to improve endometrial carcinoma subclassification is warranted. We performed target enrichment sequencing on 393 endometrial carcinomas from two large cohorts, sequencing exons from the following nine genes: ARID1A, PPP2R1A, PTEN, PIK3CA, KRAS, CTNNB1, TP53, BRAF, and PPP2R5C. Based on this gene panel, each endometrial carcinoma subtype shows a distinct mutation profile. EEC-3s have significantly different frequencies of PTEN and TP53 mutations when compared to low-grade endometrioid carcinomas. ESCs and EEC-3s are distinct subtypes with significantly different frequencies of mutations in PTEN, ARID1A, PPP2R1A, TP53, and CTNNB1. From the mutation profiles, we were able to identify subtype outliers, ie cases diagnosed morphologically as one subtype but with a mutation profile suggestive of a different subtype. Careful review of these diagnostically challenging cases suggested that the original morphological classification was incorrect in most instances. The molecular profile of carcinosarcomas suggests two distinct mutation profiles for these tumours: endometrioid-type (PTEN, PIK3CA, ARID1A, KRAS mutations) and serous-type (TP53 and PPP2R1A mutations). While this nine-gene panel does not allow for a purely molecularly based classification of endometrial carcinoma, it may prove useful as an adjunct to morphological classification and serve as an aid in the classification of problematic cases. If used in practice, it may lead to improved

  12. Cooperative genomic alteration network reveals molecular classification across 12 major cancer types

    PubMed Central

    Zhang, Hongyi; Deng, Yulan; Zhang, Yong; Ping, Yanyan; Zhao, Hongying; Pang, Lin; Zhang, Xinxin; Wang, Li; Xu, Chaohan; Xiao, Yun; Li, Xia

    2017-01-01

    The accumulation of somatic genomic alterations that enables cells to gradually acquire growth advantage contributes to tumor development. This has the important implication of the widespread existence of cooperative genomic alterations in the accumulation process. Here, we proposed a computational method HCOC that simultaneously consider genetic context and downstream functional effects on cancer hallmarks to uncover somatic cooperative events in human cancers. Applying our method to 12 TCGA cancer types, we totally identified 1199 cooperative events with high heterogeneity across human cancers, and then constructed a pan-cancer cooperative alteration network. These cooperative events are associated with genomic alterations of some high-confident cancer drivers, and can trigger the dysfunction of hallmark associated pathways in a co-defect way rather than single alterations. We found that these cooperative events can be used to produce a prognostic classification that can provide complementary information with tissue-of-origin. In a further case study of glioblastoma, using 23 cooperative events identified, we stratified patients into molecularly relevant subtypes with a prognostic significance independent of the Glioma-CpG Island Methylator Phenotype (GCIMP). In summary, our method can be effectively used to discover cancer-driving cooperative events that can be valuable clinical markers for patient stratification. PMID:27899621

  13. Changing Patient Classification System for Hospital Reimbursement in Romania

    PubMed Central

    Radu, Ciprian-Paul; Chiriac, Delia Nona; Vladescu, Cristian

    2010-01-01

    Aim To evaluate the effects of the change in the diagnosis-related group (DRG) system on patient morbidity and hospital financial performance in the Romanian public health care system. Methods Three variables were assessed before and after the classification switch in July 2007: clinical outcomes, the case mix index, and hospital budgets, using the database of the National School of Public Health and Health Services Management, which contains data regularly received from hospitals reimbursed through the Romanian DRG scheme (291 in 2009). Results The lack of a Romanian system for the calculation of cost-weights imposed the necessity to use an imported system, which was criticized by some clinicians for not accurately reflecting resource consumption in Romanian hospitals. The new DRG classification system allowed a more accurate clinical classification. However, it also exposed a lack of physicians’ knowledge on diagnosing and coding procedures, which led to incorrect coding. Consequently, the reported hospital morbidity changed after the DRG switch, reflecting an increase in the national case mix index of 25% in 2009 (compared with 2007). Since hospitals received the same reimbursement over the first two years after the classification switch, the new DRG system led them sometimes to change patients' diagnoses in order to receive more funding. Conclusion Lack of oversight of hospital coding and reporting to the national reimbursement scheme allowed the increase in the case mix index. The complexity of the new classification system requires more resources (human and financial), better monitoring and evaluation, and improved legislation in order to achieve better hospital resource allocation and more efficient patient care. PMID:20564769

  14. Changing patient classification system for hospital reimbursement in Romania.

    PubMed

    Radu, Ciprian-Paul; Chiriac, Delia Nona; Vladescu, Cristian

    2010-06-01

    To evaluate the effects of the change in the diagnosis-related group (DRG) system on patient morbidity and hospital financial performance in the Romanian public health care system. Three variables were assessed before and after the classification switch in July 2007: clinical outcomes, the case mix index, and hospital budgets, using the database of the National School of Public Health and Health Services Management, which contains data regularly received from hospitals reimbursed through the Romanian DRG scheme (291 in 2009). The lack of a Romanian system for the calculation of cost-weights imposed the necessity to use an imported system, which was criticized by some clinicians for not accurately reflecting resource consumption in Romanian hospitals. The new DRG classification system allowed a more accurate clinical classification. However, it also exposed a lack of physicians' knowledge on diagnosing and coding procedures, which led to incorrect coding. Consequently, the reported hospital morbidity changed after the DRG switch, reflecting an increase in the national case-mix index of 25% in 2009 (compared with 2007). Since hospitals received the same reimbursement over the first two years after the classification switch, the new DRG system led them sometimes to change patients' diagnoses in order to receive more funding. Lack of oversight of hospital coding and reporting to the national reimbursement scheme allowed the increase in the case-mix index. The complexity of the new classification system requires more resources (human and financial), better monitoring and evaluation, and improved legislation in order to achieve better hospital resource allocation and more efficient patient care.

  15. IRIS COLOUR CLASSIFICATION SCALES--THEN AND NOW.

    PubMed

    Grigore, Mariana; Avram, Alina

    2015-01-01

    Eye colour is one of the most obvious phenotypic traits of an individual. Since the first documented classification scale developed in 1843, there have been numerous attempts to classify the iris colour. In the past centuries, iris colour classification scales has had various colour categories and mostly relied on comparison of an individual's eye with painted glass eyes. Once photography techniques were refined, standard iris photographs replaced painted eyes, but this did not solve the problem of painted/ printed colour variability in time. Early clinical scales were easy to use, but lacked objectivity and were not standardised or statistically tested for reproducibility. The era of automated iris colour classification systems came with the technological development. Spectrophotometry, digital analysis of high-resolution iris images, hyper spectral analysis of the human real iris and the dedicated iris colour analysis software, all accomplished an objective, accurate iris colour classification, but are quite expensive and limited in use to research environment. Iris colour classification systems evolved continuously due to their use in a wide range of studies, especially in the fields of anthropology, epidemiology and genetics. Despite the wide range of the existing scales, up until present there has been no generally accepted iris colour classification scale.

  16. Multiclass cancer diagnosis using tumor gene expression signatures

    DOE PAGES

    Ramaswamy, S.; Tamayo, P.; Rifkin, R.; ...

    2001-12-11

    The optimal treatment of patients with cancer depends on establishing accurate diagnoses by using a complex combination of clinical and histopathological data. In some instances, this task is difficult or impossible because of atypical clinical presentation or histopathology. To determine whether the diagnosis of multiple common adult malignancies could be achieved purely by molecular classification, we subjected 218 tumor samples, spanning 14 common tumor types, and 90 normal tissue samples to oligonucleotide microarray gene expression analysis. The expression levels of 16,063 genes and expressed sequence tags were used to evaluate the accuracy of a multiclass classifier based on a supportmore » vector machine algorithm. Overall classification accuracy was 78%, far exceeding the accuracy of random classification (9%). Poorly differentiated cancers resulted in low-confidence predictions and could not be accurately classified according to their tissue of origin, indicating that they are molecularly distinct entities with dramatically different gene expression patterns compared with their well differentiated counterparts. Taken together, these results demonstrate the feasibility of accurate, multiclass molecular cancer classification and suggest a strategy for future clinical implementation of molecular cancer diagnostics.« less

  17. Overview of classification systems in peripheral artery disease.

    PubMed

    Hardman, Rulon L; Jazaeri, Omid; Yi, J; Smith, M; Gupta, Rajan

    2014-12-01

    Peripheral artery disease (PAD), secondary to atherosclerotic disease, is currently the leading cause of morbidity and mortality in the western world. While PAD is common, it is estimated that the majority of patients with PAD are undiagnosed and undertreated. The challenge to the treatment of PAD is to accurately diagnose the symptoms and determine treatment for each patient. The varied presentations of peripheral vascular disease have led to numerous classification schemes throughout the literature. Consistent grading of patients leads to both objective criteria for treating patients and a baseline for clinical follow-up. Reproducible classification systems are also important in clinical trials and when comparing medical, surgical, and endovascular treatment paradigms. This article reviews the various classification systems for PAD and advantages to each system.

  18. Identification of an Efficient Gene Expression Panel for Glioblastoma Classification

    PubMed Central

    Zelaya, Ivette; Laks, Dan R.; Zhao, Yining; Kawaguchi, Riki; Gao, Fuying; Kornblum, Harley I.; Coppola, Giovanni

    2016-01-01

    We present here a novel genetic algorithm-based random forest (GARF) modeling technique that enables a reduction in the complexity of large gene disease signatures to highly accurate, greatly simplified gene panels. When applied to 803 glioblastoma multiforme samples, this method allowed the 840-gene Verhaak et al. gene panel (the standard in the field) to be reduced to a 48-gene classifier, while retaining 90.91% classification accuracy, and outperforming the best available alternative methods. Additionally, using this approach we produced a 32-gene panel which allows for better consistency between RNA-seq and microarray-based classifications, improving cross-platform classification retention from 69.67% to 86.07%. A webpage producing these classifications is available at http://simplegbm.semel.ucla.edu. PMID:27855170

  19. Hydrologic Landscape Regionalisation Using Deductive Classification and Random Forests

    PubMed Central

    Brown, Stuart C.; Lester, Rebecca E.; Versace, Vincent L.; Fawcett, Jonathon; Laurenson, Laurie

    2014-01-01

    Landscape classification and hydrological regionalisation studies are being increasingly used in ecohydrology to aid in the management and research of aquatic resources. We present a methodology for classifying hydrologic landscapes based on spatial environmental variables by employing non-parametric statistics and hybrid image classification. Our approach differed from previous classifications which have required the use of an a priori spatial unit (e.g. a catchment) which necessarily results in the loss of variability that is known to exist within those units. The use of a simple statistical approach to identify an appropriate number of classes eliminated the need for large amounts of post-hoc testing with different number of groups, or the selection and justification of an arbitrary number. Using statistical clustering, we identified 23 distinct groups within our training dataset. The use of a hybrid classification employing random forests extended this statistical clustering to an area of approximately 228,000 km2 of south-eastern Australia without the need to rely on catchments, landscape units or stream sections. This extension resulted in a highly accurate regionalisation at both 30-m and 2.5-km resolution, and a less-accurate 10-km classification that would be more appropriate for use at a continental scale. A smaller case study, of an area covering 27,000 km2, demonstrated that the method preserved the intra- and inter-catchment variability that is known to exist in local hydrology, based on previous research. Preliminary analysis linking the regionalisation to streamflow indices is promising suggesting that the method could be used to predict streamflow behaviour in ungauged catchments. Our work therefore simplifies current classification frameworks that are becoming more popular in ecohydrology, while better retaining small-scale variability in hydrology, thus enabling future attempts to explain and visualise broad-scale hydrologic trends at the scale of

  20. Hydrologic landscape regionalisation using deductive classification and random forests.

    PubMed

    Brown, Stuart C; Lester, Rebecca E; Versace, Vincent L; Fawcett, Jonathon; Laurenson, Laurie

    2014-01-01

    Landscape classification and hydrological regionalisation studies are being increasingly used in ecohydrology to aid in the management and research of aquatic resources. We present a methodology for classifying hydrologic landscapes based on spatial environmental variables by employing non-parametric statistics and hybrid image classification. Our approach differed from previous classifications which have required the use of an a priori spatial unit (e.g. a catchment) which necessarily results in the loss of variability that is known to exist within those units. The use of a simple statistical approach to identify an appropriate number of classes eliminated the need for large amounts of post-hoc testing with different number of groups, or the selection and justification of an arbitrary number. Using statistical clustering, we identified 23 distinct groups within our training dataset. The use of a hybrid classification employing random forests extended this statistical clustering to an area of approximately 228,000 km2 of south-eastern Australia without the need to rely on catchments, landscape units or stream sections. This extension resulted in a highly accurate regionalisation at both 30-m and 2.5-km resolution, and a less-accurate 10-km classification that would be more appropriate for use at a continental scale. A smaller case study, of an area covering 27,000 km2, demonstrated that the method preserved the intra- and inter-catchment variability that is known to exist in local hydrology, based on previous research. Preliminary analysis linking the regionalisation to streamflow indices is promising suggesting that the method could be used to predict streamflow behaviour in ungauged catchments. Our work therefore simplifies current classification frameworks that are becoming more popular in ecohydrology, while better retaining small-scale variability in hydrology, thus enabling future attempts to explain and visualise broad-scale hydrologic trends at the scale of

  1. Accurate quantum chemical calculations

    NASA Technical Reports Server (NTRS)

    Bauschlicher, Charles W., Jr.; Langhoff, Stephen R.; Taylor, Peter R.

    1989-01-01

    An important goal of quantum chemical calculations is to provide an understanding of chemical bonding and molecular electronic structure. A second goal, the prediction of energy differences to chemical accuracy, has been much harder to attain. First, the computational resources required to achieve such accuracy are very large, and second, it is not straightforward to demonstrate that an apparently accurate result, in terms of agreement with experiment, does not result from a cancellation of errors. Recent advances in electronic structure methodology, coupled with the power of vector supercomputers, have made it possible to solve a number of electronic structure problems exactly using the full configuration interaction (FCI) method within a subspace of the complete Hilbert space. These exact results can be used to benchmark approximate techniques that are applicable to a wider range of chemical and physical problems. The methodology of many-electron quantum chemistry is reviewed. Methods are considered in detail for performing FCI calculations. The application of FCI methods to several three-electron problems in molecular physics are discussed. A number of benchmark applications of FCI wave functions are described. Atomic basis sets and the development of improved methods for handling very large basis sets are discussed: these are then applied to a number of chemical and spectroscopic problems; to transition metals; and to problems involving potential energy surfaces. Although the experiences described give considerable grounds for optimism about the general ability to perform accurate calculations, there are several problems that have proved less tractable, at least with current computer resources, and these and possible solutions are discussed.

  2. A protein and mRNA expression-based classification of gastric cancer.

    PubMed

    Setia, Namrata; Agoston, Agoston T; Han, Hye S; Mullen, John T; Duda, Dan G; Clark, Jeffrey W; Deshpande, Vikram; Mino-Kenudson, Mari; Srivastava, Amitabh; Lennerz, Jochen K; Hong, Theodore S; Kwak, Eunice L; Lauwers, Gregory Y

    2016-07-01

    The overall survival of gastric carcinoma patients remains poor despite improved control over known risk factors and surveillance. This highlights the need for new classifications, driven towards identification of potential therapeutic targets. Using sophisticated molecular technologies and analysis, three groups recently provided genetic and epigenetic molecular classifications of gastric cancer (The Cancer Genome Atlas, 'Singapore-Duke' study, and Asian Cancer Research Group). Suggested by these classifications, here, we examined the expression of 14 biomarkers in a cohort of 146 gastric adenocarcinomas and performed unsupervised hierarchical clustering analysis using less expensive and widely available immunohistochemistry and in situ hybridization. Ultimately, we identified five groups of gastric cancers based on Epstein-Barr virus (EBV) positivity, microsatellite instability, aberrant E-cadherin, and p53 expression; the remaining cases constituted a group characterized by normal p53 expression. In addition, the five categories correspond to the reported molecular subgroups by virtue of clinicopathologic features. Furthermore, evaluation between these clusters and survival using the Cox proportional hazards model showed a trend for superior survival in the EBV and microsatellite-instable related adenocarcinomas. In conclusion, we offer as a proposal a simplified algorithm that is able to reproduce the recently proposed molecular subgroups of gastric adenocarcinoma, using immunohistochemical and in situ hybridization techniques.

  3. [Strategy for molecular testing in pulmonary carcinoma].

    PubMed

    Penault-Llorca, Frédérique; Tixier, Lucie; Perrot, Loïc; Cayre, Anne

    2016-01-01

    Nowadays, the analysis of theranostic molecular markers is central in the management of lung cancer. As those tumors are diagnosed in two third of the cases at an advanced stage, molecular screening is frequently performed on "small samples". The screening strategy starts by an accurate histopathological characterization, including on biopsies or cytological specimens. WHO 2015 provided a new classification for small biopsy and cytology, defining categories such as non-small cell carcinoma (NSCC), favor adenocarcinoma (TTF1 positive), or favor squamous cell carcinoma (p40 positive). Only the NSCC tumors, non-squamous, are eligible to molecular testing. A strategy aiming at tissue sparing for the small biopsies has to be organized. Tests corresponding to available drugs are prioritized. Blank slides will be prepared for immunohistochemistry and in situ hybridization based tests such as ALK. DNA will then be extracted for the other tests, EGFR mutation screening first associated or not to KRAS. Then, the emerging biomarkers (HER2, ROS1, RET, BRAF…) as well as potentially other markers in case of clinical trials, can been tested. The spread of next generation sequencing technologies, with a very sensitive all-in-one approach will allow the identification of minority clones. Eventually, the development of liquid biopsies will provide the opportunity to monitor the apparition of resistance clones during treatment. This non-invasive approach allows patients with a contraindication to perform biopsy or with non-relevant biopsies to access to molecular screening. Copyright © 2016. Published by Elsevier Masson SAS.

  4. How reliable and accurate is the AO/OTA comprehensive classification for adult long-bone fractures?

    PubMed

    Meling, Terje; Harboe, Knut; Enoksen, Cathrine H; Aarflot, Morten; Arthursson, Astvaldur J; Søreide, Kjetil

    2012-07-01

    Reliable classification of fractures is important for treatment allocation and study comparisons. The overall accuracy of scoring applied to a general population of fractures is little known. This study aimed to investigate the accuracy and reliability of the comprehensive Arbeitsgemeinschaft für Osteosynthesefragen/Orthopedic Trauma Association classification for adult long-bone fractures and identify factors associated with poor coding agreement. Adults (>16 years) with long-bone fractures coded in a Fracture and Dislocation Registry at the Stavanger University Hospital during the fiscal year 2008 were included. An unblinded reference code dataset was generated for the overall accuracy assessment by two experienced orthopedic trauma surgeons. Blinded analysis of intrarater reliability was performed by rescoring and of interrater reliability by recoding of a randomly selected fracture sample. Proportion of agreement (PA) and kappa (κ) statistics are presented. Uni- and multivariate logistic regression analyses of factors predicting accuracy were performed. During the study period, 949 fractures were included and coded by 26 surgeons. For the intrarater analysis, overall agreements were κ = 0.67 (95% confidence interval [CI]: 0.64-0.70) and PA 69%. For interrater assessment, κ = 0.67 (95% CI: 0.62-0.72) and PA 69%. The accuracy of surgeons' blinded recoding was κ = 0.68 (95% CI: 0.65- 0.71) and PA 68%. Fracture type, frequency of the fracture, and segment fractured significantly influenced accuracy whereas the coder's experience did not. Both the reliability and accuracy of the comprehensive Arbeitsgemeinschaft für Osteosynthesefragen/Orthopedic Trauma Association classification for long-bone fractures ranged from substantial to excellent. Variations in coding accuracy seem to be related more to the fracture itself than the surgeon. Diagnostic study, level I.

  5. PHOTOMETRIC SUPERNOVA CLASSIFICATION WITH MACHINE LEARNING

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Lochner, Michelle; Peiris, Hiranya V.; Lahav, Ofer

    Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models tomore » curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k -nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.« less

  6. Photometric Supernova Classification with Machine Learning

    NASA Astrophysics Data System (ADS)

    Lochner, Michelle; McEwen, Jason D.; Peiris, Hiranya V.; Lahav, Ofer; Winter, Max K.

    2016-08-01

    Automated photometric supernova classification has become an active area of research in recent years in light of current and upcoming imaging surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope, given that spectroscopic confirmation of type for all supernovae discovered will be impossible. Here, we develop a multi-faceted classification pipeline, combining existing and new approaches. Our pipeline consists of two stages: extracting descriptive features from the light curves and classification using a machine learning algorithm. Our feature extraction methods vary from model-dependent techniques, namely SALT2 fits, to more independent techniques that fit parametric models to curves, to a completely model-independent wavelet approach. We cover a range of representative machine learning algorithms, including naive Bayes, k-nearest neighbors, support vector machines, artificial neural networks, and boosted decision trees (BDTs). We test the pipeline on simulated multi-band DES light curves from the Supernova Photometric Classification Challenge. Using the commonly used area under the curve (AUC) of the Receiver Operating Characteristic as a metric, we find that the SALT2 fits and the wavelet approach, with the BDTs algorithm, each achieve an AUC of 0.98, where 1 represents perfect classification. We find that a representative training set is essential for good classification, whatever the feature set or algorithm, with implications for spectroscopic follow-up. Importantly, we find that by using either the SALT2 or the wavelet feature sets with a BDT algorithm, accurate classification is possible purely from light curve data, without the need for any redshift information.

  7. Iris Image Classification Based on Hierarchical Visual Codebook.

    PubMed

    Zhenan Sun; Hui Zhang; Tieniu Tan; Jianyu Wang

    2014-06-01

    Iris recognition as a reliable method for personal identification has been well-studied with the objective to assign the class label of each iris image to a unique subject. In contrast, iris image classification aims to classify an iris image to an application specific category, e.g., iris liveness detection (classification of genuine and fake iris images), race classification (e.g., classification of iris images of Asian and non-Asian subjects), coarse-to-fine iris identification (classification of all iris images in the central database into multiple categories). This paper proposes a general framework for iris image classification based on texture analysis. A novel texture pattern representation method called Hierarchical Visual Codebook (HVC) is proposed to encode the texture primitives of iris images. The proposed HVC method is an integration of two existing Bag-of-Words models, namely Vocabulary Tree (VT), and Locality-constrained Linear Coding (LLC). The HVC adopts a coarse-to-fine visual coding strategy and takes advantages of both VT and LLC for accurate and sparse representation of iris texture. Extensive experimental results demonstrate that the proposed iris image classification method achieves state-of-the-art performance for iris liveness detection, race classification, and coarse-to-fine iris identification. A comprehensive fake iris image database simulating four types of iris spoof attacks is developed as the benchmark for research of iris liveness detection.

  8. California desert resource inventory using multispectral classification of digitally mosaicked Landsat frames

    NASA Technical Reports Server (NTRS)

    Bryant, N. A.; Mcleod, R. G.; Zobrist, A. L.; Johnson, H. B.

    1979-01-01

    Procedures for adjustment of brightness values between frames and the digital mosaicking of Landsat frames to standard map projections are developed for providing a continuous data base for multispectral thematic classification. A combination of local terrain variations in the Californian deserts and a global sampling strategy based on transects provided the framework for accurate classification throughout the entire geographic region.

  9. Centrifuge: rapid and sensitive classification of metagenomic sequences

    PubMed Central

    Song, Li; Breitwieser, Florian P.

    2016-01-01

    Centrifuge is a novel microbial classification engine that enables rapid, accurate, and sensitive labeling of reads and quantification of species on desktop computers. The system uses an indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem. Centrifuge requires a relatively small index (4.2 GB for 4078 bacterial and 200 archaeal genomes) and classifies sequences at very high speed, allowing it to process the millions of reads from a typical high-throughput DNA sequencing run within a few minutes. Together, these advances enable timely and accurate analysis of large metagenomics data sets on conventional desktop computers. Because of its space-optimized indexing schemes, Centrifuge also makes it possible to index the entire NCBI nonredundant nucleotide sequence database (a total of 109 billion bases) with an index size of 69 GB, in contrast to k-mer-based indexing schemes, which require far more extensive space. PMID:27852649

  10. Accurate density functional prediction of molecular electron affinity with the scaling corrected Kohn–Sham frontier orbital energies

    NASA Astrophysics Data System (ADS)

    Zhang, DaDi; Yang, Xiaolong; Zheng, Xiao; Yang, Weitao

    2018-04-01

    Electron affinity (EA) is the energy released when an additional electron is attached to an atom or a molecule. EA is a fundamental thermochemical property, and it is closely pertinent to other important properties such as electronegativity and hardness. However, accurate prediction of EA is difficult with density functional theory methods. The somewhat large error of the calculated EAs originates mainly from the intrinsic delocalisation error associated with the approximate exchange-correlation functional. In this work, we employ a previously developed non-empirical global scaling correction approach, which explicitly imposes the Perdew-Parr-Levy-Balduz condition to the approximate functional, and achieve a substantially improved accuracy for the calculated EAs. In our approach, the EA is given by the scaling corrected Kohn-Sham lowest unoccupied molecular orbital energy of the neutral molecule, without the need to carry out the self-consistent-field calculation for the anion.

  11. Scanning electron microscope automatic defect classification of process induced defects

    NASA Astrophysics Data System (ADS)

    Wolfe, Scott; McGarvey, Steve

    2017-03-01

    With the integration of high speed Scanning Electron Microscope (SEM) based Automated Defect Redetection (ADR) in both high volume semiconductor manufacturing and Research and Development (R and D), the need for reliable SEM Automated Defect Classification (ADC) has grown tremendously in the past few years. In many high volume manufacturing facilities and R and D operations, defect inspection is performed on EBeam (EB), Bright Field (BF) or Dark Field (DF) defect inspection equipment. A comma separated value (CSV) file is created by both the patterned and non-patterned defect inspection tools. The defect inspection result file contains a list of the inspection anomalies detected during the inspection tools' examination of each structure, or the examination of an entire wafers surface for non-patterned applications. This file is imported into the Defect Review Scanning Electron Microscope (DRSEM). Following the defect inspection result file import, the DRSEM automatically moves the wafer to each defect coordinate and performs ADR. During ADR the DRSEM operates in a reference mode, capturing a SEM image at the exact position of the anomalies coordinates and capturing a SEM image of a reference location in the center of the wafer. A Defect reference image is created based on the Reference image minus the Defect image. The exact coordinates of the defect is calculated based on the calculated defect position and the anomalies stage coordinate calculated when the high magnification SEM defect image is captured. The captured SEM image is processed through either DRSEM ADC binning, exporting to a Yield Analysis System (YAS), or a combination of both. Process Engineers, Yield Analysis Engineers or Failure Analysis Engineers will manually review the captured images to insure that either the YAS defect binning is accurately classifying the defects or that the DRSEM defect binning is accurately classifying the defects. This paper is an exploration of the feasibility of the

  12. Effective Feature Selection for Classification of Promoter Sequences.

    PubMed

    K, Kouser; P G, Lavanya; Rangarajan, Lalitha; K, Acharya Kshitish

    2016-01-01

    Exploring novel computational methods in making sense of biological data has not only been a necessity, but also productive. A part of this trend is the search for more efficient in silico methods/tools for analysis of promoters, which are parts of DNA sequences that are involved in regulation of expression of genes into other functional molecules. Promoter regions vary greatly in their function based on the sequence of nucleotides and the arrangement of protein-binding short-regions called motifs. In fact, the regulatory nature of the promoters seems to be largely driven by the selective presence and/or the arrangement of these motifs. Here, we explore computational classification of promoter sequences based on the pattern of motif distributions, as such classification can pave a new way of functional analysis of promoters and to discover the functionally crucial motifs. We make use of Position Specific Motif Matrix (PSMM) features for exploring the possibility of accurately classifying promoter sequences using some of the popular classification techniques. The classification results on the complete feature set are low, perhaps due to the huge number of features. We propose two ways of reducing features. Our test results show improvement in the classification output after the reduction of features. The results also show that decision trees outperform SVM (Support Vector Machine), KNN (K Nearest Neighbor) and ensemble classifier LibD3C, particularly with reduced features. The proposed feature selection methods outperform some of the popular feature transformation methods such as PCA and SVD. Also, the methods proposed are as accurate as MRMR (feature selection method) but much faster than MRMR. Such methods could be useful to categorize new promoters and explore regulatory mechanisms of gene expressions in complex eukaryotic species.

  13. InterLymph hierarchical classification of lymphoid neoplasms for epidemiologic research based on the WHO classification (2008): update and future directions

    PubMed Central

    Morton, Lindsay M.; Linet, Martha S.; Clarke, Christina A.; Kadin, Marshall E.; Vajdic, Claire M.; Monnereau, Alain; Maynadié, Marc; Chiu, Brian C.-H.; Marcos-Gragera, Rafael; Costantini, Adele Seniori; Cerhan, James R.; Weisenburger, Dennis D.

    2010-01-01

    After publication of the updated World Health Organization (WHO) classification of tumors of hematopoietic and lymphoid tissues in 2008, the Pathology Working Group of the International Lymphoma Epidemiology Consortium (InterLymph) now presents an update of the hierarchical classification of lymphoid neoplasms for epidemiologic research based on the 2001 WHO classification, which we published in 2007. The updated hierarchical classification incorporates all of the major and provisional entities in the 2008 WHO classification, including newly defined entities based on age, site, certain infections, and molecular characteristics, as well as borderline categories, early and “in situ” lesions, disorders with limited capacity for clinical progression, lesions without current International Classification of Diseases for Oncology, 3rd Edition codes, and immunodeficiency-associated lymphoproliferative disorders. WHO subtypes are defined in hierarchical groupings, with newly defined groups for small B-cell lymphomas with plasmacytic differentiation and for primary cutaneous T-cell lymphomas. We suggest approaches for applying the hierarchical classification in various epidemiologic settings, including strategies for dealing with multiple coexisting lymphoma subtypes in one patient, and cases with incomplete pathologic information. The pathology materials useful for state-of-the-art epidemiology studies are also discussed. We encourage epidemiologists to adopt the updated InterLymph hierarchical classification, which incorporates the most recent WHO entities while demonstrating their relationship to older classifications. PMID:20699439

  14. InterLymph hierarchical classification of lymphoid neoplasms for epidemiologic research based on the WHO classification (2008): update and future directions.

    PubMed

    Turner, Jennifer J; Morton, Lindsay M; Linet, Martha S; Clarke, Christina A; Kadin, Marshall E; Vajdic, Claire M; Monnereau, Alain; Maynadié, Marc; Chiu, Brian C-H; Marcos-Gragera, Rafael; Costantini, Adele Seniori; Cerhan, James R; Weisenburger, Dennis D

    2010-11-18

    After publication of the updated World Health Organization (WHO) classification of tumors of hematopoietic and lymphoid tissues in 2008, the Pathology Working Group of the International Lymphoma Epidemiology Consortium (InterLymph) now presents an update of the hierarchical classification of lymphoid neoplasms for epidemiologic research based on the 2001 WHO classification, which we published in 2007. The updated hierarchical classification incorporates all of the major and provisional entities in the 2008 WHO classification, including newly defined entities based on age, site, certain infections, and molecular characteristics, as well as borderline categories, early and "in situ" lesions, disorders with limited capacity for clinical progression, lesions without current International Classification of Diseases for Oncology, 3rd Edition codes, and immunodeficiency-associated lymphoproliferative disorders. WHO subtypes are defined in hierarchical groupings, with newly defined groups for small B-cell lymphomas with plasmacytic differentiation and for primary cutaneous T-cell lymphomas. We suggest approaches for applying the hierarchical classification in various epidemiologic settings, including strategies for dealing with multiple coexisting lymphoma subtypes in one patient, and cases with incomplete pathologic information. The pathology materials useful for state-of-the-art epidemiology studies are also discussed. We encourage epidemiologists to adopt the updated InterLymph hierarchical classification, which incorporates the most recent WHO entities while demonstrating their relationship to older classifications.

  15. Flying insect detection and classification with inexpensive sensors.

    PubMed

    Chen, Yanping; Why, Adena; Batista, Gustavo; Mafra-Neto, Agenor; Keogh, Eamonn

    2014-10-15

    An inexpensive, noninvasive system that could accurately classify flying insects would have important implications for entomological research, and allow for the development of many useful applications in vector and pest control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts devoted to this task. To date, however, none of this research has had a lasting impact. In this work, we show that pseudo-acoustic optical sensors can produce superior data; that additional features, both intrinsic and extrinsic to the insect's flight behavior, can be exploited to improve insect classification; that a Bayesian classification approach allows to efficiently learn classification models that are very robust to over-fitting, and a general classification framework allows to easily incorporate arbitrary number of features. We demonstrate the findings with large-scale experiments that dwarf all previous works combined, as measured by the number of insects and the number of species considered.

  16. Flying Insect Detection and Classification with Inexpensive Sensors

    PubMed Central

    Chen, Yanping; Why, Adena; Batista, Gustavo; Mafra-Neto, Agenor; Keogh, Eamonn

    2014-01-01

    An inexpensive, noninvasive system that could accurately classify flying insects would have important implications for entomological research, and allow for the development of many useful applications in vector and pest control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts devoted to this task. To date, however, none of this research has had a lasting impact. In this work, we show that pseudo-acoustic optical sensors can produce superior data; that additional features, both intrinsic and extrinsic to the insect’s flight behavior, can be exploited to improve insect classification; that a Bayesian classification approach allows to efficiently learn classification models that are very robust to over-fitting, and a general classification framework allows to easily incorporate arbitrary number of features. We demonstrate the findings with large-scale experiments that dwarf all previous works combined, as measured by the number of insects and the number of species considered. PMID:25350921

  17. Concerning a new classification of tricyanides

    NASA Technical Reports Server (NTRS)

    Krafft, F.; Vonhansen, A.

    1979-01-01

    A new classification series of tricyanides is presented. Several tricyanides are synthesized by a simple method from aluminum chloride, benzonitrile, and a respective alkyl or phenyl chloride, purified by recrystallization and distillation, and then analyzed. Structural formulae are suggested, and molecular weights, melting points, and boiling points are determined for each.

  18. Raster Vs. Point Cloud LiDAR Data Classification

    NASA Astrophysics Data System (ADS)

    El-Ashmawy, N.; Shaker, A.

    2014-09-01

    Airborne Laser Scanning systems with light detection and ranging (LiDAR) technology is one of the fast and accurate 3D point data acquisition techniques. Generating accurate digital terrain and/or surface models (DTM/DSM) is the main application of collecting LiDAR range data. Recently, LiDAR range and intensity data have been used for land cover classification applications. Data range and Intensity, (strength of the backscattered signals measured by the LiDAR systems), are affected by the flying height, the ground elevation, scanning angle and the physical characteristics of the objects surface. These effects may lead to uneven distribution of point cloud or some gaps that may affect the classification process. Researchers have investigated the conversion of LiDAR range point data to raster image for terrain modelling. Interpolation techniques have been used to achieve the best representation of surfaces, and to fill the gaps between the LiDAR footprints. Interpolation methods are also investigated to generate LiDAR range and intensity image data for land cover classification applications. In this paper, different approach has been followed to classifying the LiDAR data (range and intensity) for land cover mapping. The methodology relies on the classification of the point cloud data based on their range and intensity and then converted the classified points into raster image. The gaps in the data are filled based on the classes of the nearest neighbour. Land cover maps are produced using two approaches using: (a) the conventional raster image data based on point interpolation; and (b) the proposed point data classification. A study area covering an urban district in Burnaby, British Colombia, Canada, is selected to compare the results of the two approaches. Five different land cover classes can be distinguished in that area: buildings, roads and parking areas, trees, low vegetation (grass), and bare soil. The results show that an improvement of around 10 % in the

  19. Evaluation of the Unified Compensation and Classification Plan.

    ERIC Educational Resources Information Center

    Dade County Public Schools, Miami, FL. Office of Educational Accountability.

    The Unified Classification and Compensation Plan of the Dade County (Florida) Public Schools consists of four interdependent activities that include: (1) developing and maintaining accurate job descriptions, (2) conducting evaluations that recommend job worth and grade, (3) developing and maintaining rates of compensation for job values, and (4)…

  20. Intratumoral and Intertumoral Genomic Heterogeneity of Multifocal Localized Prostate Cancer Impacts Molecular Classifications and Genomic Prognosticators

    PubMed Central

    Wei, Lei; Wang, Jianmin; Lampert, Erika; Schlanger, Simon; DePriest, Adam D.; Hu, Qiang; Gomez, Eduardo Cortes; Murakam, Mitsuko; Glenn, Sean T.; Conroy, Jeffrey; Morrison, Carl; Azabdaftari, Gissou; Mohler, James L.; Liu, Song; Heemers, Hannelore V.

    2018-01-01

    Background Next-generation sequencing is revealing genomic heterogeneity in localized prostate cancer (CaP). Incomplete sampling of CaP multiclonality has limited the implications for molecular subtyping, stratification, and systemic treatment. Objective To determine the impact of genomic and transcriptomic diversity within and among intraprostatic CaP foci on CaP molecular taxonomy, predictors of progression, and actionable therapeutic targets. Design, setting, and participants Four consecutive patients with clinically localized National Comprehensive Cancer Network intermediate- or high-risk CaP who did not receive neoadjuvant therapy underwent radical prostatectomy at Roswell Park Cancer Institute in June–July 2014. Presurgical information on CaP content and a customized tissue procurement procedure were used to isolate nonmicroscopic and noncontiguous CaP foci in radical prostatectomy specimens. Three cores were obtained from the index lesion and one core from smaller lesions. RNA and DNA were extracted simultaneously from 26 cores with ≥90% CaP content and analyzed using whole-exome sequencing, single-nucleotide polymorphism arrays, and RNA sequencing. Outcome measurements and statistical analysis Somatic mutations, copy number alternations, gene expression, gene fusions, and phylogeny were defined. The impact of genomic alterations on CaP molecular classification, gene sets measured in Oncotype DX, Prolaris, and Decipher assays, and androgen receptor activity among CaP cores was determined. Results and limitations There was considerable variability in genomic alterations among CaP cores, and between RNA- and DNA-based platforms. Heterogeneity was found in molecular grouping of individual CaP foci and the activity of gene sets underlying the assays for risk stratification and androgen receptor activity, and was validated in independent genomic data sets. Determination of the implications for clinical decision-making requires follow-up studies. Conclusions

  1. Molecular Dynamics in Mixed Solvents Reveals Protein-Ligand Interactions, Improves Docking, and Allows Accurate Binding Free Energy Predictions.

    PubMed

    Arcon, Juan Pablo; Defelipe, Lucas A; Modenutti, Carlos P; López, Elias D; Alvarez-Garcia, Daniel; Barril, Xavier; Turjanski, Adrián G; Martí, Marcelo A

    2017-04-24

    One of the most important biological processes at the molecular level is the formation of protein-ligand complexes. Therefore, determining their structure and underlying key interactions is of paramount relevance and has direct applications in drug development. Because of its low cost relative to its experimental sibling, molecular dynamics (MD) simulations in the presence of different solvent probes mimicking specific types of interactions have been increasingly used to analyze protein binding sites and reveal protein-ligand interaction hot spots. However, a systematic comparison of different probes and their real predictive power from a quantitative and thermodynamic point of view is still missing. In the present work, we have performed MD simulations of 18 different proteins in pure water as well as water mixtures of ethanol, acetamide, acetonitrile and methylammonium acetate, leading to a total of 5.4 μs simulation time. For each system, we determined the corresponding solvent sites, defined as space regions adjacent to the protein surface where the probability of finding a probe atom is higher than that in the bulk solvent. Finally, we compared the identified solvent sites with 121 different protein-ligand complexes and used them to perform molecular docking and ligand binding free energy estimates. Our results show that combining solely water and ethanol sites allows sampling over 70% of all possible protein-ligand interactions, especially those that coincide with ligand-based pharmacophoric points. Most important, we also show how the solvent sites can be used to significantly improve ligand docking in terms of both accuracy and precision, and that accurate predictions of ligand binding free energies, along with relative ranking of ligand affinity, can be performed.

  2. Proposed morphologic classification of prostate cancer with neuroendocrine differentiation.

    PubMed

    Epstein, Jonathan I; Amin, Mahul B; Beltran, Himisha; Lotan, Tamara L; Mosquera, Juan-Miguel; Reuter, Victor E; Robinson, Brian D; Troncoso, Patricia; Rubin, Mark A

    2014-06-01

    On July 31, 2013, the Prostate Cancer Foundation assembled a working committee on the molecular biology and pathologic classification of neuroendocrine (NE) differentiation in prostate cancer. New clinical and molecular data emerging from prostate cancers treated by contemporary androgen deprivation therapies, as well as primary lesions, have highlighted the need for refinement of diagnostic terminology to encompass the full spectrum of NE differentiation. The classification system consists of: Usual prostate adenocarcinoma with NE differentiation; 2) Adenocarcinoma with Paneth cell NE differentiation; 3) Carcinoid tumor; 4) Small cell carcinoma; 5) Large cell NE carcinoma; and 5) Mixed NE carcinoma - acinar adenocarcinoma. The article also highlights "prostate carcinoma with overlapping features of small cell carcinoma and acinar adenocarcinoma" and "castrate-resistant prostate cancer with small cell cancer-like clinical presentation". It is envisioned that specific criteria associated with the refined diagnostic terminology will lead to clinically relevant pathologic diagnoses that will stimulate further clinical and molecular investigation and identification of appropriate targeted therapies.

  3. Lauren classification and individualized chemotherapy in gastric cancer.

    PubMed

    Ma, Junli; Shen, Hong; Kapesa, Linda; Zeng, Shan

    2016-05-01

    Gastric cancer is one of the most common malignancies worldwide. During the last 50 years, the histological classification of gastric carcinoma has been largely based on Lauren's criteria, in which gastric cancer is classified into two major histological subtypes, namely intestinal type and diffuse type adenocarcinoma. This classification was introduced in 1965, and remains currently widely accepted and employed, since it constitutes a simple and robust classification approach. The two histological subtypes of gastric cancer proposed by the Lauren classification exhibit a number of distinct clinical and molecular characteristics, including histogenesis, cell differentiation, epidemiology, etiology, carcinogenesis, biological behaviors and prognosis. Gastric cancer exhibits varied sensitivity to chemotherapy drugs and significant heterogeneity; therefore, the disease may be a target for individualized therapy. The Lauren classification may provide the basis for individualized treatment for advanced gastric cancer, which is increasingly gaining attention in the scientific field. However, few studies have investigated individualized treatment that is guided by pathological classification. The aim of the current review is to analyze the two major histological subtypes of gastric cancer, as proposed by the Lauren classification, and to discuss the implications of this for personalized chemotherapy.

  4. Classification of Aerial Photogrammetric 3d Point Clouds

    NASA Astrophysics Data System (ADS)

    Becker, C.; Häni, N.; Rosinskaya, E.; d'Angelo, E.; Strecha, C.

    2017-05-01

    We present a powerful method to extract per-point semantic class labels from aerial photogrammetry data. Labelling this kind of data is important for tasks such as environmental modelling, object classification and scene understanding. Unlike previous point cloud classification methods that rely exclusively on geometric features, we show that incorporating color information yields a significant increase in accuracy in detecting semantic classes. We test our classification method on three real-world photogrammetry datasets that were generated with Pix4Dmapper Pro, and with varying point densities. We show that off-the-shelf machine learning techniques coupled with our new features allow us to train highly accurate classifiers that generalize well to unseen data, processing point clouds containing 10 million points in less than 3 minutes on a desktop computer.

  5. Learning classification trees

    NASA Technical Reports Server (NTRS)

    Buntine, Wray

    1991-01-01

    Algorithms for learning classification trees have had successes in artificial intelligence and statistics over many years. How a tree learning algorithm can be derived from Bayesian decision theory is outlined. This introduces Bayesian techniques for splitting, smoothing, and tree averaging. The splitting rule turns out to be similar to Quinlan's information gain splitting rule, while smoothing and averaging replace pruning. Comparative experiments with reimplementations of a minimum encoding approach, Quinlan's C4 and Breiman et al. Cart show the full Bayesian algorithm is consistently as good, or more accurate than these other approaches though at a computational price.

  6. Automated Decision Tree Classification of Corneal Shape

    PubMed Central

    Twa, Michael D.; Parthasarathy, Srinivasan; Roberts, Cynthia; Mahmoud, Ashraf M.; Raasch, Thomas W.; Bullimore, Mark A.

    2011-01-01

    Purpose The volume and complexity of data produced during videokeratography examinations present a challenge of interpretation. As a consequence, results are often analyzed qualitatively by subjective pattern recognition or reduced to comparisons of summary indices. We describe the application of decision tree induction, an automated machine learning classification method, to discriminate between normal and keratoconic corneal shapes in an objective and quantitative way. We then compared this method with other known classification methods. Methods The corneal surface was modeled with a seventh-order Zernike polynomial for 132 normal eyes of 92 subjects and 112 eyes of 71 subjects diagnosed with keratoconus. A decision tree classifier was induced using the C4.5 algorithm, and its classification performance was compared with the modified Rabinowitz–McDonnell index, Schwiegerling’s Z3 index (Z3), Keratoconus Prediction Index (KPI), KISA%, and Cone Location and Magnitude Index using recommended classification thresholds for each method. We also evaluated the area under the receiver operator characteristic (ROC) curve for each classification method. Results Our decision tree classifier performed equal to or better than the other classifiers tested: accuracy was 92% and the area under the ROC curve was 0.97. Our decision tree classifier reduced the information needed to distinguish between normal and keratoconus eyes using four of 36 Zernike polynomial coefficients. The four surface features selected as classification attributes by the decision tree method were inferior elevation, greater sagittal depth, oblique toricity, and trefoil. Conclusions Automated decision tree classification of corneal shape through Zernike polynomials is an accurate quantitative method of classification that is interpretable and can be generated from any instrument platform capable of raw elevation data output. This method of pattern classification is extendable to other classification

  7. Ensemble Sparse Classification of Alzheimer’s Disease

    PubMed Central

    Liu, Manhua; Zhang, Daoqiang; Shen, Dinggang

    2012-01-01

    The high-dimensional pattern classification methods, e.g., support vector machines (SVM), have been widely investigated for analysis of structural and functional brain images (such as magnetic resonance imaging (MRI)) to assist the diagnosis of Alzheimer’s disease (AD) including its prodromal stage, i.e., mild cognitive impairment (MCI). Most existing classification methods extract features from neuroimaging data and then construct a single classifier to perform classification. However, due to noise and small sample size of neuroimaging data, it is challenging to train only a global classifier that can be robust enough to achieve good classification performance. In this paper, instead of building a single global classifier, we propose a local patch-based subspace ensemble method which builds multiple individual classifiers based on different subsets of local patches and then combines them for more accurate and robust classification. Specifically, to capture the local spatial consistency, each brain image is partitioned into a number of local patches and a subset of patches is randomly selected from the patch pool to build a weak classifier. Here, the sparse representation-based classification (SRC) method, which has shown effective for classification of image data (e.g., face), is used to construct each weak classifier. Then, multiple weak classifiers are combined to make the final decision. We evaluate our method on 652 subjects (including 198 AD patients, 225 MCI and 229 normal controls) from Alzheimer’s Disease Neuroimaging Initiative (ADNI) database using MR images. The experimental results show that our method achieves an accuracy of 90.8% and an area under the ROC curve (AUC) of 94.86% for AD classification and an accuracy of 87.85% and an AUC of 92.90% for MCI classification, respectively, demonstrating a very promising performance of our method compared with the state-of-the-art methods for AD/MCI classification using MR images. PMID:22270352

  8. Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques

    PubMed Central

    Macyszyn, Luke; Akbari, Hamed; Pisapia, Jared M.; Da, Xiao; Attiah, Mark; Pigrish, Vadim; Bi, Yingtao; Pal, Sharmistha; Davuluri, Ramana V.; Roccograndi, Laura; Dahmane, Nadia; Martinez-Lage, Maria; Biros, George; Wolf, Ronald L.; Bilello, Michel; O'Rourke, Donald M.; Davatzikos, Christos

    2016-01-01

    Background MRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB). Methods One hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients. Results Survival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy. Conclusions By employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood–brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients. PMID:26188015

  9. Object-based forest classification to facilitate landscape-scale conservation in the Mississippi Alluvial Valley

    USGS Publications Warehouse

    Mitchell, Michael; Wilson, R. Randy; Twedt, Daniel J.; Mini, Anne E.; James, J. Dale

    2016-01-01

    The Mississippi Alluvial Valley is a floodplain along the southern extent of the Mississippi River extending from southern Missouri to the Gulf of Mexico. This area once encompassed nearly 10 million ha of floodplain forests, most of which has been converted to agriculture over the past two centuries. Conservation programs in this region revolve around protection of existing forest and reforestation of converted lands. Therefore, an accurate and up to date classification of forest cover is essential for conservation planning, including efforts that prioritize areas for conservation activities. We used object-based image analysis with Random Forest classification to quickly and accurately classify forest cover. We used Landsat band, band ratio, and band index statistics to identify and define similar objects as our training sets instead of selecting individual training points. This provided a single rule-set that was used to classify each of the 11 Landsat 5 Thematic Mapper scenes that encompassed the Mississippi Alluvial Valley. We classified 3,307,910±85,344 ha (32% of this region) as forest. Our overall classification accuracy was 96.9% with Kappa statistic of 0.96. Because this method of forest classification is rapid and accurate, assessment of forest cover can be regularly updated and progress toward forest habitat goals identified in conservation plans can be periodically evaluated.

  10. Effective classification of the prevalence of Schistosoma mansoni.

    PubMed

    Mitchell, Shira A; Pagano, Marcello

    2012-12-01

    To present an effective classification method based on the prevalence of Schistosoma mansoni in the community. We created decision rules (defined by cut-offs for number of positive slides), which account for imperfect sensitivity, both with a simple adjustment of fixed sensitivity and with a more complex adjustment of changing sensitivity with prevalence. To reduce screening costs while maintaining accuracy, we propose a pooled classification method. To estimate sensitivity, we use the De Vlas model for worm and egg distributions. We compare the proposed method with the standard method to investigate differences in efficiency, measured by number of slides read, and accuracy, measured by probability of correct classification. Modelling varying sensitivity lowers the lower cut-off more significantly than the upper cut-off, correctly classifying regions as moderate rather than lower, thus receiving life-saving treatment. The classification method goes directly to classification on the basis of positive pools, avoiding having to know sensitivity to estimate prevalence. For model parameter values describing worm and egg distributions among children, the pooled method with 25 slides achieves an expected 89.9% probability of correct classification, whereas the standard method with 50 slides achieves 88.7%. Among children, it is more efficient and more accurate to use the pooled method for classification of S. mansoni prevalence than the current standard method. © 2012 Blackwell Publishing Ltd.

  11. The Molecular Pathology of Myelodysplastic Syndrome.

    PubMed

    Haferlach, Torsten

    2018-05-23

    The diagnosis and classification of myelodysplastic syndromes (MDS) are based on cytomorphology and cytogenetics (WHO classification). Prognosis is best defined by the Revised International Prognostic Scoring System (IPSS-R). In recent years, an increasing number of molecular aberrations have been discovered. They are already included in the classification (e.g., SF3B1) and, more importantly, have emerged as valuable markers for better classification, particularly for defining risk groups. Mutations in genes such as SF3B1 and IDH1/2 have already had an impact on targeted treatment approaches in MDS. © 2018 S. Karger AG, Basel.

  12. IRIS COLOUR CLASSIFICATION SCALES – THEN AND NOW

    PubMed Central

    Grigore, Mariana; Avram, Alina

    2015-01-01

    Eye colour is one of the most obvious phenotypic traits of an individual. Since the first documented classification scale developed in 1843, there have been numerous attempts to classify the iris colour. In the past centuries, iris colour classification scales has had various colour categories and mostly relied on comparison of an individual’s eye with painted glass eyes. Once photography techniques were refined, standard iris photographs replaced painted eyes, but this did not solve the problem of painted/ printed colour variability in time. Early clinical scales were easy to use, but lacked objectivity and were not standardised or statistically tested for reproducibility. The era of automated iris colour classification systems came with the technological development. Spectrophotometry, digital analysis of high-resolution iris images, hyper spectral analysis of the human real iris and the dedicated iris colour analysis software, all accomplished an objective, accurate iris colour classification, but are quite expensive and limited in use to research environment. Iris colour classification systems evolved continuously due to their use in a wide range of studies, especially in the fields of anthropology, epidemiology and genetics. Despite the wide range of the existing scales, up until present there has been no generally accepted iris colour classification scale. PMID:27373112

  13. HHsvm: fast and accurate classification of profile–profile matches identified by HHsearch

    PubMed Central

    Dlakić, Mensur

    2009-01-01

    Motivation: Recently developed profile–profile methods rival structural comparisons in their ability to detect homology between distantly related proteins. Despite this tremendous progress, many genuine relationships between protein families cannot be recognized as comparisons of their profiles result in scores that are statistically insignificant. Results: Using known evolutionary relationships among protein superfamilies in SCOP database, support vector machines were trained on four sets of discriminatory features derived from the output of HHsearch. Upon validation, it was shown that the automatic classification of all profile–profile matches was superior to fixed threshold-based annotation in terms of sensitivity and specificity. The effectiveness of this approach was demonstrated by annotating several domains of unknown function from the Pfam database. Availability: Programs and scripts implementing the methods described in this manuscript are freely available from http://hhsvm.dlakiclab.org/. Contact: mdlakic@montana.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:19773335

  14. The research on medical image classification algorithm based on PLSA-BOW model.

    PubMed

    Cao, C H; Cao, H L

    2016-04-29

    With the rapid development of modern medical imaging technology, medical image classification has become more important for medical diagnosis and treatment. To solve the existence of polysemous words and synonyms problem, this study combines the word bag model with PLSA (Probabilistic Latent Semantic Analysis) and proposes the PLSA-BOW (Probabilistic Latent Semantic Analysis-Bag of Words) model. In this paper we introduce the bag of words model in text field to image field, and build the model of visual bag of words model. The method enables the word bag model-based classification method to be further improved in accuracy. The experimental results show that the PLSA-BOW model for medical image classification can lead to a more accurate classification.

  15. Neuromuscular disease classification system

    NASA Astrophysics Data System (ADS)

    Sáez, Aurora; Acha, Begoña; Montero-Sánchez, Adoración; Rivas, Eloy; Escudero, Luis M.; Serrano, Carmen

    2013-06-01

    Diagnosis of neuromuscular diseases is based on subjective visual assessment of biopsies from patients by the pathologist specialist. A system for objective analysis and classification of muscular dystrophies and neurogenic atrophies through muscle biopsy images of fluorescence microscopy is presented. The procedure starts with an accurate segmentation of the muscle fibers using mathematical morphology and a watershed transform. A feature extraction step is carried out in two parts: 24 features that pathologists take into account to diagnose the diseases and 58 structural features that the human eye cannot see, based on the assumption that the biopsy is considered as a graph, where the nodes are represented by each fiber, and two nodes are connected if two fibers are adjacent. A feature selection using sequential forward selection and sequential backward selection methods, a classification using a Fuzzy ARTMAP neural network, and a study of grading the severity are performed on these two sets of features. A database consisting of 91 images was used: 71 images for the training step and 20 as the test. A classification error of 0% was obtained. It is concluded that the addition of features undetectable by the human visual inspection improves the categorization of atrophic patterns.

  16. Comparison of Neural Networks and Tabular Nearest Neighbor Encoding for Hyperspectral Signature Classification in Unresolved Object Detection

    NASA Astrophysics Data System (ADS)

    Schmalz, M.; Ritter, G.; Key, R.

    Accurate and computationally efficient spectral signature classification is a crucial step in the nonimaging detection and recognition of spaceborne objects. In classical hyperspectral recognition applications using linear mixing models, signature classification accuracy depends on accurate spectral endmember discrimination [1]. If the endmembers cannot be classified correctly, then the signatures cannot be classified correctly, and object recognition from hyperspectral data will be inaccurate. In practice, the number of endmembers accurately classified often depends linearly on the number of inputs. This can lead to potentially severe classification errors in the presence of noise or densely interleaved signatures. In this paper, we present an comparison of emerging technologies for nonimaging spectral signature classfication based on a highly accurate, efficient search engine called Tabular Nearest Neighbor Encoding (TNE) [3,4] and a neural network technology called Morphological Neural Networks (MNNs) [5]. Based on prior results, TNE can optimize its classifier performance to track input nonergodicities, as well as yield measures of confidence or caution for evaluation of classification results. Unlike neural networks, TNE does not have a hidden intermediate data structure (e.g., the neural net weight matrix). Instead, TNE generates and exploits a user-accessible data structure called the agreement map (AM), which can be manipulated by Boolean logic operations to effect accurate classifier refinement algorithms. The open architecture and programmability of TNE's agreement map processing allows a TNE programmer or user to determine classification accuracy, as well as characterize in detail the signatures for which TNE did not obtain classification matches, and why such mis-matches occurred. In this study, we will compare TNE and MNN based endmember classification, using performance metrics such as probability of correct classification (Pd) and rate of false

  17. Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

    PubMed

    Fabelo, Himar; Ortega, Samuel; Ravi, Daniele; Kiran, B Ravi; Sosa, Coralia; Bulters, Diederik; Callicó, Gustavo M; Bulstrode, Harry; Szolna, Adam; Piñeiro, Juan F; Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O'Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto

    2018-01-01

    Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising

  18. Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

    PubMed Central

    Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O’Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto

    2018-01-01

    Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising

  19. Advanced eddy current test signal analysis for steam generator tube defect classification and characterization

    NASA Astrophysics Data System (ADS)

    McClanahan, James Patrick

    Eddy Current Testing (ECT) is a Non-Destructive Examination (NDE) technique that is widely used in power generating plants (both nuclear and fossil) to test the integrity of heat exchanger (HX) and steam generator (SG) tubing. Specifically for this research, laboratory-generated, flawed tubing data were examined. The purpose of this dissertation is to develop and implement an automated method for the classification and an advanced characterization of defects in HX and SG tubing. These two improvements enhanced the robustness of characterization as compared to traditional bobbin-coil ECT data analysis methods. A more robust classification and characterization of the tube flaw in-situ (while the SG is on-line but not when the plant is operating), should provide valuable information to the power industry. The following are the conclusions reached from this research. A feature extraction program acquiring relevant information from both the mixed, absolute and differential data was successfully implemented. The CWT was utilized to extract more information from the mixed, complex differential data. Image Processing techniques used to extract the information contained in the generated CWT, classified the data with a high success rate. The data were accurately classified, utilizing the compressed feature vector and using a Bayes classification system. An estimation of the upper bound for the probability of error, using the Bhattacharyya distance, was successfully applied to the Bayesian classification. The classified data were separated according to flaw-type (classification) to enhance characterization. The characterization routine used dedicated, flaw-type specific ANNs that made the characterization of the tube flaw more robust. The inclusion of outliers may help complete the feature space so that classification accuracy is increased. Given that the eddy current test signals appear very similar, there may not be sufficient information to make an extremely accurate (>95

  20. Can SLE classification rules be effectively applied to diagnose unclear SLE cases?

    PubMed Central

    Mesa, Annia; Fernandez, Mitch; Wu, Wensong; Narasimhan, Giri; Greidinger, Eric L.; Mills, DeEtta K.

    2016-01-01

    Summary Objective Develop a novel classification criteria to distinguish between unclear SLE and MCTD cases. Methods A total of 205 variables from 111 SLE and 55 MCTD patients were evaluated to uncover unique molecular and clinical markers for each disease. Binomial logistic regressions (BLR) were performed on currently used SLE and MCTD classification criteria sets to obtain six reduced models with power to discriminate between unclear SLE and MCTD patients which were confirmed by Receiving Operating Characteristic (ROC) curve. Decision trees were employed to delineate novel classification rules to discriminate between unclear SLE and MCTD patients. Results SLE and MCTD patients exhibited contrasting molecular markers and clinical manifestations. Furthermore, reduced models highlighted SLE patients exhibit prevalence of skin rashes and renal disease while MCTD cases show dominance of myositis and muscle weakness. Additionally decision trees analyses revealed a novel classification rule tailored to differentiate unclear SLE and MCTD patients (Lu-vs-M) with an overall accuracy of 88%. Conclusions Validation of our novel proposed classification rule (Lu-vs-M) includes novel contrasting characteristics (calcinosis, CPK elevated and anti-IgM reactivity for U1-70K, U1A and U1C) between SLE and MCTD patients and showed a 33% improvement in distinguishing these disorders when compare to currently used classification criteria sets. Pending additional validation, our novel classification rule is a promising method to distinguish between patients with unclear SLE and MCTD diagnosis. PMID:27353506

  1. Comparison of Nested PCR and RFLP for Identification and Classification of Malassezia Yeasts from Healthy Human Skin

    PubMed Central

    Oh, Byung Ho; Song, Young Chan; Choe, Yong Beom; Ahn, Kyu Joong

    2009-01-01

    Background Malassezia yeasts are normal flora of the skin found in 75~98% of healthy subjects. The accurate identification of the Malassezia species is important for determining the pathogenesis of the Malassezia yeasts with regard to various skin diseases such as Malassezia folliculitis, seborrheic dermatitis, and atopic dermatitis. Objective This research was conducted to determine a more accurate and rapid molecular test for the identification and classification of Malassezia yeasts. Methods We compared the accuracy and efficacy of restriction fragment length polymorphism (RFLP) and the nested polymerase chain reaction (PCR) for the identification of Malassezia yeasts. Results Although both methods demonstrated rapid and reliable results with regard to identification, the nested PCR method was faster. However, 7 different Malassezia species (1.2%) were identified by the nested PCR compared to the RFLP method. Conclusion Our results show that RFLP method was relatively more accurate and reliable for the detection of various Malassezia species compared to the nested PCR. But, in the aspect of simplicity and time saving, the latter method has its own advantages. In addition, the 26S rDNA, which was targeted in this study, contains highly conserved base sequences and enough sequence variation for inter-species identification of Malassezia yeasts. PMID:20523823

  2. Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery

    NASA Technical Reports Server (NTRS)

    Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn

    2011-01-01

    The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

  3. Folding molecular dynamics simulations accurately predict the effect of mutations on the stability and structure of a vammin-derived peptide.

    PubMed

    Koukos, Panagiotis I; Glykos, Nicholas M

    2014-08-28

    Folding molecular dynamics simulations amounting to a grand total of 4 μs of simulation time were performed on two peptides (with native and mutated sequences) derived from loop 3 of the vammin protein and the results compared with the experimentally known peptide stabilities and structures. The simulations faithfully and accurately reproduce the major experimental findings and show that (a) the native peptide is mostly disordered in solution, (b) the mutant peptide has a well-defined and stable structure, and (c) the structure of the mutant is an irregular β-hairpin with a non-glycine β-bulge, in excellent agreement with the peptide's known NMR structure. Additionally, the simulations also predict the presence of a very small β-hairpin-like population for the native peptide but surprisingly indicate that this population is structurally more similar to the structure of the native peptide as observed in the vammin protein than to the NMR structure of the isolated mutant peptide. We conclude that, at least for the given system, force field, and simulation protocol, folding molecular dynamics simulations appear to be successful in reproducing the experimentally accessible physical reality to a satisfactory level of detail and accuracy.

  4. The 7th lung cancer TNM classification and staging system: Review of the changes and implications.

    PubMed

    Mirsadraee, Saeed; Oswal, Dilip; Alizadeh, Yalda; Caulo, Andrea; van Beek, Edwin

    2012-04-28

    Lung cancer is the most common cause of death from cancer in males, accounting for more than 1.4 million deaths in 2008. It is a growing concern in China, Asia and Africa as well. Accurate staging of the disease is an important part of the management as it provides estimation of patient's prognosis and identifies treatment sterategies. It also helps to build a database for future staging projects. A major revision of lung cancer staging has been announced with effect from January 2010. The new classification is based on a larger surgical and non-surgical cohort of patients, and thus more accurate in terms of outcome prediction compared to the previous classification. There are several original papers regarding this new classification which give comprehensive description of the methodology, the changes in the staging and the statistical analysis. This overview is a simplified description of the changes in the new classification and their potential impact on patients' treatment and prognosis.

  5. Molecular Biology of Archaebacteria.

    DTIC Science & Technology

    1988-03-31

    Security Classification) Molecular Biology of Archaebacteria .. d. 12. PERSONAL AUTHOR(S) Patrick P. Dennis * 13a. TYPE OF REPORT 13b. TIME COVERED 14...Escherichia coli Research Objectives i) to characterize the principles of gene organization and regulation of gene expression in archaebacteria ; (ii) to...biophysical and molecular terms some of the mechanisms that allow archaebacteria to inhabit extreme environments. Progress - Year I A’ Ribosomal protein

  6. Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting

    PubMed Central

    Khan, Tarik A.; Friedensohn, Simon; de Vries, Arthur R. Gorter; Straszewski, Jakub; Ruscheweyh, Hans-Joachim; Reddy, Sai T.

    2016-01-01

    High-throughput antibody repertoire sequencing (Ig-seq) provides quantitative molecular information on humoral immunity. However, Ig-seq is compromised by biases and errors introduced during library preparation and sequencing. By using synthetic antibody spike-in genes, we determined that primer bias from multiplex polymerase chain reaction (PCR) library preparation resulted in antibody frequencies with only 42 to 62% accuracy. Additionally, Ig-seq errors resulted in antibody diversity measurements being overestimated by up to 5000-fold. To rectify this, we developed molecular amplification fingerprinting (MAF), which uses unique molecular identifier (UID) tagging before and during multiplex PCR amplification, which enabled tagging of transcripts while accounting for PCR efficiency. Combined with a bioinformatic pipeline, MAF bias correction led to measurements of antibody frequencies with up to 99% accuracy. We also used MAF to correct PCR and sequencing errors, resulting in enhanced accuracy of full-length antibody diversity measurements, achieving 98 to 100% error correction. Using murine MAF-corrected data, we established a quantitative metric of recent clonal expansion—the intraclonal diversity index—which measures the number of unique transcripts associated with an antibody clone. We used this intraclonal diversity index along with antibody frequencies and somatic hypermutation to build a logistic regression model for prediction of the immunological status of clones. The model was able to predict clonal status with high confidence but only when using MAF error and bias corrected Ig-seq data. Improved accuracy by MAF provides the potential to greatly advance Ig-seq and its utility in immunology and biotechnology. PMID:26998518

  7. Accurate and predictive antibody repertoire profiling by molecular amplification fingerprinting.

    PubMed

    Khan, Tarik A; Friedensohn, Simon; Gorter de Vries, Arthur R; Straszewski, Jakub; Ruscheweyh, Hans-Joachim; Reddy, Sai T

    2016-03-01

    High-throughput antibody repertoire sequencing (Ig-seq) provides quantitative molecular information on humoral immunity. However, Ig-seq is compromised by biases and errors introduced during library preparation and sequencing. By using synthetic antibody spike-in genes, we determined that primer bias from multiplex polymerase chain reaction (PCR) library preparation resulted in antibody frequencies with only 42 to 62% accuracy. Additionally, Ig-seq errors resulted in antibody diversity measurements being overestimated by up to 5000-fold. To rectify this, we developed molecular amplification fingerprinting (MAF), which uses unique molecular identifier (UID) tagging before and during multiplex PCR amplification, which enabled tagging of transcripts while accounting for PCR efficiency. Combined with a bioinformatic pipeline, MAF bias correction led to measurements of antibody frequencies with up to 99% accuracy. We also used MAF to correct PCR and sequencing errors, resulting in enhanced accuracy of full-length antibody diversity measurements, achieving 98 to 100% error correction. Using murine MAF-corrected data, we established a quantitative metric of recent clonal expansion-the intraclonal diversity index-which measures the number of unique transcripts associated with an antibody clone. We used this intraclonal diversity index along with antibody frequencies and somatic hypermutation to build a logistic regression model for prediction of the immunological status of clones. The model was able to predict clonal status with high confidence but only when using MAF error and bias corrected Ig-seq data. Improved accuracy by MAF provides the potential to greatly advance Ig-seq and its utility in immunology and biotechnology.

  8. Evaluation of Hydrometeor Classification for Winter Mixed-Phase Precipitation Events

    NASA Astrophysics Data System (ADS)

    Hickman, B.; Troemel, S.; Ryzhkov, A.; Simmer, C.

    2016-12-01

    Hydrometeor classification algorithms (HCL) typically discriminate radar echoes into several classes including rain (light, medium, heavy), hail, dry snow, wet snow, ice crystals, graupel and rain-hail mixtures. Despite the strength of HCL for precipitation dominated by a single phase - especially warm-season classification - shortcomings exist for mixed-phase precipitation classification. Properly identifying mixed-phase can lead to more accurate precipitation estimates, and better forecasts for aviation weather and ground warnings. Cold season precipitation classification is also highly important due to their potentially high impact on society (e.g. black ice, ice accumulation, snow loads), but due to the varying nature of the hydrometeor - density, dielectric constant, shape - reliable classification via radar alone is not capable. With the addition of thermodynamic information of the atmosphere, either from weather models or sounding data, it has been possible to extend more and more into winter time precipitation events. Yet, inaccuracies still exist in separating more benign (ice pellets) from more the more hazardous (freezing rain) events. We have investigated winter mixed-phase precipitation cases which include freezing rain, ice pellets, and rain-snow transitions from several events in Germany in order to move towards a reliable nowcasting of winter precipitation in hopes to provide faster, more accurate winter time warnings. All events have been confirmed to have the specified precipitation from ground reports. Classification of the events is achieved via a combination of inputs from a bulk microphysics numerical weather prediction model and the German dual-polarimetric C-band radar network, into a 1D spectral bin microphysical model (SBC) which explicitly treats the processes of melting, refreezing, and ice nucleation to predict four near-surface precipitation types: rain, snow, freezing rain, ice pellets, rain/snow mixture, and freezing rain

  9. Contributions for classification of platelet rich plasma - proposal of a new classification: MARSPILL.

    PubMed

    Lana, Jose Fabio Santos Duarte; Purita, Joseph; Paulus, Christian; Huber, Stephany Cares; Rodrigues, Bruno Lima; Rodrigues, Ana Amélia; Santana, Maria Helena; Madureira, João Lopo; Malheiros Luzo, Ângela Cristina; Belangero, William Dias; Annichino-Bizzacchi, Joyce Maria

    2017-07-01

    Platelet-rich plasma (PRP) has emerged as a significant therapy used in medical conditions with heterogeneous results. There are some important classifications to try to standardize the PRP procedure. The aim of this report is to describe PRP contents studying celular and molecular components, and also propose a new classification for PRP. The main focus is on mononuclear cells, which comprise progenitor cells and monocytes. In addition, there are important variables related to PRP application incorporated in this study, which are the harvest method, activation, red blood cells, number of spins, image guidance, leukocytes number and light activation. The other focus is the discussion about progenitor cells presence on peripherial blood which are interesting due to neovasculogenesis and proliferation. The function of monocytes (in tissue-macrophages) are discussed here and also its plasticity, a potential property for regenerative medicine treatments.

  10. Rough set classification based on quantum logic

    NASA Astrophysics Data System (ADS)

    Hassan, Yasser F.

    2017-11-01

    By combining the advantages of quantum computing and soft computing, the paper shows that rough sets can be used with quantum logic for classification and recognition systems. We suggest the new definition of rough set theory as quantum logic theory. Rough approximations are essential elements in rough set theory, the quantum rough set model for set-valued data directly construct set approximation based on a kind of quantum similarity relation which is presented here. Theoretical analyses demonstrate that the new model for quantum rough sets has new type of decision rule with less redundancy which can be used to give accurate classification using principles of quantum superposition and non-linear quantum relations. To our knowledge, this is the first attempt aiming to define rough sets in representation of a quantum rather than logic or sets. The experiments on data-sets have demonstrated that the proposed model is more accuracy than the traditional rough sets in terms of finding optimal classifications.

  11. Classification

    NASA Astrophysics Data System (ADS)

    Oza, Nikunj

    2012-03-01

    would represent one sunspot’s classification (y_i) and the corresponding set of measurements (x_i). The output of a supervised learning algorithm is a model h that approximates the unknown mapping from the inputs to the outputs. In our example, h would map from the sunspot measurements to the type of sunspot. We may have a test set S—a set of examples not used in training that we use to test how well the model h predicts the outputs on new examples. Just as with the examples in T, the examples in S are assumed to be independent and identically distributed (i.i.d.) draws from the distribution D. We measure the error of h on the test set as the proportion of test cases that h misclassifies: 1/|S| Sigma(x,y union S)[I(h(x)!= y)] where I(v) is the indicator function—it returns 1 if v is true and 0 otherwise. In our sunspot classification example, we would identify additional examples of sunspots that were not used in generating the model, and use these to determine how accurate the model is—the fraction of the test samples that the model classifies correctly. An example of a classification model is the decision tree shown in Figure 23.1. We will discuss the decision tree learning algorithm in more detail later—for now, we assume that, given a training set with examples of sunspots, this decision tree is derived. This can be used to classify previously unseen examples of sunpots. For example, if a new sunspot’s inputs indicate that its "Group Length" is in the range 10-15, then the decision tree would classify the sunspot as being of type “E,” whereas if the "Group Length" is "NULL," the "Magnetic Type" is "bipolar," and the "Penumbra" is "rudimentary," then it would be classified as type "C." In this chapter, we will add to the above description of classification problems. We will discuss decision trees and several other classification models. In particular, we will discuss the learning algorithms that generate these classification models, how to use them to

  12. An accurate method of extracting fat droplets in liver images for quantitative evaluation

    NASA Astrophysics Data System (ADS)

    Ishikawa, Masahiro; Kobayashi, Naoki; Komagata, Hideki; Shinoda, Kazuma; Yamaguchi, Masahiro; Abe, Tokiya; Hashiguchi, Akinori; Sakamoto, Michiie

    2015-03-01

    The steatosis in liver pathological tissue images is a promising indicator of nonalcoholic fatty liver disease (NAFLD) and the possible risk of hepatocellular carcinoma (HCC). The resulting values are also important for ensuring the automatic and accurate classification of HCC images, because the existence of many fat droplets is likely to create errors in quantifying the morphological features used in the process. In this study we propose a method that can automatically detect, and exclude regions with many fat droplets by using the feature values of colors, shapes and the arrangement of cell nuclei. We implement the method and confirm that it can accurately detect fat droplets and quantify the fat droplet ratio of actual images. This investigation also clarifies the effective characteristics that contribute to accurate detection.

  13. Centrifuge: rapid and sensitive classification of metagenomic sequences.

    PubMed

    Kim, Daehwan; Song, Li; Breitwieser, Florian P; Salzberg, Steven L

    2016-12-01

    Centrifuge is a novel microbial classification engine that enables rapid, accurate, and sensitive labeling of reads and quantification of species on desktop computers. The system uses an indexing scheme based on the Burrows-Wheeler transform (BWT) and the Ferragina-Manzini (FM) index, optimized specifically for the metagenomic classification problem. Centrifuge requires a relatively small index (4.2 GB for 4078 bacterial and 200 archaeal genomes) and classifies sequences at very high speed, allowing it to process the millions of reads from a typical high-throughput DNA sequencing run within a few minutes. Together, these advances enable timely and accurate analysis of large metagenomics data sets on conventional desktop computers. Because of its space-optimized indexing schemes, Centrifuge also makes it possible to index the entire NCBI nonredundant nucleotide sequence database (a total of 109 billion bases) with an index size of 69 GB, in contrast to k-mer-based indexing schemes, which require far more extensive space. © 2016 Kim et al.; Published by Cold Spring Harbor Laboratory Press.

  14. Classification of cardiac patient states using artificial neural networks

    PubMed Central

    Kannathal, N; Acharya, U Rajendra; Lim, Choo Min; Sadasivan, PK; Krishnan, SM

    2003-01-01

    Electrocardiogram (ECG) is a nonstationary signal; therefore, the disease indicators may occur at random in the time scale. This may require the patient be kept under observation for long intervals in the intensive care unit of hospitals for accurate diagnosis. The present study examined the classification of the states of patients with certain diseases in the intensive care unit using their ECG and an Artificial Neural Networks (ANN) classification system. The states were classified into normal, abnormal and life threatening. Seven significant features extracted from the ECG were fed as input parameters to the ANN for classification. Three neural network techniques, namely, back propagation, self-organizing maps and radial basis functions, were used for classification of the patient states. The ANN classifier in this case was observed to be correct in approximately 99% of the test cases. This result was further improved by taking 13 features of the ECG as input for the ANN classifier. PMID:19649222

  15. Lung tumor diagnosis and subtype discovery by gene expression profiling.

    PubMed

    Wang, Lu-yong; Tu, Zhuowen

    2006-01-01

    The optimal treatment of patients with complex diseases, such as cancers, depends on the accurate diagnosis by using a combination of clinical and histopathological data. In many scenarios, it becomes tremendously difficult because of the limitations in clinical presentation and histopathology. To accurate diagnose complex diseases, the molecular classification based on gene or protein expression profiles are indispensable for modern medicine. Moreover, many heterogeneous diseases consist of various potential subtypes in molecular basis and differ remarkably in their response to therapies. It is critical to accurate predict subgroup on disease gene expression profiles. More fundamental knowledge of the molecular basis and classification of disease could aid in the prediction of patient outcome, the informed selection of therapies, and identification of novel molecular targets for therapy. In this paper, we propose a new disease diagnostic method, probabilistic boosting tree (PB tree) method, on gene expression profiles of lung tumors. It enables accurate disease classification and subtype discovery in disease. It automatically constructs a tree in which each node combines a number of weak classifiers into a strong classifier. Also, subtype discovery is naturally embedded in the learning process. Our algorithm achieves excellent diagnostic performance, and meanwhile it is capable of detecting the disease subtype based on gene expression profile.

  16. Learning semantic histopathological representation for basal cell carcinoma classification

    NASA Astrophysics Data System (ADS)

    Gutiérrez, Ricardo; Rueda, Andrea; Romero, Eduardo

    2013-03-01

    Diagnosis of a histopathology glass slide is a complex process that involves accurate recognition of several structures, their function in the tissue and their relation with other structures. The way in which the pathologist represents the image content and the relations between those objects yields a better and accurate diagnoses. Therefore, an appropriate semantic representation of the image content will be useful in several analysis tasks such as cancer classification, tissue retrieval and histopahological image analysis, among others. Nevertheless, to automatically recognize those structures and extract their inner semantic meaning are still very challenging tasks. In this paper we introduce a new semantic representation that allows to describe histopathological concepts suitable for classification. The approach herein identify local concepts using a dictionary learning approach, i.e., the algorithm learns the most representative atoms from a set of random sampled patches, and then models the spatial relations among them by counting the co-occurrence between atoms, while penalizing the spatial distance. The proposed approach was compared with a bag-of-features representation in a tissue classification task. For this purpose, 240 histological microscopical fields of view, 24 per tissue class, were collected. Those images fed a Support Vector Machine classifier per class, using 120 images as train set and the remaining ones for testing, maintaining the same proportion of each concept in the train and test sets. The obtained classification results, averaged from 100 random partitions of training and test sets, shows that our approach is more sensitive in average than the bag-of-features representation in almost 6%.

  17. Intraclass Evolution and Classification of the Colpodea (Ciliophora)

    PubMed Central

    FOISSNER, WILHELM; STOECK, THORSTEN; AGATHA, SABINE; DUNTHORN, MICAH

    2012-01-01

    Using nine new taxa and statistical inferences based on morphological and molecular data, we analyze the evolution within the class Colpodea. The molecular and cladistic analyses show four well-supported clades: platyophryids, bursariomorphids, cyrtolophosidids, and colpodids. There is a widespread occurrence of homoplasies, affecting even conspicuous morphological characteristics, e.g. the inclusion of the micronucleus in the perinuclear space of the macronucleus. The most distinct changes in the morphological classification are the lack of a basal divergence into two subclasses and the split of the cyrtolophosidids into two main clades, differing mainly by the presence vs. absence of an oral cavity. The most complex clade is that of the colpodids. We partially reconcile the morphological and molecular data using evolutionary systematics, providing a scenario in which the colpodids evolved from a Bardeliella-like ancestor and the genus Colpoda performed an intense adaptive radiation, giving rise to three main clades: Colpodina n. subord., Grossglockneriina, and Bryophryina. Three new taxa are established: Colpodina n. subord., Tillinidae n. fam., and Ottowphryidae n. fam. Colpodean evolution and classification are far from being understood because sequences are lacking for most species and half of their diversity is possibly undescribed. PMID:21762424

  18. Comparisons of neural networks to standard techniques for image classification and correlation

    NASA Technical Reports Server (NTRS)

    Paola, Justin D.; Schowengerdt, Robert A.

    1994-01-01

    Neural network techniques for multispectral image classification and spatial pattern detection are compared to the standard techniques of maximum-likelihood classification and spatial correlation. The neural network produced a more accurate classification than maximum-likelihood of a Landsat scene of Tucson, Arizona. Some of the errors in the maximum-likelihood classification are illustrated using decision region and class probability density plots. As expected, the main drawback to the neural network method is the long time required for the training stage. The network was trained using several different hidden layer sizes to optimize both the classification accuracy and training speed, and it was found that one node per class was optimal. The performance improved when 3x3 local windows of image data were entered into the net. This modification introduces texture into the classification without explicit calculation of a texture measure. Larger windows were successfully used for the detection of spatial features in Landsat and Magellan synthetic aperture radar imagery.

  19. From Classification to Epilepsy Ontology and Informatics

    PubMed Central

    Zhang, Guo-Qiang; Sahoo, Satya S; Lhatoo, Samden D

    2012-01-01

    Summary The 2010 International League Against Epilepsy (ILAE) classification and terminology commission report proposed a much needed departure from previous classifications to incorporate advances in molecular biology, neuroimaging, and genetics. It proposed an interim classification and defined two key requirements that need to be satisfied. The first is the ability to classify epilepsy in dimensions according to a variety of purposes including clinical research, patient care, and drug discovery. The second is the ability of the classification system to evolve with new discoveries. Multi-dimensionality and flexibility are crucial to the success of any future classification. In addition, a successful classification system must play a central role in the rapidly growing field of epilepsy informatics. An epilepsy ontology, based on classification, will allow information systems to facilitate data-intensive studies and provide a proven route to meeting the two foregoing key requirements. Epilepsy ontology will be a structured terminology system that accommodates proposed and evolving ILAE classifications, the NIH/NINDS Common Data Elements, the ICD systems and explicitly specifies all known relationships between epilepsy concepts in a proper framework. This will aid evidence based epilepsy diagnosis, investigation, treatment and research for a diverse community of clinicians and researchers. Benefits range from systematization of electronic patient records to multi-modal data repositories for research and training manuals for those involved in epilepsy care. Given the complexity, heterogeneity and pace of research advances in the epilepsy domain, such an ontology must be collaboratively developed by key stakeholders in the epilepsy community and experts in knowledge engineering and computer science. PMID:22765502

  20. Molecular Thermometry

    PubMed Central

    McCabe, Kevin M.; Hernandez, Mark

    2010-01-01

    Conventional temperature measurements rely on material responses to heat, which can be detected visually. When Galileo developed an air expansion based device to detect temperature changes, Santorio, a contemporary physician, added a scale to create the first thermometer. With this instrument, patients’ temperatures could be measured, recorded and related to changing health conditions. Today, advances in materials science and bioengineering provide new ways to report temperature at the molecular level in real time. In this review the scientific foundations and history of thermometry underpin a discussion of the discoveries emerging from the field of molecular thermometry. Intracellular nanogels and heat sensing biomolecules have been shown to accurately report temperature changes at the nano-scale. Various systems will soon provide the ability to accurately measure temperature changes at the tissue, cellular, and even sub-cellular level, allowing for detection and monitoring of very small changes in local temperature. In the clinic this will lead to enhanced detection of tumors and localized infection, and accurate and precise monitoring of hyperthermia based therapies. Some nanomaterial systems have even demonstrated a theranostic capacity for heat-sensitive, local delivery of chemotherapeutics. Just as early thermometry moved into the clinic, so too will these molecular thermometers. PMID:20139796

  1. Colorectal Cancer Classification and Cell Heterogeneity: A Systems Oncology Approach

    PubMed Central

    Blanco-Calvo, Moisés; Concha, Ángel; Figueroa, Angélica; Garrido, Federico; Valladares-Ayerbes, Manuel

    2015-01-01

    Colorectal cancer is a heterogeneous disease that manifests through diverse clinical scenarios. During many years, our knowledge about the variability of colorectal tumors was limited to the histopathological analysis from which generic classifications associated with different clinical expectations are derived. However, currently we are beginning to understand that under the intense pathological and clinical variability of these tumors there underlies strong genetic and biological heterogeneity. Thus, with the increasing available information of inter-tumor and intra-tumor heterogeneity, the classical pathological approach is being displaced in favor of novel molecular classifications. In the present article, we summarize the most relevant proposals of molecular classifications obtained from the analysis of colorectal tumors using powerful high throughput techniques and devices. We also discuss the role that cancer systems biology may play in the integration and interpretation of the high amount of data generated and the challenges to be addressed in the future development of precision oncology. In addition, we review the current state of implementation of these novel tools in the pathological laboratory and in clinical practice. PMID:26084042

  2. A new epileptic seizure classification based exclusively on ictal semiology.

    PubMed

    Lüders, H; Acharya, J; Baumgartner, C; Benbadis, S; Bleasel, A; Burgess, R; Dinner, D S; Ebner, A; Foldvary, N; Geller, E; Hamer, H; Holthausen, H; Kotagal, P; Morris, H; Meencke, H J; Noachtar, S; Rosenow, F; Sakamoto, A; Steinhoff, B J; Tuxhorn, I; Wyllie, E

    1999-03-01

    Historically, seizure semiology was the main feature in the differential diagnosis of epileptic syndromes. With the development of clinical EEG, the definition of electroclinical complexes became an essential tool to define epileptic syndromes, particularly focal epileptic syndromes. Modern advances in diagnostic technology, particularly in neuroimaging and molecular biology, now permit better definitions of epileptic syndromes. At the same time detailed studies showed that there does not necessarily exist a one-to-one relationship between epileptic seizures or electroclinical complexes and epileptic syndromes. These developments call for the reintroduction of an epileptic seizure classification based exclusively on clinical semiology, similar to the seizure classifications which were used by neurologists before the introduction of the modern diagnostic methods. This classification of epileptic seizures should always be complemented by an epileptic syndrome classification based on all the available clinical information (clinical history, neurological exam, ictal semiology, EEG, anatomical and functional neuroimaging, etc.). Such an approach is more consistent with mainstream clinical neurology and would avoid the current confusion between the classification of epileptic seizures (which in the International Seizure Classification is actually a classification of electroclinical complexes) and the classification of epileptic syndromes.

  3. Link prediction boosted psychiatry disorder classification for functional connectivity network

    NASA Astrophysics Data System (ADS)

    Li, Weiwei; Mei, Xue; Wang, Hao; Zhou, Yu; Huang, Jiashuang

    2017-02-01

    Functional connectivity network (FCN) is an effective tool in psychiatry disorders classification, and represents cross-correlation of the regional blood oxygenation level dependent signal. However, FCN is often incomplete for suffering from missing and spurious edges. To accurate classify psychiatry disorders and health control with the incomplete FCN, we first `repair' the FCN with link prediction, and then exact the clustering coefficients as features to build a weak classifier for every FCN. Finally, we apply a boosting algorithm to combine these weak classifiers for improving classification accuracy. Our method tested by three datasets of psychiatry disorder, including Alzheimer's Disease, Schizophrenia and Attention Deficit Hyperactivity Disorder. The experimental results show our method not only significantly improves the classification accuracy, but also efficiently reconstructs the incomplete FCN.

  4. Molecular Pathogenesis and Diagnostic, Prognostic and Predictive Molecular Markers in Sarcoma.

    PubMed

    Mariño-Enríquez, Adrián; Bovée, Judith V M G

    2016-09-01

    Sarcomas are infrequent mesenchymal neoplasms characterized by notable morphological and molecular heterogeneity. Molecular studies in sarcoma provide refinements to morphologic classification, and contribute diagnostic information (frequently), prognostic stratification (rarely) and predict therapeutic response (occasionally). Herein, we summarize the major molecular mechanisms underlying sarcoma pathogenesis and present clinically useful diagnostic, prognostic and predictive molecular markers for sarcoma. Five major molecular alterations are discussed, illustrated with representative sarcoma types, including 1. the presence of chimeric transcription factors, in vascular tumors; 2. abnormal kinase signaling, in gastrointestinal stromal tumor; 3. epigenetic deregulation, in chondrosarcoma, chondroblastoma, and other tumors; 4. deregulated cell survival and proliferation, due to focal copy number alterations, in dedifferentiated liposarcoma; 5. extreme genomic instability, in conventional osteosarcoma as a representative example of sarcomas with highly complex karyotype. Copyright © 2016 Elsevier Inc. All rights reserved.

  5. Evolutionary lineages of marine snails identified using molecular phylogenetics and geometric morphometric analysis of shells.

    PubMed

    Vaux, Felix; Trewick, Steven A; Crampton, James S; Marshall, Bruce A; Beu, Alan G; Hills, Simon F K; Morgan-Richards, Mary

    2018-06-15

    The relationship between morphology and inheritance is of perennial interest in evolutionary biology and palaeontology. Using three marine snail genera Penion, Antarctoneptunea and Kelletia, we investigate whether systematics based on shell morphology accurately reflect evolutionary lineages indicated by molecular phylogenetics. Members of these gastropod genera have been a taxonomic challenge due to substantial variation in shell morphology, conservative radular and soft tissue morphology, few known ecological differences, and geographical overlap between numerous species. Sampling all sixteen putative taxa identified across the three genera, we infer mitochondrial and nuclear ribosomal DNA phylogenetic relationships within the group, and compare this to variation in adult shell shape and size. Results of phylogenetic analysis indicate that each genus is monophyletic, although the status of some phylogenetically derived and likely more recently evolved taxa within Penion is uncertain. The recently described species P. lineatus is supported by genetic evidence. Morphology, captured using geometric morphometric analysis, distinguishes the genera and matches the molecular phylogeny, although using the same dataset, species and phylogenetic subclades are not identified with high accuracy. Overall, despite abundant variation, we find that shell morphology accurately reflects genus-level classification and the corresponding deep phylogenetic splits identified in this group of marine snails. Copyright © 2018 Elsevier Inc. All rights reserved.

  6. Diverse Region-Based CNN for Hyperspectral Image Classification.

    PubMed

    Zhang, Mengmeng; Li, Wei; Du, Qian

    2018-06-01

    Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification. The proposed method exploiting diverse region-based inputs to learn contextual interactional features is expected to have more discriminative power. The joint representation containing rich spectral and spatial information is then fed to a fully connected network and the label of each pixel vector is predicted by a softmax layer. Experimental results with widely used hyperspectral image data sets demonstrate that the proposed method can surpass any other conventional deep learning-based classifiers and other state-of-the-art classifiers.

  7. Hyperspectral Image Classification via Multitask Joint Sparse Representation and Stepwise MRF Optimization.

    PubMed

    Yuan, Yuan; Lin, Jianzhe; Wang, Qi

    2016-12-01

    Hyperspectral image (HSI) classification is a crucial issue in remote sensing. Accurate classification benefits a large number of applications such as land use analysis and marine resource utilization. But high data correlation brings difficulty to reliable classification, especially for HSI with abundant spectral information. Furthermore, the traditional methods often fail to well consider the spatial coherency of HSI that also limits the classification performance. To address these inherent obstacles, a novel spectral-spatial classification scheme is proposed in this paper. The proposed method mainly focuses on multitask joint sparse representation (MJSR) and a stepwise Markov random filed framework, which are claimed to be two main contributions in this procedure. First, the MJSR not only reduces the spectral redundancy, but also retains necessary correlation in spectral field during classification. Second, the stepwise optimization further explores the spatial correlation that significantly enhances the classification accuracy and robustness. As far as several universal quality evaluation indexes are concerned, the experimental results on Indian Pines and Pavia University demonstrate the superiority of our method compared with the state-of-the-art competitors.

  8. Accurate positioning based on acoustic and optical sensors

    NASA Astrophysics Data System (ADS)

    Cai, Kerong; Deng, Jiahao; Guo, Hualing

    2009-11-01

    Unattended laser target designator (ULTD) was designed to partly take the place of conventional LTDs for accurate positioning and laser marking. Analyzed the precision, accuracy and errors of acoustic sensor array, the requirements of laser generator, and the technology of image analysis and tracking, the major system modules were determined. The target's classification, velocity and position can be measured by sensors, and then coded laser beam will be emitted intelligently to mark the excellent position at the excellent time. The conclusion shows that, ULTD can not only avoid security threats, be deployed massively, and accomplish battle damage assessment (BDA), but also be fit for information-based warfare.

  9. Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems.

    PubMed

    Oh, Sang-Il; Kang, Hang-Bong

    2017-01-22

    To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.

  10. Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems

    PubMed Central

    Oh, Sang-Il; Kang, Hang-Bong

    2017-01-01

    To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226×370 image, whereas the original selective search method extracted approximately 106×n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset. PMID:28117742

  11. Identification of Microorganisms by High Resolution Tandem Mass Spectrometry with Accurate Statistical Significance

    NASA Astrophysics Data System (ADS)

    Alves, Gelio; Wang, Guanghui; Ogurtsov, Aleksey Y.; Drake, Steven K.; Gucek, Marjan; Suffredini, Anthony F.; Sacks, David B.; Yu, Yi-Kuo

    2016-02-01

    Correct and rapid identification of microorganisms is the key to the success of many important applications in health and safety, including, but not limited to, infection treatment, food safety, and biodefense. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is challenging correct microbial identification because of the large number of choices present. To properly disentangle candidate microbes, one needs to go beyond apparent morphology or simple `fingerprinting'; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptidome profiles of microbes to better separate them and by designing an analysis method that yields accurate statistical significance. Here, we present an analysis pipeline that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using MS/MS data of 81 samples, each composed of a single known microorganism, that the proposed pipeline can correctly identify microorganisms at least at the genus and species levels. We have also shown that the proposed pipeline computes accurate statistical significances, i.e., E-values for identified peptides and unified E-values for identified microorganisms. The proposed analysis pipeline has been implemented in MiCId, a freely available software for Microorganism Classification and Identification. MiCId is available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.

  12. Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

    PubMed

    Chang, P; Grinband, J; Weinberg, B D; Bardis, M; Khy, M; Cadena, G; Su, M-Y; Cha, S; Filippi, C G; Bota, D; Baldi, P; Poisson, L M; Jain, R; Chow, D

    2018-05-10

    The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation. MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate dehydrogenase 1 ( IDH1 ) mutation status, 1p/19q codeletion, and O6-methylguanine-DNA methyltransferase ( MGMT ) promotor methylation status. Principal component analysis of the final convolutional neural network layer was used to extract the key imaging features critical for successful classification. Classification had high accuracy: IDH1 mutation status, 94%; 1p/19q codeletion, 92%; and MGMT promotor methylation status, 83%. Each genetic category was also associated with distinctive imaging features such as definition of tumor margins, T1 and FLAIR suppression, extent of edema, extent of necrosis, and textural features. Our results indicate that for The Cancer Imaging Archives dataset, machine-learning approaches allow classification of individual genetic mutations of both low- and high-grade gliomas. We show that relevant MR imaging features acquired from an added dimensionality-reduction technique demonstrate that neural networks are capable of learning key imaging components without prior feature selection or human-directed training. © 2018 by American Journal of Neuroradiology.

  13. Molecular subtype classification of urothelial carcinoma in Lynch syndrome.

    PubMed

    Therkildsen, Christina; Eriksson, Pontus; Höglund, Mattias; Jönsson, Mats; Sjödahl, Gottfrid; Nilbert, Mef; Liedberg, Fredrik

    2018-05-23

    Lynch syndrome confers an increased risk for urothelial carcinoma (UC). Molecular subtypes may be relevant to prognosis and therapeutic possibilities, but have to date not been defined in Lynch syndrome-associated urothelial cancer. We aimed to provide a molecular description of Lynch syndrome-associated UC. Thus, Lynch syndrome-associated UCs of the upper urinary tract and the urinary bladder were identified in the Danish hereditary nonpolyposis colorectal cancer (HNPCC) register and were transcriptionally and immunohistochemically profiled and further related to data from 307 sporadic urothelial carcinomas. Whole-genome mRNA expression profiles of 41 tumors and immunohistochemical stainings against FGFR3, KRT5, CCNB1, RB1, and CDKN2A (p16) of 37 tumors from patients with Lynch syndrome were generated. Pathological data, microsatellite instability, anatomic location, and overall survival data were analyzed and compared with sporadic bladder cancer. The 41 Lynch syndrome-associated UC developed at a mean age of 61 years with 59% women. mRNA expression profiling and immunostaining classified the majority of the Lynch syndrome-associated UC as urothelial-like tumors with only 20% being genomically unstable, basal/SCC-like, or other subtypes. The subtypes were associated with stage, grade, and microsatellite instability. Comparison to larger datasets revealed that Lynch syndrome-associated UC shares molecular similarities with sporadic UC. In conclusion, transcriptomic and immunohistochemical profiling identifies a predominance of the urothelial-like molecular subtype in Lynch syndrome and reveals that the molecular subtypes of sporadic bladder cancer are relevant also within this hereditary, mismatch-repair defective subset. © 2018 The Authors. Published by FEBS Press and John Wiley & Sons Ltd.

  14. Molecular Phylogeny of the Cliff Ferns (Woodsiaceae: Polypodiales) with a Proposed Infrageneric Classification

    PubMed Central

    Zhang, Xianchun; Xiang, Qiaoping

    2015-01-01

    The cliff fern family Woodsiaceae has experienced frequent taxonomic changes at the familial and generic ranks since its establishment. The bulk of its species were placed in Woodsia, while Cheilanthopsis, Hymenocystis, Physematium, and Protowoodsia are segregates recognized by some authors. Phylogenetic relationships among the genera of Woodsiaceae remain unclear because of the extreme morphological diversity and inadequate taxon sampling in phylogenetic studies to date. In this study, we carry out comprehensive phylogenetic analyses of Woodsiaceae using molecular evidence from four chloroplast DNA markers (atpA, matK, rbcL and trnL–F) and covering over half the currently recognized species. Our results show three main clades in Woodsiaceae corresponding to Physematium (clade I), Cheilanthopsis–Protowoodsia (clade II) and Woodsia s.s. (clade III). In the interest of preserving monophyly and taxonomic stability, a broadly defined Woodsia including the other segregates is proposed, which is characterized by the distinctive indument and inferior indusia. Therefore, we present a new subgeneric classification of the redefined Woodsia based on phylogenetic and ancestral state reconstructions to better reflect the morphological variation, geographic distribution pattern, and evolutionary history of the genus. Our analyses of the cytological character evolution support multiple aneuploidy events that have resulted in the reduction of chromosome base number from 41 to 33, 37, 38, 39 and 40 during the evolutionary history of the cliff ferns. PMID:26348852

  15. Two fast and accurate heuristic RBF learning rules for data classification.

    PubMed

    Rouhani, Modjtaba; Javan, Dawood S

    2016-03-01

    This paper presents new Radial Basis Function (RBF) learning methods for classification problems. The proposed methods use some heuristics to determine the spreads, the centers and the number of hidden neurons of network in such a way that the higher efficiency is achieved by fewer numbers of neurons, while the learning algorithm remains fast and simple. To retain network size limited, neurons are added to network recursively until termination condition is met. Each neuron covers some of train data. The termination condition is to cover all training data or to reach the maximum number of neurons. In each step, the center and spread of the new neuron are selected based on maximization of its coverage. Maximization of coverage of the neurons leads to a network with fewer neurons and indeed lower VC dimension and better generalization property. Using power exponential distribution function as the activation function of hidden neurons, and in the light of new learning approaches, it is proved that all data became linearly separable in the space of hidden layer outputs which implies that there exist linear output layer weights with zero training error. The proposed methods are applied to some well-known datasets and the simulation results, compared with SVM and some other leading RBF learning methods, show their satisfactory and comparable performance. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. Molecular Identification of the Schwannomatosis Locus

    DTIC Science & Technology

    2005-07-01

    AD Award Number: DAMD17-03-1-0445 TITLE: Molecular Identification of the Schwannomatosis Locus PRINCIPAL INVESTIGATOR: Mia M. MacCollin, M.D...NUMBER Molecular Identification of the Schwannomatosis Locus 5b. GRANT NUMBER DAMD17-03-1-0445 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER...can be found on next page. 15. SUBJECT TERMS schwannomatosis , tumor suppressor gene, NF2, molecular genetics 16. SECURITY CLASSIFICATION OF: 17

  17. Classification of light sources and their interaction with active and passive environments

    NASA Astrophysics Data System (ADS)

    El-Dardiry, Ramy G. S.; Faez, Sanli; Lagendijk, Ad

    2011-03-01

    Emission from a molecular light source depends on its optical and chemical environment. This dependence is different for various sources. We present a general classification in terms of constant-amplitude and constant-power sources. Using this classification, we have described the response to both changes in the local density of states and stimulated emission. The unforeseen consequences of this classification are illustrated for photonic studies by random laser experiments and are in good agreement with our correspondingly developed theory. Our results require a revision of studies on sources in complex media.

  18. Classification of Children Intelligence with Fuzzy Logic Method

    NASA Astrophysics Data System (ADS)

    Syahminan; ika Hidayati, Permata

    2018-04-01

    Intelligence of children s An Important Thing To Know The Parents Early on. Typing Can be done With a Child’s intelligence Grouping Dominant Characteristics Of each Type of Intelligence. To Make it easier for Parents in Determining The type of Children’s intelligence And How to Overcome them, for It Created A Classification System Intelligence Grouping Children By Using Fuzzy logic method For determination Of a Child’s degree of intelligence type. From the analysis We concluded that The presence of Intelligence Classification systems Pendulum Children With Fuzzy Logic Method Of determining The type of The Child’s intelligence Can be Done in a way That is easier And The results More accurate Conclusions Than Manual tests.

  19. Texture as a basis for acoustic classification of substrate in the nearshore region

    NASA Astrophysics Data System (ADS)

    Dennison, A.; Wattrus, N. J.

    2016-12-01

    Segmentation and classification of substrate type from two locations in Lake Superior, are predicted using multivariate statistical processing of textural measures derived from shallow-water, high-resolution multibeam bathymetric data. During a multibeam sonar survey, both bathymetric and backscatter data are collected. It is well documented that the statistical characteristic of a sonar backscatter mosaic is dependent on substrate type. While classifying the bottom-type on the basis on backscatter alone can accurately predict and map bottom-type, it lacks the ability to resolve and capture fine textural details, an important factor in many habitat mapping studies. Statistical processing can capture the pertinent details about the bottom-type that are rich in textural information. Further multivariate statistical processing can then isolate characteristic features, and provide the basis for an accurate classification scheme. Preliminary results from an analysis of bathymetric data and ground-truth samples collected from the Amnicon River, Superior, Wisconsin, and the Lester River, Duluth, Minnesota, demonstrate the ability to process and develop a novel classification scheme of the bottom type in two geomorphologically distinct areas.

  20. Gastric precancerous diseases classification using CNN with a concise model.

    PubMed

    Zhang, Xu; Hu, Weiling; Chen, Fei; Liu, Jiquan; Yang, Yuanhang; Wang, Liangjing; Duan, Huilong; Si, Jianmin

    2017-01-01

    Gastric precancerous diseases (GPD) may deteriorate into early gastric cancer if misdiagnosed, so it is important to help doctors recognize GPD accurately and quickly. In this paper, we realize the classification of 3-class GPD, namely, polyp, erosion, and ulcer using convolutional neural networks (CNN) with a concise model called the Gastric Precancerous Disease Network (GPDNet). GPDNet introduces fire modules from SqueezeNet to reduce the model size and parameters about 10 times while improving speed for quick classification. To maintain classification accuracy with fewer parameters, we propose an innovative method called iterative reinforced learning (IRL). After training GPDNet from scratch, we apply IRL to fine-tune the parameters whose values are close to 0, and then we take the modified model as a pretrained model for the next training. The result shows that IRL can improve the accuracy about 9% after 6 iterations. The final classification accuracy of our GPDNet was 88.90%, which is promising for clinical GPD recognition.

  1. Headache classification: criticism and suggestions.

    PubMed

    Manzoni, G C; Torelli, P

    2004-10-01

    The International Classification of Headache Disorders 2nd Edition (ICHD-II), published in 2004, marks an unquestionable progress from the preceding 1988 edition, but the in-depth analysis it offers is not immune from drawbacks and shortcomings. First of all, it is still basically a classification of attacks and not of syndromes. For the migraine group, while the revised classification more accurately characterises migraine with aura, it fails to provide a sufficiently structured description of those forms of migraine without aura that over the years evolve to so-called daily chronic forms. These forms are not adequately recognised as chronic migraine, which ICHD-II includes among the complications of migraine. The inclusion of short-lasting unilateral neuralgiform headache attacks with conjunctival injection and tearing (SUNCT) in the cluster headache group is bound to generate some perplexity, while the recognition of new daily persistent headache (NDPH) included in the group of other primary headaches as a separate clinical entity appears somewhat premature. Doubts are also raised by the actual existence of triptan-overuse headache, which ICHD-II includes in Group 8 among medication-overuse headaches. Finally, the addition of headache attributed to psychiatric disorder, which is certainly a good option in perspective, is not yet supported by an adequate systematisation.

  2. A topological approach for protein classification

    DOE PAGES

    Cang, Zixuan; Mu, Lin; Wu, Kedi; ...

    2015-11-04

    Here, protein function and dynamics are closely related to its sequence and structure. However, prediction of protein function and dynamics from its sequence and structure is still a fundamental challenge in molecular biology. Protein classification, which is typically done through measuring the similarity between proteins based on protein sequence or physical information, serves as a crucial step toward the understanding of protein function and dynamics.

  3. Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia

    NASA Astrophysics Data System (ADS)

    Rokni Deilmai, B.; Ahmad, B. Bin; Zabihi, H.

    2014-06-01

    Mapping is essential for the analysis of the land use and land cover, which influence many environmental processes and properties. For the purpose of the creation of land cover maps, it is important to minimize error. These errors will propagate into later analyses based on these land cover maps. The reliability of land cover maps derived from remotely sensed data depends on an accurate classification. In this study, we have analyzed multispectral data using two different classifiers including Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). To pursue this aim, Landsat Thematic Mapper data and identical field-based training sample datasets in Johor Malaysia used for each classification method, which results indicate in five land cover classes forest, oil palm, urban area, water, rubber. Classification results indicate that SVM was more accurate than MLC. With demonstrated capability to produce reliable cover results, the SVM methods should be especially useful for land cover classification.

  4. JDINAC: joint density-based non-parametric differential interaction network analysis and classification using high-dimensional sparse omics data.

    PubMed

    Ji, Jiadong; He, Di; Feng, Yang; He, Yong; Xue, Fuzhong; Xie, Lei

    2017-10-01

    A complex disease is usually driven by a number of genes interwoven into networks, rather than a single gene product. Network comparison or differential network analysis has become an important means of revealing the underlying mechanism of pathogenesis and identifying clinical biomarkers for disease classification. Most studies, however, are limited to network correlations that mainly capture the linear relationship among genes, or rely on the assumption of a parametric probability distribution of gene measurements. They are restrictive in real application. We propose a new Joint density based non-parametric Differential Interaction Network Analysis and Classification (JDINAC) method to identify differential interaction patterns of network activation between two groups. At the same time, JDINAC uses the network biomarkers to build a classification model. The novelty of JDINAC lies in its potential to capture non-linear relations between molecular interactions using high-dimensional sparse data as well as to adjust confounding factors, without the need of the assumption of a parametric probability distribution of gene measurements. Simulation studies demonstrate that JDINAC provides more accurate differential network estimation and lower classification error than that achieved by other state-of-the-art methods. We apply JDINAC to a Breast Invasive Carcinoma dataset, which includes 114 patients who have both tumor and matched normal samples. The hub genes and differential interaction patterns identified were consistent with existing experimental studies. Furthermore, JDINAC discriminated the tumor and normal sample with high accuracy by virtue of the identified biomarkers. JDINAC provides a general framework for feature selection and classification using high-dimensional sparse omics data. R scripts available at https://github.com/jijiadong/JDINAC. lxie@iscb.org. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford

  5. Impact of recent molecular phylogenetic studies on classification of ascomycete yeasts

    USDA-ARS?s Scientific Manuscript database

    Analyses of concatenated gene sequences as well as whole genome sequences are resolving relationships among the ascomycete yeasts (Saccharomycotina), thus allowing classification of members of this subphylum to be based on phylogeny. In addition, changes implemented in the new Botanical Code [Intern...

  6. Diffuse low-grade glioma: a review on the new molecular classification, natural history and current management strategies.

    PubMed

    Delgado-López, P D; Corrales-García, E M; Martino, J; Lastra-Aras, E; Dueñas-Polo, M T

    2017-08-01

    The management of diffuse supratentorial WHO grade II glioma remains a challenge because of the infiltrative nature of the tumor, which precludes curative therapy after total or even supratotal resection. When possible, functional-guided resection is the preferred initial treatment. Total and subtotal resections correlate with increased overall survival. High-risk patients (age >40, partial resection), especially IDH-mutated and 1p19q-codeleted oligodendroglial lesions, benefit from surgery plus adjuvant chemoradiation. Under the new 2016 WHO brain tumor classification, which now incorporates molecular parameters, all diffusely infiltrating gliomas are grouped together since they share specific genetic mutations and prognostic factors. Although low-grade gliomas cannot be regarded as benign tumors, large observational studies have shown that median survival can actually be doubled if an early, aggressive, multi-stage and personalized therapy is applied, as compared to prior wait-and-see policy series. Patients need an honest long-term therapeutic strategy that should ideally anticipate neurological, cognitive and histopathologic worsening.

  7. Novel gene sets improve set-level classification of prokaryotic gene expression data.

    PubMed

    Holec, Matěj; Kuželka, Ondřej; Železný, Filip

    2015-10-28

    Set-level classification of gene expression data has received significant attention recently. In this setting, high-dimensional vectors of features corresponding to genes are converted into lower-dimensional vectors of features corresponding to biologically interpretable gene sets. The dimensionality reduction brings the promise of a decreased risk of overfitting, potentially resulting in improved accuracy of the learned classifiers. However, recent empirical research has not confirmed this expectation. Here we hypothesize that the reported unfavorable classification results in the set-level framework were due to the adoption of unsuitable gene sets defined typically on the basis of the Gene ontology and the KEGG database of metabolic networks. We explore an alternative approach to defining gene sets, based on regulatory interactions, which we expect to collect genes with more correlated expression. We hypothesize that such more correlated gene sets will enable to learn more accurate classifiers. We define two families of gene sets using information on regulatory interactions, and evaluate them on phenotype-classification tasks using public prokaryotic gene expression data sets. From each of the two gene-set families, we first select the best-performing subtype. The two selected subtypes are then evaluated on independent (testing) data sets against state-of-the-art gene sets and against the conventional gene-level approach. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. The novel gene sets are indeed more correlated than the conventional ones, and lead to significantly more accurate classifiers. Novel gene sets defined on the basis of regulatory interactions improve set-level classification of gene expression data. The experimental scripts and other material needed to reproduce the experiments are available at http://ida.felk.cvut.cz/novelgenesets.tar.gz.

  8. Usefulness of Ilae 2010 classification in Mexican epilepsy patients.

    PubMed

    Leyva, Ildefonso Rodríguez; Gómez, Juan Francisco Hernández; Enríquez, Fernando Cortés; Sierra, Juan Francisco Hernández

    2017-05-15

    Advances in neuroimaging, genomics, and molecular biology have improved the understanding of the pathogenesis of epilepsy. That is why the International League Against Epilepsy (ILAE) has created a new classification system. The present study aims to evaluate the association between epilepsy cases classified by the ILAE 2010 classification proposal, electroencephalography (EEG), and magnetic resonance imaging brain findings (MRI). Prospective cross-sectional design of 277 cases of epilepsy seen at the Epilepsy Clinic, Hospital Central "Dr. Ignacio Morones Prieto", were compared with the ILAE classification based on the etiology and clinical manifestations and their MRI and EEG findings. Cochran, Mantell, Haenzel test with significance p<0.05. MRI findings were associated with the etiology of the ILAE classification. According to EEG findings, the structural-metabolic etiology patients had more dysfunctional reports than genetic or unknown etiology patients (p<0.05). The adoption of the ILAE classification is recommended, as it can provide useful guidance towards the etiology of cases of epilepsy even when brain MRIs and EEGs are not available. Copyright © 2017 Elsevier B.V. All rights reserved.

  9. Classification of Pelteobagrus fish in Poyang Lake based on mitochondrial COI gene sequence.

    PubMed

    Zhong, Bin; Chen, Ting-Ting; Gong, Rui-Yue; Zhao, Zhe-Xia; Wang, Binhua; Fang, Chunlin; Mao, Hui-Ling

    2016-11-01

    We use DNA molecular marker technology to correct the deficiency of traditional morphological taxonomy. Totality 770 Pelteobagrus fish from Poyang Lake were collected. After preliminary morphological classification, random selected eight samples in each species for DNA extraction. Mitochondrial COI gene sequence was cloned with universal primers and sequenced. The results showed that there are four species of Pelteobagrus living in Poyang Lake. The average of intraspecific genetic distance value was 0.003, while the average interspecific genetic distance was 0.128. The interspecific genetic distance is far more than intraspecific genetic distance. Besides, phylogenetic tree analysis revealed that molecular systematics was in accord with morphological classification. It indicated that COI gene is an effective DNA molecular marker in Pelteobagrus classification. Surprisingly, the intraspecific difference of some individuals (P. e6, P. n6, P. e5, and P. v4) from their original named exceeded species threshold (2%), which should be renewedly classified into Pelteobagrus fulvidraco. However, another individual P. v3 was very different, because its genetic distance was over 8.4% difference from original named Pelteobagrus vachelli. Its taxonomic status remained to be further studied.

  10. Spatial Mutual Information Based Hyperspectral Band Selection for Classification

    PubMed Central

    2015-01-01

    The amount of information involved in hyperspectral imaging is large. Hyperspectral band selection is a popular method for reducing dimensionality. Several information based measures such as mutual information have been proposed to reduce information redundancy among spectral bands. Unfortunately, mutual information does not take into account the spatial dependency between adjacent pixels in images thus reducing its robustness as a similarity measure. In this paper, we propose a new band selection method based on spatial mutual information. As validation criteria, a supervised classification method using support vector machine (SVM) is used. Experimental results of the classification of hyperspectral datasets show that the proposed method can achieve more accurate results. PMID:25918742

  11. Deep learning for brain tumor classification

    NASA Astrophysics Data System (ADS)

    Paul, Justin S.; Plassard, Andrew J.; Landman, Bennett A.; Fabbri, Daniel

    2017-03-01

    Recent research has shown that deep learning methods have performed well on supervised machine learning, image classification tasks. The purpose of this study is to apply deep learning methods to classify brain images with different tumor types: meningioma, glioma, and pituitary. A dataset was publicly released containing 3,064 T1-weighted contrast enhanced MRI (CE-MRI) brain images from 233 patients with either meningioma, glioma, or pituitary tumors split across axial, coronal, or sagittal planes. This research focuses on the 989 axial images from 191 patients in order to avoid confusing the neural networks with three different planes containing the same diagnosis. Two types of neural networks were used in classification: fully connected and convolutional neural networks. Within these two categories, further tests were computed via the augmentation of the original 512×512 axial images. Training neural networks over the axial data has proven to be accurate in its classifications with an average five-fold cross validation of 91.43% on the best trained neural network. This result demonstrates that a more general method (i.e. deep learning) can outperform specialized methods that require image dilation and ring-forming subregions on tumors.

  12. Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features.

    PubMed

    Li, Linyi; Xu, Tingbao; Chen, Yun

    2017-01-01

    In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images.

  13. Automatic classification of protein structures using physicochemical parameters.

    PubMed

    Mohan, Abhilash; Rao, M Divya; Sunderrajan, Shruthi; Pennathur, Gautam

    2014-09-01

    Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

  14. Rotationally Invariant Image Representation for Viewing Direction Classification in Cryo-EM

    PubMed Central

    Zhao, Zhizhen; Singer, Amit

    2014-01-01

    We introduce a new rotationally invariant viewing angle classification method for identifying, among a large number of cryo-EM projection images, similar views without prior knowledge of the molecule. Our rotationally invariant features are based on the bispectrum. Each image is denoised and compressed using steerable principal component analysis (PCA) such that rotating an image is equivalent to phase shifting the expansion coefficients. Thus we are able to extend the theory of bispectrum of 1D periodic signals to 2D images. The randomized PCA algorithm is then used to efficiently reduce the dimensionality of the bispectrum coefficients, enabling fast computation of the similarity between any pair of images. The nearest neighbors provide an initial classification of similar viewing angles. In this way, rotational alignment is only performed for images with their nearest neighbors. The initial nearest neighbor classification and alignment are further improved by a new classification method called vector diffusion maps. Our pipeline for viewing angle classification and alignment is experimentally shown to be faster and more accurate than reference-free alignment with rotationally invariant K-means clustering, MSA/MRA 2D classification, and their modern approximations. PMID:24631969

  15. Three-Way Analysis of Spectrospatial Electromyography Data: Classification and Interpretation

    PubMed Central

    Kauppi, Jukka-Pekka; Hahne, Janne; Müller, Klaus-Robert; Hyvärinen, Aapo

    2015-01-01

    Classifying multivariate electromyography (EMG) data is an important problem in prosthesis control as well as in neurophysiological studies and diagnosis. With modern high-density EMG sensor technology, it is possible to capture the rich spectrospatial structure of the myoelectric activity. We hypothesize that multi-way machine learning methods can efficiently utilize this structure in classification as well as reveal interesting patterns in it. To this end, we investigate the suitability of existing three-way classification methods to EMG-based hand movement classification in spectrospatial domain, as well as extend these methods by sparsification and regularization. We propose to use Fourier-domain independent component analysis as preprocessing to improve classification and interpretability of the results. In high-density EMG experiments on hand movements across 10 subjects, three-way classification yielded higher average performance compared with state-of-the art classification based on temporal features, suggesting that the three-way analysis approach can efficiently utilize detailed spectrospatial information of high-density EMG. Phase and amplitude patterns of features selected by the classifier in finger-movement data were found to be consistent with known physiology. Thus, our approach can accurately resolve hand and finger movements on the basis of detailed spectrospatial information, and at the same time allows for physiological interpretation of the results. PMID:26039100

  16. Study design in high-dimensional classification analysis.

    PubMed

    Sánchez, Brisa N; Wu, Meihua; Song, Peter X K; Wang, Wen

    2016-10-01

    Advances in high throughput technology have accelerated the use of hundreds to millions of biomarkers to construct classifiers that partition patients into different clinical conditions. Prior to classifier development in actual studies, a critical need is to determine the sample size required to reach a specified classification precision. We develop a systematic approach for sample size determination in high-dimensional (large [Formula: see text] small [Formula: see text]) classification analysis. Our method utilizes the probability of correct classification (PCC) as the optimization objective function and incorporates the higher criticism thresholding procedure for classifier development. Further, we derive the theoretical bound of maximal PCC gain from feature augmentation (e.g. when molecular and clinical predictors are combined in classifier development). Our methods are motivated and illustrated by a study using proteomics markers to classify post-kidney transplantation patients into stable and rejecting classes. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  17. The role of amino acid PET in the light of the new WHO classification 2016 for brain tumors.

    PubMed

    Suchorska, Bogdana; Albert, Nathalie L; Bauer, Elena K; Tonn, Jörg-Christian; Galldiks, Norbert

    2018-04-26

    Since its introduction in 2016, the revision of the World Health Organization (WHO) classification of central nervous system tumours has already changed the diagnostic and therapeutic approach in glial tumors. Blurring the lines between entities formerly labelled as "high-grade" or "low-grade", molecular markers define distinct biological subtypes with different clinical course. This new classification raises the demand for non-invasive imaging methods focussing on depicting metabolic processes. We performed a review of current literature on the use of amino acid PET (AA-PET) for obtaining diagnostic or prognostic information on glioma in the setting of the current WHO 2016 classification. So far, only a few studies have focussed on combining molecular genetic information and metabolic imaging using AA-PET. The current review summarizes the information available on "molecular grading" as well as prognostic information obtained from AA-PET and delivers an insight into a possible interrelation between metabolic imaging and glioma genetics. Within the framework of molecular characterization of gliomas, metabolic imaging using AA-PET is a promising tool for non-invasive characterisation of molecular features and to provide additional prognostic information. Further studies incorporating molecular and metabolic features are necessary to improve the explanatory power of AA-PET in glial tumors.

  18. A thyroid nodule classification method based on TI-RADS

    NASA Astrophysics Data System (ADS)

    Wang, Hao; Yang, Yang; Peng, Bo; Chen, Qin

    2017-07-01

    Thyroid Imaging Reporting and Data System(TI-RADS) is a valuable tool for differentiating the benign and the malignant thyroid nodules. In clinic, doctors can determine the extent of being benign or malignant in terms of different classes by using TI-RADS. Classification represents the degree of malignancy of thyroid nodules. TI-RADS as a classification standard can be used to guide the ultrasonic doctor to examine thyroid nodules more accurately and reliably. In this paper, we aim to classify the thyroid nodules with the help of TI-RADS. To this end, four ultrasound signs, i.e., cystic and solid, echo pattern, boundary feature and calcification of thyroid nodules are extracted and converted into feature vectors. Then semi-supervised fuzzy C-means ensemble (SS-FCME) model is applied to obtain the classification results. The experimental results demonstrate that the proposed method can help doctors diagnose the thyroid nodules effectively.

  19. The Optimization of Trained and Untrained Image Classification Algorithms for Use on Large Spatial Datasets

    NASA Technical Reports Server (NTRS)

    Kocurek, Michael J.

    2005-01-01

    The HARVIST project seeks to automatically provide an accurate, interactive interface to predict crop yield over the entire United States. In order to accomplish this goal, large images must be quickly and automatically classified by crop type. Current trained and untrained classification algorithms, while accurate, are highly inefficient when operating on large datasets. This project sought to develop new variants of two standard trained and untrained classification algorithms that are optimized to take advantage of the spatial nature of image data. The first algorithm, harvist-cluster, utilizes divide-and-conquer techniques to precluster an image in the hopes of increasing overall clustering speed. The second algorithm, harvistSVM, utilizes support vector machines (SVMs), a type of trained classifier. It seeks to increase classification speed by applying a "meta-SVM" to a quick (but inaccurate) SVM to approximate a slower, yet more accurate, SVM. Speedups were achieved by tuning the algorithm to quickly identify when the quick SVM was incorrect, and then reclassifying low-confidence pixels as necessary. Comparing the classification speeds of both algorithms to known baselines showed a slight speedup for large values of k (the number of clusters) for harvist-cluster, and a significant speedup for harvistSVM. Future work aims to automate the parameter tuning process required for harvistSVM, and further improve classification accuracy and speed. Additionally, this research will move documents created in Canvas into ArcGIS. The launch of the Mars Reconnaissance Orbiter (MRO) will provide a wealth of image data such as global maps of Martian weather and high resolution global images of Mars. The ability to store this new data in a georeferenced format will support future Mars missions by providing data for landing site selection and the search for water on Mars.

  20. Automatic Classification of Medical Text: The Influence of Publication Form1

    PubMed Central

    Cole, William G.; Michael, Patricia A.; Stewart, James G.; Blois, Marsden S.

    1988-01-01

    Previous research has shown that within the domain of medical journal abstracts the statistical distribution of words is neither random nor uniform, but is highly characteristic. Many words are used mainly or solely by one medical specialty or when writing about one particular level of description. Due to this regularity of usage, automatic classification within journal abstracts has proved quite successful. The present research asks two further questions. It investigates whether this statistical regularity and automatic classification success can also be achieved in medical textbook chapters. It then goes on to see whether the statistical distribution found in textbooks is sufficiently similar to that found in abstracts to permit accurate classification of abstracts based solely on previous knowledge of textbooks. 14 textbook chapters and 45 MEDLINE abstracts were submitted to an automatic classification program that had been trained only on chapters drawn from a standard textbook series. Statistical analysis of the properties of abstracts vs. chapters revealed important differences in word use. Automatic classification performance was good for chapters, but poor for abstracts.

  1. Automatic Classification of Time-variable X-Ray Sources

    NASA Astrophysics Data System (ADS)

    Lo, Kitty K.; Farrell, Sean; Murphy, Tara; Gaensler, B. M.

    2014-05-01

    To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, and other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ~97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7-500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.

  2. Automatic classification of time-variable X-ray sources

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Lo, Kitty K.; Farrell, Sean; Murphy, Tara

    2014-05-01

    To maximize the discovery potential of future synoptic surveys, especially in the field of transient science, it will be necessary to use automatic classification to identify some of the astronomical sources. The data mining technique of supervised classification is suitable for this problem. Here, we present a supervised learning method to automatically classify variable X-ray sources in the Second XMM-Newton Serendipitous Source Catalog (2XMMi-DR2). Random Forest is our classifier of choice since it is one of the most accurate learning algorithms available. Our training set consists of 873 variable sources and their features are derived from time series, spectra, andmore » other multi-wavelength contextual information. The 10 fold cross validation accuracy of the training data is ∼97% on a 7 class data set. We applied the trained classification model to 411 unknown variable 2XMM sources to produce a probabilistically classified catalog. Using the classification margin and the Random Forest derived outlier measure, we identified 12 anomalous sources, of which 2XMM J180658.7–500250 appears to be the most unusual source in the sample. Its X-ray spectra is suggestive of a ultraluminous X-ray source but its variability makes it highly unusual. Machine-learned classification and anomaly detection will facilitate scientific discoveries in the era of all-sky surveys.« less

  3. Classification of Strawberry Fruit Shape by Machine Learning

    NASA Astrophysics Data System (ADS)

    Ishikawa, T.; Hayashi, A.; Nagamatsu, S.; Kyutoku, Y.; Dan, I.; Wada, T.; Oku, K.; Saeki, Y.; Uto, T.; Tanabata, T.; Isobe, S.; Kochi, N.

    2018-05-01

    Shape is one of the most important traits of agricultural products due to its relationships with the quality, quantity, and value of the products. For strawberries, the nine types of fruit shape were defined and classified by humans based on the sampler patterns of the nine types. In this study, we tested the classification of strawberry shapes by machine learning in order to increase the accuracy of the classification, and we introduce the concept of computerization into this field. Four types of descriptors were extracted from the digital images of strawberries: (1) the Measured Values (MVs) including the length of the contour line, the area, the fruit length and width, and the fruit width/length ratio; (2) the Ellipse Similarity Index (ESI); (3) Elliptic Fourier Descriptors (EFDs), and (4) Chain Code Subtraction (CCS). We used these descriptors for the classification test along with the random forest approach, and eight of the nine shape types were classified with combinations of MVs + CCS + EFDs. CCS is a descriptor that adds human knowledge to the chain codes, and it showed higher robustness in classification than the other descriptors. Our results suggest machine learning's high ability to classify fruit shapes accurately. We will attempt to increase the classification accuracy and apply the machine learning methods to other plant species.

  4. Molecular diagnostics in the management of rhabdomyosarcoma.

    PubMed

    Arnold, Michael A; Barr, Fredric G

    2017-02-01

    A classification of rhabdomyosarcoma (RMS) with prognostic relevance has primarily relied on clinical features and histologic classification as either embryonal or alveolar RMS. The PAX3-FOXO1 and PAX7-FOXO1 gene fusions occur in 80% of cases with the alveolar subtype and are more predictive of outcome than histologic classification. Identifying additional molecular hallmarks that further subclassify RMS is an active area of research. Areas Covered: The authors review the current state of the PAX3-FOXO1 and PAX7-FOXO1 fusions as prognostic biomarkers. Emerging biomarkers, including mRNA expression profiling, MYOD1 mutations, RAS pathway mutations and gene fusions involving NCOA2 or VGLL2 are also reviewed. Expert commentary: Strategies for modifying RMS risk stratification based on molecular biomarkers are emerging with the potential to transform the clinical management of RMS, ultimately improving patient outcomes by tailoring therapy to predicted patient risk and identifying targets for novel therapies.

  5. Auto-simultaneous laser treatment and Ohshiro's classification of laser treatment

    NASA Astrophysics Data System (ADS)

    Ohshiro, Toshio

    2005-07-01

    When the laser was first applied in medicine and surgery in the late 1960"s and early 1970"s, early adopters reported better wound healing and less postoperative pain with laser procedures compared with the same procedure performed with the cold scalpel or with electrothermy, and multiple surgical effects such as incision, vaporization and hemocoagulation could be achieved with the same laser beam. There was thus an added beneficial component which was associated only with laser surgery. This was first recognized as the `?-effect", was then classified by the author as simultaneous laser therapy, but is now more accurately classified by the author as part of the auto-simultaneous aspect of laser treatment. Indeed, with the dramatic increase of the applications of the laser in surgery and medicine over the last 2 decades there has been a parallel increase in the need for a standardized classification of laser treatment. Some classifications have been machine-based, and thus inaccurate because at appropriate parameters, a `low-power laser" can produce a surgical effect and a `high power laser", a therapeutic one . A more accurate classification based on the tissue reaction is presented, developed by the author. In addition to this, the author has devised a graphical representation of laser surgical and therapeutic beams whereby the laser type, parameters, penetration depth, and tissue reaction can all be shown in a single illustration, which the author has termed the `Laser Apple", due to the typical pattern generated when a laser beam is incident on tissue. Laser/tissue reactions fall into three broad groups. If the photoreaction in the tissue is irreversible, then it is classified as high-reactive level laser treatment (HLLT). If some irreversible damage occurs together with reversible photodamage, as in tissue welding, the author refers to this as mid-reactive level laser treatment (MLLT). If the level of reaction in the target tissue is lower than the cells

  6. Halitosis: a new definition and classification.

    PubMed

    Aydin, M; Harvey-Woodworth, C N

    2014-07-11

    There is no universally accepted, precise definition, nor standardisation in terminology and classification of halitosis. To propose a new definition, free from subjective descriptions (faecal, fish odour, etc), one-time sulphide detector readings and organoleptic estimation of odour levels, and excludes temporary exogenous odours (for example, from dietary sources). Some terms previously used in the literature are revised. A new aetiologic classification is proposed, dividing pathologic halitosis into Type 1 (oral), Type 2 (airway), Type 3 (gastroesophageal), Type 4 (blood-borne) and Type 5 (subjective). In reality, any halitosis complaint is potentially the sum of these types in any combination, superimposed on the Type 0 (physiologic odour) present in health. This system allows for multiple diagnoses in the same patient, reflecting the multifactorial nature of the complaint. It represents the most accurate model to understand halitosis and forms an efficient and logical basis for clinical management of the complaint.

  7. Coarse gaining of molecular crystals: limitations imposed by molecular flexibility

    NASA Astrophysics Data System (ADS)

    Picu, Catalin; Pal, Anirban

    Molecular crystals include molecular electronics, energetic materials, pharmaceuticals and some food components. In many of these applications the small scale mechanical behavior of the crystal is important such as for example in energetic materials where detonation is induced by the formation of hot spots which are induced thermomechanically, and in pharmaceuticals where phase stability is critical for the biochemical activity of the drug. Accurate modeling of these processes requires resolving the atomistic scale details of the material. However, the cost of these models is very large due to the complexity of the molecules forming the crystal, and some form of coarse graning is necessary. In this study we identify the limitations imposed by the need to accurately capture molecular flexibility on the development of coarse grained models for the energetic molecular crystal RDX. We define guidelines for the definition of coarse grained models that target elastic and plastic crystal scale properties such as elastic constants, thermal expansion, compressibility, the critical stress for the motion of dislocations (Peierls stress) and the stacking fault energy This work was supported by the ARO through Grant W911NF-09-1-0330 and AFRL through Grant FA8651-16-1-0004.

  8. Automated classification of cell morphology by coherence-controlled holographic microscopy

    NASA Astrophysics Data System (ADS)

    Strbkova, Lenka; Zicha, Daniel; Vesely, Pavel; Chmelik, Radim

    2017-08-01

    In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.

  9. Alabama-Mississippi Coastal Classification Maps - Perdido Pass to Cat Island

    USGS Publications Warehouse

    Morton, Robert A.; Peterson, Russell L.

    2005-01-01

    The primary purpose of the USGS National Assessment of Coastal Change Project is to provide accurate representations of pre-storm ground conditions for areas that are designated high-priority because they have dense populations or valuable resources that are at risk from storm waves. Another purpose of the project is to develop a geomorphic (land feature) coastal classification that, with only minor modification, can be applied to most coastal regions in the United States. A Coastal Classification Map describing local geomorphic features is the first step toward determining the hazard vulnerability of an area. The Coastal Classification Maps of the National Assessment of Coastal Change Project present ground conditions such as beach width, dune elevations, overwash potential, and density of development. In order to complete a hazard vulnerability assessment, that information must be integrated with other information, such as prior storm impacts and beach stability. The Coastal Classification Maps provide much of the basic information for such an assessment and represent a critical component of a storm-impact forecasting capability. The map above shows the areas covered by this web site. Click on any of the location names or outlines to view the Coastal Classification Map for that area.

  10. Automated classification of articular cartilage surfaces based on surface texture.

    PubMed

    Stachowiak, G P; Stachowiak, G W; Podsiadlo, P

    2006-11-01

    In this study the automated classification system previously developed by the authors was used to classify articular cartilage surfaces with different degrees of wear. This automated system classifies surfaces based on their texture. Plug samples of sheep cartilage (pins) were run on stainless steel discs under various conditions using a pin-on-disc tribometer. Testing conditions were specifically designed to produce different severities of cartilage damage due to wear. Environmental scanning electron microscope (SEM) (ESEM) images of cartilage surfaces, that formed a database for pattern recognition analysis, were acquired. The ESEM images of cartilage were divided into five groups (classes), each class representing different wear conditions or wear severity. Each class was first examined and assessed visually. Next, the automated classification system (pattern recognition) was applied to all classes. The results of the automated surface texture classification were compared to those based on visual assessment of surface morphology. It was shown that the texture-based automated classification system was an efficient and accurate method of distinguishing between various cartilage surfaces generated under different wear conditions. It appears that the texture-based classification method has potential to become a useful tool in medical diagnostics.

  11. Agent Collaborative Target Localization and Classification in Wireless Sensor Networks

    PubMed Central

    Wang, Xue; Bi, Dao-wei; Ding, Liang; Wang, Sheng

    2007-01-01

    Wireless sensor networks (WSNs) are autonomous networks that have been frequently deployed to collaboratively perform target localization and classification tasks. Their autonomous and collaborative features resemble the characteristics of agents. Such similarities inspire the development of heterogeneous agent architecture for WSN in this paper. The proposed agent architecture views WSN as multi-agent systems and mobile agents are employed to reduce in-network communication. According to the architecture, an energy based acoustic localization algorithm is proposed. In localization, estimate of target location is obtained by steepest descent search. The search algorithm adapts to measurement environments by dynamically adjusting its termination condition. With the agent architecture, target classification is accomplished by distributed support vector machine (SVM). Mobile agents are employed for feature extraction and distributed SVM learning to reduce communication load. Desirable learning performance is guaranteed by combining support vectors and convex hull vectors. Fusion algorithms are designed to merge SVM classification decisions made from various modalities. Real world experiments with MICAz sensor nodes are conducted for vehicle localization and classification. Experimental results show the proposed agent architecture remarkably facilitates WSN designs and algorithm implementation. The localization and classification algorithms also prove to be accurate and energy efficient.

  12. [Molecular Genetics as Best Evidence in Glioma Diagnostics].

    PubMed

    Masui, Kenta; Komori, Takashi

    2016-03-01

    The development of a genomic landscape of gliomas has led to the internally consistent, molecularly-based classifiers. However, development of a biologically insightful classification to guide therapy is still ongoing. Further, tumors are heterogeneous, and they change and adapt in response to drugs. The challenge of developing molecular classifiers that provide meaningful ways to stratify patients for therapy remains a major challenge for the field. Therefore, by incorporating molecular markers into the new World Health Organization (WHO) classification of tumors of the central nervous system, the traditional principle of diagnosis based on histologic criteria will be replaced by a multilayered approach combining histologic features and molecular information in an "integrated diagnosis", to define tumor entities as narrowly as possible. We herein review the current status of diagnostic molecular markers for gliomas, focusing on IDH mutation, ATRX mutation, 1p/19q co-deletion, and TERT promoter mutation in adult tumors, as well as BRAF and H3F3A aberrations in pediatric gliomas, the combination of which will be a promising endeavor to render molecular genetics as a best evidence in the glioma diagnositics.

  13. An efficient ensemble learning method for gene microarray classification.

    PubMed

    Osareh, Alireza; Shadgar, Bita

    2013-01-01

    The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable features of an ensemble architecture, that is, accuracy and diversity. To select a concise subset of informative genes, 5 different feature selection algorithms are considered. To assess the efficiency of the RotBoost, other nonensemble/ensemble techniques including Decision Trees, Support Vector Machines, Rotation Forest, AdaBoost, and Bagging are also deployed. Experimental results have revealed that the combination of the fast correlation-based feature selection method with ICA-based RotBoost ensemble is highly effective for gene classification. In fact, the proposed method can create ensemble classifiers which outperform not only the classifiers produced by the conventional machine learning but also the classifiers generated by two widely used conventional ensemble learning methods, that is, Bagging and AdaBoost.

  14. Non-Destructive Classification Approaches for Equilbrated Ordinary Chondrites

    NASA Technical Reports Server (NTRS)

    Righter, K.; Harrington, R.; Schroeder, C.; Morris, R. V.

    2013-01-01

    Classification of meteorites is most effectively carried out by petrographic and mineralogic studies of thin sections, but a rapid and accurate classification technique for the many samples collected in dense collection areas (hot and cold deserts) is of great interest. Oil immersion techniques have been used to classify a large proportion of the US Antarctic meteorite collections since the mid-1980s [1]. This approach has allowed rapid characterization of thousands of samples over time, but nonetheless utilizes a piece of the sample that has been ground to grains or a powder. In order to compare a few non-destructive techniques with the standard approaches, we have characterized a group of chondrites from the Larkman Nunatak region using magnetic susceptibility and Moessbauer spectroscopy.

  15. Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques.

    PubMed

    Macyszyn, Luke; Akbari, Hamed; Pisapia, Jared M; Da, Xiao; Attiah, Mark; Pigrish, Vadim; Bi, Yingtao; Pal, Sharmistha; Davuluri, Ramana V; Roccograndi, Laura; Dahmane, Nadia; Martinez-Lage, Maria; Biros, George; Wolf, Ronald L; Bilello, Michel; O'Rourke, Donald M; Davatzikos, Christos

    2016-03-01

    MRI characteristics of brain gliomas have been used to predict clinical outcome and molecular tumor characteristics. However, previously reported imaging biomarkers have not been sufficiently accurate or reproducible to enter routine clinical practice and often rely on relatively simple MRI measures. The current study leverages advanced image analysis and machine learning algorithms to identify complex and reproducible imaging patterns predictive of overall survival and molecular subtype in glioblastoma (GB). One hundred five patients with GB were first used to extract approximately 60 diverse features from preoperative multiparametric MRIs. These imaging features were used by a machine learning algorithm to derive imaging predictors of patient survival and molecular subtype. Cross-validation ensured generalizability of these predictors to new patients. Subsequently, the predictors were evaluated in a prospective cohort of 29 new patients. Survival curves yielded a hazard ratio of 10.64 for predicted long versus short survivors. The overall, 3-way (long/medium/short survival) accuracy in the prospective cohort approached 80%. Classification of patients into the 4 molecular subtypes of GB achieved 76% accuracy. By employing machine learning techniques, we were able to demonstrate that imaging patterns are highly predictive of patient survival. Additionally, we found that GB subtypes have distinctive imaging phenotypes. These results reveal that when imaging markers related to infiltration, cell density, microvascularity, and blood-brain barrier compromise are integrated via advanced pattern analysis methods, they form very accurate predictive biomarkers. These predictive markers used solely preoperative images, hence they can significantly augment diagnosis and treatment of GB patients. © The Author(s) 2015. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  16. Cloud field classification based on textural features

    NASA Technical Reports Server (NTRS)

    Sengupta, Sailes Kumar

    1989-01-01

    An essential component in global climate research is accurate cloud cover and type determination. Of the two approaches to texture-based classification (statistical and textural), only the former is effective in the classification of natural scenes such as land, ocean, and atmosphere. In the statistical approach that was adopted, parameters characterizing the stochastic properties of the spatial distribution of grey levels in an image are estimated and then used as features for cloud classification. Two types of textural measures were used. One is based on the distribution of the grey level difference vector (GLDV), and the other on a set of textural features derived from the MaxMin cooccurrence matrix (MMCM). The GLDV method looks at the difference D of grey levels at pixels separated by a horizontal distance d and computes several statistics based on this distribution. These are then used as features in subsequent classification. The MaxMin tectural features on the other hand are based on the MMCM, a matrix whose (I,J)th entry give the relative frequency of occurrences of the grey level pair (I,J) that are consecutive and thresholded local extremes separated by a given pixel distance d. Textural measures are then computed based on this matrix in much the same manner as is done in texture computation using the grey level cooccurrence matrix. The database consists of 37 cloud field scenes from LANDSAT imagery using a near IR visible channel. The classification algorithm used is the well known Stepwise Discriminant Analysis. The overall accuracy was estimated by the percentage or correct classifications in each case. It turns out that both types of classifiers, at their best combination of features, and at any given spatial resolution give approximately the same classification accuracy. A neural network based classifier with a feed forward architecture and a back propagation training algorithm is used to increase the classification accuracy, using these two classes

  17. [Histological and molecular classification of endometrial carcinoma and therapeutical implications].

    PubMed

    Genestie, Catherine; Leary, Alexandra; Devouassoux, Mojgan; Auguste, Aurélie

    2017-12-01

    Endometrial cancer is the fourth cause of cancer in women in France and is the second most common cancer of the gynecologic cancer after breast cancer with 7275 new cases in 2012. The incidence of this neoplasm tends to increase with population aging, diabetes and obesity's augmentation. In rare cases, a hereditary factor has been described: Lynch's syndrome. The therapeutic management of the patient depends on the endometrial biopsy which specifies the histological type and the histo-prognostic grade as well as the MRI which allow the tumor staging. Within the last decade, improvement in technologies such as genomic, transcriptomic and histological analyses, allowed the establishment of new and finer classifications of endometrial carcinomas. The latest classification proposed by The Cancer Genomic Atlas (TCGA), has been made routinely applicable through the international consortium TransPORTEC. It consists of 4 groups listed from good to poor prognosis: (1) ultra-mutated "POLE"; (2) hyper-mutated "MSI"; (3) low copy number "NSMP" and (4) high number of copies "TP53 mutated" (serous-like). This integrated characterization combined with mutational data opens new opportunities for therapeutic strategies. Copyright © 2017 Société Française du Cancer. Published by Elsevier Masson SAS. All rights reserved.

  18. 2008 International Conference on Ectodermal Dysplasias Classification Conference Report

    PubMed Central

    Salinas, Carlos F.; Jorgenson, Ronald J.; Wright, J. Timothy; DiGiovanna, John J.; Fete, Mary D.

    2009-01-01

    There are many ways to classify ectodermal dysplasia syndromes. Clinicians in practice use a list of syndromes from which to choose a potential diagnosis, paging through a volume, such as Freire-Maia and Pinheiro's corpus, matching their patient's findings to listed syndromes. Medical researchers may want a list of syndromes that share one (monothetic system) or several (polythetic system) traits in order to focus research on a narrowly defined group. Special interest groups may want a list from which they can choose constituencies, and insurance companies and government agencies may want a list to determine for whom to provide (or deny) health care coverage. Furthermore, various molecular biologists are now promoting classification systems based on gene mutation (e.g. TP63 associated syndromes) or common molecular pathways. The challenge will be to balance comprehensiveness within the classification with usability and accessibility so that the benefits truly serve the needs of researchers, health care providers and ultimately the individuals and families directly affected by ectodermal dysplasias. It is also recognized that a new classification approach is an ongoing process and will require periodical reviews or updates. Whatever scheme is developed, however, will have far-reaching application for other groups of disorders for which classification is complicated by the number of interested parties and advances in diagnostic acumen. Consensus among interested parties is necessary for optimizing communication among the diverse groups whether it be for equitable distribution of funds, correctness of diagnosis and treatment, or focusing research efforts. PMID:19681152

  19. HIPPI: highly accurate protein family classification with ensembles of HMMs.

    PubMed

    Nguyen, Nam-Phuong; Nute, Michael; Mirarab, Siavash; Warnow, Tandy

    2016-11-11

    Given a new biological sequence, detecting membership in a known family is a basic step in many bioinformatics analyses, with applications to protein structure and function prediction and metagenomic taxon identification and abundance profiling, among others. Yet family identification of sequences that are distantly related to sequences in public databases or that are fragmentary remains one of the more difficult analytical problems in bioinformatics. We present a new technique for family identification called HIPPI (Hierarchical Profile Hidden Markov Models for Protein family Identification). HIPPI uses a novel technique to represent a multiple sequence alignment for a given protein family or superfamily by an ensemble of profile hidden Markov models computed using HMMER. An evaluation of HIPPI on the Pfam database shows that HIPPI has better overall precision and recall than blastp, HMMER, and pipelines based on HHsearch, and maintains good accuracy even for fragmentary query sequences and for protein families with low average pairwise sequence identity, both conditions where other methods degrade in accuracy. HIPPI provides accurate protein family identification and is robust to difficult model conditions. Our results, combined with observations from previous studies, show that ensembles of profile Hidden Markov models can better represent multiple sequence alignments than a single profile Hidden Markov model, and thus can improve downstream analyses for various bioinformatic tasks. Further research is needed to determine the best practices for building the ensemble of profile Hidden Markov models. HIPPI is available on GitHub at https://github.com/smirarab/sepp .

  20. Molecular Profiling Reveals Biologically Discrete Subsets and Pathways of Progression in Diffuse Glioma.

    PubMed

    Ceccarelli, Michele; Barthel, Floris P; Malta, Tathiane M; Sabedot, Thais S; Salama, Sofie R; Murray, Bradley A; Morozova, Olena; Newton, Yulia; Radenbaugh, Amie; Pagnotta, Stefano M; Anjum, Samreen; Wang, Jiguang; Manyam, Ganiraju; Zoppoli, Pietro; Ling, Shiyun; Rao, Arjun A; Grifford, Mia; Cherniack, Andrew D; Zhang, Hailei; Poisson, Laila; Carlotti, Carlos Gilberto; Tirapelli, Daniela Pretti da Cunha; Rao, Arvind; Mikkelsen, Tom; Lau, Ching C; Yung, W K Alfred; Rabadan, Raul; Huse, Jason; Brat, Daniel J; Lehman, Norman L; Barnholtz-Sloan, Jill S; Zheng, Siyuan; Hess, Kenneth; Rao, Ganesh; Meyerson, Matthew; Beroukhim, Rameen; Cooper, Lee; Akbani, Rehan; Wrensch, Margaret; Haussler, David; Aldape, Kenneth D; Laird, Peter W; Gutmann, David H; Noushmehr, Houtan; Iavarone, Antonio; Verhaak, Roel G W

    2016-01-28

    Therapy development for adult diffuse glioma is hindered by incomplete knowledge of somatic glioma driving alterations and suboptimal disease classification. We defined the complete set of genes associated with 1,122 diffuse grade II-III-IV gliomas from The Cancer Genome Atlas and used molecular profiles to improve disease classification, identify molecular correlations, and provide insights into the progression from low- to high-grade disease. Whole-genome sequencing data analysis determined that ATRX but not TERT promoter mutations are associated with increased telomere length. Recent advances in glioma classification based on IDH mutation and 1p/19q co-deletion status were recapitulated through analysis of DNA methylation profiles, which identified clinically relevant molecular subsets. A subtype of IDH mutant glioma was associated with DNA demethylation and poor outcome; a group of IDH-wild-type diffuse glioma showed molecular similarity to pilocytic astrocytoma and relatively favorable survival. Understanding of cohesive disease groups may aid improved clinical outcomes. Copyright © 2016 Elsevier Inc. All rights reserved.

  1. Subliminal priming with nearly perfect performance in the prime-classification task.

    PubMed

    Finkbeiner, Matthew

    2011-05-01

    The subliminal priming paradigm is widely used by cognitive scientists, and claims of subliminal perception are common nowadays. Nevertheless, there are still those who remain skeptical. In a recent critique of subliminal priming, Pratte and Rouder (Attention, Perception, & Psychophysics, 71, 1276-1283, 2009) suggested that previous claims of subliminal priming may have been due to a failure to control the task difficulty between the experiment proper and the prime-classification task. Essentially, because the prime-classification task is more difficult than the experiment proper, the prime-classification task results may underrepresent the subjects' true ability to perceive the prime stimuli. To address this possibility, prime words were here presented in color. In the experiment proper, priming was observed. In the prime-classification task, subjects reported the color of the primes very accurately, indicating almost perfect control of task difficulty, but they could not identify the primes. Thus, I conclude that controlling for task difficulty does not eliminate subliminal priming.

  2. Potential role of photodynamic techniques combined with new generation flexible ureterorenoscopes and molecular markers for the management of urothelial carcinoma of the upper urinary tract.

    PubMed

    Audenet, François; Traxer, Olivier; Yates, David R; Cussenot, Olivier; Rouprêt, Morgan

    2012-02-01

    •  To discuss how the development of new generation flexible ureterorenoscopes in combination with photodynamic diagnosis (PDD) improves the assessment of urothelial cell carcinoma of the upper urinary tract (UUT-UCC). •  Ultimately, this may allow accurate tumour classification and the ability to select which patients would benefit from conservative treatment as opposed to radical surgery. •  We conducted an exhaustive Pubmed literature search using a combination of keywords including: ureterorenoscopy, UUT-UCC diagnosis, PDD, narrow band imaging, conservative treatment UUT-UCC and molecular urinalysis. •  We then selected salient high calibre articles relevant to our objective. •  We give specific consideration to anatomical aspects of UUT-UCC investigation, PDD in UCC, aminolevulinic acid and its derivatives, autofluorescence, narrow band imaging, molecular marker analysis and the recent advances in ureterorenoscopic technology. •  The traditional pitfalls of UUT-UCC diagnosis, namely poor visualisation and difficulty in obtaining representative histological samples, are being circumvented by the introduction of modern digital flexible ureteroscopes that can be combined with PDD and molecular analysis to improve tumour classification, deferring to conservative treatment accordingly. •  The accuracy of the diagnostic work-up of UUT-UCC is improving due to advances in technology, pharmaceutical agents and incorporation of molecular markers, all factors allowing us to characterise tumours of the UUT more definitively. © 2011 THE AUTHORS. BJU INTERNATIONAL © 2011 BJU INTERNATIONAL.

  3. Understanding Molecular-Ion Neutral Atom Collisions for the Production of Ultracold Molecular Ions

    DTIC Science & Technology

    2014-02-03

    SECURITY CLASSIFICATION OF: This project was superseded and replaced by another ARO-funded project of the same name, which is still continuing. The goal...cooled atoms," IOTA -COST Workshop on molecular ions, Arosa, Switzerland. 5. E.R. Hudson, "Sympathetic cooling of molecules with laser cooled

  4. Classification of forest land attributes using multi-source remotely sensed data

    NASA Astrophysics Data System (ADS)

    Pippuri, Inka; Suvanto, Aki; Maltamo, Matti; Korhonen, Kari T.; Pitkänen, Juho; Packalen, Petteri

    2016-02-01

    The aim of the study was to (1) examine the classification of forest land using airborne laser scanning (ALS) data, satellite images and sample plots of the Finnish National Forest Inventory (NFI) as training data and to (2) identify best performing metrics for classifying forest land attributes. Six different schemes of forest land classification were studied: land use/land cover (LU/LC) classification using both national classes and FAO (Food and Agricultural Organization of the United Nations) classes, main type, site type, peat land type and drainage status. Special interest was to test different ALS-based surface metrics in classification of forest land attributes. Field data consisted of 828 NFI plots collected in 2008-2012 in southern Finland and remotely sensed data was from summer 2010. Multinomial logistic regression was used as the classification method. Classification of LU/LC classes were highly accurate (kappa-values 0.90 and 0.91) but also the classification of site type, peat land type and drainage status succeeded moderately well (kappa-values 0.51, 0.69 and 0.52). ALS-based surface metrics were found to be the most important predictor variables in classification of LU/LC class, main type and drainage status. In best classification models of forest site types both spectral metrics from satellite data and point cloud metrics from ALS were used. In turn, in the classification of peat land types ALS point cloud metrics played the most important role. Results indicated that the prediction of site type and forest land category could be incorporated into stand level forest management inventory system in Finland.

  5. Misclassification Errors in Unsupervised Classification Methods. Comparison Based on the Simulation of Targeted Proteomics Data

    PubMed Central

    Andreev, Victor P; Gillespie, Brenda W; Helfand, Brian T; Merion, Robert M

    2016-01-01

    Unsupervised classification methods are gaining acceptance in omics studies of complex common diseases, which are often vaguely defined and are likely the collections of disease subtypes. Unsupervised classification based on the molecular signatures identified in omics studies have the potential to reflect molecular mechanisms of the subtypes of the disease and to lead to more targeted and successful interventions for the identified subtypes. Multiple classification algorithms exist but none is ideal for all types of data. Importantly, there are no established methods to estimate sample size in unsupervised classification (unlike power analysis in hypothesis testing). Therefore, we developed a simulation approach allowing comparison of misclassification errors and estimating the required sample size for a given effect size, number, and correlation matrix of the differentially abundant proteins in targeted proteomics studies. All the experiments were performed in silico. The simulated data imitated the expected one from the study of the plasma of patients with lower urinary tract dysfunction with the aptamer proteomics assay Somascan (SomaLogic Inc, Boulder, CO), which targeted 1129 proteins, including 330 involved in inflammation, 180 in stress response, 80 in aging, etc. Three popular clustering methods (hierarchical, k-means, and k-medoids) were compared. K-means clustering performed much better for the simulated data than the other two methods and enabled classification with misclassification error below 5% in the simulated cohort of 100 patients based on the molecular signatures of 40 differentially abundant proteins (effect size 1.5) from among the 1129-protein panel. PMID:27524871

  6. Using Copula Distributions to Support More Accurate Imaging-Based Diagnostic Classifiers for Neuropsychiatric Disorders

    PubMed Central

    Bansal, Ravi; Hao, Xuejun; Liu, Jun; Peterson, Bradley S.

    2014-01-01

    Many investigators have tried to apply machine learning techniques to magnetic resonance images (MRIs) of the brain in order to diagnose neuropsychiatric disorders. Usually the number of brain imaging measures (such as measures of cortical thickness and measures of local surface morphology) derived from the MRIs (i.e., their dimensionality) has been large (e.g. >10) relative to the number of participants who provide the MRI data (<100). Sparse data in a high dimensional space increases the variability of the classification rules that machine learning algorithms generate, thereby limiting the validity, reproducibility, and generalizability of those classifiers. The accuracy and stability of the classifiers can improve significantly if the multivariate distributions of the imaging measures can be estimated accurately. To accurately estimate the multivariate distributions using sparse data, we propose to estimate first the univariate distributions of imaging data and then combine them using a Copula to generate more accurate estimates of their multivariate distributions. We then sample the estimated Copula distributions to generate dense sets of imaging measures and use those measures to train classifiers. We hypothesize that the dense sets of brain imaging measures will generate classifiers that are stable to variations in brain imaging measures, thereby improving the reproducibility, validity, and generalizability of diagnostic classification algorithms in imaging datasets from clinical populations. In our experiments, we used both computer-generated and real-world brain imaging datasets to assess the accuracy of multivariate Copula distributions in estimating the corresponding multivariate distributions of real-world imaging data. Our experiments showed that diagnostic classifiers generated using imaging measures sampled from the Copula were significantly more accurate and more reproducible than were the classifiers generated using either the real-world imaging

  7. Traditional taxonomic groupings mask evolutionary history: a molecular phylogeny and new classification of the chromodorid nudibranchs.

    PubMed

    Johnson, Rebecca Fay; Gosliner, Terrence M

    2012-01-01

    Chromodorid nudibranchs (16 genera, 300+ species) are beautiful, brightly colored sea slugs found primarily in tropical coral reef habitats and subtropical coastal waters. The chromodorids are the most speciose family of opisthobranchs and one of the most diverse heterobranch clades. Chromodorids have the potential to be a model group with which to study diversification, color pattern evolution, are important source organisms in natural products chemistry and represent a stunning and widely compelling example of marine biodiversity. Here, we present the most complete molecular phylogeny of the chromodorid nudibranchs to date, with a broad sample of 244 specimens (142 new), representing 157 (106 new) chromodorid species, four actinocylcid species and four additional dorid species utilizing two mitochondrial markers (16s and COI). We confirmed the monophyly of the Chromodorididae and its sister group relationship with the Actinocyclidae. We were also able to, for the first time, test generic monophyly by including more than one member of all 14 of the non-monotypic chromodorid genera. Every one of these 14 traditional chromodorid genera are either non-monophyletic, or render another genus paraphyletic. Additionally, both the monotypic genera Verconia and Diversidoris are nested within clades. Based on data shown here, there are three individual species and five clades limited to the eastern Pacific and Atlantic Oceans (or just one of these ocean regions), while the majority of chromodorid clades and species are strictly Indo-Pacific in distribution. We present a new classification of the chromodorid nudibranchs. We use molecular data to untangle evolutionary relationships and retain a historical connection to traditional systematics by using generic names attached to type species as clade names.

  8. Optimization of the ANFIS using a genetic algorithm for physical work rate classification.

    PubMed

    Habibi, Ehsanollah; Salehi, Mina; Yadegarfar, Ghasem; Taheri, Ali

    2018-03-13

    Recently, a new method was proposed for physical work rate classification based on an adaptive neuro-fuzzy inference system (ANFIS). This study aims to present a genetic algorithm (GA)-optimized ANFIS model for a highly accurate classification of physical work rate. Thirty healthy men participated in this study. Directly measured heart rate and oxygen consumption of the participants in the laboratory were used for training the ANFIS classifier model in MATLAB version 8.0.0 using a hybrid algorithm. A similar process was done using the GA as an optimization technique. The accuracy, sensitivity and specificity of the ANFIS classifier model were increased successfully. The mean accuracy of the model was increased from 92.95 to 97.92%. Also, the calculated root mean square error of the model was reduced from 5.4186 to 3.1882. The maximum estimation error of the optimized ANFIS during the network testing process was ± 5%. The GA can be effectively used for ANFIS optimization and leads to an accurate classification of physical work rate. In addition to high accuracy, simple implementation and inter-individual variability consideration are two other advantages of the presented model.

  9. Is geography an accurate predictor of evolutionary history in the millipede family Xystodesmidae?

    PubMed Central

    Marek, Paul E.

    2017-01-01

    For the past several centuries, millipede taxonomists have used the morphology of male copulatory structures (modified legs called gonopods), which are strongly variable and suggestive of species-level differences, as a source to understand taxon relationships. Millipedes in the family Xystodesmidae are blind, dispersal-limited and have narrow habitat requirements. Therefore, geographical proximity may instead be a better predictor of evolutionary relationship than morphology, especially since gonopodal anatomy is extremely divergent and similarities may be masked by evolutionary convergence. Here we provide a phylogenetics-based test of the power of morphological versus geographical character sets for resolving phylogenetic relationships in xystodesmid millipedes. Molecular data from 90 species-group taxa in the family were included in a six-gene phylogenetic analysis to provide the basis for comparing trees generated from these alternative character sets. The molecular phylogeny was compared to topologies representing three hypotheses: (1) a prior classification formulated using morphological and geographical data, (2) hierarchical groupings derived from Euclidean geographical distance, and (3) one based solely on morphological data. Euclidean geographical distance was not found to be a better predictor of evolutionary relationship than the prior classification, the latter of which was the most similar to the molecular topology. However, all three of the alternative topologies were highly divergent (Bayes factor >10) from the molecular topology, with the tree inferred exclusively from morphology being the most divergent. The results of this analysis show that a high degree of morphological convergence from substantial gonopod shape divergence generated spurious phylogenetic relationships. These results indicate the impact that a high degree of morphological homoplasy may have had on prior treatments of the family. Using the results of our phylogenetic analysis

  10. Effects of uncertainty and variability on population declines and IUCN Red List classifications.

    PubMed

    Rueda-Cediel, Pamela; Anderson, Kurt E; Regan, Tracey J; Regan, Helen M

    2018-01-22

    The International Union for Conservation of Nature (IUCN) Red List Categories and Criteria is a quantitative framework for classifying species according to extinction risk. Population models may be used to estimate extinction risk or population declines. Uncertainty and variability arise in threat classifications through measurement and process error in empirical data and uncertainty in the models used to estimate extinction risk and population declines. Furthermore, species traits are known to affect extinction risk. We investigated the effects of measurement and process error, model type, population growth rate, and age at first reproduction on the reliability of risk classifications based on projected population declines on IUCN Red List classifications. We used an age-structured population model to simulate true population trajectories with different growth rates, reproductive ages and levels of variation, and subjected them to measurement error. We evaluated the ability of scalar and matrix models parameterized with these simulated time series to accurately capture the IUCN Red List classification generated with true population declines. Under all levels of measurement error tested and low process error, classifications were reasonably accurate; scalar and matrix models yielded roughly the same rate of misclassifications, but the distribution of errors differed; matrix models led to greater overestimation of extinction risk than underestimations; process error tended to contribute to misclassifications to a greater extent than measurement error; and more misclassifications occurred for fast, rather than slow, life histories. These results indicate that classifications of highly threatened taxa (i.e., taxa with low growth rates) under criterion A are more likely to be reliable than for less threatened taxa when assessed with population models. Greater scrutiny needs to be placed on data used to parameterize population models for species with high growth rates

  11. Accurate diagnosis of prenatal cleft lip/palate by understanding the embryology

    PubMed Central

    Smarius, Bram; Loozen, Charlotte; Manten, Wendy; Bekker, Mireille; Pistorius, Lou; Breugem, Corstiaan

    2017-01-01

    Cleft lip with or without cleft palate (CP) is one of the most common congenital malformations. Ultrasonographers involved in the routine 20-wk ultrasound screening could encounter these malformations. The face and palate develop in a very characteristic way. For ultrasonographers involved in screening these patients it is crucial to have a thorough understanding of the embryology of the face. This could help them to make a more accurate diagnosis and save time during the ultrasound. Subsequently, the current postnatal classification will be discussed to facilitate the communication with the CP teams. PMID:29026689

  12. Correlation between molecular analysis, diagnosis according to the 2015 WHO classification of unresected lung tumours and TTF1 expression in small biopsies and cytology specimens from 344 non-small cell lung carcinoma patients.

    PubMed

    Russell, Prudence A; Rogers, Toni-Maree; Solomon, Benjamin; Alam, Naveed; Barnett, Stephen A; Rathi, Vivek; Williams, Richard A; Wright, Gavin M; Conron, Matthew

    2017-10-01

    We investigated correlations between diagnosis according to the 2015 World Health Organization (WHO) classification of unresected lung tumours, molecular analysis and TTF1 expression in small biopsy and cytology specimens from 344 non-small cell lung carcinoma (NSCLC) patients. One case failed testing for EGFR, KRAS and ALK abnormalities and six had insufficient tumour for ALK testing. Overall mutation rate in 343 cases was 48% for the genes tested, with 19% EGFR, 33% KRAS and 4% BRAF mutations, and 5% ALK rearrangements detected. More EGFR-mutant (78%) and ALK-rearranged (75%) tumours had morphologic adenocarcinoma than KRAS-mutant (56%) tumours. Despite no significant difference in the overall rate of any molecular abnormality between morphologic adenocarcinoma (52%) and NSCLC, favour adenocarcinoma (47%) (p = 0.18), KRAS mutations were detected more frequently in the latter group. No significant difference in the overall rate of any molecular abnormality between TTF1 positive (49%) and TTF1 negative tumours (44%) (p = 0.92) was detected, but more EGFR-mutant (97%) and ALK-rearranged tumours (92%) were TTF1 positive than KRAS-mutant tumours (68%). Rates of EGFR, KRAS and BRAF mutations and ALK rearrangements in this Australian NSCLC patient population are consistent with the published international literature. Our findings suggest that 2015 WHO classification of unresected tumours may assist in identifying molecular subsets of advanced NSCLC. Copyright © 2017 Royal College of Pathologists of Australasia. Published by Elsevier B.V. All rights reserved.

  13. Construction of a Calibrated Probabilistic Classification Catalog: Application to 50k Variable Sources in the All-Sky Automated Survey

    NASA Astrophysics Data System (ADS)

    Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.; Bloom, Joshua S.; Butler, Nathaniel R.; Brink, Henrik; Crellin-Quick, Arien

    2012-12-01

    With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In addition to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.

  14. CONSTRUCTION OF A CALIBRATED PROBABILISTIC CLASSIFICATION CATALOG: APPLICATION TO 50k VARIABLE SOURCES IN THE ALL-SKY AUTOMATED SURVEY

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Richards, Joseph W.; Starr, Dan L.; Miller, Adam A.

    2012-12-15

    With growing data volumes from synoptic surveys, astronomers necessarily must become more abstracted from the discovery and introspection processes. Given the scarcity of follow-up resources, there is a particularly sharp onus on the frameworks that replace these human roles to provide accurate and well-calibrated probabilistic classification catalogs. Such catalogs inform the subsequent follow-up, allowing consumers to optimize the selection of specific sources for further study and permitting rigorous treatment of classification purities and efficiencies for population studies. Here, we describe a process to produce a probabilistic classification catalog of variability with machine learning from a multi-epoch photometric survey. In additionmore » to producing accurate classifications, we show how to estimate calibrated class probabilities and motivate the importance of probability calibration. We also introduce a methodology for feature-based anomaly detection, which allows discovery of objects in the survey that do not fit within the predefined class taxonomy. Finally, we apply these methods to sources observed by the All-Sky Automated Survey (ASAS), and release the Machine-learned ASAS Classification Catalog (MACC), a 28 class probabilistic classification catalog of 50,124 ASAS sources in the ASAS Catalog of Variable Stars. We estimate that MACC achieves a sub-20% classification error rate and demonstrate that the class posterior probabilities are reasonably calibrated. MACC classifications compare favorably to the classifications of several previous domain-specific ASAS papers and to the ASAS Catalog of Variable Stars, which had classified only 24% of those sources into one of 12 science classes.« less

  15. Uav-Based Crops Classification with Joint Features from Orthoimage and Dsm Data

    NASA Astrophysics Data System (ADS)

    Liu, B.; Shi, Y.; Duan, Y.; Wu, W.

    2018-04-01

    Accurate crops classification remains a challenging task due to the same crop with different spectra and different crops with same spectrum phenomenon. Recently, UAV-based remote sensing approach gains popularity not only for its high spatial and temporal resolution, but also for its ability to obtain spectraand spatial data at the same time. This paper focus on how to take full advantages of spatial and spectrum features to improve crops classification accuracy, based on an UAV platform equipped with a general digital camera. Texture and spatial features extracted from the RGB orthoimage and the digital surface model of the monitoring area are analysed and integrated within a SVM classification framework. Extensive experiences results indicate that the overall classification accuracy is drastically improved from 72.9 % to 94.5 % when the spatial features are combined together, which verified the feasibility and effectiveness of the proposed method.

  16. Deep learning for EEG-Based preference classification

    NASA Astrophysics Data System (ADS)

    Teo, Jason; Hou, Chew Lin; Mountstephens, James

    2017-10-01

    Electroencephalogram (EEG)-based emotion classification is rapidly becoming one of the most intensely studied areas of brain-computer interfacing (BCI). The ability to passively identify yet accurately correlate brainwaves with our immediate emotions opens up truly meaningful and previously unattainable human-computer interactions such as in forensic neuroscience, rehabilitative medicine, affective entertainment and neuro-marketing. One particularly useful yet rarely explored areas of EEG-based emotion classification is preference recognition [1], which is simply the detection of like versus dislike. Within the limited investigations into preference classification, all reported studies were based on musically-induced stimuli except for a single study which used 2D images. The main objective of this study is to apply deep learning, which has been shown to produce state-of-the-art results in diverse hard problems such as in computer vision, natural language processing and audio recognition, to 3D object preference classification over a larger group of test subjects. A cohort of 16 users was shown 60 bracelet-like objects as rotating visual stimuli on a computer display while their preferences and EEGs were recorded. After training a variety of machine learning approaches which included deep neural networks, we then attempted to classify the users' preferences for the 3D visual stimuli based on their EEGs. Here, we show that that deep learning outperforms a variety of other machine learning classifiers for this EEG-based preference classification task particularly in a highly challenging dataset with large inter- and intra-subject variability.

  17. Morphological classification of plant cell deaths.

    PubMed

    van Doorn, W G; Beers, E P; Dangl, J L; Franklin-Tong, V E; Gallois, P; Hara-Nishimura, I; Jones, A M; Kawai-Yamada, M; Lam, E; Mundy, J; Mur, L A J; Petersen, M; Smertenko, A; Taliansky, M; Van Breusegem, F; Wolpert, T; Woltering, E; Zhivotovsky, B; Bozhkov, P V

    2011-08-01

    Programmed cell death (PCD) is an integral part of plant development and of responses to abiotic stress or pathogens. Although the morphology of plant PCD is, in some cases, well characterised and molecular mechanisms controlling plant PCD are beginning to emerge, there is still confusion about the classification of PCD in plants. Here we suggest a classification based on morphological criteria. According to this classification, the use of the term 'apoptosis' is not justified in plants, but at least two classes of PCD can be distinguished: vacuolar cell death and necrosis. During vacuolar cell death, the cell contents are removed by a combination of autophagy-like process and release of hydrolases from collapsed lytic vacuoles. Necrosis is characterised by early rupture of the plasma membrane, shrinkage of the protoplast and absence of vacuolar cell death features. Vacuolar cell death is common during tissue and organ formation and elimination, whereas necrosis is typically found under abiotic stress. Some examples of plant PCD cannot be ascribed to either major class and are therefore classified as separate modalities. These are PCD associated with the hypersensitive response to biotrophic pathogens, which can express features of both necrosis and vacuolar cell death, PCD in starchy cereal endosperm and during self-incompatibility. The present classification is not static, but will be subject to further revision, especially when specific biochemical pathways are better defined.

  18. Impact of Information based Classification on Network Epidemics

    PubMed Central

    Mishra, Bimal Kumar; Haldar, Kaushik; Sinha, Durgesh Nandini

    2016-01-01

    Formulating mathematical models for accurate approximation of malicious propagation in a network is a difficult process because of our inherent lack of understanding of several underlying physical processes that intrinsically characterize the broader picture. The aim of this paper is to understand the impact of available information in the control of malicious network epidemics. A 1-n-n-1 type differential epidemic model is proposed, where the differentiality allows a symptom based classification. This is the first such attempt to add such a classification into the existing epidemic framework. The model is incorporated into a five class system called the DifEpGoss architecture. Analysis reveals an epidemic threshold, based on which the long-term behavior of the system is analyzed. In this work three real network datasets with 22002, 22469 and 22607 undirected edges respectively, are used. The datasets show that classification based prevention given in the model can have a good role in containing network epidemics. Further simulation based experiments are used with a three category classification of attack and defense strengths, which allows us to consider 27 different possibilities. These experiments further corroborate the utility of the proposed model. The paper concludes with several interesting results. PMID:27329348

  19. Advances in Spectral-Spatial Classification of Hyperspectral Images

    NASA Technical Reports Server (NTRS)

    Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

    2012-01-01

    Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation and contrast of the spatial structures present in the image. Then the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines using the available spectral information and the extracted spatial information. Spatial post-processing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple classifier system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

  20. Advances in Spectral-Spatial Classification of Hyperspectral Images

    NASA Technical Reports Server (NTRS)

    Fauvel, Mathieu; Tarabalka, Yuliya; Benediktsson, Jon Atli; Chanussot, Jocelyn; Tilton, James C.

    2012-01-01

    Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral–spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.

  1. Automated retinal vessel type classification in color fundus images

    NASA Astrophysics Data System (ADS)

    Yu, H.; Barriga, S.; Agurto, C.; Nemeth, S.; Bauman, W.; Soliz, P.

    2013-02-01

    Automated retinal vessel type classification is an essential first step toward machine-based quantitative measurement of various vessel topological parameters and identifying vessel abnormalities and alternations in cardiovascular disease risk analysis. This paper presents a new and accurate automatic artery and vein classification method developed for arteriolar-to-venular width ratio (AVR) and artery and vein tortuosity measurements in regions of interest (ROI) of 1.5 and 2.5 optic disc diameters from the disc center, respectively. This method includes illumination normalization, automatic optic disc detection and retinal vessel segmentation, feature extraction, and a partial least squares (PLS) classification. Normalized multi-color information, color variation, and multi-scale morphological features are extracted on each vessel segment. We trained the algorithm on a set of 51 color fundus images using manually marked arteries and veins. We tested the proposed method in a previously unseen test data set consisting of 42 images. We obtained an area under the ROC curve (AUC) of 93.7% in the ROI of AVR measurement and 91.5% of AUC in the ROI of tortuosity measurement. The proposed AV classification method has the potential to assist automatic cardiovascular disease early detection and risk analysis.

  2. Using beta binomials to estimate classification uncertainty for ensemble models.

    PubMed

    Clark, Robert D; Liang, Wenkel; Lee, Adam C; Lawless, Michael S; Fraczkiewicz, Robert; Waldman, Marvin

    2014-01-01

    Quantitative structure-activity (QSAR) models have enormous potential for reducing drug discovery and development costs as well as the need for animal testing. Great strides have been made in estimating their overall reliability, but to fully realize that potential, researchers and regulators need to know how confident they can be in individual predictions. Submodels in an ensemble model which have been trained on different subsets of a shared training pool represent multiple samples of the model space, and the degree of agreement among them contains information on the reliability of ensemble predictions. For artificial neural network ensembles (ANNEs) using two different methods for determining ensemble classification - one using vote tallies and the other averaging individual network outputs - we have found that the distribution of predictions across positive vote tallies can be reasonably well-modeled as a beta binomial distribution, as can the distribution of errors. Together, these two distributions can be used to estimate the probability that a given predictive classification will be in error. Large data sets comprised of logP, Ames mutagenicity, and CYP2D6 inhibition data are used to illustrate and validate the method. The distributions of predictions and errors for the training pool accurately predicted the distribution of predictions and errors for large external validation sets, even when the number of positive and negative examples in the training pool were not balanced. Moreover, the likelihood of a given compound being prospectively misclassified as a function of the degree of consensus between networks in the ensemble could in most cases be estimated accurately from the fitted beta binomial distributions for the training pool. Confidence in an individual predictive classification by an ensemble model can be accurately assessed by examining the distributions of predictions and errors as a function of the degree of agreement among the constituent

  3. Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features

    PubMed Central

    Xu, Tingbao; Chen, Yun

    2017-01-01

    In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images. PMID:28761440

  4. The Australian experience in dental classification.

    PubMed

    Mahoney, Greg

    2008-01-01

    The Australian Defence Health Service uses a disease-risk management strategy to achieve two goals: first, to identify Australian Defence Force (ADF) members who are at high risk of developing an adverse health event, and second, to deliver intervention strategies efficiently so that maximum benefits for health within the ADF are achieved with the least cost. The present dental classification system utilized by the ADF, while an excellent dental triage tool, has been found not to be predictive of an ADF member having an adverse dental event in the following 12-month period. Clearly, there is a need for further research to establish a predictive risk-based dental classification system. This risk assessment must be sensitive enough to accurately estimate the probability that an ADF member will experience dental pain, dysfunction, or other adverse dental events within a forthcoming period, typically 12 months. Furthermore, there needs to be better epidemiological data collected in the field to assist in the research.

  5. Automated Classification of Asteroids into Families at Work

    NASA Astrophysics Data System (ADS)

    Knežević, Zoran; Milani, Andrea; Cellino, Alberto; Novaković, Bojan; Spoto, Federica; Paolicchi, Paolo

    2014-07-01

    We have recently proposed a new approach to the asteroid family classification by combining the classical HCM method with an automated procedure to add newly discovered members to existing families. This approach is specifically intended to cope with ever increasing asteroid data sets, and consists of several steps to segment the problem and handle the very large amount of data in an efficient and accurate manner. We briefly present all these steps and show the results from three subsequent updates making use of only the automated step of attributing the newly numbered asteroids to the known families. We describe the changes of the individual families membership, as well as the evolution of the classification due to the newly added intersections between the families, resolved candidate family mergers, and emergence of the new candidates for the mergers. We thus demonstrate how by the new approach the asteroid family classification becomes stable in general terms (converging towards a permanent list of confirmed families), and in the same time evolving in details (to account for the newly discovered asteroids) at each update.

  6. Using reconstructed IVUS images for coronary plaque classification.

    PubMed

    Caballero, Karla L; Barajas, Joel; Pujol, Oriol; Rodriguez, Oriol; Radeva, Petia

    2007-01-01

    Coronary plaque rupture is one of the principal causes of sudden death in western societies. Reliable diagnostic of the different plaque types are of great interest for the medical community the predicting their evolution and applying an effective treatment. To achieve this, a tissue classification must be performed. Intravascular Ultrasound (IVUS) represents a technique to explore the vessel walls and to observe its histological properties. In this paper, a method to reconstruct IVUS images from the raw Radio Frequency (RF) data coming from ultrasound catheter is proposed. This framework offers a normalization scheme to compare accurately different patient studies. The automatic tissue classification is based on texture analysis and Adapting Boosting (Adaboost) learning technique combined with Error Correcting Output Codes (ECOC). In this study, 9 in-vivo cases are reconstructed with 7 different parameter set. This method improves the classification rate based on images, yielding a 91% of well-detected tissue using the best parameter set. It also reduces the inter-patient variability compared with the analysis of DICOM images, which are obtained from the commercial equipment.

  7. Cognitive-motivational deficits in ADHD: development of a classification system.

    PubMed

    Gupta, Rashmi; Kar, Bhoomika R; Srinivasan, Narayanan

    2011-01-01

    The classification systems developed so far to detect attention deficit/hyperactivity disorder (ADHD) do not have high sensitivity and specificity. We have developed a classification system based on several neuropsychological tests that measure cognitive-motivational functions that are specifically impaired in ADHD children. A total of 240 (120 ADHD children and 120 healthy controls) children in the age range of 6-9 years and 32 Oppositional Defiant Disorder (ODD) children (aged 9 years) participated in the study. Stop-Signal, Task-Switching, Attentional Network, and Choice Delay tests were administered to all the participants. Receiver operating characteristic (ROC) analysis indicated that percentage choice of long-delay reward best classified the ADHD children from healthy controls. Single parameters were not helpful in making a differential classification of ADHD with ODD. Multinominal logistic regression (MLR) was performed with multiple parameters (data fusion) that produced improved overall classification accuracy. A combination of stop-signal reaction time, posterror-slowing, mean delay, switch cost, and percentage choice of long-delay reward produced an overall classification accuracy of 97.8%; with internal validation, the overall accuracy was 92.2%. Combining parameters from different tests of control functions not only enabled us to accurately classify ADHD children from healthy controls but also in making a differential classification with ODD. These results have implications for the theories of ADHD.

  8. Theory of bi-molecular association dynamics in 2D for accurate model and experimental parameterization of binding rates

    PubMed Central

    Yogurtcu, Osman N.; Johnson, Margaret E.

    2015-01-01

    The dynamics of association between diffusing and reacting molecular species are routinely quantified using simple rate-equation kinetics that assume both well-mixed concentrations of species and a single rate constant for parameterizing the binding rate. In two-dimensions (2D), however, even when systems are well-mixed, the assumption of a single characteristic rate constant for describing association is not generally accurate, due to the properties of diffusional searching in dimensions d ≤ 2. Establishing rigorous bounds for discriminating between 2D reactive systems that will be accurately described by rate equations with a single rate constant, and those that will not, is critical for both modeling and experimentally parameterizing binding reactions restricted to surfaces such as cellular membranes. We show here that in regimes of intrinsic reaction rate (ka) and diffusion (D) parameters ka/D > 0.05, a single rate constant cannot be fit to the dynamics of concentrations of associating species independently of the initial conditions. Instead, a more sophisticated multi-parametric description than rate-equations is necessary to robustly characterize bimolecular reactions from experiment. Our quantitative bounds derive from our new analysis of 2D rate-behavior predicted from Smoluchowski theory. Using a recently developed single particle reaction-diffusion algorithm we extend here to 2D, we are able to test and validate the predictions of Smoluchowski theory and several other theories of reversible reaction dynamics in 2D for the first time. Finally, our results also mean that simulations of reactive systems in 2D using rate equations must be undertaken with caution when reactions have ka/D > 0.05, regardless of the simulation volume. We introduce here a simple formula for an adaptive concentration dependent rate constant for these chemical kinetics simulations which improves on existing formulas to better capture non-equilibrium reaction dynamics from dilute

  9. VizieR Online Data Catalog: LAMOST-Kepler MKCLASS spectral classification (Gray+, 2016)

    NASA Astrophysics Data System (ADS)

    Gray, R. O.; Corbally, C. J.; De Cat, P.; Fu, J. N.; Ren, A. B.; Shi, J. R.; Luo, A. L.; Zhang, H. T.; Wu, Y.; Cao, Z.; Li, G.; Zhang, Y.; Hou, Y.; Wang, Y.

    2016-07-01

    The data for the LAMOST-Kepler project are supplied by the Large Sky Area Multi Object Fiber Spectroscopic Telescope (LAMOST, also known as the Guo Shou Jing Telescope). This unique astronomical instrument is located at the Xinglong observatory in China, and combines a large aperture (4 m) telescope with a 5° circular field of view (Wang et al. 1996ApOpt..35.5155W). Our role in this project is to supply accurate two-dimensional spectral types for the observed targets. The large number of spectra obtained for this project (101086) makes traditional visual classification techniques impractical, so we have utilized the MKCLASS code to perform these classifications. The MKCLASS code (Gray & Corbally 2014AJ....147...80G, v1.07 http://www.appstate.edu/~grayro/mkclass/), an expert system designed to classify blue-violet spectra on the MK Classification system, was employed to produce the spectral classifications reported in this paper. MKCLASS was designed to reproduce the steps skilled human classifiers employ in the classification process. (2 data files).

  10. Toward functional classification of neuronal types.

    PubMed

    Sharpee, Tatyana O

    2014-09-17

    How many types of neurons are there in the brain? This basic neuroscience question remains unsettled despite many decades of research. Classification schemes have been proposed based on anatomical, electrophysiological, or molecular properties. However, different schemes do not always agree with each other. This raises the question of whether one can classify neurons based on their function directly. For example, among sensory neurons, can a classification scheme be devised that is based on their role in encoding sensory stimuli? Here, theoretical arguments are outlined for how this can be achieved using information theory by looking at optimal numbers of cell types and paying attention to two key properties: correlations between inputs and noise in neural responses. This theoretical framework could help to map the hierarchical tree relating different neuronal classes within and across species. Copyright © 2014 Elsevier Inc. All rights reserved.

  11. Ensemble MD simulations restrained via crystallographic data: Accurate structure leads to accurate dynamics

    PubMed Central

    Xue, Yi; Skrynnikov, Nikolai R

    2014-01-01

    Currently, the best existing molecular dynamics (MD) force fields cannot accurately reproduce the global free-energy minimum which realizes the experimental protein structure. As a result, long MD trajectories tend to drift away from the starting coordinates (e.g., crystallographic structures). To address this problem, we have devised a new simulation strategy aimed at protein crystals. An MD simulation of protein crystal is essentially an ensemble simulation involving multiple protein molecules in a crystal unit cell (or a block of unit cells). To ensure that average protein coordinates remain correct during the simulation, we introduced crystallography-based restraints into the MD protocol. Because these restraints are aimed at the ensemble-average structure, they have only minimal impact on conformational dynamics of the individual protein molecules. So long as the average structure remains reasonable, the proteins move in a native-like fashion as dictated by the original force field. To validate this approach, we have used the data from solid-state NMR spectroscopy, which is the orthogonal experimental technique uniquely sensitive to protein local dynamics. The new method has been tested on the well-established model protein, ubiquitin. The ensemble-restrained MD simulations produced lower crystallographic R factors than conventional simulations; they also led to more accurate predictions for crystallographic temperature factors, solid-state chemical shifts, and backbone order parameters. The predictions for 15N R1 relaxation rates are at least as accurate as those obtained from conventional simulations. Taken together, these results suggest that the presented trajectories may be among the most realistic protein MD simulations ever reported. In this context, the ensemble restraints based on high-resolution crystallographic data can be viewed as protein-specific empirical corrections to the standard force fields. PMID:24452989

  12. Classification of neocortical interneurons using affinity propagation.

    PubMed

    Santana, Roberto; McGarry, Laura M; Bielza, Concha; Larrañaga, Pedro; Yuste, Rafael

    2013-01-01

    In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. In fact, neuronal classification is a difficult problem because it is unclear how to designate a neuronal cell class and what are the best characteristics to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological, or molecular characteristics, have provided quantitative and unbiased identification of distinct neuronal subtypes, when applied to selected datasets. However, better and more robust classification methods are needed for increasingly complex and larger datasets. Here, we explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. Affinity propagation outperformed Ward's method, a current standard clustering approach, in classifying the neurons into 4 subtypes. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.

  13. Alexnet Feature Extraction and Multi-Kernel Learning for Objectoriented Classification

    NASA Astrophysics Data System (ADS)

    Ding, L.; Li, H.; Hu, C.; Zhang, W.; Wang, S.

    2018-04-01

    In view of the fact that the deep convolutional neural network has stronger ability of feature learning and feature expression, an exploratory research is done on feature extraction and classification for high resolution remote sensing images. Taking the Google image with 0.3 meter spatial resolution in Ludian area of Yunnan Province as an example, the image segmentation object was taken as the basic unit, and the pre-trained AlexNet deep convolution neural network model was used for feature extraction. And the spectral features, AlexNet features and GLCM texture features are combined with multi-kernel learning and SVM classifier, finally the classification results were compared and analyzed. The results show that the deep convolution neural network can extract more accurate remote sensing image features, and significantly improve the overall accuracy of classification, and provide a reference value for earthquake disaster investigation and remote sensing disaster evaluation.

  14. Robust tissue classification for reproducible wound assessment in telemedicine environments

    NASA Astrophysics Data System (ADS)

    Wannous, Hazem; Treuillet, Sylvie; Lucas, Yves

    2010-04-01

    In telemedicine environments, a standardized and reproducible assessment of wounds, using a simple free-handled digital camera, is an essential requirement. However, to ensure robust tissue classification, particular attention must be paid to the complete design of the color processing chain. We introduce the key steps including color correction, merging of expert labeling, and segmentation-driven classification based on support vector machines. The tool thus developed ensures stability under lighting condition, viewpoint, and camera changes, to achieve accurate and robust classification of skin tissues. Clinical tests demonstrate that such an advanced tool, which forms part of a complete 3-D and color wound assessment system, significantly improves the monitoring of the healing process. It achieves an overlap score of 79.3 against 69.1% for a single expert, after mapping on the medical reference developed from the image labeling by a college of experts.

  15. Subordinate-level object classification reexamined.

    PubMed

    Biederman, I; Subramaniam, S; Bar, M; Kalocsai, P; Fiser, J

    1999-01-01

    The classification of a table as round rather than square, a car as a Mazda rather than a Ford, a drill bit as 3/8-inch rather than 1/4-inch, and a face as Tom have all been regarded as a single process termed "subordinate classification." Despite the common label, the considerable heterogeneity of the perceptual processing required to achieve such classifications requires, minimally, a more detailed taxonomy. Perceptual information relevant to subordinate-level shape classifications can be presumed to vary on continua of (a) the type of distinctive information that is present, nonaccidental or metric, (b) the size of the relevant contours or surfaces, and (c) the similarity of the to-be-discriminated features, such as whether a straight contour has to be distinguished from a contour of low curvature versus high curvature. We consider three, relatively pure cases. Case 1 subordinates may be distinguished by a representation, a geon structural description (GSD), specifying a nonaccidental characterization of an object's large parts and the relations among these parts, such as a round table versus a square table. Case 2 subordinates are also distinguished by GSDs, except that the distinctive GSDs are present at a small scale in a complex object so the location and mapping of the GSDs are contingent on an initial basic-level classification, such as when we use a logo to distinguish various makes of cars. Expertise for Cases 1 and 2 can be easily achieved through specification, often verbal, of the GSDs. Case 3 subordinates, which have furnished much of the grist for theorizing with "view-based" template models, require fine metric discriminations. Cases 1 and 2 account for the overwhelming majority of shape-based basic- and subordinate-level object classifications that people can and do make in their everyday lives. These classifications are typically made quickly, accurately, and with only modest costs of viewpoint changes. Whereas the activation of an array of

  16. Computational approaches for the classification of seed storage proteins.

    PubMed

    Radhika, V; Rao, V Sree Hari

    2015-07-01

    Seed storage proteins comprise a major part of the protein content of the seed and have an important role on the quality of the seed. These storage proteins are important because they determine the total protein content and have an effect on the nutritional quality and functional properties for food processing. Transgenic plants are being used to develop improved lines for incorporation into plant breeding programs and the nutrient composition of seeds is a major target of molecular breeding programs. Hence, classification of these proteins is crucial for the development of superior varieties with improved nutritional quality. In this study we have applied machine learning algorithms for classification of seed storage proteins. We have presented an algorithm based on nearest neighbor approach for classification of seed storage proteins and compared its performance with decision tree J48, multilayer perceptron neural (MLP) network and support vector machine (SVM) libSVM. The model based on our algorithm has been able to give higher classification accuracy in comparison to the other methods.

  17. Performance-scalable volumetric data classification for online industrial inspection

    NASA Astrophysics Data System (ADS)

    Abraham, Aby J.; Sadki, Mustapha; Lea, R. M.

    2002-03-01

    Non-intrusive inspection and non-destructive testing of manufactured objects with complex internal structures typically requires the enhancement, analysis and visualization of high-resolution volumetric data. Given the increasing availability of fast 3D scanning technology (e.g. cone-beam CT), enabling on-line detection and accurate discrimination of components or sub-structures, the inherent complexity of classification algorithms inevitably leads to throughput bottlenecks. Indeed, whereas typical inspection throughput requirements range from 1 to 1000 volumes per hour, depending on density and resolution, current computational capability is one to two orders-of-magnitude less. Accordingly, speeding up classification algorithms requires both reduction of algorithm complexity and acceleration of computer performance. A shape-based classification algorithm, offering algorithm complexity reduction, by using ellipses as generic descriptors of solids-of-revolution, and supporting performance-scalability, by exploiting the inherent parallelism of volumetric data, is presented. A two-stage variant of the classical Hough transform is used for ellipse detection and correlation of the detected ellipses facilitates position-, scale- and orientation-invariant component classification. Performance-scalability is achieved cost-effectively by accelerating a PC host with one or more COTS (Commercial-Off-The-Shelf) PCI multiprocessor cards. Experimental results are reported to demonstrate the feasibility and cost-effectiveness of the data-parallel classification algorithm for on-line industrial inspection applications.

  18. DeepPap: Deep Convolutional Networks for Cervical Cell Classification.

    PubMed

    Zhang, Ling; Le Lu; Nogues, Isabella; Summers, Ronald M; Liu, Shaoxiong; Yao, Jianhua

    2017-11-01

    Automation-assisted cervical screening via Pap smear or liquid-based cytology (LBC) is a highly effective cell imaging based cancer detection tool, where cells are partitioned into "abnormal" and "normal" categories. However, the success of most traditional classification methods relies on the presence of accurate cell segmentations. Despite sixty years of research in this field, accurate segmentation remains a challenge in the presence of cell clusters and pathologies. Moreover, previous classification methods are only built upon the extraction of hand-crafted features, such as morphology and texture. This paper addresses these limitations by proposing a method to directly classify cervical cells-without prior segmentation-based on deep features, using convolutional neural networks (ConvNets). First, the ConvNet is pretrained on a natural image dataset. It is subsequently fine-tuned on a cervical cell dataset consisting of adaptively resampled image patches coarsely centered on the nuclei. In the testing phase, aggregation is used to average the prediction scores of a similar set of image patches. The proposed method is evaluated on both Pap smear and LBC datasets. Results show that our method outperforms previous algorithms in classification accuracy (98.3%), area under the curve (0.99) values, and especially specificity (98.3%), when applied to the Herlev benchmark Pap smear dataset and evaluated using five-fold cross validation. Similar superior performances are also achieved on the HEMLBC (H&E stained manual LBC) dataset. Our method is promising for the development of automation-assisted reading systems in primary cervical screening.

  19. A Functional-Phylogenetic Classification System for Transmembrane Solute Transporters

    PubMed Central

    Saier, Milton H.

    2000-01-01

    A comprehensive classification system for transmembrane molecular transporters has been developed and recently approved by the transport panel of the nomenclature committee of the International Union of Biochemistry and Molecular Biology. This system is based on (i) transporter class and subclass (mode of transport and energy coupling mechanism), (ii) protein phylogenetic family and subfamily, and (iii) substrate specificity. Almost all of the more than 250 identified families of transporters include members that function exclusively in transport. Channels (115 families), secondary active transporters (uniporters, symporters, and antiporters) (78 families), primary active transporters (23 families), group translocators (6 families), and transport proteins of ill-defined function or of unknown mechanism (51 families) constitute distinct categories. Transport mode and energy coupling prove to be relatively immutable characteristics and therefore provide primary bases for classification. Phylogenetic grouping reflects structure, function, mechanism, and often substrate specificity and therefore provides a reliable secondary basis for classification. Substrate specificity and polarity of transport prove to be more readily altered during evolutionary history and therefore provide a tertiary basis for classification. With very few exceptions, a phylogenetic family of transporters includes members that function by a single transport mode and energy coupling mechanism, although a variety of substrates may be transported, sometimes with either inwardly or outwardly directed polarity. In this review, I provide cross-referencing of well-characterized constituent transporters according to (i) transport mode, (ii) energy coupling mechanism, (iii) phylogenetic grouping, and (iv) substrates transported. The structural features and distribution of recognized family members throughout the living world are also evaluated. The tabulations should facilitate familial and functional

  20. Classification of Dynamical Diffusion States in Single Molecule Tracking Microscopy

    PubMed Central

    Bosch, Peter J.; Kanger, Johannes S.; Subramaniam, Vinod

    2014-01-01

    Single molecule tracking of membrane proteins by fluorescence microscopy is a promising method to investigate dynamic processes in live cells. Translating the trajectories of proteins to biological implications, such as protein interactions, requires the classification of protein motion within the trajectories. Spatial information of protein motion may reveal where the protein interacts with cellular structures, because binding of proteins to such structures often alters their diffusion speed. For dynamic diffusion systems, we provide an analytical framework to determine in which diffusion state a molecule is residing during the course of its trajectory. We compare different methods for the quantification of motion to utilize this framework for the classification of two diffusion states (two populations with different diffusion speed). We found that a gyration quantification method and a Bayesian statistics-based method are the most accurate in diffusion-state classification for realistic experimentally obtained datasets, of which the gyration method is much less computationally demanding. After classification of the diffusion, the lifetime of the states can be determined, and images of the diffusion states can be reconstructed at high resolution. Simulations validate these applications. We apply the classification and its applications to experimental data to demonstrate the potential of this approach to obtain further insights into the dynamics of cell membrane proteins. PMID:25099798

  1. Automated classification of cell morphology by coherence-controlled holographic microscopy.

    PubMed

    Strbkova, Lenka; Zicha, Daniel; Vesely, Pavel; Chmelik, Radim

    2017-08-01

    In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

  2. MoleculeNet: a benchmark for molecular machine learning† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc02664a

    PubMed Central

    Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N.; Gomes, Joseph; Geniesse, Caleb; Pappu, Aneesh S.; Leswing, Karl

    2017-01-01

    Molecular machine learning has been maturing rapidly over the last few years. Improved methods and the presence of larger datasets have enabled machine learning algorithms to make increasingly accurate predictions about molecular properties. However, algorithmic progress has been limited due to the lack of a standard benchmark to compare the efficacy of proposed methods; most new algorithms are benchmarked on different datasets making it challenging to gauge the quality of proposed methods. This work introduces MoleculeNet, a large scale benchmark for molecular machine learning. MoleculeNet curates multiple public datasets, establishes metrics for evaluation, and offers high quality open-source implementations of multiple previously proposed molecular featurization and learning algorithms (released as part of the DeepChem open source library). MoleculeNet benchmarks demonstrate that learnable representations are powerful tools for molecular machine learning and broadly offer the best performance. However, this result comes with caveats. Learnable representations still struggle to deal with complex tasks under data scarcity and highly imbalanced classification. For quantum mechanical and biophysical datasets, the use of physics-aware featurizations can be more important than choice of particular learning algorithm. PMID:29629118

  3. Pathogenesis of Gastric Cancer: Genetics and Molecular Classification.

    PubMed

    Figueiredo, Ceu; Camargo, M C; Leite, Marina; Fuentes-Pananá, Ezequiel M; Rabkin, Charles S; Machado, José C

    Gastric cancer is the fifth most incident and the third most common cause of cancer-related death in the world. Infection with Helicobacter pylori is the major risk factor for this disease. Gastric cancer is the final outcome of a cascade of events that takes decades to occur and results from the accumulation of multiple genetic and epigenetic alterations. These changes are crucial for tumor cells to expedite and sustain the array of pathways involved in the cancer development, such as cell cycle, DNA repair, metabolism, cell-to-cell and cell-to-matrix interactions, apoptosis, angiogenesis, and immune surveillance. Comprehensive molecular analyses of gastric cancer have disclosed the complex heterogeneity of this disease. In particular, these analyses have confirmed that Epstein-Barr virus (EBV)-positive gastric cancer is a distinct entity. The identification of gastric cancer subtypes characterized by recognizable molecular profiles may pave the way for a more personalized clinical management and to the identification of novel therapeutic targets and biomarkers for screening, prognosis, prediction of response to treatment, and monitoring of gastric cancer progression.

  4. Forested land cover classification on the Cumberland Plateau, Jackson County, Alabama: a comparison of Landsat ETM+ and SPOT5 images

    Treesearch

    Yong Wang; Shanta Parajuli; Callie Schweitzer; Glendon Smalley; Dawn Lemke; Wubishet Tadesse; Xiongwen Chen

    2010-01-01

    Forest cover classifications focus on the overall growth form (physiognomy) of the community, dominant vegetation, and species composition of the existing forest. Accurately classifying the forest cover type is important for forest inventory and silviculture. We compared classification accuracy based on Landsat Enhanced Thematic Mapper Plus (Landsat ETM+) and Satellite...

  5. Accurate Structural Correlations from Maximum Likelihood Superpositions

    PubMed Central

    Theobald, Douglas L; Wuttke, Deborah S

    2008-01-01

    The cores of globular proteins are densely packed, resulting in complicated networks of structural interactions. These interactions in turn give rise to dynamic structural correlations over a wide range of time scales. Accurate analysis of these complex correlations is crucial for understanding biomolecular mechanisms and for relating structure to function. Here we report a highly accurate technique for inferring the major modes of structural correlation in macromolecules using likelihood-based statistical analysis of sets of structures. This method is generally applicable to any ensemble of related molecules, including families of nuclear magnetic resonance (NMR) models, different crystal forms of a protein, and structural alignments of homologous proteins, as well as molecular dynamics trajectories. Dominant modes of structural correlation are determined using principal components analysis (PCA) of the maximum likelihood estimate of the correlation matrix. The correlations we identify are inherently independent of the statistical uncertainty and dynamic heterogeneity associated with the structural coordinates. We additionally present an easily interpretable method (“PCA plots”) for displaying these positional correlations by color-coding them onto a macromolecular structure. Maximum likelihood PCA of structural superpositions, and the structural PCA plots that illustrate the results, will facilitate the accurate determination of dynamic structural correlations analyzed in diverse fields of structural biology. PMID:18282091

  6. Data Clustering and Evolving Fuzzy Decision Tree for Data Base Classification Problems

    NASA Astrophysics Data System (ADS)

    Chang, Pei-Chann; Fan, Chin-Yuan; Wang, Yen-Wen

    Data base classification suffers from two well known difficulties, i.e., the high dimensionality and non-stationary variations within the large historic data. This paper presents a hybrid classification model by integrating a case based reasoning technique, a Fuzzy Decision Tree (FDT), and Genetic Algorithms (GA) to construct a decision-making system for data classification in various data base applications. The model is major based on the idea that the historic data base can be transformed into a smaller case-base together with a group of fuzzy decision rules. As a result, the model can be more accurately respond to the current data under classifying from the inductions by these smaller cases based fuzzy decision trees. Hit rate is applied as a performance measure and the effectiveness of our proposed model is demonstrated by experimentally compared with other approaches on different data base classification applications. The average hit rate of our proposed model is the highest among others.

  7. Single-trial EEG RSVP classification using convolutional neural networks

    NASA Astrophysics Data System (ADS)

    Shamwell, Jared; Lee, Hyungtae; Kwon, Heesung; Marathe, Amar R.; Lawhern, Vernon; Nothwang, William

    2016-05-01

    Traditionally, Brain-Computer Interfaces (BCI) have been explored as a means to return function to paralyzed or otherwise debilitated individuals. An emerging use for BCIs is in human-autonomy sensor fusion where physiological data from healthy subjects is combined with machine-generated information to enhance the capabilities of artificial systems. While human-autonomy fusion of physiological data and computer vision have been shown to improve classification during visual search tasks, to date these approaches have relied on separately trained classification models for each modality. We aim to improve human-autonomy classification performance by developing a single framework that builds codependent models of human electroencephalograph (EEG) and image data to generate fused target estimates. As a first step, we developed a novel convolutional neural network (CNN) architecture and applied it to EEG recordings of subjects classifying target and non-target image presentations during a rapid serial visual presentation (RSVP) image triage task. The low signal-to-noise ratio (SNR) of EEG inherently limits the accuracy of single-trial classification and when combined with the high dimensionality of EEG recordings, extremely large training sets are needed to prevent overfitting and achieve accurate classification from raw EEG data. This paper explores a new deep CNN architecture for generalized multi-class, single-trial EEG classification across subjects. We compare classification performance from the generalized CNN architecture trained across all subjects to the individualized XDAWN, HDCA, and CSP neural classifiers which are trained and tested on single subjects. Preliminary results show that our CNN meets and slightly exceeds the performance of the other classifiers despite being trained across subjects.

  8. A Modified ELISA Accurately Measures Secretion of High Molecular Weight Hyaluronan (HA) by Graves' Disease Orbital Cells

    PubMed Central

    Krieger, Christine C.

    2014-01-01

    Excess production of hyaluronan (hyaluronic acid [HA]) in the retro-orbital space is a major component of Graves' ophthalmopathy, and regulation of HA production by orbital cells is a major research area. In most previous studies, HA was measured by ELISAs that used HA-binding proteins for detection and rooster comb HA as standards. We show that the binding efficiency of HA-binding protein in the ELISA is a function of HA polymer size. Using gel electrophoresis, we show that HA secreted from orbital cells is primarily comprised of polymers more than 500 000. We modified a commercially available ELISA by using 1 million molecular weight HA as standard to accurately measure HA of this size. We demonstrated that IL-1β-stimulated HA secretion is at least 2-fold greater than previously reported, and activation of the TSH receptor by an activating antibody M22 from a patient with Graves' disease led to more than 3-fold increase in HA production in both fibroblasts/preadipocytes and adipocytes. These effects were not consistently detected with the commercial ELISA using rooster comb HA as standard and suggest that fibroblasts/preadipocytes may play a more prominent role in HA remodeling in Graves' ophthalmopathy than previously appreciated. PMID:24302624

  9. Histological, molecular and functional subtypes of breast cancers

    PubMed Central

    Malhotra, Gautam K; Zhao, Xiangshan; Band, Hamid

    2010-01-01

    Increased understanding of the molecular heterogeneity that is intrinsic to the various subtypes of breast cancer will likely shape the future of breast cancer diagnosis, prognosis and treatment. Advances in the field over the last several decades have been remarkable and have clearly translated into better patient care as evidenced by the earlier detection, better prognosis and new targeted therapies. There have been two recent advances in the breast cancer research field that have lead to paradigm shifts: first, the identification of intrinsic breast tumor subtypes, which has changed the way we think about breast cancer and second, the recent characterization of cancer stem cells (CSCs), which are suspected to be responsible for tumor initiation, recurrence and resistance to therapy. These findings have opened new exciting avenues to think about breast cancer therapeutic strategies. While these advances constitute major paradigm shifts within the research realm, the clinical arena has yet to adopt and apply our understanding of the molecular basis of the disease to early diagnosis, prognosis and therapy of breast cancers. Here, we will review the current clinical approach to classification of breast cancers, newer molecular-based classification schemes and potential future of biomarkers representing a functional classification of breast cancer. PMID:21057215

  10. An assessment of the effectiveness of a random forest classifier for land-cover classification

    NASA Astrophysics Data System (ADS)

    Rodriguez-Galiano, V. F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J. P.

    2012-01-01

    Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning. In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classifications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significance level.

  11. The Role of Facial Attractiveness and Facial Masculinity/Femininity in Sex Classification of Faces

    PubMed Central

    Hoss, Rebecca A.; Ramsey, Jennifer L.; Griffin, Angela M.; Langlois, Judith H.

    2005-01-01

    We tested whether adults (Experiment 1) and 4–5-year-old children (Experiment 2) identify the sex of high attractive faces faster and more accurately than low attractive faces in a reaction time task. We also assessed whether facial masculinity/femininity facilitated identification of sex. Results showed that attractiveness facilitated adults’ sex classification of both female and male faces and children’s sex classification of female, but not male, faces. Moreover, attractiveness affected the speed and accuracy of sex classification independent of masculinity/femininity. High masculinity in male faces, but not high femininity in female faces, also facilitated sex classification for both adults and children. These findings provide important new data on how the facial cues of attractiveness and masculinity/femininity contribute to the task of sex classification and provide evidence for developmental differences in how adults and children use these cues. Additionally, these findings provide support for Langlois and Roggman’s (1990) averageness theory of attractiveness. PMID:16457167

  12. What is new in genetics and osteogenesis imperfecta classification?

    PubMed

    Valadares, Eugênia R; Carneiro, Túlio B; Santos, Paula M; Oliveira, Ana Cristina; Zabel, Bernhard

    2014-01-01

    Literature review of new genes related to osteogenesis imperfecta (OI) and update of its classification. Literature review in the PubMed and OMIM databases, followed by selection of relevant references. In 1979, Sillence et al. developed a classification of OI subtypes based on clinical features and disease severity: OI type I, mild, common, with blue sclera; OI type II, perinatal lethal form; OI type III, severe and progressively deforming, with normal sclera; and OI type IV, moderate severity with normal sclera. Approximately 90% of individuals with OI are heterozygous for mutations in the COL1A1 and COL1A2 genes, with dominant pattern of inheritance or sporadic mutations. After 2006, mutations were identified in the CRTAP, FKBP10, LEPRE1, PLOD2, PPIB, SERPINF1, SERPINH1, SP7, WNT1, BMP1, and TMEM38B genes, associated with recessive OI and mutation in the IFITM5 gene associated with dominant OI. Mutations in PLS3 were recently identified in families with osteoporosis and fractures, with X-linked inheritance pattern. In addition to the genetic complexity of the molecular basis of OI, extensive phenotypic variability resulting from individual loci has also been documented. Considering the discovery of new genes and limited genotype-phenotype correlation, the use of next-generation sequencing tools has become useful in molecular studies of OI cases. The recommendation of the Nosology Group of the International Society of Skeletal Dysplasias is to maintain the classification of Sillence as the prototypical form, universally accepted to classify the degree of severity in OI, while maintaining it free from direct molecular reference. Copyright © 2014 Sociedade Brasileira de Pediatria. Published by Elsevier Editora Ltda. All rights reserved.

  13. Reliability of a four-column classification for tibial plateau fractures.

    PubMed

    Martínez-Rondanelli, Alfredo; Escobar-González, Sara Sofía; Henao-Alzate, Alejandro; Martínez-Cano, Juan Pablo

    2017-09-01

    A four-column classification system offers a different way of evaluating tibial plateau fractures. The aim of this study is to compare the intra-observer and inter-observer reliability between four-column and classic classifications. This is a reliability study, which included patients presenting with tibial plateau fractures between January 2013 and September 2015 in a level-1 trauma centre. Four orthopaedic surgeons blindly classified each fracture according to four different classifications: AO, Schatzker, Duparc and four-column. Kappa, intra-observer and inter-observer concordance were calculated for the reliability analysis. Forty-nine patients were included. The mean age was 39 ± 14.2 years, with no gender predominance (men: 51%; women: 49%), and 67% of the fractures included at least one of the posterior columns. The intra-observer and inter-observer concordance were calculated for each classification: four-column (84%/79%), Schatzker (60%/71%), AO (50%/59%) and Duparc (48%/58%), with a statistically significant difference among them (p = 0.001/p = 0.003). Kappa coefficient for intr-aobserver and inter-observer evaluations: Schatzker 0.48/0.39, four-column 0.61/0.34, Duparc 0.37/0.23, and AO 0.34/0.11. The proposed four-column classification showed the highest intra and inter-observer agreement. When taking into account the agreement that occurs by chance, Schatzker classification showed the highest inter-observer kappa, but again the four-column had the highest intra-observer kappa value. The proposed classification is a more inclusive classification for the posteromedial and posterolateral fractures. We suggest, therefore, that it be used in addition to one of the classic classifications in order to better understand the fracture pattern, as it allows more attention to be paid to the posterior columns, it improves the surgical planning and allows the surgical approach to be chosen more accurately.

  14. Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data

    Treesearch

    Weiqi Zhou; Austin Troy; Morgan Grove

    2008-01-01

    Accurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and results of an object-based classification and post-classification change detection of multitemporal high-spatial resolution Emerge aerial imagery in the...

  15. New Insights into the Phylogeny and Molecular Classification of Nicotinamide Mononucleotide Deamidases

    PubMed Central

    Sánchez-Carrón, Guiomar; Martínez-Moñino, Ana Belén; Sola-Carvajal, Agustín; Takami, Hideto; García-Carmona, Francisco; Sánchez-Ferrer, Álvaro

    2013-01-01

    Nicotinamide mononucleotide (NMN) deamidase is one of the key enzymes of the bacterial pyridine nucleotide cycle (PNC). It catalyzes the conversion of NMN to nicotinic acid mononucleotide, which is later converted to NAD+ by entering the Preiss-Handler pathway. However, very few biochemical data are available regarding this enzyme. This paper represents the first complete molecular characterization of a novel NMN deamidase from the halotolerant and alkaliphilic bacterium Oceanobacillus iheyensis (OiPncC). The enzyme was active over a broad pH range, with an optimum at pH 7.4, whilst maintaining 90 % activity at pH 10.0. Surprisingly, the enzyme was quite stable at such basic pH, maintaining 61 % activity after 21 days. As regard temperature, it had an optimum at 65 °C but its stability was better below 50 °C. OiPncC was a Michaelian enzyme towards its only substrate NMN, with a K m value of 0.18 mM and a kcat/K m of 2.1 mM-1 s-1. To further our understanding of these enzymes, a complete phylogenetic and structural analysis was carried out taking into account the two Pfam domains usually associated with them (MocF and CinA). This analysis sheds light on the evolution of NMN deamidases, and enables the classification of NMN deamidases into 12 different subgroups, pointing to a novel domain architecture never before described. Using a Logo representation, conserved blocks were determined, providing new insights on the crucial residues involved in the binding and catalysis of both CinA and MocF domains. The analysis of these conserved blocks within new protein sequences could permit the more efficient data curation of incoming NMN deamidases. PMID:24340054

  16. Remote sensing imagery classification using multi-objective gravitational search algorithm

    NASA Astrophysics Data System (ADS)

    Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

    2016-10-01

    Simultaneous optimization of different validity measures can capture different data characteristics of remote sensing imagery (RSI) and thereby achieving high quality classification results. In this paper, two conflicting cluster validity indices, the Xie-Beni (XB) index and the fuzzy C-means (FCM) (Jm) measure, are integrated with a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA) to present a novel multi-objective optimization based RSI classification method. In this method, the Gabor filter method is firstly implemented to extract texture features of RSI. Then, the texture features are syncretized with the spectral features to construct the spatial-spectral feature space/set of the RSI. Afterwards, cluster of the spectral-spatial feature set is carried out on the basis of the proposed method. To be specific, cluster centers are randomly generated initially. After that, the cluster centers are updated and optimized adaptively by employing the DMMOGSA. Accordingly, a set of non-dominated cluster centers are obtained. Therefore, numbers of image classification results of RSI are produced and users can pick up the most promising one according to their problem requirements. To quantitatively and qualitatively validate the effectiveness of the proposed method, the proposed classification method was applied to classifier two aerial high-resolution remote sensing imageries. The obtained classification results are compared with that produced by two single cluster validity index based and two state-of-the-art multi-objective optimization algorithms based classification results. Comparison results show that the proposed method can achieve more accurate RSI classification.

  17. TIME-INTEGRATED EXPOSURE MEASURES TO IMPROVE THE PREDICTIVE POWER OF EXPOSURE CLASSIFICATION FOR EPIDEMIOLOGIC STUDIES

    EPA Science Inventory

    Accurate exposure classification tools are required to link exposure with health effects in epidemiological studies. Although long-term integrated exposure measurements are a critical component of exposure assessment, the ability to include these measurements into epidemiologic...

  18. A novel risk classification system for 30-day mortality in children undergoing surgery

    PubMed Central

    Walter, Arianne I.; Jones, Tamekia L.; Huang, Eunice Y.; Davis, Robert L.

    2018-01-01

    A simple, objective and accurate way of grouping children undergoing surgery into clinically relevant risk groups is needed. The purpose of this study, is to develop and validate a preoperative risk classification system for postsurgical 30-day mortality for children undergoing a wide variety of operations. The National Surgical Quality Improvement Project-Pediatric participant use file data for calendar years 2012–2014 was analyzed to determine preoperative variables most associated with death within 30 days of operation (D30). Risk groups were created using classification tree analysis based on these preoperative variables. The resulting risk groups were validated using 2015 data, and applied to neonates and higher risk CPT codes to determine validity in high-risk subpopulations. A five-level risk classification was found to be most accurate. The preoperative need for ventilation, oxygen support, inotropic support, sepsis, the need for emergent surgery and a do not resuscitate order defined non-overlapping groups with observed rates of D30 that vary from 0.075% (Very Low Risk) to 38.6% (Very High Risk). When CPT codes where death was never observed are eliminated or when the system is applied to neonates, the groupings remained predictive of death in an ordinal manner. PMID:29351327

  19. Minimally Invasive Molecular Staging (MIMS) RT-PCR Breast Cancer Study

    DTIC Science & Technology

    2007-03-31

    per se. 15. SUBJECT TERMS Breast cancer, real-time PCR, molecular diagnostics , micrometastases 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF 18...a superior molecular marker for detection of non- small cell lung cancer in peripheral blood. Journal of Molecular Diagnostics . 5:23 7-42, 2003. With...Journal of Molecular Diagnostics . 5:237-42, 2003 10. Mikhitarian K, Allen A, Reott S, Hoover L, Allen A, Cole DJ, Gillanders WE, and Mitas M. Enhanced

  20. A detailed procedure for the use of small-scale photography in land use classification

    NASA Technical Reports Server (NTRS)

    Vegas, P. L.

    1974-01-01

    A procedure developed to produce accurate land use maps from available high-altitude, small-scale photography in a cost-effective manner is presented. An alternative procedure, for use when the capability for updating the resultant land use map is not required, is also presented. The technical approach is discussed in detail, and personnel and equipment needs are analyzed. Accuracy percentages are listed, and costs are cited. The experiment land use classification categories are explained, and a proposed national land use classification system is recommended.

  1. Wavelet-based energy features for glaucomatous image classification.

    PubMed

    Dua, Sumeet; Acharya, U Rajendra; Chowriappa, Pradeep; Sree, S Vinitha

    2012-01-01

    Texture features within images are actively pursued for accurate and efficient glaucoma classification. Energy distribution over wavelet subbands is applied to find these important texture features. In this paper, we investigate the discriminatory potential of wavelet features obtained from the daubechies (db3), symlets (sym3), and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. We propose a novel technique to extract energy signatures obtained using 2-D discrete wavelet transform, and subject these signatures to different feature ranking and feature selection strategies. We have gauged the effectiveness of the resultant ranked and selected subsets of features using a support vector machine, sequential minimal optimization, random forest, and naïve Bayes classification strategies. We observed an accuracy of around 93% using tenfold cross validations to demonstrate the effectiveness of these methods.

  2. A new taxonomic classification for species in Gomphus sensu lato

    Treesearch

    Admir J. Giachini; Michael A. Castellano

    2011-01-01

    Taxonomy of the Gomphales has been revisited by combining morphology and molecular data (DNA sequences) to provide a natural classification for the species of Gomphus sensu lato. Results indicate Gomphus s.l. to be non-monophyletic, leading to new combinations and the placement of its species into four genera: Gomphus...

  3. Comparative Performance Analysis of Support Vector Machine, Random Forest, Logistic Regression and k-Nearest Neighbours in Rainbow Trout (Oncorhynchus Mykiss) Classification Using Image-Based Features

    PubMed Central

    Císař, Petr; Labbé, Laurent; Souček, Pavel; Pelissier, Pablo; Kerneis, Thierry

    2018-01-01

    The main aim of this study was to develop a new objective method for evaluating the impacts of different diets on the live fish skin using image-based features. In total, one-hundred and sixty rainbow trout (Oncorhynchus mykiss) were fed either a fish-meal based diet (80 fish) or a 100% plant-based diet (80 fish) and photographed using consumer-grade digital camera. Twenty-three colour features and four texture features were extracted. Four different classification methods were used to evaluate fish diets including Random forest (RF), Support vector machine (SVM), Logistic regression (LR) and k-Nearest neighbours (k-NN). The SVM with radial based kernel provided the best classifier with correct classification rate (CCR) of 82% and Kappa coefficient of 0.65. Although the both LR and RF methods were less accurate than SVM, they achieved good classification with CCR 75% and 70% respectively. The k-NN was the least accurate (40%) classification model. Overall, it can be concluded that consumer-grade digital cameras could be employed as the fast, accurate and non-invasive sensor for classifying rainbow trout based on their diets. Furthermore, these was a close association between image-based features and fish diet received during cultivation. These procedures can be used as non-invasive, accurate and precise approaches for monitoring fish status during the cultivation by evaluating diet’s effects on fish skin. PMID:29596375

  4. Comparative Performance Analysis of Support Vector Machine, Random Forest, Logistic Regression and k-Nearest Neighbours in Rainbow Trout (Oncorhynchus Mykiss) Classification Using Image-Based Features.

    PubMed

    Saberioon, Mohammadmehdi; Císař, Petr; Labbé, Laurent; Souček, Pavel; Pelissier, Pablo; Kerneis, Thierry

    2018-03-29

    The main aim of this study was to develop a new objective method for evaluating the impacts of different diets on the live fish skin using image-based features. In total, one-hundred and sixty rainbow trout ( Oncorhynchus mykiss ) were fed either a fish-meal based diet (80 fish) or a 100% plant-based diet (80 fish) and photographed using consumer-grade digital camera. Twenty-three colour features and four texture features were extracted. Four different classification methods were used to evaluate fish diets including Random forest (RF), Support vector machine (SVM), Logistic regression (LR) and k -Nearest neighbours ( k -NN). The SVM with radial based kernel provided the best classifier with correct classification rate (CCR) of 82% and Kappa coefficient of 0.65. Although the both LR and RF methods were less accurate than SVM, they achieved good classification with CCR 75% and 70% respectively. The k -NN was the least accurate (40%) classification model. Overall, it can be concluded that consumer-grade digital cameras could be employed as the fast, accurate and non-invasive sensor for classifying rainbow trout based on their diets. Furthermore, these was a close association between image-based features and fish diet received during cultivation. These procedures can be used as non-invasive, accurate and precise approaches for monitoring fish status during the cultivation by evaluating diet's effects on fish skin.

  5. Moving beyond the van Krevelen Diagram: A New Stoichiometric Approach for Compound Classification in Organisms

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Rivas-Ubach, Albert; Liu, Yina; Bianchi, Thomas S.

    van Krevelen diagrams (O:C vs H:C ratios of elemental formulas) have been widely used in studies to obtain an estimation of the main compound categories present in environmental samples. However, the limits defining a specific compound category based solely on O:C and H:C ratios of elemental formulas have never been accurately listed or proposed to classify metabolites in biological samples. Furthermore, while O:C vs. H:C ratios of elemental formulas can provide an overview of the compound categories, such classification is inefficient because of the large overlap among different compound categories along both axes. We propose a more accurate compound classificationmore » for biological samples analyzed by high-resolution mass spectrometry-based on an assessment of the C:H:O:N:P stoichiometric ratios of over 130,000 elemental formulas of compounds classified in 6 main categories: lipids, peptides, amino-sugars, carbohydrates, nucleotides and phytochemical compounds (oxy-aromatic compounds). Our multidimensional stoichiometric compound classification (MSCC) constraints showed a highly accurate categorization of elemental formulas to the main compound categories in biological samples with over 98% of accuracy representing a substantial improvement over any classification based on the classic van Krevelen diagram. This method represents a significant step forward in environmental research, especially ecological stoichiometry and eco-metabolomics studies, by providing a novel and robust tool to further our understanding the ecosystem structure and function through the chemical characterization of different biological samples.« less

  6. Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review.

    PubMed

    Uddin, M B; Chow, C M; Su, S W

    2018-03-26

    Sleep apnea (SA), a common sleep disorder, can significantly decrease the quality of life, and is closely associated with major health risks such as cardiovascular disease, sudden death, depression, and hypertension. The normal diagnostic process of SA using polysomnography is costly and time consuming. In addition, the accuracy of different classification methods to detect SA varies with the use of different physiological signals. If an effective, reliable, and accurate classification method is developed, then the diagnosis of SA and its associated treatment will be time-efficient and economical. This study aims to systematically review the literature and present an overview of classification methods to detect SA using respiratory and oximetry signals and address the automated detection approach. Sixty-two included studies revealed the application of single and multiple signals (respiratory and oximetry) for the diagnosis of SA. Both airflow and oxygen saturation signals alone were effective in detecting SA in the case of binary decision-making, whereas multiple signals were good for multi-class detection. In addition, some machine learning methods were superior to the other classification methods for SA detection using respiratory and oximetry signals. To deal with the respiratory and oximetry signals, a good choice of classification method as well as the consideration of associated factors would result in high accuracy in the detection of SA. An accurate classification method should provide a high detection rate with an automated (independent of human action) analysis of respiratory and oximetry signals. Future high-quality automated studies using large samples of data from multiple patient groups or record batches are recommended.

  7. A Unified Methodology for Computing Accurate Quaternion Color Moments and Moment Invariants.

    PubMed

    Karakasis, Evangelos G; Papakostas, George A; Koulouriotis, Dimitrios E; Tourassis, Vassilios D

    2014-02-01

    In this paper, a general framework for computing accurate quaternion color moments and their corresponding invariants is proposed. The proposed unified scheme arose by studying the characteristics of different orthogonal polynomials. These polynomials are used as kernels in order to form moments, the invariants of which can easily be derived. The resulted scheme permits the usage of any polynomial-like kernel in a unified and consistent way. The resulted moments and moment invariants demonstrate robustness to noisy conditions and high discriminative power. Additionally, in the case of continuous moments, accurate computations take place to avoid approximation errors. Based on this general methodology, the quaternion Tchebichef, Krawtchouk, Dual Hahn, Legendre, orthogonal Fourier-Mellin, pseudo Zernike and Zernike color moments, and their corresponding invariants are introduced. A selected paradigm presents the reconstruction capability of each moment family, whereas proper classification scenarios evaluate the performance of color moment invariants.

  8. Indexed variation graphs for efficient and accurate resistome profiling.

    PubMed

    Rowe, Will P M; Winn, Martyn D

    2018-05-14

    Antimicrobial resistance remains a major threat to global health. Profiling the collective antimicrobial resistance genes within a metagenome (the "resistome") facilitates greater understanding of antimicrobial resistance gene diversity and dynamics. In turn, this can allow for gene surveillance, individualised treatment of bacterial infections and more sustainable use of antimicrobials. However, resistome profiling can be complicated by high similarity between reference genes, as well as the sheer volume of sequencing data and the complexity of analysis workflows. We have developed an efficient and accurate method for resistome profiling that addresses these complications and improves upon currently available tools. Our method combines a variation graph representation of gene sets with an LSH Forest indexing scheme to allow for fast classification of metagenomic sequence reads using similarity-search queries. Subsequent hierarchical local alignment of classified reads against graph traversals enables accurate reconstruction of full-length gene sequences using a scoring scheme. We provide our implementation, GROOT, and show it to be both faster and more accurate than a current reference-dependent tool for resistome profiling. GROOT runs on a laptop and can process a typical 2 gigabyte metagenome in 2 minutes using a single CPU. Our method is not restricted to resistome profiling and has the potential to improve current metagenomic workflows. GROOT is written in Go and is available at https://github.com/will-rowe/groot (MIT license). will.rowe@stfc.ac.uk. Supplementary data are available at Bioinformatics online.

  9. Hydration free energies of cyanide and hydroxide ions from molecular dynamics simulations with accurate force fields

    USGS Publications Warehouse

    Lee, M.W.; Meuwly, M.

    2013-01-01

    The evaluation of hydration free energies is a sensitive test to assess force fields used in atomistic simulations. We showed recently that the vibrational relaxation times, 1D- and 2D-infrared spectroscopies for CN(-) in water can be quantitatively described from molecular dynamics (MD) simulations with multipolar force fields and slightly enlarged van der Waals radii for the C- and N-atoms. To validate such an approach, the present work investigates the solvation free energy of cyanide in water using MD simulations with accurate multipolar electrostatics. It is found that larger van der Waals radii are indeed necessary to obtain results close to the experimental values when a multipolar force field is used. For CN(-), the van der Waals ranges refined in our previous work yield hydration free energy between -72.0 and -77.2 kcal mol(-1), which is in excellent agreement with the experimental data. In addition to the cyanide ion, we also study the hydroxide ion to show that the method used here is readily applicable to similar systems. Hydration free energies are found to sensitively depend on the intermolecular interactions, while bonded interactions are less important, as expected. We also investigate in the present work the possibility of applying the multipolar force field in scoring trajectories generated using computationally inexpensive methods, which should be useful in broader parametrization studies with reduced computational resources, as scoring is much faster than the generation of the trajectories.

  10. Design of a hybrid model for cardiac arrhythmia classification based on Daubechies wavelet transform.

    PubMed

    Rajagopal, Rekha; Ranganathan, Vidhyapriya

    2018-06-05

    Automation in cardiac arrhythmia classification helps medical professionals make accurate decisions about the patient's health. The aim of this work was to design a hybrid classification model to classify cardiac arrhythmias. The design phase of the classification model comprises the following stages: preprocessing of the cardiac signal by eliminating detail coefficients that contain noise, feature extraction through Daubechies wavelet transform, and arrhythmia classification using a collaborative decision from the K nearest neighbor classifier (KNN) and a support vector machine (SVM). The proposed model is able to classify 5 arrhythmia classes as per the ANSI/AAMI EC57: 1998 classification standard. Level 1 of the proposed model involves classification using the KNN and the classifier is trained with examples from all classes. Level 2 involves classification using an SVM and is trained specifically to classify overlapped classes. The final classification of a test heartbeat pertaining to a particular class is done using the proposed KNN/SVM hybrid model. The experimental results demonstrated that the average sensitivity of the proposed model was 92.56%, the average specificity 99.35%, the average positive predictive value 98.13%, the average F-score 94.5%, and the average accuracy 99.78%. The results obtained using the proposed model were compared with the results of discriminant, tree, and KNN classifiers. The proposed model is able to achieve a high classification accuracy.

  11. Classification of Ancient Mammal Individuals Using Dental Pulp MALDI-TOF MS Peptide Profiling

    PubMed Central

    Tran, Thi-Nguyen-Ny; Aboudharam, Gérard; Gardeisen, Armelle; Davoust, Bernard; Bocquet-Appel, Jean-Pierre; Flaudrops, Christophe; Belghazi, Maya; Raoult, Didier; Drancourt, Michel

    2011-01-01

    Background The classification of ancient animal corpses at the species level remains a challenging task for forensic scientists and anthropologists. Severe damage and mixed, tiny pieces originating from several skeletons may render morphological classification virtually impossible. Standard approaches are based on sequencing mitochondrial and nuclear targets. Methodology/Principal Findings We present a method that can accurately classify mammalian species using dental pulp and mass spectrometry peptide profiling. Our work was organized into three successive steps. First, after extracting proteins from the dental pulp collected from 37 modern individuals representing 13 mammalian species, trypsin-digested peptides were used for matrix-assisted laser desorption/ionization time-of-flight mass spectrometry analysis. The resulting peptide profiles accurately classified every individual at the species level in agreement with parallel cytochrome b gene sequencing gold standard. Second, using a 279–modern spectrum database, we blindly classified 33 of 37 teeth collected in 37 modern individuals (89.1%). Third, we classified 10 of 18 teeth (56%) collected in 15 ancient individuals representing five mammal species including human, from five burial sites dating back 8,500 years. Further comparison with an upgraded database comprising ancient specimen profiles yielded 100% classification in ancient teeth. Peptide sequencing yield 4 and 16 different non-keratin proteins including collagen (alpha-1 type I and alpha-2 type I) in human ancient and modern dental pulp, respectively. Conclusions/Significance Mass spectrometry peptide profiling of the dental pulp is a new approach that can be added to the arsenal of species classification tools for forensics and anthropology as a complementary method to DNA sequencing. The dental pulp is a new source for collagen and other proteins for the species classification of modern and ancient mammal individuals. PMID:21364886

  12. Conflicts in wound classification of neonatal operations.

    PubMed

    Vu, Lan T; Nobuhara, Kerilyn K; Lee, Hanmin; Farmer, Diana L

    2009-06-01

    This study sought to determine the reliability of wound classification guidelines when applied to neonatal operations. This study is a cross-sectional web-based survey of pediatric surgeons. From a random sample of 22 neonatal operations, participants classified each operation as "clean," "clean-contaminated," "contaminated," or "dirty or infected," and specified duration of perioperative antibiotics as "none," "single preoperative," "24 hours," or ">24 hours." Unweighted kappa score was calculated to estimate interrater reliability. Overall interrater reliability for wound classification was poor (kappa = 0.30). The following operations were classified as clean: pyloromyotomy, resection of sequestration, resection of sacrococcygeal teratoma, oophorectomy, and immediate repair of omphalocele; as clean-contaminated: Ladd procedure, bowel resection for midgut volvulus and meconium peritonitis, fistula ligation of tracheoesophageal fistula, primary esophageal anastomosis of esophageal atresia, thoracic lobectomy, staged closure of gastroschisis, delayed repair and primary closure of omphalocele, perineal anoplasty and diverting colostomy for imperforate anus, anal pull-through for Hirschsprung disease, and colostomy closure; and as dirty: perforated necrotizing enterocolitis. There is poor consensus on how neonatal operations are classified based on contamination. An improved classification system will provide more accurate risk assessment for development of surgical site infections and identify neonates who would benefit from antibiotic prophylaxis.

  13. New decision support tool for acute lymphoblastic leukemia classification

    NASA Astrophysics Data System (ADS)

    Madhukar, Monica; Agaian, Sos; Chronopoulos, Anthony T.

    2012-03-01

    In this paper, we build up a new decision support tool to improve treatment intensity choice in childhood ALL. The developed system includes different methods to accurately measure furthermore cell properties in microscope blood film images. The blood images are exposed to series of pre-processing steps which include color correlation, and contrast enhancement. By performing K-means clustering on the resultant images, the nuclei of the cells under consideration are obtained. Shape features and texture features are then extracted for classification. The system is further tested on the classification of spectra measured from the cell nuclei in blood samples in order to distinguish normal cells from those affected by Acute Lymphoblastic Leukemia. The results show that the proposed system robustly segments and classifies acute lymphoblastic leukemia based on complete microscopic blood images.

  14. Classification and pose estimation of objects using nonlinear features

    NASA Astrophysics Data System (ADS)

    Talukder, Ashit; Casasent, David P.

    1998-03-01

    A new nonlinear feature extraction method called the maximum representation and discrimination feature (MRDF) method is presented for extraction of features from input image data. It implements transformations similar to the Sigma-Pi neural network. However, the weights of the MRDF are obtained in closed form, and offer advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We show its use in estimating the class and pose of images of real objects and rendered solid CAD models of machine parts from single views using a feature-space trajectory (FST) neural network classifier. We show more accurate classification and pose estimation results than are achieved by standard principal component analysis (PCA) and Fukunaga-Koontz (FK) feature extraction methods.

  15. Molecular diagnostics of inflammatory disease: New tools and perspectives.

    PubMed

    Garzorz-Stark, Natalie; Lauffer, Felix

    2017-08-01

    This essay reviews current approaches to establish novel molecular diagnostic tools for inflammatory skin diseases. Moreover, it highlights the importance of stratifying patients according to molecular signatures and revising current outdated disease classification systems to eventually reach the goal of personalized medicine. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  16. Clinical Relevance of Prognostic and Predictive Molecular Markers in Gliomas.

    PubMed

    Siegal, Tali

    2016-01-01

    Sorting and grading of glial tumors by the WHO classification provide clinicians with guidance as to the predicted course of the disease and choice of treatment. Nonetheless, histologically identical tumors may have very different outcome and response to treatment. Molecular markers that carry both diagnostic and prognostic information add useful tools to traditional classification by redefining tumor subtypes within each WHO category. Therefore, molecular markers have become an integral part of tumor assessment in modern neuro-oncology and biomarker status now guides clinical decisions in some subtypes of gliomas. The routine assessment of IDH status improves histological diagnostic accuracy by differentiating diffuse glioma from reactive gliosis. It carries a favorable prognostic implication for all glial tumors and it is predictive for chemotherapeutic response in anaplastic oligodendrogliomas with codeletion of 1p/19q chromosomes. Glial tumors that contain chromosomal codeletion of 1p/19q are defined as tumors of oligodendroglial lineage and have favorable prognosis. MGMT promoter methylation is a favorable prognostic marker in astrocytic high-grade gliomas and it is predictive for chemotherapeutic response in anaplastic gliomas with wild-type IDH1/2 and in glioblastoma of the elderly. The clinical implication of other molecular markers of gliomas like mutations of EGFR and ATRX genes and BRAF fusion or point mutation is highlighted. The potential of molecular biomarker-based classification to guide future therapeutic approach is discussed and accentuated.

  17. Method: automatic segmentation of mitochondria utilizing patch classification, contour pair classification, and automatically seeded level sets

    PubMed Central

    2012-01-01

    Background While progress has been made to develop automatic segmentation techniques for mitochondria, there remains a need for more accurate and robust techniques to delineate mitochondria in serial blockface scanning electron microscopic data. Previously developed texture based methods are limited for solving this problem because texture alone is often not sufficient to identify mitochondria. This paper presents a new three-step method, the Cytoseg process, for automated segmentation of mitochondria contained in 3D electron microscopic volumes generated through serial block face scanning electron microscopic imaging. The method consists of three steps. The first is a random forest patch classification step operating directly on 2D image patches. The second step consists of contour-pair classification. At the final step, we introduce a method to automatically seed a level set operation with output from previous steps. Results We report accuracy of the Cytoseg process on three types of tissue and compare it to a previous method based on Radon-Like Features. At step 1, we show that the patch classifier identifies mitochondria texture but creates many false positive pixels. At step 2, our contour processing step produces contours and then filters them with a second classification step, helping to improve overall accuracy. We show that our final level set operation, which is automatically seeded with output from previous steps, helps to smooth the results. Overall, our results show that use of contour pair classification and level set operations improve segmentation accuracy beyond patch classification alone. We show that the Cytoseg process performs well compared to another modern technique based on Radon-Like Features. Conclusions We demonstrated that texture based methods for mitochondria segmentation can be enhanced with multiple steps that form an image processing pipeline. While we used a random-forest based patch classifier to recognize texture, it would be

  18. Migraine classification using magnetic resonance imaging resting-state functional connectivity data.

    PubMed

    Chong, Catherine D; Gaw, Nathan; Fu, Yinlin; Li, Jing; Wu, Teresa; Schwedt, Todd J

    2017-08-01

    Background This study used machine-learning techniques to develop discriminative brain-connectivity biomarkers from resting-state functional magnetic resonance neuroimaging ( rs-fMRI) data that distinguish between individual migraine patients and healthy controls. Methods This study included 58 migraine patients (mean age = 36.3 years; SD = 11.5) and 50 healthy controls (mean age = 35.9 years; SD = 11.0). The functional connections of 33 seeded pain-related regions were used as input for a brain classification algorithm that tested the accuracy of determining whether an individual brain MRI belongs to someone with migraine or to a healthy control. Results The best classification accuracy using a 10-fold cross-validation method was 86.1%. Resting functional connectivity of the right middle temporal, posterior insula, middle cingulate, left ventromedial prefrontal and bilateral amygdala regions best discriminated the migraine brain from that of a healthy control. Migraineurs with longer disease durations were classified more accurately (>14 years; 96.7% accuracy) compared to migraineurs with shorter disease durations (≤14 years; 82.1% accuracy). Conclusions Classification of migraine using rs-fMRI provides insights into pain circuits that are altered in migraine and could potentially contribute to the development of a new, noninvasive migraine biomarker. Migraineurs with longer disease burden were classified more accurately than migraineurs with shorter disease burden, potentially indicating that disease duration leads to reorganization of brain circuitry.

  19. A patch-based convolutional neural network for remote sensing image classification.

    PubMed

    Sharma, Atharva; Liu, Xiuwen; Yang, Xiaojun; Shi, Di

    2017-11-01

    Availability of accurate land cover information over large areas is essential to the global environment sustainability; digital classification using medium-resolution remote sensing data would provide an effective method to generate the required land cover information. However, low accuracy of existing per-pixel based classification methods for medium-resolution data is a fundamental limiting factor. While convolutional neural networks (CNNs) with deep layers have achieved unprecedented improvements in object recognition applications that rely on fine image structures, they cannot be applied directly to medium-resolution data due to lack of such fine structures. In this paper, considering the spatial relation of a pixel to its neighborhood, we propose a new deep patch-based CNN system tailored for medium-resolution remote sensing data. The system is designed by incorporating distinctive characteristics of medium-resolution data; in particular, the system computes patch-based samples from multidimensional top of atmosphere reflectance data. With a test site from the Florida Everglades area (with a size of 771 square kilometers), the proposed new system has outperformed pixel-based neural network, pixel-based CNN and patch-based neural network by 24.36%, 24.23% and 11.52%, respectively, in overall classification accuracy. By combining the proposed deep CNN and the huge collection of medium-resolution remote sensing data, we believe that much more accurate land cover datasets can be produced over large areas. Copyright © 2017 Elsevier Ltd. All rights reserved.

  20. Different CHEK2 germline mutations are associated with distinct immunophenotypic molecular subtypes of breast cancer.

    PubMed

    Domagala, Pawel; Wokolorczyk, Dominika; Cybulski, Cezary; Huzarski, Tomasz; Lubinski, Jan; Domagala, Wenancjusz

    2012-04-01

    Germline mutations in BRCA1 were already linked to basal-like subtype of immunophenotypic molecular classification of breast cancer (BC). However, it is not known whether mutations in other BC susceptibility genes are associated with molecular subtypes of this cancer. We tested the hypothesis that distinct mutations in another BC susceptibility gene involved in DNA repair, i.e., CHEK2 may be associated with particular immunophenotypic molecular subtypes of this cancer. Two groups of patients: 1255 with BCs and 5496 healthy controls were genotyped for four CHEK2 mutations (I157T and three truncating mutations: 1100delC, IVS2 + 1G > A, del5395). BCs were tested by immunohistochemistry on tissue microarrays for ER, PR, HER-2, EGFR, and CK5/6 and were assigned to appropriate subtypes of immunophenotypic molecular classification. There was a significant association between CHEK2 mutations and the immunophenotypic molecular classification (P = 0.004). CHEK2-associated cancers were predominantly luminal (108/117 = 92.3%). CHEK2-I157T variant was associated with the luminal A subtype (P = 0.01), whereas CHEK2-truncating mutations were associated with the luminal B subtype (P = 0.005). Comparing the prevalence of CHEK2 mutations in BC with controls revealed that carriers of an I157T variant had OR of 1.80 for luminal A subtype and carriers of truncating mutations had OR of 6.26 for luminal B subtype of BC. To our knowledge, this is the first study showing that specific mutations in the same susceptibility gene are associated with different immunophenotypic molecular subtypes of BC. This association represents independent evidence supporting the biological significance of immunophenotypic molecular classification of BC.

  1. Automatic classification of diseases from free-text death certificates for real-time surveillance.

    PubMed

    Koopman, Bevan; Karimi, Sarvnaz; Nguyen, Anthony; McGuire, Rhydwyn; Muscatello, David; Kemp, Madonna; Truran, Donna; Zhang, Ming; Thackway, Sarah

    2015-07-15

    Death certificates provide an invaluable source for mortality statistics which can be used for surveillance and early warnings of increases in disease activity and to support the development and monitoring of prevention or response strategies. However, their value can be realised only if accurate, quantitative data can be extracted from death certificates, an aim hampered by both the volume and variable nature of certificates written in natural language. This study aims to develop a set of machine learning and rule-based methods to automatically classify death certificates according to four high impact diseases of interest: diabetes, influenza, pneumonia and HIV. Two classification methods are presented: i) a machine learning approach, where detailed features (terms, term n-grams and SNOMED CT concepts) are extracted from death certificates and used to train a set of supervised machine learning models (Support Vector Machines); and ii) a set of keyword-matching rules. These methods were used to identify the presence of diabetes, influenza, pneumonia and HIV in a death certificate. An empirical evaluation was conducted using 340,142 death certificates, divided between training and test sets, covering deaths from 2000-2007 in New South Wales, Australia. Precision and recall (positive predictive value and sensitivity) were used as evaluation measures, with F-measure providing a single, overall measure of effectiveness. A detailed error analysis was performed on classification errors. Classification of diabetes, influenza, pneumonia and HIV was highly accurate (F-measure 0.96). More fine-grained ICD-10 classification effectiveness was more variable but still high (F-measure 0.80). The error analysis revealed that word variations as well as certain word combinations adversely affected classification. In addition, anomalies in the ground truth likely led to an underestimation of the effectiveness. The high accuracy and low cost of the classification methods allow for an

  2. Comparative study of wine tannin classification using Fourier transform mid-infrared spectrometry and sensory analysis.

    PubMed

    Fernández, Katherina; Labarca, Ximena; Bordeu, Edmundo; Guesalaga, Andrés; Agosin, Eduardo

    2007-11-01

    Wine tannins are fundamental to the determination of wine quality. However, the chemical and sensorial analysis of these compounds is not straightforward and a simple and rapid technique is necessary. We analyzed the mid-infrared spectra of white, red, and model wines spiked with known amounts of skin or seed tannins, collected using Fourier transform mid-infrared (FT-MIR) transmission spectroscopy (400-4000 cm(-1)). The spectral data were classified according to their tannin source, skin or seed, and tannin concentration by means of discriminant analysis (DA) and soft independent modeling of class analogy (SIMCA) to obtain a probabilistic classification. Wines were also classified sensorially by a trained panel and compared with FT-MIR. SIMCA models gave the most accurate classification (over 97%) and prediction (over 60%) among the wine samples. The prediction was increased (over 73%) using the leave-one-out cross-validation technique. Sensory classification of the wines was less accurate than that obtained with FT-MIR and SIMCA. Overall, these results show the potential of FT-MIR spectroscopy, in combination with adequate statistical tools, to discriminate wines with different tannin levels.

  3. Integrating Human and Machine Intelligence in Galaxy Morphology Classification Tasks

    NASA Astrophysics Data System (ADS)

    Beck, Melanie Renee

    The large flood of data flowing from observatories presents significant challenges to astronomy and cosmology--challenges that will only be magnified by projects currently under development. Growth in both volume and velocity of astrophysics data is accelerating: whereas the Sloan Digital Sky Survey (SDSS) has produced 60 terabytes of data in the last decade, the upcoming Large Synoptic Survey Telescope (LSST) plans to register 30 terabytes per night starting in the year 2020. Additionally, the Euclid Mission will acquire imaging for 5 x 107 resolvable galaxies. The field of galaxy evolution faces a particularly challenging future as complete understanding often cannot be reached without analysis of detailed morphological galaxy features. Historically, morphological analysis has relied on visual classification by astronomers, accessing the human brains capacity for advanced pattern recognition. However, this accurate but inefficient method falters when confronted with many thousands (or millions) of images. In the SDSS era, efforts to automate morphological classifications of galaxies (e.g., Conselice et al., 2000; Lotz et al., 2004) are reasonably successful and can distinguish between elliptical and disk-dominated galaxies with accuracies of 80%. While this is statistically very useful, a key problem with these methods is that they often cannot say which 80% of their samples are accurate. Furthermore, when confronted with the more complex task of identifying key substructure within galaxies, automated classification algorithms begin to fail. The Galaxy Zoo project uses a highly innovative approach to solving the scalability problem of visual classification. Displaying images of SDSS galaxies to volunteers via a simple and engaging web interface, www.galaxyzoo.org asks people to classify images by eye. Within the first year hundreds of thousands of members of the general public had classified each of the 1 million SDSS galaxies an average of 40 times. Galaxy Zoo

  4. [Molecular cytogenetic analysis of chromosomal aberrations in cells of low grade gliomas and its contribution for tumour classification].

    PubMed

    Lhotská, H; Zemanová, Z; Kramář, F; Lizcová, L; Svobodová, K; Ransdorfová, S; Bystřická, D; Krejčík, Z; Hrabal, P; Dohnalová, A; Kaiser, M; Michalová, K

    2014-01-01

    Low-grade gliomas represent a heterogeneous group of primary brain malignancies. The current diagnostics of these tumors rely strongly on histological classification. With the development of molecular cytogenetic methods several genetic markers were described, contributing to a better distinction of glial subtypes. The aim of this study was to assess the frequency of acquired chromosomal aberrations in lowgrade gliomas and to search for new genomic changes associated with higher risk of tumor progression. We analysed biopsy specimens from 41 patients with histological dia-gnosis of low-grade glioma using interphase fluorescence in situ hybridization (I FISH) and single nucleotide polymorphism (SNP) array techniques (19 females and 22 males, medium age 42 years). Besides notorious and most frequent finding of combined deletion of 1p/ 19q (81.25% patients) several other recurrent aberrations were described in patients with oligodendrogliomas: deletions of p and q arms of chromosome 4 (25% patients), deletions of the short arms of chromosome 9 (18.75% patients), deletions of the long arms of chromosome 13 and monosomy of chromosome 18 (18.75% patients). In bio-psy specimens from patients with astrocytomas, we often observed deletion of 1p (24% patients), amplification of the long arms of chromosome 7 (16% patients), deletion of the long arm of chromosome 13 (20% patients), segmental uniparental disomy (UPD) of the short arms of chromosome 17 (60% patients) and deletion of the long arms of chromosome 19 (28% patients). In one patient we detected a shuttered chromosome 10 resulting from chromothripsis. Using a combination of I FISH and SNP array, we detected not only known chromosomal changes but also new or less frequent recur-rent aberrations. Their role in cancer  cell progression and their impact on low grade gliomas classification remains to be elucidated in a larger cohort of patients.

  5. Recent Advances in Conotoxin Classification by Using Machine Learning Methods.

    PubMed

    Dao, Fu-Ying; Yang, Hui; Su, Zhen-Dong; Yang, Wuritu; Wu, Yun; Hui, Ding; Chen, Wei; Tang, Hua; Lin, Hao

    2017-06-25

    Conotoxins are disulfide-rich small peptides, which are invaluable peptides that target ion channel and neuronal receptors. Conotoxins have been demonstrated as potent pharmaceuticals in the treatment of a series of diseases, such as Alzheimer's disease, Parkinson's disease, and epilepsy. In addition, conotoxins are also ideal molecular templates for the development of new drug lead compounds and play important roles in neurobiological research as well. Thus, the accurate identification of conotoxin types will provide key clues for the biological research and clinical medicine. Generally, conotoxin types are confirmed when their sequence, structure, and function are experimentally validated. However, it is time-consuming and costly to acquire the structure and function information by using biochemical experiments. Therefore, it is important to develop computational tools for efficiently and effectively recognizing conotoxin types based on sequence information. In this work, we reviewed the current progress in computational identification of conotoxins in the following aspects: (i) construction of benchmark dataset; (ii) strategies for extracting sequence features; (iii) feature selection techniques; (iv) machine learning methods for classifying conotoxins; (v) the results obtained by these methods and the published tools; and (vi) future perspectives on conotoxin classification. The paper provides the basis for in-depth study of conotoxins and drug therapy research.

  6. Machine learning for the structure–energy–property landscapes of molecular crystals† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc04665k

    PubMed Central

    Yang, Jack; Campbell, Joshua E.; Day, Graeme M.; Ceriotti, Michele

    2017-01-01

    Molecular crystals play an important role in several fields of science and technology. They frequently crystallize in different polymorphs with substantially different physical properties. To help guide the synthesis of candidate materials, atomic-scale modelling can be used to enumerate the stable polymorphs and to predict their properties, as well as to propose heuristic rules to rationalize the correlations between crystal structure and materials properties. Here we show how a recently-developed machine-learning (ML) framework can be used to achieve inexpensive and accurate predictions of the stability and properties of polymorphs, and a data-driven classification that is less biased and more flexible than typical heuristic rules. We discuss, as examples, the lattice energy and property landscapes of pentacene and two azapentacene isomers that are of interest as organic semiconductor materials. We show that we can estimate force field or DFT lattice energies with sub-kJ mol–1 accuracy, using only a few hundred reference configurations, and reduce by a factor of ten the computational effort needed to predict charge mobility in the crystal structures. The automatic structural classification of the polymorphs reveals a more detailed picture of molecular packing than that provided by conventional heuristics, and helps disentangle the role of hydrogen bonded and π-stacking interactions in determining molecular self-assembly. This observation demonstrates that ML is not just a black-box scheme to interpolate between reference calculations, but can also be used as a tool to gain intuitive insights into structure–property relations in molecular crystal engineering. PMID:29675175

  7. Molecular subtypes of metastatic colorectal cancer are associated with patient response to irinotecan-based therapies.

    PubMed

    Del Rio, M; Mollevi, C; Bibeau, F; Vie, N; Selves, J; Emile, J-F; Roger, P; Gongora, C; Robert, J; Tubiana-Mathieu, N; Ychou, M; Martineau, P

    2017-05-01

    Currently, metastatic colorectal cancer is treated as a homogeneous disease and only RAS mutational status has been approved as a negative predictive factor in patients treated with cetuximab. The aim of this study was to evaluate if recently identified molecular subtypes of colon cancer are associated with response of metastatic patients to first-line therapy. We collected and analysed 143 samples of human colorectal tumours with complete clinical annotations, including the response to treatment. Gene expression profiling was used to classify patients in three to six classes using four different molecular classifications. Correlations between molecular subtypes, response to treatment, progression-free and overall survival were analysed. We first demonstrated that the four previously described molecular classifications of colorectal cancer defined in non-metastatic patients also correctly classify stage IV patients. One of the classifications is strongly associated with response to FOLFIRI (P=0.003), but not to FOLFOX (P=0.911) and FOLFIRI + Bevacizumab (P=0.190). In particular, we identify a molecular subtype representing 28% of the patients that shows an exceptionally high response rate to FOLFIRI (87.5%). These patients have a two-fold longer overall survival (40.1 months) when treated with FOLFIRI, as first-line regimen, instead of FOLFOX (18.6 months). Our results demonstrate the interest of molecular classifications to develop tailored therapies for patients with metastatic colorectal cancer and a strong impact of the first-line regimen on the overall survival of some patients. This however remains to be confirmed in a large prospective clinical trial. Copyright © 2017 Elsevier Ltd. All rights reserved.

  8. Multi-modal classification of neurodegenerative disease by progressive graph-based transductive learning

    PubMed Central

    Wang, Zhengxia; Zhu, Xiaofeng; Adeli, Ehsan; Zhu, Yingying; Nie, Feiping; Munsell, Brent

    2018-01-01

    Graph-based transductive learning (GTL) is a powerful machine learning technique that is used when sufficient training data is not available. In particular, conventional GTL approaches first construct a fixed inter-subject relation graph that is based on similarities in voxel intensity values in the feature domain, which can then be used to propagate the known phenotype data (i.e., clinical scores and labels) from the training data to the testing data in the label domain. However, this type of graph is exclusively learned in the feature domain, and primarily due to outliers in the observed features, may not be optimal for label propagation in the label domain. To address this limitation, a progressive GTL (pGTL) method is proposed that gradually finds an intrinsic data representation that more accurately aligns imaging features with the phenotype data. In general, optimal feature-to-phenotype alignment is achieved using an iterative approach that: (1) refines inter-subject relationships observed in the feature domain by using the learned intrinsic data representation in the label domain, (2) updates the intrinsic data representation from the refined inter-subject relationships, and (3) verifies the intrinsic data representation on the training data to guarantee an optimal classification when applied to testing data. Additionally, the iterative approach is extended to multi-modal imaging data to further improve pGTL classification accuracy. Using Alzheimer’s disease and Parkinson’s disease study data, the classification accuracy of the proposed pGTL method is compared to several state-of-the-art classification methods, and the results show pGTL can more accurately identify subjects, even at different progression stages, in these two study data sets. PMID:28551556

  9. Molecular Approaches to Thyroid Cancer Diagnosis

    PubMed Central

    Hsiao, Susan J.; Nikiforov, Yuri E.

    2014-01-01

    Thyroid nodules are common, and the accurate diagnosis of cancer or benign disease is important for the effective clinical management of these patients. Molecular markers are a helpful diagnostic tool, particularly for cytologically indeterminate thyroid nodules. In the past few years, significant progress has been made in developing molecular markers for clinical use in fine needle aspiration (FNA) specimens, including gene mutation panels and gene expression classifiers. With the availability of next generation sequencing technology, gene mutation panels can be expanded to interrogate multiple genes simultaneously and to provide yet more accurate diagnostic information. In addition, recently several new molecular markers in thyroid cancer have been identified that offer diagnostic, prognostic, and therapeutic information that could potentially be of value in guiding individualized management of patients with thyroid nodules. PMID:24829266

  10. LANDSAT applications to wetlands classification in the upper Mississippi River Valley. Ph.D. Thesis. Final Report

    NASA Technical Reports Server (NTRS)

    Lillesand, T. M.; Werth, L. F. (Principal Investigator)

    1980-01-01

    A 25% improvement in average classification accuracy was realized by processing double-date vs. single-date data. Under the spectrally and spatially complex site conditions characterizing the geographical area used, further improvement in wetland classification accuracy is apparently precluded by the spectral and spatial resolution restrictions of the LANDSAT MSS. Full scene analysis of scanning densitometer data extracted from scale infrared photography failed to permit discrimination of many wetland and nonwetland cover types. When classification of photographic data was limited to wetland areas only, much more detailed and accurate classification could be made. The integration of conventional image interpretation (to simply delineate wetland boundaries) and machine assisted classification (to discriminate among cover types present within the wetland areas) appears to warrant further research to study the feasibility and cost of extending this methodology over a large area using LANDSAT and/or small scale photography.

  11. Rapid automated classification of anesthetic depth levels using GPU based parallelization of neural networks.

    PubMed

    Peker, Musa; Şen, Baha; Gürüler, Hüseyin

    2015-02-01

    The effect of anesthesia on the patient is referred to as depth of anesthesia. Rapid classification of appropriate depth level of anesthesia is a matter of great importance in surgical operations. Similarly, accelerating classification algorithms is important for the rapid solution of problems in the field of biomedical signal processing. However numerous, time-consuming mathematical operations are required when training and testing stages of the classification algorithms, especially in neural networks. In this study, to accelerate the process, parallel programming and computing platform (Nvidia CUDA) facilitates dramatic increases in computing performance by harnessing the power of the graphics processing unit (GPU) was utilized. The system was employed to detect anesthetic depth level on related electroencephalogram (EEG) data set. This dataset is rather complex and large. Moreover, the achieving more anesthetic levels with rapid response is critical in anesthesia. The proposed parallelization method yielded high accurate classification results in a faster time.

  12. Automatic classification of hyperactive children: comparing multiple artificial intelligence approaches.

    PubMed

    Delavarian, Mona; Towhidkhah, Farzad; Gharibzadeh, Shahriar; Dibajnia, Parvin

    2011-07-12

    Automatic classification of different behavioral disorders with many similarities (e.g. in symptoms) by using an automated approach will help psychiatrists to concentrate on correct disorder and its treatment as soon as possible, to avoid wasting time on diagnosis, and to increase the accuracy of diagnosis. In this study, we tried to differentiate and classify (diagnose) 306 children with many similar symptoms and different behavioral disorders such as ADHD, depression, anxiety, comorbid depression and anxiety and conduct disorder with high accuracy. Classification was based on the symptoms and their severity. With examining 16 different available classifiers, by using "Prtools", we have proposed nearest mean classifier as the most accurate classifier with 96.92% accuracy in this research. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

  13. The Value of Ensari's Proposal in Evaluating the Mucosal Pathology of Childhood Celiac Disease: Old Classification versus New Version.

    PubMed

    Güreşci, Servet; Hızlı, Samil; Simşek, Gülçin Güler

    2012-09-01

    Small intestinal biopsy remains the gold standard in diagnosing celiac disease (CD); however, the wide spectrum of histopathological states and differential diagnosis of CD is still a diagnostic problem for pathologists. Recently, Ensari reviewed the literature and proposed an update of the histopathological diagnosis and classification for CD. In this study, the histopathological materials of 54 children in whom CD was diagnosed at our hospital were reviewed to compare the previous Marsh and Modified Marsh-Oberhuber classifications with this new proposal. In this study, we show that the Ensari classification is as accurate as the Marsh and Modified Marsh classifications in describing the consecutive states of mucosal damage seen in CD. Ensari's classification is simple, practical and facilitative in diagnosing and subtyping of mucosal pathology of CD.

  14. Development of municipal solid waste classification in Korea based on fossil carbon fraction.

    PubMed

    Lee, Jeongwoo; Kang, Seongmin; Kim, Seungjin; Kim, Ki-Hyun; Jeon, Eui-Chan

    2015-10-01

    Environmental problems and climate change arising from waste incineration are taken quite seriously in the world. In Korea, the waste disposal methods are largely classified into landfill, incineration, recycling, etc. and the amount of incinerated waste has risen by 24.5% from 2002. In the analysis of CO₂emissions estimations of waste incinerators fossil carbon content are main factor by the IPCC. FCF differs depending on the characteristics of waste in each country, and a wide range of default values are proposed by the IPCC. This study conducted research on the existing classifications of the IPCC and Korean waste classification systems based on FCF for accurate greenhouse gas emissions estimation of waste incineration. The characteristics possible for sorting were classified according to FCF and form. The characteristics sorted according to fossil carbon fraction were paper, textiles, rubber, and leather. Paper was classified into pure paper and processed paper; textiles were classified into cotton and synthetic fibers; and rubber and leather were classified into artificial and natural. The analysis of FCF was implemented by collecting representative samples from each classification group, by applying the 14C method, and using AMS equipment. And the analysis values were compared with the default values proposed by the IPCC. In this study of garden and park waste and plastics, the differences were within the range of the IPCC default values or the differences were negligible. However, coated paper, synthetic textiles, natural rubber, synthetic rubber, artificial leather, and other wastes showed differences of over 10% in FCF content. IPCC is comprised of largely 9 types of qualitative classifications, in emissions estimation a great difference can occur from the combined characteristics according with the existing IPCC classification system by using the minutely classified waste characteristics as in this study. Fossil carbon fraction (FCF) differs depending

  15. Classification of urban features using airborne hyperspectral data

    NASA Astrophysics Data System (ADS)

    Ganesh Babu, Bharath

    Accurate mapping and modeling of urban environments are critical for their efficient and successful management. Superior understanding of complex urban environments is made possible by using modern geospatial technologies. This research focuses on thematic classification of urban land use and land cover (LULC) using 248 bands of 2.0 meter resolution hyperspectral data acquired from an airborne imaging spectrometer (AISA+) on 24th July 2006 in and near Terre Haute, Indiana. Three distinct study areas including two commercial classes, two residential classes, and two urban parks/recreational classes were selected for classification and analysis. Four commonly used classification methods -- maximum likelihood (ML), extraction and classification of homogeneous objects (ECHO), spectral angle mapper (SAM), and iterative self organizing data analysis (ISODATA) - were applied to each data set. Accuracy assessment was conducted and overall accuracies were compared between the twenty four resulting thematic maps. With the exception of SAM and ISODATA in a complex commercial area, all methods employed classified the designated urban features with more than 80% accuracy. The thematic classification from ECHO showed the best agreement with ground reference samples. The residential area with relatively homogeneous composition was classified consistently with highest accuracy by all four of the classification methods used. The average accuracy amongst the classifiers was 93.60% for this area. When individually observed, the complex recreational area (Deming Park) was classified with the highest accuracy by ECHO, with an accuracy of 96.80% and 96.10% Kappa. The average accuracy amongst all the classifiers was 92.07%. The commercial area with relatively high complexity was classified with the least accuracy by all classifiers. The lowest accuracy was achieved by SAM at 63.90% with 59.20% Kappa. This was also the lowest accuracy in the entire analysis. This study demonstrates the

  16. a Two-Step Classification Approach to Distinguishing Similar Objects in Mobile LIDAR Point Clouds

    NASA Astrophysics Data System (ADS)

    He, H.; Khoshelham, K.; Fraser, C.

    2017-09-01

    Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

  17. Factors Affecting the Item Parameter Estimation and Classification Accuracy of the DINA Model

    ERIC Educational Resources Information Center

    de la Torre, Jimmy; Hong, Yuan; Deng, Weiling

    2010-01-01

    To better understand the statistical properties of the deterministic inputs, noisy "and" gate cognitive diagnosis (DINA) model, the impact of several factors on the quality of the item parameter estimates and classification accuracy was investigated. Results of the simulation study indicate that the fully Bayes approach is most accurate when the…

  18. METHODS STUDIES FOR THE NATIONAL CHILDREN'S STUDY: MOLECULARLY IMPRINTED POLYMERS

    EPA Science Inventory

    Accurate exposure classification tools are required to link exposure with health effects in epidemiological studies. Although long-term integrated exposure measurements are a critical component of exposure assessment, the ability to include these measurements into epidemiologic...

  19. Using genetically modified tomato crop plants with purple leaves for absolute weed/crop classification.

    PubMed

    Lati, Ran N; Filin, Sagi; Aly, Radi; Lande, Tal; Levin, Ilan; Eizenberg, Hanan

    2014-07-01

    Weed/crop classification is considered the main problem in developing precise weed-management methodologies, because both crops and weeds share similar hues. Great effort has been invested in the development of classification models, most based on expensive sensors and complicated algorithms. However, satisfactory results are not consistently obtained due to imaging conditions in the field. We report on an innovative approach that combines advances in genetic engineering and robust image-processing methods to detect weeds and distinguish them from crop plants by manipulating the crop's leaf color. We demonstrate this on genetically modified tomato (germplasm AN-113) which expresses a purple leaf color. An autonomous weed/crop classification is performed using an invariant-hue transformation that is applied to images acquired by a standard consumer camera (visible wavelength) and handles variations in illumination intensities. The integration of these methodologies is simple and effective, and classification results were accurate and stable under a wide range of imaging conditions. Using this approach, we simplify the most complicated stage in image-based weed/crop classification models. © 2013 Society of Chemical Industry.

  20. Classification of US hydropower dams by their modes of operation

    DOE PAGES

    McManamay, Ryan A.; Oigbokie, II, Clement O.; Kao, Shih -Chieh; ...

    2016-02-19

    A key challenge to understanding ecohydrologic responses to dam regulation is the absence of a universally transferable classification framework for how dams operate. In the present paper, we develop a classification system to organize the modes of operation (MOPs) for U.S. hydropower dams and powerplants. To determine the full diversity of MOPs, we mined federal documents, open-access data repositories, and internet sources. W then used CART classification trees to predict MOPs based on physical characteristics, regulation, and project generation. Finally, we evaluated how much variation MOPs explained in sub-daily discharge patterns for stream gages downstream of hydropower dams. After reviewingmore » information for 721 dams and 597 power plants, we developed a 2-tier hierarchical classification based on 1) the storage and control of flows to powerplants, and 2) the presence of a diversion around the natural stream bed. This resulted in nine tier-1 MOPs representing a continuum of operations from strictly peaking, to reregulating, to run-of-river, and two tier-2 MOPs, representing diversion and integral dam-powerhouse configurations. Although MOPs differed in physical characteristics and energy production, classification trees had low accuracies (<62%), which suggested accurate evaluations of MOPs may require individual attention. MOPs and dam storage explained 20% of the variation in downstream subdaily flow characteristics and showed consistent alterations in subdaily flow patterns from reference streams. Lastly, this standardized classification scheme is important for future research including estimating reservoir operations for large-scale hydrologic models and evaluating project economics, environmental impacts, and mitigation.« less

  1. Towards automated spectroscopic tissue classification in thyroid and parathyroid surgery.

    PubMed

    Schols, Rutger M; Alic, Lejla; Wieringa, Fokko P; Bouvy, Nicole D; Stassen, Laurents P S

    2017-03-01

    In (para-)thyroid surgery iatrogenic parathyroid injury should be prevented. To aid the surgeons' eye, a camera system enabling parathyroid-specific image enhancement would be useful. Hyperspectral camera technology might work, provided that the spectral signature of parathyroid tissue offers enough specific features to be reliably and automatically distinguished from surrounding tissues. As a first step to investigate this, we examined the feasibility of wide band diffuse reflectance spectroscopy (DRS) for automated spectroscopic tissue classification, using silicon (Si) and indium-gallium-arsenide (InGaAs) sensors. DRS (350-1830 nm) was performed during (para-)thyroid resections. From the acquired spectra 36 features at predefined wavelengths were extracted. The best features for classification of parathyroid from adipose or thyroid were assessed by binary logistic regression for Si- and InGaAs-sensor ranges. Classification performance was evaluated by leave-one-out cross-validation. In 19 patients 299 spectra were recorded (62 tissue sites: thyroid = 23, parathyroid = 21, adipose = 18). Classification accuracy of parathyroid-adipose was, respectively, 79% (Si), 82% (InGaAs) and 97% (Si/InGaAs combined). Parathyroid-thyroid classification accuracies were 80% (Si), 75% (InGaAs), 82% (Si/InGaAs combined). Si and InGaAs sensors are fairly accurate for automated spectroscopic classification of parathyroid, adipose and thyroid tissues. Combination of both sensor technologies improves accuracy. Follow-up research, aimed towards hyperspectral imaging seems justified. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  2. Classification of US hydropower dams by their modes of operation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    McManamay, Ryan A.; Oigbokie, II, Clement O.; Kao, Shih -Chieh

    A key challenge to understanding ecohydrologic responses to dam regulation is the absence of a universally transferable classification framework for how dams operate. In the present paper, we develop a classification system to organize the modes of operation (MOPs) for U.S. hydropower dams and powerplants. To determine the full diversity of MOPs, we mined federal documents, open-access data repositories, and internet sources. W then used CART classification trees to predict MOPs based on physical characteristics, regulation, and project generation. Finally, we evaluated how much variation MOPs explained in sub-daily discharge patterns for stream gages downstream of hydropower dams. After reviewingmore » information for 721 dams and 597 power plants, we developed a 2-tier hierarchical classification based on 1) the storage and control of flows to powerplants, and 2) the presence of a diversion around the natural stream bed. This resulted in nine tier-1 MOPs representing a continuum of operations from strictly peaking, to reregulating, to run-of-river, and two tier-2 MOPs, representing diversion and integral dam-powerhouse configurations. Although MOPs differed in physical characteristics and energy production, classification trees had low accuracies (<62%), which suggested accurate evaluations of MOPs may require individual attention. MOPs and dam storage explained 20% of the variation in downstream subdaily flow characteristics and showed consistent alterations in subdaily flow patterns from reference streams. Lastly, this standardized classification scheme is important for future research including estimating reservoir operations for large-scale hydrologic models and evaluating project economics, environmental impacts, and mitigation.« less

  3. Suicide Surveillance in the U.S. Military?Reporting and Classification Biases in Rate Calculations

    ERIC Educational Resources Information Center

    Carr, Joel R.; Hoge, Charles W.; Gardner, John; Potter, Robert

    2004-01-01

    The military has a well-defined population with suicide prevention programs that have been recognized as possible models for civilian suicide prevention efforts. Monitoring prevention programs requires accurate reporting. In civilian settings, several studies have confirmed problems in the reporting and classification of suicides. This analysis…

  4. [CT morphometry for calcaneal fractures and comparison of the Zwipp and Sanders classifications].

    PubMed

    Andermahr, J; Jesch, A B; Helling, H J; Jubel, A; Fischbach, R; Rehm, K E

    2002-01-01

    The aim of the study is to correlate the CT-morphological changes of fractured calcaneus and the classifications of Zwipp and Sanders with the clinical outcome. In a retrospective clinical study, the preoperative CT scans of 75 calcaneal fractures were analysed. The morphometry of the fractures was determined by measuring height, length diameter and calcaneo-cuboidal angle in comparison to the intact contralateral side. At a mean of 38 months after trauma 44 patients were clinically followed-up. The data of CT image morphometry were correlated with the severity of fracture classified by Zwipp or Sanders as well as with the functional outcome. There was a good correlation between the fracture classifications and the morphometric data. Both fracture classifying systems have a predictive impact for functional outcome. The more exacting and accurate Zwipp classification considers the most important cofactors like involvement of the calcaneo-cuboidal joint, soft tissue damage, additional fractures etc. The Sanders classification is easier to use during clinical routine. The Zwipp classification includes more relevant cofactors (fracture of the calcaneo-cuboidal-joint, soft tissue swelling, etc.) and presents a higher correlation to the choice of therapy. Both classification systems present a prognostic impact concerning the clinical outcome.

  5. Data Mining for Efficient and Accurate Large Scale Retrieval of Geophysical Parameters

    NASA Astrophysics Data System (ADS)

    Obradovic, Z.; Vucetic, S.; Peng, K.; Han, B.

    2004-12-01

    Our effort is devoted to developing data mining technology for improving efficiency and accuracy of the geophysical parameter retrievals by learning a mapping from observation attributes to the corresponding parameters within the framework of classification and regression. We will describe a method for efficient learning of neural network-based classification and regression models from high-volume data streams. The proposed procedure automatically learns a series of neural networks of different complexities on smaller data stream chunks and then properly combines them into an ensemble predictor through averaging. Based on the idea of progressive sampling the proposed approach starts with a very simple network trained on a very small chunk and then gradually increases the model complexity and the chunk size until the learning performance no longer improves. Our empirical study on aerosol retrievals from data obtained with the MISR instrument mounted at Terra satellite suggests that the proposed method is successful in learning complex concepts from large data streams with near-optimal computational effort. We will also report on a method that complements deterministic retrievals by constructing accurate predictive algorithms and applying them on appropriately selected subsets of observed data. The method is based on developing more accurate predictors aimed to catch global and local properties synthesized in a region. The procedure starts by learning the global properties of data sampled over the entire space, and continues by constructing specialized models on selected localized regions. The global and local models are integrated through an automated procedure that determines the optimal trade-off between the two components with the objective of minimizing the overall mean square errors over a specific region. Our experimental results on MISR data showed that the combined model can increase the retrieval accuracy significantly. The preliminary results on various

  6. Integrative Genomic Analysis of Cholangiocarcinoma Identifies Distinct IDH-Mutant Molecular Profiles.

    PubMed

    Farshidfar, Farshad; Zheng, Siyuan; Gingras, Marie-Claude; Newton, Yulia; Shih, Juliann; Robertson, A Gordon; Hinoue, Toshinori; Hoadley, Katherine A; Gibb, Ewan A; Roszik, Jason; Covington, Kyle R; Wu, Chia-Chin; Shinbrot, Eve; Stransky, Nicolas; Hegde, Apurva; Yang, Ju Dong; Reznik, Ed; Sadeghi, Sara; Pedamallu, Chandra Sekhar; Ojesina, Akinyemi I; Hess, Julian M; Auman, J Todd; Rhie, Suhn K; Bowlby, Reanne; Borad, Mitesh J; Zhu, Andrew X; Stuart, Josh M; Sander, Chris; Akbani, Rehan; Cherniack, Andrew D; Deshpande, Vikram; Mounajjed, Taofic; Foo, Wai Chin; Torbenson, Michael S; Kleiner, David E; Laird, Peter W; Wheeler, David A; McRee, Autumn J; Bathe, Oliver F; Andersen, Jesper B; Bardeesy, Nabeel; Roberts, Lewis R; Kwong, Lawrence N

    2017-03-14

    Cholangiocarcinoma (CCA) is an aggressive malignancy of the bile ducts, with poor prognosis and limited treatment options. Here, we describe the integrated analysis of somatic mutations, RNA expression, copy number, and DNA methylation by The Cancer Genome Atlas of a set of predominantly intrahepatic CCA cases and propose a molecular classification scheme. We identified an IDH mutant-enriched subtype with distinct molecular features including low expression of chromatin modifiers, elevated expression of mitochondrial genes, and increased mitochondrial DNA copy number. Leveraging the multi-platform data, we observed that ARID1A exhibited DNA hypermethylation and decreased expression in the IDH mutant subtype. More broadly, we found that IDH mutations are associated with an expanded histological spectrum of liver tumors with molecular features that stratify with CCA. Our studies reveal insights into the molecular pathogenesis and heterogeneity of cholangiocarcinoma and provide classification information of potential therapeutic significance. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  7. INVENTORY AND CLASSIFICATION OF GREAT LAKES COASTAL WETLANDS FOR MONITORING AND ASSESSMENT AT LARGE SPATIAL SCALES

    EPA Science Inventory

    Monitoring aquatic resources for regional assessments requires an accurate and comprehensive inventory of the resource and useful classification of exosystem similarities. Our research effort to create an electronic database and work with various ways to classify coastal wetlands...

  8. Molecular subtypes of osteosarcoma identified by reducing tumor heterogeneity through an interspecies comparative approach

    PubMed Central

    Scott, Milcah C.; Sarver, Aaron L.; Gavin, Katherine J.; Thayanithy, Venugopal; Getzy, David M.; Newman, Robert A.; Cutter, Gary R.; Lindblad-Toh, Kerstin; Kisseberth, William C.; Hunter, Lawrence E.; Subramanian, Subbaya; Breen, Matthew; Modiano, Jaime F.

    2011-01-01

    The heterogeneous and chaotic nature of osteosarcoma has confounded accurate molecular classification, prognosis, and prediction for this tumor. The occurrence of spontaneous osteosarcoma is largely confined to humans and dogs. While the clinical features are remarkably similar in both species, the organization of dogs into defined breeds provides a more homogeneous genetic background that may increase the likelihood to uncover molecular subtypes for this complex disease. We thus hypothesized that molecular profiles derived from canine osteosarcoma would aid in molecular subclassification of this disease when applied to humans. To test the hypothesis, we performed genome wide gene expression profiling in a cohort of dogs with osteosarcoma, primarily from high-risk breeds. To further reduce inter-sample heterogeneity, we assessed tumor-intrinsic properties through use of an extensive panel of osteosarcoma-derived cell lines. We observed strong differential gene expression that segregated samples into two groups with differential survival probabilities. Groupings were characterized by the inversely correlated expression of genes associated with G2/M transition and DNA damage checkpoint and microenvironment-interaction categories. This signature was preserved in data from whole tumor samples of three independent dog osteosarcoma cohorts, with stratification into the two expected groups. Significantly, this restricted signature partially overlapped a previously defined, predictive signature for soft tissue sarcomas, and it unmasked orthologous molecular subtypes and their corresponding natural histories in five independent data sets from human patients with osteosarcoma. Our results indicate that the narrower genetic diversity of dogs can be utilized to group complex human osteosarcoma into biologically and clinically relevant molecular subtypes. This in turn may enhance prognosis and prediction, and identify relevant therapeutic targets. PMID:21621658

  9. Classification of Birds and Bats Using Flight Tracks

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Cullinan, Valerie I.; Matzner, Shari; Duberstein, Corey A.

    Classification of birds and bats that use areas targeted for offshore wind farm development and the inference of their behavior is essential to evaluating the potential effects of development. The current approach to assessing the number and distribution of birds at sea involves transect surveys using trained individuals in boats or airplanes or using high-resolution imagery. These approaches are costly and have safety concerns. Based on a limited annotated library extracted from a single-camera thermal video, we provide a framework for building models that classify birds and bats and their associated behaviors. As an example, we developed a discriminant modelmore » for theoretical flight paths and applied it to data (N = 64 tracks) extracted from 5-min video clips. The agreement between model- and observer-classified path types was initially only 41%, but it increased to 73% when small-scale jitter was censored and path types were combined. Classification of 46 tracks of bats, swallows, gulls, and terns on average was 82% accurate, based on a jackknife cross-validation. Model classification of bats and terns (N = 4 and 2, respectively) was 94% and 91% correct, respectively; however, the variance associated with the tracks from these targets is poorly estimated. Model classification of gulls and swallows (N ≥ 18) was on average 73% and 85% correct, respectively. The models developed here should be considered preliminary because they are based on a small data set both in terms of the numbers of species and the identified flight tracks. Future classification models would be greatly improved by including a measure of distance between the camera and the target.« less

  10. Classification of rare missense substitutions, using risk surfaces, with genetic- and molecular-epidemiology applications.

    PubMed

    Tavtigian, Sean V; Byrnes, Graham B; Goldgar, David E; Thomas, Alun

    2008-11-01

    Many individually rare missense substitutions are encountered during deep resequencing of candidate susceptibility genes and clinical mutation screening of known susceptibility genes. BRCA1 and BRCA2 are among the most resequenced of all genes, and clinical mutation screening of these genes provides an extensive data set for analysis of rare missense substitutions. Align-GVGD is a mathematically simple missense substitution analysis algorithm, based on the Grantham difference, which has already contributed to classification of missense substitutions in BRCA1, BRCA2, and CHEK2. However, the distribution of genetic risk as a function of Align-GVGD's output variables Grantham variation (GV) and Grantham deviation (GD) has not been well characterized. Here, we used data from the Myriad Genetic Laboratories database of nearly 70,000 full-sequence tests plus two risk estimates, one approximating the odds ratio and the other reflecting strength of selection, to display the distribution of risk in the GV-GD plane as a series of surfaces. We abstracted contours from the surfaces and used the contours to define a sequence of missense substitution grades ordered from greatest risk to least risk. The grades were validated internally using a third, personal and family history-based, measure of risk. The Align-GVGD grades defined here are applicable to both the genetic epidemiology problem of classifying rare missense substitutions observed in known susceptibility genes and the molecular epidemiology problem of analyzing rare missense substitutions observed during case-control mutation screening studies of candidate susceptibility genes. (c) 2008 Wiley-Liss, Inc.

  11. Classification of Dual-Wavelength Airborne Laser Scanning Point Cloud Based on the Radiometric Properties of the Objects

    NASA Astrophysics Data System (ADS)

    Pilarska, M.

    2018-05-01

    Airborne laser scanning (ALS) is a well-known and willingly used technology. One of the advantages of this technology is primarily its fast and accurate data registration. In recent years ALS is continuously developed. One of the latest achievements is multispectral ALS, which consists in obtaining simultaneously the data in more than one laser wavelength. In this article the results of the dual-wavelength ALS data classification are presented. The data were acquired with RIEGL VQ-1560i sensor, which is equipped with two laser scanners operating in different wavelengths: 532 nm and 1064 nm. Two classification approaches are presented in the article: classification, which is based on geometric relationships between points and classification, which mostly relies on the radiometric properties of registered objects. The overall accuracy of the geometric classification was 86 %, whereas for the radiometric classification it was 81 %. As a result, it can be assumed that the radiometric features which are provided by the multispectral ALS have potential to be successfully used in ALS point cloud classification.

  12. Brain tumor classification and segmentation using sparse coding and dictionary learning.

    PubMed

    Salman Al-Shaikhli, Saif Dawood; Yang, Michael Ying; Rosenhahn, Bodo

    2016-08-01

    This paper presents a novel fully automatic framework for multi-class brain tumor classification and segmentation using a sparse coding and dictionary learning method. The proposed framework consists of two steps: classification and segmentation. The classification of the brain tumors is based on brain topology and texture. The segmentation is based on voxel values of the image data. Using K-SVD, two types of dictionaries are learned from the training data and their associated ground truth segmentation: feature dictionary and voxel-wise coupled dictionaries. The feature dictionary consists of global image features (topological and texture features). The coupled dictionaries consist of coupled information: gray scale voxel values of the training image data and their associated label voxel values of the ground truth segmentation of the training data. For quantitative evaluation, the proposed framework is evaluated using different metrics. The segmentation results of the brain tumor segmentation (MICCAI-BraTS-2013) database are evaluated using five different metric scores, which are computed using the online evaluation tool provided by the BraTS-2013 challenge organizers. Experimental results demonstrate that the proposed approach achieves an accurate brain tumor classification and segmentation and outperforms the state-of-the-art methods.

  13. A classification model of Hyperion image base on SAM combined decision tree

    NASA Astrophysics Data System (ADS)

    Wang, Zhenghai; Hu, Guangdao; Zhou, YongZhang; Liu, Xin

    2009-10-01

    Monitoring the Earth using imaging spectrometers has necessitated more accurate analyses and new applications to remote sensing. A very high dimensional input space requires an exponentially large amount of data to adequately and reliably represent the classes in that space. On the other hand, with increase in the input dimensionality the hypothesis space grows exponentially, which makes the classification performance highly unreliable. Traditional classification algorithms Classification of hyperspectral images is challenging. New algorithms have to be developed for hyperspectral data classification. The Spectral Angle Mapper (SAM) is a physically-based spectral classification that uses an ndimensional angle to match pixels to reference spectra. The algorithm determines the spectral similarity between two spectra by calculating the angle between the spectra, treating them as vectors in a space with dimensionality equal to the number of bands. The key and difficulty is that we should artificial defining the threshold of SAM. The classification precision depends on the rationality of the threshold of SAM. In order to resolve this problem, this paper proposes a new automatic classification model of remote sensing image using SAM combined with decision tree. It can automatic choose the appropriate threshold of SAM and improve the classify precision of SAM base on the analyze of field spectrum. The test area located in Heqing Yunnan was imaged by EO_1 Hyperion imaging spectrometer using 224 bands in visual and near infrared. The area included limestone areas, rock fields, soil and forests. The area was classified into four different vegetation and soil types. The results show that this method choose the appropriate threshold of SAM and eliminates the disturbance and influence of unwanted objects effectively, so as to improve the classification precision. Compared with the likelihood classification by field survey data, the classification precision of this model

  14. Genetic Bio-Ancestry and Social Construction of Racial Classification in Social Surveys in the Contemporary United States

    PubMed Central

    Guo, Guang; Fu, Yilan; Lee, Hedwig; Cai, Tianji; Harris, Kathleen Mullan; Li, Yi

    2013-01-01

    Self-reported race is generally considered the basis for racial classification in social surveys, including the U.S. census. Drawing on recent advances in human molecular genetics and social science perspectives of socially constructed race, our study takes into account both genetic bio-ancestry and social context in understanding racial classification. This article accomplishes two objectives. First, our research establishes geographic genetic bio-ancestry as a component of racial classification. Second, it shows how social forces trump biology in racial classification and/or how social context interacts with bio-ancestry in shaping racial classification. The findings were replicated in two racially and ethnically diverse data sets: the College Roommate Study (N = 2,065) and the National Longitudinal Study of Adolescent Health (N = 2,281). PMID:24019100

  15. Molecular classification of spontaneous endometrial adenocarcinomas in BDII rats.

    PubMed

    Samuelson, Emma; Hedberg, Carola; Nilsson, Staffan; Behboudi, Afrouz

    2009-03-01

    Female rats of the BDII/Han inbred strain are prone to spontaneously develop endometrial carcinomas (EC) that in cell biology and pathogenesis are very similar to those of human. Human EC are classified into two major groups: Type I displays endometroid histology, is hormone-dependent, and characterized by frequent microsatellite instability and PTEN, K-RAS, and CTNNB1 (beta-Catenin) mutations; Type II shows non-endometrioid histology, is hormone-unrelated, displays recurrent TP53 mutation, CDKN2A (P16) inactivation, over-expression of ERBB2 (Her2/neu), and reduced CDH1 (Cadherin 1 or E-Cadherin) expression. However, many human EC have overlapping clinical, morphologic, immunohistochemical, and molecular features of types I and II. The EC developed in BDII rats can be related to type I tumors, since they are hormone-related and histologically from endometrioid type. Here, we combined gene sequencing (Pten, Ifr1, and Ctnnb1) and real-time gene expression analysis (Pten, Cdh1, P16, Erbb2, Ctnnb1, Tp53, and Irf1) to further characterize molecular alterations in this tumor model with respect to different subtypes of EC in humans. No mutation in Pten and Ctnnb1 was detected, whereas three tumors displayed sequence aberrations of the Irf1 gene. Significant down regulation of Pten, Cdh1, p16, Erbb2, and Ctnnb1 gene products was found in the tumors. In conclusion, our data suggest that molecular features of spontaneous EC in BDII rats can be related to higher-grade human type I tumors and thus, this model represents an excellent experimental tool for research on this malignancy in human.

  16. The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification.

    PubMed

    Travis, William D; Brambilla, Elisabeth; Nicholson, Andrew G; Yatabe, Yasushi; Austin, John H M; Beasley, Mary Beth; Chirieac, Lucian R; Dacic, Sanja; Duhig, Edwina; Flieder, Douglas B; Geisinger, Kim; Hirsch, Fred R; Ishikawa, Yuichi; Kerr, Keith M; Noguchi, Masayuki; Pelosi, Giuseppe; Powell, Charles A; Tsao, Ming Sound; Wistuba, Ignacio

    2015-09-01

    The 2015 World Health Organization (WHO) Classification of Tumors of the Lung, Pleura, Thymus and Heart has just been published with numerous important changes from the 2004 WHO classification. The most significant changes in this edition involve (1) use of immunohistochemistry throughout the classification, (2) a new emphasis on genetic studies, in particular, integration of molecular testing to help personalize treatment strategies for advanced lung cancer patients, (3) a new classification for small biopsies and cytology similar to that proposed in the 2011 Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification, (4) a completely different approach to lung adenocarcinoma as proposed by the 2011 Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society classification, (5) restricting the diagnosis of large cell carcinoma only to resected tumors that lack any clear morphologic or immunohistochemical differentiation with reclassification of the remaining former large cell carcinoma subtypes into different categories, (6) reclassifying squamous cell carcinomas into keratinizing, nonkeratinizing, and basaloid subtypes with the nonkeratinizing tumors requiring immunohistochemistry proof of squamous differentiation, (7) grouping of neuroendocrine tumors together in one category, (8) adding NUT carcinoma, (9) changing the term sclerosing hemangioma to sclerosing pneumocytoma, (10) changing the name hamartoma to "pulmonary hamartoma," (11) creating a group of PEComatous tumors that include (a) lymphangioleiomyomatosis, (b) PEComa, benign (with clear cell tumor as a variant) and (c) PEComa, malignant, (12) introducing the entity pulmonary myxoid sarcoma with an EWSR1-CREB1 translocation, (13) adding the entities myoepithelioma and myoepithelial carcinomas, which can show EWSR1 gene rearrangements, (14) recognition of usefulness of WWTR1-CAMTA1 fusions in diagnosis of epithelioid

  17. Machine Learning of Parameters for Accurate Semiempirical Quantum Chemical Calculations

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Dral, Pavlo O.; von Lilienfeld, O. Anatole; Thiel, Walter

    2015-05-12

    We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempiricalmore » OM2 method using a set of 6095 constitutional isomers C7H10O2, for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.« less

  18. Machine learning of parameters for accurate semiempirical quantum chemical calculations

    DOE PAGES

    Dral, Pavlo O.; von Lilienfeld, O. Anatole; Thiel, Walter

    2015-04-14

    We investigate possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods through the use of machine learning (ML) models for the parameters. For a given class of compounds, ML techniques require sufficiently large training sets to develop ML models that can be used for adapting SQC parameters to reflect changes in molecular composition and geometry. The ML-SQC approach allows the automatic tuning of SQC parameters for individual molecules, thereby improving the accuracy without deteriorating transferability to molecules with molecular descriptors very different from those in the training set. The performance of this approach is demonstrated for the semiempiricalmore » OM2 method using a set of 6095 constitutional isomers C 7H 10O 2, for which accurate ab initio atomization enthalpies are available. The ML-OM2 results show improved average accuracy and a much reduced error range compared with those of standard OM2 results, with mean absolute errors in atomization enthalpies dropping from 6.3 to 1.7 kcal/mol. They are also found to be superior to the results from specific OM2 reparameterizations (rOM2) for the same set of isomers. The ML-SQC approach thus holds promise for fast and reasonably accurate high-throughput screening of materials and molecules.« less

  19. Automatically high accurate and efficient photomask defects management solution for advanced lithography manufacture

    NASA Astrophysics Data System (ADS)

    Zhu, Jun; Chen, Lijun; Ma, Lantao; Li, Dejian; Jiang, Wei; Pan, Lihong; Shen, Huiting; Jia, Hongmin; Hsiang, Chingyun; Cheng, Guojie; Ling, Li; Chen, Shijie; Wang, Jun; Liao, Wenkui; Zhang, Gary

    2014-04-01

    Defect review is a time consuming job. Human error makes result inconsistent. The defects located on don't care area would not hurt the yield and no need to review them such as defects on dark area. However, critical area defects can impact yield dramatically and need more attention to review them such as defects on clear area. With decrease in integrated circuit dimensions, mask defects are always thousands detected during inspection even more. Traditional manual or simple classification approaches are unable to meet efficient and accuracy requirement. This paper focuses on automatic defect management and classification solution using image output of Lasertec inspection equipment and Anchor pattern centric image process technology. The number of mask defect found during an inspection is always in the range of thousands or even more. This system can handle large number defects with quick and accurate defect classification result. Our experiment includes Die to Die and Single Die modes. The classification accuracy can reach 87.4% and 93.3%. No critical or printable defects are missing in our test cases. The missing classification defects are 0.25% and 0.24% in Die to Die mode and Single Die mode. This kind of missing rate is encouraging and acceptable to apply on production line. The result can be output and reloaded back to inspection machine to have further review. This step helps users to validate some unsure defects with clear and magnification images when captured images can't provide enough information to make judgment. This system effectively reduces expensive inline defect review time. As a fully inline automated defect management solution, the system could be compatible with current inspection approach and integrated with optical simulation even scoring function and guide wafer level defect inspection.

  20. Best practice guidelines for molecular genetic diagnosis of cystic fibrosis and CFTR-related disorders--updated European recommendations.

    PubMed

    Dequeker, Els; Stuhrmann, Manfred; Morris, Michael A; Casals, Teresa; Castellani, Carlo; Claustres, Mireille; Cuppens, Harry; des Georges, Marie; Ferec, Claude; Macek, Milan; Pignatti, Pier-Franco; Scheffer, Hans; Schwartz, Marianne; Witt, Michal; Schwarz, Martin; Girodon, Emmanuelle

    2009-01-01

    The increasing number of laboratories offering molecular genetic analysis of the CFTR gene and the growing use of commercial kits strengthen the need for an update of previous best practice guidelines (published in 2000). The importance of organizing regional or national laboratory networks, to provide both primary and comprehensive CFTR mutation screening, is stressed. Current guidelines focus on strategies for dealing with increasingly complex situations of CFTR testing. Diagnostic flow charts now include testing in CFTR-related disorders and in fetal bowel anomalies. Emphasis is also placed on the need to consider ethnic or geographic origins of patients and individuals, on basic principles of risk calculation and on the importance of providing accurate laboratory reports. Finally, classification of CFTR mutations is reviewed, with regard to their relevance to pathogenicity and to genetic counselling.

  1. Tumor taxonomy for the developmental lineage classification of neoplasms

    PubMed Central

    Berman, Jules J

    2004-01-01

    Background The new "Developmental lineage classification of neoplasms" was described in a prior publication. The classification is simple (the entire hierarchy is described with just 39 classifiers), comprehensive (providing a place for every tumor of man), and consistent with recent attempts to characterize tumors by cytogenetic and molecular features. A taxonomy is a list of the instances that populate a classification. The taxonomy of neoplasia attempts to list every known term for every known tumor of man. Methods The taxonomy provides each concept with a unique code and groups synonymous terms under the same concept. A Perl script validated successive drafts of the taxonomy ensuring that: 1) each term occurs only once in the taxonomy; 2) each term occurs in only one tumor class; 3) each concept code occurs in one and only one hierarchical position in the classification; and 4) the file containing the classification and taxonomy is a well-formed XML (eXtensible Markup Language) document. Results The taxonomy currently contains 122,632 different terms encompassing 5,376 neoplasm concepts. Each concept has, on average, 23 synonyms. The taxonomy populates "The developmental lineage classification of neoplasms," and is available as an XML file, currently 9+ Megabytes in length. A representation of the classification/taxonomy listing each term followed by its code, followed by its full ancestry, is available as a flat-file, 19+ Megabytes in length. The taxonomy is the largest nomenclature of neoplasms, with more than twice the number of neoplasm names found in other medical nomenclatures, including the 2004 version of the Unified Medical Language System, the Systematized Nomenclature of Medicine Clinical Terminology, the National Cancer Institute's Thesaurus, and the International Classification of Diseases Oncolology version. Conclusions This manuscript describes a comprehensive taxonomy of neoplasia that collects synonymous terms under a unique code number and

  2. Utilizing fast multipole expansions for efficient and accurate quantum-classical molecular dynamics simulations

    NASA Astrophysics Data System (ADS)

    Schwörer, Magnus; Lorenzen, Konstantin; Mathias, Gerald; Tavan, Paul

    2015-03-01

    Recently, a novel approach to hybrid quantum mechanics/molecular mechanics (QM/MM) molecular dynamics (MD) simulations has been suggested [Schwörer et al., J. Chem. Phys. 138, 244103 (2013)]. Here, the forces acting on the atoms are calculated by grid-based density functional theory (DFT) for a solute molecule and by a polarizable molecular mechanics (PMM) force field for a large solvent environment composed of several 103-105 molecules as negative gradients of a DFT/PMM hybrid Hamiltonian. The electrostatic interactions are efficiently described by a hierarchical fast multipole method (FMM). Adopting recent progress of this FMM technique [Lorenzen et al., J. Chem. Theory Comput. 10, 3244 (2014)], which particularly entails a strictly linear scaling of the computational effort with the system size, and adapting this revised FMM approach to the computation of the interactions between the DFT and PMM fragments of a simulation system, here, we show how one can further enhance the efficiency and accuracy of such DFT/PMM-MD simulations. The resulting gain of total performance, as measured for alanine dipeptide (DFT) embedded in water (PMM) by the product of the gains in efficiency and accuracy, amounts to about one order of magnitude. We also demonstrate that the jointly parallelized implementation of the DFT and PMM-MD parts of the computation enables the efficient use of high-performance computing systems. The associated software is available online.

  3. Per-field crop classification in irrigated agricultural regions in middle Asia using random forest and support vector machine ensemble

    NASA Astrophysics Data System (ADS)

    Löw, Fabian; Schorcht, Gunther; Michel, Ulrich; Dech, Stefan; Conrad, Christopher

    2012-10-01

    Accurate crop identification and crop area estimation are important for studies on irrigated agricultural systems, yield and water demand modeling, and agrarian policy development. In this study a novel combination of Random Forest (RF) and Support Vector Machine (SVM) classifiers is presented that (i) enhances crop classification accuracy and (ii) provides spatial information on map uncertainty. The methodology was implemented over four distinct irrigated sites in Middle Asia using RapidEye time series data. The RF feature importance statistics was used as feature-selection strategy for the SVM to assess possible negative effects on classification accuracy caused by an oversized feature space. The results of the individual RF and SVM classifications were combined with rules based on posterior classification probability and estimates of classification probability entropy. SVM classification performance was increased by feature selection through RF. Further experimental results indicate that the hybrid classifier improves overall classification accuracy in comparison to the single classifiers as well as useŕs and produceŕs accuracy.

  4. Proposed Morphologic Classification of Prostate Cancer With Neuroendocrine Differentiation

    PubMed Central

    Epstein, Jonathan I.; Amin, Mahul B.; Beltran, Himisha; Lotan, Tamara L.; Mosquera, Juan-Miguel; Reuter, Victor E.; Robinson, Brian D.; Troncoso, Patricia; Rubin, Mark A.

    2014-01-01

    On July 31, 2013, the Prostate Cancer Foundation assembled a working committee on the molecular biology and pathologic classification of neuroendocrine differentiation in prostate cancer. The committee consisted of genitourinary oncologists, urologists, urological surgical pathologists, basic scientists, and translational researchers, with expertise in this field. It was concluded that the proceedings of the meeting should be reported in 2 manuscripts appealing to different target audiences, one to focus on surgical pathology and the other to review the molecular aspects of this disease. New clinical and molecular data emerging from prostate cancers treated by contemporary androgen deprivation therapies, as well as primary lesions, have highlighted the need for refinement of diagnostic terminology to encompass the full spectrum of neuroendocrine differentiation. It is envisioned that specific criteria associated with the refined diagnostic terminology will lead to clinically relevant pathologic diagnoses that will stimulate further clinical and molecular investigation and identification of appropriate targeted therapies. PMID:24705311

  5. Automatic Classification of Aerial Imagery for Urban Hydrological Applications

    NASA Astrophysics Data System (ADS)

    Paul, A.; Yang, C.; Breitkopf, U.; Liu, Y.; Wang, Z.; Rottensteiner, F.; Wallner, M.; Verworn, A.; Heipke, C.

    2018-04-01

    In this paper we investigate the potential of automatic supervised classification for urban hydrological applications. In particular, we contribute to runoff simulations using hydrodynamic urban drainage models. In order to assess whether the capacity of the sewers is sufficient to avoid surcharge within certain return periods, precipitation is transformed into runoff. The transformation of precipitation into runoff requires knowledge about the proportion of drainage-effective areas and their spatial distribution in the catchment area. Common simulation methods use the coefficient of imperviousness as an important parameter to estimate the overland flow, which subsequently contributes to the pipe flow. The coefficient of imperviousness is the percentage of area covered by impervious surfaces such as roofs or road surfaces. It is still common practice to assign the coefficient of imperviousness for each particular land parcel manually by visual interpretation of aerial images. Based on classification results of these imagery we contribute to an objective automatic determination of the coefficient of imperviousness. In this context we compare two classification techniques: Random Forests (RF) and Conditional Random Fields (CRF). Experimental results performed on an urban test area show good results and confirm that the automated derivation of the coefficient of imperviousness, apart from being more objective and, thus, reproducible, delivers more accurate results than the interactive estimation. We achieve an overall accuracy of about 85 % for both classifiers. The root mean square error of the differences of the coefficient of imperviousness compared to the reference is 4.4 % for the CRF-based classification, and 3.8 % for the RF-based classification.

  6. Influence of nuclei segmentation on breast cancer malignancy classification

    NASA Astrophysics Data System (ADS)

    Jelen, Lukasz; Fevens, Thomas; Krzyzak, Adam

    2009-02-01

    Breast Cancer is one of the most deadly cancers affecting middle-aged women. Accurate diagnosis and prognosis are crucial to reduce the high death rate. Nowadays there are numerous diagnostic tools for breast cancer diagnosis. In this paper we discuss a role of nuclear segmentation from fine needle aspiration biopsy (FNA) slides and its influence on malignancy classification. Classification of malignancy plays a very important role during the diagnosis process of breast cancer. Out of all cancer diagnostic tools, FNA slides provide the most valuable information about the cancer malignancy grade which helps to choose an appropriate treatment. This process involves assessing numerous nuclear features and therefore precise segmentation of nuclei is very important. In this work we compare three powerful segmentation approaches and test their impact on the classification of breast cancer malignancy. The studied approaches involve level set segmentation, fuzzy c-means segmentation and textural segmentation based on co-occurrence matrix. Segmented nuclei were used to extract nuclear features for malignancy classification. For classification purposes four different classifiers were trained and tested with previously extracted features. The compared classifiers are Multilayer Perceptron (MLP), Self-Organizing Maps (SOM), Principal Component-based Neural Network (PCA) and Support Vector Machines (SVM). The presented results show that level set segmentation yields the best results over the three compared approaches and leads to a good feature extraction with a lowest average error rate of 6.51% over four different classifiers. The best performance was recorded for multilayer perceptron with an error rate of 3.07% using fuzzy c-means segmentation.

  7. Using the Molecular Classification of Glioblastoma to Inform Personalized Treatment

    PubMed Central

    Olar, Adriana; Aldape, Kenneth D.

    2014-01-01

    Glioblastoma is the most common and most aggressive diffuse glioma, associated with short survival and uniformly fatal outcome irrespective of treatment. It is characterized by morphologic, genetic, and gene-expression heterogeneity. The current standard of treatment is maximal surgical resection, followed by radiation, with concurrent and adjuvant chemotherapy. Due to the heterogeneity most tumors develop resistance to treatment and shortly recur. Following recurrence glioblastoma is quickly fatal in the majority of cases. Recent genetic molecular advances have contributed to a better understanding of glioblastoma pathophysiology and disease stratification. In this paper we review the basic glioblastoma pathophysiology with emphasis on clinically relevant genetic molecular alterations and potential targets for further drug development. PMID:24114756

  8. Longitudinal molecular characterization of endoscopic specimens from colorectal lesions

    PubMed Central

    Minarikova, Petra; Benesova, Lucie; Halkova, Tereza; Belsanova, Barbora; Suchanek, Stepan; Cyrany, Jiri; Tuckova, Inna; Bures, Jan; Zavoral, Miroslav; Minarik, Marek

    2016-01-01

    AIM: To compare molecular profiles of proximal colon, distal colon and rectum in large adenomas, early and late carcinomas. To assess feasibility of testing directed at molecular markers from this study in routine clinical practice. METHODS: A prospective 3-year study has resulted in the acquisition of samples from 159 large adenomas and 138 carcinomas along with associated clinical parameters including localization, grade and histological type for adenomas and localization and stage for carcinomas. A complex molecular phenotyping has been performed using multiplex ligation-dependent probe amplification technique for the evaluation of CpG-island methylator phenotype (CIMP), PCR fragment analysis for detection of microsatellite instability and denaturing capillary electrophoresis for sensitive detection of somatic mutations in KRAS, BRAF, TP53 and APC genes. RESULTS: Molecular types according to previously introduced Jass classification have been evaluated for large adenomas and early and late carcinomas. An increase in CIMP+ type, eventually accompanied with KRAS mutations, was notable between large adenomas and early carcinomas. As expected, the longitudinal observations revealed a correlation of the CIMP+/BRAF+ type with proximal location. CONCLUSION: Prospective molecular classification of tissue specimens is feasible in routine endoscopy practice. Increased frequency of some molecular types corresponds to the developmental stages of colorectal tumors. As expected, a clear distinction is notable for tumors located in proximal colon supposedly arising from the serrated (methylation) pathway. PMID:27239120

  9. Differences in the molecular biology of adenocarcinoma of the esophagus, gastric cardia, and upper gastric third.

    PubMed

    Lehmann, Kuno; Schneider, Paul M

    2010-01-01

    Adenocarcinoma of the distal esophagus, gastric cardia, and upper gastric third are grouped in type I-III by the Siewert classification. This classification is based on the endoscopic localisation of the tumor center, and is the most important diagnostic tool to group these tumors. On a molecular level, there is currently no marker that would allow to differentiate the three different types. Furthermore, the Siewert classification was not uniformly used in the recent literature, making interpretation and generalization of these results difficult. However, several potential targets have been identified that may help to separate these tumors by molecular markers, and are summarized in this chapter.

  10. Accurate label-free 3-part leukocyte recognition with single cell lens-free imaging flow cytometry.

    PubMed

    Li, Yuqian; Cornelis, Bruno; Dusa, Alexandra; Vanmeerbeeck, Geert; Vercruysse, Dries; Sohn, Erik; Blaszkiewicz, Kamil; Prodanov, Dimiter; Schelkens, Peter; Lagae, Liesbet

    2018-05-01

    Three-part white blood cell differentials which are key to routine blood workups are typically performed in centralized laboratories on conventional hematology analyzers operated by highly trained staff. With the trend of developing miniaturized blood analysis tool for point-of-need in order to accelerate turnaround times and move routine blood testing away from centralized facilities on the rise, our group has developed a highly miniaturized holographic imaging system for generating lens-free images of white blood cells in suspension. Analysis and classification of its output data, constitutes the final crucial step ensuring appropriate accuracy of the system. In this work, we implement reference holographic images of single white blood cells in suspension, in order to establish an accurate ground truth to increase classification accuracy. We also automate the entire workflow for analyzing the output and demonstrate clear improvement in the accuracy of the 3-part classification. High-dimensional optical and morphological features are extracted from reconstructed digital holograms of single cells using the ground-truth images and advanced machine learning algorithms are investigated and implemented to obtain 99% classification accuracy. Representative features of the three white blood cell subtypes are selected and give comparable results, with a focus on rapid cell recognition and decreased computational cost. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

  11. Multivariate classification of the infrared spectra of cell and tissue samples

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Haaland, D.M.; Jones, H.D.; Thomas, E.V.

    1997-03-01

    Infrared microspectroscopy of biopsied canine lymph cells and tissue was performed to investigate the possibility of using IR spectra coupled with multivariate classification methods to classify the samples as normal, hyperplastic, or neoplastic (malignant). IR spectra were obtained in transmission mode through BaF{sub 2} windows and in reflection mode from samples prepared on gold-coated microscope slides. Cytology and histopathology samples were prepared by a variety of methods to identify the optimal methods of sample preparation. Cytospinning procedures that yielded a monolayer of cells on the BaF{sub 2} windows produced a limited set of IR transmission spectra. These transmission spectra weremore » converted to absorbance and formed the basis for a classification rule that yielded 100{percent} correct classification in a cross-validated context. Classifications of normal, hyperplastic, and neoplastic cell sample spectra were achieved by using both partial least-squares (PLS) and principal component regression (PCR) classification methods. Linear discriminant analysis applied to principal components obtained from the spectral data yielded a small number of misclassifications. PLS weight loading vectors yield valuable qualitative insight into the molecular changes that are responsible for the success of the infrared classification. These successful classification results show promise for assisting pathologists in the diagnosis of cell types and offer future potential for {ital in vivo} IR detection of some types of cancer. {copyright} {ital 1997} {ital Society for Applied Spectroscopy}« less

  12. [From new genetic and histological classifications to direct treatment].

    PubMed

    Compérat, Eva; Furudoï, Adeline; Varinot, Justine; Rioux-Leclerq, Nathalie

    2016-08-01

    The most important criterion for optimal cancer treatment is a correct classification of the tumour. During the last three years, several very important progresses have been made with a better definition of urothelial carcinoma (UC), especially from a molecular point of view. We start having a global understanding of UC, although many details are still not completely understood. Copyright © 2016 Elsevier Masson SAS. All rights reserved.

  13. Molecular methods for pathogen detection and quantification

    USDA-ARS?s Scientific Manuscript database

    Ongoing interest in convenient, inexpensive, fast, sensitive and accurate techniques for detecting and/or quantifying the presence of soybean pathogens has resulted in increased usage of molecular tools. The method of extracting a molecular target (usually DNA or RNA) for detection depends wholly up...

  14. FPGA Coprocessor for Accelerated Classification of Images

    NASA Technical Reports Server (NTRS)

    Pingree, Paula J.; Scharenbroich, Lucas J.; Werne, Thomas A.

    2008-01-01

    An effort related to that described in the preceding article focuses on developing a spaceborne processing platform for fast and accurate onboard classification of image data, a critical part of modern satellite image processing. The approach again has been to exploit the versatility of recently developed hybrid Virtex-4FX field-programmable gate array (FPGA) to run diverse science applications on embedded processors while taking advantage of the reconfigurable hardware resources of the FPGAs. In this case, the FPGA serves as a coprocessor that implements legacy C-language support-vector-machine (SVM) image-classification algorithms to detect and identify natural phenomena such as flooding, volcanic eruptions, and sea-ice break-up. The FPGA provides hardware acceleration for increased onboard processing capability than previously demonstrated in software. The original C-language program demonstrated on an imaging instrument aboard the Earth Observing-1 (EO-1) satellite implements a linear-kernel SVM algorithm for classifying parts of the images as snow, water, ice, land, or cloud or unclassified. Current onboard processors, such as on EO-1, have limited computing power, extremely limited active storage capability and are no longer considered state-of-the-art. Using commercially available software that translates C-language programs into hardware description language (HDL) files, the legacy C-language program, and two newly formulated programs for a more capable expanded-linear-kernel and a more accurate polynomial-kernel SVM algorithm, have been implemented in the Virtex-4FX FPGA. In tests, the FPGA implementations have exhibited significant speedups over conventional software implementations running on general-purpose hardware.

  15. Accurate indel prediction using paired-end short reads

    PubMed Central

    2013-01-01

    Background One of the major open challenges in next generation sequencing (NGS) is the accurate identification of structural variants such as insertions and deletions (indels). Current methods for indel calling assign scores to different types of evidence or counter-evidence for the presence of an indel, such as the number of split read alignments spanning the boundaries of a deletion candidate or reads that map within a putative deletion. Candidates with a score above a manually defined threshold are then predicted to be true indels. As a consequence, structural variants detected in this manner contain many false positives. Results Here, we present a machine learning based method which is able to discover and distinguish true from false indel candidates in order to reduce the false positive rate. Our method identifies indel candidates using a discriminative classifier based on features of split read alignment profiles and trained on true and false indel candidates that were validated by Sanger sequencing. We demonstrate the usefulness of our method with paired-end Illumina reads from 80 genomes of the first phase of the 1001 Genomes Project ( http://www.1001genomes.org) in Arabidopsis thaliana. Conclusion In this work we show that indel classification is a necessary step to reduce the number of false positive candidates. We demonstrate that missing classification may lead to spurious biological interpretations. The software is available at: http://agkb.is.tuebingen.mpg.de/Forschung/SV-M/. PMID:23442375

  16. [Cystic renal neoplasms. New entities and molecular findings].

    PubMed

    Moch, H

    2010-10-01

    Renal neoplasms with dominant cysts represent a broad spectrum of known as well as novel renal tumor entities. Established renal tumors with dominant cysts include cystic nephroma, mixed epithelial and stromal tumor, synovial sarcoma and multilocular cystic renal cancer (WHO classification 2004). Novel tumor types have recently been reported, which are also characterized by marked cyst formation. Examples are tubulocystic renal cancer and renal cancer in end-stage renal disease. These tumors are very likely to be included in a future WHO classification due to their characteristic phenotype and molecular features. Cysts and clear cell renal cell carcinoma frequently coexist in the kidneys of patients with von Hippel-Lindau disease. Cysts are also a component of many sporadic clear cell renal cell carcinomas. Multilocular cystic renal cell carcinoma is composed almost exclusively of cysts and is regarded as a specific subtype of clear cell renal cancer. Recent molecular findings suggest that clear cell renal cancer may develop via a cyst-dependent mechanism in von Hippel-Lindau syndrome as well as via cyst-independent molecular pathways in sporadic clear cell renal cancer.

  17. Strength in Numbers: Using Big Data to Simplify Sentiment Classification.

    PubMed

    Filippas, Apostolos; Lappas, Theodoros

    2017-09-01

    Sentiment classification, the task of assigning a positive or negative label to a text segment, is a key component of mainstream applications such as reputation monitoring, sentiment summarization, and item recommendation. Even though the performance of sentiment classification methods has steadily improved over time, their ever-increasing complexity renders them comprehensible by only a shrinking minority of expert practitioners. For all others, such highly complex methods are black-box predictors that are hard to tune and even harder to justify to decision makers. Motivated by these shortcomings, we introduce BigCounter: a new algorithm for sentiment classification that substitutes algorithmic complexity with Big Data. Our algorithm combines standard data structures with statistical testing to deliver accurate and interpretable predictions. It is also parameter free and suitable for use virtually "out of the box," which makes it appealing for organizations wanting to leverage their troves of unstructured data without incurring the significant expense of creating in-house teams of data scientists. Finally, BigCounter's efficient and parallelizable design makes it applicable to very large data sets. We apply our method on such data sets toward a study on the limits of Big Data for sentiment classification. Our study finds that, after a certain point, predictive performance tends to converge and additional data have little benefit. Our algorithmic design and findings provide the foundations for future research on the data-over-computation paradigm for classification problems.

  18. Classification algorithm of lung lobe for lung disease cases based on multislice CT images

    NASA Astrophysics Data System (ADS)

    Matsuhiro, M.; Kawata, Y.; Niki, N.; Nakano, Y.; Mishima, M.; Ohmatsu, H.; Tsuchida, T.; Eguchi, K.; Kaneko, M.; Moriyama, N.

    2011-03-01

    With the development of multi-slice CT technology, to obtain an accurate 3D image of lung field in a short time is possible. To support that, a lot of image processing methods need to be developed. In clinical setting for diagnosis of lung cancer, it is important to study and analyse lung structure. Therefore, classification of lung lobe provides useful information for lung cancer analysis. In this report, we describe algorithm which classify lungs into lung lobes for lung disease cases from multi-slice CT images. The classification algorithm of lung lobes is efficiently carried out using information of lung blood vessel, bronchus, and interlobar fissure. Applying the classification algorithms to multi-slice CT images of 20 normal cases and 5 lung disease cases, we demonstrate the usefulness of the proposed algorithms.

  19. Is overall similarity classification less effortful than single-dimension classification?

    PubMed

    Wills, Andy J; Milton, Fraser; Longmore, Christopher A; Hester, Sarah; Robinson, Jo

    2013-01-01

    It is sometimes argued that the implementation of an overall similarity classification is less effortful than the implementation of a single-dimension classification. In the current article, we argue that the evidence securely in support of this view is limited, and report additional evidence in support of the opposite proposition--overall similarity classification is more effortful than single-dimension classification. Using a match-to-standards procedure, Experiments 1A, 1B and 2 demonstrate that concurrent load reduces the prevalence of overall similarity classification, and that this effect is robust to changes in the concurrent load task employed, the level of time pressure experienced, and the short-term memory requirements of the classification task. Experiment 3 demonstrates that participants who produced overall similarity classifications from the outset have larger working memory capacities than those who produced single-dimension classifications initially, and Experiment 4 demonstrates that instructions to respond meticulously increase the prevalence of overall similarity classification.

  20. A framework for farmland parcels extraction based on image classification

    NASA Astrophysics Data System (ADS)

    Liu, Guoying; Ge, Wenying; Song, Xu; Zhao, Hongdan

    2018-03-01

    It is very important for the government to build an accurate national basic cultivated land database. In this work, farmland parcels extraction is one of the basic steps. However, during the past years, people had to spend much time on determining an area is a farmland parcel or not, since they were bounded to understand remote sensing images only from the mere visual interpretation. In order to overcome this problem, in this study, a method was proposed to extract farmland parcels by means of image classification. In the proposed method, farmland areas and ridge areas of the classification map are semantically processed independently and the results are fused together to form the final results of farmland parcels. Experiments on high spatial remote sensing images have shown the effectiveness of the proposed method.

  1. Classification of HCV and HIV-1 Sequences with the Branching Index

    PubMed Central

    Hraber, Peter; Kuiken, Carla; Waugh, Mark; Geer, Shaun; Bruno, William J.; Leitner, Thomas

    2009-01-01

    SUMMARY Classification of viral sequences should be fast, objective, accurate, and reproducible. Most methods that classify sequences use either pairwise distances or phylogenetic relations, but cannot discern when a sequence is unclassifiable. The branching index (BI) combines distance and phylogeny methods to compute a ratio that quantifies how closely a query sequence clusters with a subtype clade. In the hypothesis-testing framework of statistical inference, the BI is compared with a threshold to test whether sufficient evidence exists for the query sequence to be classified among known sequences. If above the threshold, the null hypothesis of no support for the subtype relation is rejected and the sequence is taken as belonging to the subtype clade with which it clusters on the tree. This study evaluates statistical properties of the branching index for subtype classification in HCV and HIV-1. Pairs of BI values with known positive and negative test results were computed from 10,000 random fragments of reference alignments. Sampled fragments were of sufficient length to contain phylogenetic signal that groups reference sequences together properly into subtype clades. For HCV, a threshold BI of 0.71 yields 95.1% agreement with reference subtypes, with equal false positive and false negative rates. For HIV-1, a threshold of 0.66 yields 93.5% agreement. Higher thresholds can be used where lower false positive rates are required. In synthetic recombinants, regions without breakpoints are recognized accurately; regions with breakpoints do not uniquely represent any known subtype. Web-based services for viral subtype classification with the branching index are available online. PMID:18753218

  2. pyQms enables universal and accurate quantification of mass spectrometry data.

    PubMed

    Leufken, Johannes; Niehues, Anna; Sarin, L Peter; Wessel, Florian; Hippler, Michael; Leidel, Sebastian A; Fufezan, Christian

    2017-10-01

    Quantitative mass spectrometry (MS) is a key technique in many research areas (1), including proteomics, metabolomics, glycomics, and lipidomics. Because all of the corresponding molecules can be described by chemical formulas, universal quantification tools are highly desirable. Here, we present pyQms, an open-source software for accurate quantification of all types of molecules measurable by MS. pyQms uses isotope pattern matching that offers an accurate quality assessment of all quantifications and the ability to directly incorporate mass spectrometer accuracy. pyQms is, due to its universal design, applicable to every research field, labeling strategy, and acquisition technique. This opens ultimate flexibility for researchers to design experiments employing innovative and hitherto unexplored labeling strategies. Importantly, pyQms performs very well to accurately quantify partially labeled proteomes in large scale and high throughput, the most challenging task for a quantification algorithm. © 2017 by The American Society for Biochemistry and Molecular Biology, Inc.

  3. Endometrial stromal tumours revisited: an update based on the 2014 WHO classification.

    PubMed

    Ali, Rola H; Rouzbahman, Marjan

    2015-05-01

    Endometrial stromal tumours (EST) are rare tumours of endometrial stromal origin that account for less than 2% of all uterine tumours. Recent cytogenetic and molecular advances in this area have improved our understanding of ESTs and helped refine their classification into more meaningful categories. Accordingly, the newly released 2014 WHO classification system recognises four categories: endometrial stromal nodule (ESN), low-grade endometrial stromal sarcoma (LGESS), high-grade endometrial stromal sarcoma (HGESS) and undifferentiated uterine sarcoma (UUS). At the molecular level, these tumours may demonstrate a relatively simple karyotype with a defining chromosomal rearrangement (as in the majority of ESNs, LGESSs and YWHAE-rearranged HGESS) or demonstrate complex cytogenetic aberrations lacking specific rearrangements (as in UUSs). Herein we provide an update on this topic aimed at the practicing pathologist. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  4. Classification and grading of muscle injuries: a narrative review

    PubMed Central

    Hamilton, Bruce; Valle, Xavier; Rodas, Gil; Til, Luis; Grive, Ricard Pruna; Rincon, Josep Antoni Gutierrez; Tol, Johannes L

    2015-01-01

    A limitation to the accurate study of muscle injuries and their management has been the lack of a uniform approach to the categorisation and grading of muscle injuries. The goal of this narrative review was to provide a framework from which to understand the historical progression of the classification and grading of muscle injuries. We reviewed the classification and grading of muscle injuries in the literature to critically illustrate the strengths, weaknesses, contradictions or controversies. A retrospective, citation-based methodology was applied to search for English language literature which evaluated or utilised a novel muscle classification or grading system. While there is an abundance of literature classifying and grading muscle injuries, it is predominantly expert opinion, and there remains little evidence relating any of the clinical or radiological features to an established pathology or clinical outcome. While the categorical grading of injury severity may have been a reasonable solution to a clinical challenge identified in the middle of the 20th century, it is time to recognise the complexity of the injury, cease trying to oversimplify it and to develop appropriately powered research projects to answer important questions. PMID:25394420

  5. Application of advanced cytometric and molecular technologies to minimal residual disease monitoring

    NASA Astrophysics Data System (ADS)

    Leary, James F.; He, Feng; Reece, Lisa M.

    2000-04-01

    Minimal residual disease monitoring presents a number of theoretical and practical challenges. Recently it has been possible to meet some of these challenges by combining a number of new advanced biotechnologies. To monitor the number of residual tumor cells requires complex cocktails of molecular probes that collectively provide sensitivities of detection on the order of one residual tumor cell per million total cells. Ultra-high-speed, multi parameter flow cytometry is capable of analyzing cells at rates in excess of 100,000 cells/sec. Residual tumor selection marker cocktails can be optimized by use of receiver operating characteristic analysis. New data minimizing techniques when combined with multi variate statistical or neural network classifications of tumor cells can more accurately predict residual tumor cell frequencies. The combination of these techniques can, under at least some circumstances, detect frequencies of tumor cells as low as one cell in a million with an accuracy of over 98 percent correct classification. Detection of mutations in tumor suppressor genes requires insolation of these rare tumor cells and single-cell DNA sequencing. Rare residual tumor cells can be isolated at single cell level by high-resolution single-cell cell sorting. Molecular characterization of tumor suppressor gene mutations can be accomplished using a combination of single- cell polymerase chain reaction amplification of specific gene sequences followed by TA cloning techniques and DNA sequencing. Mutations as small as a single base pair in a tumor suppressor gene of a single sorted tumor cell have been detected using these methods. Using new amplification procedures and DNA micro arrays it should be possible to extend the capabilities shown in this paper to screening of multiple DNA mutations in tumor suppressor and other genes on small numbers of sorted metastatic tumor cells.

  6. Emerging insights into the molecular and cellular basis of glioblastoma

    PubMed Central

    Dunn, Gavin P.; Rinne, Mikael L.; Wykosky, Jill; Genovese, Giannicola; Quayle, Steven N.; Dunn, Ian F.; Agarwalla, Pankaj K.; Chheda, Milan G.; Campos, Benito; Wang, Alan; Brennan, Cameron; Ligon, Keith L.; Furnari, Frank; Cavenee, Webster K.; Depinho, Ronald A.; Chin, Lynda; Hahn, William C.

    2012-01-01

    Glioblastoma is both the most common and lethal primary malignant brain tumor. Extensive multiplatform genomic characterization has provided a higher-resolution picture of the molecular alterations underlying this disease. These studies provide the emerging view that “glioblastoma” represents several histologically similar yet molecularly heterogeneous diseases, which influences taxonomic classification systems, prognosis, and therapeutic decisions. PMID:22508724

  7. Cancer classification in the genomic era: five contemporary problems.

    PubMed

    Song, Qingxuan; Merajver, Sofia D; Li, Jun Z

    2015-10-19

    Classification is an everyday instinct as well as a full-fledged scientific discipline. Throughout the history of medicine, disease classification is central to how we develop knowledge, make diagnosis, and assign treatment. Here, we discuss the classification of cancer and the process of categorizing cancer subtypes based on their observed clinical and biological features. Traditionally, cancer nomenclature is primarily based on organ location, e.g., "lung cancer" designates a tumor originating in lung structures. Within each organ-specific major type, finer subgroups can be defined based on patient age, cell type, histological grades, and sometimes molecular markers, e.g., hormonal receptor status in breast cancer or microsatellite instability in colorectal cancer. In the past 15+ years, high-throughput technologies have generated rich new data regarding somatic variations in DNA, RNA, protein, or epigenomic features for many cancers. These data, collected for increasingly large tumor cohorts, have provided not only new insights into the biological diversity of human cancers but also exciting opportunities to discover previously unrecognized cancer subtypes. Meanwhile, the unprecedented volume and complexity of these data pose significant challenges for biostatisticians, cancer biologists, and clinicians alike. Here, we review five related issues that represent contemporary problems in cancer taxonomy and interpretation. (1) How many cancer subtypes are there? (2) How can we evaluate the robustness of a new classification system? (3) How are classification systems affected by intratumor heterogeneity and tumor evolution? (4) How should we interpret cancer subtypes? (5) Can multiple classification systems co-exist? While related issues have existed for a long time, we will focus on those aspects that have been magnified by the recent influx of complex multi-omics data. Exploration of these problems is essential for data-driven refinement of cancer classification

  8. Classification of human coronary atherosclerotic plaques using ex vivo high-resolution multicontrast-weighted MRI compared with histopathology.

    PubMed

    Li, Tao; Li, Xin; Zhao, Xihai; Zhou, Weihua; Cai, Zulong; Yang, Li; Guo, Aitao; Zhao, Shaohong

    2012-05-01

    The objective of our study was to evaluate the feasibility of ex vivo high-resolution multicontrast-weighted MRI to accurately classify human coronary atherosclerotic plaques according to the American Heart Association classification. Thirteen human cadaver heart specimens were imaged using high-resolution multicontrast-weighted MR technique (T1-weighted, proton density-weighted, and T2-weighted). All MR images were matched with histopathologic sections according to the landmark of the bifurcation of the left main coronary artery. The sensitivity and specificity of MRI for the classification of plaques were determined, and Cohen's kappa analysis was applied to evaluate the agreement between MRI and histopathology in the classification of atherosclerotic plaques. One hundred eleven MR cross-sectional images obtained perpendicular to the long axis of the proximal left anterior descending artery were successfully matched with the histopathologic sections. For the classification of plaques, the sensitivity and specificity of MRI were as follows: type I-II (near normal), 60% and 100%; type III (focal lipid pool), 80% and 100%; type IV-V (lipid, necrosis, fibrosis), 96.2% and 88.2%; type VI (hemorrhage), 100% and 99.0%; type VII (calcification), 93% and 100%; and type VIII (fibrosis without lipid core), 100% and 99.1%, respectively. Isointensity, which indicates lipid composition on histopathology, was detected on MRI in 48.8% of calcified plaques. Agreement between MRI and histopathology for plaque classification was 0.86 (p < 0.001). Ex vivo high-resolution multicontrast-weighted MRI can accurately classify advanced atherosclerotic plaques in human coronary arteries.

  9. Classification in Australia.

    ERIC Educational Resources Information Center

    McKinlay, John

    Despite some inroads by the Library of Congress Classification and short-lived experimentation with Universal Decimal Classification and Bliss Classification, Dewey Decimal Classification, with its ability in recent editions to be hospitable to local needs, remains the most widely used classification system in Australia. Although supplemented at…

  10. Recursive heuristic classification

    NASA Technical Reports Server (NTRS)

    Wilkins, David C.

    1994-01-01

    The author will describe a new problem-solving approach called recursive heuristic classification, whereby a subproblem of heuristic classification is itself formulated and solved by heuristic classification. This allows the construction of more knowledge-intensive classification programs in a way that yields a clean organization. Further, standard knowledge acquisition and learning techniques for heuristic classification can be used to create, refine, and maintain the knowledge base associated with the recursively called classification expert system. The method of recursive heuristic classification was used in the Minerva blackboard shell for heuristic classification. Minerva recursively calls itself every problem-solving cycle to solve the important blackboard scheduler task, which involves assigning a desirability rating to alternative problem-solving actions. Knowing these ratings is critical to the use of an expert system as a component of a critiquing or apprenticeship tutoring system. One innovation of this research is a method called dynamic heuristic classification, which allows selection among dynamically generated classification categories instead of requiring them to be prenumerated.

  11. A Molecular Phylogeny for the Leaf-Roller Moths (Lepidoptera: Tortricidae) and Its Implications for Classification and Life History Evolution

    PubMed Central

    Regier, Jerome C.; Brown, John W.; Mitter, Charles; Baixeras, Joaquín; Cho, Soowon; Cummings, Michael P.; Zwick, Andreas

    2012-01-01

    Background Tortricidae, one of the largest families of microlepidopterans, comprise about 10,000 described species worldwide, including important pests, biological control agents and experimental models. Understanding of tortricid phylogeny, the basis for a predictive classification, is currently provisional. We present the first detailed molecular estimate of relationships across the tribes and subfamilies of Tortricidae, assess its concordance with previous morphological evidence, and re-examine postulated evolutionary trends in host plant use and biogeography. Methodology/Principal Findings We sequenced up to five nuclear genes (6,633 bp) in each of 52 tortricids spanning all three subfamilies and 19 of the 22 tribes, plus up to 14 additional genes, for a total of 14,826 bp, in 29 of those taxa plus all 14 outgroup taxa. Maximum likelihood analyses yield trees that, within Tortricidae, differ little among data sets and character treatments and are nearly always strongly supported at all levels of divergence. Support for several nodes was greatly increased by the additional 14 genes sequenced in just 29 of 52 tortricids, with no evidence of phylogenetic artifacts from deliberately incomplete gene sampling. There is strong support for the monophyly of Tortricinae and of Olethreutinae, and for grouping of these to the exclusion of Chlidanotinae. Relationships among tribes are robustly resolved in Tortricinae and mostly so in Olethreutinae. Feeding habit (internal versus external) is strongly conserved on the phylogeny. Within Tortricinae, a clade characterized by eggs being deposited in large clusters, in contrast to singly or in small batches, has markedly elevated incidence of polyphagous species. The five earliest-branching tortricid lineages are all species-poor tribes with mainly southern/tropical distributions, consistent with a hypothesized Gondwanan origin for the family. Conclusions/Significance We present the first robustly supported phylogeny for

  12. Using discordance to improve classification in narrative clinical databases: an application to community-acquired pneumonia.

    PubMed

    Hripcsak, George; Knirsch, Charles; Zhou, Li; Wilcox, Adam; Melton, Genevieve B

    2007-03-01

    Data mining in electronic medical records may facilitate clinical research, but much of the structured data may be miscoded, incomplete, or non-specific. The exploitation of narrative data using natural language processing may help, although nesting, varying granularity, and repetition remain challenges. In a study of community-acquired pneumonia using electronic records, these issues led to poor classification. Limiting queries to accurate, complete records led to vastly reduced, possibly biased samples. We exploited knowledge latent in the electronic records to improve classification. A similarity metric was used to cluster cases. We defined discordance as the degree to which cases within a cluster give different answers for some query that addresses a classification task of interest. Cases with higher discordance are more likely to be incorrectly classified, and can be reviewed manually to adjust the classification, improve the query, or estimate the likely accuracy of the query. In a study of pneumonia--in which the ICD9-CM coding was found to be very poor--the discordance measure was statistically significantly correlated with classification correctness (.45; 95% CI .15-.62).

  13. Numeric pathologic lymph node classification shows prognostic superiority to topographic pN classification in esophageal squamous cell carcinoma.

    PubMed

    Sugawara, Kotaro; Yamashita, Hiroharu; Uemura, Yukari; Mitsui, Takashi; Yagi, Koichi; Nishida, Masato; Aikou, Susumu; Mori, Kazuhiko; Nomura, Sachiyo; Seto, Yasuyuki

    2017-10-01

    The current eighth tumor node metastasis lymph node category pathologic lymph node staging system for esophageal squamous cell carcinoma is based solely on the number of metastatic nodes and does not consider anatomic distribution. We aimed to assess the prognostic capability of the eighth tumor node metastasis pathologic lymph node staging system (numeric-based) compared with the 11th Japan Esophageal Society (topography-based) pathologic lymph node staging system in patients with esophageal squamous cell carcinoma. We retrospectively reviewed the clinical records of 289 patients with esophageal squamous cell carcinoma who underwent esophagectomy with extended lymph node dissection during the period from January 2006 through June 2016. We compared discrimination abilities for overall survival, recurrence-free survival, and cancer-specific survival between these 2 staging systems using C-statistics. The median number of dissected and metastatic nodes was 61 (25% to 75% quartile range, 45 to 79) and 1 (25% to 75% quartile range, 0 to 3), respectively. The eighth tumor node metastasis pathologic lymph node staging system had a greater ability to accurately determine overall survival (C-statistics: tumor node metastasis classification, 0.69, 95% confidence interval, 0.62-0.76; Japan Esophageal Society classification; 0.65, 95% confidence interval, 0.58-0.71; P = .014) and cancer-specific survival (C-statistics: tumor node metastasis classification, 0.78, 95% confidence interval, 0.70-0.87; Japan Esophageal Society classification; 0.72, 95% confidence interval, 0.64-0.80; P = .018). Rates of total recurrence rose as the eighth tumor node metastasis pathologic lymph node stage increased, while stratification of patients according to the topography-based node classification system was not feasible. Numeric nodal staging is an essential tool for stratifying the oncologic outcomes of patients with esophageal squamous cell carcinoma even in the cohort in which adequate

  14. Optical beam classification using deep learning: a comparison with rule- and feature-based classification

    NASA Astrophysics Data System (ADS)

    Alom, Md. Zahangir; Awwal, Abdul A. S.; Lowe-Webb, Roger; Taha, Tarek M.

    2017-08-01

    Deep-learning methods are gaining popularity because of their state-of-the-art performance in image classification tasks. In this paper, we explore classification of laser-beam images from the National Ignition Facility (NIF) using a novel deeplearning approach. NIF is the world's largest, most energetic laser. It has nearly 40,000 optics that precisely guide, reflect, amplify, and focus 192 laser beams onto a fusion target. NIF utilizes four petawatt lasers called the Advanced Radiographic Capability (ARC) to produce backlighting X-ray illumination to capture implosion dynamics of NIF experiments with picosecond temporal resolution. In the current operational configuration, four independent short-pulse ARC beams are created and combined in a split-beam configuration in each of two NIF apertures at the entry of the pre-amplifier. The subaperture beams then propagate through the NIF beampath up to the ARC compressor. Each ARC beamlet is separately compressed with a dedicated set of four gratings and recombined as sub-apertures for transport to the parabola vessel, where the beams are focused using parabolic mirrors and pointed to the target. Small angular errors in the compressor gratings can cause the sub-aperture beams to diverge from one another and prevent accurate alignment through the transport section between the compressor and parabolic mirrors. This is an off-normal condition that must be detected and corrected. The goal of the off-normal check is to determine whether the ARC beamlets are sufficiently overlapped into a merged single spot or diverged into two distinct spots. Thus, the objective of the current work is three-fold: developing a simple algorithm to perform off-normal classification, exploring the use of Convolutional Neural Network (CNN) for the same task, and understanding the inter-relationship of the two approaches. The CNN recognition results are compared with other machine-learning approaches, such as Deep Neural Network (DNN) and Support

  15. Constant size descriptors for accurate machine learning models of molecular properties

    NASA Astrophysics Data System (ADS)

    Collins, Christopher R.; Gordon, Geoffrey J.; von Lilienfeld, O. Anatole; Yaron, David J.

    2018-06-01

    Two different classes of molecular representations for use in machine learning of thermodynamic and electronic properties are studied. The representations are evaluated by monitoring the performance of linear and kernel ridge regression models on well-studied data sets of small organic molecules. One class of representations studied here counts the occurrence of bonding patterns in the molecule. These require only the connectivity of atoms in the molecule as may be obtained from a line diagram or a SMILES string. The second class utilizes the three-dimensional structure of the molecule. These include the Coulomb matrix and Bag of Bonds, which list the inter-atomic distances present in the molecule, and Encoded Bonds, which encode such lists into a feature vector whose length is independent of molecular size. Encoded Bonds' features introduced here have the advantage of leading to models that may be trained on smaller molecules and then used successfully on larger molecules. A wide range of feature sets are constructed by selecting, at each rank, either a graph or geometry-based feature. Here, rank refers to the number of atoms involved in the feature, e.g., atom counts are rank 1, while Encoded Bonds are rank 2. For atomization energies in the QM7 data set, the best graph-based feature set gives a mean absolute error of 3.4 kcal/mol. Inclusion of 3D geometry substantially enhances the performance, with Encoded Bonds giving 2.4 kcal/mol, when used alone, and 1.19 kcal/mol, when combined with graph features.

  16. Leishmania infections: Molecular targets and diagnosis.

    PubMed

    Akhoundi, Mohammad; Downing, Tim; Votýpka, Jan; Kuhls, Katrin; Lukeš, Julius; Cannet, Arnaud; Ravel, Christophe; Marty, Pierre; Delaunay, Pascal; Kasbari, Mohamed; Granouillac, Bruno; Gradoni, Luigi; Sereno, Denis

    2017-10-01

    Progress in the diagnosis of leishmaniases depends on the development of effective methods and the discovery of suitable biomarkers. We propose firstly an update classification of Leishmania species and their synonymies. We demonstrate a global map highlighting the geography of known endemic Leishmania species pathogenic to humans. We summarize a complete list of techniques currently in use and discuss their advantages and limitations. The available data highlights the benefits of molecular markers in terms of their sensitivity and specificity to quantify variation from the subgeneric level to species complexes, (sub) species within complexes, and individual populations and infection foci. Each DNA-based detection method is supplied with a comprehensive description of markers and primers and proposal for a classification based on the role of each target and primer in the detection, identification and quantification of leishmaniasis infection. We outline a genome-wide map of genes informative for diagnosis that have been used for Leishmania genotyping. Furthermore, we propose a classification method based on the suitability of well-studied molecular markers for typing the 21 known Leishmania species pathogenic to humans. This can be applied to newly discovered species and to hybrid strains originating from inter-species crosses. Developing more effective and sensitive diagnostic methods and biomarkers is vital for enhancing Leishmania infection control programs. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

  17. Machine Learning of Human Pluripotent Stem Cell-Derived Engineered Cardiac Tissue Contractility for Automated Drug Classification.

    PubMed

    Lee, Eugene K; Tran, David D; Keung, Wendy; Chan, Patrick; Wong, Gabriel; Chan, Camie W; Costa, Kevin D; Li, Ronald A; Khine, Michelle

    2017-11-14

    Accurately predicting cardioactive effects of new molecular entities for therapeutics remains a daunting challenge. Immense research effort has been focused toward creating new screening platforms that utilize human pluripotent stem cell (hPSC)-derived cardiomyocytes and three-dimensional engineered cardiac tissue constructs to better recapitulate human heart function and drug responses. As these new platforms become increasingly sophisticated and high throughput, the drug screens result in larger multidimensional datasets. Improved automated analysis methods must therefore be developed in parallel to fully comprehend the cellular response across a multidimensional parameter space. Here, we describe the use of machine learning to comprehensively analyze 17 functional parameters derived from force readouts of hPSC-derived ventricular cardiac tissue strips (hvCTS) electrically paced at a range of frequencies and exposed to a library of compounds. A generated metric is effective for then determining the cardioactivity of a given drug. Furthermore, we demonstrate a classification model that can automatically predict the mechanistic action of an unknown cardioactive drug. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

  18. Comparing Features for Classification of MEG Responses to Motor Imagery.

    PubMed

    Halme, Hanna-Leena; Parkkonen, Lauri

    2016-01-01

    Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest. MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio-spectral decomposition (SSD), and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject. The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7%) and MI-vs-rest (mean 81.3%) classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%). There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results. We obtained good accuracy in sensor-level decoding of MI from single-trial MEG data. Feature extraction

  19. Comparing Features for Classification of MEG Responses to Motor Imagery

    PubMed Central

    Halme, Hanna-Leena; Parkkonen, Lauri

    2016-01-01

    Background Motor imagery (MI) with real-time neurofeedback could be a viable approach, e.g., in rehabilitation of cerebral stroke. Magnetoencephalography (MEG) noninvasively measures electric brain activity at high temporal resolution and is well-suited for recording oscillatory brain signals. MI is known to modulate 10- and 20-Hz oscillations in the somatomotor system. In order to provide accurate feedback to the subject, the most relevant MI-related features should be extracted from MEG data. In this study, we evaluated several MEG signal features for discriminating between left- and right-hand MI and between MI and rest. Methods MEG was measured from nine healthy participants imagining either left- or right-hand finger tapping according to visual cues. Data preprocessing, feature extraction and classification were performed offline. The evaluated MI-related features were power spectral density (PSD), Morlet wavelets, short-time Fourier transform (STFT), common spatial patterns (CSP), filter-bank common spatial patterns (FBCSP), spatio—spectral decomposition (SSD), and combined SSD+CSP, CSP+PSD, CSP+Morlet, and CSP+STFT. We also compared four classifiers applied to single trials using 5-fold cross-validation for evaluating the classification accuracy and its possible dependence on the classification algorithm. In addition, we estimated the inter-session left-vs-right accuracy for each subject. Results The SSD+CSP combination yielded the best accuracy in both left-vs-right (mean 73.7%) and MI-vs-rest (mean 81.3%) classification. CSP+Morlet yielded the best mean accuracy in inter-session left-vs-right classification (mean 69.1%). There were large inter-subject differences in classification accuracy, and the level of the 20-Hz suppression correlated significantly with the subjective MI-vs-rest accuracy. Selection of the classification algorithm had only a minor effect on the results. Conclusions We obtained good accuracy in sensor-level decoding of MI from single

  20. A Three-Phase Decision Model of Computer-Aided Coding for the Iranian Classification of Health Interventions (IRCHI)

    PubMed Central

    Azadmanjir, Zahra; Safdari, Reza; Ghazisaeedi, Marjan; Mokhtaran, Mehrshad; Kameli, Mohammad Esmail

    2017-01-01

    Introduction: Accurate coded data in the healthcare are critical. Computer-Assisted Coding (CAC) is an effective tool to improve clinical coding in particular when a new classification will be developed and implemented. But determine the appropriate method for development need to consider the specifications of existing CAC systems, requirements for each type, our infrastructure and also, the classification scheme. Aim: The aim of the study was the development of a decision model for determining accurate code of each medical intervention in Iranian Classification of Health Interventions (IRCHI) that can be implemented as a suitable CAC system. Methods: first, a sample of existing CAC systems was reviewed. Then feasibility of each one of CAC types was examined with regard to their prerequisites for their implementation. The next step, proper model was proposed according to the structure of the classification scheme and was implemented as an interactive system. Results: There is a significant relationship between the level of assistance of a CAC system and integration of it with electronic medical documents. Implementation of fully automated CAC systems is impossible due to immature development of electronic medical record and problems in using language for medical documenting. So, a model was proposed to develop semi-automated CAC system based on hierarchical relationships between entities in the classification scheme and also the logic of decision making to specify the characters of code step by step through a web-based interactive user interface for CAC. It was composed of three phases to select Target, Action and Means respectively for an intervention. Conclusion: The proposed model was suitable the current status of clinical documentation and coding in Iran and also, the structure of new classification scheme. Our results show it was practical. However, the model needs to be evaluated in the next stage of the research. PMID:28883671

  1. A Three-Phase Decision Model of Computer-Aided Coding for the Iranian Classification of Health Interventions (IRCHI).

    PubMed

    Azadmanjir, Zahra; Safdari, Reza; Ghazisaeedi, Marjan; Mokhtaran, Mehrshad; Kameli, Mohammad Esmail

    2017-06-01

    Accurate coded data in the healthcare are critical. Computer-Assisted Coding (CAC) is an effective tool to improve clinical coding in particular when a new classification will be developed and implemented. But determine the appropriate method for development need to consider the specifications of existing CAC systems, requirements for each type, our infrastructure and also, the classification scheme. The aim of the study was the development of a decision model for determining accurate code of each medical intervention in Iranian Classification of Health Interventions (IRCHI) that can be implemented as a suitable CAC system. first, a sample of existing CAC systems was reviewed. Then feasibility of each one of CAC types was examined with regard to their prerequisites for their implementation. The next step, proper model was proposed according to the structure of the classification scheme and was implemented as an interactive system. There is a significant relationship between the level of assistance of a CAC system and integration of it with electronic medical documents. Implementation of fully automated CAC systems is impossible due to immature development of electronic medical record and problems in using language for medical documenting. So, a model was proposed to develop semi-automated CAC system based on hierarchical relationships between entities in the classification scheme and also the logic of decision making to specify the characters of code step by step through a web-based interactive user interface for CAC. It was composed of three phases to select Target, Action and Means respectively for an intervention. The proposed model was suitable the current status of clinical documentation and coding in Iran and also, the structure of new classification scheme. Our results show it was practical. However, the model needs to be evaluated in the next stage of the research.

  2. Spectral dependence of texture features integrated with hyperspectral data for area target classification improvement

    NASA Astrophysics Data System (ADS)

    Bangs, Corey F.; Kruse, Fred A.; Olsen, Chris R.

    2013-05-01

    Hyperspectral data were assessed to determine the effect of integrating spectral data and extracted texture feature data on classification accuracy. Four separate spectral ranges (hundreds of spectral bands total) were used from the Visible and Near Infrared (VNIR) and Shortwave Infrared (SWIR) portions of the electromagnetic spectrum. Haralick texture features (contrast, entropy, and correlation) were extracted from the average gray-level image for each of the four spectral ranges studied. A maximum likelihood classifier was trained using a set of ground truth regions of interest (ROIs) and applied separately to the spectral data, texture data, and a fused dataset containing both. Classification accuracy was measured by comparison of results to a separate verification set of test ROIs. Analysis indicates that the spectral range (source of the gray-level image) used to extract the texture feature data has a significant effect on the classification accuracy. This result applies to texture-only classifications as well as the classification of integrated spectral data and texture feature data sets. Overall classification improvement for the integrated data sets was near 1%. Individual improvement for integrated spectral and texture classification of the "Urban" class showed approximately 9% accuracy increase over spectral-only classification. Texture-only classification accuracy was highest for the "Dirt Path" class at approximately 92% for the spectral range from 947 to 1343nm. This research demonstrates the effectiveness of texture feature data for more accurate analysis of hyperspectral data and the importance of selecting the correct spectral range to be used for the gray-level image source to extract these features.

  3. Clinical Variant Classification: A Comparison of Public Databases and a Commercial Testing Laboratory.

    PubMed

    Gradishar, William; Johnson, KariAnne; Brown, Krystal; Mundt, Erin; Manley, Susan

    2017-07-01

    There is a growing move to consult public databases following receipt of a genetic test result from a clinical laboratory; however, the well-documented limitations of these databases call into question how often clinicians will encounter discordant variant classifications that may introduce uncertainty into patient management. Here, we evaluate discordance in BRCA1 and BRCA2 variant classifications between a single commercial testing laboratory and a public database commonly consulted in clinical practice. BRCA1 and BRCA2 variant classifications were obtained from ClinVar and compared with the classifications from a reference laboratory. Full concordance and discordance were determined for variants whose ClinVar entries were of the same pathogenicity (pathogenic, benign, or uncertain). Variants with conflicting ClinVar classifications were considered partially concordant if ≥1 of the listed classifications agreed with the reference laboratory classification. Four thousand two hundred and fifty unique BRCA1 and BRCA2 variants were available for analysis. Overall, 73.2% of classifications were fully concordant and 12.3% were partially concordant. The remaining 14.5% of variants had discordant classifications, most of which had a definitive classification (pathogenic or benign) from the reference laboratory compared with an uncertain classification in ClinVar (14.0%). Here, we show that discrepant classifications between a public database and single reference laboratory potentially account for 26.7% of variants in BRCA1 and BRCA2 . The time and expertise required of clinicians to research these discordant classifications call into question the practicality of checking all test results against a database and suggest that discordant classifications should be interpreted with these limitations in mind. With the increasing use of clinical genetic testing for hereditary cancer risk, accurate variant classification is vital to ensuring appropriate medical management

  4. Coefficient of variation for use in crop area classification across multiple climates

    NASA Astrophysics Data System (ADS)

    Whelen, Tracy; Siqueira, Paul

    2018-05-01

    In this study, the coefficient of variation (CV) is introduced as a unitless statistical measurement for the classification of croplands using synthetic aperture radar (SAR) data. As a measurement of change, the CV is able to capture changing backscatter responses caused by cycles of planting, growing, and harvesting, and thus is able to differentiate these areas from a more static forest or urban area. Pixels with CV values above a given threshold are classified as crops, and below the threshold are non-crops. This paper uses cross-polarized L-band SAR data from the ALOS PALSAR satellite to classify eleven regions across the United States, covering a wide range of major crops and climates. Two separate sets of classification were done, with the first targeting the optimum classification thresholds for each dataset, and the second using a generalized threshold for all datasets to simulate a large-scale operationalized situation. Overall accuracies for the first phase of classification ranged from 66%-81%, and 62%-84% for the second phase. Visual inspection of the results shows numerous possibilities for improving the classifications while still using the same classification method, including increasing the number and temporal frequency of input images in order to better capture phenological events and mitigate the effects of major precipitation events, as well as more accurate ground truth data. These improvements would make the CV method a viable tool for monitoring agriculture throughout the year on a global scale.

  5. Molecular systematics of higher primates: genealogical relations and classification.

    PubMed Central

    Miyamoto, M M; Koop, B F; Slightom, J L; Goodman, M; Tennant, M R

    1988-01-01

    We obtained 5' and 3' flanking sequences (5.4 kilobase pairs) from the psi eta-globin gene region of the rhesus macaque (Macaca mulatta) and combined them with available nucleotide data. The completed sequence, representing 10.8 kilobase pairs of contiguous noncoding DNA, was compared to the same orthologous regions available for human (Homo sapiens, as represented by five different alleles), common chimpanzee (Pan troglodytes), gorilla (Gorilla gorilla), and orangutan (Pongo pygmaeus). The nucleotide sequence for Macaca mulatta provided the outgroup perspective needed to evaluate better the relationships of humans and great apes. Pairwise comparisons and parsimony analysis of these orthologues clearly demonstrated (i) that humans and great apes share a high degree of genetic similarity and (ii) that humans, chimpanzees, and gorillas form a natural monophyletic group. These conclusions strongly favor a genealogical classification for higher primates consisting of a single family (Hominidae) with two subfamilies (Homininae for Homo, Pan, and Gorilla and Ponginae for Pongo). PMID:3174657

  6. A High Resolution/Accurate Mass (HRAM) Data-Dependent MS3 Neutral Loss Screening, Classification, and Relative Quantitation Methodology for Carbonyl Compounds in Saliva

    NASA Astrophysics Data System (ADS)

    Dator, Romel; Carrà, Andrea; Maertens, Laura; Guidolin, Valeria; Villalta, Peter W.; Balbo, Silvia

    2017-04-01

    Reactive carbonyl compounds (RCCs) are ubiquitous in the environment and are generated endogenously as a result of various physiological and pathological processes. These compounds can react with biological molecules inducing deleterious processes believed to be at the basis of their toxic effects. Several of these compounds are implicated in neurotoxic processes, aging disorders, and cancer. Therefore, a method characterizing exposures to these chemicals will provide insights into how they may influence overall health and contribute to disease pathogenesis. Here, we have developed a high resolution accurate mass (HRAM) screening strategy allowing simultaneous identification and relative quantitation of DNPH-derivatized carbonyls in human biological fluids. The screening strategy involves the diagnostic neutral loss of hydroxyl radical triggering MS3 fragmentation, which is only observed in positive ionization mode of DNPH-derivatized carbonyls. Unique fragmentation pathways were used to develop a classification scheme for characterizing known and unanticipated/unknown carbonyl compounds present in saliva. Furthermore, a relative quantitation strategy was implemented to assess variations in the levels of carbonyl compounds before and after exposure using deuterated d 3 -DNPH. This relative quantitation method was tested on human samples before and after exposure to specific amounts of alcohol. The nano-electrospray ionization (nano-ESI) in positive mode afforded excellent sensitivity with detection limits on-column in the high-attomole levels. To the best of our knowledge, this is the first report of a method using HRAM neutral loss screening of carbonyl compounds. In addition, the method allows simultaneous characterization and relative quantitation of DNPH-derivatized compounds using nano-ESI in positive mode.

  7. A Comprehensive Strategy for Accurate Mutation Detection of the Highly Homologous PMS2.

    PubMed

    Li, Jianli; Dai, Hongzheng; Feng, Yanming; Tang, Jia; Chen, Stella; Tian, Xia; Gorman, Elizabeth; Schmitt, Eric S; Hansen, Terah A A; Wang, Jing; Plon, Sharon E; Zhang, Victor Wei; Wong, Lee-Jun C

    2015-09-01

    Germline mutations in the DNA mismatch repair gene PMS2 underlie the cancer susceptibility syndrome, Lynch syndrome. However, accurate molecular testing of PMS2 is complicated by a large number of highly homologous sequences. To establish a comprehensive approach for mutation detection of PMS2, we have designed a strategy combining targeted capture next-generation sequencing (NGS), multiplex ligation-dependent probe amplification, and long-range PCR followed by NGS to simultaneously detect point mutations and copy number changes of PMS2. Exonic deletions (E2 to E9, E5 to E9, E8, E10, E14, and E1 to E15), duplications (E11 to E12), and a nonsense mutation, p.S22*, were identified. Traditional multiplex ligation-dependent probe amplification and Sanger sequencing approaches cannot differentiate the origin of the exonic deletions in the 3' region when PMS2 and PMS2CL share identical sequences as a result of gene conversion. Our approach allows unambiguous identification of mutations in the active gene with a straightforward long-range-PCR/NGS method. Breakpoint analysis of multiple samples revealed that recurrent exon 14 deletions are mediated by homologous Alu sequences. Our comprehensive approach provides a reliable tool for accurate molecular analysis of genes containing multiple copies of highly homologous sequences and should improve PMS2 molecular analysis for patients with Lynch syndrome. Copyright © 2015 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.

  8. Successional stage of biological soil crusts: an accurate indicator of ecohydrological condition

    USGS Publications Warehouse

    Belnap, Jayne; Wilcox, Bradford P.; Van Scoyoc, Matthew V.; Phillips, Susan L.

    2013-01-01

    Biological soil crusts are a key component of many dryland ecosystems. Following disturbance, biological soil crusts will recover in stages. Recently, a simple classification of these stages has been developed, largely on the basis of external features of the crusts, which reflects their level of development (LOD). The classification system has six LOD classes, from low (1) to high (6). To determine whether the LOD of a crust is related to its ecohydrological function, we used rainfall simulation to evaluate differences in infiltration, runoff, and erosion among crusts in the various LODs, across a range of soil depths and with different wetting pre-treatments. We found large differences between the lowest and highest LODs, with runoff and erosion being greatest from the lowest LOD. Under dry antecedent conditions, about 50% of the water applied ran off the lowest LOD plots, whereas less than 10% ran off the plots of the two highest LODs. Similarly, sediment loss was 400 g m-2 from the lowest LOD and almost zero from the higher LODs. We scaled up the results from these simulations using the Rangeland Hydrology and Erosion Model. Modelling results indicate that erosion increases dramatically as slope length and gradient increase, especially beyond the threshold values of 10 m for slope length and 10% for slope gradient. Our findings confirm that the LOD classification is a quick, easy, nondestructive, and accurate index of hydrological condition and should be incorporated in field and modelling assessments of ecosystem health.

  9. Combining High Spatial Resolution Optical and LIDAR Data for Object-Based Image Classification

    NASA Astrophysics Data System (ADS)

    Li, R.; Zhang, T.; Geng, R.; Wang, L.

    2018-04-01

    In order to classify high spatial resolution images more accurately, in this research, a hierarchical rule-based object-based classification framework was developed based on a high-resolution image with airborne Light Detection and Ranging (LiDAR) data. The eCognition software is employed to conduct the whole process. In detail, firstly, the FBSP optimizer (Fuzzy-based Segmentation Parameter) is used to obtain the optimal scale parameters for different land cover types. Then, using the segmented regions as basic units, the classification rules for various land cover types are established according to the spectral, morphological and texture features extracted from the optical images, and the height feature from LiDAR respectively. Thirdly, the object classification results are evaluated by using the confusion matrix, overall accuracy and Kappa coefficients. As a result, a method using the combination of an aerial image and the airborne Lidar data shows higher accuracy.

  10. An accurate computational method for an order parameter with a Markov state model constructed using a manifold-learning technique

    NASA Astrophysics Data System (ADS)

    Ito, Reika; Yoshidome, Takashi

    2018-01-01

    Markov state models (MSMs) are a powerful approach for analyzing the long-time behaviors of protein motion using molecular dynamics simulation data. However, their quantitative performance with respect to the physical quantities is poor. We believe that this poor performance is caused by the failure to appropriately classify protein conformations into states when constructing MSMs. Herein, we show that the quantitative performance of an order parameter is improved when a manifold-learning technique is employed for the classification in the MSM. The MSM construction using the K-center method, which has been previously used for classification, has a poor quantitative performance.

  11. An accurate sleep stages classification system using a new class of optimally time-frequency localized three-band wavelet filter bank.

    PubMed

    Sharma, Manish; Goyal, Deepanshu; Achuth, P V; Acharya, U Rajendra

    2018-07-01

    Sleep related disorder causes diminished quality of lives in human beings. Sleep scoring or sleep staging is the process of classifying various sleep stages which helps to detect the quality of sleep. The identification of sleep-stages using electroencephalogram (EEG) signals is an arduous task. Just by looking at an EEG signal, one cannot determine the sleep stages precisely. Sleep specialists may make errors in identifying sleep stages by visual inspection. To mitigate the erroneous identification and to reduce the burden on doctors, a computer-aided EEG based system can be deployed in the hospitals, which can help identify the sleep stages, correctly. Several automated systems based on the analysis of polysomnographic (PSG) signals have been proposed. A few sleep stage scoring systems using EEG signals have also been proposed. But, still there is a need for a robust and accurate portable system developed using huge dataset. In this study, we have developed a new single-channel EEG based sleep-stages identification system using a novel set of wavelet-based features extracted from a large EEG dataset. We employed a novel three-band time-frequency localized (TBTFL) wavelet filter bank (FB). The EEG signals are decomposed using three-level wavelet decomposition, yielding seven sub-bands (SBs). This is followed by the computation of discriminating features namely, log-energy (LE), signal-fractal-dimensions (SFD), and signal-sample-entropy (SSE) from all seven SBs. The extracted features are ranked and fed to the support vector machine (SVM) and other supervised learning classifiers. In this study, we have considered five different classification problems (CPs), (two-class (CP-1), three-class (CP-2), four-class (CP-3), five-class (CP-4) and six-class (CP-5)). The proposed system yielded accuracies of 98.3%, 93.9%, 92.1%, 91.7%, and 91.5% for CP-1 to CP-5, respectively, using 10-fold cross validation (CV) technique. Copyright © 2018 Elsevier Ltd. All rights reserved.

  12. Transfer Learning of Classification Rules for Biomarker Discovery and Verification from Molecular Profiling Studies

    PubMed Central

    Ganchev, Philip; Malehorn, David; Bigbee, William L.; Gopalakrishnan, Vanathi

    2013-01-01

    We present a novel framework for integrative biomarker discovery from related but separate data sets created in biomarker profiling studies. The framework takes prior knowledge in the form of interpretable, modular rules, and uses them during the learning of rules on a new data set. The framework consists of two methods of transfer of knowledge from source to target data: transfer of whole rules and transfer of rule structures. We evaluated the methods on three pairs of data sets: one genomic and two proteomic. We used standard measures of classification performance and three novel measures of amount of transfer. Preliminary evaluation shows that whole-rule transfer improves classification performance over using the target data alone, especially when there is more source data than target data. It also improves performance over using the union of the data sets. PMID:21571094

  13. 78 FR 68983 - Cotton Futures Classification: Optional Classification Procedure

    Federal Register 2010, 2011, 2012, 2013, 2014

    2013-11-18

    ...-AD33 Cotton Futures Classification: Optional Classification Procedure AGENCY: Agricultural Marketing... regulations to allow for the addition of an optional cotton futures classification procedure--identified and known as ``registration'' by the U.S. cotton industry and the Intercontinental Exchange (ICE). In...

  14. Bacteriocins from Gram-Negative Bacteria: A Classification?

    NASA Astrophysics Data System (ADS)

    Rebuffat, Sylvie

    Bacteria produce an arsenal of toxic peptides and proteins, which are termed bacteriocins and play a role in mediating the dynamics of microbial populations and communities. Bacteriocins from Gram-negative bacteria arise mainly from Enterobacteriaceae. They assemble into two main families: high molecular mass modular proteins (30-80 kDa) termed colicins and low molecular mass peptides (between 1 and 10 kDa) termed microcins. The production of colicins is mediated by the SOS response regulon, which plays a role in the response of many bacteria to DNA damages. Microcins are highly stable hydrophobic peptides that are produced under stress conditions, particularly nutrient depletion. Colicins and microcins are found essentially in Escherichia coli, but several other Gram-negative species also produce bacteriocin-like substances. This chapter presents the basis of a classification of colicins and microcins.

  15. Cartography of Pathway Signal Perturbations Identifies Distinct Molecular Pathomechanisms in Malignant and Chronic Lung Diseases

    PubMed Central

    Arakelyan, Arsen; Nersisyan, Lilit; Petrek, Martin; Löffler-Wirth, Henry; Binder, Hans

    2016-01-01

    Lung diseases are described by a wide variety of developmental mechanisms and clinical manifestations. Accurate classification and diagnosis of lung diseases are the bases for development of effective treatments. While extensive studies are conducted toward characterization of various lung diseases at molecular level, no systematic approach has been developed so far. Here we have applied a methodology for pathway-centered mining of high throughput gene expression data to describe a wide range of lung diseases in the light of shared and specific pathway activity profiles. We have applied an algorithm combining a Pathway Signal Flow (PSF) algorithm for estimation of pathway activity deregulation states in lung diseases and malignancies, and a Self Organizing Maps algorithm for classification and clustering of the pathway activity profiles. The analysis results allowed clearly distinguish between cancer and non-cancer lung diseases. Lung cancers were characterized by pathways implicated in cell proliferation, metabolism, while non-malignant lung diseases were characterized by deregulations in pathways involved in immune/inflammatory response and fibrotic tissue remodeling. In contrast to lung malignancies, chronic lung diseases had relatively heterogeneous pathway deregulation profiles. We identified three groups of interstitial lung diseases and showed that the development of characteristic pathological processes, such as fibrosis, can be initiated by deregulations in different signaling pathways. In conclusion, this paper describes the pathobiology of lung diseases from systems viewpoint using pathway centered high-dimensional data mining approach. Our results contribute largely to current understanding of pathological events in lung cancers and non-malignant lung diseases. Moreover, this paper provides new insight into molecular mechanisms of a number of interstitial lung diseases that have been studied to a lesser extent. PMID:27200087

  16. Accurate van der Waals coefficients from density functional theory

    PubMed Central

    Tao, Jianmin; Perdew, John P.; Ruzsinszky, Adrienn

    2012-01-01

    The van der Waals interaction is a weak, long-range correlation, arising from quantum electronic charge fluctuations. This interaction affects many properties of materials. A simple and yet accurate estimate of this effect will facilitate computer simulation of complex molecular materials and drug design. Here we develop a fast approach for accurate evaluation of dynamic multipole polarizabilities and van der Waals (vdW) coefficients of all orders from the electron density and static multipole polarizabilities of each atom or other spherical object, without empirical fitting. Our dynamic polarizabilities (dipole, quadrupole, octupole, etc.) are exact in the zero- and high-frequency limits, and exact at all frequencies for a metallic sphere of uniform density. Our theory predicts dynamic multipole polarizabilities in excellent agreement with more expensive many-body methods, and yields therefrom vdW coefficients C6, C8, C10 for atom pairs with a mean absolute relative error of only 3%. PMID:22205765

  17. A bayesian hierarchical model for classification with selection of functional predictors.

    PubMed

    Zhu, Hongxiao; Vannucci, Marina; Cox, Dennis D

    2010-06-01

    In functional data classification, functional observations are often contaminated by various systematic effects, such as random batch effects caused by device artifacts, or fixed effects caused by sample-related factors. These effects may lead to classification bias and thus should not be neglected. Another issue of concern is the selection of functions when predictors consist of multiple functions, some of which may be redundant. The above issues arise in a real data application where we use fluorescence spectroscopy to detect cervical precancer. In this article, we propose a Bayesian hierarchical model that takes into account random batch effects and selects effective functions among multiple functional predictors. Fixed effects or predictors in nonfunctional form are also included in the model. The dimension of the functional data is reduced through orthonormal basis expansion or functional principal components. For posterior sampling, we use a hybrid Metropolis-Hastings/Gibbs sampler, which suffers slow mixing. An evolutionary Monte Carlo algorithm is applied to improve the mixing. Simulation and real data application show that the proposed model provides accurate selection of functional predictors as well as good classification.

  18. Functional odor classification through a medicinal chemistry approach.

    PubMed

    Poivet, Erwan; Tahirova, Narmin; Peterlin, Zita; Xu, Lu; Zou, Dong-Jing; Acree, Terry; Firestein, Stuart

    2018-02-01

    Crucial for any hypothesis about odor coding is the classification and prediction of sensory qualities in chemical compounds. The relationship between perceptual quality and molecular structure has occupied olfactory scientists throughout the 20th century, but details of the mechanism remain elusive. Odor molecules are typically organic compounds of low molecular weight that may be aliphatic or aromatic, may be saturated or unsaturated, and may have diverse functional polar groups. However, many molecules conforming to these characteristics are odorless. One approach recently used to solve this problem was to apply machine learning strategies to a large set of odors and human classifiers in an attempt to find common and unique chemical features that would predict a chemical's odor. We use an alternative method that relies more on the biological responses of olfactory sensory neurons and then applies the principles of medicinal chemistry, a technique widely used in drug discovery. We demonstrate the effectiveness of this strategy through a classification for esters, an important odorant for the creation of flavor in wine. Our findings indicate that computational approaches that do not account for biological responses will be plagued by both false positives and false negatives and fail to provide meaningful mechanistic data. However, the two approaches used in tandem could resolve many of the paradoxes in odor perception.

  19. Elucidation of molecular kinetic schemes from macroscopic traces using system identification

    PubMed Central

    González-Maeso, Javier; Sealfon, Stuart C.; Galocha-Iragüen, Belén; Brezina, Vladimir

    2017-01-01

    Overall cellular responses to biologically-relevant stimuli are mediated by networks of simpler lower-level processes. Although information about some of these processes can now be obtained by visualizing and recording events at the molecular level, this is still possible only in especially favorable cases. Therefore the development of methods to extract the dynamics and relationships between the different lower-level (microscopic) processes from the overall (macroscopic) response remains a crucial challenge in the understanding of many aspects of physiology. Here we have devised a hybrid computational-analytical method to accomplish this task, the SYStems-based MOLecular kinetic scheme Extractor (SYSMOLE). SYSMOLE utilizes system-identification input-output analysis to obtain a transfer function between the stimulus and the overall cellular response in the Laplace-transformed domain. It then derives a Markov-chain state molecular kinetic scheme uniquely associated with the transfer function by means of a classification procedure and an analytical step that imposes general biological constraints. We first tested SYSMOLE with synthetic data and evaluated its performance in terms of its rate of convergence to the correct molecular kinetic scheme and its robustness to noise. We then examined its performance on real experimental traces by analyzing macroscopic calcium-current traces elicited by membrane depolarization. SYSMOLE derived the correct, previously known molecular kinetic scheme describing the activation and inactivation of the underlying calcium channels and correctly identified the accepted mechanism of action of nifedipine, a calcium-channel blocker clinically used in patients with cardiovascular disease. Finally, we applied SYSMOLE to study the pharmacology of a new class of glutamate antipsychotic drugs and their crosstalk mechanism through a heteromeric complex of G protein-coupled receptors. Our results indicate that our methodology can be successfully

  20. A Novel Feature Level Fusion for Heart Rate Variability Classification Using Correntropy and Cauchy-Schwarz Divergence.

    PubMed

    Goshvarpour, Ateke; Goshvarpour, Atefeh

    2018-04-30

    Heart rate variability (HRV) analysis has become a widely used tool for monitoring pathological and psychological states in medical applications. In a typical classification problem, information fusion is a process whereby the effective combination of the data can achieve a more accurate system. The purpose of this article was to provide an accurate algorithm for classifying HRV signals in various psychological states. Therefore, a novel feature level fusion approach was proposed. First, using the theory of information, two similarity indicators of the signal were extracted, including correntropy and Cauchy-Schwarz divergence. Applying probabilistic neural network (PNN) and k-nearest neighbor (kNN), the performance of each index in the classification of meditators and non-meditators HRV signals was appraised. Then, three fusion rules, including division, product, and weighted sum rules were used to combine the information of both similarity measures. For the first time, we propose an algorithm to define the weights of each feature based on the statistical p-values. The performance of HRV classification using combined features was compared with the non-combined features. Totally, the accuracy of 100% was obtained for discriminating all states. The results showed the strong ability and proficiency of division and weighted sum rules in the improvement of the classifier accuracies.