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Sample records for molecular features predicting

  1. Genes associated with histopathologic features of triple negative breast tumors predict molecular subtypes.

    PubMed

    Purrington, Kristen S; Visscher, Daniel W; Wang, Chen; Yannoukakos, Drakoulis; Hamann, Ute; Nevanlinna, Heli; Cox, Angela; Giles, Graham G; Eckel-Passow, Jeanette E; Lakis, Sotiris; Kotoula, Vassiliki; Fountzilas, George; Kabisch, Maria; Rüdiger, Thomas; Heikkilä, Päivi; Blomqvist, Carl; Cross, Simon S; Southey, Melissa C; Olson, Janet E; Gilbert, Judy; Deming-Halverson, Sandra; Kosma, Veli-Matti; Clarke, Christine; Scott, Rodney; Jones, J Louise; Zheng, Wei; Mannermaa, Arto; Eccles, Diana M; Vachon, Celine M; Couch, Fergus J

    2016-05-01

    Distinct subtypes of triple negative (TN) breast cancer have been identified by tumor expression profiling. However, little is known about the relationship between histopathologic features of TN tumors, which reflect aspects of both tumor behavior and tumor microenvironment, and molecular TN subtypes. The histopathologic features of TN tumors were assessed by central review and 593 TN tumors were subjected to whole genome expression profiling using the Illumina Whole Genome DASL array. TN molecular subtypes were defined based on gene expression data associated with histopathologic features of TN tumors. Gene expression analysis yielded signatures for four TN subtypes (basal-like, androgen receptor positive, immune, and stromal) consistent with previous studies. Expression analysis also identified genes significantly associated with the 12 histological features of TN tumors. Development of signatures using these markers of histopathological features resulted in six distinct TN subtype signatures, including an additional basal-like and stromal signature. The additional basal-like subtype was distinguished by elevated expression of cell motility and glucose metabolism genes and reduced expression of immune signaling genes, whereas the additional stromal subtype was distinguished by elevated expression of immunomodulatory pathway genes. Histopathologic features that reflect heterogeneity in tumor architecture, cell structure, and tumor microenvironment are related to TN subtype. Accounting for histopathologic features in the development of gene expression signatures, six major subtypes of TN breast cancer were identified.

  2. Communication: Finding destructive interference features in molecular transport junctions

    SciTech Connect

    Reuter, Matthew G.; Hansen, Thorsten

    2014-11-14

    Associating molecular structure with quantum interference features in electrode-molecule-electrode transport junctions has been difficult because existing guidelines for understanding interferences only apply to conjugated hydrocarbons. Herein we use linear algebra and the Landauer-Büttiker theory for electron transport to derive a general rule for predicting the existence and locations of interference features. Our analysis illustrates that interferences can be directly determined from the molecular Hamiltonian and the molecule–electrode couplings, and we demonstrate its utility with several examples.

  3. Communication: Finding destructive interference features in molecular transport junctions.

    PubMed

    Reuter, Matthew G; Hansen, Thorsten

    2014-11-14

    Associating molecular structure with quantum interference features in electrode-molecule-electrode transport junctions has been difficult because existing guidelines for understanding interferences only apply to conjugated hydrocarbons. Herein we use linear algebra and the Landauer-Büttiker theory for electron transport to derive a general rule for predicting the existence and locations of interference features. Our analysis illustrates that interferences can be directly determined from the molecular Hamiltonian and the molecule-electrode couplings, and we demonstrate its utility with several examples. PMID:25399124

  4. Molecular Dynamics Simulations Of Nanometer-Scale Feature Etch

    SciTech Connect

    Vegh, J. J.; Graves, D. B.

    2008-09-23

    Molecular dynamics (MD) simulations have been carried out to examine fundamental etch limitations. Beams of Ar{sup +}, Ar{sup +}/F and CF{sub x}{sup +} (x = 2,3) with 2 nm diameter cylindrical confinement were utilized to mimic 'perfect' masks for small feature etching in silicon. The holes formed during etch exhibit sidewall damage and passivation as a result of ion-induced mixing. The MD results predict a minimum hole diameter of {approx}5 nm after post-etch cleaning of the sidewall.

  5. Predicting the molecular complexity of sequencing libraries.

    PubMed

    Daley, Timothy; Smith, Andrew D

    2013-04-01

    Predicting the molecular complexity of a genomic sequencing library is a critical but difficult problem in modern sequencing applications. Methods to determine how deeply to sequence to achieve complete coverage or to predict the benefits of additional sequencing are lacking. We introduce an empirical bayesian method to accurately characterize the molecular complexity of a DNA sample for almost any sequencing application on the basis of limited preliminary sequencing. PMID:23435259

  6. Learning through Feature Prediction: An Initial Investigation into Teaching Categories to Children with Autism through Predicting Missing Features

    ERIC Educational Resources Information Center

    Sweller, Naomi

    2015-01-01

    Individuals with autism have difficulty generalising information from one situation to another, a process that requires the learning of categories and concepts. Category information may be learned through: (1) classifying items into categories, or (2) predicting missing features of category items. Predicting missing features has to this point been…

  7. Molecular prognostic prediction in liver cirrhosis.

    PubMed

    Goossens, Nicolas; Nakagawa, Shigeki; Hoshida, Yujin

    2015-09-28

    The natural history of cirrhosis varies and therefore prognostic prediction is critical given the sizable patient population. A variety of clinical prognostic indicators have been developed and enable patient risk stratification although their performance is somewhat limited especially within relatively earlier stage of disease. Molecular prognostic indicators are expected to refine the prediction, and potentially link a subset of patients with molecular targeted interventions that counteract poor prognosis. Here we overview clinical and molecular prognostic indicators in the literature, and discuss critical issues to successfully define, evaluate, and deploy prognostic indicators as clinical scores or tests. The use of liver biopsy has been diminishing due to sampling variability on fibrosis assessment and emergence of imaging- or lab test-based fibrosis assessment methods. However, recent rapid developments of genomics technologies and selective molecular targeted agents has highlighted the need for biopsy tissue specimen to explore and establish molecular information-guided personalized/stratified clinical care, and eventually achieve "precision medicine".

  8. Predicting Clinical Outcomes Using Molecular Biomarkers

    PubMed Central

    Burke, Harry B.

    2016-01-01

    Over the past 20 years, there has been an exponential increase in the number of biomarkers. At the last count, there were 768,259 papers indexed in PubMed.gov directly related to biomarkers. Although many of these papers claim to report clinically useful molecular biomarkers, embarrassingly few are currently in clinical use. It is suggested that a failure to properly understand, clinically assess, and utilize molecular biomarkers has prevented their widespread adoption in treatment, in comparative benefit analyses, and their integration into individualized patient outcome predictions for clinical decision-making and therapy. A straightforward, general approach to understanding how to predict clinical outcomes using risk, diagnostic, and prognostic molecular biomarkers is presented. In the future, molecular biomarkers will drive advances in risk, diagnosis, and prognosis, they will be the targets of powerful molecular therapies, and they will individualize and optimize therapy. Furthermore, clinical predictions based on molecular biomarkers will be displayed on the clinician’s screen during the physician–patient interaction, they will be an integral part of physician–patient-shared decision-making, and they will improve clinical care and patient outcomes. PMID:27279751

  9. Stabilizing l1-norm prediction models by supervised feature grouping.

    PubMed

    Kamkar, Iman; Gupta, Sunil Kumar; Phung, Dinh; Venkatesh, Svetha

    2016-02-01

    Emerging Electronic Medical Records (EMRs) have reformed the modern healthcare. These records have great potential to be used for building clinical prediction models. However, a problem in using them is their high dimensionality. Since a lot of information may not be relevant for prediction, the underlying complexity of the prediction models may not be high. A popular way to deal with this problem is to employ feature selection. Lasso and l1-norm based feature selection methods have shown promising results. But, in presence of correlated features, these methods select features that change considerably with small changes in data. This prevents clinicians to obtain a stable feature set, which is crucial for clinical decision making. Grouping correlated variables together can improve the stability of feature selection, however, such grouping is usually not known and needs to be estimated for optimal performance. Addressing this problem, we propose a new model that can simultaneously learn the grouping of correlated features and perform stable feature selection. We formulate the model as a constrained optimization problem and provide an efficient solution with guaranteed convergence. Our experiments with both synthetic and real-world datasets show that the proposed model is significantly more stable than Lasso and many existing state-of-the-art shrinkage and classification methods. We further show that in terms of prediction performance, the proposed method consistently outperforms Lasso and other baselines. Our model can be used for selecting stable risk factors for a variety of healthcare problems, so it can assist clinicians toward accurate decision making.

  10. Transmission line icing prediction based on DWT feature extraction

    NASA Astrophysics Data System (ADS)

    Ma, T. N.; Niu, D. X.; Huang, Y. L.

    2016-08-01

    Transmission line icing prediction is the premise of ensuring the safe operation of the network as well as the very important basis for the prevention of freezing disasters. In order to improve the prediction accuracy of icing, a transmission line icing prediction model based on discrete wavelet transform (DWT) feature extraction was built. In this method, a group of high and low frequency signals were obtained by DWT decomposition, and were fitted and predicted by using partial least squares regression model (PLS) and wavelet least square support vector model (w-LSSVM). Finally, the final result of the icing prediction was obtained by adding the predicted values of the high and low frequency signals. The results showed that the method is effective and feasible in the prediction of transmission line icing.

  11. Prediction of Drug Combinations by Integrating Molecular and Pharmacological Data

    PubMed Central

    Zhao, Xing-Ming; Iskar, Murat; Zeller, Georg; Kuhn, Michael; van Noort, Vera; Bork, Peer

    2011-01-01

    Combinatorial therapy is a promising strategy for combating complex disorders due to improved efficacy and reduced side effects. However, screening new drug combinations exhaustively is impractical considering all possible combinations between drugs. Here, we present a novel computational approach to predict drug combinations by integrating molecular and pharmacological data. Specifically, drugs are represented by a set of their properties, such as their targets or indications. By integrating several of these features, we show that feature patterns enriched in approved drug combinations are not only predictive for new drug combinations but also provide insights into mechanisms underlying combinatorial therapy. Further analysis confirmed that among our top ranked predictions of effective combinations, 69% are supported by literature, while the others represent novel potential drug combinations. We believe that our proposed approach can help to limit the search space of drug combinations and provide a new way to effectively utilize existing drugs for new purposes. PMID:22219721

  12. Molecular predictive and prognostic factors in ependymoma.

    PubMed

    Benson, Rony; Mallick, Supriya; Julka, Pramod K; Rath, Goura K

    2016-01-01

    An ependymoma is an uncommon glial tumor, which arises from different parts of the neuroaxis. Considerable variation in presentation and survival in tumors in different locations after an optimum treatment indicates inherent molecular and genetic differences in tumorigenesis between them. A number of genetic aberrations have been identified to distinctly characterize different subgroups of ependymomas that include a posterior fossa tumor, a supratentorial tumor, and a pediatric tumor. These different groups have substantial genetic alterations, and also distinct demography, clinical characteristics, and prognosis. This article is intended to review the diverse molecular and genetic aberrations that may be helpful in prognostication and prediction of survival in patients suffering from an ependymoma. PMID:26954807

  13. NSCLC tumor shrinkage prediction using quantitative image features.

    PubMed

    Hunter, Luke A; Chen, Yi Pei; Zhang, Lifei; Matney, Jason E; Choi, Haesun; Kry, Stephen F; Martel, Mary K; Stingo, Francesco; Liao, Zhongxing; Gomez, Daniel; Yang, Jinzhong; Court, Laurence E

    2016-04-01

    The objective of this study was to develop a quantitative image feature model to predict non-small cell lung cancer (NSCLC) volume shrinkage from pre-treatment CT images. 64 stage II-IIIB NSCLC patients with similar treatments were all imaged using the same CT scanner and protocol. For each patient, the planning gross tumor volume (GTV) was deformed onto the week 6 treatment image, and tumor shrinkage was quantified as the deformed GTV volume divided by the planning GTV volume. Geometric, intensity histogram, absolute gradient image, co-occurrence matrix, and run-length matrix image features were extracted from each planning GTV. Prediction models were generated using principal component regression with simulated annealing subset selection. Performance was quantified using the mean squared error (MSE) between the predicted and observed tumor shrinkages. Permutation tests were used to validate the results. The optimal prediction model gave a strong correlation between the observed and predicted tumor shrinkages with r=0.81 and MSE=8.60×10(-3). Compared to predictions based on the mean population shrinkage this resulted in a 2.92 fold reduction in MSE. In conclusion, this study indicated that quantitative image features extracted from existing pre-treatment CT images can successfully predict tumor shrinkage and provide additional information for clinical decisions regarding patient risk stratification, treatment, and prognosis. PMID:26878137

  14. Classification performance prediction using parametric scattering feature models

    NASA Astrophysics Data System (ADS)

    Chiang, Hung-Chih; Moses, Randolph L.; Potter, Lee C.

    2000-08-01

    We consider a method for estimating classification performance of a model-based synthetic aperture radar (SAR) automatic target recognition system. Target classification is performed by comparing an unordered feature set extracted from a measured SAR image chip with an unordered feature set predicted from a hypothesized target class and pose. A Bayes likelihood metric that incorporates uncertainty in both the predicted and extracted feature vectors is used to compute the match score. Evaluation of the match likelihoods requires a correspondence between the unordered predicted and extracted feature sets. This is a bipartite graph matching problem with insertions and deletions; we show that the optimal match can be found in polynomial time. We extend the results in 1 to estimate classification performance for a ten-class SAR ATR problem. We consider a synthetic classification problem to validate the classifier and to address resolution and robustness questions in the likelihood scoring method. Specifically, we consider performance versus SAR resolution, performance degradation due to mismatch between the assumed and actual feature statistics, and performance impact of correlated feature attributes.

  15. BDDCS class prediction for new molecular entities.

    PubMed

    Broccatelli, Fabio; Cruciani, Gabriele; Benet, Leslie Z; Oprea, Tudor I

    2012-03-01

    The Biopharmaceutics Drug Disposition Classification System (BDDCS) was successfully employed for predicting drug-drug interactions (DDIs) with respect to drug metabolizing enzymes (DMEs), drug transporters and their interplay. The major assumption of BDDCS is that the extent of metabolism (EoM) predicts high versus low intestinal permeability rate, and vice versa, at least when uptake transporters or paracellular transport is not involved. We recently published a collection of over 900 marketed drugs classified for BDDCS. We suggest that a reliable model for predicting BDDCS class, integrated with in vitro assays, could anticipate disposition and potential DDIs of new molecular entities (NMEs). Here we describe a computational procedure for predicting BDDCS class from molecular structures. The model was trained on a set of 300 oral drugs, and validated on an external set of 379 oral drugs, using 17 descriptors calculated or derived from the VolSurf+ software. For each molecule, a probability of BDDCS class membership was given, based on predicted EoM, FDA solubility (FDAS) and their confidence scores. The accuracy in predicting FDAS was 78% in training and 77% in validation, while for EoM prediction the accuracy was 82% in training and 79% in external validation. The actual BDDCS class corresponded to the highest ranked calculated class for 55% of the validation molecules, and it was within the top two ranked more than 92% of the time. The unbalanced stratification of the data set did not affect the prediction, which showed highest accuracy in predicting classes 2 and 3 with respect to the most populated class 1. For class 4 drugs a general lack of predictability was observed. A linear discriminant analysis (LDA) confirming the degree of accuracy for the prediction of the different BDDCS classes is tied to the structure of the data set. This model could routinely be used in early drug discovery to prioritize in vitro tests for NMEs (e.g., affinity to transporters

  16. How to Predict Molecular Interactions between Species?

    PubMed Central

    Schulze, Sylvie; Schleicher, Jana; Guthke, Reinhard; Linde, Jörg

    2016-01-01

    Organisms constantly interact with other species through physical contact which leads to changes on the molecular level, for example the transcriptome. These changes can be monitored for all genes, with the help of high-throughput experiments such as RNA-seq or microarrays. The adaptation of the gene expression to environmental changes within cells is mediated through complex gene regulatory networks. Often, our knowledge of these networks is incomplete. Network inference predicts gene regulatory interactions based on transcriptome data. An emerging application of high-throughput transcriptome studies are dual transcriptomics experiments. Here, the transcriptome of two or more interacting species is measured simultaneously. Based on a dual RNA-seq data set of murine dendritic cells infected with the fungal pathogen Candida albicans, the software tool NetGenerator was applied to predict an inter-species gene regulatory network. To promote further investigations of molecular inter-species interactions, we recently discussed dual RNA-seq experiments for host-pathogen interactions and extended the applied tool NetGenerator (Schulze et al., 2015). The updated version of NetGenerator makes use of measurement variances in the algorithmic procedure and accepts gene expression time series data with missing values. Additionally, we tested multiple modeling scenarios regarding the stimuli functions of the gene regulatory network. Here, we summarize the work by Schulze et al. (2015) and put it into a broader context. We review various studies making use of the dual transcriptomics approach to investigate the molecular basis of interacting species. Besides the application to host-pathogen interactions, dual transcriptomics data are also utilized to study mutualistic and commensalistic interactions. Furthermore, we give a short introduction into additional approaches for the prediction of gene regulatory networks and discuss their application to dual transcriptomics data. We

  17. Clinical and molecular features of young-onset colorectal cancer

    PubMed Central

    Ballester, Veroushka; Rashtak, Shahrooz; Boardman, Lisa

    2016-01-01

    Colorectal cancer (CRC) is one of the leading causes of cancer related mortality worldwide. Although young-onset CRC raises the possibility of a hereditary component, hereditary CRC syndromes only explain a minority of young-onset CRC cases. There is evidence to suggest that young-onset CRC have a different molecular profile than late-onset CRC. While the pathogenesis of young-onset CRC is well characterized in individuals with an inherited CRC syndrome, knowledge regarding the molecular features of sporadic young-onset CRC is limited. Understanding the molecular mechanisms of young-onset CRC can help us tailor specific screening and management strategies. While the incidence of late-onset CRC has been decreasing, mainly attributed to an increase in CRC screening, the incidence of young-onset CRC is increasing. Differences in the molecular biology of these tumors and low suspicion of CRC in young symptomatic individuals, may be possible explanations. Currently there is no evidence that supports that screening of average risk individuals less than 50 years of age will translate into early detection or increased survival. However, increasing understanding of the underlying molecular mechanisms of young-onset CRC could help us tailor specific screening and management strategies. The purpose of this review is to evaluate the current knowledge about young-onset CRC, its clinicopathologic features, and the newly recognized molecular alterations involved in tumor progression. PMID:26855533

  18. Exploiting Information Diffusion Feature for Link Prediction in Sina Weibo

    NASA Astrophysics Data System (ADS)

    Li, Dong; Zhang, Yongchao; Xu, Zhiming; Chu, Dianhui; Li, Sheng

    2016-01-01

    The rapid development of online social networks (e.g., Twitter and Facebook) has promoted research related to social networks in which link prediction is a key problem. Although numerous attempts have been made for link prediction based on network structure, node attribute and so on, few of the current studies have considered the impact of information diffusion on link creation and prediction. This paper mainly addresses Sina Weibo, which is the largest microblog platform with Chinese characteristics, and proposes the hypothesis that information diffusion influences link creation and verifies the hypothesis based on real data analysis. We also detect an important feature from the information diffusion process, which is used to promote link prediction performance. Finally, the experimental results on Sina Weibo dataset have demonstrated the effectiveness of our methods.

  19. Borderline personality disorder features predict negative outcomes 2 years later.

    PubMed

    Bagge, Courtney; Nickell, Angela; Stepp, Stephanie; Durrett, Christine; Jackson, Kristina; Trull, Timothy J

    2004-05-01

    In a sample of 351 young adults, the authors assessed whether borderline personality disorder (BPD) features prospectively predicted negative outcomes (poorer academic achievement and social maladjustment) over the subsequent 2 years, over and above gender and both Axis I and Axis II psychopathology. Borderline traits were significantly related to these outcomes, with impulsivity and affective instability the most highly associated. The present findings suggest that the impulsivity and affective instability associated with BPD leads to impairment in relating well with others, in meeting social role obligations, and in academic or occupational achievement. Therefore, these may be especially important features to target in interventions for BPD.

  20. Predictive features of breast cancer on Mexican screening mammography patients

    NASA Astrophysics Data System (ADS)

    Rodriguez-Rojas, Juan; Garza-Montemayor, Margarita; Trevino-Alvarado, Victor; Tamez-Pena, José Gerardo

    2013-02-01

    Breast cancer is the most common type of cancer worldwide. In response, breast cancer screening programs are becoming common around the world and public programs now serve millions of women worldwide. These programs are expensive, requiring many specialized radiologists to examine all images. Nevertheless, there is a lack of trained radiologists in many countries as in Mexico, which is a barrier towards decreasing breast cancer mortality, pointing at the need of a triaging system that prioritizes high risk cases for prompt interpretation. Therefore we explored in an image database of Mexican patients whether high risk cases can be distinguished using image features. We collected a set of 200 digital screening mammography cases from a hospital in Mexico, and assigned low or high risk labels according to its BIRADS score. Breast tissue segmentation was performed using an automatic procedure. Image features were obtained considering only the segmented region on each view and comparing the bilateral di erences of the obtained features. Predictive combinations of features were chosen using a genetic algorithms based feature selection procedure. The best model found was able to classify low-risk and high-risk cases with an area under the ROC curve of 0.88 on a 150-fold cross-validation test. The features selected were associated to the differences of signal distribution and tissue shape on bilateral views. The model found can be used to automatically identify high risk cases and trigger the necessary measures to provide prompt treatment.

  1. Quantitative imaging features to predict cancer status in lung nodules

    NASA Astrophysics Data System (ADS)

    Liu, Ying; Balagurunathan, Yoganand; Atwater, Thomas; Antic, Sanja; Li, Qian; Walker, Ronald; Smith, Gary T.; Massion, Pierre P.; Schabath, Matthew B.; Gillies, Robert J.

    2016-03-01

    Background: We propose a systematic methodology to quantify incidentally identified lung nodules based on observed radiological traits on a point scale. These quantitative traits classification model was used to predict cancer status. Materials and Methods: We used 102 patients' low dose computed tomography (LDCT) images for this study, 24 semantic traits were systematically scored from each image. We built a machine learning classifier in cross validation setting to find best predictive imaging features to differentiate malignant from benign lung nodules. Results: The best feature triplet to discriminate malignancy was based on long axis, concavity and lymphadenopathy with average AUC of 0.897 (Accuracy of 76.8%, Sensitivity of 64.3%, Specificity of 90%). A similar semantic triplet optimized on Sensitivity/Specificity (Youden's J index) included long axis, vascular convergence and lymphadenopathy which had an average AUC of 0.875 (Accuracy of 81.7%, Sensitivity of 76.2%, Specificity of 95%). Conclusions: Quantitative radiological image traits can differentiate malignant from benign lung nodules. These semantic features along with size measurement enhance the prediction accuracy.

  2. Automated Analysis and Classification of Histological Tissue Features by Multi-Dimensional Microscopic Molecular Profiling

    PubMed Central

    Riordan, Daniel P.; Varma, Sushama; West, Robert B.; Brown, Patrick O.

    2015-01-01

    Characterization of the molecular attributes and spatial arrangements of cells and features within complex human tissues provides a critical basis for understanding processes involved in development and disease. Moreover, the ability to automate steps in the analysis and interpretation of histological images that currently require manual inspection by pathologists could revolutionize medical diagnostics. Toward this end, we developed a new imaging approach called multidimensional microscopic molecular profiling (MMMP) that can measure several independent molecular properties in situ at subcellular resolution for the same tissue specimen. MMMP involves repeated cycles of antibody or histochemical staining, imaging, and signal removal, which ultimately can generate information analogous to a multidimensional flow cytometry analysis on intact tissue sections. We performed a MMMP analysis on a tissue microarray containing a diverse set of 102 human tissues using a panel of 15 informative antibody and 5 histochemical stains plus DAPI. Large-scale unsupervised analysis of MMMP data, and visualization of the resulting classifications, identified molecular profiles that were associated with functional tissue features. We then directly annotated H&E images from this MMMP series such that canonical histological features of interest (e.g. blood vessels, epithelium, red blood cells) were individually labeled. By integrating image annotation data, we identified molecular signatures that were associated with specific histological annotations and we developed statistical models for automatically classifying these features. The classification accuracy for automated histology labeling was objectively evaluated using a cross-validation strategy, and significant accuracy (with a median per-pixel rate of 77% per feature from 15 annotated samples) for de novo feature prediction was obtained. These results suggest that high-dimensional profiling may advance the development of computer

  3. Application of optimal prediction to molecular dynamics

    SciTech Connect

    Barber, IV, John Letherman

    2004-12-01

    Optimal prediction is a general system reduction technique for large sets of differential equations. In this method, which was devised by Chorin, Hald, Kast, Kupferman, and Levy, a projection operator formalism is used to construct a smaller system of equations governing the dynamics of a subset of the original degrees of freedom. This reduced system consists of an effective Hamiltonian dynamics, augmented by an integral memory term and a random noise term. Molecular dynamics is a method for simulating large systems of interacting fluid particles. In this thesis, I construct a formalism for applying optimal prediction to molecular dynamics, producing reduced systems from which the properties of the original system can be recovered. These reduced systems require significantly less computational time than the original system. I initially consider first-order optimal prediction, in which the memory and noise terms are neglected. I construct a pair approximation to the renormalized potential, and ignore three-particle and higher interactions. This produces a reduced system that correctly reproduces static properties of the original system, such as energy and pressure, at low-to-moderate densities. However, it fails to capture dynamical quantities, such as autocorrelation functions. I next derive a short-memory approximation, in which the memory term is represented as a linear frictional force with configuration-dependent coefficients. This allows the use of a Fokker-Planck equation to show that, in this regime, the noise is δ-correlated in time. This linear friction model reproduces not only the static properties of the original system, but also the autocorrelation functions of dynamical variables.

  4. Proteomic Features Predict Seroreactivity against Leptospiral Antigens in Leptospirosis Patients

    PubMed Central

    2015-01-01

    With increasing efficiency, accuracy, and speed we can access complete genome sequences from thousands of infectious microorganisms; however, the ability to predict antigenic targets of the immune system based on amino acid sequence alone is still needed. Here we use a Leptospira interrogans microarray expressing 91% (3359) of all leptospiral predicted ORFs (3667) and make an empirical accounting of all antibody reactive antigens recognized in sera from naturally infected humans; 191 antigens elicited an IgM or IgG response, representing 5% of the whole proteome. We classified the reactive antigens into 26 annotated COGs (clusters of orthologous groups), 26 JCVI Mainrole annotations, and 11 computationally predicted proteomic features. Altogether, 14 significantly enriched categories were identified, which are associated with immune recognition including mass spectrometry evidence of in vitro expression and in vivo mRNA up-regulation. Together, this group of 14 enriched categories accounts for just 25% of the leptospiral proteome but contains 50% of the immunoreactive antigens. These findings are consistent with our previous studies of other Gram-negative bacteria. This genome-wide approach provides an empirical basis to predict and classify antibody reactive antigens based on structural, physical–chemical, and functional proteomic features and a framework for understanding the breadth and specificity of the immune response to L. interrogans. PMID:25358092

  5. Using weighted features to predict recombination hotspots in Saccharomyces cerevisiae.

    PubMed

    Liu, Guoqing; Xing, Yongqiang; Cai, Lu

    2015-10-01

    Characterization and accurate prediction of recombination hotspots and coldspots have crucial implications for the mechanism of recombination. Several models have predicted recombination hot/cold spots successfully, but there is still much room for improvement. We present a novel classifier in which k-mer frequency, physical and thermodynamic properties of DNA sequences are incorporated in the form of weighted features. Applying the classifier to recombination hot/cold ORFs in Saccharomyces cerevisiae, we achieved an accuracy of 90%, which is ~5% higher than existing methods, such as iRSpot-PseDNC, IDQD and Random Forest. The model also predicted non-ORF recombination hot/cold spots sequences in S. cerevisiae with high accuracy. A broad applicability of the model in the field of classification is expected.

  6. Identifying predictive morphologic features of malignancy in eyelid lesions

    PubMed Central

    Leung, Christina; Johnson, Davin; Pang, Renee; Kratky, Vladimir

    2015-01-01

    Abstract Objective To determine features of eyelid lesions most predictive of malignancy, and to design a key to assist general practitioners in the triaging of such lesions. Design Prospective observational study. Setting Department of Ophthalmology at Queen’s University in Kingston, Ont. Participants A total of 199 consecutive periocular lesions requiring biopsy or excision were included. Main outcome measures First, potential features suggestive of malignancy for eyelid lesions were identified based on a survey sent to Canadian oculoplastic surgeons. The sensitivity, specificity, and odds ratios (ORs) of these features were then determined using 199 consecutive photographed eyelid lesions of patients who presented to the Department of Ophthalmology and underwent biopsy or excision. A triage key was then created based on the features with the highest ORs, and it was pilot-tested by a group of medical students. Results Of the 199 lesions included, 161 (80.9%) were benign and 38 (19.1%) were malignant. The 3 features with the highest ORs in predicting malignancy were infiltration (OR = 18.2, P < .01), ulceration (OR = 14.7, P < .01), and loss of eyelashes (OR = 6.0, P < .01). The acronym LUI (loss of eyelashes, ulceration, infiltration) was created to assist in memory recall. After watching a video describing the LUI triage key, the mean total score of a group of medical students for correctly identifying malignant lesions increased from 46% to 70% (P < .001). Conclusion Differentiating benign from malignant eyelid lesions can be difficult even for experienced physicians. The LUI triage key provides physicians with an evidence-based, easy-to-remember system for assisting in the triaging of these lesions. PMID:25756148

  7. Predicting Malignancy in Thyroid Nodules: Molecular Advances

    PubMed Central

    Melck, Adrienne L.; Yip, Linwah

    2016-01-01

    Over the last several years, a clearer understanding of the genetic alterations underlying thyroid carcinogenesis has developed. This knowledge can be utilized to tackle one of the greatest challenges facing thyroidologists: management of the indeterminate thyroid nodule. Despite the accuracy of fine needle aspiration cytology, many patients undergo invasive surgery in order to determine if a follicular or Hurthle cell neoplasm is malignant, and better diagnostic tools are required. A number of biomarkers have recently been studied and show promise in this setting. In particular, BRAF, RAS, PAX8-PPARγ, microRNAs and loss of heterozygosity have each been demonstrated as useful molecular tools for predicting malignancy and can thereby guide decisions regarding surgical management of nodular thyroid disease. This review summarizes the current literature surrounding each of these markers and highlights our institution’s prospective analysis of these markers and their subsequent incorporation into our management algorithms for thyroid nodules. PMID:21818817

  8. Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features

    PubMed Central

    Mohammad-Noori, Morteza; Beer, Michael A.

    2014-01-01

    Abstract Oligomers of length k, or k-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, k-mers suffer from the inherent limitation that if the parameter k is increased to resolve longer features, the probability of observing any specific k-mer becomes very small, and k-mer counts approach a binary variable, with most k-mers absent and a few present once. Thus, any statistical learning approach using k-mers as features becomes susceptible to noisy training set k-mer frequencies once k becomes large. To address this problem, we introduce alternative feature sets using gapped k-mers, a new classifier, gkm-SVM, and a general method for robust estimation of k-mer frequencies. To make the method applicable to large-scale genome wide applications, we develop an efficient tree data structure for computing the kernel matrix. We show that compared to our original kmer-SVM and alternative approaches, our gkm-SVM predicts functional genomic regulatory elements and tissue specific enhancers with significantly improved accuracy, increasing the precision by up to a factor of two. We then show that gkm-SVM consistently outperforms kmer-SVM on human ENCODE ChIP-seq datasets, and further demonstrate the general utility of our method using a Naïve-Bayes classifier. Although developed for regulatory sequence analysis, these methods can be applied to any sequence classification problem. PMID:25033408

  9. A Prediction Model for Membrane Proteins Using Moments Based Features.

    PubMed

    Butt, Ahmad Hassan; Khan, Sher Afzal; Jamil, Hamza; Rasool, Nouman; Khan, Yaser Daanial

    2016-01-01

    The most expedient unit of the human body is its cell. Encapsulated within the cell are many infinitesimal entities and molecules which are protected by a cell membrane. The proteins that are associated with this lipid based bilayer cell membrane are known as membrane proteins and are considered to play a significant role. These membrane proteins exhibit their effect in cellular activities inside and outside of the cell. According to the scientists in pharmaceutical organizations, these membrane proteins perform key task in drug interactions. In this study, a technique is presented that is based on various computationally intelligent methods used for the prediction of membrane protein without the experimental use of mass spectrometry. Statistical moments were used to extract features and furthermore a Multilayer Neural Network was trained using backpropagation for the prediction of membrane proteins. Results show that the proposed technique performs better than existing methodologies.

  10. A Prediction Model for Membrane Proteins Using Moments Based Features

    PubMed Central

    Butt, Ahmad Hassan; Khan, Sher Afzal; Jamil, Hamza; Rasool, Nouman; Khan, Yaser Daanial

    2016-01-01

    The most expedient unit of the human body is its cell. Encapsulated within the cell are many infinitesimal entities and molecules which are protected by a cell membrane. The proteins that are associated with this lipid based bilayer cell membrane are known as membrane proteins and are considered to play a significant role. These membrane proteins exhibit their effect in cellular activities inside and outside of the cell. According to the scientists in pharmaceutical organizations, these membrane proteins perform key task in drug interactions. In this study, a technique is presented that is based on various computationally intelligent methods used for the prediction of membrane protein without the experimental use of mass spectrometry. Statistical moments were used to extract features and furthermore a Multilayer Neural Network was trained using backpropagation for the prediction of membrane proteins. Results show that the proposed technique performs better than existing methodologies. PMID:26966690

  11. Automated Feature Detection and Solar Flare Prediction Using SDO Data

    NASA Astrophysics Data System (ADS)

    Qahwaji, Rami; Ahmed, Omar; Colak, Tufan

    The importance of real-time processing of solar data especially for space weather applica-tions is increasing continuously, especially with the launch of SDO which will provide sev-eral times more data compared to previous solar satellites. In this paper, we will show the initial results of applying our Automated Solar Activity Prediction (ASAP) system for the short-term prediction of significant solar flares to SDO data. This automated system is cur-rently working in real-time mode with SOHO/MDI images and its results are available online (http://spaceweather.inf.brad.ac.uk/) whenever a new solar image available. This system inte-grates image processing and machine learning to deliver these predictions. A machine learning-based system is designed to analyse years of sunspots and flares data to extract knowledge and to create associations that can be represented using computer-based learning rules. An imaging-based real time system that provides automated detection, grouping and then clas-sification of recent sunspots based on the McIntosh classification and integrated within this system. The results of current feature detections and flare predictions of ASAP using SOHO data will be compared to those results of ASAP using SDO data and will also be presented in this paper.

  12. Emerging predictable features of replicated biological invasion fronts.

    PubMed

    Giometto, Andrea; Rinaldo, Andrea; Carrara, Francesco; Altermatt, Florian

    2014-01-01

    Biological dispersal shapes species' distribution and affects their coexistence. The spread of organisms governs the dynamics of invasive species, the spread of pathogens, and the shifts in species ranges due to climate or environmental change. Despite its relevance for fundamental ecological processes, however, replicated experimentation on biological dispersal is lacking, and current assessments point at inherent limitations to predictability, even in the simplest ecological settings. In contrast, we show, by replicated experimentation on the spread of the ciliate Tetrahymena sp. in linear landscapes, that information on local unconstrained movement and reproduction allows us to predict reliably the existence and speed of traveling waves of invasion at the macroscopic scale. Furthermore, a theoretical approach introducing demographic stochasticity in the Fisher-Kolmogorov framework of reaction-diffusion processes captures the observed fluctuations in range expansions. Therefore, predictability of the key features of biological dispersal overcomes the inherent biological stochasticity. Our results establish a causal link from the short-term individual level to the long-term, broad-scale population patterns and may be generalized, possibly providing a general predictive framework for biological invasions in natural environments.

  13. Assist feature printability prediction by 3-D resist profile reconstruction

    NASA Astrophysics Data System (ADS)

    Zheng, Xin; Huang, Jensheng; Chin, Fook; Kazarian, Aram; Kuo, Chun-Chieh

    2012-06-01

    Sub-resolution Assist Features (SRAFs) are powerful tools to enhance the focus margin of drawn patterns. SRAFs are placed and sized so they do not print on the wafer, but the larger the SRAF, the more effective it becomes at enhancing through-focus stability. The size and location of an SRAF that will image on a wafer is highly dependent upon neighboring patterns and models of SRAF printability are, at present, unreliable. Model-based SRAF placement has been used to enhance resolution at 20nm node processes and below with stringent requirements that inserted SRAFs will not be imaged on wafer. However, despite widespread SRAF use and hard data as to SRAF effectiveness, it has been very difficult to develop a process model that accurately predicts under what process conditions an SRAF will image on a wafer. More accurate models of SRAF printing should allow model based SRAF placement to be relaxed, resulting in more effective SRAF placement and broader focus margins. One of the first problems with the concept of SRAF printability is the definition of an SRAF printing on a wafer. This is not obvious because two different states of printing exist. The first print state is when a residue is left on a wafer from the SRAF. The first state can be considered printing from the point of view that photoresist is on the wafer and the photoresist may even lift off and cause defects. However, the first state can be considered non-printing because the over etch from the etch process will generally remove the photoresist residual and the material underneath. The second state is when a pattern is formed and etched into the substrate, a state at which the pattern has clearly printed on the wafer. Of course, intermediate states may also be defined. In order to be applicable, an SRAF printability model must be able to predict both printing states. In addition, the model must be able to extrapolate to configurations beyond those used to develop the model in the first place. These model

  14. Pathological and molecular features of adrenocortical carcinoma: an update.

    PubMed

    Volante, M; Buttigliero, C; Greco, E; Berruti, A; Papotti, M

    2008-07-01

    The pathological diagnosis of adrenocortical carcinoma (ACC), which is based on gross and microscopic criteria, is subjective. None of the features are absolutely indicative of malignancy, although their combination in a scoring system may correctly identify ACC. The Weiss system, which is currently the most popular, combines nine morphological parameters, of which three are structural ("dark" cytoplasm, diffuse architecture, necrosis), three are cytological (atypia, mitotic count, atypical mitotic figures) and three are related to invasion (of sinusoids, veins and tumour capsule). Although there are strictly defined criteria for each feature, some are straightforward and objective, while others are potentially more problematic (diffuse architecture, necrosis, sinusoidal, venous and capsular invasions). The classification of oncocytic and paediatric adrenocortical tumours is even more challenging, as not all of the above morphological parameters are predictors of malignancy in these tumour types. As an alternative to the morphological approach, a wide array of chromosomal, genetic, molecular and immunohistochemical markers have been tested in ACC to identify reliable diagnostic and prognostic factors. Genetic and epigenetic alterations of p53, IGF-2 and molecules involved in cancer cell invasive properties seem the most promising. These molecular markers may not only play a role in the biology of these tumours and have prognostic implications, but may also be used as potential targets for treatment. However, these markers are not sufficiently sensitive and specific to replace conventional morphological criteria. PMID:18430754

  15. Predicting the Presence of Large Fish through Benthic Geomorphic Features

    NASA Astrophysics Data System (ADS)

    Knuth, F.; Sautter, L.; Levine, N. S.; Kracker, L.

    2013-12-01

    Marine Protected Areas are critical in sustaining the resilience of fish populations to commercial fishing operations. Using acoustic data to survey these areas promises efficiency, accuracy, and minimal environmental impact. In July, 2013, the NOAA Ship Pisces collected bathymetric, backscatter and water column data for 10 proposed MPA sites along the U.S. Southeast Atlantic continental shelf. A total of 205 km2 of seafloor were mapped between Mayport, FL and Wilmington, NC, using the SIMRAD ME70 and EK60 echosounder systems. These data were processed in Caris HIPS, QPS FMGT, MATLAB and ArcGIS. The backscatter and bathymetry reveal various benthic geomorphic features, including flat sand, rippled sand, and rugose hard bottom. Water column data directly above highly rugose hardbottom contains the greatest counts for large fish populations. Using spatial statistics, such as a geographically weighted regression model, we aim to identify features of the benthic profile, including rugosity, curvature and slope, that can predict the presence of large fish. The success of this approach will greatly expedite fishery surveys, minimize operational cost and aid in making timely management decisions.

  16. Molecular markers in prostate cancer. Part I: predicting lethality

    PubMed Central

    Agrawal, Sachin; Dunsmuir, William D.

    2009-01-01

    Assessing the lethality of 'early,' potentially organ-confined prostate cancer (PCa) is one of the central controversies in modern-day urological clinical practice. Such cases are often considered for radical 'curative' treatment, although active surveillance may be equally appropriate for many men. Moreover, the balance between judicious intervention and overtreatment can be difficult to judge. The patient's age, comorbidities, family history and philosophy of self-health care can be weighed against clinical features such as the palpability of disease, the number and percentage of biopsy cores involved with the disease, histological grade, presenting prostate-specific antigen (PSA) and possible previous PSA kinetics. For many years, scientists and physicians have sought additional molecular factors that may be predictive for disease stage, progression and lethality. Usually, claims for a 'new' unique marker fall short of true clinical value. More often than not, such molecular markers are useful only in multivariate models. This review summarizes relevant molecular markers and models reported up to and including 2008. PMID:19050690

  17. Molecular features related to HIV integrase inhibition obtained from structure- and ligand-based approaches.

    PubMed

    de Carvalho, Luciana L; Maltarollo, Vinícius G; de Lima, Emmanuela Ferreira; Weber, Karen C; Honorio, Kathia M; da Silva, Albérico B F

    2014-01-01

    Among several biological targets to treat AIDS, HIV integrase is a promising enzyme that can be employed to develop new anti-HIV agents. The aim of this work is to propose a mechanistic interpretation of HIV-1 integrase inhibition and to rationalize the molecular features related to the binding affinity of studied ligands. A set of 79 HIV-1 integrase inhibitors and its relationship with biological activity are investigated employing 2D and 3D QSAR models, docking analysis and DFT studies. Analyses of docking poses and frontier molecular orbitals revealed important features on the main ligand-receptor interactions. 2D and 3D models presenting good internal consistency, predictive power and stability were obtained in all cases. Significant correlation coefficients (r(2) = 0.908 and q(2)= 0.643 for 2D model; r(2)= 0.904 and q(2)= 0.719 for 3D model) were obtained, indicating the potential of these models for untested compounds. The generated holograms and contribution maps revealed important molecular requirements to HIV-1 IN inhibition and several evidences for molecular modifications. The final models along with information resulting from molecular orbitals, 2D contribution and 3D contour maps should be useful in the design of new inhibitors with increased potency and selectivity within the chemical diversity of the data. PMID:24416129

  18. Molecular features of hypothalamic plaques in Alzheimer's disease.

    PubMed Central

    Standaert, D. G.; Lee, V. M.; Greenberg, B. D.; Lowery, D. E.; Trojanowski, J. Q.

    1991-01-01

    The pathology of Alzheimer's disease (AD) involves subcortical as well as cortical structures. The authors have used immunohistochemical methods to study the molecular composition of AD plaques in the hypothalamus. In contrast to previous studies using histochemical methods, the authors observed large numbers of diffuse plaques in the AD hypothalamus labeled with an antiserum to the beta-amyloid, or A4 peptide, of the beta-amyloid precursor proteins (beta APPs), whereas A4-immunoreactive plaques were uncommon in the hypothalamus of patients without AD. Unlike plaques in the cortex and hippocampus of AD patients, hypothalamic plaques did not contain epitopes corresponding to other regions of the beta APPs, nor did they contain tau-, neurofilament-, or microtubule-associated protein-reactive epitopes, and did not disrupt the neuropil or produce astrogliosis. These findings demonstrate that there are substantial molecular and cellular differences in the pathologic features of AD in the hypothalamus compared with those observed in hippocampal and cortical structures, which may provide insight into the pathogenetic mechanisms of AD. Images Figure 1 Figure 2 Figure 3 Figure 4 PMID:1653521

  19. Predictive Features of a Cockpit Traffic Display: A Workload Assessment

    NASA Technical Reports Server (NTRS)

    Wickens, Christopher D.; Morphew, Ephimia

    1997-01-01

    Eighteen pilots flew a series of traffic avoidance maneuvers in an experiment designed to assess the support offered and workload imposed by different levels of traffic display information in a free flight simulation. Three display prototypes were compared which differed in traffic information provided. A BASELINE (BL) display provided current and (2nd order) predicted information regarding ownship and current information of an intruder aircraft, represented on lateral and vertical displays in a coplanar suite. An INTRUDER PREDICTOR (IP) display, augmented the baseline display by providing lateral and vertical prediction of the intruder aircraft. A THREAT VECTOR (TV) display added to the IP display a vector that indicates the direction from ownship to the intruder at the predicted point of closest contact (POCC). The length of the vector corresponds to the radius of the protected zone, and the distance of the intersection of the vector with ownship predictor, corresponds to the time available till POCC or loss of separation. Pilots time shared the traffic avoidance task with a secondary task requiring them to monitor the top of the display for faint targets. This task simulated the visual demands of out-of-cockpit scanning, and hence was used to estimate the head-down time required by the different display formats. The results revealed that both display augmentations improved performance (safety) as assessed by predicted and actual loss of separation (i.e., penetration of the protected zone). Both enhancements also reduced workload, as assessed by the NASA TLX scale. The intruder predictor display produced these benefits with no substantial impact on the qualitative nature of the avoidance maneuvers that were selected. The threat vector produced the safety benefits by inducing a greater degree of (effective) lateral maneuvering, thus partially offsetting the benefits of reduced workload. The three displays did not differ in terms of their effect on performance of

  20. Sequence-only evolutionary and predicted structural features for the prediction of stability changes in protein mutants

    PubMed Central

    2013-01-01

    Background Even a single amino acid substitution in a protein sequence may result in significant changes in protein stability, structure, and therefore in protein function as well. In the post-genomic era, computational methods for predicting stability changes from only the sequence of a protein are of importance. While evolutionary relationships of protein mutations can be extracted from large protein databases holding millions of protein sequences, relevant evolutionary features for the prediction of stability changes have not been proposed. Also, the use of predicted structural features in situations when a protein structure is not available has not been explored. Results We proposed a number of evolutionary and predicted structural features for the prediction of stability changes and analysed which of them capture the determinants of protein stability the best. We trained and evaluated our machine learning method on a non-redundant data set of experimentally measured stability changes. When only the direction of the stability change was predicted, we found that the best performance improvement can be achieved by the combination of the evolutionary features mutation likelihood and SIFTscore in conjunction with the predicted structural feature secondary structure. The same two evolutionary features in the combination with the predicted structural feature accessible surface area achieved the lowest error when the prediction of actual values of stability changes was assessed. Compared to similar studies, our method achieved improvements in prediction performance. Conclusion Although the strongest feature for the prediction of stability changes appears to be the vector of amino acid identities in the sequential neighbourhood of the mutation, the most relevant combination of evolutionary and predicted structural features further improves prediction performance. Even the predicted structural features, which did not perform well on their own, turn out to be beneficial

  1. PredictProtein—an open resource for online prediction of protein structural and functional features

    PubMed Central

    Yachdav, Guy; Kloppmann, Edda; Kajan, Laszlo; Hecht, Maximilian; Goldberg, Tatyana; Hamp, Tobias; Hönigschmid, Peter; Schafferhans, Andrea; Roos, Manfred; Bernhofer, Michael; Richter, Lothar; Ashkenazy, Haim; Punta, Marco; Schlessinger, Avner; Bromberg, Yana; Schneider, Reinhard; Vriend, Gerrit; Sander, Chris; Ben-Tal, Nir; Rost, Burkhard

    2014-01-01

    PredictProtein is a meta-service for sequence analysis that has been predicting structural and functional features of proteins since 1992. Queried with a protein sequence it returns: multiple sequence alignments, predicted aspects of structure (secondary structure, solvent accessibility, transmembrane helices (TMSEG) and strands, coiled-coil regions, disulfide bonds and disordered regions) and function. The service incorporates analysis methods for the identification of functional regions (ConSurf), homology-based inference of Gene Ontology terms (metastudent), comprehensive subcellular localization prediction (LocTree3), protein–protein binding sites (ISIS2), protein–polynucleotide binding sites (SomeNA) and predictions of the effect of point mutations (non-synonymous SNPs) on protein function (SNAP2). Our goal has always been to develop a system optimized to meet the demands of experimentalists not highly experienced in bioinformatics. To this end, the PredictProtein results are presented as both text and a series of intuitive, interactive and visually appealing figures. The web server and sources are available at http://ppopen.rostlab.org. PMID:24799431

  2. Fusobacterium in colonic flora and molecular features of colorectal carcinoma.

    PubMed

    Tahara, Tomomitsu; Yamamoto, Eiichiro; Suzuki, Hiromu; Maruyama, Reo; Chung, Woonbok; Garriga, Judith; Jelinek, Jaroslav; Yamano, Hiro-o; Sugai, Tamotsu; An, Byonggu; Shureiqi, Imad; Toyota, Minoru; Kondo, Yutaka; Estécio, Marcos R H; Issa, Jean-Pierre J

    2014-03-01

    Fusobacterium species are part of the gut microbiome in humans. Recent studies have identified overrepresentation of Fusobacterium in colorectal cancer tissues, but it is not yet clear whether this is pathogenic or simply an epiphenomenon. In this study, we evaluated the relationship between Fusobacterium status and molecular features in colorectal cancers through quantitative real-time PCR in 149 colorectal cancer tissues, 89 adjacent normal appearing mucosae and 72 colonic mucosae from cancer-free individuals. Results were correlated with CpG island methylator phenotype (CIMP) status, microsatellite instability (MSI), and mutations in BRAF, KRAS, TP53, CHD7, and CHD8. Whole-exome capture sequencing data were also available in 11 cases. Fusobacterium was detectable in 111 of 149 (74%) colorectal cancer tissues and heavily enriched in 9% (14/149) of the cases. As expected, Fusobacterium was also detected in normal appearing mucosae from both cancer and cancer-free individuals, but the amount of bacteria was much lower compared with colorectal cancer tissues (a mean of 250-fold lower for Pan-fusobacterium). We found the Fusobacterium-high colorectal cancer group (FB-high) to be associated with CIMP positivity (P = 0.001), TP53 wild-type (P = 0.015), hMLH1 methylation positivity (P = 0.0028), MSI (P = 0.018), and CHD7/8 mutation positivity (P = 0.002). Among the 11 cases where whole-exome sequencing data were available, two that were FB-high cases also had the highest number of somatic mutations (a mean of 736 per case in FB-high vs. 225 per case in all others). Taken together, our findings show that Fusobacterium enrichment is associated with specific molecular subsets of colorectal cancers, offering support for a pathogenic role in colorectal cancer for this gut microbiome component.

  3. Beyond [lambda][subscript max] Part 2: Predicting Molecular Color

    ERIC Educational Resources Information Center

    Williams, Darren L.; Flaherty, Thomas J.; Alnasleh, Bassam K.

    2009-01-01

    A concise roadmap for using computational chemistry programs (i.e., Gaussian 03W) to predict the color of a molecular species is presented. A color-predicting spreadsheet is available with the online material that uses transition wavelengths and peak-shape parameters to predict the visible absorbance spectrum, transmittance spectrum, chromaticity…

  4. Delta hepatitis: molecular biology and clinical and epidemiological features.

    PubMed Central

    Polish, L B; Gallagher, M; Fields, H A; Hadler, S C

    1993-01-01

    Hepatitis delta virus, discovered in 1977, requires the help of hepatitis B virus to replicate in hepatocytes and is an important cause of acute, fulminant, and chronic liver disease in many regions of the world. Because of the helper function of hepatitis delta virus, infection with it occurs either as a coinfection with hepatitis B or as a superinfection of a carrier of hepatitis B surface antigen. Although the mechanisms of transmission are similar to those of hepatitis B virus, the patterns of transmission of delta virus vary widely around the world. In regions of the world in which hepatitis delta virus infection is not endemic, the disease is confined to groups at high risk of acquiring hepatitis B infection and high-risk hepatitis B carriers. Because of the propensity of this viral infection to cause fulminant as well as chronic liver disease, continued incursion of hepatitis delta virus into areas of the world where persistent hepatitis B infection is endemic will have serious implications. Prevention depends on the widespread use of hepatitis B vaccine. This review focuses on the molecular biology and the clinical and epidemiologic features of this important viral infection. PMID:8358704

  5. Lung cancer prediction using neural network ensemble with histogram of oriented gradient genomic features.

    PubMed

    Adetiba, Emmanuel; Olugbara, Oludayo O

    2015-01-01

    This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their "nonensemble" variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations. PMID:25802891

  6. Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features

    PubMed Central

    Adetiba, Emmanuel; Olugbara, Oludayo O.

    2015-01-01

    This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their “nonensemble” variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations. PMID:25802891

  7. Radiogenomic analysis of breast cancer: dynamic contrast enhanced - magnetic resonance imaging based features are associated with molecular subtypes

    NASA Astrophysics Data System (ADS)

    Wang, Shijian; Fan, Ming; Zhang, Juan; Zheng, Bin; Wang, Xiaojia; Li, Lihua

    2016-03-01

    Breast cancer is one of the most common malignant tumor with upgrading incidence in females. The key to decrease the mortality is early diagnosis and reasonable treatment. Molecular classification could provide better insights into patient-directed therapy and prognosis prediction of breast cancer. It is known that different molecular subtypes have different characteristics in magnetic resonance imaging (MRI) examination. Therefore, we assumed that imaging features can reflect molecular information in breast cancer. In this study, we investigated associations between dynamic contrasts enhanced MRI (DCE-MRI) features and molecular subtypes in breast cancer. Sixty patients with breast cancer were enrolled and the MR images were pre-processed for noise reduction, registration and segmentation. Sixty-five dimensional imaging features including statistical characteristics, morphology, texture and dynamic enhancement in breast lesion and background regions were semiautomatically extracted. The associations between imaging features and molecular subtypes were assessed by using statistical analyses, including univariate logistic regression and multivariate logistic regression. The results of multivariate regression showed that imaging features are significantly associated with molecular subtypes of Luminal A (p=0.00473), HER2-enriched (p=0.00277) and Basal like (p=0.0117), respectively. The results indicated that three molecular subtypes are correlated with DCE-MRI features in breast cancer. Specifically, patients with a higher level of compactness or lower level of skewness in breast lesion are more likely to be Luminal A subtype. Besides, the higher value of the dynamic enhancement at T1 time in normal side reflect higher possibility of HER2-enriched subtype in breast cancer.

  8. Embedded prediction in feature extraction: application to single-trial EEG discrimination.

    PubMed

    Hsu, Wei-Yen

    2013-01-01

    In this study, an analysis system embedding neuron-fuzzy prediction in feature extraction is proposed for brain-computer interface (BCI) applications. Wavelet-fractal features combined with neuro-fuzzy predictions are applied for feature extraction in motor imagery (MI) discrimination. The features are extracted from the electroencephalography (EEG) signals recorded from participants performing left and right MI. Time-series predictions are performed by training 2 adaptive neuro-fuzzy inference systems (ANFIS) for respective left and right MI data. Features are then calculated from the difference in multi-resolution fractal feature vector (MFFV) between the predicted and actual signals through a window of EEG signals. Finally, the support vector machine is used for classification. The proposed method estimates its performance in comparison with the linear adaptive autoregressive (AAR) model and the AAR time-series prediction of 6 participants from 2 data sets. The results indicate that the proposed method is promising in MI classification. PMID:23248335

  9. INFUSE: Interactive Feature Selection for Predictive Modeling of High Dimensional Data.

    PubMed

    Krause, Josua; Perer, Adam; Bertini, Enrico

    2014-12-01

    Predictive modeling techniques are increasingly being used by data scientists to understand the probability of predicted outcomes. However, for data that is high-dimensional, a critical step in predictive modeling is determining which features should be included in the models. Feature selection algorithms are often used to remove non-informative features from models. However, there are many different classes of feature selection algorithms. Deciding which one to use is problematic as the algorithmic output is often not amenable to user interpretation. This limits the ability for users to utilize their domain expertise during the modeling process. To improve on this limitation, we developed INFUSE, a novel visual analytics system designed to help analysts understand how predictive features are being ranked across feature selection algorithms, cross-validation folds, and classifiers. We demonstrate how our system can lead to important insights in a case study involving clinical researchers predicting patient outcomes from electronic medical records.

  10. Interval Prediction of Molecular Properties in Parametrized Quantum Chemistry

    NASA Astrophysics Data System (ADS)

    Edwards, David E.; Zubarev, Dmitry Yu.; Packard, Andrew; Lester, William A.; Frenklach, Michael

    2014-06-01

    The accurate evaluation of molecular properties lies at the core of predictive physical models. Most reliable quantum-chemical calculations are limited to smaller molecular systems while purely empirical approaches are limited in accuracy and reliability. A promising approach is to employ a quantum-mechanical formalism with simplifications and to compensate for the latter with parametrization. We propose a strategy of directly predicting the uncertainty interval for a property of interest, based on training-data uncertainties, which sidesteps the need for an optimum set of parameters.

  11. Prediction of OCR accuracy using simple image features

    SciTech Connect

    Blando, L.R.; Kanai, Junichi; Nartker, T.A.

    1995-04-01

    A classifier for predicting the character accuracy of a given page achieved by any Optical Character Recognition (OCR) system is presented. This classifier is based on measuring the amount of white speckle, the amount of character fragments, and overall size information in the page. No output from the OCR system is used. The given page is classified as either good quality (i.e., high OCR accuracy expected) or poor (i.e., low OCR accuracy expected). Six OCR systems processed two different sets of test data: a set of 439 pages obtained from technical and scientific documents and a set of 200 pages obtained from magazines. For every system, approximately 85% of the pages in each data set were correctly predicted. The performance of this classifier is also compared with the ideal-case performance of a prediction method based upon the number of reject markers in OCR generated text. In several cases, this method matched or exceeded the performance of the reject based approach.

  12. Generic eukaryotic core promoter prediction using structural features of DNA.

    PubMed

    Abeel, Thomas; Saeys, Yvan; Bonnet, Eric; Rouzé, Pierre; Van de Peer, Yves

    2008-02-01

    Despite many recent efforts, in silico identification of promoter regions is still in its infancy. However, the accurate identification and delineation of promoter regions is important for several reasons, such as improving genome annotation and devising experiments to study and understand transcriptional regulation. Current methods to identify the core region of promoters require large amounts of high-quality training data and often behave like black box models that output predictions that are difficult to interpret. Here, we present a novel approach for predicting promoters in whole-genome sequences by using large-scale structural properties of DNA. Our technique requires no training, is applicable to many eukaryotic genomes, and performs extremely well in comparison with the best available promoter prediction programs. Moreover, it is fast, simple in design, and has no size constraints, and the results are easily interpretable. We compared our approach with 14 current state-of-the-art implementations using human gene and transcription start site data and analyzed the ENCODE region in more detail. We also validated our method on 12 additional eukaryotic genomes, including vertebrates, invertebrates, plants, fungi, and protists.

  13. Personalized Cancer Medicine: Molecular Diagnostics, Predictive biomarkers, and Drug Resistance

    PubMed Central

    Gonzalez de Castro, D; Clarke, P A; Al-Lazikani, B; Workman, P

    2013-01-01

    The progressive elucidation of the molecular pathogenesis of cancer has fueled the rational development of targeted drugs for patient populations stratified by genetic characteristics. Here we discuss general challenges relating to molecular diagnostics and describe predictive biomarkers for personalized cancer medicine. We also highlight resistance mechanisms for epidermal growth factor receptor (EGFR) kinase inhibitors in lung cancer. We envisage a future requiring the use of longitudinal genome sequencing and other omics technologies alongside combinatorial treatment to overcome cellular and molecular heterogeneity and prevent resistance caused by clonal evolution. PMID:23361103

  14. Epileptic Seizure Prediction based on Ratio and Differential Linear Univariate Features

    PubMed Central

    Rasekhi, Jalil; Mollaei, Mohammad Reza Karami; Bandarabadi, Mojtaba; Teixeira, César A.; Dourado, António

    2015-01-01

    Bivariate features, obtained from multichannel electroencephalogram recordings, quantify the relation between different brain regions. Studies based on bivariate features have shown optimistic results for tackling epileptic seizure prediction problem in patients suffering from refractory epilepsy. A new bivariate approach using univariate features is proposed here. Differences and ratios of 22 linear univariate features were calculated using pairwise combination of 6 electroencephalograms channels, to create 330 differential, and 330 relative features. The feature subsets were classified using support vector machines separately, as one of the two classes of preictal and nonpreictal. Furthermore, minimum Redundancy Maximum Relevance feature reduction method is employed to improve the predictions and reduce the number of false alarms. The studies were carried out on features obtained from 10 patients. For reduced subset of 30 features and using differential approach, the seizures were on average predicted in 60.9% of the cases (28 out of 46 in 737.9 h of test data), with a low false prediction rate of 0.11 h−1. Results of bivariate approaches were compared with those achieved from original linear univariate features, extracted from 6 channels. The advantage of proposed bivariate features is the smaller number of false predictions in comparison to the original 22 univariate features. In addition, reduction in feature dimension could provide a less complex and the more cost-effective algorithm. Results indicate that applying machine learning methods on a multidimensional feature space resulting from relative/differential pairwise combination of 22 univariate features could predict seizure onsets with high performance. PMID:25709936

  15. Fold prediction problem: the application of new physical and physicochemical-based features.

    PubMed

    Dehzangi, Abdollah; Phon-Amnuaisuk, Somnuk

    2011-02-01

    One of the most important goals in bioinformatics is the ability to predict tertiary structure of a protein from its amino acid sequence. In this paper, new feature groups based on the physical and physicochemical properties of amino acids (size of the amino acids' side chains, predicted secondary structure based on normalized frequency of β-Strands, Turns, and Reverse Turns) are proposed to tackle this task. The proposed features are extracted using a modified feature extraction method adapted from Dubchak et al. To study the effectiveness of the proposed features and the modified feature extraction method, AdaBoost.M1, Multi Layer Perceptron (MLP), and Support Vector Machine (SVM) that have been commonly and successfully applied to the protein folding problem are employed. Our experimental results show that the new feature groups altogether with the modified feature extraction method are capable of enhancing the protein fold prediction accuracy better than the previous works found in the literature.

  16. Identification of Prognostic Molecular Features in the Reactive Stroma of Human Breast and Prostate Cancer

    PubMed Central

    Provero, Paolo; Fusco, Carlo; Delorenzi, Mauro; Stehle, Jean-Christophe; Stamenkovic, Ivan

    2011-01-01

    Primary tumor growth induces host tissue responses that are believed to support and promote tumor progression. Identification of the molecular characteristics of the tumor microenvironment and elucidation of its crosstalk with tumor cells may therefore be crucial for improving our understanding of the processes implicated in cancer progression, identifying potential therapeutic targets, and uncovering stromal gene expression signatures that may predict clinical outcome. A key issue to resolve, therefore, is whether the stromal response to tumor growth is largely a generic phenomenon, irrespective of the tumor type or whether the response reflects tumor-specific properties. To address similarity or distinction of stromal gene expression changes during cancer progression, oligonucleotide-based Affymetrix microarray technology was used to compare the transcriptomes of laser-microdissected stromal cells derived from invasive human breast and prostate carcinoma. Invasive breast and prostate cancer-associated stroma was observed to display distinct transcriptomes, with a limited number of shared genes. Interestingly, both breast and prostate tumor-specific dysregulated stromal genes were observed to cluster breast and prostate cancer patients, respectively, into two distinct groups with statistically different clinical outcomes. By contrast, a gene signature that was common to the reactive stroma of both tumor types did not have survival predictive value. Univariate Cox analysis identified genes whose expression level was most strongly associated with patient survival. Taken together, these observations suggest that the tumor microenvironment displays distinct features according to the tumor type that provides survival-predictive value. PMID:21611158

  17. Accelerating ab initio molecular dynamics simulations by linear prediction methods

    NASA Astrophysics Data System (ADS)

    Herr, Jonathan D.; Steele, Ryan P.

    2016-09-01

    Acceleration of ab initio molecular dynamics (AIMD) simulations can be reliably achieved by extrapolation of electronic data from previous timesteps. Existing techniques utilize polynomial least-squares regression to fit previous steps' Fock or density matrix elements. In this work, the recursive Burg 'linear prediction' technique is shown to be a viable alternative to polynomial regression, and the extrapolation-predicted Fock matrix elements were three orders of magnitude closer to converged elements. Accelerations of 1.8-3.4× were observed in test systems, and in all cases, linear prediction outperformed polynomial extrapolation. Importantly, these accelerations were achieved without reducing the MD integration timestep.

  18. Rational Prediction with Molecular Dynamics for Hit Identification

    PubMed Central

    Nichols, Sara E; Swift, Robert V; Amaro, Rommie E

    2012-01-01

    Although the motions of proteins are fundamental for their function, for pragmatic reasons, the consideration of protein elasticity has traditionally been neglected in drug discovery and design. This review details protein motion, its relevance to biomolecular interactions and how it can be sampled using molecular dynamics simulations. Within this context, two major areas of research in structure-based prediction that can benefit from considering protein flexibility, binding site detection and molecular docking, are discussed. Basic classification metrics and statistical analysis techniques, which can facilitate performance analysis, are also reviewed. With hardware and software advances, molecular dynamics in combination with traditional structure-based prediction methods can potentially reduce the time and costs involved in the hit identification pipeline. PMID:23110535

  19. 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. PMID:27523972

  20. [Basal cell carcinoma. Molecular genetics and unusual clinical features].

    PubMed

    Reifenberger, J

    2007-05-01

    Basal cell carcinoma is the most common human cancer. Its incidence is steadily increasing. The development of basal cell carcinoma is linked to genetic factors, including the individual skin phototype, as well as the cumulative exposure to UVB. The vast majority of basal cell carcinomas are sporadic tumors, while familial cases associated with certain hereditary syndromes are less common. At the molecular level, basal cell carcinomas are characterized by aberrant activation of sonic hedgehog signaling, usually due to mutations either in the ptch or smoh genes. In addition, about half of the cases carry mutations in the tp53 tumor suppressor gene, which are often UVB-associated C-->T transition mutations. Clinically, basal cell carcinomas may show a high degree of phenotypical variability. In particular, tumors occurring in atypical locations, showing an unusual clinical appearance, or imitating other skin diseases may cause diagnostic problems. This review article summarizes the current state of the art concerning the etiology, predisposition and molecular genetics of basal cell carcinoma. In addition, examples of unusual clinical manifestations are illustrated. PMID:17440702

  1. Skeletal Muscle Laminopathies: A Review of Clinical and Molecular Features

    PubMed Central

    Maggi, Lorenzo; Carboni, Nicola; Bernasconi, Pia

    2016-01-01

    LMNA-related disorders are caused by mutations in the LMNA gene, which encodes for the nuclear envelope proteins, lamin A and C, via alternative splicing. Laminopathies are associated with a wide range of disease phenotypes, including neuromuscular, cardiac, metabolic disorders and premature aging syndromes. The most frequent diseases associated with mutations in the LMNA gene are characterized by skeletal and cardiac muscle involvement. This review will focus on genetics and clinical features of laminopathies affecting primarily skeletal muscle. Although only symptomatic treatment is available for these patients, many achievements have been made in clarifying the pathogenesis and improving the management of these diseases. PMID:27529282

  2. Skeletal Muscle Laminopathies: A Review of Clinical and Molecular Features.

    PubMed

    Maggi, Lorenzo; Carboni, Nicola; Bernasconi, Pia

    2016-01-01

    LMNA-related disorders are caused by mutations in the LMNA gene, which encodes for the nuclear envelope proteins, lamin A and C, via alternative splicing. Laminopathies are associated with a wide range of disease phenotypes, including neuromuscular, cardiac, metabolic disorders and premature aging syndromes. The most frequent diseases associated with mutations in the LMNA gene are characterized by skeletal and cardiac muscle involvement. This review will focus on genetics and clinical features of laminopathies affecting primarily skeletal muscle. Although only symptomatic treatment is available for these patients, many achievements have been made in clarifying the pathogenesis and improving the management of these diseases. PMID:27529282

  3. Protein-ligand binding region prediction (PLB-SAVE) based on geometric features and CUDA acceleration

    PubMed Central

    2013-01-01

    Background Protein-ligand interactions are key processes in triggering and controlling biological functions within cells. Prediction of protein binding regions on the protein surface assists in understanding the mechanisms and principles of molecular recognition. In silico geometrical shape analysis plays a primary step in analyzing the spatial characteristics of protein binding regions and facilitates applications of bioinformatics in drug discovery and design. Here, we describe the novel software, PLB-SAVE, which uses parallel processing technology and is ideally suited to extract the geometrical construct of solid angles from surface atoms. Representative clusters and corresponding anchors were identified from all surface elements and were assigned according to the ranking of their solid angles. In addition, cavity depth indicators were obtained by proportional transformation of solid angles and cavity volumes were calculated by scanning multiple directional vectors within each selected cavity. Both depth and volume characteristics were combined with various weighting coefficients to rank predicted potential binding regions. Results Two test datasets from LigASite, each containing 388 bound and unbound structures, were used to predict binding regions using PLB-SAVE and two well-known prediction systems, SiteHound and MetaPocket2.0 (MPK2). PLB-SAVE outperformed the other programs with accuracy rates of 94.3% for unbound proteins and 95.5% for bound proteins via a tenfold cross-validation process. Additionally, because the parallel processing architecture was designed to enhance the computational efficiency, we obtained an average of 160-fold increase in computational time. Conclusions In silico binding region prediction is considered the initial stage in structure-based drug design. To improve the efficacy of biological experiments for drug development, we developed PLB-SAVE, which uses only geometrical features of proteins and achieves a good overall performance

  4. Extraction of Molecular Features through Exome to Transcriptome Alignment

    PubMed Central

    Mudvari, Prakriti; Kowsari, Kamran; Cole, Charles; Mazumder, Raja; Horvath, Anelia

    2014-01-01

    Integrative Next Generation Sequencing (NGS) DNA and RNA analyses have very recently become feasible, and the published to date studies have discovered critical disease implicated pathways, and diagnostic and therapeutic targets. A growing number of exomes, genomes and transcriptomes from the same individual are quickly accumulating, providing unique venues for mechanistic and regulatory features analysis, and, at the same time, requiring new exploration strategies. In this study, we have integrated variation and expression information of four NGS datasets from the same individual: normal and tumor breast exomes and transcriptomes. Focusing on SNPcentered variant allelic prevalence, we illustrate analytical algorithms that can be applied to extract or validate potential regulatory elements, such as expression or growth advantage, imprinting, loss of heterozygosity (LOH), somatic changes, and RNA editing. In addition, we point to some critical elements that might bias the output and recommend alternative measures to maximize the confidence of findings. The need for such strategies is especially recognized within the growing appreciation of the concept of systems biology: integrative exploration of genome and transcriptome features reveal mechanistic and regulatory insights that reach far beyond linear addition of the individual datasets. PMID:24791251

  5. Prediction and Analysis of Quorum Sensing Peptides Based on Sequence Features

    PubMed Central

    Rajput, Akanksha; Gupta, Amit Kumar; Kumar, Manoj

    2015-01-01

    Quorum sensing peptides (QSPs) are the signaling molecules used by the Gram-positive bacteria in orchestrating cell-to-cell communication. In spite of their enormous importance in signaling process, their detailed bioinformatics analysis is lacking. In this study, QSPs and non-QSPs were examined according to their amino acid composition, residues position, motifs and physicochemical properties. Compositional analysis concludes that QSPs are enriched with aromatic residues like Trp, Tyr and Phe. At the N-terminal, Ser was a dominant residue at maximum positions, namely, first, second, third and fifth while Phe was a preferred residue at first, third and fifth positions from the C-terminal. A few motifs from QSPs were also extracted. Physicochemical properties like aromaticity, molecular weight and secondary structure were found to be distinguishing features of QSPs. Exploiting above properties, we have developed a Support Vector Machine (SVM) based predictive model. During 10-fold cross-validation, SVM achieves maximum accuracy of 93.00%, Mathew’s correlation coefficient (MCC) of 0.86 and Receiver operating characteristic (ROC) of 0.98 on the training/testing dataset (T200p+200n). Developed models performed equally well on the validation dataset (V20p+20n). The server also integrates several useful analysis tools like “QSMotifScan”, “ProtFrag”, “MutGen” and “PhysicoProp”. Our analysis reveals important characteristics of QSPs and on the basis of these unique features, we have developed a prediction algorithm “QSPpred” (freely available at: http://crdd.osdd.net/servers/qsppred). PMID:25781990

  6. Immunoglobulin M myeloma: evaluation of molecular features and cytokine expression.

    PubMed

    Konduri, Kartik; Sahota, Surinder S; Babbage, Gavin; Tong, Alex W; Kumar, Padmasini; Newman, Joseph T; Stone, Marvin J

    2005-03-01

    Immunoglobulin (Ig) M myeloma is a distinct entity with features of multiple myeloma (MM) and Waldenstrom's macroglobulinemia (WM). The malignant cells in IgM myeloma have a distinctive chromosomal translocation that differentiates them from WM. These cells are postgerminal-center in origin with isotype-switch transcripts. They appear to be arrested at a point of maturation between that of WM and MM. Preliminary data indicate that a pattern of osteoclast-activating factor and osteoprotegerin expression similar to that observed in classic MM is present in IgM myeloma. Additional studies on patients with this rare tumor may provide further insight into the pathogenesis of bone disease in plasma cell dyscrasias.

  7. Molecular features in arsenic-induced lung tumors

    PubMed Central

    2013-01-01

    Arsenic is a well-known human carcinogen, which potentially affects ~160 million people worldwide via exposure to unsafe levels in drinking water. Lungs are one of the main target organs for arsenic-related carcinogenesis. These tumors exhibit particular features, such as squamous cell-type specificity and high incidence among never smokers. Arsenic-induced malignant transformation is mainly related to the biotransformation process intended for the metabolic clearing of the carcinogen, which results in specific genetic and epigenetic alterations that ultimately affect key pathways in lung carcinogenesis. Based on this, lung tumors induced by arsenic exposure could be considered an additional subtype of lung cancer, especially in the case of never-smokers, where arsenic is a known etiological agent. In this article, we review the current knowledge on the various mechanisms of arsenic carcinogenicity and the specific roles of this metalloid in signaling pathways leading to lung cancer. PMID:23510327

  8. Circular features with predictable size on Xanadu region of Titan

    NASA Astrophysics Data System (ADS)

    Kochemasov, G. G.

    2008-09-01

    Planets' satellites in the Solar system (rocky and icy) have in common one fundamental property: all of them move simultaneously in two orbits - around Sun and around their planets (planets have only one orbit in the Solar system). As was shown by the wave planetology [1-6] " orbits make structures'. This means that movements in elliptical keplerian orbits imply periodically changing increasing and decreasing accelerations. Multiplied by celestial body mass this produces inertia-gravity forces (Newton: F=m • a). These forces warp celestial bodies in form of standing waves propagating in rotating bodies in four interfering orthogonal and diagonal directions. This interference gives three kinds of regularly disposed tectonic blocks: uprising (+), subsiding (-), neutral (0)(Fig. 1). Their size depends on warping wavelengths. The fundamental wave1 and its first overtone wave2 (and weaker ones) are responsible for ubiquitous tectonic dichotomy - two hemispheres - segments and sectoring. These superimposed global tectonic features are adorned by tectonic granulations size of which is inversely proportional to orbital frequencies: higher frequency - smaller granule, lower frequency - larger granule. A row of the planets granulations is as follows: Mercury πR/16, Venus πR/6, Earth πR/4, Mars πR/2, asteroids πR/1, Jupiter 3πR, Saturn 7.5πR, Uranus 21πR, Neptune 41πR, Pluto 62πR (a granule size is a half of a wavelength; a scale is Earth with πR/4 granule corresponding to 1/1 year orbital frequency; R-radius). So, orbits make structures. They are simpler for planets, but much more complicated for moons. Their surfaces are saturated with granules related to two main frequencies and at least two modulated side frequencies. Two orbits imply a wave modulation. The lower circum-Sun frequency modulates the higher circum-planet frequency by dividing and multiplying it thus producing two side frequencies with corresponding waves and granules. In case of Titan for the

  9. Random forests for feature selection in QSPR Models - an application for predicting standard enthalpy of formation of hydrocarbons

    PubMed Central

    2013-01-01

    Background One of the main topics in the development of quantitative structure-property relationship (QSPR) predictive models is the identification of the subset of variables that represent the structure of a molecule and which are predictors for a given property. There are several automated feature selection methods, ranging from backward, forward or stepwise procedures, to further elaborated methodologies such as evolutionary programming. The problem lies in selecting the minimum subset of descriptors that can predict a certain property with a good performance, computationally efficient and in a more robust way, since the presence of irrelevant or redundant features can cause poor generalization capacity. In this paper an alternative selection method, based on Random Forests to determine the variable importance is proposed in the context of QSPR regression problems, with an application to a manually curated dataset for predicting standard enthalpy of formation. The subsequent predictive models are trained with support vector machines introducing the variables sequentially from a ranked list based on the variable importance. Results The model generalizes well even with a high dimensional dataset and in the presence of highly correlated variables. The feature selection step was shown to yield lower prediction errors with RMSE values 23% lower than without feature selection, albeit using only 6% of the total number of variables (89 from the original 1485). The proposed approach further compared favourably with other feature selection methods and dimension reduction of the feature space. The predictive model was selected using a 10-fold cross validation procedure and, after selection, it was validated with an independent set to assess its performance when applied to new data and the results were similar to the ones obtained for the training set, supporting the robustness of the proposed approach. Conclusions The proposed methodology seemingly improves the prediction

  10. Adaptive modelling of structured molecular representations for toxicity prediction

    NASA Astrophysics Data System (ADS)

    Bertinetto, Carlo; Duce, Celia; Micheli, Alessio; Solaro, Roberto; Tiné, Maria Rosaria

    2012-12-01

    We investigated the possibility of modelling structure-toxicity relationships by direct treatment of the molecular structure (without using descriptors) through an adaptive model able to retain the appropriate structural information. With respect to traditional descriptor-based approaches, this provides a more general and flexible way to tackle prediction problems that is particularly suitable when little or no background knowledge is available. Our method employs a tree-structured molecular representation, which is processed by a recursive neural network (RNN). To explore the realization of RNN modelling in toxicological problems, we employed a data set containing growth impairment concentrations (IGC50) for Tetrahymena pyriformis.

  11. Interaction of proteases with legume seed inhibitors. Molecular features.

    PubMed

    de Seidl, D S

    1996-12-01

    After having found that raw black beans (Phaseolus vulgaris) were toxic, while the cooked ones constitute the basic diet of the underdeveloped peoples of the world, in the sixties, our research directed by Dr. Jaffé, concentrated mainly around the detection and identification of the heat labile toxic factors in legume seeds. A micromethod for the detection of protease inhibitors (PI) in individual seeds was developed, for the purpose of establishing that the multiple trypsin inhibitors (TI) found in the Cubagua variety were expressions of single seeds and not a mixture of a non homogenous bean lot. Six isoinhibitors were isolated and purified, all of which were "double-headed" and interacted with trypsin (T) and chymotrypsin (CHT) independently and simultaneously, as shown by electrophoresis of their binary and ternary complexes with each and both enzymes. However, their affinity for the enzymes, including elastases, was rather variable, as well as their amino acid composition which consisted of 51 units for inhibitor V, the smallest, and 83 amino acids for inhibitor I, the largest. A low molecular weight protein fraction that inhibited subtilisin (S), but recognized neither T, CHT nor pancreatic elastase was detected in 63 varieties of Phaseolus vulgaris as well as in broad beans (Vicia faba), chick peas (Cicer arietinum), jack beans (Canavalia ensiformis), kidney beans (Vigna aureus), etc., It was absent though, in soybeans (Glycine max), lentils (Lens culinaris), green peas (Pisum sativum), cowpea (Vigna sinensis) and lupine seeds (Lupinus sp). Subtilisin inhibitors (SI) were isolated from black beans, broad beans, chick peas and jack beans. Their Mr is between 8-9KD and they show a rather high stability in the presence of denaturing agents. They are specific toward microbial proteases, in addition to subtilisins, Carlsberg and BPN', they inhibit the alkaline protease from Tritirachium album (Protease K), from Aspergillus oryzae and one isolated from

  12. Clinical, Epidemiologic, Histopathologic and Molecular Features of an Unexplained Dermopathy

    PubMed Central

    Pearson, Michele L.; Selby, Joseph V.; Katz, Kenneth A.; Cantrell, Virginia; Braden, Christopher R.; Parise, Monica E.; Paddock, Christopher D.; Lewin-Smith, Michael R.; Kalasinsky, Victor F.; Goldstein, Felicia C.; Hightower, Allen W.; Papier, Arthur; Lewis, Brian; Motipara, Sarita; Eberhard, Mark L.

    2012-01-01

    Background Morgellons is a poorly characterized constellation of symptoms, with the primary manifestations involving the skin. We conducted an investigation of this unexplained dermopathy to characterize the clinical and epidemiologic features and explore potential etiologies. Methods A descriptive study was conducted among persons at least 13 years of age and enrolled in Kaiser Permanente Northern California (KPNC) during 2006–2008. A case was defined as the self-reported emergence of fibers or materials from the skin accompanied by skin lesions and/or disturbing skin sensations. We collected detailed epidemiologic data, performed clinical evaluations and geospatial analyses and analyzed materials collected from participants' skin. Results We identified 115 case-patients. The prevalence was 3.65 (95% CI = 2.98, 4.40) cases per 100,000 enrollees. There was no clustering of cases within the 13-county KPNC catchment area (p = .113). Case-patients had a median age of 52 years (range: 17–93) and were primarily female (77%) and Caucasian (77%). Multi-system complaints were common; 70% reported chronic fatigue and 54% rated their overall health as fair or poor with mean Physical Component Scores and Mental Component Scores of 36.63 (SD = 12.9) and 35.45 (SD = 12.89), respectively. Cognitive deficits were detected in 59% of case-patients and 63% had evidence of clinically significant somatic complaints; 50% had drugs detected in hair samples and 78% reported exposure to solvents. Solar elastosis was the most common histopathologic abnormality (51% of biopsies); skin lesions were most consistent with arthropod bites or chronic excoriations. No parasites or mycobacteria were detected. Most materials collected from participants' skin were composed of cellulose, likely of cotton origin. Conclusions This unexplained dermopathy was rare among this population of Northern California residents, but associated with significantly reduced health-related quality of

  13. Predictive Value of Morphological Features in Patients with Autism versus Normal Controls

    ERIC Educational Resources Information Center

    Ozgen, H.; Hellemann, G. S.; de Jonge, M. V.; Beemer, F. A.; van Engeland, H.

    2013-01-01

    We investigated the predictive power of morphological features in 224 autistic patients and 224 matched-pairs controls. To assess the relationship between the morphological features and autism, we used the receiver operator curves (ROC). In addition, we used recursive partitioning (RP) to determine a specific pattern of abnormalities that is…

  14. Ground-state features in the THz spectra of molecular clusters of β-HMX.

    PubMed

    Huang, Lulu; Shabaev, Andrew; Lambrakos, Samuel G; Massa, Lou

    2012-10-01

    We present calculations of absorption spectra arising from molecular vibrations at THz frequencies for molecular clusters of the explosive HMX using density functional theory (DFT). The features of these spectra can be shown to follow from the coupling of vibrational modes. In particular, the coupling among ground-state vibrational modes provides a reasonable molecular-level interpretation of spectral features associated with the vibrational modes of molecular clusters. THz excitation from the ground state is associated with frequencies that characteristically perturb molecular electronic states, in contrast to frequencies, which are usually substantially above the mid-infrared (mid-IR) range, that can induce appreciable electronic-state transition. Owing to this characteristic of THz excitation, one is able to make a direct association between local oscillations about ground-state minima of molecules, either isolated or comprising a cluster, and THz absorption spectra. The DFT software program GAUSSIAN was used for the calculations of the absorption spectra presented here.

  15. Improving structure-based function prediction using molecular dynamics

    PubMed Central

    Glazer, Dariya S.; Radmer, Randall J.; Altman, Russ B.

    2009-01-01

    Summary The number of molecules with solved three-dimensional structure but unknown function is increasing rapidly. Particularly problematic are novel folds with little detectable similarity to molecules of known function. Experimental assays can determine the functions of such molecules, but are time-consuming and expensive. Computational approaches can identify potential functional sites; however, these approaches generally rely on single static structures and do not use information about dynamics. In fact, structural dynamics can enhance function prediction: we coupled molecular dynamics simulations with structure-based function prediction algorithms that identify Ca2+ binding sites. When applied to 11 challenging proteins, both methods showed substantial improvement in performance, revealing 22 more sites in one case and 12 more in the other, with a modest increase in apparent false positives. Thus, we show that treating molecules as dynamic entities improves the performance of structure-based function prediction methods. PMID:19604472

  16. Toward Fully in Silico Melting Point Prediction Using Molecular Simulations.

    PubMed

    Zhang, Yong; Maginn, Edward J

    2013-03-12

    Melting point is one of the most fundamental and practically important properties of a compound. Molecular simulation methods have been developed for the accurate computation of melting points. However, all of these methods need an experimental crystal structure as input, which means that such calculations are not really predictive since the melting point can be measured easily in experiments once a crystal structure is known. On the other hand, crystal structure prediction (CSP) has become an active field and significant progress has been made, although challenges still exist. One of the main challenges is the existence of many crystal structures (polymorphs) that are very close in energy. Thermal effects and kinetic factors make the situation even more complicated, such that it is still not trivial to predict experimental crystal structures. In this work, we exploit the fact that free energy differences are often small between crystal structures. We show that accurate melting point predictions can be made by using a reasonable crystal structure from CSP as a starting point for a free energy-based melting point calculation. The key is that most crystal structures predicted by CSP have free energies that are close to that of the experimental structure. The proposed method was tested on two rigid molecules and the results suggest that a fully in silico melting point prediction method is possible.

  17. Toward Fully in Silico Melting Point Prediction Using Molecular Simulations

    SciTech Connect

    Zhang, Y; Maginn, EJ

    2013-03-01

    Melting point is one of the most fundamental and practically important properties of a compound. Molecular computation of melting points. However, all of these methods simulation methods have been developed for the accurate need an experimental crystal structure as input, which means that such calculations are not really predictive since the melting point can be measured easily in experiments once a crystal structure is known. On the other hand, crystal structure prediction (CSP) has become an active field and significant progress has been made, although challenges still exist. One of the main challenges is the existence of many crystal structures (polymorphs) that are very close in energy. Thermal effects and kinetic factors make the situation even more complicated, such that it is still not trivial to predict experimental crystal structures. In this work, we exploit the fact that free energy differences are often small between crystal structures. We show that accurate melting point predictions can be made by using a reasonable crystal structure from CSP as a starting point for a free energy-based melting point calculation. The key is that most crystal structures predicted by CSP have free energies that are close to that of the experimental structure. The proposed method was tested on two rigid molecules and the results suggest that a fully in silico melting point prediction method is possible.

  18. Prediction of biomechanical trabecular bone properties with geometric features using MR imaging

    NASA Astrophysics Data System (ADS)

    Huber, Markus B.; Lancianese, Sarah L.; Ikpot, Imoh; Nagarajan, Mahesh B.; Lerner, Amy L.; Wismüller, Axel

    2010-03-01

    Trabecular bone parameters extracted from magnetic resonance (MR) images are compared in their ability to predict biomechanical properties determined through mechanical testing. Trabecular bone density and structural changes throughout the proximal tibia are indicative of several musculoskeletal disorders of the knee joint involving changes in the bone quality and the surrounding soft tissue. Recent studies have shown that MR imaging, most frequently applied in soft tissue imaging, also allows non-invasive 3-dimensional characterization of bone microstructure. Sophisticated MR image features that estimate local structural and geometric properties of the trabecular bone may improve the ability of MR imaging to determine local bone quality in vivo. The purpose of the current study is to use whole joint MR images to compare the performance of trabecular bone features extracted from the images in predicting biomechanical strength properties measured on the corresponding ex vivo specimens. The regional apparent bone volume fraction (appBVF) and scaling index method (SIM) derived features were calculated; a Multilayer Radial Basis Functions Network was then optimized to calculate the prediction accuracy as measured by the root mean square error (RSME) for each bone feature. The best prediction result was obtained with a SIM feature with the lowest prediction error (RSME=0.246) and the highest coefficient of determination (R2 = 0.769). The current study demonstrates that the combination of sophisticated bone structure features and supervised learning techniques can improve MR imaging as an in vivo imaging tool in determining local trabecular bone quality.

  19. Feature maps driven no-reference image quality prediction of authentically distorted images

    NASA Astrophysics Data System (ADS)

    Ghadiyaram, Deepti; Bovik, Alan C.

    2015-03-01

    Current blind image quality prediction models rely on benchmark databases comprised of singly and synthetically distorted images, thereby learning image features that are only adequate to predict human perceived visual quality on such inauthentic distortions. However, real world images often contain complex mixtures of multiple distortions. Rather than a) discounting the effect of these mixtures of distortions on an image's perceptual quality and considering only the dominant distortion or b) using features that are only proven to be efficient for singly distorted images, we deeply study the natural scene statistics of authentically distorted images, in different color spaces and transform domains. We propose a feature-maps-driven statistical approach which avoids any latent assumptions about the type of distortion(s) contained in an image, and focuses instead on modeling the remarkable consistencies in the scene statistics of real world images in the absence of distortions. We design a deep belief network that takes model-based statistical image features derived from a very large database of authentically distorted images as input and discovers good feature representations by generalizing over different distortion types, mixtures, and severities, which are later used to learn a regressor for quality prediction. We demonstrate the remarkable competence of our features for improving automatic perceptual quality prediction on a benchmark database and on the newly designed LIVE Authentic Image Quality Challenge Database and show that our approach of combining robust statistical features and the deep belief network dramatically outperforms the state-of-the-art.

  20. Widespread convergence in toxin resistance by predictable molecular evolution.

    PubMed

    Ujvari, Beata; Casewell, Nicholas R; Sunagar, Kartik; Arbuckle, Kevin; Wüster, Wolfgang; Lo, Nathan; O'Meally, Denis; Beckmann, Christa; King, Glenn F; Deplazes, Evelyne; Madsen, Thomas

    2015-09-22

    The question about whether evolution is unpredictable and stochastic or intermittently constrained along predictable pathways is the subject of a fundamental debate in biology, in which understanding convergent evolution plays a central role. At the molecular level, documented examples of convergence are rare and limited to occurring within specific taxonomic groups. Here we provide evidence of constrained convergent molecular evolution across the metazoan tree of life. We show that resistance to toxic cardiac glycosides produced by plants and bufonid toads is mediated by similar molecular changes to the sodium-potassium-pump (Na(+)/K(+)-ATPase) in insects, amphibians, reptiles, and mammals. In toad-feeding reptiles, resistance is conferred by two point mutations that have evolved convergently on four occasions, whereas evidence of a molecular reversal back to the susceptible state in varanid lizards migrating to toad-free areas suggests that toxin resistance is maladaptive in the absence of selection. Importantly, resistance in all taxa is mediated by replacements of 2 of the 12 amino acids comprising the Na(+)/K(+)-ATPase H1-H2 extracellular domain that constitutes a core part of the cardiac glycoside binding site. We provide mechanistic insight into the basis of resistance by showing that these alterations perturb the interaction between the cardiac glycoside bufalin and the Na(+)/K(+)-ATPase. Thus, similar selection pressures have resulted in convergent evolution of the same molecular solution across the breadth of the animal kingdom, demonstrating how a scarcity of possible solutions to a selective challenge can lead to highly predictable evolutionary responses. PMID:26372961

  1. Widespread convergence in toxin resistance by predictable molecular evolution

    PubMed Central

    Ujvari, Beata; Casewell, Nicholas R.; Sunagar, Kartik; Arbuckle, Kevin; Wüster, Wolfgang; Lo, Nathan; O’Meally, Denis; Beckmann, Christa; King, Glenn F.; Deplazes, Evelyne; Madsen, Thomas

    2015-01-01

    The question about whether evolution is unpredictable and stochastic or intermittently constrained along predictable pathways is the subject of a fundamental debate in biology, in which understanding convergent evolution plays a central role. At the molecular level, documented examples of convergence are rare and limited to occurring within specific taxonomic groups. Here we provide evidence of constrained convergent molecular evolution across the metazoan tree of life. We show that resistance to toxic cardiac glycosides produced by plants and bufonid toads is mediated by similar molecular changes to the sodium-potassium-pump (Na+/K+-ATPase) in insects, amphibians, reptiles, and mammals. In toad-feeding reptiles, resistance is conferred by two point mutations that have evolved convergently on four occasions, whereas evidence of a molecular reversal back to the susceptible state in varanid lizards migrating to toad-free areas suggests that toxin resistance is maladaptive in the absence of selection. Importantly, resistance in all taxa is mediated by replacements of 2 of the 12 amino acids comprising the Na+/K+-ATPase H1–H2 extracellular domain that constitutes a core part of the cardiac glycoside binding site. We provide mechanistic insight into the basis of resistance by showing that these alterations perturb the interaction between the cardiac glycoside bufalin and the Na+/K+-ATPase. Thus, similar selection pressures have resulted in convergent evolution of the same molecular solution across the breadth of the animal kingdom, demonstrating how a scarcity of possible solutions to a selective challenge can lead to highly predictable evolutionary responses. PMID:26372961

  2. Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using MRI and Structural Network Features.

    PubMed

    Wei, Rizhen; Li, Chuhan; Fogelson, Noa; Li, Ling

    2016-01-01

    Optimized magnetic resonance imaging (MRI) features and abnormalities of brain network architectures may allow earlier detection and accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). In this study, we proposed a classification framework to distinguish MCI converters (MCIc) from MCI non-converters (MCInc) by using a combination of FreeSurfer-derived MRI features and nodal features derived from the thickness network. At the feature selection step, we first employed sparse linear regression with stability selection, for the selection of discriminative features in the iterative combinations of MRI and network measures. Subsequently the top K features of available combinations were selected as optimal features for classification. To obtain unbiased results, support vector machine (SVM) classifiers with nested cross validation were used for classification. The combination of 10 features including those from MRI and network measures attained accuracies of 66.04, 76.39, 74.66, and 73.91% for mixed conversion time, 6, 12, and 18 months before diagnosis of probable AD, respectively. Analysis of the diagnostic power of different time periods before diagnosis of probable AD showed that short-term prediction (6 and 12 months) achieved more stable and higher AUC scores compared with long-term prediction (18 months), with K-values from 1 to 30. The present results suggest that meaningful predictors composed of MRI and network measures may offer the possibility for early detection of progression from MCI to AD. PMID:27148045

  3. Prediction of Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using MRI and Structural Network Features

    PubMed Central

    Wei, Rizhen; Li, Chuhan; Fogelson, Noa; Li, Ling

    2016-01-01

    Optimized magnetic resonance imaging (MRI) features and abnormalities of brain network architectures may allow earlier detection and accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). In this study, we proposed a classification framework to distinguish MCI converters (MCIc) from MCI non-converters (MCInc) by using a combination of FreeSurfer-derived MRI features and nodal features derived from the thickness network. At the feature selection step, we first employed sparse linear regression with stability selection, for the selection of discriminative features in the iterative combinations of MRI and network measures. Subsequently the top K features of available combinations were selected as optimal features for classification. To obtain unbiased results, support vector machine (SVM) classifiers with nested cross validation were used for classification. The combination of 10 features including those from MRI and network measures attained accuracies of 66.04, 76.39, 74.66, and 73.91% for mixed conversion time, 6, 12, and 18 months before diagnosis of probable AD, respectively. Analysis of the diagnostic power of different time periods before diagnosis of probable AD showed that short-term prediction (6 and 12 months) achieved more stable and higher AUC scores compared with long-term prediction (18 months), with K-values from 1 to 30. The present results suggest that meaningful predictors composed of MRI and network measures may offer the possibility for early detection of progression from MCI to AD. PMID:27148045

  4. Harnessing Computational Biology for Exact Linear B-Cell Epitope Prediction: A Novel Amino Acid Composition-Based Feature Descriptor.

    PubMed

    Saravanan, Vijayakumar; Gautham, Namasivayam

    2015-10-01

    Proteins embody epitopes that serve as their antigenic determinants. Epitopes occupy a central place in integrative biology, not to mention as targets for novel vaccine, pharmaceutical, and systems diagnostics development. The presence of T-cell and B-cell epitopes has been extensively studied due to their potential in synthetic vaccine design. However, reliable prediction of linear B-cell epitope remains a formidable challenge. Earlier studies have reported discrepancy in amino acid composition between the epitopes and non-epitopes. Hence, this study proposed and developed a novel amino acid composition-based feature descriptor, Dipeptide Deviation from Expected Mean (DDE), to distinguish the linear B-cell epitopes from non-epitopes effectively. In this study, for the first time, only exact linear B-cell epitopes and non-epitopes have been utilized for developing the prediction method, unlike the use of epitope-containing regions in earlier reports. To evaluate the performance of the DDE feature vector, models have been developed with two widely used machine-learning techniques Support Vector Machine and AdaBoost-Random Forest. Five-fold cross-validation performance of the proposed method with error-free dataset and dataset from other studies achieved an overall accuracy between nearly 61% and 73%, with balance between sensitivity and specificity metrics. Performance of the DDE feature vector was better (with accuracy difference of about 2% to 12%), in comparison to other amino acid-derived features on different datasets. This study reflects the efficiency of the DDE feature vector in enhancing the linear B-cell epitope prediction performance, compared to other feature representations. The proposed method is made as a stand-alone tool available freely for researchers, particularly for those interested in vaccine design and novel molecular target development for systems therapeutics and diagnostics: https://github.com/brsaran/LBEEP.

  5. Prediction of structural features and application to outer membrane protein identification

    NASA Astrophysics Data System (ADS)

    Yan, Renxiang; Wang, Xiaofeng; Huang, Lanqing; Yan, Feidi; Xue, Xiaoyu; Cai, Weiwen

    2015-06-01

    Protein three-dimensional (3D) structures provide insightful information in many fields of biology. One-dimensional properties derived from 3D structures such as secondary structure, residue solvent accessibility, residue depth and backbone torsion angles are helpful to protein function prediction, fold recognition and ab initio folding. Here, we predict various structural features with the assistance of neural network learning. Based on an independent test dataset, protein secondary structure prediction generates an overall Q3 accuracy of ~80%. Meanwhile, the prediction of relative solvent accessibility obtains the highest mean absolute error of 0.164, and prediction of residue depth achieves the lowest mean absolute error of 0.062. We further improve the outer membrane protein identification by including the predicted structural features in a scoring function using a simple profile-to-profile alignment. The results demonstrate that the accuracy of outer membrane protein identification can be improved by ~3% at a 1% false positive level when structural features are incorporated. Finally, our methods are available as two convenient and easy-to-use programs. One is PSSM-2-Features for predicting secondary structure, relative solvent accessibility, residue depth and backbone torsion angles, the other is PPA-OMP for identifying outer membrane proteins from proteomes.

  6. Exponential repulsion improves structural predictability of molecular docking.

    PubMed

    Bazgier, Václav; Berka, Karel; Otyepka, Michal; Banáš, Pavel

    2016-10-30

    Molecular docking is a powerful tool for theoretical prediction of the preferred conformation and orientation of small molecules within protein active sites. The obtained poses can be used for estimation of binding energies, which indicate the inhibition effect of designed inhibitors, and therefore might be used for in silico drug design. However, the evaluation of ligand binding affinity critically depends on successful prediction of the native binding mode. Contemporary docking methods are often based on scoring functions derived from molecular mechanical potentials. In such potentials, nonbonded interactions are typically represented by electrostatic interactions between atom-centered partial charges and standard 6-12 Lennard-Jones potential. Here, we present implementation and testing of a scoring function based on more physically justified exponential repulsion instead of the standard Lennard-Jones potential. We found that this scoring function significantly improved prediction of the native binding modes in proteins bearing narrow active sites such as serine proteases and kinases. © 2016 Wiley Periodicals, Inc. PMID:27620738

  7. Ultra-low-molecular-weight heparins: precise structural features impacting specific anticoagulant activities.

    PubMed

    Lima, Marcelo A; Viskov, Christian; Herman, Frederic; Gray, Angel L; de Farias, Eduardo H C; Cavalheiro, Renan P; Sassaki, Guilherme L; Hoppensteadt, Debra; Fareed, Jawed; Nader, Helena B

    2013-03-01

    Ultra-low-molecular-weight heparins (ULMWHs) with better efficacy and safety ratios are under development; however, there are few structural data available. The main structural features and molecular weight of ULMWHs were studied and compared to enoxaparin. Their monosaccharide composition and average molecular weights were determined and preparations studied by nuclear magnetic resonance spectroscopy, scanning ultraviolet spectroscopy, circular dichroism and gel permeation chromatography. In general, ULMWHs presented higher 3-O-sulphated glucosamine and unsaturated uronic acid residues, the latter being comparable with their higher degree of depolymerisation. The analysis showed that ULMWHs are structurally related to LMWHs; however, their monosaccharide/oligosaccharide compositions and average molecular weights differed considerably explaining their different anticoagulant activities. The results relate structural features to activity, assisting the development of new and improved therapeutic agents, based on depolymerised heparin, for the prophylaxis and treatment of thrombotic disorders.

  8. MINT: Mutual Information Based Transductive Feature Selection for Genetic Trait Prediction.

    PubMed

    He, Dan; Rish, Irina; Haws, David; Parida, Laxmi

    2016-01-01

    Whole genome prediction of complex phenotypic traits using high-density genotyping arrays has attracted a lot of attention, as it is relevant to the fields of plant and animal breeding and genetic epidemiology. Since the number of genotypes is generally much bigger than the number of samples, predictive models suffer from the curse of dimensionality. The curse of dimensionality problem not only affects the computational efficiency of a particular genomic selection method, but can also lead to a poor performance, mainly due to possible overfitting, or un-informative features. In this work, we propose a novel transductive feature selection method, called MINT, which is based on the MRMR (Max-Relevance and Min-Redundancy) criterion. We apply MINT on genetic trait prediction problems and show that, in general, MINT is a better feature selection method than the state-of-the-art inductive method MRMR. PMID:27295642

  9. Prediction of outcome in traumatic brain injury patients using long-term qEEG features.

    PubMed

    Mikola, Annika; Ratsep, Indrek; Sarkela, Mika; Lipping, Tarmo

    2015-08-01

    Treatment of patients suffering from severe traumatic brain injury (TBI) commonly involves sedation and mechanical ventilation during prolonged stay in the intensive care unit. Continuous EEG is often monitored in these patients to detect epileptic seizures. It has also been suggested that EEG has prognostic value regarding the outcome of the treatment. In this study the ability of 186 qEEG features to predict the outcome of the treatment of TBI patients is assessed. The features are based on the power spectrum of the EEG. The data underlying the study contains long term (over 24 h) recordings from 20 patients treated in the postoperative intensive care unit of the North Estonian Medical Center. 12 qEEG features were found to have predictive value when evaluated by calculating the area under the receiver operating curve constructed from feature probabilities. PMID:26736563

  10. Adaptive reliance on the most stable sensory predictions enhances perceptual feature extraction of moving stimuli

    PubMed Central

    Kumar, Neeraj

    2016-01-01

    The prediction of the sensory outcomes of action is thought to be useful for distinguishing self- vs. externally generated sensations, correcting movements when sensory feedback is delayed, and learning predictive models for motor behavior. Here, we show that aspects of another fundamental function—perception—are enhanced when they entail the contribution of predicted sensory outcomes and that this enhancement relies on the adaptive use of the most stable predictions available. We combined a motor-learning paradigm that imposes new sensory predictions with a dynamic visual search task to first show that perceptual feature extraction of a moving stimulus is poorer when it is based on sensory feedback that is misaligned with those predictions. This was possible because our novel experimental design allowed us to override the “natural” sensory predictions present when any action is performed and separately examine the influence of these two sources on perceptual feature extraction. We then show that if the new predictions induced via motor learning are unreliable, rather than just relying on sensory information for perceptual judgments, as is conventionally thought, then subjects adaptively transition to using other stable sensory predictions to maintain greater accuracy in their perceptual judgments. Finally, we show that when sensory predictions are not modified at all, these judgments are sharper when subjects combine their natural predictions with sensory feedback. Collectively, our results highlight the crucial contribution of sensory predictions to perception and also suggest that the brain intelligently integrates the most stable predictions available with sensory information to maintain high fidelity in perceptual decisions. PMID:26823516

  11. Scoring multiple features to predict drug disease associations using information fusion and aggregation.

    PubMed

    Moghadam, H; Rahgozar, M; Gharaghani, S

    2016-08-01

    Prediction of drug-disease associations is one of the current fields in drug repositioning that has turned into a challenging topic in pharmaceutical science. Several available computational methods use network-based and machine learning approaches to reposition old drugs for new indications. However, they often ignore features of drugs and diseases as well as the priority and importance of each feature, relation, or interactions between features and the degree of uncertainty. When predicting unknown drug-disease interactions there are diverse data sources and multiple features available that can provide more accurate and reliable results. This information can be collectively mined using data fusion methods and aggregation operators. Therefore, we can use the feature fusion method to make high-level features. We have proposed a computational method named scored mean kernel fusion (SMKF), which uses a new method to score the average aggregation operator called scored mean. To predict novel drug indications, this method systematically combines multiple features related to drugs or diseases at two levels: the drug-drug level and the drug-disease level. The purpose of this study was to investigate the effect of drug and disease features as well as data fusion to predict drug-disease interactions. The method was validated against a well-established drug-disease gold-standard dataset. When compared with the available methods, our proposed method outperformed them and competed well in performance with area under cover (AUC) of 0.91, F-measure of 84.9% and Matthews correlation coefficient of 70.31%. PMID:27455069

  12. Stable feature selection for clinical prediction: exploiting ICD tree structure using Tree-Lasso.

    PubMed

    Kamkar, Iman; Gupta, Sunil Kumar; Phung, Dinh; Venkatesh, Svetha

    2015-02-01

    Modern healthcare is getting reshaped by growing Electronic Medical Records (EMR). Recently, these records have been shown of great value towards building clinical prediction models. In EMR data, patients' diseases and hospital interventions are captured through a set of diagnoses and procedures codes. These codes are usually represented in a tree form (e.g. ICD-10 tree) and the codes within a tree branch may be highly correlated. These codes can be used as features to build a prediction model and an appropriate feature selection can inform a clinician about important risk factors for a disease. Traditional feature selection methods (e.g. Information Gain, T-test, etc.) consider each variable independently and usually end up having a long feature list. Recently, Lasso and related l1-penalty based feature selection methods have become popular due to their joint feature selection property. However, Lasso is known to have problems of selecting one feature of many correlated features randomly. This hinders the clinicians to arrive at a stable feature set, which is crucial for clinical decision making process. In this paper, we solve this problem by using a recently proposed Tree-Lasso model. Since, the stability behavior of Tree-Lasso is not well understood, we study the stability behavior of Tree-Lasso and compare it with other feature selection methods. Using a synthetic and two real-world datasets (Cancer and Acute Myocardial Infarction), we show that Tree-Lasso based feature selection is significantly more stable than Lasso and comparable to other methods e.g. Information Gain, ReliefF and T-test. We further show that, using different types of classifiers such as logistic regression, naive Bayes, support vector machines, decision trees and Random Forest, the classification performance of Tree-Lasso is comparable to Lasso and better than other methods. Our result has implications in identifying stable risk factors for many healthcare problems and therefore can

  13. MRI signal and texture features for the prediction of MCI to Alzheimer's disease progression

    NASA Astrophysics Data System (ADS)

    Martínez-Torteya, Antonio; Rodríguez-Rojas, Juan; Celaya-Padilla, José M.; Galván-Tejada, Jorge I.; Treviño, Victor; Tamez-Peña, José G.

    2014-03-01

    An early diagnosis of Alzheimer's disease (AD) confers many benefits. Several biomarkers from different information modalities have been proposed for the prediction of MCI to AD progression, where features extracted from MRI have played an important role. However, studies have focused almost exclusively in the morphological characteristics of the images. This study aims to determine whether features relating to the signal and texture of the image could add predictive power. Baseline clinical, biological and PET information, and MP-RAGE images for 62 subjects from the Alzheimer's Disease Neuroimaging Initiative were used in this study. Images were divided into 83 regions and 50 features were extracted from each one of these. A multimodal database was constructed, and a feature selection algorithm was used to obtain an accurate and small logistic regression model, which achieved a cross-validation accuracy of 0.96. These model included six features, five of them obtained from the MP-RAGE image, and one obtained from genotyping. A risk analysis divided the subjects into low-risk and high-risk groups according to a prognostic index, showing that both groups are statistically different (p-value of 2.04e-11). The results demonstrate that MRI features related to both signal and texture, add MCI to AD predictive power, and support the idea that multimodal biomarkers outperform single-modality biomarkers.

  14. Cellular automata with object-oriented features for parallel molecular network modeling.

    PubMed

    Zhu, Hao; Wu, Yinghui; Huang, Sui; Sun, Yan; Dhar, Pawan

    2005-06-01

    Cellular automata are an important modeling paradigm for studying the dynamics of large, parallel systems composed of multiple, interacting components. However, to model biological systems, cellular automata need to be extended beyond the large-scale parallelism and intensive communication in order to capture two fundamental properties characteristic of complex biological systems: hierarchy and heterogeneity. This paper proposes extensions to a cellular automata language, Cellang, to meet this purpose. The extended language, with object-oriented features, can be used to describe the structure and activity of parallel molecular networks within cells. Capabilities of this new programming language include object structure to define molecular programs within a cell, floating-point data type and mathematical functions to perform quantitative computation, message passing capability to describe molecular interactions, as well as new operators, statements, and built-in functions. We discuss relevant programming issues of these features, including the object-oriented description of molecular interactions with molecule encapsulation, message passing, and the description of heterogeneity and anisotropy at the cell and molecule levels. By enabling the integration of modeling at the molecular level with system behavior at cell, tissue, organ, or even organism levels, the program will help improve our understanding of how complex and dynamic biological activities are generated and controlled by parallel functioning of molecular networks. Index Terms-Cellular automata, modeling, molecular network, object-oriented. PMID:16117022

  15. The predictability of molecular evolution during functional innovation.

    PubMed

    Blank, Diana; Wolf, Luise; Ackermann, Martin; Silander, Olin K

    2014-02-25

    Determining the molecular changes that give rise to functional innovations is a major unresolved problem in biology. The paucity of examples has served as a significant hindrance in furthering our understanding of this process. Here we used experimental evolution with the bacterium Escherichia coli to quantify the molecular changes underlying functional innovation in 68 independent instances ranging over 22 different metabolic functions. Using whole-genome sequencing, we show that the relative contribution of regulatory and structural mutations depends on the cellular context of the metabolic function. In addition, we find that regulatory mutations affect genes that act in pathways relevant to the novel function, whereas structural mutations affect genes that act in unrelated pathways. Finally, we use population genetic modeling to show that the relative contributions of regulatory and structural mutations during functional innovation may be affected by population size. These results provide a predictive framework for the molecular basis of evolutionary innovation, which is essential for anticipating future evolutionary trajectories in the face of rapid environmental change.

  16. 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

  17. Unbiased Prediction and Feature Selection in High-Dimensional Survival Regression

    PubMed Central

    Laimighofer, Michael; Krumsiek, Jan; Theis, Fabian J.

    2016-01-01

    Abstract With widespread availability of omics profiling techniques, the analysis and interpretation of high-dimensional omics data, for example, for biomarkers, is becoming an increasingly important part of clinical medicine because such datasets constitute a promising resource for predicting survival outcomes. However, early experience has shown that biomarkers often generalize poorly. Thus, it is crucial that models are not overfitted and give accurate results with new data. In addition, reliable detection of multivariate biomarkers with high predictive power (feature selection) is of particular interest in clinical settings. We present an approach that addresses both aspects in high-dimensional survival models. Within a nested cross-validation (CV), we fit a survival model, evaluate a dataset in an unbiased fashion, and select features with the best predictive power by applying a weighted combination of CV runs. We evaluate our approach using simulated toy data, as well as three breast cancer datasets, to predict the survival of breast cancer patients after treatment. In all datasets, we achieve more reliable estimation of predictive power for unseen cases and better predictive performance compared to the standard CoxLasso model. Taken together, we present a comprehensive and flexible framework for survival models, including performance estimation, final feature selection, and final model construction. The proposed algorithm is implemented in an open source R package (SurvRank) available on CRAN. PMID:26894327

  18. Multivariate Feature Selection for Predicting Scour-Related Bridge Damage using a Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Anderson, I.

    2015-12-01

    Scour and hydraulic damage are the most common cause of bridge failure, reported to be responsible for over 60% of bridge failure nationwide. Scour is a complex process, and is likely an epistatic function of both bridge and stream conditions that are both stationary and in dynamic flux. Bridge inspections, conducted regularly on bridges nationwide, rate bridge health assuming a static stream condition, and typically do not include dynamically changing geomorphological adjustments. The Vermont Agency of Natural Resources stream geomorphic assessment data could add value into the current bridge inspection and scour design. The 2011 bridge damage from Tropical Storm Irene served as a case study for feature selection to improve bridge scour damage prediction in extreme events. The bridge inspection (with over 200 features on more than 300 damaged and 2,000 non-damaged bridges), and the stream geomorphic assessment (with over 300 features on more than 5000 stream reaches) constitute "Big Data", and together have the potential to generate large numbers of combined features ("epistatic relationships") that might better predict scour-related bridge damage. The potential combined features pose significant computational challenges for traditional statistical techniques (e.g., multivariate logistic regression). This study uses a genetic algorithm to perform a search of the multivariate feature space to identify epistatic relationships that are indicative of bridge scour damage. The combined features identified could be used to improve bridge scour design, and to better monitor and rate bridge scour vulnerability.

  19. Patient feature based dosimetric Pareto front prediction in esophageal cancer radiotherapy

    SciTech Connect

    Wang, Jiazhou; Zhao, Kuaike; Peng, Jiayuan; Xie, Jiang; Chen, Junchao; Zhang, Zhen; Hu, Weigang; Jin, Xiance; Studenski, Matthew

    2015-02-15

    Purpose: To investigate the feasibility of the dosimetric Pareto front (PF) prediction based on patient’s anatomic and dosimetric parameters for esophageal cancer patients. Methods: Eighty esophagus patients in the authors’ institution were enrolled in this study. A total of 2928 intensity-modulated radiotherapy plans were obtained and used to generate PF for each patient. On average, each patient had 36.6 plans. The anatomic and dosimetric features were extracted from these plans. The mean lung dose (MLD), mean heart dose (MHD), spinal cord max dose, and PTV homogeneity index were recorded for each plan. Principal component analysis was used to extract overlap volume histogram (OVH) features between PTV and other organs at risk. The full dataset was separated into two parts; a training dataset and a validation dataset. The prediction outcomes were the MHD and MLD. The spearman’s rank correlation coefficient was used to evaluate the correlation between the anatomical features and dosimetric features. The stepwise multiple regression method was used to fit the PF. The cross validation method was used to evaluate the model. Results: With 1000 repetitions, the mean prediction error of the MHD was 469 cGy. The most correlated factor was the first principal components of the OVH between heart and PTV and the overlap between heart and PTV in Z-axis. The mean prediction error of the MLD was 284 cGy. The most correlated factors were the first principal components of the OVH between heart and PTV and the overlap between lung and PTV in Z-axis. Conclusions: It is feasible to use patients’ anatomic and dosimetric features to generate a predicted Pareto front. Additional samples and further studies are required improve the prediction model.

  20. Size effects in molecular dynamics thermal conductivity predictions

    NASA Astrophysics Data System (ADS)

    Sellan, D. P.; Landry, E. S.; Turney, J. E.; McGaughey, A. J. H.; Amon, C. H.

    2010-06-01

    We predict the bulk thermal conductivity of Lennard-Jones argon and Stillinger-Weber silicon using the Green-Kubo (GK) and direct methods in classical molecular dynamics simulations. While system-size-independent thermal conductivities can be obtained with less than 1000 atoms for both materials using the GK method, the linear extrapolation procedure [Schelling , Phys. Rev. B 65, 144306 (2002)] must be applied to direct method results for multiple system sizes. We find that applying the linear extrapolation procedure in a manner consistent with previous researchers can lead to an underprediction of the GK thermal conductivity (e.g., by a factor of 2.5 for Stillinger-Weber silicon at a temperature of 500 K). To understand this discrepancy, we perform lattice dynamics calculations to predict phonon properties and from these, length-dependent thermal conductivities. From these results, we find that the linear extrapolation procedure is only accurate when the minimum system size used in the direct method simulations is comparable to the largest mean-free paths of the phonons that dominate the thermal transport. This condition has not typically been satisfied in previous works. To aid in future studies, we present a simple metric for determining if the system sizes used in direct method simulations are sufficiently large so that the linear extrapolation procedure can accurately predict the bulk thermal conductivity.

  1. Synergistic combination of clinical and imaging features predicts abnormal imaging patterns of pulmonary infections

    PubMed Central

    Bagci, Ulas; Jaster-Miller, Kirsten; Olivier, Kenneth N.; Yao, Jianhua; Mollura, Daniel J.

    2013-01-01

    We designed and tested a novel hybrid statistical model that accepts radiologic image features and clinical variables, and integrates this information in order to automatically predict abnormalities in chest computed-tomography (CT) scans and identify potentially important infectious disease biomarkers. In 200 patients, 160 with various pulmonary infections and 40 healthy controls, we extracted 34 clinical variables from laboratory tests and 25 textural features from CT images. From the CT scans, pleural effusion (PE), linear opacity (or thickening) (LT), tree-in-bud (TIB), pulmonary nodules, ground glass opacity (GGO), and consolidation abnormality patterns were analyzed and predicted through clinical, textural (imaging), or combined attributes. The presence and severity of each abnormality pattern was validated by visual analysis of the CT scans. The proposed biomarker identification system included two important steps: (i) a coarse identification of an abnormal imaging pattern by adaptively selected features (AmRMR), and (ii) a fine selection of the most important features from the previous step, and assigning them as biomarkers, depending on the prediction accuracy. Selected biomarkers were used to classify normal and abnormal patterns by using a boosted decision tree (BDT) classifier. For all abnormal imaging patterns, an average prediction accuracy of 76.15% was obtained. Experimental results demonstrated that our proposed biomarker identification approach is promising and may advance the data processing in clinical pulmonary infection research and diagnostic techniques. PMID:23930819

  2. Critical Features Predicting Sustained Implementation of School-Wide Positive Behavioral Interventions and Supports

    ERIC Educational Resources Information Center

    Mathews, Susanna; McIntosh, Kent; Frank, Jennifer L.; May, Seth L.

    2014-01-01

    The current study explored the extent to which a common measure of perceived implementation of critical features of Positive Behavioral Interventions and Supports (PBIS) predicted fidelity of implementation 3 years later. Respondents included school personnel from 261 schools across the United States implementing PBIS. School teams completed the…

  3. Critical Features Predicting Sustained Implementation of School-Wide Positive Behavior Support

    ERIC Educational Resources Information Center

    Mathews, Susanna; McIntosh, Kent; Frank, Jennifer; May, Seth

    2014-01-01

    The current study explored the extent to which a common measure of perceived implementation of critical features of School-wide Positive Behavior Support (SWPBS) predicted fidelity of implementation 3 years later. Respondents included school personnel from 261 schools across the United States implementing SWPBS. School teams completed the…

  4. Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set.

    PubMed

    Wuyun, Qiqige; Zheng, Wei; Zhang, Yanping; Ruan, Jishou; Hu, Gang

    2016-01-01

    Lysine acetylation is a major post-translational modification. It plays a vital role in numerous essential biological processes, such as gene expression and metabolism, and is related to some human diseases. To fully understand the regulatory mechanism of acetylation, identification of acetylation sites is first and most important. However, experimental identification of protein acetylation sites is often time consuming and expensive. Therefore, the alternative computational methods are necessary. Here, we developed a novel tool, KA-predictor, to predict species-specific lysine acetylation sites based on support vector machine (SVM) classifier. We incorporated different types of features and employed an efficient feature selection on each type to form the final optimal feature set for model learning. And our predictor was highly competitive for the majority of species when compared with other methods. Feature contribution analysis indicated that HSE features, which were firstly introduced for lysine acetylation prediction, significantly improved the predictive performance. Particularly, we constructed a high-accurate structure dataset of H.sapiens from PDB to analyze the structural properties around lysine acetylation sites. Our datasets and a user-friendly local tool of KA-predictor can be freely available at http://sourceforge.net/p/ka-predictor. PMID:27183223

  5. Improved Species-Specific Lysine Acetylation Site Prediction Based on a Large Variety of Features Set

    PubMed Central

    Wuyun, Qiqige; Zheng, Wei; Zhang, Yanping; Ruan, Jishou; Hu, Gang

    2016-01-01

    Lysine acetylation is a major post-translational modification. It plays a vital role in numerous essential biological processes, such as gene expression and metabolism, and is related to some human diseases. To fully understand the regulatory mechanism of acetylation, identification of acetylation sites is first and most important. However, experimental identification of protein acetylation sites is often time consuming and expensive. Therefore, the alternative computational methods are necessary. Here, we developed a novel tool, KA-predictor, to predict species-specific lysine acetylation sites based on support vector machine (SVM) classifier. We incorporated different types of features and employed an efficient feature selection on each type to form the final optimal feature set for model learning. And our predictor was highly competitive for the majority of species when compared with other methods. Feature contribution analysis indicated that HSE features, which were firstly introduced for lysine acetylation prediction, significantly improved the predictive performance. Particularly, we constructed a high-accurate structure dataset of H.sapiens from PDB to analyze the structural properties around lysine acetylation sites. Our datasets and a user-friendly local tool of KA-predictor can be freely available at http://sourceforge.net/p/ka-predictor. PMID:27183223

  6. Genomic Signal Processing: Predicting Basic Molecular Biological Principles

    NASA Astrophysics Data System (ADS)

    Alter, Orly

    2005-03-01

    Advances in high-throughput technologies enable acquisition of different types of molecular biological data, monitoring the flow of biological information as DNA is transcribed to RNA, and RNA is translated to proteins, on a genomic scale. Future discovery in biology and medicine will come from the mathematical modeling of these data, which hold the key to fundamental understanding of life on the molecular level, as well as answers to questions regarding diagnosis, treatment and drug development. Recently we described data-driven models for genome-scale molecular biological data, which use singular value decomposition (SVD) and the comparative generalized SVD (GSVD). Now we describe an integrative data-driven model, which uses pseudoinverse projection (1). We also demonstrate the predictive power of these matrix algebra models (2). The integrative pseudoinverse projection model formulates any number of genome-scale molecular biological data sets in terms of one chosen set of data samples, or of profiles extracted mathematically from data samples, designated the ``basis'' set. The mathematical variables of this integrative model, the pseudoinverse correlation patterns that are uncovered in the data, represent independent processes and corresponding cellular states (such as observed genome-wide effects of known regulators or transcription factors, the biological components of the cellular machinery that generate the genomic signals, and measured samples in which these regulators or transcription factors are over- or underactive). Reconstruction of the data in the basis simulates experimental observation of only the cellular states manifest in the data that correspond to those of the basis. Classification of the data samples according to their reconstruction in the basis, rather than their overall measured profiles, maps the cellular states of the data onto those of the basis, and gives a global picture of the correlations and possibly also causal coordination of

  7. Music-induced emotions can be predicted from a combination of brain activity and acoustic features.

    PubMed

    Daly, Ian; Williams, Duncan; Hallowell, James; Hwang, Faustina; Kirke, Alexis; Malik, Asad; Weaver, James; Miranda, Eduardo; Nasuto, Slawomir J

    2015-12-01

    It is widely acknowledged that music can communicate and induce a wide range of emotions in the listener. However, music is a highly-complex audio signal composed of a wide range of complex time- and frequency-varying components. Additionally, music-induced emotions are known to differ greatly between listeners. Therefore, it is not immediately clear what emotions will be induced in a given individual by a piece of music. We attempt to predict the music-induced emotional response in a listener by measuring the activity in the listeners electroencephalogram (EEG). We combine these measures with acoustic descriptors of the music, an approach that allows us to consider music as a complex set of time-varying acoustic features, independently of any specific music theory. Regression models are found which allow us to predict the music-induced emotions of our participants with a correlation between the actual and predicted responses of up to r=0.234,p<0.001. This regression fit suggests that over 20% of the variance of the participant's music induced emotions can be predicted by their neural activity and the properties of the music. Given the large amount of noise, non-stationarity, and non-linearity in both EEG and music, this is an encouraging result. Additionally, the combination of measures of brain activity and acoustic features describing the music played to our participants allows us to predict music-induced emotions with significantly higher accuracies than either feature type alone (p<0.01).

  8. Non-linear feature extraction from HRV signal for mortality prediction of ICU cardiovascular patient.

    PubMed

    Karimi Moridani, Mohammad; Setarehdan, Seyed Kamaledin; Motie Nasrabadi, Ali; Hajinasrollah, Esmaeil

    2016-01-01

    Intensive care unit (ICU) patients are at risk of in-ICU morbidities and mortality, making specific systems for identifying at-risk patients a necessity for improving clinical care. This study presents a new method for predicting in-hospital mortality using heart rate variability (HRV) collected from the times of a patient's ICU stay. In this paper, a HRV time series processing based method is proposed for mortality prediction of ICU cardiovascular patients. HRV signals were obtained measuring R-R time intervals. A novel method, named return map, is then developed that reveals useful information from the HRV time series. This study also proposed several features that can be extracted from the return map, including the angle between two vectors, the area of triangles formed by successive points, shortest distance to 45° line and their various combinations. Finally, a thresholding technique is proposed to extract the risk period and to predict mortality. The data used to evaluate the proposed algorithm obtained from 80 cardiovascular ICU patients, from the first 48 h of the first ICU stay of 40 males and 40 females. This study showed that the angle feature has on average a sensitivity of 87.5% (with 12 false alarms), the area feature has on average a sensitivity of 89.58% (with 10 false alarms), the shortest distance feature has on average a sensitivity of 85.42% (with 14 false alarms) and, finally, the combined feature has on average a sensitivity of 92.71% (with seven false alarms). The results showed that the last half an hour before the patient's death is very informative for diagnosing the patient's condition and to save his/her life. These results confirm that it is possible to predict mortality based on the features introduced in this paper, relying on the variations of the HRV dynamic characteristics.

  9. Predicting and explaining the movement of mesoscale oceanographic features using CLIPS

    NASA Technical Reports Server (NTRS)

    Bridges, Susan; Chen, Liang-Chun; Lybanon, Matthew

    1994-01-01

    The Naval Research Laboratory has developed an oceanographic expert system that describes the evolution of mesoscale features in the Gulf Stream region of the northwest Atlantic Ocean. These features include the Gulf Stream current and the warm and cold core eddies associated with the Gulf Stream. An explanation capability was added to the eddy prediction component of the expert system in order to allow the system to justify the reasoning process it uses to make predictions. The eddy prediction and explanation components of the system have recently been redesigned and translated from OPS83 to C and CLIPS and the new system is called WATE (Where Are Those Eddies). The new design has improved the system's readability, understandability and maintainability and will also allow the system to be incorporated into the Semi-Automated Mesoscale Analysis System which will eventually be embedded into the Navy's Tactical Environmental Support System, Third Generation, TESS(3).

  10. The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures.

    PubMed

    Haury, Anne-Claire; Gestraud, Pierre; Vert, Jean-Philippe

    2011-01-01

    Biomarker discovery from high-dimensional data is a crucial problem with enormous applications in biology and medicine. It is also extremely challenging from a statistical viewpoint, but surprisingly few studies have investigated the relative strengths and weaknesses of the plethora of existing feature selection methods. In this study we compare 32 feature selection methods on 4 public gene expression datasets for breast cancer prognosis, in terms of predictive performance, stability and functional interpretability of the signatures they produce. We observe that the feature selection method has a significant influence on the accuracy, stability and interpretability of signatures. Surprisingly, complex wrapper and embedded methods generally do not outperform simple univariate feature selection methods, and ensemble feature selection has generally no positive effect. Overall a simple Student's t-test seems to provide the best results.

  11. Molecular Markers for Breast Cancer: Prediction on Tumor Behavior

    PubMed Central

    Banin Hirata, Bruna Karina; Oda, Julie Massayo Maeda; Losi Guembarovski, Roberta; Ariza, Carolina Batista; de Oliveira, Carlos Eduardo Coral; Watanabe, Maria Angelica Ehara

    2014-01-01

    Breast cancer is one of the most common cancers with greater than 1,300,000 cases and 450,000 deaths each year worldwide. The development of breast cancer involves a progression through intermediate stages until the invasive carcinoma and finally into metastatic disease. Given the variability in clinical progression, the identification of markers that could predict the tumor behavior is particularly important in breast cancer. The determination of tumor markers is a useful tool for clinical management in cancer patients, assisting in diagnostic, staging, evaluation of therapeutic response, detection of recurrence and metastasis, and development of new treatment modalities. In this context, this review aims to discuss the main tumor markers in breast carcinogenesis. The most well-established breast molecular markers with prognostic and/or therapeutic value like hormone receptors, HER-2 oncogene, Ki-67, and p53 proteins, and the genes for hereditary breast cancer will be presented. Furthermore, this review shows the new molecular targets in breast cancer: CXCR4, caveolin, miRNA, and FOXP3, as promising candidates for future development of effective and targeted therapies, also with lower toxicity. PMID:24591761

  12. Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

    SciTech Connect

    Aghaei, Faranak; Tan, Maxine; Liu, Hong; Zheng, Bin; Hollingsworth, Alan B.; Qian, Wei

    2015-11-15

    Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy.

  13. Computer-aided breast MR image feature analysis for prediction of tumor response to chemotherapy

    PubMed Central

    Aghaei, Faranak; Tan, Maxine; Hollingsworth, Alan B.; Qian, Wei; Liu, Hong; Zheng, Bin

    2015-01-01

    Purpose: To identify a new clinical marker based on quantitative kinetic image features analysis and assess its feasibility to predict tumor response to neoadjuvant chemotherapy. Methods: The authors assembled a dataset involving breast MR images acquired from 68 cancer patients before undergoing neoadjuvant chemotherapy. Among them, 25 patients had complete response (CR) and 43 had partial and nonresponse (NR) to chemotherapy based on the response evaluation criteria in solid tumors. The authors developed a computer-aided detection scheme to segment breast areas and tumors depicted on the breast MR images and computed a total of 39 kinetic image features from both tumor and background parenchymal enhancement regions. The authors then applied and tested two approaches to classify between CR and NR cases. The first one analyzed each individual feature and applied a simple feature fusion method that combines classification results from multiple features. The second approach tested an attribute selected classifier that integrates an artificial neural network (ANN) with a wrapper subset evaluator, which was optimized using a leave-one-case-out validation method. Results: In the pool of 39 features, 10 yielded relatively higher classification performance with the areas under receiver operating characteristic curves (AUCs) ranging from 0.61 to 0.78 to classify between CR and NR cases. Using a feature fusion method, the maximum AUC = 0.85 ± 0.05. Using the ANN-based classifier, AUC value significantly increased to 0.96 ± 0.03 (p < 0.01). Conclusions: This study demonstrated that quantitative analysis of kinetic image features computed from breast MR images acquired prechemotherapy has potential to generate a useful clinical marker in predicting tumor response to chemotherapy. PMID:26520742

  14. Comprehensible Predictive Modeling Using Regularized Logistic Regression and Comorbidity Based Features.

    PubMed

    Stiglic, Gregor; Povalej Brzan, Petra; Fijacko, Nino; Wang, Fei; Delibasic, Boris; Kalousis, Alexandros; Obradovic, Zoran

    2015-01-01

    Different studies have demonstrated the importance of comorbidities to better understand the origin and evolution of medical complications. This study focuses on improvement of the predictive model interpretability based on simple logical features representing comorbidities. We use group lasso based feature interaction discovery followed by a post-processing step, where simple logic terms are added. In the final step, we reduce the feature set by applying lasso logistic regression to obtain a compact set of non-zero coefficients that represent a more comprehensible predictive model. The effectiveness of the proposed approach was demonstrated on a pediatric hospital discharge dataset that was used to build a readmission risk estimation model. The evaluation of the proposed method demonstrates a reduction of the initial set of features in a regression model by 72%, with a slight improvement in the Area Under the ROC Curve metric from 0.763 (95% CI: 0.755-0.771) to 0.769 (95% CI: 0.761-0.777). Additionally, our results show improvement in comprehensibility of the final predictive model using simple comorbidity based terms for logistic regression.

  15. Predicting the Occurrence of Cave-Inhabiting Fauna Based on Features of the Earth Surface Environment.

    PubMed

    Christman, Mary C; Doctor, Daniel H; Niemiller, Matthew L; Weary, David J; Young, John A; Zigler, Kirk S; Culver, David C

    2016-01-01

    One of the most challenging fauna to study in situ is the obligate cave fauna because of the difficulty of sampling. Cave-limited species display patchy and restricted distributions, but it is often unclear whether the observed distribution is a sampling artifact or a true restriction in range. Further, the drivers of the distribution could be local environmental conditions, such as cave humidity, or they could be associated with surface features that are surrogates for cave conditions. If surface features can be used to predict the distribution of important cave taxa, then conservation management is more easily obtained. We examined the hypothesis that the presence of major faunal groups of cave obligate species could be predicted based on features of the earth surface. Georeferenced records of cave obligate amphipods, crayfish, fish, isopods, beetles, millipedes, pseudoscorpions, spiders, and springtails within the area of Appalachian Landscape Conservation Cooperative in the eastern United States (Illinois to Virginia and New York to Alabama) were assigned to 20 x 20 km grid cells. Habitat suitability for these faunal groups was modeled using logistic regression with twenty predictor variables within each grid cell, such as percent karst, soil features, temperature, precipitation, and elevation. Models successfully predicted the presence of a group greater than 65% of the time (mean = 88%) for the presence of single grid cell endemics, and for all faunal groups except pseudoscorpions. The most common predictor variables were latitude, percent karst, and the standard deviation of the Topographic Position Index (TPI), a measure of landscape rugosity within each grid cell. The overall success of these models points to a number of important connections between the surface and cave environments, and some of these, especially soil features and topographic variability, suggest new research directions. These models should prove to be useful tools in predicting the

  16. Predicting the Occurrence of Cave-Inhabiting Fauna Based on Features of the Earth Surface Environment

    PubMed Central

    Doctor, Daniel H.; Niemiller, Matthew L.; Weary, David J.; Young, John A.; Zigler, Kirk S.

    2016-01-01

    One of the most challenging fauna to study in situ is the obligate cave fauna because of the difficulty of sampling. Cave-limited species display patchy and restricted distributions, but it is often unclear whether the observed distribution is a sampling artifact or a true restriction in range. Further, the drivers of the distribution could be local environmental conditions, such as cave humidity, or they could be associated with surface features that are surrogates for cave conditions. If surface features can be used to predict the distribution of important cave taxa, then conservation management is more easily obtained. We examined the hypothesis that the presence of major faunal groups of cave obligate species could be predicted based on features of the earth surface. Georeferenced records of cave obligate amphipods, crayfish, fish, isopods, beetles, millipedes, pseudoscorpions, spiders, and springtails within the area of Appalachian Landscape Conservation Cooperative in the eastern United States (Illinois to Virginia and New York to Alabama) were assigned to 20 x 20 km grid cells. Habitat suitability for these faunal groups was modeled using logistic regression with twenty predictor variables within each grid cell, such as percent karst, soil features, temperature, precipitation, and elevation. Models successfully predicted the presence of a group greater than 65% of the time (mean = 88%) for the presence of single grid cell endemics, and for all faunal groups except pseudoscorpions. The most common predictor variables were latitude, percent karst, and the standard deviation of the Topographic Position Index (TPI), a measure of landscape rugosity within each grid cell. The overall success of these models points to a number of important connections between the surface and cave environments, and some of these, especially soil features and topographic variability, suggest new research directions. These models should prove to be useful tools in predicting the

  17. Predicting the Occurrence of Cave-Inhabiting Fauna Based on Features of the Earth Surface Environment.

    PubMed

    Christman, Mary C; Doctor, Daniel H; Niemiller, Matthew L; Weary, David J; Young, John A; Zigler, Kirk S; Culver, David C

    2016-01-01

    One of the most challenging fauna to study in situ is the obligate cave fauna because of the difficulty of sampling. Cave-limited species display patchy and restricted distributions, but it is often unclear whether the observed distribution is a sampling artifact or a true restriction in range. Further, the drivers of the distribution could be local environmental conditions, such as cave humidity, or they could be associated with surface features that are surrogates for cave conditions. If surface features can be used to predict the distribution of important cave taxa, then conservation management is more easily obtained. We examined the hypothesis that the presence of major faunal groups of cave obligate species could be predicted based on features of the earth surface. Georeferenced records of cave obligate amphipods, crayfish, fish, isopods, beetles, millipedes, pseudoscorpions, spiders, and springtails within the area of Appalachian Landscape Conservation Cooperative in the eastern United States (Illinois to Virginia and New York to Alabama) were assigned to 20 x 20 km grid cells. Habitat suitability for these faunal groups was modeled using logistic regression with twenty predictor variables within each grid cell, such as percent karst, soil features, temperature, precipitation, and elevation. Models successfully predicted the presence of a group greater than 65% of the time (mean = 88%) for the presence of single grid cell endemics, and for all faunal groups except pseudoscorpions. The most common predictor variables were latitude, percent karst, and the standard deviation of the Topographic Position Index (TPI), a measure of landscape rugosity within each grid cell. The overall success of these models points to a number of important connections between the surface and cave environments, and some of these, especially soil features and topographic variability, suggest new research directions. These models should prove to be useful tools in predicting the

  18. Biased ART: a neural architecture that shifts attention toward previously disregarded features following an incorrect prediction.

    PubMed

    Carpenter, Gail A; Gaddam, Sai Chaitanya

    2010-04-01

    Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved successful for both brain models and applications, computational examples show that attention to early critical features may later distort memory representations during online fast learning. For supervised learning, biased ARTMAP (bARTMAP) solves the problem of over-emphasis on early critical features by directing attention away from previously attended features after the system makes a predictive error. Small-scale, hand-computed analog and binary examples illustrate key model dynamics. Two-dimensional simulation examples demonstrate the evolution of bARTMAP memories as they are learned online. Benchmark simulations show that featural biasing also improves performance on large-scale examples. One example, which predicts movie genres and is based, in part, on the Netflix Prize database, was developed for this project. Both first principles and consistent performance improvements on all simulation studies suggest that featural biasing should be incorporated by default in all ARTMAP systems. Benchmark datasets and bARTMAP code are available from the CNS Technology Lab Website: http://techlab.bu.edu/bART/.

  19. Prediction of hot spots in protein interfaces using a random forest model with hybrid features.

    PubMed

    Wang, Lin; Liu, Zhi-Ping; Zhang, Xiang-Sun; Chen, Luonan

    2012-03-01

    Prediction of hot spots in protein interfaces provides crucial information for the research on protein-protein interaction and drug design. Existing machine learning methods generally judge whether a given residue is likely to be a hot spot by extracting features only from the target residue. However, hot spots usually form a small cluster of residues which are tightly packed together at the center of protein interface. With this in mind, we present a novel method to extract hybrid features which incorporate a wide range of information of the target residue and its spatially neighboring residues, i.e. the nearest contact residue in the other face (mirror-contact residue) and the nearest contact residue in the same face (intra-contact residue). We provide a novel random forest (RF) model to effectively integrate these hybrid features for predicting hot spots in protein interfaces. Our method can achieve accuracy (ACC) of 82.4% and Matthew's correlation coefficient (MCC) of 0.482 in Alanine Scanning Energetics Database, and ACC of 77.6% and MCC of 0.429 in Binding Interface Database. In a comparison study, performance of our RF model exceeds other existing methods, such as Robetta, FOLDEF, KFC, KFC2, MINERVA and HotPoint. Of our hybrid features, three physicochemical features of target residues (mass, polarizability and isoelectric point), the relative side-chain accessible surface area and the average depth index of mirror-contact residues are found to be the main discriminative features in hot spots prediction. We also confirm that hot spots tend to form large contact surface areas between two interacting proteins. Source data and code are available at: http://www.aporc.org/doc/wiki/HotSpot. PMID:22258275

  20. GalNAc-transferase specificity prediction based on feature selection method.

    PubMed

    Lu, Lin; Niu, Bing; Zhao, Jun; Liu, Liang; Lu, Wen-Cong; Liu, Xiao-Jun; Li, Yi-Xue; Cai, Yu-Dong

    2009-02-01

    GalNAc-transferase can catalyze the biosynthesis of O-linked oligosaccharides. The specificity of GalNAc-transferase is composed of nine amino acid residues denoted by R4, R3, R2, R1, R0, R1', R2', R3', R4'. To predict whether the reducing monosaccharide will be covalently linked to the central residue R0(Ser or Thr), a new method based on feature selection has been proposed in our work. 277 nonapeptides from reference [Chou KC. A sequence-coupled vector-projection model for predicting the specificity of GalNAc-transferase. Protein Sci 1995;4:1365-83] are chosen for training set. Each nonapeptide is represented by hundreds of amino acid properties collected by Amino Acid Index database (http://www.genome.jp/aaindex) and transformed into a numeric vector with 4554 features. The Maximum Relevance Minimum Redundancy (mRMR) method combining with Incremental Feature Selection (IFS) and Feature Forward Selection (FFS) are then applied for feature selection. Nearest Neighbor Algorithm (NNA) is used to build prediction models. The optimal model contains 54 features and its correct rate tested by Jackknife cross-validation test reaches 91.34%. Final feature analysis indicates that amino acid residues at position R3' play the most important role in the recognition of GalNAc-transferase specificity, which were confirmed by the experiments [Elhammer AP, Poorman RA, Brown E, Maggiora LL, Hoogerheide JG, Kezdy FJ. The specificity of UDP-GalNAc:polypeptide N-acetylgalactosaminyltransferase as inferred from a database of in vivo substrates and from the in vitro glycosylation of proteins and peptides. J Biol Chem 1993;268:10029-38; O'Connell BC, Hagen FK, Tabak LA. The influence of flanking sequence on the O-glycosylation of threonine in vitro. J Biol Chem 1992;267:25010-8; Yoshida A, Suzuki M, Ikenaga H, Takeuchi M. Discovery of the shortest sequence motif for high level mucin-type O-glycosylation. J Biol Chem 1997;272:16884-8]. Our method can be used as a tool for predicting O

  1. Can we predict lattice energy from molecular structure?

    PubMed

    Ouvrard, Carole; Mitchell, John B O

    2003-10-01

    By using simply the numbers of occurrences of different atom types as descriptors, a conceptually transparent and remarkably accurate model for the prediction of the enthalpies of sublimation of organic compounds has been generated. The atom types are defined on the basis of atomic number, hybridization state and bonded environment. Models of this kind were applied firstly to aliphatic hydrocarbons, secondly to both aliphatic and aromatic hydrocarbons, thirdly to a wide range of non-hydrogen-bonding molecules, and finally to a set of 226 organic compounds including 70 containing hydrogen-bond donors and acceptors. The final model gives squared correlation coefficients of 0.925 for the 226 compounds in the training set and 0.937 for an independent test set of 35 compounds. The success of such a simple model implies that the enthalpy of sublimation can be predicted accurately without knowledge of the crystal packing. This hypothesis is in turn consistent with the idea that, rather than being determined by the particular features of the lowest-energy packing, the lattice energy is similar for a number of hypothetical alternative crystal structures of a molecule.

  2. Relationship of carbohydrate molecular spectroscopic features in combined feeds to carbohydrate utilization and availability in ruminants

    NASA Astrophysics Data System (ADS)

    Zhang, Xuewei; Yu, Peiqiang

    To date, there is no study on the relationship between carbohydrate (CHO) molecular structures and nutrient availability of combined feeds in ruminants. The objective of this study was to use molecular spectroscopy to reveal the relationship between CHO molecular spectral profiles (in terms of functional groups (biomolecular, biopolymer) spectral peak area and height intensity) and CHO chemical profiles, CHO subfractions, energy values, and CHO rumen degradation kinetics of combined feeds of hulless barley with pure wheat dried distillers grains with solubles (DDGS) at five different combination ratios (hulless barley to pure wheat DDGS: 100:0, 75:25, 50:50, 25:75, 0:100). The molecular spectroscopic parameters assessed included: lignin biopolymer molecular spectra profile (peak area and height, region and baseline: ca. 1539-1504 cm-1); structural carbohydrate (STCHO, peaks area region and baseline: ca. 1485-1186 cm-1) mainly associated with hemi- and cellulosic compounds; cellulosic materials peak area (centered at ca. 1240 cm-1 with region and baseline: ca. 1272-1186 cm-1); total carbohydrate (CHO, peaks area region and baseline: ca. 1186-946 cm-1). The results showed that the functional groups (biomolecular, biopolymer) in the combined feeds are sensitive to the changes of carbohydrate chemical and nutrient profiles. The changes of the CHO molecular spectroscopic features in the combined feeds were highly correlated with CHO chemical profiles, CHO subfractions, in situ CHO rumen degradation kinetics and fermentable organic matter supply. Further study is needed to investigate possibility of using CHO molecular spectral features as a predictor to estimate nutrient availability in combined feeds for animals and quantify their relationship.

  3. Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data

    PubMed Central

    Grissa, Dhouha; Pétéra, Mélanie; Brandolini, Marion; Napoli, Amedeo; Comte, Blandine; Pujos-Guillot, Estelle

    2016-01-01

    Untargeted metabolomics is a powerful phenotyping tool for better understanding biological mechanisms involved in human pathology development and identifying early predictive biomarkers. This approach, based on multiple analytical platforms, such as mass spectrometry (MS), chemometrics and bioinformatics, generates massive and complex data that need appropriate analyses to extract the biologically meaningful information. Despite various tools available, it is still a challenge to handle such large and noisy datasets with limited number of individuals without risking overfitting. Moreover, when the objective is focused on the identification of early predictive markers of clinical outcome, few years before occurrence, it becomes essential to use the appropriate algorithms and workflow to be able to discover subtle effects among this large amount of data. In this context, this work consists in studying a workflow describing the general feature selection process, using knowledge discovery and data mining methodologies to propose advanced solutions for predictive biomarker discovery. The strategy was focused on evaluating a combination of numeric-symbolic approaches for feature selection with the objective of obtaining the best combination of metabolites producing an effective and accurate predictive model. Relying first on numerical approaches, and especially on machine learning methods (SVM-RFE, RF, RF-RFE) and on univariate statistical analyses (ANOVA), a comparative study was performed on an original metabolomic dataset and reduced subsets. As resampling method, LOOCV was applied to minimize the risk of overfitting. The best k-features obtained with different scores of importance from the combination of these different approaches were compared and allowed determining the variable stabilities using Formal Concept Analysis. The results revealed the interest of RF-Gini combined with ANOVA for feature selection as these two complementary methods allowed selecting the 48

  4. Feature Selection Methods for Early Predictive Biomarker Discovery Using Untargeted Metabolomic Data.

    PubMed

    Grissa, Dhouha; Pétéra, Mélanie; Brandolini, Marion; Napoli, Amedeo; Comte, Blandine; Pujos-Guillot, Estelle

    2016-01-01

    Untargeted metabolomics is a powerful phenotyping tool for better understanding biological mechanisms involved in human pathology development and identifying early predictive biomarkers. This approach, based on multiple analytical platforms, such as mass spectrometry (MS), chemometrics and bioinformatics, generates massive and complex data that need appropriate analyses to extract the biologically meaningful information. Despite various tools available, it is still a challenge to handle such large and noisy datasets with limited number of individuals without risking overfitting. Moreover, when the objective is focused on the identification of early predictive markers of clinical outcome, few years before occurrence, it becomes essential to use the appropriate algorithms and workflow to be able to discover subtle effects among this large amount of data. In this context, this work consists in studying a workflow describing the general feature selection process, using knowledge discovery and data mining methodologies to propose advanced solutions for predictive biomarker discovery. The strategy was focused on evaluating a combination of numeric-symbolic approaches for feature selection with the objective of obtaining the best combination of metabolites producing an effective and accurate predictive model. Relying first on numerical approaches, and especially on machine learning methods (SVM-RFE, RF, RF-RFE) and on univariate statistical analyses (ANOVA), a comparative study was performed on an original metabolomic dataset and reduced subsets. As resampling method, LOOCV was applied to minimize the risk of overfitting. The best k-features obtained with different scores of importance from the combination of these different approaches were compared and allowed determining the variable stabilities using Formal Concept Analysis. The results revealed the interest of RF-Gini combined with ANOVA for feature selection as these two complementary methods allowed selecting the 48

  5. miRNAfe: A comprehensive tool for feature extraction in microRNA prediction.

    PubMed

    Yones, Cristian A; Stegmayer, Georgina; Kamenetzky, Laura; Milone, Diego H

    2015-12-01

    miRNAfe is a comprehensive tool to extract features from RNA sequences. It is freely available as a web service, allowing a single access point to almost all state-of-the-art feature extraction methods used today in a variety of works from different authors. It has a very simple user interface, where the user only needs to load a file containing the input sequences and select the features to extract. As a result, the user obtains a text file with the features extracted, which can be used to analyze the sequences or as input to a miRNA prediction software. The tool can calculate up to 80 features where many of them are multidimensional arrays. In order to simplify the web interface, the features have been divided into six pre-defined groups, each one providing information about: primary sequence, secondary structure, thermodynamic stability, statistical stability, conservation between genomes of different species and substrings analysis of the sequences. Additionally, pre-trained classifiers are provided for prediction in different species. All algorithms to extract the features have been validated, comparing the results with the ones obtained from software of the original authors. The source code is freely available for academic use under GPL license at http://sourceforge.net/projects/sourcesinc/files/mirnafe/0.90/. A user-friendly access is provided as web interface at http://fich.unl.edu.ar/sinc/web-demo/mirnafe/. A more configurable web interface can be accessed at http://fich.unl.edu.ar/sinc/web-demo/mirnafe-full/.

  6. miRNAfe: A comprehensive tool for feature extraction in microRNA prediction.

    PubMed

    Yones, Cristian A; Stegmayer, Georgina; Kamenetzky, Laura; Milone, Diego H

    2015-12-01

    miRNAfe is a comprehensive tool to extract features from RNA sequences. It is freely available as a web service, allowing a single access point to almost all state-of-the-art feature extraction methods used today in a variety of works from different authors. It has a very simple user interface, where the user only needs to load a file containing the input sequences and select the features to extract. As a result, the user obtains a text file with the features extracted, which can be used to analyze the sequences or as input to a miRNA prediction software. The tool can calculate up to 80 features where many of them are multidimensional arrays. In order to simplify the web interface, the features have been divided into six pre-defined groups, each one providing information about: primary sequence, secondary structure, thermodynamic stability, statistical stability, conservation between genomes of different species and substrings analysis of the sequences. Additionally, pre-trained classifiers are provided for prediction in different species. All algorithms to extract the features have been validated, comparing the results with the ones obtained from software of the original authors. The source code is freely available for academic use under GPL license at http://sourceforge.net/projects/sourcesinc/files/mirnafe/0.90/. A user-friendly access is provided as web interface at http://fich.unl.edu.ar/sinc/web-demo/mirnafe/. A more configurable web interface can be accessed at http://fich.unl.edu.ar/sinc/web-demo/mirnafe-full/. PMID:26499212

  7. Prediction of Golgi-resident protein types using general form of Chou's pseudo-amino acid compositions: Approaches with minimal redundancy maximal relevance feature selection.

    PubMed

    Jiao, Ya-Sen; Du, Pu-Feng

    2016-08-01

    Recently, several efforts have been made in predicting Golgi-resident proteins. However, it is still a challenging task to identify the type of a Golgi-resident protein. Precise prediction of the type of a Golgi-resident protein plays a key role in understanding its molecular functions in various biological processes. In this paper, we proposed to use a mutual information based feature selection scheme with the general form Chou's pseudo-amino acid compositions to predict the Golgi-resident protein types. The positional specific physicochemical properties were applied in the Chou's pseudo-amino acid compositions. We achieved 91.24% prediction accuracy in a jackknife test with 49 selected features. It has the best performance among all the present predictors. This result indicates that our computational model can be useful in identifying Golgi-resident protein types. PMID:27155042

  8. Prediction of Protein Cleavage Site with Feature Selection by Random Forest

    PubMed Central

    Li, Bi-Qing; Cai, Yu-Dong; Feng, Kai-Yan; Zhao, Gui-Jun

    2012-01-01

    Proteinases play critical roles in both intra and extracellular processes by binding and cleaving their protein substrates. The cleavage can either be non-specific as part of degradation during protein catabolism or highly specific as part of proteolytic cascades and signal transduction events. Identification of these targets is extremely challenging. Current computational approaches for predicting cleavage sites are very limited since they mainly represent the amino acid sequences as patterns or frequency matrices. In this work, we developed a novel predictor based on Random Forest algorithm (RF) using maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS). The features of physicochemical/biochemical properties, sequence conservation, residual disorder, amino acid occurrence frequency, secondary structure and solvent accessibility were utilized to represent the peptides concerned. Here, we compared existing prediction tools which are available for predicting possible cleavage sites in candidate substrates with ours. It is shown that our method makes much more reliable predictions in terms of the overall prediction accuracy. In addition, this predictor allows the use of a wide range of proteinases. PMID:23029276

  9. Prediction of banana quality indices from color features using support vector regression.

    PubMed

    Sanaeifar, Alireza; Bakhshipour, Adel; de la Guardia, Miguel

    2016-01-01

    Banana undergoes significant quality indices and color transformations during shelf-life process, which in turn affect important chemical and physical characteristics for the organoleptic quality of banana. A computer vision system was implemented in order to evaluate color of banana in RGB, L*a*b* and HSV color spaces, and changes in color features of banana during shelf-life were employed for the quantitative prediction of quality indices. The radial basis function (RBF) was applied as the kernel function of support vector regression (SVR) and the color features, in different color spaces, were selected as the inputs of the model, being determined total soluble solids, pH, titratable acidity and firmness as the output. Experimental results provided an improvement in predictive accuracy as compared with those obtained by using artificial neural network (ANN). PMID:26653423

  10. Prediction of banana quality indices from color features using support vector regression.

    PubMed

    Sanaeifar, Alireza; Bakhshipour, Adel; de la Guardia, Miguel

    2016-01-01

    Banana undergoes significant quality indices and color transformations during shelf-life process, which in turn affect important chemical and physical characteristics for the organoleptic quality of banana. A computer vision system was implemented in order to evaluate color of banana in RGB, L*a*b* and HSV color spaces, and changes in color features of banana during shelf-life were employed for the quantitative prediction of quality indices. The radial basis function (RBF) was applied as the kernel function of support vector regression (SVR) and the color features, in different color spaces, were selected as the inputs of the model, being determined total soluble solids, pH, titratable acidity and firmness as the output. Experimental results provided an improvement in predictive accuracy as compared with those obtained by using artificial neural network (ANN).

  11. Machine Learning Methods Enable Predictive Modeling of Antibody Feature:Function Relationships in RV144 Vaccinees

    PubMed Central

    Choi, Ickwon; Chung, Amy W.; Suscovich, Todd J.; Rerks-Ngarm, Supachai; Pitisuttithum, Punnee; Nitayaphan, Sorachai; Kaewkungwal, Jaranit; O'Connell, Robert J.; Francis, Donald; Robb, Merlin L.; Michael, Nelson L.; Kim, Jerome H.; Alter, Galit; Ackerman, Margaret E.; Bailey-Kellogg, Chris

    2015-01-01

    The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates. PMID:25874406

  12. Predictive deconvolution and hybrid feature selection for computer-aided detection of prostate cancer.

    PubMed

    Maggio, Simona; Palladini, Alessandro; Marchi, Luca De; Alessandrini, Martino; Speciale, Nicolò; Masetti, Guido

    2010-02-01

    Computer-aided detection (CAD) schemes are decision making support tools, useful to overcome limitations of problematic clinical procedures. Trans-rectal ultrasound image based CAD would be extremely important to support prostate cancer diagnosis. An effective approach to realize a CAD scheme for this purpose is described in this work, employing a multi-feature kernel classification model based on generalized discriminant analysis. The mutual information of feature value and tissue pathological state is used to select features essential for tissue characterization. System-dependent effects are reduced through predictive deconvolution of the acquired radio-frequency signals. A clinical study, performed on ground truth images from biopsy findings, provides a comparison of the classification model applied before and after deconvolution, showing in the latter case a significant gain in accuracy and area under the receiver operating characteristic curve.

  13. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features

    PubMed Central

    Yu, Kun-Hsing; Zhang, Ce; Berry, Gerald J.; Altman, Russ B.; Ré, Christopher; Rubin, Daniel L.; Snyder, Michael

    2016-01-01

    Lung cancer is the most prevalent cancer worldwide, and histopathological assessment is indispensable for its diagnosis. However, human evaluation of pathology slides cannot accurately predict patients' prognoses. In this study, we obtain 2,186 haematoxylin and eosin stained histopathology whole-slide images of lung adenocarcinoma and squamous cell carcinoma patients from The Cancer Genome Atlas (TCGA), and 294 additional images from Stanford Tissue Microarray (TMA) Database. We extract 9,879 quantitative image features and use regularized machine-learning methods to select the top features and to distinguish shorter-term survivors from longer-term survivors with stage I adenocarcinoma (P<0.003) or squamous cell carcinoma (P=0.023) in the TCGA data set. We validate the survival prediction framework with the TMA cohort (P<0.036 for both tumour types). Our results suggest that automatically derived image features can predict the prognosis of lung cancer patients and thereby contribute to precision oncology. Our methods are extensible to histopathology images of other organs. PMID:27527408

  14. Predicting visual fixations on video based on low-level visual features.

    PubMed

    Le Meur, Olivier; Le Callet, Patrick; Barba, Dominique

    2007-09-01

    To what extent can a computational model of the bottom-up visual attention predict what an observer is looking at? What is the contribution of the low-level visual features in the attention deployment? To answer these questions, a new spatio-temporal computational model is proposed. This model incorporates several visual features; therefore, a fusion algorithm is required to combine the different saliency maps (achromatic, chromatic and temporal). To quantitatively assess the model performances, eye movements were recorded while naive observers viewed natural dynamic scenes. Four completing metrics have been used. In addition, predictions from the proposed model are compared to the predictions from a state of the art model [Itti's model (Itti, L., Koch, C., & Niebur, E. (1998). A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254-1259)] and from three non-biologically plausible models (uniform, flicker and centered models). Regardless of the metric used, the proposed model shows significant improvement over the selected benchmarking models (except the centered model). Conclusions are drawn regarding both the influence of low-level visual features over time and the central bias in an eye tracking experiment.

  15. Protein subcellular localization prediction based on compartment-specific features and structure conservation

    PubMed Central

    Su, Emily Chia-Yu; Chiu, Hua-Sheng; Lo, Allan; Hwang, Jenn-Kang; Sung, Ting-Yi; Hsu, Wen-Lian

    2007-01-01

    Background Protein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming; thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins. Results We propose a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machines (SVM) model and a structural homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. In the structural homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% and 93.2% using ten-fold cross-validation on the benchmark data sets. In the assessment of the evaluation data sets, our method also attains accurate prediction accuracy of 84.0%, especially when testing on sequences with a low level of homology to the training data. A three-way data split procedure is also incorporated to prevent overestimation of the predictive performance. In addition, we show that the prediction accuracy should be approximately 85% for non-redundant data sets of sequence identity less than 30%. Conclusion Our results demonstrate that biological features derived from Gram-negative bacteria translocation pathways yield a significant

  16. Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks

    NASA Astrophysics Data System (ADS)

    Wang, Yiheng; Liu, Tong; Xu, Dong; Shi, Huidong; Zhang, Chaoyang; Mo, Yin-Yuan; Wang, Zheng

    2016-01-01

    The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named “DeepMethyl” to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/.

  17. Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks

    PubMed Central

    Wang, Yiheng; Liu, Tong; Xu, Dong; Shi, Huidong; Zhang, Chaoyang; Mo, Yin-Yuan; Wang, Zheng

    2016-01-01

    The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named “DeepMethyl” to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/. PMID:26797014

  18. Self-Adaptive MOEA Feature Selection for Classification of Bankruptcy Prediction Data

    PubMed Central

    Gaspar-Cunha, A.; Recio, G.; Costa, L.; Estébanez, C.

    2014-01-01

    Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy). The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier. PMID:24707201

  19. Self-adaptive MOEA feature selection for classification of bankruptcy prediction data.

    PubMed

    Gaspar-Cunha, A; Recio, G; Costa, L; Estébanez, C

    2014-01-01

    Bankruptcy prediction is a vast area of finance and accounting whose importance lies in the relevance for creditors and investors in evaluating the likelihood of getting into bankrupt. As companies become complex, they develop sophisticated schemes to hide their real situation. In turn, making an estimation of the credit risks associated with counterparts or predicting bankruptcy becomes harder. Evolutionary algorithms have shown to be an excellent tool to deal with complex problems in finances and economics where a large number of irrelevant features are involved. This paper provides a methodology for feature selection in classification of bankruptcy data sets using an evolutionary multiobjective approach that simultaneously minimise the number of features and maximise the classifier quality measure (e.g., accuracy). The proposed methodology makes use of self-adaptation by applying the feature selection algorithm while simultaneously optimising the parameters of the classifier used. The methodology was applied to four different sets of data. The obtained results showed the utility of using the self-adaptation of the classifier. PMID:24707201

  20. Accurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features

    PubMed Central

    Luo, Longqiang; Li, Dingfang; Zhang, Wen; Tu, Shikui; Zhu, Xiaopeng; Tian, Gang

    2016-01-01

    Background Piwi-interacting RNA (piRNA) is the largest class of small non-coding RNA molecules. The transposon-derived piRNA prediction can enrich the research contents of small ncRNAs as well as help to further understand generation mechanism of gamete. Methods In this paper, we attempt to differentiate transposon-derived piRNAs from non-piRNAs based on their sequential and physicochemical features by using machine learning methods. We explore six sequence-derived features, i.e. spectrum profile, mismatch profile, subsequence profile, position-specific scoring matrix, pseudo dinucleotide composition and local structure-sequence triplet elements, and systematically evaluate their performances for transposon-derived piRNA prediction. Finally, we consider two approaches: direct combination and ensemble learning to integrate useful features and achieve high-accuracy prediction models. Results We construct three datasets, covering three species: Human, Mouse and Drosophila, and evaluate the performances of prediction models by 10-fold cross validation. In the computational experiments, direct combination models achieve AUC of 0.917, 0.922 and 0.992 on Human, Mouse and Drosophila, respectively; ensemble learning models achieve AUC of 0.922, 0.926 and 0.994 on the three datasets. Conclusions Compared with other state-of-the-art methods, our methods can lead to better performances. In conclusion, the proposed methods are promising for the transposon-derived piRNA prediction. The source codes and datasets are available in S1 File. PMID:27074043

  1. Habitat features and predictive habitat modeling for the Colorado chipmunk in southern New Mexico

    USGS Publications Warehouse

    Rivieccio, M.; Thompson, B.C.; Gould, W.R.; Boykin, K.G.

    2003-01-01

    Two subspecies of Colorado chipmunk (state threatened and federal species of concern) occur in southern New Mexico: Tamias quadrivittatus australis in the Organ Mountains and T. q. oscuraensis in the Oscura Mountains. We developed a GIS model of potentially suitable habitat based on vegetation and elevation features, evaluated site classifications of the GIS model, and determined vegetation and terrain features associated with chipmunk occurrence. We compared GIS model classifications with actual vegetation and elevation features measured at 37 sites. At 60 sites we measured 18 habitat variables regarding slope, aspect, tree species, shrub species, and ground cover. We used logistic regression to analyze habitat variables associated with chipmunk presence/absence. All (100%) 37 sample sites (28 predicted suitable, 9 predicted unsuitable) were classified correctly by the GIS model regarding elevation and vegetation. For 28 sites predicted suitable by the GIS model, 18 sites (64%) appeared visually suitable based on habitat variables selected from logistic regression analyses, of which 10 sites (36%) were specifically predicted as suitable habitat via logistic regression. We detected chipmunks at 70% of sites deemed suitable via the logistic regression models. Shrub cover, tree density, plant proximity, presence of logs, and presence of rock outcrop were retained in the logistic model for the Oscura Mountains; litter, shrub cover, and grass cover were retained in the logistic model for the Organ Mountains. Evaluation of predictive models illustrates the need for multi-stage analyses to best judge performance. Microhabitat analyses indicate prospective needs for different management strategies between the subspecies. Sensitivities of each population of the Colorado chipmunk to natural and prescribed fire suggest that partial burnings of areas inhabited by Colorado chipmunks in southern New Mexico may be beneficial. These partial burnings may later help avoid a fire

  2. Music-induced emotions can be predicted from a combination of brain activity and acoustic features.

    PubMed

    Daly, Ian; Williams, Duncan; Hallowell, James; Hwang, Faustina; Kirke, Alexis; Malik, Asad; Weaver, James; Miranda, Eduardo; Nasuto, Slawomir J

    2015-12-01

    It is widely acknowledged that music can communicate and induce a wide range of emotions in the listener. However, music is a highly-complex audio signal composed of a wide range of complex time- and frequency-varying components. Additionally, music-induced emotions are known to differ greatly between listeners. Therefore, it is not immediately clear what emotions will be induced in a given individual by a piece of music. We attempt to predict the music-induced emotional response in a listener by measuring the activity in the listeners electroencephalogram (EEG). We combine these measures with acoustic descriptors of the music, an approach that allows us to consider music as a complex set of time-varying acoustic features, independently of any specific music theory. Regression models are found which allow us to predict the music-induced emotions of our participants with a correlation between the actual and predicted responses of up to r=0.234,p<0.001. This regression fit suggests that over 20% of the variance of the participant's music induced emotions can be predicted by their neural activity and the properties of the music. Given the large amount of noise, non-stationarity, and non-linearity in both EEG and music, this is an encouraging result. Additionally, the combination of measures of brain activity and acoustic features describing the music played to our participants allows us to predict music-induced emotions with significantly higher accuracies than either feature type alone (p<0.01). PMID:26544602

  3. Clinical features of nine males with molecularly defined deletions of the Y chromosome long arm.

    PubMed

    Salo, P; Ignatius, J; Simola, K O; Tahvanainen, E; Kääriäinen, H

    1995-09-01

    Deletions of the long arm of the Y chromosome have previously been associated with azoospermia and short stature. We report the results of a detailed clinical and molecular study of nine males with partial deletions of Yq. Special emphasis was laid on congenital anomalies and dysmorphic features. Some of the patients have developmental problems or distinct facial features, namely a small chin and mouth, a high arched or cleft palate, downward slanting palpebral fissures, high nasal bridge, and dysmorphic ears. As far as we know, similar facial dysmorphism has not been previously described in association with del(Yq). These features are not, however, simply correlated to the size of the deletion. In none of these patients could evidence of aberrant Xq-Yq interchange be found.

  4. Prediction of molecular mimicry candidates in human pathogenic bacteria.

    PubMed

    Doxey, Andrew C; McConkey, Brendan J

    2013-08-15

    Molecular mimicry of host proteins is a common strategy adopted by bacterial pathogens to interfere with and exploit host processes. Despite the availability of pathogen genomes, few studies have attempted to predict virulence-associated mimicry relationships directly from genomic sequences. Here, we analyzed the proteomes of 62 pathogenic and 66 non-pathogenic bacterial species, and screened for the top pathogen-specific or pathogen-enriched sequence similarities to human proteins. The screen identified approximately 100 potential mimicry relationships including well-characterized examples among the top-scoring hits (e.g., RalF, internalin, yopH, and others), with about 1/3 of predicted relationships supported by existing literature. Examination of homology to virulence factors, statistically enriched functions, and comparison with literature indicated that the detected mimics target key host structures (e.g., extracellular matrix, ECM) and pathways (e.g., cell adhesion, lipid metabolism, and immune signaling). The top-scoring and most widespread mimicry pattern detected among pathogens consisted of elevated sequence similarities to ECM proteins including collagens and leucine-rich repeat proteins. Unexpectedly, analysis of the pathogen counterparts of these proteins revealed that they have evolved independently in different species of bacterial pathogens from separate repeat amplifications. Thus, our analysis provides evidence for two classes of mimics: complex proteins such as enzymes that have been acquired by eukaryote-to-pathogen horizontal transfer, and simpler repeat proteins that have independently evolved to mimic the host ECM. Ultimately, computational detection of pathogen-specific and pathogen-enriched similarities to host proteins provides insights into potentially novel mimicry-mediated virulence mechanisms of pathogenic bacteria.

  5. Well-characterized sequence features of eukaryote genomes and implications for ab initio gene prediction.

    PubMed

    Huang, Ying; Chen, Shi-Yi; Deng, Feilong

    2016-01-01

    In silico analysis of DNA sequences is an important area of computational biology in the post-genomic era. Over the past two decades, computational approaches for ab initio prediction of gene structure from genome sequence alone have largely facilitated our understanding on a variety of biological questions. Although the computational prediction of protein-coding genes has already been well-established, we are also facing challenges to robustly find the non-coding RNA genes, such as miRNA and lncRNA. Two main aspects of ab initio gene prediction include the computed values for describing sequence features and used algorithm for training the discriminant function, and by which different combinations are employed into various bioinformatic tools. Herein, we briefly review these well-characterized sequence features in eukaryote genomes and applications to ab initio gene prediction. The main purpose of this article is to provide an overview to beginners who aim to develop the related bioinformatic tools. PMID:27536341

  6. Prediction models for solitary pulmonary nodules based on curvelet textural features and clinical parameters.

    PubMed

    Wang, Jing-Jing; Wu, Hai-Feng; Sun, Tao; Li, Xia; Wang, Wei; Tao, Li-Xin; Huo, Da; Lv, Ping-Xin; He, Wen; Guo, Xiu-Hua

    2013-01-01

    Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images. PMID:24289618

  7. Prediction of outer membrane proteins by combining the position- and composition-based features of sequence profiles.

    PubMed

    Yan, Renxiang; Lin, Jun; Chen, Zhen; Wang, Xiaofeng; Huang, Lanqing; Cai, Weiwen; Zhang, Ziding

    2014-05-01

    Locating the transmembrane regions of outer membrane proteins (OMPs) is highly important for deciphering their biological functions at both molecular and cellular levels. Here, we propose a novel method to predict the transmembrane regions of OMPs by employing the position- and composition-based features of sequence profiles. Furthermore, a simple probability-based prediction model, which is estimated by the secondary structures of structurally known OMPs, is also developed. Considering that these two methods are both effective and well complementary, we integrate them into a method called TransOMP, which is also capable of identifying OMPs. Furthermore, we develop an OMP identification measure I_CScore by considering transmembrane regions by TransOMP and secondary structural topology by SSEA-OMP. Our methods were benchmarked against state-of-the-art methods and assessed in the genome of Escherichia coli. Benchmark results confirmed that our methods were reliable and useful. Meanwhile, we constructed an OMP prediction web server, which can be used for OMP identification, transmembrane region location, and 3D model building.

  8. Histologic Features of Conjunctival Melanoma Predictive of Metastasis and Death (An American Ophthalmological Thesis)

    PubMed Central

    Esmaeli, Bita; Roberts, Dianna; Ross, Merrick; Fellman, Melissa; Cruz, Hilda; Kim, Stella K.; Prieto, Victor G.

    2012-01-01

    Purpose: In conjunctival melanoma, tumor thickness and nonlimbal location are associated with poor prognosis. However, other established high-risk features for cutaneous melanoma, including ulceration, mitotic figures, epithelioid cell type, and lymphovascular invasion, have not previously been studied extensively for their prognostic value in conjunctival melanoma. We examined the hypothesis that these features also predict regional nodal metastasis and death in conjunctival melanoma. Methods: The medical records of 44 of 46 consecutive conjunctival melanoma patients treated between June 2003 and December 2009 were retrospectively reviewed; tumor tissue was not available for the two excluded patients. Demographic and clinicopathologic features, including tumor location, tumor thickness, ulceration, mitotic rate, histology, lymphovascular invasion, and microsatellitosis, were reviewed. Outcome measures included regional nodal metastasis, distant metastasis, and death. Results: Twenty-six women and 18 men had a median age of 62 years. Regional nodal metastasis occurred in 7 patients (16%) and distant metastasis in 9 (20%). Median follow-up was 40 months. At last follow-up, 10 patients (23%) had died of disease. Tumor thickness >2.0 mm, ulceration, and mitotic figure >1/mm2 predicted regional nodal metastasis and death from disease. In addition to these three histologic features, vascular invasion, epithelioid cell type, and microsatellitosis significantly predicted death from disease. Tumor location (bulbar vs nonbulbar) was not correlated with regional nodal metastasis or death. Conclusions: In conjunctival melanoma, as in cutaneous melanoma, thicker tumor, ulceration, and higher mitotic rate are correlated with regional nodal metastasis. In addition, lymphovascular invasion, epithelioid cell type, and microsatellitosis are correlated with melanoma-related death. PMID:23818735

  9. Combining features in a graphical model to predict protein binding sites.

    PubMed

    Wierschin, Torsten; Wang, Keyu; Welter, Marlon; Waack, Stephan; Stanke, Mario

    2015-05-01

    Large efforts have been made in classifying residues as binding sites in proteins using machine learning methods. The prediction task can be translated into the computational challenge of assigning each residue the label binding site or non-binding site. Observational data comes from various possibly highly correlated sources. It includes the structure of the protein but not the structure of the complex. The model class of conditional random fields (CRFs) has previously successfully been used for protein binding site prediction. Here, a new CRF-approach is presented that models the dependencies of residues using a general graphical structure defined as a neighborhood graph and thus our model makes fewer independence assumptions on the labels than sequential labeling approaches. A novel node feature "change in free energy" is introduced into the model, which is then denoted by ΔF-CRF. Parameters are trained with an online large-margin algorithm. Using the standard feature class relative accessible surface area alone, the general graph-structure CRF already achieves higher prediction accuracy than the linear chain CRF of Li et al. ΔF-CRF performs significantly better on a large range of false positive rates than the support-vector-machine-based program PresCont of Zellner et al. on a homodimer set containing 128 chains. ΔF-CRF has a broader scope than PresCont since it is not constrained to protein subgroups and requires no multiple sequence alignment. The improvement is attributed to the advantageous combination of the novel node feature with the standard feature and to the adopted parameter training method.

  10. Transmembrane helix prediction using amino acid property features and latent semantic analysis

    PubMed Central

    Ganapathiraju, Madhavi; Balakrishnan, N; Reddy, Raj; Klein-Seetharaman, Judith

    2008-01-01

    Background Prediction of transmembrane (TM) helices by statistical methods suffers from lack of sufficient training data. Current best methods use hundreds or even thousands of free parameters in their models which are tuned to fit the little data available for training. Further, they are often restricted to the generally accepted topology "cytoplasmic-transmembrane-extracellular" and cannot adapt to membrane proteins that do not conform to this topology. Recent crystal structures of channel proteins have revealed novel architectures showing that the above topology may not be as universal as previously believed. Thus, there is a need for methods that can better predict TM helices even in novel topologies and families. Results Here, we describe a new method "TMpro" to predict TM helices with high accuracy. To avoid overfitting to existing topologies, we have collapsed cytoplasmic and extracellular labels to a single state, non-TM. TMpro is a binary classifier which predicts TM or non-TM using multiple amino acid properties (charge, polarity, aromaticity, size and electronic properties) as features. The features are extracted from sequence information by applying the framework used for latent semantic analysis of text documents and are input to neural networks that learn the distinction between TM and non-TM segments. The model uses only 25 free parameters. In benchmark analysis TMpro achieves 95% segment F-score corresponding to 50% reduction in error rate compared to the best methods not requiring an evolutionary profile of a protein to be known. Performance is also improved when applied to more recent and larger high resolution datasets PDBTM and MPtopo. TMpro predictions in membrane proteins with unusual or disputed TM structure (K+ channel, aquaporin and HIV envelope glycoprotein) are discussed. Conclusion TMpro uses very few free parameters in modeling TM segments as opposed to the very large number of free parameters used in state-of-the-art membrane

  11. A Prediction Model for ROS1-Rearranged Lung Adenocarcinomas based on Histologic Features

    PubMed Central

    Zheng, Jing; Kong, Mei; Sun, Ke; Wang, Bo; Chen, Xi; Ding, Wei; Zhou, Jianying

    2016-01-01

    Aims To identify the clinical and histological characteristics of ROS1-rearranged non-small-cell lung carcinomas (NSCLCs) and build a prediction model to prescreen suitable patients for molecular testing. Methods and Results We identified 27 cases of ROS1-rearranged lung adenocarcinomas in 1165 patients with NSCLCs confirmed by real-time PCR and FISH and performed univariate and multivariate analyses to identify predictive factors associated with ROS1 rearrangement and finally developed prediction model. Detected with ROS1 immunochemistry, 59 cases of 1165 patients had a certain degree of ROS1 expression. Among these cases, 19 cases (68%, 19/28) with 3+ and 8 cases (47%, 8/17) with 2+ staining were ROS1 rearrangement verified by real-time PCR and FISH. In the resected group, the acinar-predominant growth pattern was the most commonly observed (57%, 8/14), while in the biopsy group, solid patterns were the most frequently observed (78%, 7/13). Based on multiple logistic regression analysis, we determined that female sex, cribriform structure and the presence of psammoma body were the three most powerful indicators of ROS1 rearrangement, and we have developed a predictive model for the presence of ROS1 rearrangements in lung adenocarcinomas. Conclusions Female, cribriform structure and presence of psammoma body were the three most powerful indicator of ROS1 rearrangement status, and predictive formula was helpful in screening ROS1-rearranged NSCLC, especially for ROS1 immunochemistry equivocal cases. PMID:27648828

  12. Feature activated molecular dynamics: an efficient approach for atomistic simulation of solid-state aggregation phenomena.

    PubMed

    Prasad, Manish; Sinno, Talid

    2004-11-01

    An efficient approach is presented for performing efficient molecular dynamics simulations of solute aggregation in crystalline solids. The method dynamically divides the total simulation space into "active" regions centered about each minority species, in which regular molecular dynamics is performed. The number, size, and shape of these regions is updated periodically based on the distribution of solute atoms within the overall simulation cell. The remainder of the system is essentially static except for periodic rescaling of the entire simulation cell in order to balance the pressure between the isolated molecular dynamics regions. The method is shown to be accurate and robust for the Environment-Dependant Interatomic Potential (EDIP) for silicon and an Embedded Atom Method potential (EAM) for copper. Several tests are performed beginning with the diffusion of a single vacancy all the way to large-scale simulations of vacancy clustering. In both material systems, the predicted evolutions agree closely with the results of standard molecular dynamics simulations. Computationally, the method is demonstrated to scale almost linearly with the concentration of solute atoms, but is essentially independent of the total system size. This scaling behavior allows for the full dynamical simulation of aggregation under conditions that are more experimentally realizable than would be possible with standard molecular dynamics.

  13. Quantitative Description of a Protein Fitness Landscape Based on Molecular Features.

    PubMed

    Meini, María-Rocío; Tomatis, Pablo E; Weinreich, Daniel M; Vila, Alejandro J

    2015-07-01

    Understanding the driving forces behind protein evolution requires the ability to correlate the molecular impact of mutations with organismal fitness. To address this issue, we employ here metallo-β-lactamases as a model system, which are Zn(II) dependent enzymes that mediate antibiotic resistance. We present a study of all the possible evolutionary pathways leading to a metallo-β-lactamase variant optimized by directed evolution. By studying the activity, stability and Zn(II) binding capabilities of all mutants in the preferred evolutionary pathways, we show that this local fitness landscape is strongly conditioned by epistatic interactions arising from the pleiotropic effect of mutations in the different molecular features of the enzyme. Activity and stability assays in purified enzymes do not provide explanatory power. Instead, measurement of these molecular features in an environment resembling the native one provides an accurate description of the observed antibiotic resistance profile. We report that optimization of Zn(II) binding abilities of metallo-β-lactamases during evolution is more critical than stabilization of the protein to enhance fitness. A global analysis of these parameters allows us to connect genotype with fitness based on quantitative biochemical and biophysical parameters.

  14. Quantitative Description of a Protein Fitness Landscape Based on Molecular Features

    PubMed Central

    Meini, María-Rocío; Tomatis, Pablo E.; Weinreich, Daniel M.; Vila, Alejandro J.

    2015-01-01

    Understanding the driving forces behind protein evolution requires the ability to correlate the molecular impact of mutations with organismal fitness. To address this issue, we employ here metallo-β-lactamases as a model system, which are Zn(II) dependent enzymes that mediate antibiotic resistance. We present a study of all the possible evolutionary pathways leading to a metallo-β-lactamase variant optimized by directed evolution. By studying the activity, stability and Zn(II) binding capabilities of all mutants in the preferred evolutionary pathways, we show that this local fitness landscape is strongly conditioned by epistatic interactions arising from the pleiotropic effect of mutations in the different molecular features of the enzyme. Activity and stability assays in purified enzymes do not provide explanatory power. Instead, measurement of these molecular features in an environment resembling the native one provides an accurate description of the observed antibiotic resistance profile. We report that optimization of Zn(II) binding abilities of metallo-β-lactamases during evolution is more critical than stabilization of the protein to enhance fitness. A global analysis of these parameters allows us to connect genotype with fitness based on quantitative biochemical and biophysical parameters. PMID:25767204

  15. Molecular Features of Subtype-Specific Progression from Ductal Carcinoma In Situ to Invasive Breast Cancer.

    PubMed

    Lesurf, Robert; Aure, Miriam Ragle; Mørk, Hanne Håberg; Vitelli, Valeria; Lundgren, Steinar; Børresen-Dale, Anne-Lise; Kristensen, Vessela; Wärnberg, Fredrik; Hallett, Michael; Sørlie, Therese

    2016-07-26

    Breast cancer consists of at least five main molecular "intrinsic" subtypes that are reflected in both pre-invasive and invasive disease. Although previous studies have suggested that many of the molecular features of invasive breast cancer are established early, it is unclear what mechanisms drive progression and whether the mechanisms of progression are dependent or independent of subtype. We have generated mRNA, miRNA, and DNA copy-number profiles from a total of 59 in situ lesions and 85 invasive tumors in order to comprehensively identify those genes, signaling pathways, processes, and cell types that are involved in breast cancer progression. Our work provides evidence that there are molecular features associated with disease progression that are unique to the intrinsic subtypes. We additionally establish subtype-specific signatures that are able to identify a small proportion of pre-invasive tumors with expression profiles that resemble invasive carcinoma, indicating a higher likelihood of future disease progression. PMID:27396337

  16. Quantitative Description of a Protein Fitness Landscape Based on Molecular Features.

    PubMed

    Meini, María-Rocío; Tomatis, Pablo E; Weinreich, Daniel M; Vila, Alejandro J

    2015-07-01

    Understanding the driving forces behind protein evolution requires the ability to correlate the molecular impact of mutations with organismal fitness. To address this issue, we employ here metallo-β-lactamases as a model system, which are Zn(II) dependent enzymes that mediate antibiotic resistance. We present a study of all the possible evolutionary pathways leading to a metallo-β-lactamase variant optimized by directed evolution. By studying the activity, stability and Zn(II) binding capabilities of all mutants in the preferred evolutionary pathways, we show that this local fitness landscape is strongly conditioned by epistatic interactions arising from the pleiotropic effect of mutations in the different molecular features of the enzyme. Activity and stability assays in purified enzymes do not provide explanatory power. Instead, measurement of these molecular features in an environment resembling the native one provides an accurate description of the observed antibiotic resistance profile. We report that optimization of Zn(II) binding abilities of metallo-β-lactamases during evolution is more critical than stabilization of the protein to enhance fitness. A global analysis of these parameters allows us to connect genotype with fitness based on quantitative biochemical and biophysical parameters. PMID:25767204

  17. Remote health monitoring: predicting outcome success based on contextual features for cardiovascular disease.

    PubMed

    Alshurafa, Nabil; Eastwood, Jo-Ann; Pourhomayoun, Mohammad; Liu, Jason J; Sarrafzadeh, Majid

    2014-01-01

    Current studies have produced a plethora of remote health monitoring (RHM) systems designed to enhance the care of patients with chronic diseases. Many RHM systems are designed to improve patient risk factors for cardiovascular disease, including physiological parameters such as body mass index (BMI) and waist circumference, and lipid profiles such as low density lipoprotein (LDL) and high density lipoprotein (HDL). There are several patient characteristics that could be determining factors for a patient's RHM outcome success, but these characteristics have been largely unidentified. In this paper, we analyze results from an RHM system deployed in a six month Women's Heart Health study of 90 patients, and apply advanced feature selection and machine learning algorithms to identify patients' key baseline contextual features and build effective prediction models that help determine RHM outcome success. We introduce Wanda-CVD, a smartphone-based RHM system designed to help participants with cardiovascular disease risk factors by motivating participants through wireless coaching using feedback and prompts as social support. We analyze key contextual features that secure positive patient outcomes in both physiological parameters and lipid profiles. Results from the Women's Heart Health study show that health threat of heart disease, quality of life, family history, stress factors, social support, and anxiety at baseline all help predict patient RHM outcome success. PMID:25570321

  18. Remote health monitoring: predicting outcome success based on contextual features for cardiovascular disease.

    PubMed

    Alshurafa, Nabil; Eastwood, Jo-Ann; Pourhomayoun, Mohammad; Liu, Jason J; Sarrafzadeh, Majid

    2014-01-01

    Current studies have produced a plethora of remote health monitoring (RHM) systems designed to enhance the care of patients with chronic diseases. Many RHM systems are designed to improve patient risk factors for cardiovascular disease, including physiological parameters such as body mass index (BMI) and waist circumference, and lipid profiles such as low density lipoprotein (LDL) and high density lipoprotein (HDL). There are several patient characteristics that could be determining factors for a patient's RHM outcome success, but these characteristics have been largely unidentified. In this paper, we analyze results from an RHM system deployed in a six month Women's Heart Health study of 90 patients, and apply advanced feature selection and machine learning algorithms to identify patients' key baseline contextual features and build effective prediction models that help determine RHM outcome success. We introduce Wanda-CVD, a smartphone-based RHM system designed to help participants with cardiovascular disease risk factors by motivating participants through wireless coaching using feedback and prompts as social support. We analyze key contextual features that secure positive patient outcomes in both physiological parameters and lipid profiles. Results from the Women's Heart Health study show that health threat of heart disease, quality of life, family history, stress factors, social support, and anxiety at baseline all help predict patient RHM outcome success.

  19. Prediction of bacterial type IV secreted effectors by C-terminal features

    PubMed Central

    2014-01-01

    Background Many bacteria can deliver pathogenic proteins (effectors) through type IV secretion systems (T4SSs) to eukaryotic cytoplasm, causing host diseases. The inherent property, such as sequence diversity and global scattering throughout the whole genome, makes it a big challenge to effectively identify the full set of T4SS effectors. Therefore, an effective inter-species T4SS effector prediction tool is urgently needed to help discover new effectors in a variety of bacterial species, especially those with few known effectors, e.g., Helicobacter pylori. Results In this research, we first manually annotated a full list of validated T4SS effectors from different bacteria and then carefully compared their C-terminal sequential and position-specific amino acid compositions, possible motifs and structural features. Based on the observed features, we set up several models to automatically recognize T4SS effectors. Three of the models performed strikingly better than the others and T4SEpre_Joint had the best performance, which could distinguish the T4SS effectors from non-effectors with a 5-fold cross-validation sensitivity of 89% at a specificity of 97%, based on the training datasets. An inter-species cross prediction showed that T4SEpre_Joint could recall most known effectors from a variety of species. The inter-species prediction tool package, T4SEpre, was further used to predict new T4SS effectors from H. pylori, an important human pathogen associated with gastritis, ulcer and cancer. In total, 24 new highly possible H. pylori T4S effector genes were computationally identified. Conclusions We conclude that T4SEpre, as an effective inter-species T4SS effector prediction software package, will help find new pathogenic T4SS effectors efficiently in a variety of pathogenic bacteria. PMID:24447430

  20. Identifying predictive features in drug response using machine learning: opportunities and challenges.

    PubMed

    Vidyasagar, Mathukumalli

    2015-01-01

    This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction problems are divided into two categories: sparse classification and sparse regression. In classification, the clinical parameter to be predicted is binary, whereas in regression, the parameter is a real number. Well-known methods for both classes of problems are briefly discussed. These include the SVM (support vector machine) for classification and various algorithms such as ridge regression, LASSO (least absolute shrinkage and selection operator), and EN (elastic net) for regression. In addition, several well-established methods that do not directly fall into machine learning theory are also reviewed, including neural networks, PAM (pattern analysis for microarrays), SAM (significance analysis for microarrays), GSEA (gene set enrichment analysis), and k-means clustering. Several references indicative of the application of these methods to cancer biology are discussed.

  1. kmer-SVM: a web server for identifying predictive regulatory sequence features in genomic data sets

    PubMed Central

    Fletez-Brant, Christopher; Lee, Dongwon; McCallion, Andrew S.; Beer, Michael A.

    2013-01-01

    Massively parallel sequencing technologies have made the generation of genomic data sets a routine component of many biological investigations. For example, Chromatin immunoprecipitation followed by sequence assays detect genomic regions bound (directly or indirectly) by specific factors, and DNase-seq identifies regions of open chromatin. A major bottleneck in the interpretation of these data is the identification of the underlying DNA sequence code that defines, and ultimately facilitates prediction of, these transcription factor (TF) bound or open chromatin regions. We have recently developed a novel computational methodology, which uses a support vector machine (SVM) with kmer sequence features (kmer-SVM) to identify predictive combinations of short transcription factor-binding sites, which determine the tissue specificity of these genomic assays (Lee, Karchin and Beer, Discriminative prediction of mammalian enhancers from DNA sequence. Genome Res. 2011; 21:2167–80). This regulatory information can (i) give confidence in genomic experiments by recovering previously known binding sites, and (ii) reveal novel sequence features for subsequent experimental testing of cooperative mechanisms. Here, we describe the development and implementation of a web server to allow the broader research community to independently apply our kmer-SVM to analyze and interpret their genomic datasets. We analyze five recently published data sets and demonstrate how this tool identifies accessory factors and repressive sequence elements. kmer-SVM is available at http://kmersvm.beerlab.org. PMID:23771147

  2. A predictive model of intein insertion site for use in the engineering of molecular switches.

    PubMed

    Apgar, James; Ross, Mary; Zuo, Xiao; Dohle, Sarah; Sturtevant, Derek; Shen, Binzhang; de la Vega, Humberto; Lessard, Philip; Lazar, Gabor; Raab, R Michael

    2012-01-01

    Inteins are intervening protein domains with self-splicing ability that can be used as molecular switches to control activity of their host protein. Successfully engineering an intein into a host protein requires identifying an insertion site that permits intein insertion and splicing while allowing for proper folding of the mature protein post-splicing. By analyzing sequence and structure based properties of native intein insertion sites we have identified four features that showed significant correlation with the location of the intein insertion sites, and therefore may be useful in predicting insertion sites in other proteins that provide native-like intein function. Three of these properties, the distance to the active site and dimer interface site, the SVM score of the splice site cassette, and the sequence conservation of the site showed statistically significant correlation and strong predictive power, with area under the curve (AUC) values of 0.79, 0.76, and 0.73 respectively, while the distance to secondary structure/loop junction showed significance but with less predictive power (AUC of 0.54). In a case study of 20 insertion sites in the XynB xylanase, two features of native insertion sites showed correlation with the splice sites and demonstrated predictive value in selecting non-native splice sites. Structural modeling of intein insertions at two sites highlighted the role that the insertion site location could play on the ability of the intein to modulate activity of the host protein. These findings can be used to enrich the selection of insertion sites capable of supporting intein splicing and hosting an intein switch.

  3. A Predictive Model of Intein Insertion Site for Use in the Engineering of Molecular Switches

    PubMed Central

    Apgar, James; Ross, Mary; Zuo, Xiao; Dohle, Sarah; Sturtevant, Derek; Shen, Binzhang; de la Vega, Humberto; Lessard, Philip; Lazar, Gabor; Raab, R. Michael

    2012-01-01

    Inteins are intervening protein domains with self-splicing ability that can be used as molecular switches to control activity of their host protein. Successfully engineering an intein into a host protein requires identifying an insertion site that permits intein insertion and splicing while allowing for proper folding of the mature protein post-splicing. By analyzing sequence and structure based properties of native intein insertion sites we have identified four features that showed significant correlation with the location of the intein insertion sites, and therefore may be useful in predicting insertion sites in other proteins that provide native-like intein function. Three of these properties, the distance to the active site and dimer interface site, the SVM score of the splice site cassette, and the sequence conservation of the site showed statistically significant correlation and strong predictive power, with area under the curve (AUC) values of 0.79, 0.76, and 0.73 respectively, while the distance to secondary structure/loop junction showed significance but with less predictive power (AUC of 0.54). In a case study of 20 insertion sites in the XynB xylanase, two features of native insertion sites showed correlation with the splice sites and demonstrated predictive value in selecting non-native splice sites. Structural modeling of intein insertions at two sites highlighted the role that the insertion site location could play on the ability of the intein to modulate activity of the host protein. These findings can be used to enrich the selection of insertion sites capable of supporting intein splicing and hosting an intein switch. PMID:22649521

  4. BioCAST/IFCT-1002: epidemiological and molecular features of lung cancer in never-smokers.

    PubMed

    Couraud, Sébastien; Souquet, Pierre-Jean; Paris, Christophe; Dô, Pascal; Doubre, Hélène; Pichon, Eric; Dixmier, Adrien; Monnet, Isabelle; Etienne-Mastroianni, Bénédicte; Vincent, Michel; Trédaniel, Jean; Perrichon, Marielle; Foucher, Pascal; Coudert, Bruno; Moro-Sibilot, Denis; Dansin, Eric; Labonne, Stéphanie; Missy, Pascale; Morin, Franck; Blanché, Hélène; Zalcman, Gérard

    2015-05-01

    Lung cancer in never-smokers (LCINS) (fewer than 100 cigarettes in lifetime) is considered as a distinct entity and harbours an original molecular profile. However, the epidemiological and molecular features of LCINS in Europe remain poorly understood. All consecutive newly diagnosed LCINS patients were included in this prospective observational study by 75 participating centres during a 14-month period. Each patient completed a detailed questionnaire about risk factor exposure. Biomarker and pathological analyses were also collected. We report the main descriptive overall results with a focus on sex differences. 384 patients were included: 65 men and 319 women. 66% had been exposed to passive smoking (significantly higher among women). Definite exposure to main occupational carcinogens was significantly higher in men (35% versus 8% in women). A targetable molecular alteration was found in 73% of patients (without any significant sex difference): EGFR in 51%, ALK in 8%, KRAS in 6%, HER2 in 3%, BRAF in 3%, PI3KCA in less than 1%, and multiple in 2%. We present the largest and most comprehensive LCINS analysis in a European population. Physicians should track occupational exposure in men (35%), and a somatic molecular alteration in both sexes (73%).

  5. Respiratory trace feature analysis for the prediction of respiratory-gated PET quantification

    NASA Astrophysics Data System (ADS)

    Wang, Shouyi; Bowen, Stephen R.; Chaovalitwongse, W. Art; Sandison, George A.; Grabowski, Thomas J.; Kinahan, Paul E.

    2014-02-01

    The benefits of respiratory gating in quantitative PET/CT vary tremendously between individual patients. Respiratory pattern is among many patient-specific characteristics that are thought to play an important role in gating-induced imaging improvements. However, the quantitative relationship between patient-specific characteristics of respiratory pattern and improvements in quantitative accuracy from respiratory-gated PET/CT has not been well established. If such a relationship could be estimated, then patient-specific respiratory patterns could be used to prospectively select appropriate motion compensation during image acquisition on a per-patient basis. This study was undertaken to develop a novel statistical model that predicts quantitative changes in PET/CT imaging due to respiratory gating. Free-breathing static FDG-PET images without gating and respiratory-gated FDG-PET images were collected from 22 lung and liver cancer patients on a PET/CT scanner. PET imaging quality was quantified with peak standardized uptake value (SUVpeak) over lesions of interest. Relative differences in SUVpeak between static and gated PET images were calculated to indicate quantitative imaging changes due to gating. A comprehensive multidimensional extraction of the morphological and statistical characteristics of respiratory patterns was conducted, resulting in 16 features that characterize representative patterns of a single respiratory trace. The six most informative features were subsequently extracted using a stepwise feature selection approach. The multiple-regression model was trained and tested based on a leave-one-subject-out cross-validation. The predicted quantitative improvements in PET imaging achieved an accuracy higher than 90% using a criterion with a dynamic error-tolerance range for SUVpeak values. The results of this study suggest that our prediction framework could be applied to determine which patients would likely benefit from respiratory motion compensation

  6. Observations of the interstellar ice grain feature in the Taurus molecular clouds

    SciTech Connect

    Whittet, D.C.B.; Bode, H.F.; Longmore, A.J.; Baines, D.W.T.; Evans, A.

    1983-01-01

    Although water ice was originally proposed as a major constituent of the interstellar grain population (e.g. Oort and van de Hulst, 1946), the advent of infrared astronomy has shown that the expected absorption due to O-H stretching vibrations at 3 ..mu..m is illusive. Observations have in fact revealed that the carrier of this feature is apparently restricted to regions deep within dense molecular clouds (Merrill et al., 1976; Willner et al., 1982). However, the exact carrier of this feature is still controversial, and many questions remain as to the conditions required for its appearance. It is also uncertain whether it is restricted to circumstellar shells, rather than the general cloud medium. Detailed discussion of the 3 ..mu..m band properties is given elsewhere in this volume. 15 references, 4 figures.

  7. A child's view: social and physical environmental features differentially predict parent and child perceived neighborhood safety.

    PubMed

    Côté-Lussier, Carolyn; Jackson, Jonathan; Kestens, Yan; Henderson, Melanie; Barnett, Tracie A

    2015-02-01

    Parent and child perceived neighborhood safety predicts child health outcomes such as sleep quality, asthma, physical activity, and psychological distress. Although previous studies identify environmental predictors of parent perceived safety, little is known about predictors of child perceived safety. This study aims to identify the social and physical environmental neighborhood features that predict child and parent perceived neighborhood safety and, simultaneously, to assess the association between child and parent perceptions. Data were from the QUebec Adipose and Lifestyle InvesTigation in Youth (QUALITY) cohort, an ongoing study of Caucasian children (aged 8-10 years) with a parental history of obesity, and their biological parents from Québec, Canada. Measures of social and physical neighborhood features were collected using a spatial data infrastructure and in-person audits. Structural equation modeling was used to test direct and indirect associations between neighborhood features, child and parent perceived safety. Results suggest that among children (N = 494), trees and lighting were positively associated with perceived neighborhood safety, whereas a high proportion of visible minorities was associated with poorer perceived safety. Parents' perceptions of safety were more strongly tied to indicators of disorder and a lack of community involvement, and to traffic. Child perceived safety was partly explained by parent perceived safety, suggesting moderate concordance between perceptions. Although associated with each other, parent and child perceived safety seemed to be determined by distinct environmental features. Though this study focused on determinants of child and parent perceived safety, future research investigating the impact of neighborhood safety on child health should consider both child and parent perspectives.

  8. Feature Detection” vs. “Predictive Coding” Models of Plant Behavior

    PubMed Central

    Calvo, Paco; Baluška, František; Sims, Andrew

    2016-01-01

    In this article we consider the possibility that plants exhibit anticipatory behavior, a mark of intelligence. If plants are able to anticipate and respond accordingly to varying states of their surroundings, as opposed to merely responding online to environmental contingencies, then such capacity may be in principle testable, and subject to empirical scrutiny. Our main thesis is that adaptive behavior can only take place by way of a mechanism that predicts the environmental sources of sensory stimulation. We propose to test for anticipation in plants experimentally by contrasting two empirical hypotheses: “feature detection” and “predictive coding.” We spell out what these contrasting hypotheses consist of by way of illustration from the animal literature, and consider how to transfer the rationale involved to the plant literature. PMID:27757094

  9. Spiking neurons can discover predictive features by aggregate-label learning.

    PubMed

    Gütig, Robert

    2016-03-01

    The brain routinely discovers sensory clues that predict opportunities or dangers. However, it is unclear how neural learning processes can bridge the typically long delays between sensory clues and behavioral outcomes. Here, I introduce a learning concept, aggregate-label learning, that enables biologically plausible model neurons to solve this temporal credit assignment problem. Aggregate-label learning matches a neuron's number of output spikes to a feedback signal that is proportional to the number of clues but carries no information about their timing. Aggregate-label learning outperforms stochastic reinforcement learning at identifying predictive clues and is able to solve unsegmented speech-recognition tasks. Furthermore, it allows unsupervised neural networks to discover reoccurring constellations of sensory features even when they are widely dispersed across space and time.

  10. ERα-Negative and Triple Negative Breast Cancer: Molecular Features and Potential Therapeutic Approaches

    PubMed Central

    Chen, Jin-Qiang; Russo, Jose

    2010-01-01

    Triple negative breast cancer (TNBC) is a type of aggressive breast cancer lacking the expression of estrogen receptors (ER), progesterone receptors (PR) and human epidermal growth factor receptor-2 (HER-2). TNBC patients account for approximately 15% of total breast cancer patients and are more prevalent among young African, African-American and Latino women patients. The currently available ER-targeted and Her-2-based therapies are not effective for treating TNBC. Recent studies have revealed a number of novel features of TNBC. In the present work, we comprehensively addressed these features and discussed potential therapeutic approaches based on these features for TNBC, with particular focus on: 1) the pathological features of TNBC/basal-like breast cancer; 2) E2/ERβ – mediated signaling pathways; 3) G-protein coupling receptor-30/epithelial growth factor receptor (GPCR-30/EGFR) signaling pathway; 4) interactions of ERβ with breast cancer 1/2 (BRCA1/2); 5) chemokine CXCL8 and related chemokines; 6) altered microRNA signatures and suppression of ERα expression/ERα-signaling by micro-RNAs; 7) altered expression of several pro-oncongenic and tumor suppressor proteins; and 8) genotoxic effects caused by oxidative estrogen metabolites. Gaining better insights into these molecular pathways in TNBC may lead to identification of novel biomarkers and targets for development of diagnostic and therapeutic approaches for prevention and treatment of TNBC. PMID:19527773

  11. A switchable bis-branched [1]rotaxane featuring dual-mode molecular motions and tunable molecular aggregation.

    PubMed

    Li, Hong; Li, Xin; Cao, Zhan-Qi; Qu, Da-Hui; Ågren, Hans; Tian, He

    2014-01-01

    A multifunctional bis-branched [1]rotaxane containing a perylene bisimide (PBI) core and two identical bistable[1]rotaxane arms terminated with ferrocene units was prepared and characterized by (1)H NMR, (13)C NMR, and 2D ROESY NMR spectroscopies and by HR-ESI spectrometry. The system is shown to possess several key features: (1) In acetone solution, external acid-base stimuli can result in relative mechanical movements of its ring and thread, which can induce extension and contraction movements of the whole system accompanied by a rotational movement of the ferrocene units, thus realizing dual-mode molecular motions, and the optimized conformations at different states are obtained through molecular dynamics simulations employing the general Amber force field. (2) The introduction of PBI enables the system fluorescence encoding through distance-dependent photoinduced electron transfer process from the ferrocene units to the PBI fluorophore. (3) The addition of Zn(2+) can increase the degree of aggregation of the system, while adding base hinders aggregation because of the movement of the macrocycle. The tunable aggregated nanostructural morphologies of [1]rotaxane were examined by scanning electron microscopy. These results can pave the way to achieve precise control of integrated and coupling nanomechanical motions at a single-molecule level and provide more insight into controlling the aggregate behavior of switchable mechanically interlocked molecules. PMID:25302680

  12. Molecular features of interaction between VEGFA and anti-angiogenic drugs used in retinal diseases: a computational approach

    PubMed Central

    Platania, Chiara B. M.; Di Paola, Luisa; Leggio, Gian M.; Romano, Giovanni L.; Drago, Filippo; Salomone, Salvatore; Bucolo, Claudio

    2015-01-01

    Anti-angiogenic agents are biological drugs used for treatment of retinal neovascular degenerative diseases. In this study, we aimed at in silico analysis of interaction of vascular endothelial growth factor A (VEGFA), the main mediator of angiogenesis, with binding domains of anti-angiogenic agents used for treatment of retinal diseases, such as ranibizumab, bevacizumab and aflibercept. The analysis of anti-VEGF/VEGFA complexes was carried out by means of protein-protein docking and molecular dynamics (MD) coupled to molecular mechanics-Poisson Boltzmann Surface Area (MM-PBSA) calculation. Molecular dynamics simulation was further analyzed by protein contact networks. Rough energetic evaluation with protein-protein docking scores revealed that aflibercept/VEGFA complex was characterized by electrostatic stabilization, whereas ranibizumab and bevacizumab complexes were stabilized by Van der Waals (VdW) energy term; these results were confirmed by MM-PBSA. Comparison of MM-PBSA predicted energy terms with experimental binding parameters reported in literature indicated that the high association rate (Kon) of aflibercept to VEGFA was consistent with high stabilizing electrostatic energy. On the other hand, the relatively low experimental dissociation rate (Koff) of ranibizumab may be attributed to lower conformational fluctuations of the ranibizumab/VEGFA complex, higher number of contacts and hydrogen bonds in comparison to bevacizumab and aflibercept. Thus, the anti-angiogenic agents have been found to be considerably different both in terms of molecular interactions and stabilizing energy. Characterization of such features can improve the design of novel biological drugs potentially useful in clinical practice. PMID:26578958

  13. Application of Molecular Dynamics Simulations in Molecular Property Prediction I: Density and Heat of Vaporization

    PubMed Central

    Wang, Junmei; Tingjun, Hou

    2011-01-01

    Molecular mechanical force field (FF) methods are useful in studying condensed phase properties. They are complementary to experiment and can often go beyond experiment in atomic details. Even a FF is specific for studying structures, dynamics and functions of biomolecules, it is still important for the FF to accurately reproduce the experimental liquid properties of small molecules that represent the chemical moieties of biomolecules. Otherwise, the force field may not describe the structures and energies of macromolecules in aqueous solutions properly. In this work, we have carried out a systematic study to evaluate the General AMBER Force Field (GAFF) in studying densities and heats of vaporization for a large set of organic molecules that covers the most common chemical functional groups. The latest techniques, such as the particle mesh Ewald (PME) for calculating electrostatic energies, and Langevin dynamics for scaling temperatures, have been applied in the molecular dynamics (MD) simulations. For density, the average percent error (APE) of 71 organic compounds is 4.43% when compared to the experimental values. More encouragingly, the APE drops to 3.43% after the exclusion of two outliers and four other compounds for which the experimental densities have been measured with pressures higher than 1.0 atm. For heat of vaporization, several protocols have been investigated and the best one, P4/ntt0, achieves an average unsigned error (AUE) and a root-mean-square error (RMSE) of 0.93 and 1.20 kcal/mol, respectively. How to reduce the prediction errors through proper van der Waals (vdW) parameterization has been discussed. An encouraging finding in vdW parameterization is that both densities and heats of vaporization approach their “ideal” values in a synchronous fashion when vdW parameters are tuned. The following hydration free energy calculation using thermodynamic integration further justifies the vdW refinement. We conclude that simple vdW parameterization

  14. Structural features of cholesteryl ester transfer protein: a molecular dynamics simulation study.

    PubMed

    Lei, Dongsheng; Zhang, Xing; Jiang, Shengbo; Cai, Zhaodi; Rames, Matthew J; Zhang, Lei; Ren, Gang; Zhang, Shengli

    2013-03-01

    Cholesteryl ester transfer protein (CETP) mediates the net transfer of cholesteryl esters (CEs) from atheroprotective high-density lipoproteins (HDLs) to atherogenic low-density lipoproteins (LDLs) or very-low-density lipoproteins (VLDLs). Inhibition of CETP raises HDL cholesterol (good cholesterol) levels and reduces LDL cholesterol (bad cholesterol) levels, making it a promising drug target for the prevention and treatment of coronary heart disease. Although the crystal structure of CETP has been determined, the molecular mechanism mediating CEs transfer is still unknown, even the structural features of CETP in a physiological environment remain elusive. We performed molecular dynamics simulations to explore the structural features of CETP in an aqueous solution. Results show that the distal portion flexibility of N-terminal β-barrel domain is considerably greater in solution than in crystal; conversely, the flexibility of helix X is slightly less. During the simulations the distal end of C-terminal β-barrel domain expanded while the hydrophilic surface increasing more than the hydrophobic surface. In addition, a new surface pore was generated in this domain. This surface pore and all cavities in CETP are stable. These results suggest that the formation of a continuous tunnel within CETP by connecting cavities is permitted in solution. PMID:23042613

  15. Search performance is better predicted by tileability than presence of a unique basic feature.

    PubMed

    Chang, Honghua; Rosenholtz, Ruth

    2016-08-01

    Traditional models of visual search such as feature integration theory (FIT; Treisman & Gelade, 1980), have suggested that a key factor determining task difficulty consists of whether or not the search target contains a "basic feature" not found in the other display items (distractors). Here we discriminate between such traditional models and our recent texture tiling model (TTM) of search (Rosenholtz, Huang, Raj, Balas, & Ilie, 2012b), by designing new experiments that directly pit these models against each other. Doing so is nontrivial, for two reasons. First, the visual representation in TTM is fully specified, and makes clear testable predictions, but its complexity makes getting intuitions difficult. Here we elucidate a rule of thumb for TTM, which enables us to easily design new and interesting search experiments. FIT, on the other hand, is somewhat ill-defined and hard to pin down. To get around this, rather than designing totally new search experiments, we start with five classic experiments that FIT already claims to explain: T among Ls, 2 among 5s, Q among Os, O among Qs, and an orientation/luminance-contrast conjunction search. We find that fairly subtle changes in these search tasks lead to significant changes in performance, in a direction predicted by TTM, providing definitive evidence in favor of the texture tiling model as opposed to traditional views of search. PMID:27548090

  16. FFPred 3: feature-based function prediction for all Gene Ontology domains.

    PubMed

    Cozzetto, Domenico; Minneci, Federico; Currant, Hannah; Jones, David T

    2016-01-01

    Predicting protein function has been a major goal of bioinformatics for several decades, and it has gained fresh momentum thanks to recent community-wide blind tests aimed at benchmarking available tools on a genomic scale. Sequence-based predictors, especially those performing homology-based transfers, remain the most popular but increasing understanding of their limitations has stimulated the development of complementary approaches, which mostly exploit machine learning. Here we present FFPred 3, which is intended for assigning Gene Ontology terms to human protein chains, when homology with characterized proteins can provide little aid. Predictions are made by scanning the input sequences against an array of Support Vector Machines (SVMs), each examining the relationship between protein function and biophysical attributes describing secondary structure, transmembrane helices, intrinsically disordered regions, signal peptides and other motifs. This update features a larger SVM library that extends its coverage to the cellular component sub-ontology for the first time, prompted by the establishment of a dedicated evaluation category within the Critical Assessment of Functional Annotation. The effectiveness of this approach is demonstrated through benchmarking experiments, and its usefulness is illustrated by analysing the potential functional consequences of alternative splicing in human and their relationship to patterns of biological features. PMID:27561554

  17. FFPred 3: feature-based function prediction for all Gene Ontology domains

    PubMed Central

    Cozzetto, Domenico; Minneci, Federico; Currant, Hannah; Jones, David T.

    2016-01-01

    Predicting protein function has been a major goal of bioinformatics for several decades, and it has gained fresh momentum thanks to recent community-wide blind tests aimed at benchmarking available tools on a genomic scale. Sequence-based predictors, especially those performing homology-based transfers, remain the most popular but increasing understanding of their limitations has stimulated the development of complementary approaches, which mostly exploit machine learning. Here we present FFPred 3, which is intended for assigning Gene Ontology terms to human protein chains, when homology with characterized proteins can provide little aid. Predictions are made by scanning the input sequences against an array of Support Vector Machines (SVMs), each examining the relationship between protein function and biophysical attributes describing secondary structure, transmembrane helices, intrinsically disordered regions, signal peptides and other motifs. This update features a larger SVM library that extends its coverage to the cellular component sub-ontology for the first time, prompted by the establishment of a dedicated evaluation category within the Critical Assessment of Functional Annotation. The effectiveness of this approach is demonstrated through benchmarking experiments, and its usefulness is illustrated by analysing the potential functional consequences of alternative splicing in human and their relationship to patterns of biological features. PMID:27561554

  18. Search performance is better predicted by tileability than presence of a unique basic feature.

    PubMed

    Chang, Honghua; Rosenholtz, Ruth

    2016-08-01

    Traditional models of visual search such as feature integration theory (FIT; Treisman & Gelade, 1980), have suggested that a key factor determining task difficulty consists of whether or not the search target contains a "basic feature" not found in the other display items (distractors). Here we discriminate between such traditional models and our recent texture tiling model (TTM) of search (Rosenholtz, Huang, Raj, Balas, & Ilie, 2012b), by designing new experiments that directly pit these models against each other. Doing so is nontrivial, for two reasons. First, the visual representation in TTM is fully specified, and makes clear testable predictions, but its complexity makes getting intuitions difficult. Here we elucidate a rule of thumb for TTM, which enables us to easily design new and interesting search experiments. FIT, on the other hand, is somewhat ill-defined and hard to pin down. To get around this, rather than designing totally new search experiments, we start with five classic experiments that FIT already claims to explain: T among Ls, 2 among 5s, Q among Os, O among Qs, and an orientation/luminance-contrast conjunction search. We find that fairly subtle changes in these search tasks lead to significant changes in performance, in a direction predicted by TTM, providing definitive evidence in favor of the texture tiling model as opposed to traditional views of search.

  19. Prediction of phosphothreonine sites in human proteins by fusing different features

    PubMed Central

    Zhao, Ya-Wei; Lai, Hong-Yan; Tang, Hua; Chen, Wei; Lin, Hao

    2016-01-01

    Phosphorylation is one of the most important protein post-translation modifications. With the rapid development of high-throughput mass spectrometry, phosphorylation site data is rapidly accumulating, which provides us an opportunity to systematically investigate and predict phosphorylation in proteins. The phosphorylation of threonine is the addition of a phosphoryl group to its polar side chains group. In this work, we statistically analyzed the distribution of the different properties including position conservation, secondary structure, accessibility and some other physicochemical properties of the residues surrounding the phosphothreonine site and non-phosphothreonine site. We found that the distributions of those features are non-symmetrical. Based on the distribution of properties, we developed a new model by using optimal window size strategy and feature selection technique. The cross-validated results show that the area under receiver operating characteristic curve reaches to 0.847, suggesting that our model may play a complementary role to other existing methods for predicting phosphothreonine site in proteins. PMID:27698459

  20. Pathology Features in Bethesda Guidelines Predict Colorectal Cancer Microsatellite Instability: A Population-Based Study

    PubMed Central

    Jenkins, Mark A.; Hayashi, Shinichi; O’shea, Anne-Marie; Burgart, Lawrence J.; Smyrk, Tom C.; Shimizu, David; Waring, Paul M.; Ruszkiewicz, Andrew R.; Pollett, Aaron F.; Redston, Mark; Barker, Melissa A.; Baron, John A.; Casey, Graham R.; Dowty, James G.; Giles, Graham G.; Limburg, Paul; Newcomb, Polly; Young, Joanne P.; Walsh, Michael D.; Thibodeau, Stephen N.; Lindor, Noralane M.; Lemarchand, Loïc; Gallinger, Steven; Haile, Robert W.; Potter, John D.; Hopper, John L.; Jass, Jeremy R.

    2010-01-01

    Background & Aims The revised Bethesda guidelines for Lynch syndrome recommend microsatellite instability (MSI) testing all colorectal cancers in patients diagnosed before age 50 years and colorectal cancers diagnosed in patients between ages 50 and 59 years with particular pathology features. Our aim was to identify pathology and other features that independently predict high MSI (MSI-H). Methods Archival tissue from 1098 population-based colorectal cancers diagnosed before age 60 years was tested for MSI. Pathology features, site, and age at diagnosis were obtained. Multiple logistic regression was performed to determine the predictive value of each feature, as measured by an odds ratio (OR), from which a scoring system (MsPath) was developed to estimate the probability a colorectal cancer is MSI-H. Results Fifteen percent of tumors (162) were MSI-H. Independent predictors were tumor-infiltrating lymphocytes (OR, 9.1; 95% confidence interval [CI], 5.9 –14.1), proximal subsite (OR, 4.7; 95% CI, 3.1–7.3), mucinous histology (OR, 2.8; 95% CI, 1.7– 4.8), poor differentiation (OR, 1.9; 95% CI, 1.2–3.1), Crohn’s-like reaction (OR, 1.9; 95% CI, 1.2–2.9), and diagnosis before age 50 years (OR, 1.9; 95% CI, 1.3–2.9). MsPath score ≥ 1.0 had a sensitivity of 93% and a specificity of 55% for MSI-H. Conclusions The probability an individual colorectal cancer is MSI-H is predicted well by the MsPath score. There is little value in testing for DNA mismatch repair loss in tumors, or for germline mismatch repair mutations, for colorectal cancers diagnosed in patients before age 60 years with an MSPath score <1 (approximately 50%). Pathology can identify almost all MSI-H colorectal cancers diagnosed before age 60 years. PMID:17631130

  1. QSAR prediction of HIV-1 protease inhibitory activities using docking derived molecular descriptors.

    PubMed

    Fatemi, Mohammad H; Heidari, Afsane; Gharaghani, Sajjad

    2015-03-21

    In this study, application of a new hybrid docking-quantitative structure activity relationship (QSAR) methodology to model and predict the HIV-1 protease inhibitory activities of a series of newly synthesized chemicals is reported. This hybrid docking-QSAR approach can provide valuable information about the most important chemical and structural features of the ligands that affect their inhibitory activities. Docking studies were used to find the actual conformations of chemicals in active site of HIV-1 protease. Then the molecular descriptors were calculated from these conformations. Multiple linear regression (MLR) and least square support vector machine (LS-SVM) were used as QSAR models, respectively. The obtained results reveal that statistical parameters of the LS-SVM model are better than the MLR model, which indicate that there are some non-linear relations between selected molecular descriptors and anti-HIV activities of interested chemicals. The correlation coefficient (R), root mean square error (RMSE) and average absolute error (AAE) for LS-SVM are: R=0.988, RMSE=0.207 and AAE=0.145 for the training set, and R=0.965, RMSE=0.403 and AAE=0.338 for the test set. Leave one out cross validation test was used for assessment of the predictive power and validity of models which led to cross-validation correlation coefficient QUOTE of 0.864 and 0.850 and standardized predicted relative error sum of squares (SPRESS) of 0.553 and 0.581 for LS-SVM and MLR models, respectively.

  2. In silico predictive studies of mAHR congener binding using homology modelling and molecular docking.

    PubMed

    Panda, Roshni; Cleave, A Suneetha Susan; Suresh, P K

    2014-09-01

    The aryl hydrocarbon receptor (AHR) is one of the principal xenobiotic, nuclear receptor that is responsible for the early events involved in the transcription of a complex set of genes comprising the CYP450 gene family. In the present computational study, homology modelling and molecular docking were carried out with the objective of predicting the relationship between the binding efficiency and the lipophilicity of different polychlorinated biphenyl (PCB) congeners and the AHR in silico. Homology model of the murine AHR was constructed by several automated servers and assessed by PROCHECK, ERRAT, VERIFY3D and WHAT IF. The resulting model of the AHR by MODWEB was used to carry out molecular docking of 36 PCB congeners using PatchDock server. The lipophilicity of the congeners was predicted using the XLOGP3 tool. The results suggest that the lipophilicity influences binding energy scores and is positively correlated with the same. Score and Log P were correlated with r = +0.506 at p = 0.01 level. In addition, the number of chlorine (Cl) atoms and Log P were highly correlated with r = +0.900 at p = 0.01 level. The number of Cl atoms and scores also showed a moderate positive correlation of r = +0.481 at p = 0.01 level. To the best of our knowledge, this is the first study employing PatchDock in the docking of AHR to the environmentally deleterious congeners and attempting to correlate structural features of the AHR with its biochemical properties with regards to PCBs. The result of this study are consistent with those of other computational studies reported in the previous literature that suggests that a combination of docking, scoring and ranking organic pollutants could be a possible predictive tool for investigating ligand-mediated toxicity, for their subsequent validation using wet lab-based studies.

  3. MELANCHOLIC DEPRESSION PREDICTION BY IDENTIFYING REPRESENTATIVE FEATURES IN METABOLIC AND MICROARRAY PROFILES WITH MISSING VALUES

    PubMed Central

    Nie, Zhi; Yang, Tao; Liu, Yashu; Lin, Binbin; Li, Qingyang; Narayan, Vaibhav A; Wittenberg, Gayle; Ye, Jieping

    2014-01-01

    Recent studies have revealed that melancholic depression, one major subtype of depression, is closely associated with the concentration of some metabolites and biological functions of certain genes and pathways. Meanwhile, recent advances in biotechnologies have allowed us to collect a large amount of genomic data, e.g., metabolites and microarray gene expression. With such a huge amount of information available, one approach that can give us new insights into the understanding of the fundamental biology underlying melancholic depression is to build disease status prediction models using classification or regression methods. However, the existence of strong empirical correlations, e.g., those exhibited by genes sharing the same biological pathway in microarray profiles, tremendously limits the performance of these methods. Furthermore, the occurrence of missing values which are ubiquitous in biomedical applications further complicates the problem. In this paper, we hypothesize that the problem of missing values might in some way benefit from the correlation between the variables and propose a method to learn a compressed set of representative features through an adapted version of sparse coding which is capable of identifying correlated variables and addressing the issue of missing values simultaneously. An efficient algorithm is also developed to solve the proposed formulation. We apply the proposed method on metabolic and microarray profiles collected from a group of subjects consisting of both patients with melancholic depression and healthy controls. Results show that the proposed method can not only produce meaningful clusters of variables but also generate a set of representative features that achieve superior classification performance over those generated by traditional clustering and data imputation techniques. In particular, on both datasets, we found that in comparison with the competing algorithms, the representative features learned by the proposed

  4. Infrared images of reflection nebulae and Orion's bar: Fluorescent molecular hydrogen and the 3.3 micron feature

    NASA Technical Reports Server (NTRS)

    Burton, Michael G.; Moorhouse, Alan; Brand, P. W. J. L.; Roche, Patrick F.; Geballe, T. R.

    1989-01-01

    Images were obtained of the (fluorescent) molecular hydrogen 1-0 S(1) line, and of the 3.3 micron emission feature, in Orion's Bar and three reflection nebulae. The emission from these species appears to come from the same spatial locations in all sources observed. This suggests that the 3.3 micron feature is excited by the same energetic UV-photons which cause the molecular hydrogen to fluoresce.

  5. Machine Learning Approaches for Integrating Clinical and Imaging Features in LLD Classification and Response Prediction

    PubMed Central

    Patel, Meenal J.; Andreescu, Carmen; Price, Julie C.; Edelman, Kathryn L.; Reynolds, Charles F.; Aizenstein, Howard J.

    2015-01-01

    Objective Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate accurate prediction models for late-life depression diagnosis and treatment response using multiple machine learning methods with inputs of multi-modal imaging and non-imaging whole brain and network-based features. Methods Late-life depression patients (medicated post-recruitment) [n=33] and elderly non-depressed individuals [n=35] were recruited. Their demographics and cognitive ability scores were recorded, and brain characteristics were acquired using multi-modal magnetic resonance imaging pre-treatment. Linear and nonlinear learning methods were tested for estimating accurate prediction models. Results A learning method called alternating decision trees estimated the most accurate prediction models for late-life depression diagnosis (87.27% accuracy) and treatment response (89.47% accuracy). The diagnosis model included measures of age, mini-mental state examination score, and structural imaging (e.g. whole brain atrophy and global white mater hyperintensity burden). The treatment response model included measures of structural and functional connectivity. Conclusions Combinations of multi-modal imaging and/or non-imaging measures may help better predict late-life depression diagnosis and treatment response. As a preliminary observation, we speculate the results may also suggest that different underlying brain characteristics defined by multi-modal imaging measures—rather than region-based differences—are associated with depression versus depression recovery since to our knowledge this is the first depression study to accurately predict both using the same approach. These findings may help better understand late-life depression and identify preliminary steps towards personalized late-life depression treatment

  6. Ribonucleotide reductases reveal novel viral diversity and predict biological and ecological features of unknown marine viruses.

    PubMed

    Sakowski, Eric G; Munsell, Erik V; Hyatt, Mara; Kress, William; Williamson, Shannon J; Nasko, Daniel J; Polson, Shawn W; Wommack, K Eric

    2014-11-01

    Virioplankton play a crucial role in aquatic ecosystems as top-down regulators of bacterial populations and agents of horizontal gene transfer and nutrient cycling. However, the biology and ecology of virioplankton populations in the environment remain poorly understood. Ribonucleotide reductases (RNRs) are ancient enzymes that reduce ribonucleotides to deoxyribonucleotides and thus prime DNA synthesis. Composed of three classes according to O2 reactivity, RNRs can be predictive of the physiological conditions surrounding DNA synthesis. RNRs are universal among cellular life, common within viral genomes and virioplankton shotgun metagenomes (viromes), and estimated to occur within >90% of the dsDNA virioplankton sampled in this study. RNRs occur across diverse viral groups, including all three morphological families of tailed phages, making these genes attractive for studies of viral diversity. Differing patterns in virioplankton diversity were clear from RNRs sampled across a broad oceanic transect. The most abundant RNRs belonged to novel lineages of podoviruses infecting α-proteobacteria, a bacterial class critical to oceanic carbon cycling. RNR class was predictive of phage morphology among cyanophages and RNR distribution frequencies among cyanophages were largely consistent with the predictions of the "kill the winner-cost of resistance" model. RNRs were also identified for the first time to our knowledge within ssDNA viromes. These data indicate that RNR polymorphism provides a means of connecting the biological and ecological features of virioplankton populations.

  7. Time Score: A New Feature for Link Prediction in Social Networks

    NASA Astrophysics Data System (ADS)

    Munasinghe, Lankeshwara; Ichise, Ryutaro

    Link prediction in social networks, such as friendship networks and coauthorship networks, has recently attracted a great deal of attention. There have been numerous attempts to address the problem of link prediction through diverse approaches. In the present paper, we focus on the temporal behavior of the link strength, particularly the relationship between the time stamps of interactions or links and the temporal behavior of link strength and how link strength affects future link evolution. Most previous studies have not sufficiently discussed either the impact of time stamps of the interactions or time stamps of the links on link evolution. The gap between the current time and the time stamps of the interactions or links is also important to link evolution. In the present paper, we introduce a new time-aware feature, referred to as time score, that captures the important aspects of time stamps of interactions and the temporality of the link strengths. We also analyze the effectiveness of time score with different parameter settings for different network data sets. The results of the analysis revealed that the time score was sensitive to different networks and different time measures. We applied time score to two social network data sets, namely, Facebook friendship network data set and a coauthorship network data set. The results revealed a significant improvement in predicting future links.

  8. Accurate single-sequence prediction of solvent accessible surface area using local and global features.

    PubMed

    Faraggi, Eshel; Zhou, Yaoqi; Kloczkowski, Andrzej

    2014-11-01

    We present a new approach for predicting the Accessible Surface Area (ASA) using a General Neural Network (GENN). The novelty of the new approach lies in not using residue mutation profiles generated by multiple sequence alignments as descriptive inputs. Instead we use solely sequential window information and global features such as single-residue and two-residue compositions of the chain. The resulting predictor is both highly more efficient than sequence alignment-based predictors and of comparable accuracy to them. Introduction of the global inputs significantly helps achieve this comparable accuracy. The predictor, termed ASAquick, is tested on predicting the ASA of globular proteins and found to perform similarly well for so-called easy and hard cases indicating generalizability and possible usability for de-novo protein structure prediction. The source code and a Linux executables for GENN and ASAquick are available from Research and Information Systems at http://mamiris.com, from the SPARKS Lab at http://sparks-lab.org, and from the Battelle Center for Mathematical Medicine at http://mathmed.org. PMID:25204636

  9. An approach to predict Sudden Cardiac Death (SCD) using time domain and bispectrum features from HRV signal.

    PubMed

    Houshyarifar, Vahid; Chehel Amirani, Mehdi

    2016-08-12

    In this paper we present a method to predict Sudden Cardiac Arrest (SCA) with higher order spectral (HOS) and linear (Time) features extracted from heart rate variability (HRV) signal. Predicting the occurrence of SCA is important in order to avoid the probability of Sudden Cardiac Death (SCD). This work is a challenge to predict five minutes before SCA onset. The method consists of four steps: pre-processing, feature extraction, feature reduction, and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the HRV signal is extracted. In second step, bispectrum features of HRV signal and time-domain features are obtained. Six features are extracted from bispectrum and two features from time-domain. In the next step, these features are reduced to one feature by the linear discriminant analysis (LDA) technique. Finally, KNN and support vector machine-based classifiers are used to classify the HRV signals. We used two database named, MIT/BIH Sudden Cardiac Death (SCD) Database and Physiobank Normal Sinus Rhythm (NSR). In this work we achieved prediction of SCD occurrence for six minutes before the SCA with the accuracy over 91%. PMID:27567781

  10. Molecular features assisting in diagnosis, surgery, and treatment decision making in low-grade gliomas.

    PubMed

    Chen, Ricky; Ravindra, Vijay M; Cohen, Adam L; Jensen, Randy L; Salzman, Karen L; Prescot, Andrew P; Colman, Howard

    2015-03-01

    The preferred management of suspected low-grade gliomas (LGGs) has been disputed, and the implications of molecular changes for medical and surgical management of LGGs are important to consider. Current strategies that make use of molecular markers and imaging techniques and therapeutic considerations offer additional options for management of LGGs. Mutations in the isocitrate dehydrogenase 1 and 2 (IDH1 and IDH2) genes suggest a role for this abnormal metabolic pathway in the pathogenesis and progression of these primary brain tumors. Use of magnetic resonance spectroscopy can provide preoperative detection of IDH-mutated gliomas and affect surgical planning. In addition, IDH1 and IDH2 mutation status may have an effect on surgical resectability of gliomas. The IDH-mutated tumors exhibit better prognosis throughout every grade of glioma, and mutation may be an early genetic event, preceding lineage-specific secondary and tertiary alterations that transform LGGs into secondary glioblastomas. The O6-methylguanine-DNAmethyltransferase (MGMT) promoter methylation and 1p19q codeletion status can predict sensitivity to chemotherapy and radiation in low- and intermediate-grade gliomas. Thus, these recent advances, which have led to a better understanding of how molecular, genetic, and epigenetic alterations influence the pathogenicity of the different histological grades of gliomas, can lead to better prognostication and may lead to specific targeted surgical interventions and medical therapies. PMID:25727224

  11. Larval description of Drusus bosnicus Klapálek 1899 (Trichoptera: Limnephilidae), with distributional, molecular and ecological features

    PubMed Central

    KUČINIĆ, MLADEN; PREVIŠIĆ, ANA; GRAF, WOLFRAM; MIHOCI, IVA; ŠOUFEK, MARIN; STANIĆ-KOŠTROMAN, SVJETLANA; LELO, SUVAD; VITECEK, SIMON; WARINGER, JOHANN

    2016-01-01

    In this study we present morphological, molecular and ecological features of the last instar larvae of Drusus bosnicus with data about distribution of this species in Bosnia and Herzegovina. We also included are the most important diagnostic features enabling separation of larvae of D. bosnicus from larvae of the other European Drusinae and Trichoptera species. PMID:26249056

  12. Histopathological features predictive of a clinical diagnosis of ophthalmic granulomatosis with polyangiitis (GPA)

    PubMed Central

    Isa, Hazlita; Lightman, Sue; Luthert, Philip J; Rose, Geoffrey E; Verity, David H; Taylor, Simon RJ

    2012-01-01

    Background The limited form of Granulomatosis with Polyangiitis (GPA), formerly known as Wegener’s Granulomatosis (WG) primarily involves the head and neck region, including the orbit, but is often a diagnostic challenge, particularly as it commonly lacks positive anti-neutrophil cytoplasm antibody (ANCA) titres or classical features on diagnostic orbital biopsies. The purpose of this study was to relate biopsy findings with clinical outcome and to determine which histopathological features are predictive of a clinical diagnosis of GPA. Methods Retrospective case series of 234 patients identified from the database of the UCL Institute of Ophthalmology Department of Eye Pathology as having had orbital biopsies of orbital inflammatory disorders performed between 1988 and 2009. Clinical records were obtained for the patients and analysed to see whether patients had GPA or not, according to a standard set of diagnostic criteria (excluding any histopathological findings). Biopsy features were then correlated with the clinical diagnosis in univariate and multivariate analyses to determine factors predictive of GPA. Results Of the 234 patients, 36 were diagnosed with GPA and 198 with other orbital pathologies. The majority of biopsies were from orbital masses (47%). Histology showed a range of acute and chronic inflammatory pictures in all biopsies, but the presence of neutrophils (P<0.001), vasculitis (P<0.001), necrosis (P<0.001), eosinophils (P<0.02) and macrophages (P=0.05) were significantly associated with a later clinical diagnosis of GPA. In a multivariate analysis, only tissue neutrophils (OR=3.6, P=0.01) and vasculitis (OR=2.6, P=0.02) were independently associated with GPA, in contrast to previous reports associating eosinophils and necrosis with the diagnosis. Conclusions Neutrophil, eosinophil and macrophage infiltration of orbital tissues, together with vasculitis and necrosis, are all associated with a clinical diagnosis of GPA, but only neutrophil

  13. A data-driven feature extraction framework for predicting the severity of condition of congestive heart failure patients.

    PubMed

    Sideris, Costas; Alshurafa, Nabil; Pourhomayoun, Mohammad; Shahmohammadi, Farhad; Samy, Lauren; Sarrafzadeh, Majid

    2015-01-01

    In this paper, we propose a novel methodology for utilizing disease diagnostic information to predict severity of condition for Congestive Heart Failure (CHF) patients. Our methodology relies on a novel, clustering-based, feature extraction framework using disease diagnostic information. To reduce the dimensionality we identify disease clusters using cooccurence frequencies. We then utilize these clusters as features to predict patient severity of condition. We build our clustering and feature extraction algorithm using the 2012 National Inpatient Sample (NIS), Healthcare Cost and Utilization Project (HCUP) which contains 7 million discharge records and ICD-9-CM codes. The proposed framework is tested on Ronald Reagan UCLA Medical Center Electronic Health Records (EHR) from 3041 patients. We compare our cluster-based feature set with another that incorporates the Charlson comorbidity score as a feature and demonstrate an accuracy improvement of up to 14% in the predictability of the severity of condition. PMID:26736808

  14. Assessment of Ultrasound Features Predicting Axillary Nodal Metastasis in Breast Cancer: The Impact of Cortical Thickness

    PubMed Central

    Stachs, A.; Thi, A. Tra-Ha; Dieterich, M.; Stubert, J.; Hartmann, S.; Glass, Ä.; Reimer, T.; Gerber, B.

    2015-01-01

    Purpose: To evaluate the accuracy of axillary ultrasound (AUS) in detecting nodal metastasis in patients with early-stage breast cancer and to identify AUS features with high predictive power. Materials and Methods: Prospective single-center preliminary study in 105 patients with a primary diagnosis of breast cancer and clinically negative axilla. AUS was performed using a 12 MHz linear-array transducer before ultrasound-guided needle biopsy. Nodal characteristics (shape, longitudinal-transverse [LT] axis ratio, margins, cortical thickness, hyperechoic hilum) were correlated with histopathological nodal status after SLNB or axillary lymph node dissection (ALND). Results: Nodal metastases were present in 42/105 patients (40.0%). Univariate analyses showed that absence of hyperechoic hilum, round shape, LT axis ratio<2, sharp margins and cortical thickness>3 mm were associated with lymph node metastasis. Multivariate logistic regression analysis revealed cortical thickness > 3 mm as an independent predictive parameter for nodal involvement. Sensitivity, specificity, positive predictive value, negative predictive value and accuracy were 66.7, 74.6, 63.6, 77.0% and 71.4% respectively when cortical thickness > 3 mm was applied as the criterion for AUS positivity. Axillary tumor volume was low in patients with pT1/2 tumors and negative AUS, since only 3.2% of patients had > 2 metastatic lymph nodes. Conclusion: Cortical thickness>3 mm is a reliable predictor of nodal metastatic involvement. Negative AUS does not exclude lymph node metastases, but extensive axillary tumor volume is rare.

  15. Clinicopathologic and molecular features associated with patient age in gastric cancer

    PubMed Central

    Seo, Ji Yeon; Jin, Eun Hyo; Jo, Hyun Jin; Yoon, Hyuk; Shin, Cheol Min; Park, Young Soo; Kim, Nayoung; Jung, Hyun Chae; Lee, Dong Ho

    2015-01-01

    AIM: To compare characteristics and prognosis of gastric cancer based on age. METHODS: A retrospective study was conducted on clinical and molecular data from patients (n = 1658) with confirmed cases of gastric cancer in Seoul National University Bundang Hospital (Seoul, South Korea) from 2003 to 2010 after exclusion of patients diagnosed with lymphoma, gastrointestinal stromal tumor, and metastatic cancer in the stomach. DNA was isolated from tumor and adjacent normal tissue, and a set of five markers was amplified by polymerase chain reaction to assess microsatellite instability (MSI). MSI was categorized as high, low, or stable if ≥ 2, 1, or 0 markers, respectively, had changed. Immunohistochemistry was performed on tissue sections to detect levels of expression of p53, human epidermal growth factor receptor (HER)-2, and epidermal growth factor receptor. Statistical analysis of clinical and molecular data was performed to assess prognosis based on the stratification of patients by age (≤ 45 and > 45 years). RESULTS: Among the 1658 gastric cancer patients, the number of patients with an age ≤ 45 years was 202 (12.2%; 38.9 ± 0.4 years) and the number of patients > 45 years was 1456 (87.8%; 64.1 ± 0.3 years). Analyses revealed that females were predominant in the younger group (P < 0.001). Gastric cancers in the younger patients exhibited more aggressive features and were at a more advanced stage than those in older patients. Precancerous lesions, such as atrophic gastritis and intestinal metaplasia, were observed less frequently in the older than in the younger group (P < 0.001). Molecular characteristics, including overexpression of p53 (P < 0.001), overexpression of HER-2 (P = 0.006), and MSI (P = 0.006), were less frequent in gastric cancer of younger patients. Cancer related mortality was higher in younger patients (P = 0.048), but this difference was not significant after adjusting for the stage of cancer. CONCLUSION: Gastric cancer is distinguishable

  16. Predicting the Poaceae pollen season: six month-ahead forecasting and identification of relevant features

    NASA Astrophysics Data System (ADS)

    Navares, Ricardo; Aznarte, José Luis

    2016-09-01

    In this paper, we approach the problem of predicting the concentrations of Poaceae pollen which define the main pollination season in the city of Madrid. A classification-based approach, based on a computational intelligence model (random forests), is applied to forecast the dates in which risk concentration levels are to be observed. Unlike previous works, the proposal extends the range of forecasting horizons up to 6 months ahead. Furthermore, the proposed model allows to determine the most influential factors for each horizon, making no assumptions about the significance of the weather features. The performace of the proposed model proves it as a successful tool for allergy patients in preventing and minimizing the exposure to risky pollen concentrations and for researchers to gain a deeper insight on the factors driving the pollination season.

  17. Prediction of neurotoxins by support vector machine based on multiple feature vectors.

    PubMed

    Guang, Xuan-Min; Guo, Yan-Zhi; Wang, Xia; Li, Meng-Long

    2010-09-01

    Neurotoxin is a toxin which acts on nerve cells by interacting with membrane proteins. Different neurotoxins have different functions and sources. With much more knowledge of neurotoxins it would be greatly helpful for the development of drug design. The support vector machine (SVM) was used to predict the neurotoxin based on multiple feature vector descriptors, including the amino acid composition, length of the protein sequence, weight of the protein and the evolution information described by position specific scoring matrix (PSSM). After a five-fold cross-validation procedure, the method achieved an accuracy of 100% in discriminating neurotoxins from non-toxins. As for classifying neurotoxins based on their sources and functions, the accuracy was 99.50% and 99.38% respectively. At last, the method yielded a good performance in sub-classification of ion channels inhibitors with the total accuracy of 87.27%. These results indicate that this method outperforms previously described NTXpred method.

  18. Body Composition Features Predict Overall Survival in Patients With Hepatocellular Carcinoma

    PubMed Central

    Singal, Amit G; Zhang, Peng; Waljee, Akbar K; Ananthakrishnan, Lakshmi; Parikh, Neehar D; Sharma, Pratima; Barman, Pranab; Krishnamurthy, Venkataramu; Wang, Lu; Wang, Stewart C; Su, Grace L

    2016-01-01

    Objectives: Existing prognostic models for patients with hepatocellular carcinoma (HCC) have limitations. Analytic morphomics, a novel process to measure body composition using computational image-processing algorithms, may offer further prognostic information. The aim of this study was to develop and validate a prognostic model for HCC patients using body composition features and objective clinical information. Methods: Using computed tomography scans from a cohort of HCC patients at the VA Ann Arbor Healthcare System between January 2006 and December 2013, we developed a prognostic model using analytic morphomics and routine clinical data based on multivariate Cox regression and regularization methods. We assessed model performance using C-statistics and validated predicted survival probabilities. We validated model performance in an external cohort of HCC patients from Parkland Hospital, a safety-net health system in Dallas County. Results: The derivation cohort consisted of 204 HCC patients (20.1% Barcelona Clinic Liver Cancer classification (BCLC) 0/A), and the validation cohort had 225 patients (22.2% BCLC 0/A). The analytic morphomics model had good prognostic accuracy in the derivation cohort (C-statistic 0.80, 95% confidence interval (CI) 0.71–0.89) and external validation cohort (C-statistic 0.75, 95% CI 0.68–0.82). The accuracy of the analytic morphomics model was significantly higher than that of TNM and BCLC staging systems in derivation (P<0.001 for both) and validation (P<0.001 for both) cohorts. For calibration, mean absolute errors in predicted 1-year survival probabilities were 5.3% (90% quantile of 7.5%) and 7.6% (90% quantile of 12.5%) in the derivation and validation cohorts, respectively. Conclusion: Body composition features, combined with readily available clinical data, can provide valuable prognostic information for patients with newly diagnosed HCC. PMID:27228403

  19. Unveiling atomic-scale features of inherent heterogeneity in metallic glass by molecular dynamics simulations

    NASA Astrophysics Data System (ADS)

    Hu, Y. C.; Guan, P. F.; Li, M. Z.; Liu, C. T.; Yang, Y.; Bai, H. Y.; Wang, W. H.

    2016-06-01

    Heterogeneity is commonly believed to be intrinsic to metallic glasses (MGs). Nevertheless, how to distinguish and characterize the heterogeneity at the atomic level is still debated. Based on the extensive molecular dynamics simulations that combine isoconfigurational ensemble and atomic pinning methods, we directly reveal that MG contains flow units and the elastic matrix which can be well distinguished by their distinctive atomic-level responsiveness and mechanical performance. The microscopic features of the flow units, such as the shape, spatial distribution dimensionality, and correlation length, are characterized from atomic position analyses. Furthermore, the correlation between the flow units and the landscape of energy state, free volume, atomic-level stress, and especially the local bond orientational order parameter is discussed.

  20. Molecular effective coverage surface area of optical clearing agents for predicting optical clearing potential

    NASA Astrophysics Data System (ADS)

    Feng, Wei; Ma, Ning; Zhu, Dan

    2015-03-01

    The improvement of methods for optical clearing agent prediction exerts an important impact on tissue optical clearing technique. The molecular dynamic simulation is one of the most convincing and simplest approaches to predict the optical clearing potential of agents by analyzing the hydrogen bonds, hydrogen bridges and hydrogen bridges type forming between agents and collagen. However, the above analysis methods still suffer from some problem such as analysis of cyclic molecule by reason of molecular conformation. In this study, a molecular effective coverage surface area based on the molecular dynamic simulation was proposed to predict the potential of optical clearing agents. Several typical cyclic molecules, fructose, glucose and chain molecules, sorbitol, xylitol were analyzed by calculating their molecular effective coverage surface area, hydrogen bonds, hydrogen bridges and hydrogen bridges type, respectively. In order to verify this analysis methods, in vitro skin samples optical clearing efficacy were measured after 25 min immersing in the solutions, fructose, glucose, sorbitol and xylitol at concentration of 3.5 M using 1951 USAF resolution test target. The experimental results show accordance with prediction of molecular effective coverage surface area. Further to compare molecular effective coverage surface area with other parameters, it can show that molecular effective coverage surface area has a better performance in predicting OCP of agents.

  1. Somatic molecular changes and histo-pathological features of colorectal cancer in Tunisia

    PubMed Central

    Aissi, Sana; Buisine, Marie Pierre; Zerimech, Farid; Kourda, Nadia; Moussa, Amel; Manai, Mohamed; Porchet, Nicole

    2013-01-01

    AIM: To determine correlations between family history, clinical features and mutational status of genes involved in the progression of colorectal cancer (CRC). METHODS: Histo-pathological features and molecular changes [KRAS, BRAF and CTNNB1 genes mutations, microsatellite instability (MSI) phenotype, expression of mismatch repair (MMR) and mucin (MUC) 5AC proteins, mutation and expression analysis of TP53, MLH1 promoter hypermethylation analysis] were examined in a series of 51 unselected Tunisian CRC patients, 10 of them had a proven or probable hereditary disease, on the track of new tumoral markers for CRC susceptibility in Tunisian patients. RESULTS: As expected, MSI and MMR expression loss were associated to the presence of familial CRC (75% vs 9%, P < 0.001). However, no significant associations have been detected between personal or familial cancer history and KRAS (codons 12 and 13) or TP53 (exons 4-9) alterations. A significant inverse relationship has been observed between the presence of MSI and TP53 accumulation (10.0% vs 48.8%, P = 0.0335) in CRC tumors, suggesting different molecular pathways to CRC that in turn may reflect different environmental exposures. Interestingly, MUC5AC expression was significantly associated to the presence of MSI (46.7% vs 8.3%, P = 0.0039), MMR expression loss (46.7% vs 8.3%, P = 0.0039) and the presence of familial CRC (63% vs 23%, P = 0.039). CONCLUSION: These findings suggest that MUC5AC expression analysis may be useful in the screening of Tunisian patients with high risk of CRC. PMID:23983431

  2. Association of Fusobacterium nucleatum with clinical and molecular features in colorectal serrated pathway.

    PubMed

    Ito, Miki; Kanno, Shinichi; Nosho, Katsuhiko; Sukawa, Yasutaka; Mitsuhashi, Kei; Kurihara, Hiroyoshi; Igarashi, Hisayoshi; Takahashi, Taiga; Tachibana, Mami; Takahashi, Hiroaki; Yoshii, Shinji; Takenouchi, Toshinao; Hasegawa, Tadashi; Okita, Kenji; Hirata, Koichi; Maruyama, Reo; Suzuki, Hiromu; Imai, Kohzoh; Yamamoto, Hiroyuki; Shinomura, Yasuhisa

    2015-09-15

    Human gut microbiota is being increasingly recognized as a player in colorectal cancers (CRCs). Evidence suggests that Fusobacterium nucleatum (F. nucleatum) may contribute to disease progression and is associated with CpG island methylator phenotype (CIMP) and microsatellite instability (MSI) in CRCs; however, to date, there are no reports about the relationship between F. nucleatum and molecular features in the early stage of colorectal tumorigenesis. Therefore, we investigated the presence of F. nucleatum in premalignant colorectal lesions. In total, 465 premalignant lesions (343 serrated lesions and 122 non-serrated adenomas) and 511 CRCs were studied. We determined the presence of F. nucleatum and analyzed its association with molecular features including CIMP, MSI and microRNA-31 status. F. nucleatum was detected in 24% of hyperplastic polyps, 35% of sessile serrated adenomas (SSAs), 30% of traditional serrated adenomas (TSAs) and 33% of non-serrated adenomas. F. nucleatum was more frequently detected in CIMP-high premalignant lesions than in CIMP-low/zero lesions (p = 0.0023). In SSAs, F. nucleatum positivity increased gradually from sigmoid colon to cecum (p = 0.042). F. nucleatum positivity was significantly higher in CRCs (56%) than in premalignant lesions of any histological type (p < 0.0001). In conclusion, F. nucleatum was identified in premalignant colorectal lesions regardless of histopathology but was more frequently associated with CIMP-high lesions. Moreover, F. nucleatum positivity increased according to histological grade, suggesting that it may contribute to the progression of colorectal neoplasia. Our data also indicate that F. nucleatum positivity in SSAs may support the "colorectal continuum" concept.

  3. Molecular crosstalk between tumour and brain parenchyma instructs histopathological features in glioblastoma.

    PubMed

    Bougnaud, Sébastien; Golebiewska, Anna; Oudin, Anaïs; Keunen, Olivier; Harter, Patrick N; Mäder, Lisa; Azuaje, Francisco; Fritah, Sabrina; Stieber, Daniel; Kaoma, Tony; Vallar, Laurent; Brons, Nicolaas H C; Daubon, Thomas; Miletic, Hrvoje; Sundstrøm, Terje; Herold-Mende, Christel; Mittelbronn, Michel; Bjerkvig, Rolf; Niclou, Simone P

    2016-05-31

    The histopathological and molecular heterogeneity of glioblastomas represents a major obstacle for effective therapies. Glioblastomas do not develop autonomously, but evolve in a unique environment that adapts to the growing tumour mass and contributes to the malignancy of these neoplasms. Here, we show that patient-derived glioblastoma xenografts generated in the mouse brain from organotypic spheroids reproducibly give rise to three different histological phenotypes: (i) a highly invasive phenotype with an apparent normal brain vasculature, (ii) a highly angiogenic phenotype displaying microvascular proliferation and necrosis and (iii) an intermediate phenotype combining features of invasion and vessel abnormalities. These phenotypic differences were visible during early phases of tumour development suggesting an early instructive role of tumour cells on the brain parenchyma. Conversely, we found that tumour-instructed stromal cells differentially influenced tumour cell proliferation and migration in vitro, indicating a reciprocal crosstalk between neoplastic and non-neoplastic cells. We did not detect any transdifferentiation of tumour cells into endothelial cells. Cell type-specific transcriptomic analysis of tumour and endothelial cells revealed a strong phenotype-specific molecular conversion between the two cell types, suggesting co-evolution of tumour and endothelial cells. Integrative bioinformatic analysis confirmed the reciprocal crosstalk between tumour and microenvironment and suggested a key role for TGFβ1 and extracellular matrix proteins as major interaction modules that shape glioblastoma progression. These data provide novel insight into tumour-host interactions and identify novel stroma-specific targets that may play a role in combinatorial treatment strategies against glioblastoma.

  4. Molecular features contributing to the lower viscosity of phosphonium ionic liquids compared to their ammonium analogues.

    PubMed

    Scarbath-Evers, Laura Katharina; Hunt, Patricia A; Kirchner, Barbara; MacFarlane, Douglas R; Zahn, Stefan

    2015-08-21

    Molecular features contributing to the lower viscosity of phosphonium based ionic liquids (ILs) compared to ammonium based ILs are investigated by static quantum chemistry calculations and classical molecular dynamics simulations. The larger bond distance and the higher flexibility of bond angles and dihedral angles in the phosphonium compounds tend to reduce their viscosity compared to ammonium analogues, while the strongly localized charge at the central atom has the opposite effect. Fast translational ion dynamics is also found to be related to a short counter-ion association lifetime in the investigated compounds. Furthermore, a weak structuring between the center of charges also seems to increase mobility. Interestingly, the order of ion pair interaction energies in the gas phase is reversed compared to the order of counter-ion association lifetimes in the liquid, which highlights the important role of solvation in ILs. Overall, the higher flexibility of the bond and dihedral angles of the phosphonium compounds appears to be the most important factor in producing the lower viscosity of these ILs compared to their ammonium analogues.

  5. Beyond intensity: Spectral features effectively predict music-induced subjective arousal.

    PubMed

    Gingras, Bruno; Marin, Manuela M; Fitch, W Tecumseh

    2014-01-01

    Emotions in music are conveyed by a variety of acoustic cues. Notably, the positive association between sound intensity and arousal has particular biological relevance. However, although amplitude normalization is a common procedure used to control for intensity in music psychology research, direct comparisons between emotional ratings of original and amplitude-normalized musical excerpts are lacking. In this study, 30 nonmusicians retrospectively rated the subjective arousal and pleasantness induced by 84 six-second classical music excerpts, and an additional 30 nonmusicians rated the same excerpts normalized for amplitude. Following the cue-redundancy and Brunswik lens models of acoustic communication, we hypothesized that arousal and pleasantness ratings would be similar for both versions of the excerpts, and that arousal could be predicted effectively by other acoustic cues besides intensity. Although the difference in mean arousal and pleasantness ratings between original and amplitude-normalized excerpts correlated significantly with the amplitude adjustment, ratings for both sets of excerpts were highly correlated and shared a similar range of values, thus validating the use of amplitude normalization in music emotion research. Two acoustic parameters, spectral flux and spectral entropy, accounted for 65% of the variance in arousal ratings for both sets, indicating that spectral features can effectively predict arousal. Additionally, we confirmed that amplitude-normalized excerpts were adequately matched for loudness. Overall, the results corroborate our hypotheses and support the cue-redundancy and Brunswik lens models.

  6. Beyond intensity: Spectral features effectively predict music-induced subjective arousal.

    PubMed

    Gingras, Bruno; Marin, Manuela M; Fitch, W Tecumseh

    2014-01-01

    Emotions in music are conveyed by a variety of acoustic cues. Notably, the positive association between sound intensity and arousal has particular biological relevance. However, although amplitude normalization is a common procedure used to control for intensity in music psychology research, direct comparisons between emotional ratings of original and amplitude-normalized musical excerpts are lacking. In this study, 30 nonmusicians retrospectively rated the subjective arousal and pleasantness induced by 84 six-second classical music excerpts, and an additional 30 nonmusicians rated the same excerpts normalized for amplitude. Following the cue-redundancy and Brunswik lens models of acoustic communication, we hypothesized that arousal and pleasantness ratings would be similar for both versions of the excerpts, and that arousal could be predicted effectively by other acoustic cues besides intensity. Although the difference in mean arousal and pleasantness ratings between original and amplitude-normalized excerpts correlated significantly with the amplitude adjustment, ratings for both sets of excerpts were highly correlated and shared a similar range of values, thus validating the use of amplitude normalization in music emotion research. Two acoustic parameters, spectral flux and spectral entropy, accounted for 65% of the variance in arousal ratings for both sets, indicating that spectral features can effectively predict arousal. Additionally, we confirmed that amplitude-normalized excerpts were adequately matched for loudness. Overall, the results corroborate our hypotheses and support the cue-redundancy and Brunswik lens models. PMID:24215647

  7. Search performance is better predicted by tileability than presence of a unique basic feature

    PubMed Central

    Chang, Honghua; Rosenholtz, Ruth

    2016-01-01

    Traditional models of visual search such as feature integration theory (FIT; Treisman & Gelade, 1980), have suggested that a key factor determining task difficulty consists of whether or not the search target contains a “basic feature” not found in the other display items (distractors). Here we discriminate between such traditional models and our recent texture tiling model (TTM) of search (Rosenholtz, Huang, Raj, Balas, & Ilie, 2012b), by designing new experiments that directly pit these models against each other. Doing so is nontrivial, for two reasons. First, the visual representation in TTM is fully specified, and makes clear testable predictions, but its complexity makes getting intuitions difficult. Here we elucidate a rule of thumb for TTM, which enables us to easily design new and interesting search experiments. FIT, on the other hand, is somewhat ill-defined and hard to pin down. To get around this, rather than designing totally new search experiments, we start with five classic experiments that FIT already claims to explain: T among Ls, 2 among 5s, Q among Os, O among Qs, and an orientation/luminance-contrast conjunction search. We find that fairly subtle changes in these search tasks lead to significant changes in performance, in a direction predicted by TTM, providing definitive evidence in favor of the texture tiling model as opposed to traditional views of search. PMID:27548090

  8. Spatial Habitat Features Derived from Multiparametric Magnetic Resonance Imaging Data Are Associated with Molecular Subtype and 12-Month Survival Status in Glioblastoma Multiforme

    PubMed Central

    Lee, Joonsang; Narang, Shivali; Martinez, Juan; Rao, Ganesh; Rao, Arvind

    2015-01-01

    One of the most common and aggressive malignant brain tumors is Glioblastoma multiforme. Despite the multimodality treatment such as radiation therapy and chemotherapy (temozolomide: TMZ), the median survival rate of glioblastoma patient is less than 15 months. In this study, we investigated the association between measures of spatial diversity derived from spatial point pattern analysis of multiparametric magnetic resonance imaging (MRI) data with molecular status as well as 12-month survival in glioblastoma. We obtained 27 measures of spatial proximity (diversity) via spatial point pattern analysis of multiparametric T1 post-contrast and T2 fluid-attenuated inversion recovery MRI data. These measures were used to predict 12-month survival status (≤12 or >12 months) in 74 glioblastoma patients. Kaplan-Meier with receiver operating characteristic analyses was used to assess the relationship between derived spatial features and 12-month survival status as well as molecular subtype status in patients with glioblastoma. Kaplan-Meier survival analysis revealed that 14 spatial features were capable of stratifying overall survival in a statistically significant manner. For prediction of 12-month survival status based on these diversity indices, sensitivity and specificity were 0.86 and 0.64, respectively. The area under the receiver operating characteristic curve and the accuracy were 0.76 and 0.75, respectively. For prediction of molecular subtype status, proneural subtype shows highest accuracy of 0.93 among all molecular subtypes based on receiver operating characteristic analysis. We find that measures of spatial diversity from point pattern analysis of intensity habitats from T1 post-contrast and T2 fluid-attenuated inversion recovery images are associated with both tumor subtype status and 12-month survival status and may therefore be useful indicators of patient prognosis, in addition to providing potential guidance for molecularly-targeted therapies in

  9. Wetland features and landscape context predict the risk of wetland habitat loss.

    PubMed

    Gutzwiller, Kevin J; Flather, Curtis H

    2011-04-01

    Wetlands generally provide significant ecosystem services and function as important harbors of biodiversity. To ensure that these habitats are conserved, an efficient means of identifying wetlands at risk of conversion is needed, especially in the southern United States where the rate of wetland loss has been highest in recent decades. We used multivariate adaptive regression splines to develop a model to predict the risk of wetland habitat loss as a function of wetland features and landscape context. Fates of wetland habitats from 1992 to 1997 were obtained from the National Resources Inventory for the U.S. Forest Service's Southern Region, and land-cover data were obtained from the National Land Cover Data. We randomly selected 70% of our 40 617 observations to build the model (n = 28 432), and randomly divided the remaining 30% of the data into five Test data sets (n = 2437 each). The wetland and landscape variables that were important in the model, and their relative contributions to the model's predictive ability (100 = largest, 0 = smallest), were land-cover/ land-use of the surrounding landscape (100.0), size and proximity of development patches within 570 m (39.5), land ownership (39.1), road density within 570 m (37.5), percent woody and herbaceous wetland cover within 570 m (27.8), size and proximity of development patches within 5130 m (25.7), percent grasslands/herbaceous plants and pasture/hay cover within 5130 m (21.7), wetland type (21.2), and percent woody and herbaceous wetland cover within 1710 m (16.6). For the five Test data sets, Kappa statistics (0.40, 0.50, 0.52, 0.55, 0.56; P < 0.0001), area-under-the-receiver-operating-curve (AUC) statistics (0.78, 0.82, 0.83, 0.83, 0.84; P < 0.0001), and percent correct prediction of wetland habitat loss (69.1, 80.4, 81.7, 82.3, 83.1) indicated the model generally had substantial predictive ability across the South. Policy analysts and land-use planners can use the model and associated maps to prioritize

  10. Clinical, Pathological, and Molecular Features of Lung Adenocarcinomas with AXL Expression

    PubMed Central

    Suda, Kenichi; Shimizu, Shigeki; Sakai, Kazuko; Mizuuchi, Hiroshi; Tomizawa, Kenji; Takemoto, Toshiki; Nishio, Kazuto; Mitsudomi, Tetsuya

    2016-01-01

    The receptor tyrosine kinase AXL is a member of the Tyro3-Axl-Mer receptor tyrosine kinase subfamily. AXL affects several cellular functions, including growth and migration. AXL aberration is reportedly a marker for poor prognosis and treatment resistance in various cancers. In this study, we analyzed clinical, pathological, and molecular features of AXL expression in lung adenocarcinomas (LADs). We examined 161 LAD specimens from patients who underwent pulmonary resections. When AXL protein expression was quantified (0, 1+, 2+, 3+) according to immunohistochemical staining intensity, results were 0: 35%; 1+: 20%; 2+: 37%; and 3+: 7% for the 161 samples. AXL expression status did not correlate with clinical features, including smoking status and pathological stage. However, patients whose specimens showed strong AXL expression (3+) had markedly poorer prognoses than other groups (P = 0.0033). Strong AXL expression was also significantly associated with downregulation of E-cadherin (P = 0.025) and CD44 (P = 0.0010). In addition, 9 of 12 specimens with strong AXL expression had driver gene mutations (6 with EGFR, 2 with KRAS, 1 with ALK). In conclusion, we found that strong AXL expression in surgically resected LADs was a predictor of poor prognosis. LADs with strong AXL expression were characterized by mesenchymal status, higher expression of stem-cell-like markers, and frequent driver gene mutations. PMID:27100677

  11. Evaluating stability of histomorphometric features across scanner and staining variations: predicting biochemical recurrence from prostate cancer whole slide images

    NASA Astrophysics Data System (ADS)

    Leo, Patrick; Lee, George; Madabhushi, Anant

    2016-03-01

    Quantitative histomorphometry (QH) is the process of computerized extraction of features from digitized tissue slide images. Typically these features are used in machine learning classifiers to predict disease presence, behavior and outcome. Successful robust classifiers require features that both discriminate between classes of interest and are stable across data from multiple sites. Feature stability may be compromised by variation in slide staining and scanning procedures. These laboratory specific variables include dye batch, slice thickness and the whole slide scanner used to digitize the slide. The key therefore is to be able to identify features that are not only discriminating between the classes of interest (e.g. cancer and non-cancer or biochemical recurrence and non- recurrence) but also features that will not wildly fluctuate on slides representing the same tissue class but from across multiple different labs and sites. While there has been some recent efforts at understanding feature stability in the context of radiomics applications (i.e. feature analysis of radiographic images), relatively few attempts have been made at studying the trade-off between feature stability and discriminability for histomorphometric and digital pathology applications. In this paper we present two new measures, preparation-induced instability score (PI) and latent instability score (LI), to quantify feature instability across and within datasets. Dividing PI by LI yields a ratio for how often a feature for a specific tissue class (e.g. low grade prostate cancer) is different between datasets from different sites versus what would be expected from random chance alone. Using this ratio we seek to quantify feature vulnerability to variations in slide preparation and digitization. Since our goal is to identify stable QH features we evaluate these features for their stability and thus inclusion in machine learning based classifiers in a use case involving prostate cancer

  12. Neuroendocrine Tumors of the Large Intestine: Clinicopathological Features and Predictive Factors of Lymph Node Metastasis.

    PubMed

    Kojima, Motohiro; Ikeda, Koji; Saito, Norio; Sakuyama, Naoki; Koushi, Kenichi; Kawano, Shingo; Watanabe, Toshiaki; Sugihara, Kenichi; Ito, Masaaki; Ochiai, Atsushi

    2016-01-01

    A new histological classification of neuroendocrine tumors (NETs) was established in WHO 2010. ENET and NCCN proposed treatment algorithms for colorectal NET. Retrospective study of NET of the large intestine (colorectal and appendiceal NET) was performed among institutions allied with the Japanese Society for Cancer of the Colon and Rectum, and 760 neuroendocrine tumors from 2001 to 2011 were re-assessed using WHO 2010 criteria to elucidate the clinicopathological features of NET in the large intestine. Next, the clinicopathological relationship with lymph node metastasis was analyzed to predict lymph node metastasis in locally resected rectal NET. The primary site was rectum in 718/760 cases (94.5%), colon in 30/760 cases (3.9%), and appendix in 12/760 cases (1.6%). Patients were predominantly men (61.6%) with a mean age of 58.7 years. Tumor size was <10 mm in 65.4% of cases. Proportions of NET G1, G2, G3, and mixed adeno-neuroendocrine carcinoma (MANEC) were 88.4, 6.3, 3.9, and 1.3%, respectively. Of the 760 tumors, 468 were locally resected, and 292 were surgically resected with lymph node dissection. Rectal NET showed a higher proportion of NET G1, and colonic and appendiceal NET was more commonly G3 and MANEC. Of the 292 surgically resected cases, 233 NET G1 and G2 located in the rectum were used for the prediction of lymph node metastasis. Lymphatic and blood vessel invasion were independent predictive factors of lymph node metastasis. NET G2 cases showed more frequent lymph node metastasis than that seen in NET G1 cases, but this was not an independent predictor of lymph node metastasis. Of the 98 surgically resected cases <10 mm in size, we found 9 cases with lymph node metastasis (9.2%). All cases were NET G1, and eight of the nine cases were positive either for lymphatic invasion or blood vessel invasion. Using the WHO classification, we found NET in the large intestine showed a tumor-site-dependent variety of histological and clinicopathological

  13. Neuroendocrine Tumors of the Large Intestine: Clinicopathological Features and Predictive Factors of Lymph Node Metastasis.

    PubMed

    Kojima, Motohiro; Ikeda, Koji; Saito, Norio; Sakuyama, Naoki; Koushi, Kenichi; Kawano, Shingo; Watanabe, Toshiaki; Sugihara, Kenichi; Ito, Masaaki; Ochiai, Atsushi

    2016-01-01

    A new histological classification of neuroendocrine tumors (NETs) was established in WHO 2010. ENET and NCCN proposed treatment algorithms for colorectal NET. Retrospective study of NET of the large intestine (colorectal and appendiceal NET) was performed among institutions allied with the Japanese Society for Cancer of the Colon and Rectum, and 760 neuroendocrine tumors from 2001 to 2011 were re-assessed using WHO 2010 criteria to elucidate the clinicopathological features of NET in the large intestine. Next, the clinicopathological relationship with lymph node metastasis was analyzed to predict lymph node metastasis in locally resected rectal NET. The primary site was rectum in 718/760 cases (94.5%), colon in 30/760 cases (3.9%), and appendix in 12/760 cases (1.6%). Patients were predominantly men (61.6%) with a mean age of 58.7 years. Tumor size was <10 mm in 65.4% of cases. Proportions of NET G1, G2, G3, and mixed adeno-neuroendocrine carcinoma (MANEC) were 88.4, 6.3, 3.9, and 1.3%, respectively. Of the 760 tumors, 468 were locally resected, and 292 were surgically resected with lymph node dissection. Rectal NET showed a higher proportion of NET G1, and colonic and appendiceal NET was more commonly G3 and MANEC. Of the 292 surgically resected cases, 233 NET G1 and G2 located in the rectum were used for the prediction of lymph node metastasis. Lymphatic and blood vessel invasion were independent predictive factors of lymph node metastasis. NET G2 cases showed more frequent lymph node metastasis than that seen in NET G1 cases, but this was not an independent predictor of lymph node metastasis. Of the 98 surgically resected cases <10 mm in size, we found 9 cases with lymph node metastasis (9.2%). All cases were NET G1, and eight of the nine cases were positive either for lymphatic invasion or blood vessel invasion. Using the WHO classification, we found NET in the large intestine showed a tumor-site-dependent variety of histological and clinicopathological

  14. Computer extracted texture features on T2w MRI to predict biochemical recurrence following radiation therapy for prostate cancer

    NASA Astrophysics Data System (ADS)

    Ginsburg, Shoshana B.; Rusu, Mirabela; Kurhanewicz, John; Madabhushi, Anant

    2014-03-01

    In this study we explore the ability of a novel machine learning approach, in conjunction with computer-extracted features describing prostate cancer morphology on pre-treatment MRI, to predict whether a patient will develop biochemical recurrence within ten years of radiation therapy. Biochemical recurrence, which is characterized by a rise in serum prostate-specific antigen (PSA) of at least 2 ng/mL above the nadir PSA, is associated with increased risk of metastasis and prostate cancer-related mortality. Currently, risk of biochemical recurrence is predicted by the Kattan nomogram, which incorporates several clinical factors to predict the probability of recurrence-free survival following radiation therapy (but has limited prediction accuracy). Semantic attributes on T2w MRI, such as the presence of extracapsular extension and seminal vesicle invasion and surrogate measure- ments of tumor size, have also been shown to be predictive of biochemical recurrence risk. While the correlation between biochemical recurrence and factors like tumor stage, Gleason grade, and extracapsular spread are well- documented, it is less clear how to predict biochemical recurrence in the absence of extracapsular spread and for small tumors fully contained in the capsule. Computer{extracted texture features, which quantitatively de- scribe tumor micro-architecture and morphology on MRI, have been shown to provide clues about a tumor's aggressiveness. However, while computer{extracted features have been employed for predicting cancer presence and grade, they have not been evaluated in the context of predicting risk of biochemical recurrence. This work seeks to evaluate the role of computer-extracted texture features in predicting risk of biochemical recurrence on a cohort of sixteen patients who underwent pre{treatment 1.5 Tesla (T) T2w MRI. We extract a combination of first-order statistical, gradient, co-occurrence, and Gabor wavelet features from T2w MRI. To identify which of these

  15. Predicting hot spots in protein interfaces based on protrusion index, pseudo hydrophobicity and electron-ion interaction pseudopotential features

    PubMed Central

    Xia, Junfeng; Yue, Zhenyu; Di, Yunqiang; Zhu, Xiaolei; Zheng, Chun-Hou

    2016-01-01

    The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming more important for the research of drug design and cancer development. Based on our previous methods (APIS and KFC2), here we proposed a novel hot spot prediction method. For each hot spot residue, we firstly constructed a wide variety of 108 sequence, structural, and neighborhood features to characterize potential hot spot residues, including conventional ones and new one (pseudo hydrophobicity) exploited in this study. We then selected 3 top-ranking features that contribute the most in the classification by a two-step feature selection process consisting of minimal-redundancy-maximal-relevance algorithm and an exhaustive search method. We used support vector machines to build our final prediction model. When testing our model on an independent test set, our method showed the highest F1-score of 0.70 and MCC of 0.46 comparing with the existing state-of-the-art hot spot prediction methods. Our results indicate that these features are more effective than the conventional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spots in protein interfaces. PMID:26934646

  16. Predicting hot spots in protein interfaces based on protrusion index, pseudo hydrophobicity and electron-ion interaction pseudopotential features.

    PubMed

    Xia, Junfeng; Yue, Zhenyu; Di, Yunqiang; Zhu, Xiaolei; Zheng, Chun-Hou

    2016-04-01

    The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming more important for the research of drug design and cancer development. Based on our previous methods (APIS and KFC2), here we proposed a novel hot spot prediction method. For each hot spot residue, we firstly constructed a wide variety of 108 sequence, structural, and neighborhood features to characterize potential hot spot residues, including conventional ones and new one (pseudo hydrophobicity) exploited in this study. We then selected 3 top-ranking features that contribute the most in the classification by a two-step feature selection process consisting of minimal-redundancy-maximal-relevance algorithm and an exhaustive search method. We used support vector machines to build our final prediction model. When testing our model on an independent test set, our method showed the highest F1-score of 0.70 and MCC of 0.46 comparing with the existing state-of-the-art hot spot prediction methods. Our results indicate that these features are more effective than the conventional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spots in protein interfaces.

  17. Predicting hot spots in protein interfaces based on protrusion index, pseudo hydrophobicity and electron-ion interaction pseudopotential features.

    PubMed

    Xia, Junfeng; Yue, Zhenyu; Di, Yunqiang; Zhu, Xiaolei; Zheng, Chun-Hou

    2016-04-01

    The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming more important for the research of drug design and cancer development. Based on our previous methods (APIS and KFC2), here we proposed a novel hot spot prediction method. For each hot spot residue, we firstly constructed a wide variety of 108 sequence, structural, and neighborhood features to characterize potential hot spot residues, including conventional ones and new one (pseudo hydrophobicity) exploited in this study. We then selected 3 top-ranking features that contribute the most in the classification by a two-step feature selection process consisting of minimal-redundancy-maximal-relevance algorithm and an exhaustive search method. We used support vector machines to build our final prediction model. When testing our model on an independent test set, our method showed the highest F1-score of 0.70 and MCC of 0.46 comparing with the existing state-of-the-art hot spot prediction methods. Our results indicate that these features are more effective than the conventional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spots in protein interfaces. PMID:26934646

  18. Molecular modelling: An analytical tool with a predictive character for investigating reactivity in molten salt media.

    NASA Astrophysics Data System (ADS)

    Picard, Gérard S.; Bouyer, Frédéric C.

    1995-04-01

    Possibilities offered by Molecular Modelling for studying homogeneous and interfacial processes and reactions in melts are discussed. A few typical illustrative examples covering some of the main research fields of molten salt chemistry and electrochemistry are given. Quantum chemistry calculations, Molecular Dynamics and Monte Carlo methods appear to be fantastic tools for analyzing and predicting reactivity in molten salts.

  19. Anion pairs in room temperature ionic liquids predicted by molecular dynamics simulation, verified by spectroscopic characterization

    SciTech Connect

    Schwenzer, Birgit; Kerisit, Sebastien N.; Vijayakumar, M.

    2014-01-01

    Molecular-level spectroscopic analyses of an aprotic and a protic room-temperature ionic liquid, BMIM OTf and BMIM HSO4, respectively, have been carried out with the aim of verifying molecular dynamics simulations that predict anion pair formation in these fluid structures. Fourier-transform infrared spectroscopy, Raman spectroscopy and nuclear magnetic resonance spectroscopy of various nuclei support the theoretically-determined average molecular arrangements.

  20. Predicting the biomechanical strength of proximal femur specimens with bone mineral density features and support vector regression

    NASA Astrophysics Data System (ADS)

    Huber, Markus B.; Yang, Chien-Chun; Carballido-Gamio, Julio; Bauer, Jan S.; Baum, Thomas; Nagarajan, Mahesh B.; Eckstein, Felix; Lochmüller, Eva; Majumdar, Sharmila; Link, Thomas M.; Wismüller, Axel

    2012-03-01

    To improve the clinical assessment of osteoporotic hip fracture risk, recent computer-aided diagnosis systems explore new approaches to estimate the local trabecular bone quality beyond bone density alone to predict femoral bone strength. In this context, statistical bone mineral density (BMD) features extracted from multi-detector computed tomography (MDCT) images of proximal femur specimens and different function approximations methods were compared in their ability to predict the biomechanical strength. MDCT scans were acquired in 146 proximal femur specimens harvested from human cadavers. The femurs' failure load (FL) was determined through biomechanical testing. An automated volume of interest (VOI)-fitting algorithm was used to define a consistent volume in the femoral head of each specimen. In these VOIs, the trabecular bone was represented by statistical moments of the BMD distribution and by pairwise spatial occurrence of BMD values using the gray-level co-occurrence (GLCM) approach. A linear multi-regression analysis (MultiReg) and a support vector regression algorithm with a linear kernel (SVRlin) were used to predict the FL from the image feature sets. The prediction performance was measured by the root mean square error (RMSE) for each image feature on independent test sets; in addition the coefficient of determination R2 was calculated. The best prediction result was obtained with a GLCM feature set using SVRlin, which had the lowest prediction error (RSME = 1.040+/-0.143, R2 = 0.544) and which was significantly lower that the standard approach of using BMD.mean and MultiReg (RSME = 1.093+/-0.133, R2 = 0.490, p<0.0001). The combined sets including BMD.mean and GLCM features had a similar or slightly lower performance than using only GLCM features. The results indicate that the performance of high-dimensional BMD features extracted from MDCT images in predicting the biomechanical strength of proximal femur specimens can be significantly improved by

  1. Attentional Selection Can Be Predicted by Reinforcement Learning of Task-relevant Stimulus Features Weighted by Value-independent Stickiness.

    PubMed

    Balcarras, Matthew; Ardid, Salva; Kaping, Daniel; Everling, Stefan; Womelsdorf, Thilo

    2016-02-01

    Attention includes processes that evaluate stimuli relevance, select the most relevant stimulus against less relevant stimuli, and bias choice behavior toward the selected information. It is not clear how these processes interact. Here, we captured these processes in a reinforcement learning framework applied to a feature-based attention task that required macaques to learn and update the value of stimulus features while ignoring nonrelevant sensory features, locations, and action plans. We found that value-based reinforcement learning mechanisms could account for feature-based attentional selection and choice behavior but required a value-independent stickiness selection process to explain selection errors while at asymptotic behavior. By comparing different reinforcement learning schemes, we found that trial-by-trial selections were best predicted by a model that only represents expected values for the task-relevant feature dimension, with nonrelevant stimulus features and action plans having only a marginal influence on covert selections. These findings show that attentional control subprocesses can be described by (1) the reinforcement learning of feature values within a restricted feature space that excludes irrelevant feature dimensions, (2) a stochastic selection process on feature-specific value representations, and (3) value-independent stickiness toward previous feature selections akin to perseveration in the motor domain. We speculate that these three mechanisms are implemented by distinct but interacting brain circuits and that the proposed formal account of feature-based stimulus selection will be important to understand how attentional subprocesses are implemented in primate brain networks.

  2. Identification of critical chemical features for Aurora kinase-B inhibitors using Hip-Hop, virtual screening and molecular docking

    NASA Astrophysics Data System (ADS)

    Sakkiah, Sugunadevi; Thangapandian, Sundarapandian; John, Shalini; Lee, Keun Woo

    2011-01-01

    This study was performed to find the selective chemical features for Aurora kinase-B inhibitors using the potent methods like Hip-Hop, virtual screening, homology modeling, molecular dynamics and docking. The best hypothesis, Hypo1 was validated toward a wide range of test set containing the selective inhibitors of Aurora kinase-B. Homology modeling and molecular dynamics studies were carried out to perform the molecular docking studies. The best hypothesis Hypo1 was used as a 3D query to screen the chemical databases. The screened molecules from the databases were sorted based on ADME and drug like properties. The selective hit compounds were docked and the hydrogen bond interactions with the critical amino acids present in Aurora kinase-B were compared with the chemical features present in the Hypo1. Finally, we suggest that the chemical features present in the Hypo1 are vital for a molecule to inhibit the Aurora kinase-B activity.

  3. BCR-ABL-positive acute myeloid leukemia: a new entity? Analysis of clinical and molecular features.

    PubMed

    Neuendorff, Nina Rosa; Burmeister, Thomas; Dörken, Bernd; Westermann, Jörg

    2016-08-01

    BCR-ABL-positive acute myeloid leukemia (AML) is a rare subtype of AML that is now included as a provisional entity in the 2016 revised WHO classification of myeloid malignancies. Since a clear distinction between de novo BCR-ABL+ AML and chronic myeloid leukemia (CML) blast crisis is challenging in many cases, the existence of de novo BCR-ABL+ AML has been a matter of debate for a long time. However, there is increasing evidence suggesting that BCR-ABL+ AML is in fact a distinct subgroup of AML. In this study, we analyzed all published cases since 1975 as well as cases from our institution in order to present common clinical and molecular features of this rare disease. Our analysis shows that BCR-ABL predominantly occurs in AML-NOS, CBF leukemia, and AML with myelodysplasia-related changes. The most common BCR-ABL transcripts (p190 and p210) are nearly equally distributed. Based on the analysis of published data, we provide a clinical algorithm for the initial differential diagnosis of BCR-ABL+ AML. The prognosis of BCR-ABL+ AML seems to depend on the cytogenetic and/or molecular background rather than on BCR-ABL itself. A therapy with tyrosine kinase inhibitors (TKIs) such as imatinib, dasatinib, or nilotinib is reasonable, but-due to a lack of systematic clinical data-their use cannot be routinely recommended in first-line therapy. Beyond first-line treatment of AML, the use of TKI remains an individual decision, both in combination with intensive chemotherapy and/or as a bridge to allogeneic stem cell transplantation. In each single case, potential benefits have to be weighed against potential risks. PMID:27297971

  4. Features of exciton dynamics in molecular nanoclusters (J-aggregates): Exciton self-trapping (Review Article)

    NASA Astrophysics Data System (ADS)

    Malyukin, Yu. V.; Sorokin, A. V.; Semynozhenko, V. P.

    2016-06-01

    We present thoroughly analyzed experimental results that demonstrate the anomalous manifestation of the exciton self-trapping effect, which is already well-known in bulk crystals, in ordered molecular nanoclusters called J-aggregates. Weakly-coupled one-dimensional (1D) molecular chains are the main structural feature of J-aggregates, wherein the electron excitations are manifested as 1D Frenkel excitons. According to the continuum theory of Rashba-Toyozawa, J-aggregates can have only self-trapped excitons, because 1D excitons must adhere to barrier-free self-trapping at any exciton-phonon coupling constant g = ɛLR/2β, wherein ɛLR is the lattice relaxation energy, and 2β is the half-width of the exciton band. In contrast, very often only the luminescence of free, mobile excitons would manifest in experiments involving J-aggregates. Using the Urbach rule in order to analyze the low-frequency region of the low-temperature exciton absorption spectra has shown that J-aggregates can have both a weak (g < 1) and a strong (g > 1) exciton-phonon coupling. Moreover, it is experimentally demonstrated that under certain conditions, the J-aggregate excited state can have both free and self-trapped excitons, i.e., we establish the existence of a self-trapping barrier for 1D Frenkel excitons. We demonstrate and analyze the reasons behind the anomalous existence of both free and self-trapped excitons in J-aggregates, and demonstrate how exciton-self trapping efficiency can be managed in J-aggregates by varying the values of g, which is fundamentally impossible in bulk crystals. We discuss how the exciton-self trapping phenomenon can be used as an alternate interpretation of the wide band emission of some J-aggregates, which has thus far been explained by the strongly localized exciton model.

  5. STAT3 Expression, Molecular Features, Inflammation Patterns and Prognosis in a Database of 724 Colorectal Cancers

    PubMed Central

    Morikawa, Teppei; Baba, Yoshifumi; Yamauchi, Mai; Kuchiba, Aya; Nosho, Katsuhiko; Shima, Kaori; Tanaka, Noriko; Huttenhower, Curtis; Frank, David A.; Fuchs, Charles S.; Ogino, Shuji

    2010-01-01

    Purpose STAT3 (signal transducer and activator of transcription 3) is a transcription factor that is constitutively activated in some cancers. STAT3 appears to play crucial roles in cell proliferation and survival, angiogenesis, tumor-promoting inflammation and suppression of anti-tumor host immune response in the tumor microenvironment. Although the STAT3 signaling pathway is a potential drug target, clinical, pathologic, molecular or prognostic features of STAT3-activated colorectal cancer remain uncertain. Experimental Design Utilizing a database of 724 colon and rectal cancer cases, we evaluated phosphorylated STAT3 (p-STAT3) expression by immunohistochemistry. Cox proportional hazards model was used to compute mortality hazard ratio (HR), adjusting for clinical, pathologic and molecular features, including microsatellite instability (MSI), the CpG island methylator phenotype (CIMP), LINE-1 methylation, 18q loss of heterozygosity, TP53 (p53), CTNNB1 (β-catenin), JC virus T-antigen, and KRAS, BRAF, and PIK3CA mutations. Results Among the 724 tumors, 131 (18%) showed high-level p-STAT3 expression (p-STAT3-high), 244 (34%) showed low-level expression (p-STAT3-low), and the remaining 349 (48%) were negative for p-STAT3. p-STAT3 overexpression was associated with significantly higher colorectal cancer-specific mortality [log-rank p=0.0020; univariate HR (p-STAT3-high vs. p-STAT3-negative) 1.85, 95% confidence interval (CI) 1.30–2.63, Ptrend =0.0005; multivariate HR, 1.61, 95% CI 1.11–2.34, Ptrend =0.015). p-STAT3 expression was positively associated with peritumoral lymphocytic reaction (multivariate odds ratio 3.23; 95% CI, 1.89–5.53; p<0.0001). p-STAT3 expression was not associated with MSI, CIMP, or LINE-1 hypomethylation. Conclusions STAT3 activation in colorectal cancer is associated with adverse clinical outcome, supporting its potential roles as a prognostic biomarker and a chemoprevention and/or therapeutic target. PMID:21310826

  6. Colorectal carcinomas with KRAS mutation are associated with distinctive morphological and molecular features.

    PubMed

    Rosty, Christophe; Young, Joanne P; Walsh, Michael D; Clendenning, Mark; Walters, Rhiannon J; Pearson, Sally; Pavluk, Erika; Nagler, Belinda; Pakenas, David; Jass, Jeremy R; Jenkins, Mark A; Win, Aung Ko; Southey, Melissa C; Parry, Susan; Hopper, John L; Giles, Graham G; Williamson, Elizabeth; English, Dallas R; Buchanan, Daniel D

    2013-06-01

    KRAS-mutated carcinomas comprise 35-40% of all colorectal carcinomas but little is known about their characteristics. The aim of this study was to examine the pathological and molecular features of KRAS-mutated colorectal carcinomas and to compare them with other carcinoma subgroups. KRAS mutation testing was performed in 776 incident tumors from the Melbourne Collaborative Cohort Study. O(6)-methylguanine DNA methyltransferase (MGMT) status was assessed using both immunohistochemistry and MethyLight techniques. Microsatellite instability (MSI) phenotype and BRAF V600E mutation status were derived from earlier studies. Mutation in KRAS codon 12 or codon 13 was present in 28% of colorectal carcinomas. Compared with KRAS wild-type carcinomas, KRAS-mutated carcinomas were more frequently observed in contiguity with a residual polyp (38 vs 21%; P<0.001), demonstrated mucinous differentiation (46 vs 31%; P=0.001) and were associated with different MSI status (P<0.001) and with MGMT methylation (47 vs 21%; P=0.001). Compared with tumors demonstrating neither BRAF nor KRAS mutation, KRAS-mutated carcinomas showed more frequent location in the proximal colon (41 vs 27%; P=0.001), mucinous differentiation (46 vs 25%; P<0.001), presence of a contiguous polyp (38 vs 22%; P<0.001), MGMT methylation (47 vs 26%; P=0.01) and loss of MGMT immunohistochemical expression (27 vs 19%; P=0.02). KRAS-mutated carcinomas were distributed in a bimodal pattern along the proximal-distal axis of the colorectum. Compared with male subjects, female subjects were more likely to have KRAS-mutated carcinoma in the transverse colon and descending colon (39 vs 15%; P=0.02). No difference in overall survival was observed in patients according to their tumor KRAS mutation status. In summary, KRAS-mutated carcinomas frequently develop in contiguity with a residual polyp and show molecular features distinct from other colorectal carcinomas, in particular from tumors with neither BRAF nor KRAS mutation.

  7. Lung Adenocarcinoma with EGFR Amplification has Distinct Clinicopathologic and Molecular Features in Never-Smokers

    PubMed Central

    Sholl, Lynette M.; Yeap, Beow Y.; Iafrate, A. John; Holmes-Tisch, Alison J.; Chou, Yi-Ping; Wu, Ming-Tsang; Goan, Yih-Gang; Su, Li; Benedittini, Elisa; Yu, Jian; Loda, Massimo; Jänne, Pasi A.; Christiani, David C.; Chirieac, Lucian R.

    2009-01-01

    In a subset of lung adenocarcinomas the epidermal growth factor receptor (EGFR) is activated by kinase domain mutations and/or gene amplification, but the interaction between the two types of abnormalities is complex and unclear. We selected to study 99 consecutive never-smoking women of East Asian origin with lung adenocarcinomas that were characterized by histologic subtype. We analyzed EGFR mutations by PCR-capillary sequencing, EGFR copy number abnormalities by fluorescence and chromogenic in situ hybridization and quantitative PCR, and EGFR expression by immunohistochemistry with both specific antibodies against exon 19 deletion-mutated EGFR and total EGFR. We compared molecular and clinicopathologic features with disease-free survival. Lung adenocarcinomas with EGFR amplification had significantly more EGFR exon 19 deletion mutations than adenocarcinomas with disomy, low and high polysomy (100% v 54%, P=0.009). EGFR amplification occurred invariably on the mutated and not the wildtype allele (median mutated:wildtype ratios 14.0 v .33, P=0.003), was associated with solid histology (P=0.008), and advanced clinical stage (P=0.009). EGFR amplification was focally distributed in lung cancer specimens, mostly in regions with solid histology. Patients with EGFR amplification had a significantly worse outcome in univariate analysis (median disease-free survival 16 v 31 months, P=0.01) and when adjusted for stage (P=0.027). Lung adenocarcinomas with EGFR amplification have a unique association with exon 19 deletion mutations and demonstrate distinct clinicopathologic features associated with a significantly worsened prognosis. In these cases, EGFR amplification is heterogeneously distributed, mostly in areas with a solid histology. PMID:19826035

  8. Complete mitochondrial DNA sequences of six snakes: phylogenetic relationships and molecular evolution of genomic features.

    PubMed

    Dong, Songyu; Kumazawa, Yoshinori

    2005-07-01

    Complete mitochondrial DNA (mtDNA) sequences were determined for representative species from six snake families: the acrochordid little file snake, the bold boa constrictor, the cylindrophiid red pipe snake, the viperid himehabu, the pythonid ball python, and the xenopeltid sunbeam snake. Thirteen protein-coding genes, 22 tRNA genes, 2 rRNA genes, and 2 control regions were identified in these mtDNAs. Duplication of the control region and translocation of the tRNALeu gene were two notable features of the snake mtDNAs. The duplicate control regions had nearly identical nucleotide sequences within species but they were divergent among species, suggesting concerted sequence evolution of the two control regions. In addition, the duplicate control regions appear to have facilitated an interchange of some flanking tRNA genes in the viperid lineage. Phylogenetic analyses were conducted using a large number of sites (9570 sites in total) derived from the complete mtDNA sequences. Our data strongly suggested a new phylogenetic relationship among the major families of snakes: ((((Viperidae, Colubridae), Acrochordidae), (((Pythonidae, Xenopeltidae), Cylindrophiidae), Boidae)), Leptotyphlopidae). This conclusion was distinct from a widely accepted view based on morphological characters in denying the sister-group relationship of boids and pythonids, as well as the basal divergence of nonmacrostomatan cylindrophiids. These results imply the significance to reconstruct the snake phylogeny with ample molecular data, such as those from complete mtDNA sequences.

  9. Molecular features and toxicological properties of four common pesticides, acetamiprid, deltamethrin, chlorpyriphos and fipronil.

    PubMed

    Taillebois, Emiliane; Alamiddine, Zakaria; Brazier, Christine; Graton, Jérôme; Laurent, Adèle D; Thany, Steeve H; Le Questel, Jean-Yves

    2015-04-01

    Structural features and selected physicochemical properties of four common pesticides: acetamiprid (neonicotinoid), chlorpyriphos (organophosphate insecticide), deltamethrin (pyrethroid) and fipronil (phenylpyrazole) have been investigated by Density Functional Theory quantum chemical calculations. The high flexible character of these insecticides is revealed by the numerous conformers obtained, located within a 20kJmol(-1) range in the gas phase. In line with this trend, a redistribution of the energetic minima is observed in water medium. Molecular electrostatic potential calculations provide a ranking of the potential interaction sites of the four insecticides. The theoretical studies reported in the present work are completed by comparative toxicological assays against three aphid strains. Thus, the same toxicity order for the two susceptible strains Myzus persicae 4106A and Acyrthosiphon pisum LSR1: acetamiprid>fipronil>deltamethrin>chlorpyriphos is revealed. In the resistant strain M. persicae 1300145, the toxicity order is modified: acetamiprid>fipronil>chlorpyriphos>deltamethrin. Interestingly, the strain 1300145 which is known to be resistant to neonicotinoids, is also less sensitive to deltamethrin, chlorpyriphos and fipronil. PMID:25716006

  10. Cryptosporidiosis in HIV/AIDS patients in Kenya: clinical features, epidemiology, molecular characterization and antibody responses.

    PubMed

    Wanyiri, Jane W; Kanyi, Henry; Maina, Samuel; Wang, David E; Steen, Aaron; Ngugi, Paul; Kamau, Timothy; Waithera, Tabitha; O'Connor, Roberta; Gachuhi, Kimani; Wamae, Claire N; Mwamburi, Mkaya; Ward, Honorine D

    2014-08-01

    We investigated the epidemiological and clinical features of cryptosporidiosis, the molecular characteristics of infecting species and serum antibody responses to three Cryptosporidium-specific antigens in human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) patients in Kenya. Cryptosporidium was the most prevalent enteric pathogen and was identified in 56 of 164 (34%) of HIV/AIDS patients, including 25 of 70 (36%) with diarrhea and 31 of 94 (33%) without diarrhea. Diarrhea in patients exclusively infected with Cryptosporidium was significantly associated with the number of children per household, contact with animals, and water treatment. Cryptosporidium hominis was the most prevalent species and the most prevalent subtype family was Ib. Patients without diarrhea had significantly higher serum IgG levels to Chgp15, Chgp40 and Cp23, and higher fecal IgA levels to Chgp15 and Chgp40 than those with diarrhea suggesting that antibody responses to these antigens may be associated with protection from diarrhea and supporting further investigation of these antigens as vaccine candidates.

  11. Physiological and molecular features of Puccinellia tenuiflora tolerating salt and alkaline-salt stress.

    PubMed

    Zhang, Xia; Wei, Liqin; Wang, Zizhang; Wang, Tai

    2013-03-01

    Saline-alkali soil seriously threatens agriculture productivity; therefore, understanding the mechanism of plant tolerance to alkaline-salt stress has become a major challenge. Halophytic Puccinellia tenuiflora can tolerate salt and alkaline-salt stress, and is thus an ideal plant for studying this tolerance mechanism. In this study, we examined the salt and alkaline-salt stress tolerance of P. tenuiflora, and analyzed gene expression profiles under these stresses. Physiological experiments revealed that P. tenuiflora can grow normally with maximum stress under 600 mmol/L NaCl and 150 mmol/L Na2 CO3 (pH 11.0) for 6 d. We identified 4,982 unigenes closely homologous to rice and barley. Furthermore, 1,105 genes showed differentially expressed profiles under salt and alkaline-salt treatments. Differentially expressed genes were overrepresented in functions of photosynthesis, oxidation reduction, signal transduction, and transcription regulation. Almost all genes downregulated under salt and alkaline-salt stress were related to cell structure, photosynthesis, and protein synthesis. Comparing with salt stress, alkaline-salt stress triggered more differentially expressed genes and significantly upregulated genes related to H(+) transport and citric acid synthesis. These data indicate common and diverse features of salt and alkaline-salt stress tolerance, and give novel insights into the molecular and physiological mechanisms of plant salt and alkaline-salt tolerance.

  12. Association of Fusobacterium species in pancreatic cancer tissues with molecular features and prognosis.

    PubMed

    Mitsuhashi, Kei; Nosho, Katsuhiko; Sukawa, Yasutaka; Matsunaga, Yasutaka; Ito, Miki; Kurihara, Hiroyoshi; Kanno, Shinichi; Igarashi, Hisayoshi; Naito, Takafumi; Adachi, Yasushi; Tachibana, Mami; Tanuma, Tokuma; Maguchi, Hiroyuki; Shinohara, Toshiya; Hasegawa, Tadashi; Imamura, Masafumi; Kimura, Yasutoshi; Hirata, Koichi; Maruyama, Reo; Suzuki, Hiromu; Imai, Kohzoh; Yamamoto, Hiroyuki; Shinomura, Yasuhisa

    2015-03-30

    Recently, bacterial infection causing periodontal disease has attracted considerable attention as a risk factor for pancreatic cancer. Fusobacterium species is an oral bacterial group of the human microbiome. Some evidence suggests that Fusobacterium species promote colorectal cancer development; however, no previous studies have reported the association between Fusobacterium species and pancreatic cancer. Therefore, we examined whether Fusobacterium species exist in pancreatic cancer tissue. Using a database of 283 patients with pancreatic ductal adenocarcinoma (PDAC), we tested cancer tissue specimens for Fusobacterium species. We also tested the specimens for KRAS, NRAS, BRAF and PIK3CA mutations and measured microRNA-21 and microRNA-31. In addition, we assessed epigenetic alterations, including CpG island methylator phenotype (CIMP). Our data showed an 8.8% detection rate of Fusobacterium species in pancreatic cancers; however, tumor Fusobacterium status was not associated with any clinical and molecular features. In contrast, in multivariate Cox regression analysis, compared with the Fusobacterium species-negative group, we observed significantly higher cancer-specific mortality rates in the positive group (p = 0.023). In conclusion, Fusobacterium species were detected in pancreatic cancer tissue. Tumor Fusobacterium species status is independently associated with a worse prognosis of pancreatic cancer, suggesting that Fusobacterium species may be a prognostic biomarker of pancreatic cancer.

  13. Clinical and molecular features and therapeutic perspectives of spinal muscular atrophy with respiratory distress type 1

    PubMed Central

    Vanoli, Fiammetta; Rinchetti, Paola; Porro, Francesca; Parente, Valeria; Corti, Stefania

    2015-01-01

    Spinal muscular atrophy with respiratory distress (SMARD1) is an autosomal recessive neuromuscular disease caused by mutations in the IGHMBP2 gene, encoding the immunoglobulin μ-binding protein 2, leading to motor neuron degeneration. It is a rare and fatal disease with an early onset in infancy in the majority of the cases. The main clinical features are muscular atrophy and diaphragmatic palsy, which requires prompt and permanent supportive ventilation. The human disease is recapitulated in the neuromuscular degeneration (nmd) mouse. No effective treatment is available yet, but novel therapeutical approaches tested on the nmd mouse, such as the use of neurotrophic factors and stem cell therapy, have shown positive effects. Gene therapy demonstrated effectiveness in SMA, being now at the stage of clinical trial in patients and therefore representing a possible treatment for SMARD1 as well. The significant advancement in understanding of both SMARD1 clinical spectrum and molecular mechanisms makes ground for a rapid translation of pre-clinical therapeutic strategies in humans. PMID:26095024

  14. Can Structural Features of Kinase Receptors Provide Clues on Selectivity and Inhibition?: A Molecular Modeling Study

    PubMed Central

    Ravichandran, Sarangan; Luke, Brian T.; Collins, Jack R.

    2015-01-01

    Cancer is a complex disease resulting from the uncontrolled proliferation of cell signaling events. Protein kinases have been identified as central molecules that participate overwhelmingly in oncogenic events, thus becoming key targets for anticancer drugs. A majority of studies converged on the idea that ligand-binding pockets of kinases retain clues to the inhibiting abilities and cross-reacting tendencies of inhibitor drugs. Even though these ideas are critical for drug discovery, validating them using experiments is not only difficult, but in some cases infeasible. To overcome these limitations and to test these ideas at the molecular level, we present here the results of receptor-focused in-silico docking of nine marketed drugs to 19 different wild-type and mutated kinases chosen from a wide range of families. This investigation highlights the need for using relevant models to explain the correct inhibition trends and the results are used to make predictions that might be able to influence future experiments. Our simulation studies are able to correctly predict the primary targets for each drug studied in majority of cases and our results agree with the existing findings. Our study shows that the conformations a given receptor acquires during kinase activation, and their micro-environment, defines the ligand partners. Type II drugs display high compatibility and selectivity for DFG-out kinase conformations. On the other hand Type I drugs are less selective and show binding preferences for both the open and closed forms of selected kinases. Using this receptor-focused approach, it is possible to capture the observed fold change in binding affinities between the wild-type and disease-centric mutations in ABL kinase for Imatinib and the second-generation ABL drugs. The effects of mutation are also investigated for two other systems, EGFR and B-Raf. Finally, by including pathway information in the design it is possible to model kinase inhibitors with potentially

  15. A CELL-BASED MOLECULAR TRANSPORT SIMULATOR FOR PHARMACOKINETIC PREDICTION AND CHEMINFORMATIC EXPLORATION

    PubMed Central

    Zhang, Xinyuan; Shedden, Kerby; Rosania, Gus R.

    2009-01-01

    In the body, cell monolayers serve as permeability barriers, determining transport of drug molecules from one organ or tissue compartment to another. After oral administration, for example, drug transport across the epithelial cell monolayer lining the lumen of the intestine determines the fraction of drug in the gut that is absorbed by the body. By modeling passive transcellular transport properties in the presence of an apical to basolateral concentration gradient, we demonstrate how a computational, cell-based molecular transport simulator can be used to define a physicochemical property space occupied by molecules with desirable permeability and intracellular retention characteristics. Considering extracellular domains of cell surface receptors located on the opposite side of a cell monolayer as a drug’s desired site-of-action, simulation of transcellular transport can be used to define the physicochemical properties of molecules with maximal transcellular permeability but minimal intracellular retention. Arguably, these molecules would possess very desirable features: least likely to exhibit non-specific toxicity, metabolism and side effects associated with high (undesirable) intracellular accumulation; and, most likely to exhibit favorable bioavailability and efficacy associated with maximal rates of transport across cells and minimal intracellular retention, resulting in (desirable) accumulation at the extracellular site-of-action. Calculated permeability predictions showed good correlations with PAMPA, Caco2, and intestinal permeability measurements, without “training” the model and without resorting to statistical regression techniques to “fit” the data. Therefore, cell-based molecular transport simulators could be useful in silico screening tools for chemical genomics and drug discovery. PMID:17140258

  16. Molecular Mechanism and Prediction of Sorafenib Chemoresistance in Human Hepatocellular Carcinoma.

    PubMed

    Nishida, Naoshi; Kitano, Masayuki; Sakurai, Toshiharu; Kudo, Masatoshi

    2015-10-01

    Hepatocellular carcinoma (HCC) is the second leading cause of cancer death worldwide, and prognosis remains unsatisfactory when the disease is diagnosed at an advanced stage. Many molecular targeted agents are being developed for the treatment of advanced HCC; however, the only promising drug to have been developed is sorafenib, which acts as a multi-kinase inhibitor. Unfortunately, a subgroup of HCC is resistant to sorafenib, and the majority of these HCC patients show disease progression even after an initial satisfactory response. To date, a number of studies have examined the underlying mechanisms involved in the response to sorafenib, and trials have been performed to overcome the acquisition of drug resistance. The anti-tumor activity of sorafenib is largely attributed to the blockade of the signals from growth factors, such as vascular endothelial growth factor receptor and platelet-derived growth factor receptor, and the downstream RAF/mitogen-activated protein/extracellular signal-regulated kinase (ERK) kinase (MEK)/ERK cascade. The activation of an escape pathway from RAF/MEK/ERK possibly results in chemoresistance. In addition, there are several features of HCCs indicating sorafenib resistance, such as epithelial-mesenchymal transition and positive stem cell markers. Here, we review the recent reports and focus on the mechanism and prediction of chemoresistance to sorafenib in HCC.

  17. Molecular Mechanism and Prediction of Sorafenib Chemoresistance in Human Hepatocellular Carcinoma.

    PubMed

    Nishida, Naoshi; Kitano, Masayuki; Sakurai, Toshiharu; Kudo, Masatoshi

    2015-10-01

    Hepatocellular carcinoma (HCC) is the second leading cause of cancer death worldwide, and prognosis remains unsatisfactory when the disease is diagnosed at an advanced stage. Many molecular targeted agents are being developed for the treatment of advanced HCC; however, the only promising drug to have been developed is sorafenib, which acts as a multi-kinase inhibitor. Unfortunately, a subgroup of HCC is resistant to sorafenib, and the majority of these HCC patients show disease progression even after an initial satisfactory response. To date, a number of studies have examined the underlying mechanisms involved in the response to sorafenib, and trials have been performed to overcome the acquisition of drug resistance. The anti-tumor activity of sorafenib is largely attributed to the blockade of the signals from growth factors, such as vascular endothelial growth factor receptor and platelet-derived growth factor receptor, and the downstream RAF/mitogen-activated protein/extracellular signal-regulated kinase (ERK) kinase (MEK)/ERK cascade. The activation of an escape pathway from RAF/MEK/ERK possibly results in chemoresistance. In addition, there are several features of HCCs indicating sorafenib resistance, such as epithelial-mesenchymal transition and positive stem cell markers. Here, we review the recent reports and focus on the mechanism and prediction of chemoresistance to sorafenib in HCC. PMID:26488287

  18. Robust prediction of B-factor profile from sequence using two-stage SVR based on random forest feature selection.

    PubMed

    Pan, Xiao-Yong; Shen, Hong-Bin

    2009-01-01

    B-factor is highly correlated with protein internal motion, which is used to measure the uncertainty in the position of an atom within a crystal structure. Although the rapid progress of structural biology in recent years makes more accurate protein structures available than ever, with the avalanche of new protein sequences emerging during the post-genomic Era, the gap between the known protein sequences and the known protein structures becomes wider and wider. It is urgent to develop automated methods to predict B-factor profile from the amino acid sequences directly, so as to be able to timely utilize them for basic research. In this article, we propose a novel approach, called PredBF, to predict the real value of B-factor. We firstly extract both global and local features from the protein sequences as well as their evolution information, then the random forests feature selection is applied to rank their importance and the most important features are inputted to a two-stage support vector regression (SVR) for prediction, where the initial predicted outputs from the 1(st) SVR are further inputted to the 2nd layer SVR for final refinement. Our results have revealed that a systematic analysis of the importance of different features makes us have deep insights into the different contributions of features and is very necessary for developing effective B-factor prediction tools. The two-layer SVR prediction model designed in this study further enhanced the robustness of predicting the B-factor profile. As a web server, PredBF is freely available at: http://www.csbio.sjtu.edu.cn/bioinf/PredBF for academic use.

  19. Prediction of troponin-T degradation using color image texture features in 10d aged beef longissimus steaks.

    PubMed

    Sun, X; Chen, K J; Berg, E P; Newman, D J; Schwartz, C A; Keller, W L; Maddock Carlin, K R

    2014-02-01

    The objective was to use digital color image texture features to predict troponin-T degradation in beef. Image texture features, including 88 gray level co-occurrence texture features, 81 two-dimension fast Fourier transformation texture features, and 48 Gabor wavelet filter texture features, were extracted from color images of beef strip steaks (longissimus dorsi, n = 102) aged for 10d obtained using a digital camera and additional lighting. Steaks were designated degraded or not-degraded based on troponin-T degradation determined on d 3 and d 10 postmortem by immunoblotting. Statistical analysis (STEPWISE regression model) and artificial neural network (support vector machine model, SVM) methods were designed to classify protein degradation. The d 3 and d 10 STEPWISE models were 94% and 86% accurate, respectively, while the d 3 and d 10 SVM models were 63% and 71%, respectively, in predicting protein degradation in aged meat. STEPWISE and SVM models based on image texture features show potential to predict troponin-T degradation in meat. PMID:24200578

  20. Predicted Molecular Effects of Sequence Variants Link to System Level of Disease.

    PubMed

    Reeb, Jonas; Hecht, Maximilian; Mahlich, Yannick; Bromberg, Yana; Rost, Burkhard

    2016-08-01

    Developments in experimental and computational biology are advancing our understanding of how protein sequence variation impacts molecular protein function. However, the leap from the micro level of molecular function to the macro level of the whole organism, e.g. disease, remains barred. Here, we present new results emphasizing earlier work that suggested some links from molecular function to disease. We focused on non-synonymous single nucleotide variants, also referred to as single amino acid variants (SAVs). Building upon OMIA (Online Mendelian Inheritance in Animals), we introduced a curated set of 117 disease-causing SAVs in animals. Methods optimized to capture effects upon molecular function often correctly predict human (OMIM) and animal (OMIA) Mendelian disease-causing variants. We also predicted effects of human disease-causing variants in the mouse model, i.e. we put OMIM SAVs into mouse orthologs. Overall, fewer variants were predicted with effect in the model organism than in the original organism. Our results, along with other recent studies, demonstrate that predictions of molecular effects capture some important aspects of disease. Thus, in silico methods focusing on the micro level of molecular function can help to understand the macro system level of disease. PMID:27536940

  1. Predicted Molecular Effects of Sequence Variants Link to System Level of Disease

    PubMed Central

    Bromberg, Yana; Rost, Burkhard

    2016-01-01

    Developments in experimental and computational biology are advancing our understanding of how protein sequence variation impacts molecular protein function. However, the leap from the micro level of molecular function to the macro level of the whole organism, e.g. disease, remains barred. Here, we present new results emphasizing earlier work that suggested some links from molecular function to disease. We focused on non-synonymous single nucleotide variants, also referred to as single amino acid variants (SAVs). Building upon OMIA (Online Mendelian Inheritance in Animals), we introduced a curated set of 117 disease-causing SAVs in animals. Methods optimized to capture effects upon molecular function often correctly predict human (OMIM) and animal (OMIA) Mendelian disease-causing variants. We also predicted effects of human disease-causing variants in the mouse model, i.e. we put OMIM SAVs into mouse orthologs. Overall, fewer variants were predicted with effect in the model organism than in the original organism. Our results, along with other recent studies, demonstrate that predictions of molecular effects capture some important aspects of disease. Thus, in silico methods focusing on the micro level of molecular function can help to understand the macro system level of disease. PMID:27536940

  2. The Clinical Significance and Molecular Features of the Spatial Tumor Shapes in Breast Cancers

    PubMed Central

    Jeong, Seongmun; Lee, Minju; Moon, HyunHye; Kim, Jongjin; Yoo, Tae-Kyung; Lee, Han-Byoel; Kim, Jisun; Noh, Dong-Young; Han, Wonshik

    2015-01-01

    Each breast cancer has its unique spatial shape, but the clinical importance and the underlying mechanism for the three-dimensional tumor shapes are mostly unknown. We collected the data on the three-dimensional tumor size and tumor volume data of invasive breast cancers from 2,250 patients who underwent surgery between Jan 2000 and Jul 2007. The degree of tumor eccentricity was estimated by using the difference between the spheroid tumor volume and ellipsoid tumor volume (spheroid-ellipsoid discrepancy, SED). In 41 patients, transcriptome and exome sequencing data obtained. Estimation of more accurate tumor burden by calculating ellipsoid tumor volumes did not improve the outcome prediction when compared to the traditional longest diameter measurement. However, the spatial tumor eccentricity, which was measured by SED, showed significant variation between the molecular subtypes of breast cancer. Additionally, the degree of tumor eccentricity was associated with well-known prognostic factors of breast cancer such as tumor size and lymph node metastasis. Transcriptome data from 41 patients showed significant association between MMP13 and spatial tumor shapes. Network analysis and analysis of TCGA gene expression data suggest that MMP13 is regulated by ERBB2 and S100A7A. The present study validates the usefulness of the current tumor size method in determining tumor stages. Furthermore, we show that the tumors with high eccentricity are more likely to have aggressive tumor characteristics. Genes involved in the extracellular matrix remodeling can be candidate regulators of the spatial tumor shapes in breast cancer. PMID:26669540

  3. Accelerated Molecular Dynamics Simulation of Hypersonic Flow Features in Dilute Gases

    NASA Astrophysics Data System (ADS)

    Schwartzentruber, Thomas; Valentini, Paolo

    2009-11-01

    Accurate simulation of high-altitude hypersonic flows requires advanced physical models capable of predicting the transfer of energy between translational, rotational, vibrational, and chemical modes of a gas in strong thermochemical non-equilibrium. A combined Event-Driven / Time-Driven (ED/TD) Molecular Dynamics (MD) algorithm is presented that greatly accelerates the MD simulation of dilute gases. The goal of this research is to utilize advances in computational chemistry to study thermochemical non-equilibrium processes in hypersonic flows. The ED/TD MD method identifies impending collisions (including multi-body collisions) and advances molecules directly to their next interaction, however, then integrates each interaction accurately using an arbitrary interatomic potential via conventional MD with small timesteps. First, the ED/TD MD algorithm and efficiency will be detailed. Next, ED/TD MD simulations of normal shock waves in dilute argon will be validated with experiment and direct simulation Monte Carlo simulations employing the variable-hard-sphere collision model. Profiling of the code reveals that the relative computational time required for the MD integration of collisions is extremely low and the potential for incorporating advanced classical and first-principles interatomic potentials within the ED/TD MD method will be discussed.

  4. Identification of Molecular Targets for Predicting Colon Adenocarcinoma

    PubMed Central

    Wang, Yansheng; Zhang, Jun; Li, Li; Xu, Xin; Zhang, Yong; Teng, Zhaowei; Wu, Feihu

    2016-01-01

    Background Colon adenocarcinoma mostly happens at the junction of the rectum and is a common gastrointestinal malignancy. Accumulated evidence has indicated that colon adenocarcinoma develops by genetic alterations and is a complicated disease. The aim of this study was to screen differentially expressed miRNAs (DEMs) and genes with diagnostic and prognostic potentials in colon adenocarcinoma. Material/Methods In this study we screened DEMs and their target genes (DEGs) between 100 colon adenocarcinoma and normal samples in The Cancer Genome Atlas (TCGA) database by using the DEseq toolkit in Bioconductor. Then Go enrichment and KEGG pathway analysis were performed on the selected differential genes by use of the DAVID online tool. A regulation network of miRNA-gene was constructed and analyzed by Cytoscape. Finally, we performed ROC analysis of 8 miRNAs and ROC curves were drawn. Results A total of 159 DEMs and 1921 DEGs were screened, and 1881 pairs of miRNA-target genes with significant negative correlations were also obtained. A regulatory network of miRNA-gene, including 60 cancer-related genes and 47 miRNAs, was successfully constructed. In addition, 5 clusters with several miRNAs regulating a set of target genes simultaneously were identified through cluster analysis. There were 8 miRNAs involved in these 5 clusters, and these miRNAs could serve as molecular biomarkers to distinguish colon adenocarcinoma and normal samples indicated by ROC analysis. Conclusions The identified 8 miRNAs were closely associated with colon adenocarcinoma, which may have great clinical value as diagnostic and prognostic biomarkers and provide new ideas for targeted therapy. PMID:26868022

  5. Experimental indication of a naphthalene-base molecular aggregate for the carrier of the 2175 angstroms interstellar extinction feature

    NASA Technical Reports Server (NTRS)

    Beegle, L. W.; Wdowiak, T. J.; Robinson, M. S.; Cronin, J. R.; McGehee, M. D.; Clemett, S. J.; Gillette, S.

    1997-01-01

    Experiments where the simple polycyclic aromatic hydrocarbon (PAH) naphthalene (C10H8) is subjected to the energetic environment of a plasma have resulted in the synthesis of a molecular aggregate that has ultraviolet spectral characteristics that suggest it provides insight into the nature of the carrier of the 2175 angstroms interstellar extinction feature and may be a laboratory analog. Ultraviolet, visible, infrared, and mass spectroscopy, along with gas chromatography, indicate that it is a molecular aggregate in which an aromatic double ring ("naphthalene") structural base serves as the electron "box" chromophore that gives rise to the envelope of the 2175 angstroms feature. This chromophore can also provide the peak of the feature or function as a mantle in concert with another peak provider such as graphite. The molecular base/chromophore manifests itself both as a structural component of an alkyl-aromatic polymer and as a substructure of hydrogenated PAH species. Its spectral and molecular characteristics are consistent with what is generally expected for a complex molecular aggregate that has a role as an interstellar constituent.

  6. Experimental indication of a naphthalene-base molecular aggregate for the carrier of the 2175 angstroms interstellar extinction feature.

    PubMed

    Beegle, L W; Wdowiak, T J; Robinson, M S; Cronin, J R; McGehee, M D; Clemett, S J; Gillette, S

    1997-10-01

    Experiments where the simple polycyclic aromatic hydrocarbon (PAH) naphthalene (C10H8) is subjected to the energetic environment of a plasma have resulted in the synthesis of a molecular aggregate that has ultraviolet spectral characteristics that suggest it provides insight into the nature of the carrier of the 2175 angstroms interstellar extinction feature and may be a laboratory analog. Ultraviolet, visible, infrared, and mass spectroscopy, along with gas chromatography, indicate that it is a molecular aggregate in which an aromatic double ring ("naphthalene") structural base serves as the electron "box" chromophore that gives rise to the envelope of the 2175 angstroms feature. This chromophore can also provide the peak of the feature or function as a mantle in concert with another peak provider such as graphite. The molecular base/chromophore manifests itself both as a structural component of an alkyl-aromatic polymer and as a substructure of hydrogenated PAH species. Its spectral and molecular characteristics are consistent with what is generally expected for a complex molecular aggregate that has a role as an interstellar constituent.

  7. A Prediction of Brown Dwarfs in Ultracold Molecular Gas

    NASA Astrophysics Data System (ADS)

    Elmegreen, Bruce G.

    1999-09-01

    A recent model for the stellar initial mass function (IMF), in which the stellar masses are randomly sampled down to the thermal Jeans mass from hierarchically structured prestellar clouds, predicts that regions of ultracold CO gas, such as those recently found in nearby galaxies by Allen and collaborators, should make an abundance of brown dwarfs with relatively few normal stars. This result comes from the low value of the thermal Jeans mass, which scales as MJ~T2/P1/2 for temperature T and pressure P, considering that the hierarchical cloud model always gives the Salpeter IMF slope above this lower mass limit. The ultracold CO clouds in the inner disk of M31 have T~3 K and pressures that are probably 10 times higher than in the solar neighborhood. This gives a mass at the peak of the IMF equal to 0.01 Msolar, well below the brown dwarf limit of 0.08 Msolar. Using a functional approximation to the IMF given by [1-e-(M/MJ)2]M-1.35dlogM for M>MJ, which fits the local IMF for the expected value of MJ~0.3 Msolar, an IMF with MJ=0.01 Msolar in M31 has 50% of the mass and 90% of the objects below the brown dwarf limit. The brightest of the brown dwarfs in M31 should have an apparent extinction-corrected K-band magnitude of ~30 mag in their pre-main-sequence phase. For typical star formation efficiencies of <=10%, brown dwarfs and any associated stars up to ~2.5 Msolar should not heat the gas noticeably, but if the IMF continues up to arbitrarily high masses, then the star formation efficiency must be <=10-4 to avoid heating from massive stars.

  8. Computing Molecular Signatures as Optima of a Bi-Objective Function: Method and Application to Prediction in Oncogenomics

    PubMed Central

    Gardeux, Vincent; Chelouah, Rachid; Wanderley, Maria F Barbosa; Siarry, Patrick; Braga, Antônio P; Reyal, Fabien; Rouzier, Roman; Pusztai, Lajos; Natowicz, René

    2015-01-01

    BACKGROUND Filter feature selection methods compute molecular signatures by selecting subsets of genes in the ranking of a valuation function. The motivations of the valuation functions choice are almost always clearly stated, but those for selecting the genes according to their ranking are hardly ever explicit. METHOD We addressed the computation of molecular signatures by searching the optima of a bi-objective function whose solution space was the set of all possible molecular signatures, ie, the set of subsets of genes. The two objectives were the size of the signature–to be minimized–and the interclass distance induced by the signature–to be maximized–. RESULTS We showed that: 1) the convex combination of the two objectives had exactly n optimal non empty signatures where n was the number of genes, 2) the n optimal signatures were nested, and 3) the optimal signature of size k was the subset of k top ranked genes that contributed the most to the interclass distance. We applied our feature selection method on five public datasets in oncology, and assessed the prediction performances of the optimal signatures as input to the diagonal linear discriminant analysis (DLDA) classifier. They were at the same level or better than the best-reported ones. The predictions were robust, and the signatures were almost always significantly smaller. We studied in more details the performances of our predictive modeling on two breast cancer datasets to predict the response to a preoperative chemotherapy: the performances were higher than the previously reported ones, the signatures were three times smaller (11 versus 30 gene signatures), and the genes member of the signature were known to be involved in the response to chemotherapy. CONCLUSIONS Defining molecular signatures as the optima of a bi-objective function that combined the signature size and the interclass distance was well founded and efficient for prediction in oncogenomics. The complexity of the computation

  9. Predicted Rupture Force of a Single Molecular Bond Becomes Rate Independent at Ultralow Loading Rates

    NASA Astrophysics Data System (ADS)

    Li, Dechang; Ji, Baohua

    2014-02-01

    We present for the first time a theoretical model of studying the saturation of the rupture force of a single molecular bond that causes the rupture force to be rate independent under an ultralow loading rate. This saturation will obviously bring challenges to understanding the rupture behavior of the molecular bond using conventional methods. This intriguing feature implies that the molecular bond has a nonzero strength at a vanishing loading rate. We find that the saturation behavior is caused by bond rebinding when the loading rate is lower than a limiting value depending on the loading stiffness.

  10. TU-C-17A-10: Patient Features Based Dosimetric Pareto Front Prediction In Esophagus Cancer Radiotherapy

    SciTech Connect

    Wang, J; Zhao, K; Peng, J; Hu, W; Jin, X

    2014-06-15

    Purpose: The purpose of this study is to study the feasibility of the dosimetric pareto front (PF) prediction based on patient anatomic and dosimetric parameters for esophagus cancer patients. Methods: Sixty esophagus patients in our institution were enrolled in this study. A total 2920 IMRT plans were created to generated PF for each patient. On average, each patient had 48 plans. The anatomic and dosimetric features were extracted from those plans. The mean lung dose (MLD), mean heart dose (MHD), spinal cord max dose and PTV homogeneous index (PTVHI) were recorded for each plan. The principal component analysis (PCA) was used to extract overlap volume histogram (OVH) features between PTV and other critical organs. The full dataset was separated into two parts include the training dataset and the validation dataset. The prediction outcomes were the MHD and MLD for the current study. The spearman rank correlation coefficient was used to evaluate the correlation between the anatomical features and dosimetric features. The PF was fit by the the stepwise multiple regression method. The cross-validation method was used to evaluation the model. Results: The mean prediction error of the MHD was 465 cGy with 100 repetitions. The most correlated factors were the first principal components of the OVH between heart and PTV, and the overlap between heart and PTV in Z-axis. The mean prediction error of the MLD was 195 cGy. The most correlated factors were the first principal components of the OVH between lung and PTV, and the overlap between lung and PTV in Z-axis. Conclusion: It is feasible to use patients anatomic and dosimetric features to generate a predicted PF. Additional samples and further studies were required to get a better prediction model.

  11. Molecular recognition features (MoRFs) in three domains of life.

    PubMed

    Yan, Jing; Dunker, A Keith; Uversky, Vladimir N; Kurgan, Lukasz

    2016-03-01

    Intrinsically disordered proteins and protein regions offer numerous advantages in the context of protein-protein interactions when compared to the structured proteins and domains. These advantages include ability to interact with multiple partners, to fold into different conformations when bound to different partners, and to undergo disorder-to-order transitions concomitant with their functional activity. Molecular recognition features (MoRFs) are widespread elements located in disordered regions that undergo disorder-to-order transition upon binding to their protein partners. We characterize abundance, composition, and functions of MoRFs and their association with the disordered regions across 868 species spread across Eukaryota, Bacteria and Archaea. We found that although disorder is substantially elevated in Eukaryota, MoRFs have similar abundance and amino acid composition across the three domains of life. The abundance of MoRFs is highly correlated with the amount of intrinsic disorder in Bacteria and Archaea but only modestly correlated in Eukaryota. Proteins with MoRFs have significantly more disorder and MoRFs are present in many disordered regions, with Eukaryota having more MoRF-free disordered regions. MoRF-containing proteins are enriched in the ribosome, nucleus, nucleolus and microtubule and are involved in translation, protein transport, protein folding, and interactions with DNAs. Our insights into the nature and function of MoRFs enhance our understanding of the mechanisms underlying the disorder-to-order transition and protein-protein recognition and interactions. The fMoRFpred method that we used to annotate MoRFs is available at http://biomine.ece.ualberta.ca/fMoRFpred/. PMID:26651072

  12. Fiber-specific molecular features of tumors induced in rat peritoneum.

    PubMed Central

    Unfried, K; Roller, M; Pott, F; Friemann, J; Dehnen, W

    1997-01-01

    Molecular markers such as mutational spectra or mRNA expression patterns may give some indication of the mechanisms of carcinogenesis induced by fibers and other carcinogens. In our study, tumors were induced by application of crocidolite asbestos or benzo[a]pyrene (B[a]P) to rat peritoneum. DNA and RNA of these tumors were subjected to analysis of point mutations and to investigation of mRNA expression patterns. With both assays we found typical features depending on the type of carcinogen applied. The analysis of point mutations in the tumor suppressor gene p53 revealed mutations in the B[a]P-induced tumors. However, in the tumors induced by crocidolite asbestos that were of the same tumor type as those induced by B[a]P, mutations in p53 were not detectable. Every mutation detected on the DNA level causes an amino acid substitution within one of the functional domains of the tumor suppressor protein. Therefore, these mutations seem to be of biological relevance for tumor progression and indicate a difference in the carcinogenesis regarding the type of the carcinogenic substance. An additional specificity of crocidolite-induced tumors was detectable by analyzing the mRNA expression of the tumor suppressor gene WT1, which is known to be expressed in human mesothelial and mesothelioma cells. A relatively high amount of WT1 mRNA was measured by quantitative competitive reverse transcription-polymerase using RNA extracted from crocidolite-induced tumors. However, WT1 seems to be expressed on a rather low level in tumors induced by B[a]P. Images Figure 1. Figure 2. Figure 2. Figure 2. Figure 2. PMID:9400707

  13. Molecular self-diffusion in nanoscale cylindrical pores and classical Fick's law predictions.

    PubMed

    Cui, S T

    2005-08-01

    Molecular-dynamics calculations are carried out to study the self-diffusion of water molecules confined in cylindrical pores. It is found that the classical Fick's law description provides a surprisingly accurate prediction for the general behaviors of self-diffusion even for pore size of a few molecular diameters. The diffusion coefficient in the axial direction is reduced relative to bulk fluids for pore size less than about ten molecular diameters. In the radial direction, the mean-square displacement accurately follows Fick's law prediction, but with an average diffusion coefficient slightly lower than the bulk value. The origin of the diffusion behaviors is traced to the molecular motion in the restricted geometry of the cylindrical pores.

  14. Molecular Features of Triple Negative Breast Cancer: Microarray Evidence and Further Integrated Analysis

    PubMed Central

    Chen, Weicai; Wu, Huisheng; Yuan, Zishan; Wang, Kun; Li, Guojin; Sun, Jie; Yu, Limin

    2015-01-01

    Purpose Breast cancer is a heterogeneous disease usually including four molecular subtypes such as luminal A, luminal B, HER2-enriched, and triple-negative breast cancer (TNBC). TNBC is more aggressive than other breast cancer subtypes. Despite major advances in ER-positive or HER2-amplified breast cancer, there is no targeted agent currently available for TNBC, so it is urgent to identify new potential therapeutic targets for TNBC. Methods We first used microarray analysis to compare gene expression profiling between TNBC and non-TNBC. Furthermore an integrated analysis was conducted based on our own and published data, leading to more robust, reproducible and accurate predictions. Additionally, we performed qRT-PCR in breast cancer cell lines to verify the findings in integrated analysis. Results After searching Gene Expression Omnibus database (GEO), two microarray studies were obtained according to the inclusion criteria. The integrated analysis was conducted, including 30 samples of TNBC and 77 samples of non-TNBC. 556 genes were found to be consistently differentially expressed (344 up-regulated genes and 212 down-regulated genes in TNBC). Functional annotation for these differentially expressed genes (DEGs) showed that the most significantly enriched Gene Ontology (GO) term for molecular functions was protein binding (GO: 0005515, P = 6.09E-21), while that for biological processes was signal transduction (GO: 0007165, P = 9.46E-08), and that for cellular component was cytoplasm (GO: 0005737, P = 2.09E-21). The most significant pathway was Pathways in cancer (P = 6.54E-05) based on Kyoto Encyclopedia of Genes and Genomes (KEGG). DUSP1 (Degree = 21), MYEOV2 (Degree = 15) and UQCRQ (Degree = 14) were identified as the significant hub proteins in the protein-protein interaction (PPI) network. Five genes were selected to perform qRT-PCR in seven breast cancer cell lines, and qRT-PCR results showed that the expression pattern of selected genes in TNBC lines and

  15. Performance comparison of the Prophecy (forecasting) Algorithm in FFT form for unseen feature and time-series prediction

    NASA Astrophysics Data System (ADS)

    Jaenisch, Holger; Handley, James

    2013-06-01

    We introduce a generalized numerical prediction and forecasting algorithm. We have previously published it for malware byte sequence feature prediction and generalized distribution modeling for disparate test article analysis. We show how non-trivial non-periodic extrapolation of a numerical sequence (forecast and backcast) from the starting data is possible. Our ancestor-progeny prediction can yield new options for evolutionary programming. Our equations enable analytical integrals and derivatives to any order. Interpolation is controllable from smooth continuous to fractal structure estimation. We show how our generalized trigonometric polynomial can be derived using a Fourier transform.

  16. Correlation of chemical shifts predicted by molecular dynamics simulations for partially disordered proteins

    PubMed Central

    Karp, Jerome M.; Erylimaz, Ertan

    2015-01-01

    There has been a longstanding interest in being able to accurately predict NMR chemical shifts from structural data. Recent studies have focused on using molecular dynamics (MD) simulation data as input for improved prediction. Here we examine the accuracy of chemical shift prediction for intein systems, which have regions of intrinsic disorder. We find that using MD simulation data as input for chemical shift prediction does not consistently improve prediction accuracy over use of a static X-ray crystal structure. This appears to result from the complex conformational ensemble of the disordered protein segments. We show that using accelerated molecular dynamics (aMD) simulations improves chemical shift prediction, suggesting that methods which better sample the conformational ensemble like aMD are more appropriate tools for use in chemical shift prediction for proteins with disordered regions. Moreover, our study suggests that data accurately reflecting protein dynamics must be used as input for chemical shift prediction in order to correctly predict chemical shifts in systems with disorder. PMID:25416617

  17. Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review

    PubMed Central

    Mamy, Laure; Patureau, Dominique; Barriuso, Enrique; Bedos, Carole; Bessac, Fabienne; Louchart, Xavier; Martin-laurent, Fabrice; Miege, Cecile; Benoit, Pierre

    2015-01-01

    A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pKa), water dissolution or hydrophobic behavior (especially through the KOW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (EHOMO) and the energy of the lowest unoccupied molecular orbital (ELUMO), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment. PMID:25866458

  18. Andic soil features and debris flows in Italy. New perspective towards prediction

    NASA Astrophysics Data System (ADS)

    Scognamiglio, Solange; Calcaterra, Domenico; Iamarino, Michela; Langella, Giuliano; Orefice, Nadia; Vingiani, Simona; Terribile, Fabio

    2016-04-01

    Debris flows are dangerous hazards causing fatalities and damage. Previous works have demonstrated that the materials involved by debris flows in Campania (southern Italy) are soils classified as Andosols. These soils have peculiar chemical and physical properties which make them fertile but also vulnerable to landslide. In Italy, andic soil properties are found both in volcanic and non-volcanic mountain ecosystems (VME and NVME). Here, we focused on the assessment of the main chemical and physical properties of the soils in the detachment areas of eight debris flows occurred in NVME of Italy in the last 70 years. Such landslides were selected by consulting the official Italian geodatabase (IFFI Project). Andic properties (by means of ammonium oxalate extractable Fe, Si and Al forms for the calculation of Alo+1/2Feo) were also evaluated and a comparison with soils of VME was performed to assess possible common features. Landslide source areas were characterised by slope gradient ranging from 25° to 50° and lithological heterogeneity of the bedrock. The soils showed similar, i.e. all were very deep, had a moderately thick topsoil with a high organic carbon (OC) content decreasing regularly with depth. The cation exchange capacity trend was generally consistent with the OC and the pH varied from extremely to slightly acid, but increased with depth. Furthermore, the soils had high water retention values both at saturation (0.63 to 0.78 cm3 cm‑3) and in the dryer part of the water retention curve, and displayed a prevalent loamy texture. Such properties denote the chemical and physical fertility of the investigated ecosystems. The values of Alo+1/2Feoindicated that the soils had vitric or andic features and can be classified as Andosols. The comparison between NVME soils and those of VME showed similar depth, thickness of soil horizons, and family texture, whereas soil pH, degree of development of andic properties and allophane content were higher for VME soils

  19. Andic soil features and debris flows in Italy. New perspective towards prediction

    NASA Astrophysics Data System (ADS)

    Scognamiglio, Solange; Calcaterra, Domenico; Iamarino, Michela; Langella, Giuliano; Orefice, Nadia; Vingiani, Simona; Terribile, Fabio

    2016-04-01

    Debris flows are dangerous hazards causing fatalities and damage. Previous works have demonstrated that the materials involved by debris flows in Campania (southern Italy) are soils classified as Andosols. These soils have peculiar chemical and physical properties which make them fertile but also vulnerable to landslide. In Italy, andic soil properties are found both in volcanic and non-volcanic mountain ecosystems (VME and NVME). Here, we focused on the assessment of the main chemical and physical properties of the soils in the detachment areas of eight debris flows occurred in NVME of Italy in the last 70 years. Such landslides were selected by consulting the official Italian geodatabase (IFFI Project). Andic properties (by means of ammonium oxalate extractable Fe, Si and Al forms for the calculation of Alo+1/2Feo) were also evaluated and a comparison with soils of VME was performed to assess possible common features. Landslide source areas were characterised by slope gradient ranging from 25° to 50° and lithological heterogeneity of the bedrock. The soils showed similar, i.e. all were very deep, had a moderately thick topsoil with a high organic carbon (OC) content decreasing regularly with depth. The cation exchange capacity trend was generally consistent with the OC and the pH varied from extremely to slightly acid, but increased with depth. Furthermore, the soils had high water retention values both at saturation (0.63 to 0.78 cm3 cm-3) and in the dryer part of the water retention curve, and displayed a prevalent loamy texture. Such properties denote the chemical and physical fertility of the investigated ecosystems. The values of Alo+1/2Feoindicated that the soils had vitric or andic features and can be classified as Andosols. The comparison between NVME soils and those of VME showed similar depth, thickness of soil horizons, and family texture, whereas soil pH, degree of development of andic properties and allophane content were higher for VME soils. Such

  20. Investigating the molecular structural features of hulless barley (Hordeum vulgare L.) in relation to metabolic characteristics using synchrotron-based fourier transform infrared microspectroscopy.

    PubMed

    Yang, Ling; Christensen, David A; McKinnon, John J; Beattie, Aaron D; Xin, Hangshu; Yu, Peiqiang

    2013-11-27

    The synchrotron-based Fourier transform infrared microspectroscopy (SR-FTIRM) technique was used to quantify molecular structural features of the four hulless barley lines with altered carbohydrate traits [amylose, 1-40% of dry matter (DM); β-glucan, 5-10% of DM] in relation to rumen degradation kinetics, intestinal nutrient digestion, and predicted protein supply. Spectral features of β-glucan (both area and heights) in hulless barley lines showed a negative correlation with protein availability in the small intestine, including truly digested protein in the small intestine (DVE) (r = -0.76, P < 0.01; r = -0.84, P < 0.01) and total metabolizable protein (MP) (r = -0.71, P < 0.05; r = -0.84, P < 0.01). Variation in absorption intensities of total carbohydrate (CHO) was observed with negative effects on protein degradation, digestion, and potential protein supply (P < 0.05). Molecular structural features of CHO in hulless barley have negative effects on the supply of true protein to ruminants. The results clearly indicated the impact of the carbohydrate-protein structure and matrix.

  1. Apocalypse...now? Molecular epidemiology, predictive genetic tests, and social communication of genetic contents.

    PubMed

    Castiel, L D

    1999-01-01

    The author analyzes the underlying theoretical aspects in the construction of the molecular watershed of epidemiology and the concept of genetic risk, focusing on issues raised by contemporary reality: new technologies, globalization, proliferation of communications strategies, and the dilution of identity matrices. He discusses problems pertaining to the establishment of such new interdisciplinary fields as molecular epidemiology and molecular genetics. Finally, he analyzes the repercussions of the social communication of genetic content, especially as related to predictive genetic tests and cloning of animals, based on triumphal, deterministic metaphors sustaining beliefs relating to the existence and supremacy of concepts such as 'purity', 'essence', and 'unification' of rational, integrated 'I's/egos'. PMID:10089550

  2. Prediction of Selected Physical and Mechanical Properties of a Telechelic Polybenzoxazine by Molecular Simulation

    PubMed Central

    Wan Hassan, Wan Aminah; Hamerton, Ian; Howlin, Brendan J.

    2013-01-01

    Molecular simulation is becoming an important tool for both understanding polymeric structures and predicting their physical and mechanical properties. In this study, temperature ramped molecular dynamics simulations are used to predict two physical properties (i.e., glass transition temperature and thermal degradation temperature) of a previously synthesised and published telechelic benzoxazine. Plots of simulated density versus temperature show decreases in density within the same temperature range as experimental values for the thermal degradation. The predicted value for the thermal degradation temperature for the cured polybenzoxazine based on the telechelic polyetherketone (PEK) monomer was ca. 400°C, in line with the experimental thermal degradation temperature range of 450°C to 500°C. Mechanical Properties of both the unmodified PEK and the telechelic benzoxazines are simulated and compared to experimental values (where available). The introduction of the benoxazine moieties are predicted to increase the elastic moduli in line with the increase of crosslinking in the system. PMID:23577206

  3. Predicting molecular scale skin-effect in electrochemical impedance due to anomalous subdiffusion mediated adsorption phenomenon

    NASA Astrophysics Data System (ADS)

    Kushagra, Arindam

    2016-02-01

    Anomalous subdiffusion governs the processes which are not energetically driven, on a molecular scale. This paper proposes a model to predict the response of electrochemical impedance due to such diffusion process. Previous works considered the use of fractional calculus to predict the impedance behaviour in response to the anomalous diffusion. Here, we have developed an expression which predicts the skin-effect, marked by an increase in the impedance with increasing frequency, in this regime. Negative inductances have also been predicted as a consequence of the inertial response of adsorbed species upon application of frequency-mediated perturbations. It might help the researchers in the fields of impedimetric sensors to choose the working frequency and those working in the field of batteries to choose the parameters, likewise. This work would shed some light into the molecular mechanisms governing the impedance when exposed to frequency-based perturbations like electromagnetic waves (microwaves to ionizing radiations) and in charge storage devices like batteries etc.

  4. Clinical Features That Predict the Need for Operative Intervention in Gluteus Medius Tears

    PubMed Central

    Chandrasekaran, Sivashankar; Vemula, S. Pavan; Gui, Chengcheng; Suarez-Ahedo, Carlos; Lodhia, Parth; Domb, Benjamin G.

    2015-01-01

    Background: Gluteus medius tears are a common cause of lateral hip pain. Operative intervention is usually prescribed for patients with pain despite physical therapy and/or peritrochanteric injections. Purpose: To identify clinical features that predict operative intervention in gluteus medius tears. Study Design: Case control study; Level of evidence, 3. Methods: A matched-pair controlled study was conducted on patients who underwent endoscopic gluteus medius repairs from June 2008 to August 2014 for full-thickness tears. The exclusion criterion was previous hip disorders (eg, Legg-Calve-Perthes disease, avascular necrosis). The control group contained patients with full-thickness gluteus medius tears on magnetic resonance imaging (MRI) who did not require operative intervention. Both groups had a minimum trial of 3 months of nonoperative management. Matching criteria included age within 5 years, sex, and body mass index (BMI) class. The following clinical parameters were analyzed: presence of lateral-sided hip pain, duration of symptoms, power of resisted hip abduction, gait deviation (antalgic or Trendelenburg), greater trochanter tenderness, and hip passive range of abduction. Results: Twenty-four patients who underwent isolated endoscopic gluteus medius repairs were identified; all patients were females, with a mean age of 65 years (range, 52-82 years) and mean BMI of 29.2 kg/m2 (range, 21.55-44.398 kg/m2). The matched control cohort contained 12 females treated nonoperatively for gluteus medius tears with mean age of 66 years (range, 52-81 years) and mean BMI of 29.9 kg/m2 (range, 20.20-43.59 kg/m2). There were significant differences between the groups in power of resisted abduction and presence of gait deviation. The operative cohort had a mean power grading of 3.63 (95% CI, 3.28-3.98) compared with 4.58 (95% CI, 4.29-4.87) for the matched cohort (P < .05). Abnormal gait was found in 75% of the operative cohort, compared with 33% of the matched cohort (P

  5. Application of computer-extracted breast tissue texture features in predicting false-positive recalls from screening mammography

    NASA Astrophysics Data System (ADS)

    Ray, Shonket; Choi, Jae Y.; Keller, Brad M.; Chen, Jinbo; Conant, Emily F.; Kontos, Despina

    2014-03-01

    Mammographic texture features have been shown to have value in breast cancer risk assessment. Previous models have also been developed that use computer-extracted mammographic features of breast tissue complexity to predict the risk of false-positive (FP) recall from breast cancer screening with digital mammography. This work details a novel locallyadaptive parenchymal texture analysis algorithm that identifies and extracts mammographic features of local parenchymal tissue complexity potentially relevant for false-positive biopsy prediction. This algorithm has two important aspects: (1) the adaptive nature of automatically determining an optimal number of region-of-interests (ROIs) in the image and each ROI's corresponding size based on the parenchymal tissue distribution over the whole breast region and (2) characterizing both the local and global mammographic appearances of the parenchymal tissue that could provide more discriminative information for FP biopsy risk prediction. Preliminary results show that this locallyadaptive texture analysis algorithm, in conjunction with logistic regression, can predict the likelihood of false-positive biopsy with an ROC performance value of AUC=0.92 (p<0.001) with a 95% confidence interval [0.77, 0.94]. Significant texture feature predictors (p<0.05) included contrast, sum variance and difference average. Sensitivity for false-positives was 51% at the 100% cancer detection operating point. Although preliminary, clinical implications of using prediction models incorporating these texture features may include the future development of better tools and guidelines regarding personalized breast cancer screening recommendations. Further studies are warranted to prospectively validate our findings in larger screening populations and evaluate their clinical utility.

  6. From molecular signatures to predictive biomarkers: modeling disease pathophysiology and drug mechanism of action.

    PubMed

    Heinzel, Andreas; Perco, Paul; Mayer, Gert; Oberbauer, Rainer; Lukas, Arno; Mayer, Bernd

    2014-01-01

    Omics profiling significantly expanded the molecular landscape describing clinical phenotypes. Association analysis resulted in first diagnostic and prognostic biomarker signatures entering clinical utility. However, utilizing Omics for deepening our understanding of disease pathophysiology, and further including specific interference with drug mechanism of action on a molecular process level still sees limited added value in the clinical setting. We exemplify a computational workflow for expanding from statistics-based association analysis toward deriving molecular pathway and process models for characterizing phenotypes and drug mechanism of action. Interference analysis on the molecular model level allows identification of predictive biomarker candidates for testing drug response. We discuss this strategy on diabetic nephropathy (DN), a complex clinical phenotype triggered by diabetes and presenting with renal as well as cardiovascular endpoints. A molecular pathway map indicates involvement of multiple molecular mechanisms, and selected biomarker candidates reported as associated with disease progression are identified for specific molecular processes. Selective interference of drug mechanism of action and disease-associated processes is identified for drug classes in clinical use, in turn providing precision medicine hypotheses utilizing predictive biomarkers.

  7. Distinguishing Molecular Features and Clinical Characteristics of a Putative New Rhinovirus Species, Human Rhinovirus C (HRV C)

    PubMed Central

    McErlean, Peter; Shackelton, Laura A.; Andrews, Emily; Webster, Dale R.; Lambert, Stephen B.; Nissen, Michael D.; Sloots, Theo P.; Mackay, Ian M.

    2008-01-01

    Background Human rhinoviruses (HRVs) are the most frequently detected pathogens in acute respiratory tract infections (ARTIs) and yet little is known about the prevalence, recurrence, structure and clinical impact of individual members. During 2007, the complete coding sequences of six previously unknown and highly divergent HRV strains were reported. To catalogue the molecular and clinical features distinguishing the divergent HRV strains, we undertook, for the first time, in silico analyses of all available polyprotein sequences and performed retrospective reviews of the medical records of cases in which variants of the prototype strain, HRV-QPM, had been detected. Methodology/Principle Findings Genomic analyses revealed that the six divergent strains, residing within a clade we previously called HRV A2, had the shortest polyprotein of all picornaviruses investigated. Structure-based amino acid alignments identified conserved motifs shared among members of the genus Rhinovirus as well as substantive deletions and insertions unique to the divergent strains. Deletions mostly affected regions encoding proteins traditionally involved in antigenicity and serving as HRV and HEV receptor footprints. Because the HRV A2 strains cannot yet be cultured, we created homology models of predicted HRV-QPM structural proteins. In silico comparisons confirmed that HRV-QPM was most closely related to the major group HRVs. HRV-QPM was most frequently detected in infants with expiratory wheezing or persistent cough who had been admitted to hospital and required supplemental oxygen. It was the only virus detected in 65% of positive individuals. These observations contributed to an objective clinical impact ranging from mild to severe. Conclusions The divergent strains did not meet classification requirements for any existing species of the genus Rhinovirus or Enterovirus. HRV A2 strains should be partitioned into at least one new species, putatively called Human rhinovirus C

  8. Tracking the Correlation Between CpG Island Methylator Phenotype and Other Molecular Features and Clinicopathological Features in Human Colorectal Cancers: A Systematic Review and Meta-Analysis

    PubMed Central

    Zong, Liang; Abe, Masanobu; Ji, Jiafu; Zhu, Wei-Guo; Yu, Duonan

    2016-01-01

    Objectives: The controversy of CpG island methylator phenotype (CIMP) in colorectal cancers (CRCs) persists, despite many studies that have been conducted on its correlation with molecular and clinicopathological features. To drive a more precise estimate of the strength of this postulated relationship, a meta-analysis was performed. Methods: A comprehensive search for studies reporting molecular and clinicopathological features of CRCs stratified by CIMP was performed within the PubMed, EMBASE, and Cochrane Library. CIMP was defined by either one of the three panels of gene-specific CIMP markers (Weisenberger panel, classic panel, or a mixture panel of the previous two) or the genome-wide DNA methylation profile. The associations of CIMP with outcome parameters were estimated using odds ratio (OR) or weighted mean difference (WMD) or hazard ratios (HRs) with 95% confidence interval (CI) for each study using a fixed effects or random effects model. Results: A total of 29 studies involving 9,393 CRC patients were included for analysis. We observed more BRAF mutations (OR 34.87; 95% CI, 22.49–54.06) and microsatellite instability (MSI) (OR 12.85 95% CI, 8.84–18.68) in CIMP-positive vs. -negative CRCs, whereas KRAS mutations were less frequent (OR 0.47; 95% CI, 0.30–0.75). Subgroup analysis showed that only the genome-wide methylation profile-defined CIMP subset encompassed all BRAF-mutated CRCs. As expected, CIMP-positive CRCs displayed significant associations with female (OR 0.64; 95% CI, 0.56–0.72), older age at diagnosis (WMD 2.77; 95% CI, 1.15–4.38), proximal location (OR 6.91; 95% CI, 5.17–9.23), mucinous histology (OR 3.81; 95% CI, 2.93–4.95), and poor differentiation (OR 4.22; 95% CI, 2.52–7.08). Although CIMP did not show a correlation with tumor stage (OR 1.10; 95% CI, 0.82–1.46), it was associated with shorter overall survival (HR 1.73; 95% CI, 1.27–2.37). Conclusions: The meta-analysis highlights that CIMP-positive CRCs take their own

  9. Caudate nucleus reactivity predicts perceptual learning rate for visual feature conjunctions.

    PubMed

    Reavis, Eric A; Frank, Sebastian M; Tse, Peter U

    2015-04-15

    Useful information in the visual environment is often contained in specific conjunctions of visual features (e.g., color and shape). The ability to quickly and accurately process such conjunctions can be learned. However, the neural mechanisms responsible for such learning remain largely unknown. It has been suggested that some forms of visual learning might involve the dopaminergic neuromodulatory system (Roelfsema et al., 2010; Seitz and Watanabe, 2005), but this hypothesis has not yet been directly tested. Here we test the hypothesis that learning visual feature conjunctions involves the dopaminergic system, using functional neuroimaging, genetic assays, and behavioral testing techniques. We use a correlative approach to evaluate potential associations between individual differences in visual feature conjunction learning rate and individual differences in dopaminergic function as indexed by neuroimaging and genetic markers. We find a significant correlation between activity in the caudate nucleus (a component of the dopaminergic system connected to visual areas of the brain) and visual feature conjunction learning rate. Specifically, individuals who showed a larger difference in activity between positive and negative feedback on an unrelated cognitive task, indicative of a more reactive dopaminergic system, learned visual feature conjunctions more quickly than those who showed a smaller activity difference. This finding supports the hypothesis that the dopaminergic system is involved in visual learning, and suggests that visual feature conjunction learning could be closely related to associative learning. However, no significant, reliable correlations were found between feature conjunction learning and genotype or dopaminergic activity in any other regions of interest.

  10. Structural and molecular features of intestinal strictures in rats with Crohn's-like disease

    PubMed Central

    Talapka, Petra; Berkó, Anikó; Nagy, Lajos István; Chandrakumar, Lalitha; Bagyánszki, Mária; Puskás, László Géza; Fekete, Éva; Bódi, Nikolett

    2016-01-01

    AIM: To develop a new rat model we wanted to gain a better understanding of stricture formation in Crohn’s disease (CD). METHODS: Chronic colitis was induced locally by the administration of 2,4,6-trinitrobenzenesulfonic acid (TNBS). The relapsing inflammation characteristic to CD was mimicked by repeated TNBS treatments. Animals were randomly divided into control, once, twice and three times TNBS-treated groups. Control animals received an enema of saline. Tissue samples were taken from the strictured colonic segments and also adjacent proximally and distally to its 60, 90 or 120 d after the last TNBS or saline administrations. The frequency and macroscopic extent of the strictures were measured on digital photographs. The structural features of strictured gut wall were studied by light- and electron microscopy. Inflammation related alterations in TGF-beta 2 and 3, matrix metalloproteinases 9 (MMP9) and TIMP1 mRNA and protein expression were determined by quantitative real-time PCR and western blot analysis. The quantitative distribution of caspase 9 was determined by post-embedding immunohistochemistry. RESULTS: Intestinal strictures first appeared 60 d after TNBS treatments and the frequency of them increased up to day 120. From day 90 an intact lamina epithelialis, reversible thickening of lamina muscularis mucosae and irreversible thickening of the muscularis externa were demonstrated in the strictured colonic segments. Nevertheless the morphological signs of apoptosis were frequently seen and excess extracellular matrix deposition was recorded between smooth muscle cells (SMCs). Enhanced caspase 9 expression on day 90 in the SMCs and on day 120 also in myenteric neurons indicated the induction of apoptosis. The mRNA expression profile of TGF-betas after repeated TNBS doses was characteristic to CD, TGF-beta 2, but not TGF-beta 3 was up-regulated. Overexpression of MMP9 and down-regulation of TIMP1 were demonstrated. The progressive increase in the amount of

  11. Application of Molecular Dynamics Simulations in Molecular Property Prediction II: Diffusion Coefficient

    PubMed Central

    Wang, Junmei; Hou, Tingjun

    2011-01-01

    In this work, we have evaluated how well the General AMBER force field (GAFF) performs in studying the dynamic properties of liquids. Diffusion coefficients (D) have been predicted for 17 solvents, 5 organic compounds in aqueous solutions, 4 proteins in aqueous solutions, and 9 organic compounds in non-aqueous solutions. An efficient sampling strategy has been proposed and tested in the calculation of the diffusion coefficients of solutes in solutions. There are two major findings of this study. First of all, the diffusion coefficients of organic solutes in aqueous solution can be well predicted: the average unsigned error (AUE) and the root-mean-square error (RMSE) are 0.137 and 0.171 ×10−5 cm−2s−1, respectively. Second, although the absolute values of D cannot be predicted, good correlations have been achieved for 8 organic solvents with experimental data (R2 = 0.784), 4 proteins in aqueous solutions (R2 = 0.996) and 9 organic compounds in non-aqueous solutions (R2 = 0.834). The temperature dependent behaviors of three solvents, namely, TIP3P water, dimethyl sulfoxide (DMSO) and cyclohexane have been studied. The major MD settings, such as the sizes of simulation boxes and with/without wrapping the coordinates of MD snapshots into the primary simulation boxes have been explored. We have concluded that our sampling strategy that averaging the mean square displacement (MSD) collected in multiple short-MD simulations is efficient in predicting diffusion coefficients of solutes at infinite dilution. PMID:21953689

  12. Collision cross section prediction of deprotonated phenolics in a travelling-wave ion mobility spectrometer using molecular descriptors and chemometrics.

    PubMed

    Gonzales, Gerard Bryan; Smagghe, Guy; Coelus, Sofie; Adriaenssens, Dieter; De Winter, Karel; Desmet, Tom; Raes, Katleen; Van Camp, John

    2016-06-14

    The combination of ion mobility and mass spectrometry (MS) affords significant improvements over conventional MS/MS, especially in the characterization of isomeric metabolites due to the differences in their collision cross sections (CCS). Experimentally obtained CCS values are typically matched with theoretical CCS values from Trajectory Method (TM) and/or Projection Approximation (PA) calculations. In this paper, predictive models for CCS of deprotonated phenolics were developed using molecular descriptors and chemometric tools, stepwise multiple linear regression (SMLR), principal components regression (PCR), and partial least squares regression (PLS). A total of 102 molecular descriptors were generated and reduced to 28 after employing a feature selection tool, composed of mass, topological descriptors, Jurs descriptors and shadow indices. Therefore, the generated models considered the effects of mass, 3D conformation and partial charge distribution on CCS, which are the main parameters for either TM or PA (only 3D conformation) calculations. All three techniques yielded highly predictive models for both the training (R(2)SMLR = 0.9911; R(2)PCR = 0.9917; R(2)PLS = 0.9918) and validation datasets (R(2)SMLR = 0.9489; R(2)PCR = 0.9761; R(2)PLS = 0.9760). Also, the high cross validated R(2) values indicate that the generated models are robust and highly predictive (Q(2)SMLR = 0.9859; Q(2)PCR = 0.9748; Q(2)PLS = 0.9760). The predictions were also very comparable to the results from TM calculations using modified mobcal (N2). Most importantly, this method offered a rapid (<10 min) alternative to TM calculations without compromising predictive ability. These methods could therefore be used in routine analysis and could be easily integrated to metabolite identification platforms. PMID:27181646

  13. Machine learning for molecular scattering dynamics: Gaussian Process models for improved predictions of molecular collision observables

    NASA Astrophysics Data System (ADS)

    Krems, Roman; Cui, Jie; Li, Zhiying

    2016-05-01

    We show how statistical learning techniques based on kriging (Gaussian Process regression) can be used for improving the predictions of classical and/or quantum scattering theory. In particular, we show how Gaussian Process models can be used for: (i) efficient non-parametric fitting of multi-dimensional potential energy surfaces without the need to fit ab initio data with analytical functions; (ii) obtaining scattering observables as functions of individual PES parameters; (iii) using classical trajectories to interpolate quantum results; (iv) extrapolation of scattering observables from one molecule to another; (v) obtaining scattering observables with error bars reflecting the inherent inaccuracy of the underlying potential energy surfaces. We argue that the application of Gaussian Process models to quantum scattering calculations may potentially elevate the theoretical predictions to the same level of certainty as the experimental measurements and can be used to identify the role of individual atoms in determining the outcome of collisions of complex molecules. We will show examples and discuss the applications of Gaussian Process models to improving the predictions of scattering theory relevant for the cold molecules research field. Work supported by NSERC of Canada.

  14. Features of Knowledge Building in Biology: Understanding Undergraduate Students' Ideas about Molecular Mechanisms

    ERIC Educational Resources Information Center

    Southard, Katelyn; Wince, Tyler; Meddleton, Shanice; Bolger, Molly S.

    2016-01-01

    Research has suggested that teaching and learning in molecular and cellular biology (MCB) is difficult. We used a new lens to understand undergraduate reasoning about molecular mechanisms: the knowledge-integration approach to conceptual change. Knowledge integration is the dynamic process by which learners acquire new ideas, develop connections…

  15. A highly accurate protein structural class prediction approach using auto cross covariance transformation and recursive feature elimination.

    PubMed

    Li, Xiaowei; Liu, Taigang; Tao, Peiying; Wang, Chunhua; Chen, Lanming

    2015-12-01

    Structural class characterizes the overall folding type of a protein or its domain. Many methods have been proposed to improve the prediction accuracy of protein structural class in recent years, but it is still a challenge for the low-similarity sequences. In this study, we introduce a feature extraction technique based on auto cross covariance (ACC) transformation of position-specific score matrix (PSSM) to represent a protein sequence. Then support vector machine-recursive feature elimination (SVM-RFE) is adopted to select top K features according to their importance and these features are input to a support vector machine (SVM) to conduct the prediction. Performance evaluation of the proposed method is performed using the jackknife test on three low-similarity datasets, i.e., D640, 1189 and 25PDB. By means of this method, the overall accuracies of 97.2%, 96.2%, and 93.3% are achieved on these three datasets, which are higher than those of most existing methods. This suggests that the proposed method could serve as a very cost-effective tool for predicting protein structural class especially for low-similarity datasets.

  16. Advances in Rosetta structure prediction for difficult molecular-replacement problems

    SciTech Connect

    DiMaio, Frank

    2013-11-01

    Modeling advances using Rosetta structure prediction to aid in solving difficult molecular-replacement problems are discussed. Recent work has shown the effectiveness of structure-prediction methods in solving difficult molecular-replacement problems. The Rosetta protein structure modeling suite can aid in the solution of difficult molecular-replacement problems using templates from 15 to 25% sequence identity; Rosetta refinement guided by noisy density has consistently led to solved structures where other methods fail. In this paper, an overview of the use of Rosetta for these difficult molecular-replacement problems is provided and new modeling developments that further improve model quality are described. Several variations to the method are introduced that significantly reduce the time needed to generate a model and the sampling required to improve the starting template. The improvements are benchmarked on a set of nine difficult cases and it is shown that this improved method obtains consistently better models in less running time. Finally, strategies for best using Rosetta to solve difficult molecular-replacement problems are presented and future directions for the role of structure-prediction methods in crystallography are discussed.

  17. Computer-aided global breast MR image feature analysis for prediction of tumor response to chemotherapy: performance assessment

    NASA Astrophysics Data System (ADS)

    Aghaei, Faranak; Tan, Maxine; Hollingsworth, Alan B.; Zheng, Bin; Cheng, Samuel

    2016-03-01

    Dynamic contrast-enhanced breast magnetic resonance imaging (DCE-MRI) has been used increasingly in breast cancer diagnosis and assessment of cancer treatment efficacy. In this study, we applied a computer-aided detection (CAD) scheme to automatically segment breast regions depicting on MR images and used the kinetic image features computed from the global breast MR images acquired before neoadjuvant chemotherapy to build a new quantitative model to predict response of the breast cancer patients to the chemotherapy. To assess performance and robustness of this new prediction model, an image dataset involving breast MR images acquired from 151 cancer patients before undergoing neoadjuvant chemotherapy was retrospectively assembled and used. Among them, 63 patients had "complete response" (CR) to chemotherapy in which the enhanced contrast levels inside the tumor volume (pre-treatment) was reduced to the level as the normal enhanced background parenchymal tissues (post-treatment), while 88 patients had "partially response" (PR) in which the high contrast enhancement remain in the tumor regions after treatment. We performed the studies to analyze the correlation among the 22 global kinetic image features and then select a set of 4 optimal features. Applying an artificial neural network trained with the fusion of these 4 kinetic image features, the prediction model yielded an area under ROC curve (AUC) of 0.83+/-0.04. This study demonstrated that by avoiding tumor segmentation, which is often difficult and unreliable, fusion of kinetic image features computed from global breast MR images without tumor segmentation can also generate a useful clinical marker in predicting efficacy of chemotherapy.

  18. KFC2: a knowledge-based hot spot prediction method based on interface solvation, atomic density, and plasticity features.

    PubMed

    Zhu, Xiaolei; Mitchell, Julie C

    2011-09-01

    Hot spots constitute a small fraction of protein-protein interface residues, yet they account for a large fraction of the binding affinity. Based on our previous method (KFC), we present two new methods (KFC2a and KFC2b) that outperform other methods at hot spot prediction. A number of improvements were made in developing these new methods. First, we created a training data set that contained a similar number of hot spot and non-hot spot residues. In addition, we generated 47 different features, and different numbers of features were used to train the models to avoid over-fitting. Finally, two feature combinations were selected: One (used in KFC2a) is composed of eight features that are mainly related to solvent accessible surface area and local plasticity; the other (KFC2b) is composed of seven features, only two of which are identical to those used in KFC2a. The two models were built using support vector machines (SVM). The two KFC2 models were then tested on a mixed independent test set, and compared with other methods such as Robetta, FOLDEF, HotPoint, MINERVA, and KFC. KFC2a showed the highest predictive accuracy for hot spot residues (True Positive Rate: TPR = 0.85); however, the false positive rate was somewhat higher than for other models. KFC2b showed the best predictive accuracy for hot spot residues (True Positive Rate: TPR = 0.62) among all methods other than KFC2a, and the False Positive Rate (FPR = 0.15) was comparable with other highly predictive methods.

  19. PREDICTION OF MOLECULAR PROPERTIES WITH MID-INFRARED SPECTRA AND INTERFEROGRAMS

    EPA Science Inventory

    We have built infrared spectroscopy-based partial least squares (PLS) models for molecular polarizabilities using a 97 member training set and a 59 member independent prediction set. These 156 compounds span a very wide range of chemical structure. Our goal was to use this well...

  20. Molecular modeling as a predictive tool for the development of solid dispersions.

    PubMed

    Maniruzzaman, Mohammed; Pang, Jiayun; Morgan, David J; Douroumis, Dennis

    2015-04-01

    In this study molecular modeling is introduced as a novel approach for the development of pharmaceutical solid dispersions. A computational model based on quantum mechanical (QM) calculations was used to predict the miscibility of various drugs in various polymers by predicting the binding strength between the drug and dimeric form of the polymer. The drug/polymer miscibility was also estimated by using traditional approaches such as Van Krevelen/Hoftyzer and Bagley solubility parameters or Flory-Huggins interaction parameter in comparison to the molecular modeling approach. The molecular modeling studies predicted successfully the drug-polymer binding energies and the preferable site of interaction between the functional groups. The drug-polymer miscibility and the physical state of bulk materials, physical mixtures, and solid dispersions were determined by thermal analysis (DSC/MTDSC) and X-ray diffraction. The produced solid dispersions were analyzed by X-ray photoelectron spectroscopy (XPS), which confirmed not only the exact type of the intermolecular interactions between the drug-polymer functional groups but also the binding strength by estimating the N coefficient values. The findings demonstrate that QM-based molecular modeling is a powerful tool to predict the strength and type of intermolecular interactions in a range of drug/polymeric systems for the development of solid dispersions. PMID:25734898

  1. Molecular modeling as a predictive tool for the development of solid dispersions.

    PubMed

    Maniruzzaman, Mohammed; Pang, Jiayun; Morgan, David J; Douroumis, Dennis

    2015-04-01

    In this study molecular modeling is introduced as a novel approach for the development of pharmaceutical solid dispersions. A computational model based on quantum mechanical (QM) calculations was used to predict the miscibility of various drugs in various polymers by predicting the binding strength between the drug and dimeric form of the polymer. The drug/polymer miscibility was also estimated by using traditional approaches such as Van Krevelen/Hoftyzer and Bagley solubility parameters or Flory-Huggins interaction parameter in comparison to the molecular modeling approach. The molecular modeling studies predicted successfully the drug-polymer binding energies and the preferable site of interaction between the functional groups. The drug-polymer miscibility and the physical state of bulk materials, physical mixtures, and solid dispersions were determined by thermal analysis (DSC/MTDSC) and X-ray diffraction. The produced solid dispersions were analyzed by X-ray photoelectron spectroscopy (XPS), which confirmed not only the exact type of the intermolecular interactions between the drug-polymer functional groups but also the binding strength by estimating the N coefficient values. The findings demonstrate that QM-based molecular modeling is a powerful tool to predict the strength and type of intermolecular interactions in a range of drug/polymeric systems for the development of solid dispersions.

  2. Molecular dynamics-based ion-surface interaction models for ionized physical vapor deposition feature scale simulations

    SciTech Connect

    Coronell, D.G.; Hansen, D.E.; Voter, A.F.; Liu, C.; Liu, X.; Kress, J.D.

    1998-12-01

    A procedure is presented for incorporating the results of atomistic simulations of ion{endash}surface interactions into integrated circuit topographic simulations of ionized physical vapor deposition (PVD). Energy and angular dependent sticking probabilities for energetic Cu atoms impacting a {l_brace}111{r_brace} Cu surface, obtained from molecular dynamics simulations, were implemented in a simple Monte Carlo flux model. The resulting flux-averaged Cu sticking probability was found to vary significantly with position within submicron features and with the feature geometry. This illustrates the shortcomings of a constant (energy and angle independent) sticking probability model for ionized PVD. {copyright} {ital 1998 American Institute of Physics.}

  3. Predicting Ki67% expression from DCE-MR images of breast tumors using textural kinetic features in tumor habitats

    NASA Astrophysics Data System (ADS)

    Chaudhury, Baishali; Zhou, Mu; Farhidzadeh, Hamidreza; Goldgof, Dmitry B.; Hall, Lawrence O.; Gatenby, Robert A.; Gillies, Robert J.; Weinfurtner, Robert J.; Drukteinis, Jennifer S.

    2016-03-01

    The use of Ki67% expression, a cell proliferation marker, as a predictive and prognostic factor has been widely studied in the literature. Yet its usefulness is limited due to inconsistent cut off scores for Ki67% expression, subjective differences in its assessment in various studies, and spatial variation in expression, which makes it difficult to reproduce as a reliable independent prognostic factor. Previous studies have shown that there are significant spatial variations in Ki67% expression, which may limit its clinical prognostic utility after core biopsy. These variations are most evident when examining the periphery of the tumor vs. the core. To date, prediction of Ki67% expression from quantitative image analysis of DCE-MRI is very limited. This work presents a novel computer aided diagnosis framework to use textural kinetics to (i) predict the ratio of periphery Ki67% expression to core Ki67% expression, and (ii) predict Ki67% expression from individual tumor habitats. The pilot cohort consists of T1 weighted fat saturated DCE-MR images from 17 patients. Support vector regression with a radial basis function was used for predicting the Ki67% expression and ratios. The initial results show that texture features from individual tumor habitats are more predictive of the Ki67% expression ratio and spatial Ki67% expression than features from the whole tumor. The Ki67% expression ratio could be predicted with a root mean square error (RMSE) of 1.67%. Quantitative image analysis of DCE-MRI using textural kinetic habitats, has the potential to be used as a non-invasive method for predicting Ki67 percentage and ratio, thus more accurately reporting high KI-67 expression for patient prognosis.

  4. Comparison of Algorithms for Prediction of Protein Structural Features from Evolutionary Data

    PubMed Central

    Bywater, Robert P.

    2016-01-01

    Proteins have many functions and predicting these is still one of the major challenges in theoretical biophysics and bioinformatics. Foremost amongst these functions is the need to fold correctly thereby allowing the other genetically dictated tasks that the protein has to carry out to proceed efficiently. In this work, some earlier algorithms for predicting protein domain folds are revisited and they are compared with more recently developed methods. In dealing with intractable problems such as fold prediction, when different algorithms show convergence onto the same result there is every reason to take all algorithms into account such that a consensus result can be arrived at. In this work it is shown that the application of different algorithms in protein structure prediction leads to results that do not converge as such but rather they collude in a striking and useful way that has never been considered before. PMID:26963911

  5. Comparison of Algorithms for Prediction of Protein Structural Features from Evolutionary Data.

    PubMed

    Bywater, Robert P

    2016-01-01

    Proteins have many functions and predicting these is still one of the major challenges in theoretical biophysics and bioinformatics. Foremost amongst these functions is the need to fold correctly thereby allowing the other genetically dictated tasks that the protein has to carry out to proceed efficiently. In this work, some earlier algorithms for predicting protein domain folds are revisited and they are compared with more recently developed methods. In dealing with intractable problems such as fold prediction, when different algorithms show convergence onto the same result there is every reason to take all algorithms into account such that a consensus result can be arrived at. In this work it is shown that the application of different algorithms in protein structure prediction leads to results that do not converge as such but rather they collude in a striking and useful way that has never been considered before.

  6. Investigation on the isoform selectivity of novel kinesin-like protein 1 (KIF11) inhibitor using chemical feature based pharmacophore, molecular docking, and quantum mechanical studies.

    PubMed

    Karunagaran, Subramanian; Subhashchandrabose, Subramaniyan; Lee, Keun Woo; Meganathan, Chandrasekaran

    2016-04-01

    Kinesin-like protein (KIF11) is a molecular motor protein that is essential in mitosis. Removal of KIF11 prevents centrosome migration and causes cell arrest in mitosis. KIF11 defects are linked to the disease of microcephaly, lymph edema or mental retardation. The human KIF11 protein has been actively studied for its role in mitosis and its potential as a therapeutic target for cancer treatment. Pharmacophore modeling, molecular docking and density functional theory approaches was employed to reveal the structural, chemical and electronic features essential for the development of small molecule inhibitor for KIF11. Hence we have developed chemical feature based pharmacophore models using Discovery Studio v 2.5 (DS). The best hypothesis (Hypo1) consisting of four chemical features (two hydrogen bond acceptor, one hydrophobic and one ring aromatic) has exhibited high correlation co-efficient of 0.9521, cost difference of 70.63 and low RMS value of 0.9475. This Hypo1 is cross validated by Cat Scramble method; test set and decoy set to prove its robustness, statistical significance and predictability respectively. The well validated Hypo1 was used as 3Dquery to perform virtual screening. The hits obtained from the virtual screening were subjected to various scrupulous drug-like filters such as Lipinski's rule of five and ADMET properties. Finally, six hit compounds were identified based on the molecular interaction and its electronic properties. Our final lead compound could serve as a powerful tool for the discovery of potent inhibitor as KIF11 agonists. PMID:26815769

  7. Induction of CaSR expression circumvents the molecular features of malignant CaSR null colon cancer cells.

    PubMed

    Singh, Navneet; Chakrabarty, Subhas

    2013-11-15

    We recently reported on the isolation and characterization of calcium sensing receptor (CaSR) null human colon cancer cells (Singh et al., Int J Cancer 2013; 132: 1996-2005). CaSR null cells possess a myriad of molecular features that are linked to a highly malignant and drug resistant phenotype of colon cancer. The CaSR null phenotype can be maintained in defined human embryonic stem cell culture medium. We now show that the CaSR null cells can be induced to differentiate in conventional culture medium, regained the expression of CaSR with a concurrent reversal of the cellular and molecular features associated with the null phenotype. These features include cellular morphology, expression of colon cancer stem cell markers, expression of survivin and thymidylate synthase and sensitivity to fluorouracil. Other features include the expression of epithelial mesenchymal transition linked molecules and transcription factors, oncogenic miRNAs and tumor suppressive molecule and miRNA. With the exception of cancer stem cell markers, the reversal of molecular features, upon the induction of CaSR expression, is directly linked to the expression and function of CaSR because blocking CaSR induction by shRNA circumvented such reversal. We further report that methylation and demethylation of the CaSR gene promoter underlie CaSR expression. Due to the malignant nature of the CaSR null cells, inclusion of the CaSR null phenotype in disease management may improve on the mortality of this disease. Because CaSR is a robust promoter of differentiation and mediates its action through diverse mechanisms and pathways, inactivation of CaSR may serve as a new paradigm in colon carcinogenesis.

  8. The Use of Molecular Modeling and VSEPR Theory in the Undergraduate Curriculum to Predict the Three-Dimensional Structure of Molecules

    NASA Astrophysics Data System (ADS)

    Pfennig, Brian W.; Frock, Richard L.

    1999-07-01

    This paper illustrates how valence shell electron pair repulsion (VSEPR) theory and molecular modeling can be used in a complimentary fashion in the undergraduate curriculum to predict the three-dimensional structure of molecules. Students use the familiar VSEPR model to sketch the three-dimensional structures of molecules predicted by a comparison of the relative magnitudes of electrostatic repulsion between electron pair domains on the central atom. Despite the simplicity and elegance of the VSEPR model, however, students often have difficulty visualizing the three-dimensional shapes of molecules and learning the more subtle features of the model, such as the bond length and bond angle deviations from ideal geometry that accompany the presence of lone pair or multiple bond domains or that result from differences in the electronegativity of the bonded atoms, partial charges and molecular dipole moments, and site preferences in the trigonal bipyramidal electron geometry. Students therefore also employ a molecular modeling approach to predict the three-dimensional shapes of molecules by performing both molecular mechanics and semiempirical quantum mechanical calculations to minimize the structures of these molecules. A major strength of the molecular modeling approach is that it allows students to quantitatively explore the more subtle implications of VSEPR theory using an alternative model. Student responses on end-of-the-semester evaluations ranked this exercise highly among the seven projects performed during the semester, commenting specifically on how the software allowed them to visualize the three-dimensional aspects of molecules more effectively than simple ball-and-stick models and on how the molecular modeling helped them to explore the more detailed structural features predicted by VSEPR theory.

  9. Applying quantitative adiposity feature analysis models to predict benefit of bevacizumab-based chemotherapy in ovarian cancer patients

    NASA Astrophysics Data System (ADS)

    Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; More, Kathleen; Ding, Kai; Liu, Hong; Zheng, Bin

    2016-03-01

    How to rationally identify epithelial ovarian cancer (EOC) patients who will benefit from bevacizumab or other antiangiogenic therapies is a critical issue in EOC treatments. The motivation of this study is to quantitatively measure adiposity features from CT images and investigate the feasibility of predicting potential benefit of EOC patients with or without receiving bevacizumab-based chemotherapy treatment using multivariate statistical models built based on quantitative adiposity image features. A dataset involving CT images from 59 advanced EOC patients were included. Among them, 32 patients received maintenance bevacizumab after primary chemotherapy and the remaining 27 patients did not. We developed a computer-aided detection (CAD) scheme to automatically segment subcutaneous fat areas (VFA) and visceral fat areas (SFA) and then extracted 7 adiposity-related quantitative features. Three multivariate data analysis models (linear regression, logistic regression and Cox proportional hazards regression) were performed respectively to investigate the potential association between the model-generated prediction results and the patients' progression-free survival (PFS) and overall survival (OS). The results show that using all 3 statistical models, a statistically significant association was detected between the model-generated results and both of the two clinical outcomes in the group of patients receiving maintenance bevacizumab (p<0.01), while there were no significant association for both PFS and OS in the group of patients without receiving maintenance bevacizumab. Therefore, this study demonstrated the feasibility of using quantitative adiposity-related CT image features based statistical prediction models to generate a new clinical marker and predict the clinical outcome of EOC patients receiving maintenance bevacizumab-based chemotherapy.

  10. Downstream Antisense Transcription Predicts Genomic Features That Define the Specific Chromatin Environment at Mammalian Promoters.

    PubMed

    Lavender, Christopher A; Cannady, Kimberly R; Hoffman, Jackson A; Trotter, Kevin W; Gilchrist, Daniel A; Bennett, Brian D; Burkholder, Adam B; Burd, Craig J; Fargo, David C; Archer, Trevor K

    2016-08-01

    Antisense transcription is a prevalent feature at mammalian promoters. Previous studies have primarily focused on antisense transcription initiating upstream of genes. Here, we characterize promoter-proximal antisense transcription downstream of gene transcription starts sites in human breast cancer cells, investigating the genomic context of downstream antisense transcription. We find extensive correlations between antisense transcription and features associated with the chromatin environment at gene promoters. Antisense transcription downstream of promoters is widespread, with antisense transcription initiation observed within 2 kb of 28% of gene transcription start sites. Antisense transcription initiates between nucleosomes regularly positioned downstream of these promoters. The nucleosomes between gene and downstream antisense transcription start sites carry histone modifications associated with active promoters, such as H3K4me3 and H3K27ac. This region is bound by chromatin remodeling and histone modifying complexes including SWI/SNF subunits and HDACs, suggesting that antisense transcription or resulting RNA transcripts contribute to the creation and maintenance of a promoter-associated chromatin environment. Downstream antisense transcription overlays additional regulatory features, such as transcription factor binding, DNA accessibility, and the downstream edge of promoter-associated CpG islands. These features suggest an important role for antisense transcription in the regulation of gene expression and the maintenance of a promoter-associated chromatin environment. PMID:27487356

  11. Downstream Antisense Transcription Predicts Genomic Features That Define the Specific Chromatin Environment at Mammalian Promoters

    PubMed Central

    Lavender, Christopher A.; Hoffman, Jackson A.; Trotter, Kevin W.; Gilchrist, Daniel A.; Bennett, Brian D.; Burkholder, Adam B.; Fargo, David C.; Archer, Trevor K.

    2016-01-01

    Antisense transcription is a prevalent feature at mammalian promoters. Previous studies have primarily focused on antisense transcription initiating upstream of genes. Here, we characterize promoter-proximal antisense transcription downstream of gene transcription starts sites in human breast cancer cells, investigating the genomic context of downstream antisense transcription. We find extensive correlations between antisense transcription and features associated with the chromatin environment at gene promoters. Antisense transcription downstream of promoters is widespread, with antisense transcription initiation observed within 2 kb of 28% of gene transcription start sites. Antisense transcription initiates between nucleosomes regularly positioned downstream of these promoters. The nucleosomes between gene and downstream antisense transcription start sites carry histone modifications associated with active promoters, such as H3K4me3 and H3K27ac. This region is bound by chromatin remodeling and histone modifying complexes including SWI/SNF subunits and HDACs, suggesting that antisense transcription or resulting RNA transcripts contribute to the creation and maintenance of a promoter-associated chromatin environment. Downstream antisense transcription overlays additional regulatory features, such as transcription factor binding, DNA accessibility, and the downstream edge of promoter-associated CpG islands. These features suggest an important role for antisense transcription in the regulation of gene expression and the maintenance of a promoter-associated chromatin environment. PMID:27487356

  12. Baseline Shifts do not Predict Attentional Modulation of Target Processing During Feature-Based Visual Attention

    PubMed Central

    Fannon, Sean P.; Saron, Clifford D.; Mangun, George R.

    2007-01-01

    Cues that direct selective attention to a spatial location have been observed to increase baseline neural activity in visual areas that represent a to-be-attended stimulus location. Analogous attention-related baseline shifts have also been observed in response to attention-directing cues for non-spatial stimulus features. It has been proposed that baseline shifts with preparatory attention may serve as the mechanism by which attention modulates the responses to subsequent visual targets that match the attended location or feature. Using functional MRI, we localized color- and motion-sensitive visual areas in individual subjects and investigated the relationship between cue-induced baseline shifts and the subsequent attentional modulation of task-relevant target stimuli. Although attention-directing cues often led to increased background neural activity in feature specific visual areas, these increases were not correlated with either behavior in the task or subsequent attentional modulation of the visual targets. These findings cast doubt on the hypothesis that attention-related shifts in baseline neural activity result in selective sensory processing of visual targets during feature-based selective attention. PMID:18958221

  13. Predicting error in detecting mammographic masses among radiology trainees using statistical models based on BI-RADS features

    SciTech Connect

    Grimm, Lars J. Ghate, Sujata V.; Yoon, Sora C.; Kim, Connie; Kuzmiak, Cherie M.; Mazurowski, Maciej A.

    2014-03-15

    Purpose: The purpose of this study is to explore Breast Imaging-Reporting and Data System (BI-RADS) features as predictors of individual errors made by trainees when detecting masses in mammograms. Methods: Ten radiology trainees and three expert breast imagers reviewed 100 mammograms comprised of bilateral medial lateral oblique and craniocaudal views on a research workstation. The cases consisted of normal and biopsy proven benign and malignant masses. For cases with actionable abnormalities, the experts recorded breast (density and axillary lymph nodes) and mass (shape, margin, and density) features according to the BI-RADS lexicon, as well as the abnormality location (depth and clock face). For each trainee, a user-specific multivariate model was constructed to predict the trainee's likelihood of error based on BI-RADS features. The performance of the models was assessed using area under the receive operating characteristic curves (AUC). Results: Despite the variability in errors between different trainees, the individual models were able to predict the likelihood of error for the trainees with a mean AUC of 0.611 (range: 0.502–0.739, 95% Confidence Interval: 0.543–0.680,p < 0.002). Conclusions: Patterns in detection errors for mammographic masses made by radiology trainees can be modeled using BI-RADS features. These findings may have potential implications for the development of future educational materials that are personalized to individual trainees.

  14. Quality Assessment of Predicted Protein Models Using Energies Calculated by the Fragment Molecular Orbital Method.

    PubMed

    Simoncini, David; Nakata, Hiroya; Ogata, Koji; Nakamura, Shinichiro; Zhang, Kam Yj

    2015-02-01

    Protein structure prediction directly from sequences is a very challenging problem in computational biology. One of the most successful approaches employs stochastic conformational sampling to search an empirically derived energy function landscape for the global energy minimum state. Due to the errors in the empirically derived energy function, the lowest energy conformation may not be the best model. We have evaluated the use of energy calculated by the fragment molecular orbital method (FMO energy) to assess the quality of predicted models and its ability to identify the best model among an ensemble of predicted models. The fragment molecular orbital method implemented in GAMESS was used to calculate the FMO energy of predicted models. When tested on eight protein targets, we found that the model ranking based on FMO energies is better than that based on empirically derived energies when there is sufficient diversity among these models. This model diversity can be estimated prior to the FMO energy calculations. Our result demonstrates that the FMO energy calculated by the fragment molecular orbital method is a practical and promising measure for the assessment of protein model quality and the selection of the best protein model among many generated.

  15. A simple feature construction method for predicting upstream/downstream signal flow in human protein-protein interaction networks

    PubMed Central

    Mei, Suyu; Zhu, Hao

    2015-01-01

    Signaling pathways play important roles in understanding the underlying mechanism of cell growth, cell apoptosis, organismal development and pathways-aberrant diseases. Protein-protein interaction (PPI) networks are commonly-used infrastructure to infer signaling pathways. However, PPI networks generally carry no information of upstream/downstream relationship between interacting proteins, which retards our inferring the signal flow of signaling pathways. In this work, we propose a simple feature construction method to train a SVM (support vector machine) classifier to predict PPI upstream/downstream relations. The domain based asymmetric feature representation naturally embodies domain-domain upstream/downstream relations, providing an unconventional avenue to predict the directionality between two objects. Moreover, we propose a semantically interpretable decision function and a macro bag-level performance metric to satisfy the need of two-instance depiction of an interacting protein pair. Experimental results show that the proposed method achieves satisfactory cross validation performance and independent test performance. Lastly, we use the trained model to predict the PPIs in HPRD, Reactome and IntAct. Some predictions have been validated against recent literature. PMID:26648121

  16. Predicting peptide binding to MHC pockets via molecular modeling, implicit solvation, and global optimization.

    PubMed

    Schafroth, Heather D; Floudas, Christodoulos A

    2004-02-15

    Development of a computational prediction method based on molecular modeling, global optimization, and implicit solvation has produced accurate structure and relative binding affinity predictions for peptide amino acids binding to five pockets of the MHC molecule HLA-DRB1*0101. Because peptide binding to MHC molecules is essential to many immune responses, development of such a method for understanding and predicting the forces that drive binding is crucial for pharmaceutical design and disease treatment. Underlying the development of this prediction method are two hypotheses. The first is that pockets formed by the peptide binding groove of MHC molecules are independent, separating the prediction of peptide amino acids that bind within individual pockets from those that bind between pockets. The second hypothesis is that the native state of a system composed of an amino acid bound to a protein pocket corresponds to the system's lowest free energy. The prediction method developed from these hypotheses uses atomistic-level modeling, deterministic global optimization, and three methods of implicit solvation: solvent-accessible area, solvent-accessible volume, and Poisson-Boltzmann electrostatics. The method predicts relative binding affinities of peptide amino acids for pockets of HLA-DRB1*0101 by determining computationally an amino acid's global minimum energy conformation. Prediction results from the method are in agreement with X-ray crystallography data and experimental binding assays.

  17. Texture feature analysis for prediction of postoperative liver failure prior to surgery

    NASA Astrophysics Data System (ADS)

    Simpson, Amber L.; Do, Richard K.; Parada, E. Patricia; Miga, Michael I.; Jarnagin, William R.

    2014-03-01

    Texture analysis of preoperative CT images of the liver is undertaken in this study. Standard texture features were extracted from portal-venous phase contrast-enhanced CT scans of 36 patients prior to major hepatic resection and correlated to postoperative liver failure. Differences between patients with and without postoperative liver failure were statistically significant for contrast (measure of local variation), correlation (linear dependency of gray levels on neighboring pixels), cluster prominence (asymmetry), and normalized inverse difference moment (local homogeneity). Though texture features have been used to diagnose and characterize lesions, to our knowledge, parenchymal statistical variation has not been quantified and studied. We demonstrate that texture analysis is a valuable tool for quantifying liver function prior to surgery, which may help to identify and change the preoperative management of patients at higher risk for overall morbidity.

  18. Machine learning identification of EEG features predicting working memory performance in schizophrenia and healthy adults

    PubMed Central

    Johannesen, Jason K.; Bi, Jinbo; Jiang, Ruhua; Kenney, Joshua G.; Chen, Chi-Ming A.

    2016-01-01

    Background With millisecond-level resolution, electroencephalographic (EEG) recording provides a sensitive tool to assay neural dynamics of human cognition. However, selection of EEG features used to answer experimental questions is typically determined a priori. The utility of machine learning was investigated as a computational framework for extracting the most relevant features from EEG data empirically. Methods Schizophrenia (SZ; n = 40) and healthy community (HC; n = 12) subjects completed a Sternberg Working Memory Task (SWMT) during EEG recording. EEG was analyzed to extract 5 frequency components (theta1, theta2, alpha, beta, gamma) at 4 processing stages (baseline, encoding, retention, retrieval) and 3 scalp sites (frontal-Fz, central-Cz, occipital-Oz) separately for correctly and incorrectly answered trials. The 1-norm support vector machine (SVM) method was used to build EEG classifiers of SWMT trial accuracy (correct vs. incorrect; Model 1) and diagnosis (HC vs. SZ; Model 2). External validity of SVM models was examined in relation to neuropsychological test performance and diagnostic classification using conventional regression-based analyses. Results SWMT performance was significantly reduced in SZ (p < .001). Model 1 correctly classified trial accuracy at 84 % in HC, and at 74 % when cross-validated in SZ data. Frontal gamma at encoding and central theta at retention provided highest weightings, accounting for 76 % of variance in SWMT scores and 42 % variance in neuropsychological test performance across samples. Model 2 identified frontal theta at baseline and frontal alpha during retrieval as primary classifiers of diagnosis, providing 87 % classification accuracy as a discriminant function. Conclusions EEG features derived by SVM are consistent with literature reports of gamma’s role in memory encoding, engagement of theta during memory retention, and elevated resting low-frequency activity in schizophrenia. Tests of model performance and cross

  19. Predicting Protein-Protein Interactions from the Molecular to the Proteome Level.

    PubMed

    Keskin, Ozlem; Tuncbag, Nurcan; Gursoy, Attila

    2016-04-27

    Identification of protein-protein interactions (PPIs) is at the center of molecular biology considering the unquestionable role of proteins in cells. Combinatorial interactions result in a repertoire of multiple functions; hence, knowledge of PPI and binding regions naturally serve to functional proteomics and drug discovery. Given experimental limitations to find all interactions in a proteome, computational prediction/modeling of protein interactions is a prerequisite to proceed on the way to complete interactions at the proteome level. This review aims to provide a background on PPIs and their types. Computational methods for PPI predictions can use a variety of biological data including sequence-, evolution-, expression-, and structure-based data. Physical and statistical modeling are commonly used to integrate these data and infer PPI predictions. We review and list the state-of-the-art methods, servers, databases, and tools for protein-protein interaction prediction. PMID:27074302

  20. Ab initio NMR Confirmed Evolutionary Structure Prediction for Organic Molecular Crystals

    NASA Astrophysics Data System (ADS)

    Pham, Cong-Huy; Kucukbenli, Emine; de Gironcoli, Stefano

    2015-03-01

    Ab initio crystal structure prediction of even small organic compounds is extremely challenging due to polymorphism, molecular flexibility and difficulties in addressing the dispersion interaction from first principles. We recently implemented vdW-aware density functionals and demonstrated their success in energy ordering of aminoacid crystals. In this work we combine this development with the evolutionary structure prediction method to study cholesterol polymorphs. Cholesterol crystals have paramount importance in various diseases, from cancer to atherosclerosis. The structure of some polymorphs (e.g. ChM, ChAl, ChAh) have already been resolved while some others, which display distinct NMR spectra and are involved in disease formation, are yet to be determined. Here we thoroughly assess the applicability of evolutionary structure prediction to address such real world problems. We validate the newly predicted structures with ab initio NMR chemical shift data using secondary referencing for an improved comparison with experiments.

  1. Isolation of a Latimeria menadoensis heat shock protein 70 (Lmhsp70) that has all the features of an inducible gene and encodes a functional molecular chaperone.

    PubMed

    Modisakeng, Keoagile W; Jiwaji, Meesbah; Pesce, Eva-Rachele; Robert, Jacques; Amemiya, Chris T; Dorrington, Rosemary A; Blatch, Gregory L

    2009-08-01

    Molecular chaperones facilitate the correct folding of other proteins, and heat shock proteins form one of the major classes of molecular chaperones. Heat shock protein 70 (Hsp70) has been extensively studied, and shown to be critically important for cellular protein homeostasis in almost all prokaryotic and eukaryotic systems studied to date. Since there have been very limited studies conducted on coelacanth chaperones, the main objective of this study was to genetically and biochemically characterize a coelacanth Hsp70. We have successfully isolated an Indonesian coelacanth (L. menadoensis) hsp70 gene, Lmhsp70, and found that it contained an intronless coding region and a potential upstream regulatory region. Lmhsp70 encoded a typical Hsp70 based on conserved structural and functional features, and the predicted upstream regulatory region was found to contain six potential promoter elements, and three potential heat shock elements (HSEs). The intronless nature of the coding region and the presence of HSEs suggested that Lmhsp70 was stress-inducible. Phylogenetic analyses provided further evidence that Lmhsp70 was probably inducible, and that it branched as a clade intermediate between bony fish and tetrapods. Recombinant LmHsp70 was successfully overproduced, purified and found to be functional using ATPase activity assays. Taken together, these data provide evidence for the first time that the coelacanth encodes a functional molecular chaperone system.

  2. Borderline Personality Features in Students: the Predicting Role of Schema, Emotion Regulation, Dissociative Experience and Suicidal Ideation

    PubMed Central

    Sajadi, Seyede Fateme; Arshadi, Nasrin; Zargar, Yadolla; Mehrabizade Honarmand, Mahnaz; Hajjari, Zahra

    2015-01-01

    Background: Numerous studies have demonstrated that early maladaptive schemas, emotional dysregulation are supposed to be the defining core of borderline personality disorder. Many studies have also found a strong association between the diagnosis of borderline personality and the occurrence of suicide ideation and dissociative symptoms. Objectives: The present study was designed to investigate the relationship between borderline personality features and schema, emotion regulation, dissociative experiences and suicidal ideation among high school students in Shiraz City, Iran. Patients and Methods: In this descriptive correlational study, 300 students (150 boys and 150 girls) were selected from the high schools in Shiraz, Iran, using the multi-stage random sampling. Data were collected using some instruments including borderline personality feature scale for children, young schema questionnaire-short form, difficulties in emotion-regulation scale (DERS), dissociative experience scale and beck suicide ideation scale. Data were analyzed using the Pearson correlation coefficient and multivariate regression analysis. Results: The results showed a significant positive correlation between schema, emotion regulation, dissociative experiences and suicide ideation with borderline personality features. Moreover, the results of multivariate regression analysis suggested that among the studied variables, schema was the most effective predicting variable of borderline features (P < 0.001). Conclusions: The findings of this study are in accordance with findings from previous studies, and generally show a meaningful association between schema, emotion regulation, dissociative experiences, and suicide ideation with borderline personality features. PMID:26401490

  3. Establishing whether the structural feature controlling the mechanical properties of starch films is molecular or crystalline.

    PubMed

    Li, Ming; Xie, Fengwei; Hasjim, Jovin; Witt, Torsten; Halley, Peter J; Gilbert, Robert G

    2015-03-01

    The effects of molecular and crystalline structures on the tensile mechanical properties of thermoplastic starch (TPS) films from waxy, normal, and high-amylose maize were investigated. Starch structural variations were obtained through extrusion and hydrothermal treatment (HTT). The molecular and crystalline structures were characterized using size-exclusion chromatography and X-ray diffractometry, respectively. TPS from high-amylose maize showed higher elongation at break and tensile strength than those from normal maize and waxy maize starches when processed with 40% plasticizer. Within the same amylose content, the mechanical properties were not affected by amylopectin molecular size or the crystallinity of TPS prior to HTT. This lack of correlation between the molecular size, crystallinity and mechanical properties may be due to the dominant effect of the plasticizer on the mechanical properties. Further crystallization of normal maize TPS by HTT increased the tensile strength and Young's modulus, while decreasing the elongation at break. The results suggest that the crystallinity from the remaining ungelatinized starch granules has less significant effect on the mechanical properties than that resulting from starch recrystallization, possibly due to a stronger network from leached-out amylose surrounding the remaining starch granules. PMID:25498634

  4. Spectroscopic features of dual fluorescence/luminescence resonance energy-transfer molecular beacons.

    PubMed

    Tsourkas, Andrew; Behlke, Mark A; Xu, Yangqing; Bao, Gang

    2003-08-01

    Molecular beacons have the potential to become a powerful tool in gene detection and quantification in living cells. Here we report a novel dual molecular beacons approach to reduce false-positive signals in detecting target nucleic acids in homogeneous assays. A pair of molecular beacons, each containing a fluorescence quencher and a reporter fluorophore, one with a donor and a second with an acceptor fluorophore, hybridize to adjacent regions on the same target resulting in fluorescence resonance energy transfer (FRET). The detection of a FRET signal leads to a substantially increased signal-to-background ratio compared with that seen in single molecular beacon assays and enables discrimination between fluorescence due to specific probe/target hybridization and a variety of possible false-positive events. Further, when a lanthanide chelate is used as a donor in a dual-probe assay, extremely high signal-to-background ratios can be achieved owing to the long lifetime and sharp emission peaks of the donor and the time-gated detection of acceptor fluorescence emission. These new approaches allow for the ultrasensitive detection of target molecules in a way that could be readily applied to real-time imaging of gene expression in living cells.

  5. Reactive oxygen species-associated molecular signature predicts survival in patients with sepsis.

    PubMed

    Bime, Christian; Zhou, Tong; Wang, Ting; Slepian, Marvin J; Garcia, Joe G N; Hecker, Louise

    2016-06-01

    Sepsis-related multiple organ dysfunction syndrome is a leading cause of death in intensive care units. There is overwhelming evidence that oxidative stress plays a significant role in the pathogenesis of sepsis-associated multiple organ failure; however, reactive oxygen species (ROS)-associated biomarkers and/or diagnostics that define mortality or predict survival in sepsis are lacking. Lung or peripheral blood gene expression analysis has gained increasing recognition as a potential prognostic and/or diagnostic tool. The objective of this study was to identify ROS-associated biomarkers predictive of survival in patients with sepsis. In-silico analyses of expression profiles allowed the identification of a 21-gene ROS-associated molecular signature that predicts survival in sepsis patients. Importantly, this signature performed well in a validation cohort consisting of sepsis patients aggregated from distinct patient populations recruited from different sites. Our signature outperforms randomly generated signatures of the same signature gene size. Our findings further validate the critical role of ROSs in the pathogenesis of sepsis and provide a novel gene signature that predicts survival in sepsis patients. These results also highlight the utility of peripheral blood molecular signatures as biomarkers for predicting mortality risk in patients with sepsis, which could facilitate the development of personalized therapies. PMID:27252846

  6. Activity Prediction and Molecular Mechanism of Bovine Blood Derived Angiotensin I-Converting Enzyme Inhibitory Peptides

    PubMed Central

    Zhang, Ting; Nie, Shaoping; Liu, Boqun; Yu, Yiding; Zhang, Yan; Liu, Jingbo

    2015-01-01

    Development of angiotensin I-converting enzyme (ACE, EC 3.4.15.1) inhibitory peptides from food protein is under extensive research as alternative for the prevention of hypertension. However, it is difficult to identify peptides released from food sources. To accelerate the progress of peptide identification, a three layer back propagation neural network model was established to predict the ACE-inhibitory activity of pentapeptides derived from bovine hemoglobin by simulated enzyme digestion. The pentapeptide WTQRF has the best predicted value with experimental IC50 23.93 μM. The potential molecular mechanism of the WTQRF / ACE interaction was investigated by flexible docking. PMID:25768442

  7. Activity prediction and molecular mechanism of bovine blood derived angiotensin I-converting enzyme inhibitory peptides.

    PubMed

    Zhang, Ting; Nie, Shaoping; Liu, Boqun; Yu, Yiding; Zhang, Yan; Liu, Jingbo

    2015-01-01

    Development of angiotensin I-converting enzyme (ACE, EC 3.4.15.1) inhibitory peptides from food protein is under extensive research as alternative for the prevention of hypertension. However, it is difficult to identify peptides released from food sources. To accelerate the progress of peptide identification, a three layer back propagation neural network model was established to predict the ACE-inhibitory activity of pentapeptides derived from bovine hemoglobin by simulated enzyme digestion. The pentapeptide WTQRF has the best predicted value with experimental IC50 23.93 μM. The potential molecular mechanism of the WTQRF / ACE interaction was investigated by flexible docking. PMID:25768442

  8. Prediction of protein structural features from sequence data based on Shannon entropy and Kolmogorov complexity.

    PubMed

    Bywater, Robert Paul

    2015-01-01

    While the genome for a given organism stores the information necessary for the organism to function and flourish it is the proteins that are encoded by the genome that perhaps more than anything else characterize the phenotype for that organism. It is therefore not surprising that one of the many approaches to understanding and predicting protein folding and properties has come from genomics and more specifically from multiple sequence alignments. In this work I explore ways in which data derived from sequence alignment data can be used to investigate in a predictive way three different aspects of protein structure: secondary structures, inter-residue contacts and the dynamics of switching between different states of the protein. In particular the use of Kolmogorov complexity has identified a novel pathway towards achieving these goals.

  9. Yeast prions and human prion-like proteins: sequence features and prediction methods.

    PubMed

    Cascarina, Sean M; Ross, Eric D

    2014-06-01

    Prions are self-propagating infectious protein isoforms. A growing number of prions have been identified in yeast, each resulting from the conversion of soluble proteins into an insoluble amyloid form. These yeast prions have served as a powerful model system for studying the causes and consequences of prion aggregation. Remarkably, a number of human proteins containing prion-like domains, defined as domains with compositional similarity to yeast prion domains, have recently been linked to various human degenerative diseases, including amyotrophic lateral sclerosis. This suggests that the lessons learned from yeast prions may help in understanding these human diseases. In this review, we examine what has been learned about the amino acid sequence basis for prion aggregation in yeast, and how this information has been used to develop methods to predict aggregation propensity. We then discuss how this information is being applied to understand human disease, and the challenges involved in applying yeast prediction methods to higher organisms.

  10. Genetic features of Huntington disease in Cuban population: implications for phenotype, epidemiology and predictive testing.

    PubMed

    Vázquez-Mojena, Yaimeé; Laguna-Salvia, Leonides; Laffita-Mesa, José M; González-Zaldívar, Yanetza; Almaguer-Mederos, Luis E; Rodríguez-Labrada, Roberto; Almaguer-Gotay, Dennis; Zayas-Feria, Pedro; Velázquez-Pérez, Luis

    2013-12-15

    Huntington disease is the most frequent polyglutamine disorder with variable worldwide prevalence. Although some Latin American populations have been studied, HD prevalence in Cuban population remains unknown. In order to characterize the disease in Cuba, the relative frequency of HD was determined by studying 130 patients with chorea and 63 unrelated healthy controls, emphasizing in the molecular epidemiology of the disease. Sixty-two patients with chorea belonging to 16 unrelated families carried a pathological CAG expansion in the HTT gene, ranging from 39 to 67 repeats. Eighty-three percent of them come from the eastern region of the country. A significant inverse correlation between age at onset and expanded CAG repeats was seen. Intermediate alleles in affected individuals and controls represented 4.8% and 3.97% respectively, which have been a putative source of de novo mutation. This study represents the largest molecular characterization of Huntington disease in the Cuban population. These results may have significant implications for an understanding of the disease, its diagnosis and prognosis in Cuban patients, giving health professionals the tools to implement confirmatory genetic testing, pre-symptomatic testing and clinical trials in this population.

  11. Predicting anti-androgenic activity of bisphenols using molecular docking and quantitative structure-activity relationships.

    PubMed

    Yang, Xianhai; Liu, Huihui; Yang, Qian; Liu, Jining; Chen, Jingwen; Shi, Lili

    2016-11-01

    Both in vivo and in vitro assay indicated that bisphenols can inhibit the androgen receptor. However, the underlying antagonistic mechanism is unclear. In this study, molecular docking was employed to probe the interaction mechanism between bisphenols and human androgen receptor (hAR). The binding pattern of ligands in hAR crystal structures was also analyzed. Results show that hydrogen bonding and hydrophobic interactions are the dominant interactions between the ligands and hAR. The critical amino acid residues involved in forming hydrogen bonding between bisphenols and hAR is Asn 705 and Gln 711. Furthermore, appropriate molecular structural descriptors were selected to characterize the non-bonded interactions. Stepwise multiple linear regressions (MLR) analysis was employed to develop quantitative structure-activity relationship (QSAR) models for predicting the anti-androgenic activity of bisphenols. Based on the QSAR development and validation guideline issued by OECD, the goodness-of-fit, robustness and predictive ability of constructed QSAR model were assessed. The model application domain was characterized by the Euclidean distance and Williams plot. The mechanisms of the constructed model were also interpreted based on the selected molecular descriptors i.e. the number of hydroxyl groups (nROH), the most positive values of the molecular surface potential (Vs,max) and the lowest unoccupied molecular orbital energy (ELUMO). Finally, based on the model developed, the data gap for other twenty-six bisphenols on their anti-androgenic activity was filled. The predicted results indicated that the anti-androgenic activity of seven bisphenols was higher than that of bisphenol A. PMID:27561732

  12. Stability, surface features, and atom leaching of palladium nanoparticles: toward prediction of catalytic functionality.

    PubMed

    Ramezani-Dakhel, Hadi; Mirau, Peter A; Naik, Rajesh R; Knecht, Marc R; Heinz, Hendrik

    2013-04-21

    Surfactant-stabilized metal nanoparticles have shown promise as catalysts although specific surface features and their influence on catalytic performance have not been well understood. We quantify the thermodynamic stability, the facet composition of the surface, and distinct atom types that affect rates of atom leaching for a series of twenty near-spherical Pd nanoparticles of 1.8 to 3.1 nm size using computational models. Cohesive energies indicate higher stability of certain particles that feature an approximate 60/20/20 ratio of {111}, {100}, and {110} facets while less stable particles exhibit widely variable facet composition. Unique patterns of atom types on the surface cause apparent differences in binding energies and changes in reactivity. Estimates of the relative rate of atom leaching as a function of particle size were obtained by the summation of Boltzmann-weighted binding energies over all surface atoms. Computed leaching rates are in good qualitative correlation with the measured catalytic activity of peptide-stabilized Pd nanoparticles of the same shape and size in Stille coupling reactions. The agreement supports rate-controlling contributions by atom leaching in the presence of reactive substrates. The computational approach provides a pathway to estimate the catalytic activity of metal nanostructures of engineered shape and size, and possible further refinements are described.

  13. A comparison of molecular dynamics and diffuse interface model predictions of Lennard-Jones fluid evaporation

    SciTech Connect

    Barbante, Paolo; Frezzotti, Aldo; Gibelli, Livio

    2014-12-09

    The unsteady evaporation of a thin planar liquid film is studied by molecular dynamics simulations of Lennard-Jones fluid. The obtained results are compared with the predictions of a diffuse interface model in which capillary Korteweg contributions are added to hydrodynamic equations, in order to obtain a unified description of the liquid bulk, liquid-vapor interface and vapor region. Particular care has been taken in constructing a diffuse interface model matching the thermodynamic and transport properties of the Lennard-Jones fluid. The comparison of diffuse interface model and molecular dynamics results shows that, although good agreement is obtained in equilibrium conditions, remarkable deviations of diffuse interface model predictions from the reference molecular dynamics results are observed in the simulation of liquid film evaporation. It is also observed that molecular dynamics results are in good agreement with preliminary results obtained from a composite model which describes the liquid film by a standard hydrodynamic model and the vapor by the Boltzmann equation. The two mathematical model models are connected by kinetic boundary conditions assuming unit evaporation coefficient.

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

    PubMed

    Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; Pronobis, Wiktor; von Lilienfeld, O Anatole; Müller, Klaus-Robert; Tkatchenko, Alexandre

    2015-06-18

    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.

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

    SciTech Connect

    Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; Pronobis, Wiktor; von Lilienfeld, O. Anatole; Müller, Klaus -Robert; Tkatchenko, Alexandre

    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 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. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies.

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

    DOE PAGES

    Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; Pronobis, Wiktor; von Lilienfeld, O. Anatole; Müller, Klaus -Robert; Tkatchenko, Alexandre

    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. Individualized treatment of gastric cancer: Impact of molecular biology and pathohistological features.

    PubMed

    Dittmar, Yves; Settmacher, Utz

    2015-11-15

    Gastric cancer is one of the most common malignancies worldwide. The overall prognosis remains poor over the last decades even though improvements in surgical outcomes have been achieved. A better understanding of the molecular biology of gastric cancer and detection of eligible molecular targets might be of central interest to further improve clinical outcome. With this intention, first steps have been made in the research of growth factor signaling. Regarding morphogens, cell cycle and nuclear factor-κB signaling, a remarkable count of target-specific agents have been developed, nevertheless the transfer into the field of clinical routine is still at the beginning. The potential utility of epigenetic targets and the further evaluation of microRNA signaling seem to have potential for the development of novel treatment strategies in the future.

  19. Individualized treatment of gastric cancer: Impact of molecular biology and pathohistological features

    PubMed Central

    Dittmar, Yves; Settmacher, Utz

    2015-01-01

    Gastric cancer is one of the most common malignancies worldwide. The overall prognosis remains poor over the last decades even though improvements in surgical outcomes have been achieved. A better understanding of the molecular biology of gastric cancer and detection of eligible molecular targets might be of central interest to further improve clinical outcome. With this intention, first steps have been made in the research of growth factor signaling. Regarding morphogens, cell cycle and nuclear factor-κB signaling, a remarkable count of target-specific agents have been developed, nevertheless the transfer into the field of clinical routine is still at the beginning. The potential utility of epigenetic targets and the further evaluation of microRNA signaling seem to have potential for the development of novel treatment strategies in the future. PMID:26600929

  20. Molecular features distinguish ten neuronal types in the mouse superficial superior colliculus.

    PubMed

    Byun, Haewon; Kwon, Soohyun; Ahn, Hee-Jeong; Liu, Hong; Forrest, Douglas; Demb, Jonathan B; Kim, In-Jung

    2016-08-01

    The superior colliculus (SC) is a midbrain center involved in controlling head and eye movements in response to inputs from multiple sensory modalities. Visual inputs arise from both the retina and visual cortex and converge onto the superficial layer of the SC (sSC). Neurons in the sSC send information to deeper layers of the SC and to thalamic nuclei that modulate visually guided behaviors. Presently, our understanding of sSC neurons is impeded by a lack of molecular markers that define specific cell types. To better understand the identity and organization of sSC neurons, we took a systematic approach to investigate gene expression within four molecular families: transcription factors, cell adhesion molecules, neuropeptides, and calcium binding proteins. Our analysis revealed 12 molecules with distinct expression patterns in mouse sSC: cadherin 7, contactin 3, netrin G2, cadherin 6, protocadherin 20, retinoid-related orphan receptor β, brain-specific homeobox/POU domain protein 3b, Ets variant gene 1, substance P, somatostatin, vasoactive intestinal polypeptide, and parvalbumin. Double labeling experiments, by either in situ hybridization or immunostaining, demonstrated that the 12 molecular markers collectively define 10 different sSC neuronal types. The characteristic positions of these cell types divide the sSC into four distinct layers. The 12 markers identified here will serve as valuable tools to examine molecular mechanisms that regulate development of sSC neuronal types. These markers could also be used to examine the connections between specific cell types that form retinocollicular, corticocollicular, or colliculothalamic pathways. J. Comp. Neurol. 524:2300-2321, 2016. © 2016 Wiley Periodicals, Inc.

  1. Some Dynamical Features of Molecular Fragmentation by Electrons and Swift Ions

    NASA Astrophysics Data System (ADS)

    Montenegro, E. C.; Sigaud, L.; Wolff, W.; Luna, H.; Natalia, Ferreira

    To date, the large majority of studies on molecular fragmentation by swift charged particles have been carried out using simple molecules, for which reliable Potential Energy Curves are available to interpret the measured fragmentation yields. For complex molecules the scenario is quite different and such guidance is not available, obscuring even a simple organization of the data which are currently obtained for a large variety of molecules of biological or technological interest. In this work we show that a general and relatively simple methodology can be used to obtain a broader picture of the fragmentation pattern of an arbitrary molecule. The electronic ionization or excitation cross section of a given molecular orbital, which is the first part of the fragmentation process, can be well scaled by a simple and general procedure at high projectile velocities. The fragmentation fractions arising from each molecular orbital can then be achieved by matching the calculated ionization with the measured fragmentation cross sections. Examples for Oxygen, Chlorodifluoromethane and Pyrimidine molecules are presented.

  2. Correlation Spectroscopy and Molecular Dynamics Simulations to Study the Structural Features of Proteins

    PubMed Central

    Varriale, Antonio; Marabotti, Anna; Mei, Giampiero; Staiano, Maria; D’Auria, Sabato

    2013-01-01

    In this work, we used a combination of fluorescence correlation spectroscopy (FCS) and molecular dynamics (MD) simulation methodologies to acquire structural information on pH-induced unfolding of the maltotriose-binding protein from Thermus thermophilus (MalE2). FCS has emerged as a powerful technique for characterizing the dynamics of molecules and it is, in fact, used to study molecular diffusion on timescale of microsecond and longer. Our results showed that keeping temperature constant, the protein diffusion coefficient decreased from 84±4 µm2/s to 44±3 µm2/s when pH was changed from 7.0 to 4.0. An even more marked decrease of the MalE2 diffusion coefficient (31±3 µm2/s) was registered when pH was raised from 7.0 to 10.0. According to the size of MalE2 (a monomeric protein with a molecular weight of 43 kDa) as well as of its globular native shape, the values of 44 µm2/s and 31 µm2/s could be ascribed to deformations of the protein structure, which enhances its propensity to form aggregates at extreme pH values. The obtained fluorescence correlation data, corroborated by circular dichroism, fluorescence emission and light-scattering experiments, are discussed together with the MD simulations results. PMID:23750215

  3. Features of Knowledge Building in Biology: Understanding Undergraduate Students' Ideas about Molecular Mechanisms.

    PubMed

    Southard, Katelyn; Wince, Tyler; Meddleton, Shanice; Bolger, Molly S

    2016-01-01

    Research has suggested that teaching and learning in molecular and cellular biology (MCB) is difficult. We used a new lens to understand undergraduate reasoning about molecular mechanisms: the knowledge-integration approach to conceptual change. Knowledge integration is the dynamic process by which learners acquire new ideas, develop connections between ideas, and reorganize and restructure prior knowledge. Semistructured, clinical think-aloud interviews were conducted with introductory and upper-division MCB students. Interviews included a written conceptual assessment, a concept-mapping activity, and an opportunity to explain the biomechanisms of DNA replication, transcription, and translation. Student reasoning patterns were explored through mixed-method analyses. Results suggested that students must sort mechanistic entities into appropriate mental categories that reflect the nature of MCB mechanisms and that conflation between these categories is common. We also showed how connections between molecular mechanisms and their biological roles are part of building an integrated knowledge network as students develop expertise. We observed differences in the nature of connections between ideas related to different forms of reasoning. Finally, we provide a tentative model for MCB knowledge integration and suggest its implications for undergraduate learning.

  4. Molecular features determining different partitioning patterns of papain and bromelain in aqueous two-phase systems.

    PubMed

    Rocha, Maria Victoria; Nerli, Bibiana Beatriz

    2013-10-01

    The partitioning patterns of papain (PAP) and bromelain (BR), two well-known cysteine-proteases, in polyethyleneglycol/sodium citrate aqueous two-phase systems (ATPSs) were determined. Polyethyleneglycols of different molecular weight (600, 1000, 2000, 4600 and 8000) were assayed. Thermodynamic characterization of partitioning process, spectroscopy measurements and computational calculations of protein surface properties were also carried out in order to explain their differential partitioning behavior. PAP was observed to be displaced to the salt-enriched phase in all the assayed systems with partition coefficients (KpPAP) values between 0.2 and 0.9, while BR exhibited a high affinity for the polymer phase in systems formed by PEGs of low molecular weight (600 and 1000) with partition coefficients (KpBR) values close to 3. KpBR values resulted higher than KpPAP in all the cases. This difference could be assigned neither to the charge nor to the size of the partitioned biomolecules since PAP and BR possess similar molecular weight (23,000) and isoelectric point (9.60). The presence of highly exposed tryptophans and positively charged residues (Lys, Arg and His) in BR molecule would be responsible for a charge transfer interaction between PEG and the protein and, therefore, the uneven distribution of BR in these systems.

  5. Features of Knowledge Building in Biology: Understanding Undergraduate Students’ Ideas about Molecular Mechanisms

    PubMed Central

    Southard, Katelyn; Wince, Tyler; Meddleton, Shanice; Bolger, Molly S.

    2016-01-01

    Research has suggested that teaching and learning in molecular and cellular biology (MCB) is difficult. We used a new lens to understand undergraduate reasoning about molecular mechanisms: the knowledge-integration approach to conceptual change. Knowledge integration is the dynamic process by which learners acquire new ideas, develop connections between ideas, and reorganize and restructure prior knowledge. Semistructured, clinical think-aloud interviews were conducted with introductory and upper-division MCB students. Interviews included a written conceptual assessment, a concept-mapping activity, and an opportunity to explain the biomechanisms of DNA replication, transcription, and translation. Student reasoning patterns were explored through mixed-method analyses. Results suggested that students must sort mechanistic entities into appropriate mental categories that reflect the nature of MCB mechanisms and that conflation between these categories is common. We also showed how connections between molecular mechanisms and their biological roles are part of building an integrated knowledge network as students develop expertise. We observed differences in the nature of connections between ideas related to different forms of reasoning. Finally, we provide a tentative model for MCB knowledge integration and suggest its implications for undergraduate learning. PMID:26931398

  6. Musical emotions: predicting second-by-second subjective feelings of emotion from low-level psychoacoustic features and physiological measurements.

    PubMed

    Coutinho, Eduardo; Cangelosi, Angelo

    2011-08-01

    We sustain that the structure of affect elicited by music is largely dependent on dynamic temporal patterns in low-level music structural parameters. In support of this claim, we have previously provided evidence that spatiotemporal dynamics in psychoacoustic features resonate with two psychological dimensions of affect underlying judgments of subjective feelings: arousal and valence. In this article we extend our previous investigations in two aspects. First, we focus on the emotions experienced rather than perceived while listening to music. Second, we evaluate the extent to which peripheral feedback in music can account for the predicted emotional responses, that is, the role of physiological arousal in determining the intensity and valence of musical emotions. Akin to our previous findings, we will show that a significant part of the listeners' reported emotions can be predicted from a set of six psychoacoustic features--loudness, pitch level, pitch contour, tempo, texture, and sharpness. Furthermore, the accuracy of those predictions is improved with the inclusion of physiological cues--skin conductance and heart rate. The interdisciplinary work presented here provides a new methodology to the field of music and emotion research based on the combination of computational and experimental work, which aid the analysis of the emotional responses to music, while offering a platform for the abstract representation of those complex relationships. Future developments may aid specific areas, such as, psychology and music therapy, by providing coherent descriptions of the emotional effects of specific music stimuli.

  7. Integrating In Silico Prediction Methods, Molecular Docking, and Molecular Dynamics Simulation to Predict the Impact of ALK Missense Mutations in Structural Perspective

    PubMed Central

    Priya Doss, C. George; Chen, Luonan

    2014-01-01

    Over the past decade, advancements in next generation sequencing technology have placed personalized genomic medicine upon horizon. Understanding the likelihood of disease causing mutations in complex diseases as pathogenic or neutral remains as a major task and even impossible in the structural context because of its time consuming and expensive experiments. Among the various diseases causing mutations, single nucleotide polymorphisms (SNPs) play a vital role in defining individual's susceptibility to disease and drug response. Understanding the genotype-phenotype relationship through SNPs is the first and most important step in drug research and development. Detailed understanding of the effect of SNPs on patient drug response is a key factor in the establishment of personalized medicine. In this paper, we represent a computational pipeline in anaplastic lymphoma kinase (ALK) for SNP-centred study by the application of in silico prediction methods, molecular docking, and molecular dynamics simulation approaches. Combination of computational methods provides a way in understanding the impact of deleterious mutations in altering the protein drug targets and eventually leading to variable patient's drug response. We hope this rapid and cost effective pipeline will also serve as a bridge to connect the clinicians and in silico resources in tailoring treatments to the patients' specific genotype. PMID:25054154

  8. Molecular-Scale Features that Govern the Effects of O-Glycosylation on a Carbohydrate-Binding Module

    DOE PAGES

    Guan, Xiaoyang; Chaffey, Patrick K.; Zeng, Chen; Greene, Eric R.; Chen, Liqun; Drake, Matthew R.; Chen, Claire; Groobman, Ari; Resch, Michael G.; Himmel, Michael E.; et al

    2015-09-21

    The protein glycosylation is a ubiquitous post-translational modification in all kingdoms of life. Despite its importance in molecular and cellular biology, the molecular-level ramifications of O-glycosylation on biomolecular structure and function remain elusive. Here, we took a small model glycoprotein and changed the glycan structure and size, amino acid residues near the glycosylation site, and glycosidic linkage while monitoring any corresponding changes to physical stability and cellulose binding affinity. The results of this study reveal the collective importance of all the studied features in controlling the most pronounced effects of O-glycosylation in this system. This study suggests the possibility ofmore » designing proteins with multiple improved properties by simultaneously varying the structures of O-glycans and amino acids local to the glycosylation site.« less

  9. Molecular-Scale Features that Govern the Effects of O-Glycosylation on a Carbohydrate-Binding Module

    SciTech Connect

    Guan, Xiaoyang; Chaffey, Patrick K.; Zeng, Chen; Greene, Eric R.; Chen, Liqun; Drake, Matthew R.; Chen, Claire; Groobman, Ari; Resch, Michael G.; Himmel, Michael E.; Beckham, Gregg T.; Tan, Zhongping

    2015-09-21

    The protein glycosylation is a ubiquitous post-translational modification in all kingdoms of life. Despite its importance in molecular and cellular biology, the molecular-level ramifications of O-glycosylation on biomolecular structure and function remain elusive. Here, we took a small model glycoprotein and changed the glycan structure and size, amino acid residues near the glycosylation site, and glycosidic linkage while monitoring any corresponding changes to physical stability and cellulose binding affinity. The results of this study reveal the collective importance of all the studied features in controlling the most pronounced effects of O-glycosylation in this system. This study suggests the possibility of designing proteins with multiple improved properties by simultaneously varying the structures of O-glycans and amino acids local to the glycosylation site.

  10. Predicting Essential Genes and Proteins Based on Machine Learning and Network Topological Features: A Comprehensive Review

    PubMed Central

    Zhang, Xue; Acencio, Marcio Luis; Lemke, Ney

    2016-01-01

    Essential proteins/genes are indispensable to the survival or reproduction of an organism, and the deletion of such essential proteins will result in lethality or infertility. The identification of essential genes is very important not only for understanding the minimal requirements for survival of an organism, but also for finding human disease genes and new drug targets. Experimental methods for identifying essential genes are costly, time-consuming, and laborious. With the accumulation of sequenced genomes data and high-throughput experimental data, many computational methods for identifying essential proteins are proposed, which are useful complements to experimental methods. In this review, we show the state-of-the-art methods for identifying essential genes and proteins based on machine learning and network topological features, point out the progress and limitations of current methods, and discuss the challenges and directions for further research. PMID:27014079

  11. MRI features of Binswanger’s disease predict prognosis and associated pathology

    PubMed Central

    Akiguchi, Ichiro; Budka, Herbert; Shirakashi, Yoshitomo; Woehrer, Adelheid; Watanabe, Toshiyuki; Shiino, Akihiko; Yamamoto, Yasumasa; Kawamoto, Yasuhiro; Krampla, Wolfgang; Jungwirth, Susanne; Fischer, Peter

    2014-01-01

    Objective To identify the prevalence of MRI features of Binswanger’s disease (BD), specifically MRI with diffuse white matter lesions and scattered multiple lacunes (BD-MRI), and to describe neurological features and pathological outcomes of a community-based cohort study. Methods Of 697 participants (all 75 years old), 503 completed neurological examinations at baseline and were followed-up every 30 months thereafter with MRIs, the mini-mental state examination (MMSE) and the Unified Parkinson Disease Rating Scale-Motor Section (UPDRSM). Data from participants with BD-MRI were compared with those from participants with predominant white matter lesions (WML-MRI), scattered multiple lacunes (ML-MRI), or normal MRIs. Results Fourteen BD-MRI patients (2.8%) were detected at baseline. The mean MMSE scores in the BD-MRI, WML-MRI, ML-MRI, and normal MRIs groups were 26.4, 28.2, 28.4, and 28.5, respectively, and the mean UPDRSM scores were 9.1, 1.3, 3.1, and 1.7, respectively. At the 30-month follow-up, mortality rates in the normal MRIs, WML-MRI and ML-MRI were 4%, 9.1%, and 22.2%, respectively, and follow-up MRIs were available for 80%, 82%, and 61% of the participants, respectively. In the BD-MRI, however, five patients were deceased, and only five follow-up individual MRIs were available (33.3%). Autopsies were performed on six of eight BD-MRI brains, and these brains fulfilled the pathological criteria for BD independent of Alzheimer disease pathology. All these six individuals also showed systemic atherosclerosis and renal arterio-arteriolosclerosis. Interpretation The BD-MRI participants had poor prognoses and showed pure BD pathology with advanced systemic vascular disease. BD-MRI appears to be a predictor of vascular neurocognitive impairment. PMID:25493272

  12. Role of electrostatic potential in the in silico prediction of molecular bioactivation and mutagenesis.

    PubMed

    Ford, Kevin A

    2013-04-01

    Electrostatic potential (ESP) is a useful physicochemical property of a molecule that provides insights into inter- and intramolecular associations, as well as prediction of likely sites of electrophilic and nucleophilic metabolic attack. Knowledge of sites of metabolic attack is of paramount importance in DMPK research since drugs frequently fail in clinical trials due to the formation of bioactivated metabolites which are often difficult to measure experimentally due to their reactive nature and relatively short half-lives. Computational chemistry methods have proven invaluable in recent years as a means to predict and study bioactivated metabolites without the need for chemical syntheses, or testing on experimental animals. Additional molecular properties (heat of formation, heat of solvation and E(LUMO) - E(HOMO)) are discussed in this paper as complementary indicators of the behavior of metabolites in vivo. Five diverse examples are presented (acetaminophen, aniline/phenylamines, imidacloprid, nefazodone and vinyl chloride) which illustrate the utility of this multidimensional approach in predicting bioactivation, and in each case the predicted data agreed with experimental data described in the scientific literature. A further example of the usefulness of calculating ESP, in combination with the molecular properties mentioned above, is provided by an examination of the use of these parameters in providing an explanation for the sites of nucleophilic attack of the nucleic acid cytosine. Exploration of sites of nucleophilic attack of nucleic acids is important as adducts of DNA have the potential to result in mutagenesis. PMID:23323940

  13. Discovery of characteristic molecular signatures for the simultaneous prediction and detection of environmental pollutants.

    PubMed

    Song, Mi-Kyung; Choi, Han-Seam; Park, Yong-Keun; Ryu, Jae-Chun

    2014-02-01

    Gene expression data may be very promising for the classification of toxicant types, but the development and application of transcriptomic-based gene classifiers for environmental toxicological applications are lacking compared to the biomedical sciences. Also, simultaneous classification across a set of toxicant types has not been investigated extensively. In the present study, we determined the transcriptomic response to three types of ubiquitous toxicants exposure in two types of human cell lines (HepG2 and HL-60), which are useful in vitro human model for evaluation of toxic substances that may affect human hepatotoxicity (e.g., polycyclic aromatic hydrocarbon [PAH] and persistent organic pollutant [POP]) and human leukemic myelopoietic proliferation (e.g., volatile organic compound [VOC]). The findings demonstrate characteristic molecular signatures that facilitated discrimination and prediction of the toxicant type. To evaluate changes in gene expression levels after exposure to environmental toxicants, we utilized 18 chemical substances; nine PAH toxicants, six VOC toxicants, and three POP toxicants. Unsupervised gene expression analysis resulted in a characteristic molecular signature for each toxicant group, and combination analysis of two separate multi-classifications indicated 265 genes as surrogate markers for predicting each group of toxicants with 100 % accuracy. Our results suggest that these expression signatures can be used as predictable and discernible surrogate markers for detection and prediction of environmental toxicant exposure. Furthermore, this approach could easily be extended to screening for other types of environmental toxicants. PMID:24197968

  14. Molecular modeling application on hapten epitope prediction: an enantioselective immunoassay for ofloxacin optical isomers.

    PubMed

    Mu, Hongtao; Lei, Hongtao; Wang, Baoling; Xu, Zhenlin; Zhang, Chijian; Ling, Li; Tian, Yuanxin; Hu, Jinsheng; Sun, Yuanming

    2014-08-01

    To deepen our understanding of the physiochemical principles that govern hapten-antibody recognition, ofloxacin enantiomers were chosen as a model for epitope prediction of small molecules. In this study, two monoclonal antibodies (mAbs) mAb-WR1 and mAb-MS1 were raised against R-ofloxacin and S-ofloxacin, respectively. The enantioselective mAbs have a high sensitivity and specificity, and the enantioselectivity is not affected by heterologous coating format reactions. The epitopes of the ofloxacin isomers were predicted using the hologram quantitative structure-activity relationship (HQSAR) and comparative molecular field analysis (CoMFA) approaches. The results consistently show that the epitope of the chiral hapten should be primarily composed of the oxazine ring and the piperazinyl ring and mAbs recognize the hapten from the side of this moiety. The enantioselectivity of mAbs is most likely due to the steric hindrance caused by the stereogenic center of the epitope. Modeling of chiral hapten-protein mimics reveals that ofloxacin isomers remain upright on the surface of the carrier protein. Suggestions to improve the enantioselectivity of antibodies against ofloxacin isomers were also proposed. This study provided a simple, efficient, and general method for predicting the epitopes of small molecules via molecular modeling. The epitope predictions for small molecules may create a theoretical guide for hapten design.

  15. Drug Side Effect Profiles as Molecular Descriptors for Predictive Modeling of Target Bioactivity.

    PubMed

    Baker, Nancy C; Fourches, Denis; Tropsha, Alexander

    2015-02-01

    We have explored the potential of using side effect profiles of drugs to predict their bioactivities at the receptor level. Serotonin 5-HT6 binding and dopamine antagonism were investigated in separate studies. A set of 5-HT6 binders and non-binders was retrieved from the PDSP Ki database, whereas dopamine antagonists were retrieved from the MeSH Pharmaceutical Action file. The side effect data was extracted from ChemoText, a data repository containing MeSH annotations pulled from MEDLINE records. These side effects profiles were treated as molecular descriptors enabling a QSAR-like approach to build models that could reliably discriminate different classes of molecules, e.g., binders versus non-binders, and dopamine antagonists versus non-antagonists. Selected models with the best external prediction performances were applied to a library of ca. 1000 chemicals with known side effects profiles in order to predict their potential 5-HT6 binding and/or dopamine antagonism. In each case the virtual screening process was able to identify putatively active compounds that through subsequent literature-based validation were found to be likely or known 5-HT6 binders or dopamine antagonists. These results demonstrate that side effect profiles can be utilized to predict a drug's unknown molecular activity, thus representing a valuable opportunity in repositioning the drug for a new indications.

  16. Studies on the Conformational Features of Neomycin-B and its Molecular Recognition by RNA and Bacterial Defense Proteins

    NASA Astrophysics Data System (ADS)

    Asensio, Juan Luis; Bastida, Agatha; Jiménez-Barbero, Jesús

    According to NMR and molecular dynamics simulations, the conformational behavior of natural aminoglycosides is characterized by a remarkable flexibility, with different conformations, even non-exo-anomeric ones, in fast exchange. Very probably, this feature allows the adaptation of these ligands to the spatial and electronic requirements of different receptors. The large diversity of structures adopted by aminoglycosides in the binding pocket of the different RNA receptors and the distinct enzymes involved in bacterial resistance are consistent with this view. This conformational diversity can, in certain favorable cases, be exploited in the design of new antibiotic derivatives not susceptible to enzymatic inactivation, by designing tailor-made conformationally locked aminoglycosides.

  17. Thermal vibration of a single-walled carbon nanotube predicted by semiquantum molecular dynamics.

    PubMed

    Liu, Rumeng; Wang, Lifeng

    2015-02-21

    Quantum effects should be considered in the thermal vibrations of carbon nanotubes (CNTs). To this end, molecular dynamics based on modified Langevin dynamics, which accounts for quantum statistics by introducing a quantum heat bath, is used to simulate the thermal vibration of a cantilevered single-walled CNT (SWCNT). A nonlocal elastic Timoshenko beam model with quantum effects (TBQN), which can take the effect of microstructure into consideration, has been established to explain the resulting power spectral density of the SWCNT. The root of mean squared (RMS) amplitude of the thermal vibration of the SWCNT obtained from semiquantum molecular dynamics (SQMD) is lower than that obtained from classical molecular dynamics, especially at very low temperature and high-order modes. The natural frequencies of the SWCNT obtained from the Timoshenko beam model are closer to those obtained from molecular dynamics if the nonlocal effect is taken into consideration. However, the nonlocal Timoshenko beam model with the law of energy equipartition (TBCN) can only predict the RMS amplitude of the SWCNT obtained from classical molecular dynamics without considering quantum effects. The RMS amplitude of the SWCNT obtained from SQMD and that obtained from TBQN coincide very well. These results indicate that quantum effects are important for the thermal vibration of the SWCNT in the case of high-order modes, short length and low temperature.

  18. Labyrinthine water flow across multilayer graphene-based membranes: Molecular dynamics versus continuum predictions.

    PubMed

    Yoshida, Hiroaki; Bocquet, Lydéric

    2016-06-21

    In this paper, we investigate the hydrodynamic permeance of water through graphene-based membranes, inspired by recent experimental findings on graphene-oxide membranes. We consider the flow across multiple graphene layers having nanoslits in a staggered alignment, with an inter-layer distance ranging from sub-nanometer to a few nanometers. We compare results for the permeability obtained by means of molecular dynamics simulations to continuum predictions obtained by using the lattice Boltzmann calculations and hydrodynamic modelization. This highlights that, in spite of extreme confinement, the permeability across the graphene-based membrane is quantitatively predicted on the basis of a continuum expression, taking properly into account entrance and slippage effects of the confined water flow. Our predictions refute the breakdown of hydrodynamics at small scales in these membrane systems. They constitute a benchmark to which we compare published experimental data. PMID:27334184

  19. Predicting the excess solubility of acetanilide, acetaminophen, phenacetin, benzocaine, and caffeine in binary water/ethanol mixtures via molecular simulation

    NASA Astrophysics Data System (ADS)

    Paluch, Andrew S.; Parameswaran, Sreeja; Liu, Shuai; Kolavennu, Anasuya; Mobley, David L.

    2015-01-01

    We present a general framework to predict the excess solubility of small molecular solids (such as pharmaceutical solids) in binary solvents via molecular simulation free energy calculations at infinite dilution with conventional molecular models. The present study used molecular dynamics with the General AMBER Force Field to predict the excess solubility of acetanilide, acetaminophen, phenacetin, benzocaine, and caffeine in binary water/ethanol solvents. The simulations are able to predict the existence of solubility enhancement and the results are in good agreement with available experimental data. The accuracy of the predictions in addition to the generality of the method suggests that molecular simulations may be a valuable design tool for solvent selection in drug development processes.

  20. Predicting the excess solubility of acetanilide, acetaminophen, phenacetin, benzocaine, and caffeine in binary water/ethanol mixtures via molecular simulation.

    PubMed

    Paluch, Andrew S; Parameswaran, Sreeja; Liu, Shuai; Kolavennu, Anasuya; Mobley, David L

    2015-01-28

    We present a general framework to predict the excess solubility of small molecular solids (such as pharmaceutical solids) in binary solvents via molecular simulation free energy calculations at infinite dilution with conventional molecular models. The present study used molecular dynamics with the General AMBER Force Field to predict the excess solubility of acetanilide, acetaminophen, phenacetin, benzocaine, and caffeine in binary water/ethanol solvents. The simulations are able to predict the existence of solubility enhancement and the results are in good agreement with available experimental data. The accuracy of the predictions in addition to the generality of the method suggests that molecular simulations may be a valuable design tool for solvent selection in drug development processes. PMID:25637996

  1. Predicting the excess solubility of acetanilide, acetaminophen, phenacetin, benzocaine, and caffeine in binary water/ethanol mixtures via molecular simulation

    PubMed Central

    Paluch, Andrew S.; Parameswaran, Sreeja; Liu, Shuai; Kolavennu, Anasuya; Mobley, David L.

    2015-01-01

    We present a general framework to predict the excess solubility of small molecular solids (such as pharmaceutical solids) in binary solvents via molecular simulation free energy calculations at infinite dilution with conventional molecular models. The present study used molecular dynamics with the General AMBER Force Field to predict the excess solubility of acetanilide, acetaminophen, phenacetin, benzocaine, and caffeine in binary water/ethanol solvents. The simulations are able to predict the existence of solubility enhancement and the results are in good agreement with available experimental data. The accuracy of the predictions in addition to the generality of the method suggests that molecular simulations may be a valuable design tool for solvent selection in drug development processes. PMID:25637996

  2. Chickenpox-related pulmonary granulomas in immunocompetent adults: clinicopathologic and molecular features of an underrated occurrence.

    PubMed

    Rossi, Giulio; Cavazza, Alberto; Gennari, William; Marchioni, Alessandro; Graziano, Paolo; Caminati, Antonella; Mengoli, Maria Cecilia; Magnani, Rita; Colby, Thomas V

    2012-10-01

    Pulmonary granulomas represent a common inflammatory reaction to several lung infective or noninfective diseases. However, little is known about the histology and clinical presentation of chickenpox-related granulomas in immunocompetent subjects. We collected a series of 8 adult patients (mean age, 40 y; range, 33 to 53 y) with several bilateral pulmonary granulomas incidentally discovered after imaging studies. All patients were asymptomatic and had experienced a varicella-zoster virus (VZV) infection as adults but were clinically suspected to have a metastatic neoplasm of unknown origin. Chest computed tomography scan revealed numerous, tiny (few millimeters to 1 cm in size) nodules randomly dispersed through the lungs. Positron emission tomography scan performed in 4 patients was negative. All patients underwent video-assisted thoracoscopic surgical resection and were still alive and well. At histology, granulomas consisted of well-defined, rounded, small nodules centered by a deeply eosinophilic, acellular necrosis rimmed by lamellar dense collagen and a chronic inflammatory infiltrate with or without multinucleated giant cells. Chickenpox-related granulomas were included in the differential diagnosis along with several other granulomatous diseases. Polymerase chain reaction-based molecular analysis for VZV performed on paraffin sections detected VZV DNA in all 8 cases. By contrast, 85 cases of pulmonary granulomas of different etiologies were simultaneously studied by molecular analysis with negative results. Pathologists should be familiar with the peculiar morphologic appearance of chickenpox-related granulomas. A careful search for a history of VZV infection in adulthood and molecular studies may be very helpful in confirming the diagnosis.

  3. Molecular Dynamics Simulations and Kinetic Measurements to Estimate and Predict Protein-Ligand Residence Times.

    PubMed

    Mollica, Luca; Theret, Isabelle; Antoine, Mathias; Perron-Sierra, Françoise; Charton, Yves; Fourquez, Jean-Marie; Wierzbicki, Michel; Boutin, Jean A; Ferry, Gilles; Decherchi, Sergio; Bottegoni, Giovanni; Ducrot, Pierre; Cavalli, Andrea

    2016-08-11

    Ligand-target residence time is emerging as a key drug discovery parameter because it can reliably predict drug efficacy in vivo. Experimental approaches to binding and unbinding kinetics are nowadays available, but we still lack reliable computational tools for predicting kinetics and residence time. Most attempts have been based on brute-force molecular dynamics (MD) simulations, which are CPU-demanding and not yet particularly accurate. We recently reported a new scaled-MD-based protocol, which showed potential for residence time prediction in drug discovery. Here, we further challenged our procedure's predictive ability by applying our methodology to a series of glucokinase activators that could be useful for treating type 2 diabetes mellitus. We combined scaled MD with experimental kinetics measurements and X-ray crystallography, promptly checking the protocol's reliability by directly comparing computational predictions and experimental measures. The good agreement highlights the potential of our scaled-MD-based approach as an innovative method for computationally estimating and predicting drug residence times. PMID:27391254

  4. Enhancing Predictive Accuracy of Cardiac Autonomic Neuropathy Using Blood Biochemistry Features and Iterative Multitier Ensembles.

    PubMed

    Abawajy, Jemal; Kelarev, Andrei; Chowdhury, Morshed U; Jelinek, Herbert F

    2016-01-01

    Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigate the role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progression. Blood biochemistry contributes to CAN, and so it is a causative factor that can provide additional power for the diagnosis of CAN especially in the absence of a complete set of Ewing tests. We introduce automated iterative multitier ensembles (AIME) and investigate their performance in comparison to base classifiers and standard ensemble classifiers for blood biochemistry attributes. AIME incorporate diverse ensembles into several tiers simultaneously and combine them into one automatically generated integrated system so that one ensemble acts as an integral part of another ensemble. We carried out extensive experimental analysis using large datasets from the diabetes screening research initiative (DiScRi) project. The results of our experiments show that several blood biochemistry attributes can be used to supplement the Ewing battery for the detection of CAN in situations where one or more of the Ewing tests cannot be completed because of the individual difficulties faced by each patient in performing the tests. The results show that AIME provide higher accuracy as a multitier CAN classification paradigm. The best predictive accuracy of 99.57% has been obtained by the AIME combining decorate on top tier with bagging on middle tier based on random forest. Practitioners can use these findings to increase the accuracy of CAN diagnosis.

  5. Assessment of a Four-View Mammographic Image Feature Based Fusion Model to Predict Near-Term Breast Cancer Risk.

    PubMed

    Tan, Maxine; Pu, Jiantao; Cheng, Samuel; Liu, Hong; Zheng, Bin

    2015-10-01

    The purpose of this study was to develop and assess a new quantitative four-view mammographic image feature based fusion model to predict the near-term breast cancer risk of the individual women after a negative screening mammography examination of interest. The dataset included fully-anonymized mammograms acquired on 870 women with two sequential full-field digital mammography examinations. For each woman, the first "prior" examination in the series was interpreted as negative (not recalled) during the original image reading. In the second "current" examination, 430 women were diagnosed with pathology verified cancers and 440 remained negative ("cancer-free"). For each of four bilateral craniocaudal and mediolateral oblique view images of left and right breasts, we computed and analyzed eight groups of global mammographic texture and tissue density image features. A risk prediction model based on three artificial neural networks was developed to fuse image features computed from two bilateral views of four images. The risk model performance was tested using a ten-fold cross-validation method and a number of performance evaluation indices including the area under the receiver operating characteristic curve (AUC) and odds ratio (OR). The highest AUC = 0.725 ± 0.026 was obtained when the model was trained by gray-level run length statistics texture features computed on dense breast regions, which was significantly higher than the AUC values achieved using the model trained by only two bilateral one-view images (p < 0.02). The adjustable OR values monotonically increased from 1.0 to 11.8 as model-generated risk score increased. The regression analysis of OR values also showed a significant increase trend in slope (p < 0.01). As a result, this preliminary study demonstrated that a new four-view mammographic image feature based risk model could provide useful and supplementary image information to help predict the near-term breast cancer risk.

  6. An NMR and molecular dynamics investigation of the avian prion hexarepeat conformational features in solution

    NASA Astrophysics Data System (ADS)

    Pietropaolo, Adriana; Raiola, Luca; Muccioli, Luca; Tiberio, Giustiniano; Zannoni, Claudio; Fattorusso, Roberto; Isernia, Carla; Mendola, Diego La; Pappalardo, Giuseppe; Rizzarelli, Enrico

    2007-07-01

    The prion protein is a copper binding glycoprotein that in mammals can misfold into a pathogenic isoform leading to prion diseases, as opposed, surprisingly, to avians. The avian prion N-terminal tandem repeat is richer in prolines than the mammal one, and understanding their effect on conformation is of great biological importance. Here we succeeded in investigating the conformations of a single avian hexarepeat by means of NMR and molecular dynamics techniques. We found a high flexibility and a strong conformational dependence on pH: local turns are present at acidic and neutral pH, while unordered regions dominate at basic conditions.

  7. Molecular Characterization of Infectious Clones of the Minute Virus of Canines Reveals Unique Features of Bocaviruses▿

    PubMed Central

    Sun, Yuning; Chen, Aaron Yun; Cheng, Fang; Guan, Wuxiang; Johnson, F. Brent; Qiu, Jianming

    2009-01-01

    Minute virus of canines (MVC) is a member of the genus Bocavirus in the family Parvoviridae. We have molecularly cloned and sequenced the 5′- and 3′-terminal palindromes of MVC. The MVC genome, 5,404 nucleotides (nt) in length, shared an identity of 52.6% and 52.1% with that of human bocavirus and bovine parvovirus, respectively. It had distinct palindromic hairpins of 183 nt and 198 nt at the left-end and right-end termini of the genome, respectively. The left-end terminus was also found in two alternative orientations (flip or flop). Both termini shared extensive similarities with those of bovine parvovirus. Four full-length molecular clones constructed with different orientations of the left-end terminus proved to be infectious in Walter Reed canine cell/3873D (WRD) canine cells. Both MVC infection and transfection of the infectious clone in WRD cells revealed an identical RNA transcription profile that was similar to that of bovine parvovirus. Mutagenesis of the infectious clone demonstrated that the middle open reading frame encodes the NP1 protein. This protein, unique to the genus Bocavirus, was essential for MVC DNA replication. Moreover, the phospholipase A2 motif in the VP1 unique region was also critical for MVC infection. Thus, our studies revealed important information about the genus Bocavirus that may eventually help us to clone the human bocavirus and study its pathogenesis. PMID:19211770

  8. Modeling emission features of salicylidene aniline molecular crystals: A QM/QM' approach.

    PubMed

    Presti, Davide; Labat, Frédéric; Pedone, Alfonso; Frisch, Michael J; Hratchian, Hrant P; Ciofini, Ilaria; Cristina Menziani, Maria; Adamo, Carlo

    2016-04-01

    A new computational protocol relying on the use of electrostatic embedding, derived from QM/QM' ONIOM calculations, to simulate the effect of the crystalline environment on the emission spectra of molecular crystals is here applied to the β-form of salicylidene aniline (SA). The first singlet excited states (S1 ) of the SA cis-keto and trans-keto conformers, surrounded by a cluster of other molecules representing the crystalline structure, were optimized by using a QM/QM' ONIOM approach with and without electronic embedding. The model system consisting of the central salicylidene aniline molecule was treated at the DFT level by using either the B3LYP, PBE0, or the CAM-B3LYP functional, whereas the real system was treated at the HF level. The CAM-B3LYP/HF level of theory provides emission energies in good agreement with experiment with differences of -20/-32 nm (cis-keto form) and -8/-14 nm (trans-keto form), respectively, whereas notably larger differences are obtained using global hybrids. Though such differences on the optical properties arise from the density functional choice, the contribution of the electronic embedding is rather independent of the functional used. This plays in favor of a more general applicability of the present protocol to other crystalline molecular systems. PMID:26919703

  9. Mouse Grueneberg ganglion neurons share molecular and functional features with C. elegans amphid neurons

    PubMed Central

    Brechbühl, Julien; Moine, Fabian; Broillet, Marie-Christine

    2013-01-01

    The mouse Grueneberg ganglion (GG) is an olfactory subsystem located at the tip of the nose close to the entry of the naris. It comprises neurons that are both sensitive to cold temperature and play an important role in the detection of alarm pheromones (APs). This chemical modality may be essential for species survival. Interestingly, GG neurons display an atypical mammalian olfactory morphology with neurons bearing deeply invaginated cilia mostly covered by ensheathing glial cells. We had previously noticed their morphological resemblance with the chemosensory amphid neurons found in the anterior region of the head of Caenorhabditis elegans (C. elegans). We demonstrate here further molecular and functional similarities. Thus, we found an orthologous expression of molecular signaling elements that was furthermore restricted to similar specific subcellular localizations. Calcium imaging also revealed a ligand selectivity for the methylated thiazole odorants that amphid neurons are known to detect. Cellular responses from GG neurons evoked by chemical or temperature stimuli were also partially cGMP-dependent. In addition, we found that, although behaviors depending on temperature sensing in the mouse, such as huddling and thermotaxis did not implicate the GG, the thermosensitivity modulated the chemosensitivity at the level of single GG neurons. Thus, the striking similarities with the chemosensory amphid neurons of C. elegans conferred to the mouse GG neurons unique multimodal sensory properties. PMID:24367309

  10. Molecular features of hepatosplenic T-cell lymphoma unravels potential novel therapeutic targets

    PubMed Central

    Travert, Marion; Huang, Yenlin; De Leval, Laurence; Martin-Garcia, Nadine; Delfau-Larue, Marie-Helene; Berger, Françoise; Bosq, Jacques; Brière, Josette; Soulier, Jean; Macintyre, Elizabeth; Marafioti, Teresa; de Reyniès, Aurélien; Gaulard, Philippe

    2012-01-01

    Hepatosplenic T-cell lymphoma (HSTL) is a rare entity mostly derived from γδ T cells that shows a fatal outcome. Its pathogenesis remains largely unknown. HSTL samples (7γδ, 2αβ) and the DERL2 HSTL-cell line were subject to combined gene expression profiling and array-based comparative genomic hybridization. Compared to other T-cell lymphomas, HSTL disclosed a distinct molecular signature irrespective of TCR cell lineage. Compared to PTCL,NOS and normal γδ cells, HSTL overexpressed genes encoding NK-cell associated molecules, oncogenes (FOS, VAV3), the Sphingosine-1-phosphatase receptor 5 involved in cell trafficking and the tyrosine kinase SYK, whereas the tumor suppressor gene AIM1 was among the most downexpressed. Methylation analysis of DERL2 cells demonstrated highly methylated CpG islands of AIM1 and decitabine treatment induced significant increase in AIM1 transcripts. Notably, Syk was demonstrated in HSTL cells with its phosphorylated form present in DERL2 cells by Western blot, and in vitro DERL2 cells were sensitive to a Syk inhibitor. Genomic profiles confirmed recurrent isochromosome 7q (n=6/9) without alterations at 9q22 and 6q21 containing SYK and AIM1 genes, respectively. The current study identifies a distinct molecular signature for HSTL and highlights oncogenic pathways which offer rationale for exploring new therapeutic options such as Syk inhibitors and demethylating agents. PMID:22510872

  11. Molecular features of a human rhabdomyosarcoma cell line with spontaneous metastatic progression

    PubMed Central

    Scholl, F A; Betts, D R; Niggli, F K; Schäfer, B W

    2000-01-01

    A novel human cell line was established from a primary botryoid rhabdomyosarcoma. Reverse transcription polymerase chain reaction investigations of this cell line, called RUCH-2, demonstrated expression of the regulatory factors PAX3, Myf3 and Myf5. After 3.5 months in culture, cells underwent a crisis after which Myf3 and Myf5 could no longer be detected, whereas PAX3 expression remained constant over the entire period. Karyotype analysis revealed breakpoints in regions similar to previously described alterations in primary rhabdomyosarcoma tumour samples. Interestingly, cells progressed to a metastatic phenotype, as observed by enhanced invasiveness in vitro and tumour growth in nude mice in vivo. On the molecular level, microarray analysis before and after progression identified extensive changes in the composition of the extracellular matrix. As expected, down-regulation of tissue inhibitors of metalloproteinases and up-regulation of matrix metalloproteinases were observed. Extensive down-regulation of several death receptors of the tumour necrosis factor family suggests that these cells might have an altered response to appropriate apoptotic stimuli. The RUCH-2 cell line represents a cellular model to study multistep tumorigenesis in human rhabdomyosarcoma, allowing molecular comparison of tumorigenic versus metastatic cancer cells. © 2000 Cancer Research Campaign PMID:10735512

  12. [Analysis of Conformational Features of Watson-Crick Duplex Fragments by Molecular Mechanics and Quantum Mechanics Methods].

    PubMed

    Poltev, V I; Anisimov, V M; Sanchez, C; Deriabina, A; Gonzalez, E; Garcia, D; Rivas, F; Polteva, N A

    2016-01-01

    It is generally accepted that the important characteristic features of the Watson-Crick duplex originate from the molecular structure of its subunits. However, it still remains to elucidate what properties of each subunit are responsible for the significant characteristic features of the DNA structure. The computations of desoxydinucleoside monophosphates complexes with Na-ions using density functional theory revealed a pivotal role of DNA conformational properties of single-chain minimal fragments in the development of unique features of the Watson-Crick duplex. We found that directionality of the sugar-phosphate backbone and the preferable ranges of its torsion angles, combined with the difference between purines and pyrimidines. in ring bases, define the dependence of three-dimensional structure of the Watson-Crick duplex on nucleotide base sequence. In this work, we extended these density functional theory computations to the minimal' fragments of DNA duplex, complementary desoxydinucleoside monophosphates complexes with Na-ions. Using several computational methods and various functionals, we performed a search for energy minima of BI-conformation for complementary desoxydinucleoside monophosphates complexes with different nucleoside sequences. Two sequences are optimized using ab initio method at the MP2/6-31++G** level of theory. The analysis of torsion angles, sugar ring puckering and mutual base positions of optimized structures demonstrates that the conformational characteristic features of complementary desoxydinucleoside monophosphates complexes with Na-ions remain within BI ranges and become closer to the corresponding characteristic features of the Watson-Crick duplex crystals. Qualitatively, the main characteristic features of each studied complementary desoxydinucleoside monophosphates complex remain invariant when different computational methods are used, although the quantitative values of some conformational parameters could vary lying within the

  13. A glimpse into the past and predictions for the future: the molecular evolution of the tuberculosis agent.

    PubMed

    Boritsch, Eva C; Supply, Philip; Honoré, Nadine; Seemann, Torsten; Seeman, Torsten; Stinear, Timothy P; Brosch, Roland

    2014-09-01

    Recent advances in genomics and molecular biology are providing an excellent opportunity to get a glimpse into the past, to examine the present, and to predict the future evolution of pathogenic mycobacteria, and in particular that of Mycobacterium tuberculosis, the agent of human tuberculosis. The recent availability of genome sequences of several Mycobacterium canettii strains, representing evolutionary early-branching tubercle bacilli, has allowed the genomic and molecular features of the putative ancestor of the M. tuberculosis complex (MTBC) to be reconstituted. Analyses have identified extensive lateral gene transfer and recombination events in M. canettii and/or the MTBC, leading to suggestions of a past environmental reservoir where the ancestor(s) of the tubercle bacilli might have adapted to an intracellular lifestyle. The daily increases in M. tuberculosis genome data and the remaining urgent Public Health problem of tuberculosis make it more important than ever to try and understand the origins and the future evolution of the MTBC. Here we critically discuss a series of questions on gene-loss, acquisition, recombination, mutation and conservation that have recently arisen and which are key to better understand the outstanding evolutionary success of one of the most widespread and most deadly bacterial pathogens in the history of humankind.

  14. Iodine atoms: a new molecular feature for the design of potent transthyretin fibrillogenesis inhibitors.

    PubMed

    Mairal, Teresa; Nieto, Joan; Pinto, Marta; Almeida, Maria Rosário; Gales, Luis; Ballesteros, Alfredo; Barluenga, José; Pérez, Juan J; Vázquez, Jesús T; Centeno, Nuria B; Saraiva, Maria Joao; Damas, Ana M; Planas, Antoni; Arsequell, Gemma; Valencia, Gregorio

    2009-01-01

    The thyroid hormone and retinol transporter protein known as transthyretin (TTR) is in the origin of one of the 20 or so known amyloid diseases. TTR self assembles as a homotetramer leaving a central hydrophobic channel with two symmetrical binding sites. The aggregation pathway of TTR into amiloid fibrils is not yet well characterized but in vitro binding of thyroid hormones and other small organic molecules to TTR binding channel results in tetramer stabilization which prevents amyloid formation in an extent which is proportional to the binding constant. Up to now, TTR aggregation inhibitors have been designed looking at various structural features of this binding channel others than its ability to host iodine atoms. In the present work, greatly improved inhibitors have been designed and tested by taking into account that thyroid hormones are unique in human biochemistry owing to the presence of multiple iodine atoms in their molecules which are probed to interact with specific halogen binding domains sitting at the TTR binding channel. The new TTR fibrillogenesis inhibitors are based on the diflunisal core structure because diflunisal is a registered salicylate drug with NSAID activity now undergoing clinical trials for TTR amyloid diseases. Biochemical and biophysical evidence confirms that iodine atoms can be an important design feature in the search for candidate drugs for TTR related amyloidosis. PMID:19125186

  15. Iodine Atoms: A New Molecular Feature for the Design of Potent Transthyretin Fibrillogenesis Inhibitors

    PubMed Central

    Pinto, Marta; Almeida, Maria Rosário; Gales, Luis; Ballesteros, Alfredo; Barluenga, José; Pérez, Juan J.; Vázquez, Jesús T.; Centeno, Nuria B.; Saraiva, Maria Joao; Damas, Ana M.; Planas, Antoni; Arsequell, Gemma; Valencia, Gregorio

    2009-01-01

    The thyroid hormone and retinol transporter protein known as transthyretin (TTR) is in the origin of one of the 20 or so known amyloid diseases. TTR self assembles as a homotetramer leaving a central hydrophobic channel with two symmetrical binding sites. The aggregation pathway of TTR into amiloid fibrils is not yet well characterized but in vitro binding of thyroid hormones and other small organic molecules to TTR binding channel results in tetramer stabilization which prevents amyloid formation in an extent which is proportional to the binding constant. Up to now, TTR aggregation inhibitors have been designed looking at various structural features of this binding channel others than its ability to host iodine atoms. In the present work, greatly improved inhibitors have been designed and tested by taking into account that thyroid hormones are unique in human biochemistry owing to the presence of multiple iodine atoms in their molecules which are probed to interact with specific halogen binding domains sitting at the TTR binding channel. The new TTR fibrillogenesis inhibitors are based on the diflunisal core structure because diflunisal is a registered salicylate drug with NSAID activity now undergoing clinical trials for TTR amyloid diseases. Biochemical and biophysical evidence confirms that iodine atoms can be an important design feature in the search for candidate drugs for TTR related amyloidosis. PMID:19125186

  16. Foreign exchange market data analysis reveals statistical features that predict price movement acceleration

    NASA Astrophysics Data System (ADS)

    Nacher, Jose C.; Ochiai, Tomoshiro

    2012-05-01

    Increasingly accessible financial data allow researchers to infer market-dynamics-based laws and to propose models that are able to reproduce them. In recent years, several stylized facts have been uncovered. Here we perform an extensive analysis of foreign exchange data that leads to the unveiling of a statistical financial law. First, our findings show that, on average, volatility increases more when the price exceeds the highest (or lowest) value, i.e., breaks the resistance line. We call this the breaking-acceleration effect. Second, our results show that the probability P(T) to break the resistance line in the past time T follows power law in both real data and theoretically simulated data. However, the probability calculated using real data is rather lower than the one obtained using a traditional Black-Scholes (BS) model. Taken together, the present analysis characterizes a different stylized fact of financial markets and shows that the market exceeds a past (historical) extreme price fewer times than expected by the BS model (the resistance effect). However, when the market does, we predict that the average volatility at that time point will be much higher. These findings indicate that any Markovian model does not faithfully capture the market dynamics.

  17. LMO2 expression reflects the different stages of blast maturation and genetic features in B-cell acute lymphoblastic leukemia and predicts clinical outcome

    PubMed Central

    Malumbres, Raquel; Fresquet, Vicente; Roman-Gomez, Jose; Bobadilla, Miriam; Robles, Eloy F.; Altobelli, Giovanna G.; Calasanz, M.ª José; Smeland, Erlend B.; Aznar, Maria Angela; Agirre, Xabier; Martin-Palanco, Vanesa; Prosper, Felipe; Lossos, Izidore S.; Martinez-Climent, Jose A.

    2011-01-01

    Background LMO2 is highly expressed at the most immature stages of lymphopoiesis. In T-lymphocytes, aberrant LMO2 expression beyond those stages leads to T-cell acute lymphoblastic leukemia, while in B cells LMO2 is also expressed in germinal center lymphocytes and diffuse large B-cell lymphomas, where it predicts better clinical outcome. The implication of LMO2 in B-cell acute lymphoblastic leukemia must still be explored. Design and Methods We measured LMO2 expression by real time RT-PCR in 247 acute lymphoblastic leukemia patient samples with cytogenetic data (144 of them also with survival and immunophenotypical data) and in normal hematopoietic and lymphoid cells. Results B-cell acute lymphoblastic leukemia cases expressed variable levels of LMO2 depending on immunophenotypical and cytogenetic features. Thus, the most immature subtype, pro-B cells, displayed three-fold higher LMO2 expression than pre-B cells, common-CD10+ or mature subtypes. Additionally, cases with TEL-AML1 or MLL rearrangements exhibited two-fold higher LMO2 expression compared to cases with BCR-ABL rearrangements or hyperdyploid karyotype. Clinically, high LMO2 expression correlated with better overall survival in adult patients (5-year survival rate 64.8% (42.5%–87.1%) vs. 25.8% (10.9%–40.7%), P= 0.001) and constituted a favorable independent prognostic factor in B-ALL with normal karyotype: 5-year survival rate 80.3% (66.4%–94.2%) vs. 63.0% (46.1%–79.9%) (P= 0.043). Conclusions Our data indicate that LMO2 expression depends on the molecular features and the differentiation stage of B-cell acute lymphoblastic leukemia cells. Furthermore, assessment of LMO2 expression in adult patients with a normal karyotype, a group which lacks molecular prognostic factors, could be of clinical relevance. PMID:21459790

  18. Retrospective analysis of molecular scores for the prediction of distant recurrence according to baseline risk factors.

    PubMed

    Sestak, Ivana; Dowsett, Mitch; Ferree, Sean; Baehner, Frederick L; Cuzick, Jack

    2016-08-01

    Clinical variables and several gene signature profiles have been investigated for the prediction of (distant) recurrence in several trials. These molecular markers are significantly correlated with overall and late distant recurrences. Here, we retrospectively explore whether age and body mass index (BMI) affect the prediction of these molecular scores for distant recurrence in postmenopausal women with hormone receptor-positive breast cancer in the transATAC trial. 940 postmenopausal women for whom the Clinical Treatment Score (CTS), immunohistochemical markers (IHC4), Oncotype Recurrence Score (RS), and the Prosigna Risk of Recurrence Score (ROR) were available were included in this retrospective analysis. Conventional BMI groups were used (N = 865), and age was split into equal tertiles (N = 940). Cox proportional hazard models were used to determine the effect of a molecular score for the prediction of distant recurrence according to BMI and age groups. In both the univariate and bivariate analyses, the effect size of the IHC4 and RS was strongest in women aged 59.8 years or younger. Trends tests for age were significant for the IHC4 and RS, but not for the CTS and ROR, for which most prognostic information was added in women aged 60 years or older. The CTS and ROR scores added significant prognostic information in all three BMI groups. In both the univariate and bivariate analyses, the IHC4 provided the most prognostic information in women with a BMI lower than 25 kg/m(2), whereas the RS did not add prognostic information for distant recurrence in women with a BMI of 30 kg/m(2) or above. Molecular scores are increasingly used in women with breast cancer to assess recurrence risk. We have shown that the effect size of the molecular scores is significantly different across age groups, but not across BMI groups. The results from this retrospective analysis may be incorporated in the identification of women who may benefit most from the use of these

  19. Structurally-modified subphthalocyanines: molecular design towards realization of expected properties from the electronic structure and structural features of subphthalocyanine.

    PubMed

    Shimizu, Soji; Kobayashi, Nagao

    2014-07-01

    This feature article summarizes recent contributions of the authors in the synthesis of structurally-modified subphthalocyanines. The structural modification covers (1) modification of the conjugated system of subphthalocyanines to create novel conjugated systems comprising three pyrroles or pyrrole-like subunits, (2) core-modification by expansion of the inner pyrrolic five-membered ring to larger six- and seven-membered ring units, and (3) exterior-modification by annulation of functional units to subphthalocyanines. These modifications in the structure of subphthalocyanines have been performed with the aim of demonstrating unique properties originating from the bowl-shaped C3v-symmetric structure as well as the electronic structure delineated by the 14π-electron conjugated system on the curved molecular surface. The possible structural modifications surveyed in this feature article and their concomitant properties will provide important future guidelines to the design of subphthalocyanine-based functional molecules, considering the fact that subphthalocyanines have recently been attracting considerable attention as potential candidates in the field of optoelectronics and molecular electronics. PMID:24710280

  20. Acyclic forms of aldohexoses and ketohexoses in aqueous and DMSO solutions: conformational features studied using molecular dynamics simulations.

    PubMed

    Plazinski, Wojciech; Plazinska, Anita; Drach, Mateusz

    2016-04-14

    The molecular properties of aldohexoses and ketohexoses are usually studied in the context of their cyclic, furanose or pyranose structures which is due to the abundance of related tautomeric forms in aqueous solution. We studied the conformational features of a complete series of D-aldohexoses (D-allose, D-altrose, D-glucose, D-mannose, D-gulose, d-idose, D-galactose and D-talose) and D-ketohexoses (D-psicose, D-fructose, D-sorbose and D-tagatose) as well as of L-psicose by using microsecond-timescale molecular dynamics in explicit water and DMSO with the use of enhanced sampling methods. In each of the studied cases the preferred conformation corresponded to an extended chain structure; the less populated conformers included the quasi-cyclic structures, close to furanose rings and common for both aldo- and ketohexoses. The orientational preferences of the aldehyde or ketone groups are correlated with the relative populations of anomers characteristic of cyclic aldo- and ketohexoses, respectively, thus indicating that basic features of anomeric equilibria are preserved even if hexose molecules are not in their cyclic forms. No analogous relationship is observed in the case of other structural characteristics, such as the preferences of acyclic molecules to form either the furanose-or pyranose-like structures or maintaining the chair-like geometry of pseudo-pyranose rings.

  1. The Hasford Score May Predict Molecular Response in Chronic Myeloid Leukemia Patients: A Single Institution Experience

    PubMed Central

    Jaźwiec, Bożena; Haus, Olga; Urbaniak-Kujda, Donata; Kapelko-Słowik, Katarzyna; Wróbel, Tomasz; Lonc, Tomasz; Sawicki, Mateusz; Mędraś, Ewa; Kaczmar-Dybko, Agnieszka; Kuliczkowski, Kazimierz

    2016-01-01

    The Sokal, Hasford, and EUTOS scores were established in different treatment eras of chronic myeloid leukemia (CML). None of them was reported to predict molecular response. In this single center study we tried to reevaluate the usefulness of three main scores in TKI era. The study group included 88 CML patients in first chronic phase treated initially with standard imatinib dose. All of them achieved major molecular response (MMR) in time points defined by European LeukemiaNet (ELN). 42 patients lost MMR in a median time of 47 months and we found a significant difference in MMR maintenance between intermediate-risk (IR) and low-risk (LR) patients assessed by Hasford score. All 42 patients were switched to second-generation TKI (2G-TKI) treatment. At 18 months of 2G-TKI therapy we have still found a significant difference in BCR-ABL transcript levels and MMR rate between IR and LR groups. We did not find any of the described differences discriminating patients by Sokal or EUTOS score. In this retrospective single center analysis we found Hasford score to be useful in predicting molecular response in first chronic phase of CML patients.

  2. Proteomics, metabolomics, and protein interactomics in the characterization of the molecular features of major depressive disorder.

    PubMed

    Martins-de-Souza, Daniel

    2014-03-01

    Omics technologies emerged as complementary strategies to genomics in the attempt to understand human illnesses. In general, proteomics technologies emerged earlier than those of metabolomics for major depressive disorder (MDD) research, but both are driven by the identification of proteins and/or metabolites that can delineate a comprehensive characterization of MDD's molecular mechanisms, as well as lead to the identification of biomarker candidates of all types-prognosis, diagnosis, treatment, and patient stratification. Also, one can explore protein and metabolite interactomes in order to pinpoint additional molecules associated with the disease that had not been picked up initially. Here, results and methodological aspects of MDD research using proteomics, metabolomics, and protein interactomics are reviewed, focusing on human samples.

  3. Clinical and Molecular Features of Laron Syndrome, A Genetic Disorder Protecting from Cancer.

    PubMed

    Janecka, Anna; Kołodziej-Rzepa, Marta; Biesaga, Beata

    2016-01-01

    Laron syndrome (LS) is a rare, genetic disorder inherited in an autosomal recessive manner. The disease is caused by mutations of the growth hormone (GH) gene, leading to GH/insulin-like growth factor type 1 (IGF1) signalling pathway defect. Patients with LS have characteristic biochemical features, such as a high serum level of GH and low IGF1 concentration. Laron syndrome was first described by the Israeli physician Zvi Laron in 1966. Globally, around 350 people are affected by this syndrome and there are two large groups living in separate geographic regions: Israel (69 individuals) and Ecuador (90 individuals). They are all characterized by typical appearance such as dwarfism, facial phenotype, obesity and hypogenitalism. Additionally, they suffer from hypoglycemia, hypercholesterolemia and sleep disorders, but surprisingly have a very low cancer risk. Therefore, studies on LS offer a unique opportunity to better understand carcinogenesis and develop new strategies of cancer treatment. PMID:27381597

  4. Cytomorphologic features in thyroid nodules read as "suspicious for malignancy" on cytology may predict thyroid cancers with the BRAF mutation.

    PubMed

    Kwon, Hyeong Ju; Kim, Eun-Kyung; Kwak, Jin Young

    2015-09-01

    Some morphologic parameters have been studied to help predict the BRAF(V600E) mutation using cytopathologic specimens, which can indicate which nodules should undergo further testing. The aim of this study was to investigate the value of cytomorphologic parameters to predict the BRAF(V600E) mutation in nodules read as "suspicious for malignancy" on cytology. This study included 142 resected nodules which were diagnosed as "suspicious for malignancy" on cytology in 142 patients. At our institution, BRAF(V600E) mutation analysis was performed at the request of the referring clinicians based on the clinical features of the patients, or the judgment of the radiologists performing US-FNA because suspicious US features were observed on the targeted nodule during this study period. Cytology smears were re-reviewed to assess the presence and amount of polygonal eosinophilic (plump) cells and microfollicles, and the presence of intranuclear pseudoinclusions, irregular nuclear membranes, nuclear grooves, sickles cells, psammoma bodies, and cystic changes. We evaluated the diagnostic performances of the cytomorphologic features to predict the BRAF(V600E) mutation. Polygonal eosinophilic (plump) cells, microfollicles, intranuclear pseudoinclusions, sickle cells, and cystic changes were significantly associated with the BRAF(V600E) mutation. The mutation was not present in all 6 thyroid nodules with microfollicles larger than 20% on cytology. Additionally, polygonal eosinophilic (plump) cells larger than 20%, cystic changes, and sickle cells on cytology had a high specificity of 95%, 96.7%, and 81.7%, respectively. Excluding 6 nodules with microfollicles larger than 20% on cytology, there were 82 (60.3%) nodules with the BRAF(V600E) mutation among the 136 nodules. Among the 136 nodules, there were 95 nodules with polygonal eosinophilic (plump) cells larger than 20%, cystic changes, or sickle cells on cytology. Of the 95 nodules, 69 (72.6%) had the mutation. Cytomorphologic

  5. COBRA: A Computational Brewing Application for Predicting the Molecular Composition of Organic Aerosols

    SciTech Connect

    Fooshee, David R.; Nguyen, Tran B.; Nizkorodov, Sergey A.; Laskin, Julia; Laskin, Alexander; Baldi, Pierre

    2012-05-08

    Atmospheric organic aerosols (OA) represent a significant fraction of airborne particulate matter and can impact climate, visibility, and human health. These mixtures are difficult to characterize experimentally due to the enormous complexity and dynamic nature of their chemical composition. We introduce a novel Computational Brewing Application (COBRA) and apply it to modeling oligomerization chemistry stemming from condensation and addition reactions of monomers pertinent to secondary organic aerosol (SOA) formed by photooxidation of isoprene. COBRA uses two lists as input: a list of chemical structures comprising the molecular starting pool, and a list of rules defining potential reactions between molecules. Reactions are performed iteratively, with products of all previous iterations serving as reactants for the next one. The simulation generated thousands of molecular structures in the mass range of 120-500 Da, and correctly predicted ~70% of the individual SOA constituents observed by high-resolution mass spectrometry (HR-MS). Selected predicted structures were confirmed with tandem mass spectrometry. Esterification and hemiacetal formation reactions were shown to play the most significant role in oligomer formation, whereas aldol condensation was shown to be insignificant. COBRA is not limited to atmospheric aerosol chemistry, but is broadly applicable to the prediction of reaction products in other complex mixtures for which reasonable reaction mechanisms and seed molecules can be supplied by experimental or theoretical methods.

  6. Predicting Low Energy Dopant Implant Profiles in Semiconductors using Molecular Dynamics

    SciTech Connect

    Beardmore, K.M.; Gronbech-Jensen, N.

    1999-05-02

    The authors present a highly efficient molecular dynamics scheme for calculating dopant density profiles in group-IV alloy, and III-V zinc blende structure materials. Their scheme incorporates several necessary methods for reducing computational overhead, plus a rare event algorithm to give statistical accuracy over several orders of magnitude change in the dopant concentration. The code uses a molecular dynamics (MD) model to describe ion-target interactions. Atomic interactions are described by a combination of 'many-body' and pair specific screened Coulomb potentials. Accumulative damage is accounted for using a Kinchin-Pease type model, inelastic energy loss is represented by a Firsov expression, and electronic stopping is described by a modified Brandt-Kitagawa model which contains a single adjustable ion-target dependent parameter. Thus, the program is easily extensible beyond a given validation range, and is therefore truly predictive over a wide range of implant energies and angles. The scheme is especially suited for calculating profiles due to low energy and to situations where a predictive capability is required with the minimum of experimental validation. They give examples of using the code to calculate concentration profiles and 2D 'point response' profiles of dopants in crystalline silicon and gallium-arsenide. Here they can predict the experimental profile over five orders of magnitude for <100> and <110> channeling and for non-channeling implants at energies up to hundreds of keV.

  7. An Efficient Molecular Dynamics Scheme for Predicting Dopant Implant Profiles in Semiconductors

    SciTech Connect

    Beardmore, K.M.; Gronbech-Jensen, N.

    1998-09-15

    The authors present a highly efficient molecular dynamics scheme for calculating the concentration profile of dopants implanted in group-IV alloy, and III-V zinc blende structure materials. The program incorporates methods for reducing computational overhead, plus a rare event algorithm to give statistical accuracy over several orders of magnitude change in the dopant concentration. The code uses a molecular dynamics (MD) model, instead of the binary collision approximation (BCA) used in implant simulators such as TRIM and Marlowe, to describe ion-target interactions. Atomic interactions are described by a combination of 'many-body' and screened Coulomb potentials. Inelastic energy loss is accounted for using a Firsov model, and electronic stopping is described by a Brandt-Kitagawa model which contains the single adjustable parameter for the entire scheme. Thus, the program is easily extensible to new ion-target combinations with the minimum of tuning, and is predictive over a wide range of implant energies and angles. The scheme is especially suited for calculating profiles due to low energy, large angle implants, and for situations where a predictive capability is required with the minimum of experimental validation. They give examples of using their code to calculate concentration profiles and 2D 'point response' profiles of dopants in crystalline silicon, silicon-germanium blends, and gallium-arsenide. They can predict the experimental profiles over five orders of magnitude for <100> and <110> channeling and for non-channeling implants at energies up to hundreds of keV.

  8. Quantitative prediction of molecular clock and ka/ks at short timescales.

    PubMed

    Peterson, Grant I; Masel, Joanna

    2009-11-01

    Recent empirical studies of taxa including humans, fish, and birds have shown elevated rates of molecular evolution between species that diverged recently. Using the Moran model, we calculate expected divergence as a function of time. Our findings suggest that the observed phenomenon of elevated rates at short timescales is consistent with standard population genetics theory. The apparent acceleration of the molecular clock at short timescales can be explained by segregating polymorphisms present at the time of the ancestral population, both neutral and slightly deleterious, and not newly arising slightly deleterious mutations as has been previously hypothesized. Our work also suggests that the duration of the rate elevation depends on the effective population size, providing a method to correct time estimates of recent divergence events. Our model concords with estimates of divergence obtained from African cichlid fish and humans. As an additional application of our model, we calculate that K(a)/K(s) is elevated within a population before decaying slowly to its long-term value. Similar to the molecular clock, the duration and magnitude of K(a)/K(s) elevation depend on the effective population size. Unlike the molecular clock, however, K(a)/K(s) elevation is caused by newly arising slightly deleterious mutations. This elevation, although not as severe in magnitude as had been previously predicted in models neglecting ancestral polymorphism, persists slightly longer.

  9. Predictive diagnostic value for the clinical features accompanying intellectual disability in children with pathogenic copy number variations: a multivariate analysis

    PubMed Central

    2014-01-01

    Background Array comparative genomic hybridization (a-CGH) has become the first-tier investigation in patients with unexplained developmental delay/intellectual disability (DD/ID). Although the costs are progressively decreasing, a-CGH is still an expensive and labour-intensive technique: for this reason a definition of the categories of patients that can benefit the most of the analysis is needed. Aim of the study was to retrospectively analyze the clinical features of children with DD/ID attending the outpatient clinic of the Mother & Child Department of the University Hospital of Modena subjected to a-CGH, to verify by uni- and multivariate analysis the independent predictors of pathogenic CNVs. Methods 116 patients were included in the study. Data relative to the CNVs and to the patients’ clinical features were analyzed for genotype/phenotype correlations. Results and conclusions 27 patients (23.3%) presented pathogenic CNVs (21 deletions, 3 duplications and 3 cases with both duplications and deletions). Univariate analysis showed a significant association of the pathogenic CNVs with the early onset of symptoms (before 1 yr of age) and the presence of malformations and dysmorphisms. Logistic regression analysis showed a significant independent predictive value for diagnosing a pathogenic CNV for malformations (P = 0.002) and dysmorphisms (P = 0.023), suggesting that those features should address a-CGH analysis as a high-priority test for diagnosis. PMID:24775911

  10. Conformational Sampling by Ab Initio Molecular Dynamics Simulations Improves NMR Chemical Shift Predictions.

    PubMed

    Dračínský, Martin; Möller, Heiko M; Exner, Thomas E

    2013-08-13

    Car-Parrinello molecular dynamics simulations were performed for N-methyl acetamide as a small test system for amide groups in protein backbones, and NMR chemical shifts were calculated based on the generated ensemble. If conformational sampling and explicit solvent molecules are taken into account, excellent agreement between the calculated and experimental chemical shifts is obtained. These results represent a landmark improvement over calculations based on classical molecular dynamics (MD) simulations especially for amide protons, which are predicted too high-field shifted based on the latter ensembles. We were able to show that the better results are caused by the solute-solvents interactions forming shorter hydrogen bonds as well as by the internal degrees of freedom of the solute. Inspired by these results, we propose our approach as a new tool for the validation of force fields due to its power of identifying the structural reasons for discrepancies between the experimental and calculated data. PMID:26584127

  11. Multiscale Reactive Molecular Dynamics for Absolute pK a Predictions and Amino Acid Deprotonation.

    PubMed

    Nelson, J Gard; Peng, Yuxing; Silverstein, Daniel W; Swanson, Jessica M J

    2014-07-01

    Accurately calculating a weak acid's pK a from simulations remains a challenging task. We report a multiscale theoretical approach to calculate the free energy profile for acid ionization, resulting in accurate absolute pK a values in addition to insights into the underlying mechanism. Importantly, our approach minimizes empiricism by mapping electronic structure data (QM/MM forces) into a reactive molecular dynamics model capable of extensive sampling. Consequently, the bulk property of interest (the absolute pK a) is the natural consequence of the model, not a parameter used to fit it. This approach is applied to create reactive models of aspartic and glutamic acids. We show that these models predict the correct pK a values and provide ample statistics to probe the molecular mechanism of dissociation. This analysis shows changes in the solvation structure and Zundel-dominated transitions between the protonated acid, contact ion pair, and bulk solvated excess proton. PMID:25061442

  12. Resistance to sunitinib in renal cell carcinoma: From molecular mechanisms to predictive markers and future perspectives.

    PubMed

    Joosten, S C; Hamming, L; Soetekouw, P M; Aarts, M J; Veeck, J; van Engeland, M; Tjan-Heijnen, V C

    2015-01-01

    The introduction of agents that inhibit tumor angiogenesis by targeting vascular endothelial growth factor (VEGF) signaling has made a significant impact on the survival of patients with metastasized renal cell carcinoma (RCC). Sunitinib, a tyrosine kinase inhibitor of the VEGF receptor, has become the mainstay of treatment for these patients. Although treatment with sunitinib substantially improved patient outcome, the initial success is overshadowed by the occurrence of resistance. The mechanisms of resistance are poorly understood. Insight into the molecular mechanisms of resistance will help to better understand the biology of RCC and can ultimately aid the development of more effective therapies for patients with this infaust disease. In this review we comprehensively discuss molecular mechanisms of resistance to sunitinib and the involved biological processes, summarize potential biomarkers that predict response and resistance to treatment with sunitinib, and elaborate on future perspectives in the treatment of metastasized RCC. PMID:25446042

  13. The prediction of novel multiple lipid-binding regions in protein translocation motor proteins: a possible general feature.

    PubMed

    Keller, Rob C A

    2011-03-01

    Protein translocation is an important cellular process. SecA is an essential protein component in the Sec system, as it contains the molecular motor that facilitates protein translocation. In this study, a bioinformatics approach was applied in the search for possible lipid-binding helix regions in protein translocation motor proteins. Novel lipid-binding regions in Escherichia coli SecA were identified. Remarkably, multiple lipid-binding sites were also identified in other motor proteins such as BiP, which is involved in ER protein translocation. The prokaryotic signal recognition particle receptor FtsY, though not a motor protein, is in many ways related to SecA, and was therefore included in this study. The results demonstrate a possible general feature for motor proteins involved in protein translocation. PMID:20957445

  14. Unraveling the distinctive features of hemorrhagic and non-hemorrhagic snake venom metalloproteinases using molecular simulations.

    PubMed

    de Souza, Raoni Almeida; Díaz, Natalia; Nagem, Ronaldo Alves Pinto; Ferreira, Rafaela Salgado; Suárez, Dimas

    2016-01-01

    Snake venom metalloproteinases are important toxins that play fundamental roles during envenomation. They share a structurally similar catalytic domain, but with diverse hemorrhagic capabilities. To understand the structural basis for this difference, we build and compare two dynamical models, one for the hemorrhagic atroxlysin-I from Bothrops atrox and the other for the non-hemorraghic leucurolysin-a from Bothrops leucurus. The analysis of the extended molecular dynamics simulations shows some changes in the local structure, flexibility and surface determinants that can contribute to explain the different hemorrhagic activity of the two enzymes. In agreement with previous results, the long Ω-loop (from residue 149 to 177) has a larger mobility in the hemorrhagic protein. In addition, we find some potentially-relevant differences at the base of the S1' pocket, what may be interesting for the structure-based design of new anti-venom agents. However, the sharpest differences in the computational models of atroxlysin-I and leucurolysin-a are observed in the surface electrostatic potential around the active site region, suggesting thus that the hemorrhagic versus non-hemorrhagic activity is probably determined by protein surface determinants. PMID:26676823

  15. Model predictions of features in microsaccade-related neural responses in a feedforward network with short-term synaptic depression

    NASA Astrophysics Data System (ADS)

    Zhou, Jian-Fang; Yuan, Wu-Jie; Zhou, Zhao; Zhou, Changsong

    2016-02-01

    Recently, the significant microsaccade-induced neural responses have been extensively observed in experiments. To explore the underlying mechanisms of the observed neural responses, a feedforward network model with short-term synaptic depression has been proposed [Yuan, W.-J., Dimigen, O., Sommer, W. and Zhou, C. Front. Comput. Neurosci. 7, 47 (2013)]. The depression model not only gave an explanation for microsaccades in counteracting visual fading, but also successfully reproduced several microsaccade-related features in experimental findings. These results strongly suggest that, the depression model is very useful to investigate microsaccade-related neural responses. In this paper, by using the model, we extensively study and predict the dependance of microsaccade-related neural responses on several key parameters, which could be tuned in experiments. Particularly, we provide a significant prediction that microsaccade-related neural response also complies with the property “sharper is better” observed in many contexts in neuroscience. Importantly, the property exhibits a power-law relationship between the width of input signal and the responsive effectiveness, which is robust against many parameters in the model. By using mean field theory, we analytically investigate the robust power-law property. Our predictions would give theoretical guidance for further experimental investigations of the functional role of microsaccades in visual information processing.

  16. Analytic Methods for Predicting Significant Multi-Quanta Effects in Collisional Molecular Energy Transfer

    NASA Technical Reports Server (NTRS)

    Bieniek, Ronald J.

    1996-01-01

    Collision-induced transitions can significantly affect molecular vibrational-rotational populations and energy transfer in atmospheres and gaseous systems. This, in turn. can strongly influence convective heat transfer through dissociation and recombination of diatomics. and radiative heat transfer due to strong vibrational coupling. It is necessary to know state-to-state rates to predict engine performance and aerothermodynamic behavior of hypersonic flows, to analyze diagnostic radiative data obtained from experimental test facilities, and to design heat shields and other thermal protective systems. Furthermore, transfer rates between vibrational and translational modes can strongly influence energy flow in various 'disturbed' environments, particularly where the vibrational and translational temperatures are not equilibrated.

  17. Zsyntax: a formal language for molecular biology with projected applications in text mining and biological prediction.

    PubMed

    Boniolo, Giovanni; D'Agostino, Marcello; Di Fiore, Pier Paolo

    2010-03-03

    We propose a formal language that allows for transposing biological information precisely and rigorously into machine-readable information. This language, which we call Zsyntax (where Z stands for the Greek word zetaomegaeta, life), is grounded on a particular type of non-classical logic, and it can be used to write algorithms and computer programs. We present it as a first step towards a comprehensive formal language for molecular biology in which any biological process can be written and analyzed as a sort of logical "deduction". Moreover, we illustrate the potential value of this language, both in the field of text mining and in that of biological prediction.

  18. Genetic features and molecular epidemiology of Enterococcus faecium isolated in two university hospitals in Brazil.

    PubMed

    da Silva, Leila Priscilla Pinheiro; Pitondo-Silva, André; Martinez, Roberto; da Costa Darini, Ana Lúcia

    2012-11-01

    The global emergence of vancomycin-resistant Enterococcus faecium (VREfm) has been characterized by a clonal spread of strains belonging to clonal complex 17 (CC17). Genetic features and clonal relationships of 53 VREfm isolated from patients in 2 hospitals in Ribeirao Preto, São Paulo, Brazil, during 2005-2010 were determined as a contribution to the Brazilian evolutionary history of these nosocomial pathogens. All isolates were daptomycin susceptible, vancomycin-resistant, and had the vanA gene. The predominant virulence genes were acm and esp. Only 5 VREfm isolated in 2005-2006 had intact Tn1546, while 81% showed Tn1546 with deleted left extremity and insertion of IS1251 between the vanS and vanH genes. Multilocus sequence typing analysis permitted the identification of 9 different sequence types (STs), with 5 being new ones (656, 657, 658, 659, and 660). Predominant STs were ST412 and ST478, all belonging to CC17, except ST658. This is the first report of the ST78 in Brazil. PMID:22959818

  19. Patient-derived xenografts recapitulate molecular features of human uveal melanomas.

    PubMed

    Laurent, Cécile; Gentien, David; Piperno-Neumann, Sophie; Némati, Fariba; Nicolas, André; Tesson, Bruno; Desjardins, Laurence; Mariani, Pascale; Rapinat, Audrey; Sastre-Garau, Xavier; Couturier, Jérôme; Hupé, Philippe; de Koning, Leanne; Dubois, Thierry; Roman-Roman, Sergio; Stern, Marc-Henri; Barillot, Emmanuel; Harbour, J William; Saule, Simon; Decaudin, Didier

    2013-06-01

    We have previously developed a new method for the development and maintenance of uveal melanoma (UM) xenografts in immunodeficient mice. Here, we compare the genetic profiles of the primary tumors to their corresponding xenografts that have been passaged over time. The study included sixteen primary UMs and corresponding xenografts at very early (P1), early (P4), and late (P9) in vivo passages. The tumors were analyzed for mutation status of GNAQ, GNA11, GNAS, GNA15, BAP1, and BRAF, chromosomal copy number alterations using Affymetrix GeneChip(®) Genome-Wide Human SNP6.0 arrays, gene expression profiles using GeneChip(®) Human Exon 1.0 ST arrays, BAP1 mRNA and protein expression, and MAPK pathway status using Reverse Phase Protein Arrays (RPPA). The UM xenografts accurately recapitulated the genetic features of primary human UMs and they exhibited genetic stability over the course of their in vivo maintenance. Our technique for establishing and maintaining primary UMs as xenograft tumors in immunodeficient mice exhibit a high degree of genetic conservation between the primary tumors and the xenograft tumors over multiple passages in vivo. These models therefore constitute valuable preclinical tool for drug screening in UM.

  20. The use of molecular markers in predicting dysplasia and guiding treatment.

    PubMed

    Zeki, Sebastian; Fitzgerald, Rebecca C

    2015-02-01

    The ability to stratify patients based on the risk of progression to oesophageal adenocarcinoma would provide benefit to patients as well as deliver a more cost effective surveillance programme. Current practice is to survey all patients with Barrett's oesophagus (BO) and use histological diagnoses to guide further management. However, reliance on histology alone has its drawbacks. We are currently unable to reliably stratify the risk of progression of patients with non-dysplastic BO based on any particular histological feature. There is also considerable variability in histological interpretation. An obvious recourse has been to rely on identifying molecular features possibly as an adjunct to histology, to better diagnose and stratify patients. To this end, p53 immunohistochemistry can be used as a useful adjunct to risk stratify and clarify histological grades, particularly low-grade dysplasia. Other markers of progression, although not yet in a clinically applicable format, are promising. Measurements of promoter methylation and also genomic instability such as loss of heterozygosity and copy number alterations show promise especially as high throughput genetic technologies reach maturity. The enduring hope is that these molecular biomarkers will make the transition to clinical applicability either in the direct endoscopic setting or even using non-endoscopic methods. PMID:25743460

  1. Predicting hydration free energies of amphetamine-type stimulants with a customized molecular model

    NASA Astrophysics Data System (ADS)

    Li, Jipeng; Fu, Jia; Huang, Xing; Lu, Diannan; Wu, Jianzhong

    2016-09-01

    Amphetamine-type stimulants (ATS) are a group of incitation and psychedelic drugs affecting the central nervous system. Physicochemical data for these compounds are essential for understanding the stimulating mechanism, for assessing their environmental impacts, and for developing new drug detection methods. However, experimental data are scarce due to tight regulation of such illicit drugs, yet conventional methods to estimate their properties are often unreliable. Here we introduce a tailor-made multiscale procedure for predicting the hydration free energies and the solvation structures of ATS molecules by a combination of first principles calculations and the classical density functional theory. We demonstrate that the multiscale procedure performs well for a training set with similar molecular characteristics and yields good agreement with a testing set not used in the training. The theoretical predictions serve as a benchmark for the missing experimental data and, importantly, provide microscopic insights into manipulating the hydrophobicity of ATS compounds by chemical modifications.

  2. Clinical and molecular prognostic and predictive biomarkers in clear cell renal cell cancer.

    PubMed

    Czarnecka, Anna M; Kukwa, Wojciech; Kornakiewicz, Anna; Lian, Fei; Szczylik, Cezary

    2014-12-01

    The natural history of clear cell renal cell cancer is highly unpredictable with various progressors and with populations where small renal masses may be accompanied by metastatic disease. Currently, there is a critical need to determine patient risk and optimize treatment regimes. For these patients, molecular markers may offer significant information in terms of prognostic and predictive values, as well as determination of valid therapeutic targets. Until now, only a few of the many identified clear cell renal cell cancer biomarkers have been clinically validated in large cohorts. And only several biomarkers are integrated in predictive or prognostic models. Therefore, a large cohesive effort is required to advance the field of clear cell renal cell cancer prognostic biomarkers through systematic discovery, verification, validation and clinical implementation.

  3. Squamousness: Next-generation sequencing reveals shared molecular features across squamous tumor types

    PubMed Central

    Schwaederle, Maria; Elkin, Sheryl K; Tomson, Brett N; Carter, Jennifer Levin; Kurzrock, Razelle

    2015-01-01

    In order to gain a better understanding of the underlying biology of squamous cell carcinoma (SCC), we tested the hypothesis that SCC originating from different organs may possess common molecular alterations. SCC samples (N = 361) were examined using clinical-grade targeted next-generation sequencing (NGS). The most frequent SCC tumor types were head and neck, lung, cutaneous, gastrointestinal and gynecologic cancers. The most common gene alterations were TP53 (64.5% of patients), PIK3CA (28.5%), CDKN2A (24.4%), SOX2 (17.7%), and CCND1 (15.8%). By comparing NGS results of our SCC cohort to a non-SCC cohort (N = 277), we found that CDKN2A, SOX2, NOTCH1, TP53, PIK3CA, CCND1, and FBXW7 were significantly more frequently altered, unlike KRAS, which was less frequently altered in SCC specimens (all P < 0.05; multivariable analysis). Therefore, we identified “squamousness” gene signatures (TP53, PIK3CA, CCND1, CDKN2A, SOX2, NOTCH 1, and FBXW7 aberrations, and absence of KRAS alterations) that were significantly more frequent in SCC versus non-SCC histologies. A multivariable co-alteration analysis established 2 SCC subgroups: (i) patients in whom TP53 and cyclin pathway (CDKN2A and CCND1) alterations strongly correlated but in whom PIK3CA aberrations were less frequent; and (ii) patients with PIK3CA alterations in whom TP53 mutations were less frequent (all P ≤ 0 .001, multivariable analysis). In conclusion, we identified a set of 8 genes altered with significantly different frequencies when SCC and non-SCC were compared, suggesting the existence of patterns for “squamousness.” Targeting the PI3K-AKT-mTOR and/or cyclin pathway components in SCC may be warranted. PMID:26030731

  4. Cellular and Molecular Features of Developmentally Programmed Genome Rearrangement in a Vertebrate (Sea Lamprey: Petromyzon marinus)

    PubMed Central

    Timoshevskiy, Vladimir A.; Herdy, Joseph R.; Keinath, Melissa C.; Smith, Jeramiah J.

    2016-01-01

    The sea lamprey (Petromyzon marinus) represents one of the few vertebrate species known to undergo large-scale programmatic elimination of genomic DNA over the course of its normal development. Programmed genome rearrangements (PGRs) result in the reproducible loss of ~20% of the genome from somatic cell lineages during early embryogenesis. Studies of PGR hold the potential to provide novel insights related to the maintenance of genome stability during the cell cycle and coordination between mechanisms responsible for the accurate distribution of chromosomes into daughter cells, yet little is known regarding the mechanistic basis or cellular context of PGR in this or any other vertebrate lineage. Here we identify epigenetic silencing events that are associated with the programmed elimination of DNA and describe the spatiotemporal dynamics of PGR during lamprey embryogenesis. In situ analyses reveal that the earliest DNA methylation (and to some extent H3K9 trimethylation) events are limited to specific extranuclear structures (micronuclei) containing eliminated DNA. During early embryogenesis a majority of micronuclei (~60%) show strong enrichment for repressive chromatin modifications (H3K9me3 and 5meC). These analyses also led to the discovery that eliminated DNA is packaged into chromatin that does not migrate with somatically retained chromosomes during anaphase, a condition that is superficially similar to lagging chromosomes observed in some cancer subtypes. Closer examination of “lagging” chromatin revealed distributions of repetitive elements, cytoskeletal contacts and chromatin contacts that provide new insights into the cellular mechanisms underlying the programmed loss of these segments. Our analyses provide additional perspective on the cellular and molecular context of PGR, identify new structures associated with elimination of DNA and reveal that PGR is completed over the course of several successive cell divisions. PMID:27341395

  5. Molecular epidemiology and clinical features of human T cell lymphotropic virus type 1 infection in Spain.

    PubMed

    Treviño, Ana; Alcantara, Luiz Carlos; Benito, Rafael; Caballero, Estrella; Aguilera, Antonio; Ramos, José Manuel; de Mendoza, Carmen; Rodríguez, Carmen; García, Juan; Rodríguez-Iglesias, Manuel; Ortiz de Lejarazu, Raúl; Roc, Lourdes; Parra, Patricia; Eiros, José; del Romero, Jorge; Soriano, Vincent

    2014-09-01

    Human T cell lymphotropic virus type 1 (HTLV-1) infection in Spain is rare and mainly affects immigrants from endemic regions and native Spaniards with a prior history of sexual intercourse with persons from endemic countries. Herein, we report the main clinical and virological features of cases reported in Spain. All individuals with HTLV-1 infection recorded at the national registry since 1989 were examined. Phylogenetic analysis was performed based on the long terminal repeat (LTR) region. A total of 229 HTLV-1 cases had been reported up to December 2012. The mean age was 41 years old and 61% were female. Their country of origin was Latin America in 59%, Africa in 15%, and Spain in 20%. Transmission had occurred following sexual contact in 41%, parenteral exposure in 12%, and vertically in 9%. HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) was diagnosed in 27 cases and adult T cell leukemia/lymphoma (ATLL) in 17 subjects. HTLV-1 subtype could be obtained for 45 patients; all but one belonged to the Cosmopolitan subtype a. One Nigerian pregnant woman harbored HTLV-1 subtype b. Within the Cosmopolitan subtype a, two individuals (from Bolivia and Peru, respectively) belonged to the Japanese subgroup B, another two (from Senegal and Mauritania) to the North African subgroup D, and 39 to the Transcontinental subgroup A. Of note, one divergent HTLV-1 strain from an Ethiopian branched off from all five known Cosmopolitan subtype 1a subgroups. Divergent HTLV-1 strains have been introduced and currently circulate in Spain. The relatively large proportion of symptomatic cases (19%) suggests that HTLV-1 infection is underdiagnosed in Spain.

  6. The Complete Chloroplast Genome of the Hare’s Ear Root, Bupleurum falcatum: Its Molecular Features

    PubMed Central

    Shin, Dong-Ho; Lee, Jeong-Hoon; Kang, Sang-Ho; Ahn, Byung-Ohg; Kim, Chang-Kug

    2016-01-01

    Bupleurum falcatum, which belongs to the family Apiaceae, has long been applied for curative treatments, especially as a liver tonic, in herbal medicine. The chloroplast (cp) genome has been an ideal model to perform the evolutionary and comparative studies because of its highly conserved features and simple structure. The Apiaceae family is taxonomically close to the Araliaceae family and there have been numerous complete chloroplast genome sequences reported in the Araliaceae family, while little is known about the Apiaceae family. In this study, the complete sequence of the B. falcatum chloroplast genome was obtained. The full-length of the cp genome is 155,989 nucleotides with a 37.66% overall guanine-cytosine (GC) content and shows a quadripartite structure composed of three nomenclatural regions: a large single-copy (LSC) region, a small single-copy (SSC) region, and a pair of inverted repeat (IR) regions. The genome occupancy is 85,912-bp, 17,517-bp, and 26,280-bp for LSC, SSC, and IR, respectively. B. falcatum was shown to contain 111 unique genes (78 for protein-coding, 29 for tRNAs, and four for rRNAs, respectively) on its chloroplast genome. Genic comparison found that B. falcatum has no pseudogenes and has two gene losses, accD in the LSC and ycf15 in the IRs. A total of 55 unique tandem repeat sequences were detected in the B. falcatum cp genome. This report is the first to describe the complete chloroplast genome sequence in B. falcatum and will open up further avenues of research to understand the evolutionary panorama and the chloroplast genome conformation in related plant species. PMID:27187480

  7. Molecular Epidemiology and Clinical Features of Human T Cell Lymphotropic Virus Type 1 Infection in Spain

    PubMed Central

    Alcantara, Luiz Carlos; Benito, Rafael; Caballero, Estrella; Aguilera, Antonio; Ramos, José Manuel; de Mendoza, Carmen; Rodríguez, Carmen; García, Juan; Rodríguez-Iglesias, Manuel; Ortiz de Lejarazu, Raúl; Roc, Lourdes; Parra, Patricia; Eiros, José; del Romero, Jorge; Soriano, Vincent

    2014-01-01

    Abstract Human T cell lymphotropic virus type 1 (HTLV-1) infection in Spain is rare and mainly affects immigrants from endemic regions and native Spaniards with a prior history of sexual intercourse with persons from endemic countries. Herein, we report the main clinical and virological features of cases reported in Spain. All individuals with HTLV-1 infection recorded at the national registry since 1989 were examined. Phylogenetic analysis was performed based on the long terminal repeat (LTR) region. A total of 229 HTLV-1 cases had been reported up to December 2012. The mean age was 41 years old and 61% were female. Their country of origin was Latin America in 59%, Africa in 15%, and Spain in 20%. Transmission had occurred following sexual contact in 41%, parenteral exposure in 12%, and vertically in 9%. HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP) was diagnosed in 27 cases and adult T cell leukemia/lymphoma (ATLL) in 17 subjects. HTLV-1 subtype could be obtained for 45 patients; all but one belonged to the Cosmopolitan subtype a. One Nigerian pregnant woman harbored HTLV-1 subtype b. Within the Cosmopolitan subtype a, two individuals (from Bolivia and Peru, respectively) belonged to the Japanese subgroup B, another two (from Senegal and Mauritania) to the North African subgroup D, and 39 to the Transcontinental subgroup A. Of note, one divergent HTLV-1 strain from an Ethiopian branched off from all five known Cosmopolitan subtype 1a subgroups. Divergent HTLV-1 strains have been introduced and currently circulate in Spain. The relatively large proportion of symptomatic cases (19%) suggests that HTLV-1 infection is underdiagnosed in Spain. PMID:24924996

  8. Energy metabolism in hypoxia: reinterpreting some features of muscle physiology on molecular grounds.

    PubMed

    Cerretelli, Paolo; Gelfi, Cecilia

    2011-03-01

    An holistic approach for interpreting classical data on the adaptation of the animal and, particularly, of the human body to hypoxic stress was promoted by the discovery of HIF-1, the "master regulator" of cell hypoxic signaling. Mitochondrial production of ROS stabilizes the O(2)-regulated HIF-1α subunit of the HIF-1 dimer promoting transaction functions in a large number of potential target genes, activating transcription of sequences into RNA and, eventually, protein production. The aim of the present preliminary study is to assess whether adaptive changes in oxygen sensing and metabolic signaling, particularly in the control of energy turnover known to occur in cultured cells exposed to hypoxia, are detectable also in the muscles of animals and man. For the present analysis, data obtained from the proteome of the rat gastrocnemius and of the vastus lateralis muscle of humans together with functional measurements were compared with homologous data from hypoxic cultured cells. In particular, the following variables were assessed: (1) the role of stress response proteins in the maintenance of ROS homeostasis, (2) the activity of the PDK1 gene on the shunting of pyruvate away from the TCA cycle in rodents and in humans, (3) the COX-4/COX-2 ratio in hypoxic rodents, (4) the overall efficiency of oxidative phosphorylation in humans during exercise in hypoxia, (5) some features of muscle mitochondrial autophagy in humans undergoing subchronic and chronic altitude exposure. Despite the limited number of observations and the differences in the experimental approach, some initial interesting results were obtained encouraging to pursue this innovative effort. PMID:20352258

  9. The Complete Chloroplast Genome of the Hare's Ear Root, Bupleurum falcatum: Its Molecular Features.

    PubMed

    Shin, Dong-Ho; Lee, Jeong-Hoon; Kang, Sang-Ho; Ahn, Byung-Ohg; Kim, Chang-Kug

    2016-01-01

    Bupleurum falcatum, which belongs to the family Apiaceae, has long been applied for curative treatments, especially as a liver tonic, in herbal medicine. The chloroplast (cp) genome has been an ideal model to perform the evolutionary and comparative studies because of its highly conserved features and simple structure. The Apiaceae family is taxonomically close to the Araliaceae family and there have been numerous complete chloroplast genome sequences reported in the Araliaceae family, while little is known about the Apiaceae family. In this study, the complete sequence of the B. falcatum chloroplast genome was obtained. The full-length of the cp genome is 155,989 nucleotides with a 37.66% overall guanine-cytosine (GC) content and shows a quadripartite structure composed of three nomenclatural regions: a large single-copy (LSC) region, a small single-copy (SSC) region, and a pair of inverted repeat (IR) regions. The genome occupancy is 85,912-bp, 17,517-bp, and 26,280-bp for LSC, SSC, and IR, respectively. B. falcatum was shown to contain 111 unique genes (78 for protein-coding, 29 for tRNAs, and four for rRNAs, respectively) on its chloroplast genome. Genic comparison found that B. falcatum has no pseudogenes and has two gene losses, accD in the LSC and ycf15 in the IRs. A total of 55 unique tandem repeat sequences were detected in the B. falcatum cp genome. This report is the first to describe the complete chloroplast genome sequence in B. falcatum and will open up further avenues of research to understand the evolutionary panorama and the chloroplast genome conformation in related plant species. PMID:27187480

  10. Simultaneous feature selection and parameter optimisation using an artificial ant colony: case study of melting point prediction

    PubMed Central

    O'Boyle, Noel M; Palmer, David S; Nigsch, Florian; Mitchell, John BO

    2008-01-01

    Background We present a novel feature selection algorithm, Winnowing Artificial Ant Colony (WAAC), that performs simultaneous feature selection and model parameter optimisation for the development of predictive quantitative structure-property relationship (QSPR) models. The WAAC algorithm is an extension of the modified ant colony algorithm of Shen et al. (J Chem Inf Model 2005, 45: 1024–1029). We test the ability of the algorithm to develop a predictive partial least squares model for the Karthikeyan dataset (J Chem Inf Model 2005, 45: 581–590) of melting point values. We also test its ability to perform feature selection on a support vector machine model for the same dataset. Results Starting from an initial set of 203 descriptors, the WAAC algorithm selected a PLS model with 68 descriptors which has an RMSE on an external test set of 46.6°C and R2 of 0.51. The number of components chosen for the model was 49, which was close to optimal for this feature selection. The selected SVM model has 28 descriptors (cost of 5, ε of 0.21) and an RMSE of 45.1°C and R2 of 0.54. This model outperforms a kNN model (RMSE of 48.3°C, R2 of 0.47) for the same data and has similar performance to a Random Forest model (RMSE of 44.5°C, R2 of 0.55). However it is much less prone to bias at the extremes of the range of melting points as shown by the slope of the line through the residuals: -0.43 for WAAC/SVM, -0.53 for Random Forest. Conclusion With a careful choice of objective function, the WAAC algorithm can be used to optimise machine learning and regression models that suffer from overfitting. Where model parameters also need to be tuned, as is the case with support vector machine and partial least squares models, it can optimise these simultaneously. The moving probabilities used by the algorithm are easily interpreted in terms of the best and current models of the ants, and the winnowing procedure promotes the removal of irrelevant descriptors. PMID:18959785

  11. Molecular Similarity Methods for Predicting Cross-Reactivity With Therapeutic Drug Monitoring Immunoassays

    PubMed Central

    Krasowski, Matthew D.; Siam, Mohamed G.; Iyer, Manisha; Ekins, Sean

    2010-01-01

    Immunoassays are used for therapeutic drug monitoring (TDM) yet may suffer from cross-reacting compounds able to bind the assay antibodies in a manner similar to the target molecule. To our knowledge, there has been no investigation using computational tools to predict cross-reactivity with TDM immunoassays. The authors used molecular similarity methods to enable calculation of structural similarity for a wide range of compounds (prescription and over-the-counter medications, illicit drugs, and clinically significant metabolites) to the target molecules of TDM immunoassays. Utilizing different molecular descriptors (MDL public keys, functional class fingerprints, and pharmacophore fingerprints) and the Tanimoto similarity coefficient, the authors compared cross-reactivity data in the package inserts of immunoassays marketed for in vitro diagnostic use. Using MDL public keys and the Tanimoto similarity coefficient showed a strong and statistically significant separation between cross-reactive and non-cross-reactive compounds. Thus, two-dimensional shape similarity of cross-reacting molecules and the target molecules of TDM immunoassays provides a fast chemoinformatics methods for a priori prediction of potential of cross-reactivity that might be otherwise undetected. These methods could be used to reliably focus cross-reactivity testing on compounds with high similarity to the target molecule and limit testing of compounds with low similarity and ultimately with a very low probability of cross-reacting with the assay in vitro. PMID:19333148

  12. How important is thermal expansion for predicting molecular crystal structures and thermochemistry at finite temperatures?

    PubMed

    Heit, Yonaton N; Beran, Gregory J O

    2016-08-01

    Molecular crystals expand appreciably upon heating due to both zero-point and thermal vibrational motion, yet this expansion is often neglected in molecular crystal modeling studies. Here, a quasi-harmonic approximation is coupled with fragment-based hybrid many-body interaction calculations to predict thermal expansion and finite-temperature thermochemical properties in crystalline carbon dioxide, ice Ih, acetic acid and imidazole. Fragment-based second-order Möller-Plesset perturbation theory (MP2) and coupled cluster theory with singles, doubles and perturbative triples [CCSD(T)] predict the thermal expansion and the temperature dependence of the enthalpies, entropies and Gibbs free energies of sublimation in good agreement with experiment. The errors introduced by neglecting thermal expansion in the enthalpy and entropy cancel somewhat in the Gibbs free energy. The resulting ∼ 1-2 kJ mol(-1) errors in the free energy near room temperature are comparable to or smaller than the errors expected from the electronic structure treatment, but they may be sufficiently large to affect free-energy rankings among energetically close polymorphs. PMID:27484373

  13. Outcome Prediction for Patients with Traumatic Brain Injury with Dynamic Features from Intracranial Pressure and Arterial Blood Pressure Signals: A Gaussian Process Approach.

    PubMed

    Pimentel, Marco A F; Brennan, Thomas; Lehman, Li-Wei; King, Nicolas Kon Kam; Ang, Beng-Ti; Feng, Mengling

    2016-01-01

    Previous work has been demonstrated that tracking features describing the dynamic and time-varying patterns in brain monitoring signals provide additional predictive information beyond that derived from static features based on snapshot measurements. To achieve more accurate predictions of outcomes of patients with traumatic brain injury (TBI), we proposed a statistical framework to extract dynamic features from brain monitoring signals based on the framework of Gaussian processes (GPs). GPs provide an explicit probabilistic, nonparametric Bayesian approach to metric regression problems. This not only provides probabilistic predictions, but also gives the ability to cope with missing data and infer model parameters such as those that control the function's shape, noise level and dynamics of the signal. Through experimental evaluation, we have demonstrated that dynamic features extracted from GPs provide additional predictive information in addition to the features based on the pressure reactivity index (PRx). Significant improvements in patient outcome prediction were achieved by combining GP-based and PRx-based dynamic features. In particular, compared with the a baseline PRx-based model, the combined model achieved over 30 % improvement in prediction accuracy and sensitivity and over 20 % improvement in specificity and the area under the receiver operating characteristic curve.

  14. Pharmacophore modeling and molecular dynamics simulation to identify the critical chemical features against human sirtuin 2 inhibitors

    NASA Astrophysics Data System (ADS)

    Sakkiah, Sugunadevi; Baek, Ayoung; Lee, Keun Woo

    2012-03-01

    Sirtuin 2 (SIRT2) is one of the emerging targets in chemotherapy field and mainly associated with many diseases such as cancer and Parkinson's. Hence, quantitative hypothesis was developed using Discovery Studio v2.5. Top ten resultant hypotheses were generated, among them Hypo1 was selected as a best hypothesis based on the statistical parameters like high cost difference (52), lowest RMS (0.71), and good correlation coefficient (0.96). Hypo1 has been validated by using well known methodologies such as Fischer's randomization method (95% confidence level), test set which has shown the correlation coefficient of 0.93 as well as the goodness of hit (0.65), and enrichment factor (8.80). All the above statistical validations confirm that the chemical features in Hypo1 (1 hydrogen bond acceptor, 1 hydrophobic, and 2 ring aromatic features) was able to inhibit the function of SIRT2. Hence, Hypo1 was used as a query in virtual screening to find a novel scaffolds by screening the various chemical databases. The screened molecules from the databases were checked for the ADMET as well as the drug-like properties. Due to the lack of SIRT2-ligand complex structure in PDB, molecular docking and molecular dynamics (MD) simulation was carried out to find the suitable orientation of ligand in the active site. The representative structure from MD simulations was used as a receptor to dock the molecules which passed the drug-like properties from the virtual screening. Finally, 29 compounds were selected as a potent candidate leads based on the interactions with the active site residues of SIRT2. Thus, the resultant pharmacophore can be used to discover and design the SIRT2 inhibitors with desired biological activity.

  15. Hydrophilic solutes in modified carbon dioxide extraction-prediction of the extractability using molecular dynamic simulation.

    PubMed

    Günther, Martina; Maus, Martin; Wagner, Karl Gerhard; Schmidt, Peter Christian

    2005-06-01

    Super- and subcritical carbon dioxide (CO2) extractions of crude drugs were simulated by molecular modelling to predict the extractability of different hydrophilic plant constituents under various extraction conditions. The CO2 extraction fluids were simulated either with pure CO2 or with solvent modified CO2 at different pressures and temperatures. Molecular modelling resulted in three different solubility parameters: the total solubility parameter delta and the partial solubility parameters delta(d) for the van der Waals and delta(EL) for the polar forces. Thus, delta(EL) enabled the estimation of the polarity of the extraction fluids and the solute molecules. If the value of delta(EL) of the extraction fluid reached the value of the solute molecule in the crude drug, i.e. minimum extraction value, the compound was soluble at the distinct extraction conditions. For a further increase in yield of the hydrophilic solutes, the polarity of the extraction fluid had to be increased, too. That means delta(EL) of the fluid exceeded the minimum extraction value. All simulations were verified by CO2 extractions of the secondary roots of Harpagophytum procumbens (harpagoside, stachyose) and the seeds of Aesculus hippocastanum (aescin). CO2 extractions of the flowers of Matricaria recutita ((-)-alpha-bisabolol) were obtained from literature data. These four constituents with different properties, like molecular size and the allocation of polar functional groups were extracted, analysed, simulated and the extract content was correlated with the extraction fluid used, respectively. PMID:15911229

  16. Molecular Markers Predict Distant Metastases After Adjuvant Chemoradiation for Rectal Cancer

    SciTech Connect

    Kim, Jun Won; Kim, Yong Bae; Choi, Jun Jeong; Koom, Woong Sub; Kim, Hoguen; Kim, Nam-Kyu; Ahn, Joong Bae; Lee, Ikjae; Cho, Jae Ho; Keum, Ki Chang

    2012-12-01

    Purpose: The outcomes of adjuvant chemoradiation for locally advanced rectal cancer are nonuniform among patients with matching prognostic factors. We explored the role of molecular markers for predicting the outcome of adjuvant chemoradiation for rectal cancer patients. Methods and Materials: The study included 68 patients with stages II to III rectal adenocarcinoma who were treated with total mesorectal excision and adjuvant chemoradiation. Chemotherapy based on 5-fluorouracil and leucovorin was intravenously administered each month for 6-12 cycles. Radiation therapy consisted of 54 Gy delivered in 30 fractions. Immunostaining of surgical specimens for COX-2, EGFR, VEGF, thymidine synthase (TS), and Raf kinase inhibitor protein (RKIP) was performed. Results: The median follow-up was 65 months. Eight locoregional (11.8%) and 13 distant (19.1%) recurrences occurred. Five-year locoregional failure-free survival (LRFFS), distant metastasis-free survival (DMFS), disease-free survival (DFS), and overall survival (OS) rates for all patients were 83.9%, 78.7%, 66.7%, and 73.8%, respectively. LRFFS was not correlated with TNM stage, surgical margin, or any of the molecular markers. VEGF overexpression was significantly correlated with decreased DMFS (P=.045), while RKIP-positive results were correlated with increased DMFS (P=.025). In multivariate analyses, positive findings for COX-2 (COX-2+) and VEGF (VEGF+) and negative findings for RKIP (RKIP-) were independent prognostic factors for DMFS, DFS, and OS (P=.035, .014, and .007 for DMFS; .021, .010, and <.0001 for DFS; and .004, .012, and .001 for OS). The combination of both COX-2+ and VEGF+ (COX-2+/VEGF+) showed a strong correlation with decreased DFS (P=.007), and the combinations of RKIP+/COX-2- and RKIP+/VEGF- showed strong correlations with improved DFS compared with the rest of the patients (P=.001 and <.0001, respectively). Conclusions: Molecular markers can be valuable in predicting treatment outcome of adjuvant

  17. Structure prediction and molecular simulation of gases diffusion pathways in hydrogenase.

    PubMed

    Sundaram, Shanthy; Tripathi, Ashutosh; Gupta, Vipul

    2010-01-01

    Although hydrogen is considered to be one of the most promising future energy sources and the technical aspects involved in using it have advanced considerably, the future supply of hydrogen from renewable sources is still unsolved. The [Fe]- hydrogenase enzymes are highly efficient H(2) catalysts found in ecologically and phylogenetically diverse microorganisms, including the photosynthetic green alga, Chlamydomonas reinhardtii. While these enzymes can occur in several forms, H(2) catalysis takes place at a unique [FeS] prosthetic group or H-cluster, located at the active site. 3D structure of the protein hydA1 hydrogenase from Chlamydomonas reinhardtti was predicted using the MODELER 8v2 software. Conserved region was depicted from the NCBI CDD Search. Template selection was done on the basis NCBI BLAST results. For single template 1FEH was used and for multiple templates 1FEH and 1HFE were used. The result of the Homology modeling was verified by uploading the file to SAVS server. On the basis of the SAVS result 3D structure predicted using single template was chosen for performing molecular simulation. For performing molecular simulation three strategies were used. First the molecular simulation of the protein was performed in solvated box containing bulk water. Then 100 H(2) molecules were randomly inserted in the solvated box and two simulations of 50 and 100 ps were performed. Similarly 100 O(2) molecules were randomly placed in the solvated box and again 50 and 100 ps simulation were performed. Energy minimization was performed before each simulation was performed. Conformations were saved after each simulation. Analysis of the gas diffusion was done on the basis of RMSD, Radius of Gyration and no. of gas molecule/ps plot. PMID:21364783

  18. Structure prediction and molecular simulation of gases diffusion pathways in hydrogenase.

    PubMed

    Sundaram, Shanthy; Tripathi, Ashutosh; Gupta, Vipul

    2010-10-06

    Although hydrogen is considered to be one of the most promising future energy sources and the technical aspects involved in using it have advanced considerably, the future supply of hydrogen from renewable sources is still unsolved. The [Fe]- hydrogenase enzymes are highly efficient H(2) catalysts found in ecologically and phylogenetically diverse microorganisms, including the photosynthetic green alga, Chlamydomonas reinhardtii. While these enzymes can occur in several forms, H(2) catalysis takes place at a unique [FeS] prosthetic group or H-cluster, located at the active site. 3D structure of the protein hydA1 hydrogenase from Chlamydomonas reinhardtti was predicted using the MODELER 8v2 software. Conserved region was depicted from the NCBI CDD Search. Template selection was done on the basis NCBI BLAST results. For single template 1FEH was used and for multiple templates 1FEH and 1HFE were used. The result of the Homology modeling was verified by uploading the file to SAVS server. On the basis of the SAVS result 3D structure predicted using single template was chosen for performing molecular simulation. For performing molecular simulation three strategies were used. First the molecular simulation of the protein was performed in solvated box containing bulk water. Then 100 H(2) molecules were randomly inserted in the solvated box and two simulations of 50 and 100 ps were performed. Similarly 100 O(2) molecules were randomly placed in the solvated box and again 50 and 100 ps simulation were performed. Energy minimization was performed before each simulation was performed. Conformations were saved after each simulation. Analysis of the gas diffusion was done on the basis of RMSD, Radius of Gyration and no. of gas molecule/ps plot.

  19. SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features.

    PubMed

    Zhou, Yuan; Zeng, Pan; Li, Yan-Hui; Zhang, Ziding; Cui, Qinghua

    2016-06-01

    N(6)-methyladenosine (m(6)A) is a prevalent RNA methylation modification involved in the regulation of degradation, subcellular localization, splicing and local conformation changes of RNA transcripts. High-throughput experiments have demonstrated that only a small fraction of the m(6)A consensus motifs in mammalian transcriptomes are modified. Therefore, accurate identification of RNA m(6)A sites becomes emergently important. For the above purpose, here a computational predictor of mammalian m(6)A site named SRAMP is established. To depict the sequence context around m(6)A sites, SRAMP combines three random forest classifiers that exploit the positional nucleotide sequence pattern, the K-nearest neighbor information and the position-independent nucleotide pair spectrum features, respectively. SRAMP uses either genomic sequences or cDNA sequences as its input. With either kind of input sequence, SRAMP achieves competitive performance in both cross-validation tests and rigorous independent benchmarking tests. Analyses of the informative features and overrepresented rules extracted from the random forest classifiers demonstrate that nucleotide usage preferences at the distal positions, in addition to those at the proximal positions, contribute to the classification. As a public prediction server, SRAMP is freely available at http://www.cuilab.cn/sramp/. PMID:26896799

  20. SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features

    PubMed Central

    Zhou, Yuan; Zeng, Pan; Li, Yan-Hui; Zhang, Ziding; Cui, Qinghua

    2016-01-01

    N6-methyladenosine (m6A) is a prevalent RNA methylation modification involved in the regulation of degradation, subcellular localization, splicing and local conformation changes of RNA transcripts. High-throughput experiments have demonstrated that only a small fraction of the m6A consensus motifs in mammalian transcriptomes are modified. Therefore, accurate identification of RNA m6A sites becomes emergently important. For the above purpose, here a computational predictor of mammalian m6A site named SRAMP is established. To depict the sequence context around m6A sites, SRAMP combines three random forest classifiers that exploit the positional nucleotide sequence pattern, the K-nearest neighbor information and the position-independent nucleotide pair spectrum features, respectively. SRAMP uses either genomic sequences or cDNA sequences as its input. With either kind of input sequence, SRAMP achieves competitive performance in both cross-validation tests and rigorous independent benchmarking tests. Analyses of the informative features and overrepresented rules extracted from the random forest classifiers demonstrate that nucleotide usage preferences at the distal positions, in addition to those at the proximal positions, contribute to the classification. As a public prediction server, SRAMP is freely available at http://www.cuilab.cn/sramp/. PMID:26896799

  1. Predictive models of biohydrogen and biomethane production based on the compositional and structural features of lignocellulosic materials.

    PubMed

    Monlau, Florian; Sambusiti, Cecilia; Barakat, Abdellatif; Guo, Xin Mei; Latrille, Eric; Trably, Eric; Steyer, Jean-Philippe; Carrere, Hélène

    2012-11-01

    In an integrated biorefinery concept, biological hydrogen and methane production from lignocellulosic substrates appears to be one of the most promising alternatives to produce energy from renewable sources. However, lignocellulosic substrates present compositional and structural features that can limit their conversion into biohydrogen and methane. In this study, biohydrogen and methane potentials of 20 lignocellulosic residues were evaluated. Compositional (lignin, cellulose, hemicelluloses, total uronic acids, proteins, and soluble sugars) as well as structural features (crystallinity) were determined for each substrate. Two predictive partial least square (PLS) models were built to determine which compositional and structural parameters affected biohydrogen or methane production from lignocellulosic substrates, among proteins, total uronic acids, soluble sugars, crystalline cellulose, amorphous holocelluloses, and lignin. Only soluble sugars had a significant positive effect on biohydrogen production. Besides, methane potentials correlated negatively to the lignin contents and, to a lower extent, crystalline cellulose showed also a negative impact, whereas soluble sugars, proteins, and amorphous hemicelluloses showed a positive impact. These findings will help to develop further pretreatment strategies for enhancing both biohydrogen and methane production.

  2. Exploring QSTR modeling and toxicophore mapping for identification of important molecular features contributing to the chemical toxicity in Escherichia coli.

    PubMed

    Pramanik, Subrata; Roy, Kunal

    2014-03-01

    Biodiversity deprivation can affect functions and services of the ecosystem. Changes in biodiversity alter ecosystem processes and change the resilience of ecosystems to ecological changes. Bacterial communities are the main form of biomass in the ecosystem and one of largest populations on the planet. Bacterial communities provide important services to biodiversity. They break down pollutants, municipal waste and ingested food, and they are the primary route for recycling of organic matter to plants and other autotrophs, conversion of inorganic matter into new biological tissue using sunlight, management of energy crisis through use of biofuel. In the present study, computational chemistry and statistical modeling have been used to develop mathematical equations which can be applied to calculate toxicity of new/unknown chemicals/biofuels/metabolites in Escherichia coli. 2D and 3D descriptors were generated from molecular structure of compounds and mathematical models have been developed using genetic function approximation followed by multiple linear regression (GFA-MLR) method. Model validity was checked through defined internal (R(2)=0.751 and Q(2)=0.711), and external (Rpred(2)=0.773) statistical parameters. Molecular features responsible for toxicity were also assessed through 3D toxicophore study. The toxicophore-based model was validated (R=0.785) using qualitative statistical metrics and randomization test (Fischer validation). PMID:24246193

  3. Exploring QSTR modeling and toxicophore mapping for identification of important molecular features contributing to the chemical toxicity in Escherichia coli.

    PubMed

    Pramanik, Subrata; Roy, Kunal

    2014-03-01

    Biodiversity deprivation can affect functions and services of the ecosystem. Changes in biodiversity alter ecosystem processes and change the resilience of ecosystems to ecological changes. Bacterial communities are the main form of biomass in the ecosystem and one of largest populations on the planet. Bacterial communities provide important services to biodiversity. They break down pollutants, municipal waste and ingested food, and they are the primary route for recycling of organic matter to plants and other autotrophs, conversion of inorganic matter into new biological tissue using sunlight, management of energy crisis through use of biofuel. In the present study, computational chemistry and statistical modeling have been used to develop mathematical equations which can be applied to calculate toxicity of new/unknown chemicals/biofuels/metabolites in Escherichia coli. 2D and 3D descriptors were generated from molecular structure of compounds and mathematical models have been developed using genetic function approximation followed by multiple linear regression (GFA-MLR) method. Model validity was checked through defined internal (R(2)=0.751 and Q(2)=0.711), and external (Rpred(2)=0.773) statistical parameters. Molecular features responsible for toxicity were also assessed through 3D toxicophore study. The toxicophore-based model was validated (R=0.785) using qualitative statistical metrics and randomization test (Fischer validation).

  4. Predicting biological activity: computational approach using novel distance based molecular descriptors.

    PubMed

    Dutt, R; Madan, A K

    2012-10-01

    Four novel distance based molecular descriptors termed as superpendentic eccentric distance sum indices 1-4 (denoted by:∫P-1EDS, ∫P-2EDS, ∫P-3EDS and ∫P-4EDS) as well as their topochemical counterparts (denoted by:∫cP-1EDS, ∫cP-2EDS, ∫cP-3EDS and ∫cP-4EDS) have been conceptualized and developed in the present study. The sensitivity towards branching, discriminating power, and degeneracy of the proposed novel descriptors were investigated. Utility of these indices was investigated for development of models through decision tree and moving average analysis for the prediction of human corticotropin releasing factor-1 receptor binding affinity of substituted pyrazines. A wide variety of 46 2D and 3D molecular descriptors including proposed indices was employed for development of models through decision tree and moving average analysis. The calculation of most of these descriptors for each compound of the dataset was performed using online E-Dragon software (version 1.0). An in-house computer programme was also employed to calculate additional topological descriptors which did not figure in E-Dragon software. The decision tree classified and correctly predicted the input data with an impressive accuracy of 92% in the training set and 71% during cross-validation. A total of three descriptors, identified by decision tree, were subsequently utilized for development of suitable models using moving average analysis. These models predicted human corticotropin releasing factor-1 receptor binding affinity with an accuracy of ≥85%. The statistical significance of models was assessed through sensitivity, specificity and Matthew's correlation coefficient. High discriminating power, high sensitivity towards branching amalgamated with negligible degeneracy offer proposed descriptors a vast potential for use in the quantitative structure-activity/property/toxicity relationships so as to facilitate drug design.

  5. In Vitro Drug Sensitivity Tests to Predict Molecular Target Drug Responses in Surgically Resected Lung Cancer

    PubMed Central

    Miyazaki, Ryohei; Anayama, Takashi; Hirohashi, Kentaro; Okada, Hironobu; Kume, Motohiko; Orihashi, Kazumasa

    2016-01-01

    Background Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) and anaplastic lymphoma kinase (ALK) inhibitors have dramatically changed the strategy of medical treatment of lung cancer. Patients should be screened for the presence of the EGFR mutation or echinoderm microtubule-associated protein-like 4 (EML4)-ALK fusion gene prior to chemotherapy to predict their clinical response. The succinate dehydrogenase inhibition (SDI) test and collagen gel droplet embedded culture drug sensitivity test (CD-DST) are established in vitro drug sensitivity tests, which may predict the sensitivity of patients to cytotoxic anticancer drugs. We applied in vitro drug sensitivity tests for cyclopedic prediction of clinical responses to different molecular targeting drugs. Methods The growth inhibitory effects of erlotinib and crizotinib were confirmed for lung cancer cell lines using SDI and CD-DST. The sensitivity of 35 cases of surgically resected lung cancer to erlotinib was examined using SDI or CD-DST, and compared with EGFR mutation status. Results HCC827 (Exon19: E746-A750 del) and H3122 (EML4-ALK) cells were inhibited by lower concentrations of erlotinib and crizotinib, respectively than A549, H460, and H1975 (L858R+T790M) cells were. The viability of the surgically resected lung cancer was 60.0 ± 9.8 and 86.8 ± 13.9% in EGFR-mutants vs. wild types in the SDI (p = 0.0003). The cell viability was 33.5 ± 21.2 and 79.0 ± 18.6% in EGFR mutants vs. wild-type cases (p = 0.026) in CD-DST. Conclusions In vitro drug sensitivity evaluated by either SDI or CD-DST correlated with EGFR gene status. Therefore, SDI and CD-DST may be useful predictors of potential clinical responses to the molecular anticancer drugs, cyclopedically. PMID:27070423

  6. Prediction of subcellular location of apoptosis proteins combining tri-gram encoding based on PSSM and recursive feature elimination.

    PubMed

    Liu, Taigang; Tao, Peiying; Li, Xiaowei; Qin, Yufang; Wang, Chunhua

    2015-02-01

    Knowledge of apoptosis proteins plays an important role in understanding the mechanism of programmed cell death. Obtaining information on subcellular location of apoptosis proteins is very helpful to reveal the apoptosis mechanism and understand the function of apoptosis proteins. Because of the cost in time and labor associated with large-scale wet-bench experiments, computational prediction of apoptosis proteins subcellular location becomes very important and many computational tools have been developed in the recent decades. Existing methods differ in the protein sequence representation techniques and classification algorithms adopted. In this study, we firstly introduce a sequence encoding scheme based on tri-grams computed directly from position-specific score matrices, which incorporates evolution information represented in the PSI-BLAST profile and sequence-order information. Then SVM-RFE algorithm is applied for feature selection and reduced vectors are input to a support vector machine classifier to predict subcellular location of apoptosis proteins. Jackknife tests on three widely used datasets show that our method provides the state-of-the-art performance in comparison with other existing methods.

  7. Prediction of subcellular location of apoptosis proteins combining tri-gram encoding based on PSSM and recursive feature elimination.

    PubMed

    Liu, Taigang; Tao, Peiying; Li, Xiaowei; Qin, Yufang; Wang, Chunhua

    2015-02-01

    Knowledge of apoptosis proteins plays an important role in understanding the mechanism of programmed cell death. Obtaining information on subcellular location of apoptosis proteins is very helpful to reveal the apoptosis mechanism and understand the function of apoptosis proteins. Because of the cost in time and labor associated with large-scale wet-bench experiments, computational prediction of apoptosis proteins subcellular location becomes very important and many computational tools have been developed in the recent decades. Existing methods differ in the protein sequence representation techniques and classification algorithms adopted. In this study, we firstly introduce a sequence encoding scheme based on tri-grams computed directly from position-specific score matrices, which incorporates evolution information represented in the PSI-BLAST profile and sequence-order information. Then SVM-RFE algorithm is applied for feature selection and reduced vectors are input to a support vector machine classifier to predict subcellular location of apoptosis proteins. Jackknife tests on three widely used datasets show that our method provides the state-of-the-art performance in comparison with other existing methods. PMID:25463695

  8. Diffuse sclerosing variant of papillary thyroid carcinoma--an update of its clinicopathological features and molecular biology.

    PubMed

    Pillai, Suja; Gopalan, Vinod; Smith, Robert A; Lam, Alfred K-Y

    2015-04-01

    Diffuse sclerosing variant of papillary thyroid carcinoma (DSVPTC) is an uncommon variant of papillary thyroid carcinoma. The aim of this review is to critically analyse the features of this entity. A search of the literature revealed 25 clinicopathological studies with in-depth analysis of features of DSVPTC. Overall, the prevalence of DSVPTC varies from 0.7-6.6% of all papillary thyroid carcinoma. Higher prevalence of DSVPTC was noted in paediatric patients and in patients affected by irradiation. DSVPTC tends to occur more frequently in women and in patients in the third decade of life. Macroscopically, DSVPTC can involve the thyroid gland extensively without forming a dominant mass. Microscopic examination of DSVPTC revealed extensive fibrosis, squamous metaplasia and numerous psammoma bodies. The latter pathological feature can aid in the pre-operative diagnosis of the entity by fine needle aspiration and ultrasound. Compared to conventional papillary thyroid carcinoma, DSVPTC had a higher incidence of lymph node metastases at presentation. Distant metastases were noted in approximately 5% of the cases. Patients with DSVPTC were recommended to be managed by aggressive treatment protocols. It is likely that as a result of this, the prognosis of the patients with DSVPTC was noted to be similar to conventional papillary thyroid carcinoma. Overall, cancer recurrence and cancer related mortality have been reported in 14% and 3%, respectively, of patients with DSVPTC. In immunohistochemical studies, DSVPTC showed different expression patterns of epithelial membrane antigen, galectin 3, cell adhesion molecules, p53 and p63 when compared to conventional papillary thyroid carcinoma. On genetic analysis, the occurrence of BRAF and RAS mutations are uncommon events in DSVPTC and activation of RET/PTC rearrangements are common. To conclude, DSVPTC has different clinical, pathological and molecular profiles when compared to conventional papillary thyroid carcinoma.

  9. Predictive Features of Severe Acquired ADAMTS13 Deficiency in Idiopathic Thrombotic Microangiopathies: The French TMA Reference Center Experience

    PubMed Central

    Coppo, Paul; Schwarzinger, Michael; Buffet, Marc; Wynckel, Alain; Clabault, Karine; Presne, Claire; Poullin, Pascale; Malot, Sandrine; Vanhille, Philippe; Azoulay, Elie; Galicier, Lionel; Lemiale, Virginie; Mira, Jean-Paul; Ridel, Christophe; Rondeau, Eric; Pourrat, Jacques; Girault, Stéphane; Bordessoule, Dominique; Saheb, Samir; Ramakers, Michel; Hamidou, Mohamed; Vernant, Jean-Paul; Guidet, Bertrand; Wolf, Martine; Veyradier, Agnès

    2010-01-01

    Severe ADAMTS13 deficiency occurs in 13% to 75% of thrombotic microangiopathies (TMA). In this context, the early identification of a severe, antibody-mediated, ADAMTS13 deficiency may allow to start targeted therapies such as B-lymphocytes-depleting monoclonal antibodies. To date, assays exploring ADAMTS13 activity require skill and are limited to only some specialized reference laboratories, given the very low incidence of the disease. To identify clinical features which may allow to predict rapidly an acquired ADAMTS13 deficiency, we performed a cross-sectional analysis of our national registry from 2000 to 2007. The clinical presentation of 160 patients with TMA and acquired ADAMTS13 deficiency was compared with that of 54 patients with detectable ADAMTS13 activity. ADAMTS13 deficiency was associated with more relapses during treatment and with a good renal prognosis. Patients with acquired ADAMTS13 deficiency had platelet count <30×109/L (adjusted odds ratio [OR] 9.1, 95% confidence interval [CI] 3.4–24.2, P<.001), serum creatinine level ≤200 µmol/L (OR 23.4, 95% CI 8.8–62.5, P<.001), and detectable antinuclear antibodies (OR 2.8, 95% CI 1.0–8.0, P<.05). When at least 1 criteria was met, patients with a severe acquired ADAMTS13 deficiency were identified with positive predictive value of 85%, negative predictive value of 93.3%, sensitivity of 98.8%, and specificity of 48.1%. Our criteria should be useful to identify rapidly newly diagnosed patients with an acquired ADAMTS13 deficiency to better tailor treatment for different pathophysiological groups. PMID:20436664

  10. PSSP-RFE: Accurate Prediction of Protein Structural Class by Recursive Feature Extraction from PSI-BLAST Profile, Physical-Chemical Property and Functional Annotations

    PubMed Central

    Yu, Sanjiu; Zhang, Yuan; Luo, Zhong; Yang, Hua; Zhou, Yue; Zheng, Xiaoqi

    2014-01-01

    Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets. PMID:24675610

  11. Numerical simulations of mechanical properties of innovative pothole patching materials featuring high toughness, low viscosity nano-molecular resins

    NASA Astrophysics Data System (ADS)

    Yuan, K. Y.; Yuan, W.; Ju, J. W.; Yang, J. M.; Kao, W.; Carlson, L.

    2012-04-01

    As asphalt pavements age and deteriorate, recurring pothole repair failures and propagating alligator cracks in the asphalt pavements have become a serious issue to our daily life and resulted in high repairing costs for pavement and vehicles. To solve this urgent issue, pothole repair materials with superior durability and long service life are needed. In the present work, revolutionary pothole patching materials with high toughness, high fatigue resistance that are reinforced with nano-molecular resins have been developed to enhance their resistance to traffic loads and service life of repaired potholes. In particular, DCPD resin (dicyclopentadiene, C10H12) with a Rhuthinium-based catalyst is employed to develop controlled properties that are compatible with aggregates and asphalt binders. In this paper, a multi-level numerical micromechanics-based model is developed to predict the mechanical properties of these innovative nanomolecular resin reinforced pothole patching materials. Coarse aggregates in the finite element analysis are modeled as irregular shapes through image processing techniques and randomly-dispersed coated particles. The overall properties of asphalt mastic, which consists of fine aggregates, asphalt binder, cured DCPD and air voids are theoretically estimated by the homogenization technique of micromechanics. Numerical predictions are compared with suitably designed experimental laboratory results.

  12. Predictive Bioinformatic Assignment of Methyl-Bearing Stereocenters, Total Synthesis, and an Additional Molecular Target of Ajudazol B.

    PubMed

    Essig, Sebastian; Schmalzbauer, Björn; Bretzke, Sebastian; Scherer, Olga; Koeberle, Andreas; Werz, Oliver; Müller, Rolf; Menche, Dirk

    2016-02-19

    Full details on the evaluation and application of an easily feasible and generally useful method for configurational assignments of isolated methyl-bearing stereocenters are reported. The analytical tool relies on a bioinformatic gene cluster analysis and utilizes a predictive enoylreductase alignment, and its feasibility was demonstrated by the full stereochemical determination of the ajudazols, highly potent inhibitors of the mitochondrial respiratory chain. Furthermore, a full account of our strategies and tactics that culminated in the total synthesis of ajudazol B, the most potent and least abundant of these structurally unique class of myxobacterial natural products, is presented. Key features include an application of an asymmetric ortholithiation strategy for synthesis of the characteristic anti-configured hydroxyisochromanone core bearing three contiguous stereocenters, a modular oxazole formation, a flexible cross-metathesis approach for terminal allyl amide synthesis, and a late-stage Z,Z-selective Suzuki coupling. This total synthesis unambiguously proves the correct stereochemistry, which was further corroborated by comparison with reisolated natural material. Finally, 5-lipoxygenase was discovered as an additional molecular target of ajudazol B. Activities against this clinically validated key enzyme of the biosynthesis of proinflammatory leukotrienes were in the range of the approved drug zileuton, which further underlines the biological importance of this unique natural product.

  13. Predictive Bioinformatic Assignment of Methyl-Bearing Stereocenters, Total Synthesis, and an Additional Molecular Target of Ajudazol B.

    PubMed

    Essig, Sebastian; Schmalzbauer, Björn; Bretzke, Sebastian; Scherer, Olga; Koeberle, Andreas; Werz, Oliver; Müller, Rolf; Menche, Dirk

    2016-02-19

    Full details on the evaluation and application of an easily feasible and generally useful method for configurational assignments of isolated methyl-bearing stereocenters are reported. The analytical tool relies on a bioinformatic gene cluster analysis and utilizes a predictive enoylreductase alignment, and its feasibility was demonstrated by the full stereochemical determination of the ajudazols, highly potent inhibitors of the mitochondrial respiratory chain. Furthermore, a full account of our strategies and tactics that culminated in the total synthesis of ajudazol B, the most potent and least abundant of these structurally unique class of myxobacterial natural products, is presented. Key features include an application of an asymmetric ortholithiation strategy for synthesis of the characteristic anti-configured hydroxyisochromanone core bearing three contiguous stereocenters, a modular oxazole formation, a flexible cross-metathesis approach for terminal allyl amide synthesis, and a late-stage Z,Z-selective Suzuki coupling. This total synthesis unambiguously proves the correct stereochemistry, which was further corroborated by comparison with reisolated natural material. Finally, 5-lipoxygenase was discovered as an additional molecular target of ajudazol B. Activities against this clinically validated key enzyme of the biosynthesis of proinflammatory leukotrienes were in the range of the approved drug zileuton, which further underlines the biological importance of this unique natural product. PMID:26796481

  14. Isotopic Soret effect in ternary mixtures: Theoretical predictions and molecular simulations

    SciTech Connect

    Artola, Pierre-Arnaud; Rousseau, Bernard

    2015-11-07

    In this paper, we study the Soret effect in ternary fluid mixtures of isotopic argon like atoms. Soret coefficients have been computed using non-equilibrium molecular dynamics and a theoretical approach based on our extended Prigogine model (with mass effect) and generalized to mixtures with any number of components. As is well known for binary mixture studies, the heaviest component always accumulates on the cold side whereas the lightest species accumulate on the hot side. An interesting behavior is observed for the species with the intermediate mass: it can accumulate on both sides, depending on composition and mass ratios. A simple picture can be given to understand this change of sign: the intermediate mass species can be seen as evolving in an equivalent fluid whose species mass varies with composition. An excellent prediction of all simulated data has been obtained using our model including the change of sign of the Soret coefficient for species with intermediate mass.

  15. Thermodynamic properties of methane/water interface predicted by molecular dynamics simulations.

    PubMed

    Sakamaki, Ryuji; Sum, Amadeu K; Narumi, Tetsu; Ohmura, Ryo; Yasuoka, Kenji

    2011-04-14

    Molecular dynamics simulations have been performed to examine the thermodynamic properties of methane/water interface using two different water models, the TIP4P/2005 and SPC/E, and two sets of combining rules. The density profiles, interfacial tensions, surface excesses, surface pressures, and coexisting densities are calculated over a wide range of pressure conditions. The TIP4P/2005 water model was used, with an optimized combining rule between water and methane fit to the solubility, to provide good predictions of interfacial properties. The use of the infinite dilution approximation to calculate the surface excesses from the interfacial tensions is examined comparing the surface pressures obtained by different approaches. It is shown that both the change of methane solubilities in pressure and position of maximum methane density profile at the interface are independent of pressure up to about 2 MPa. We have also calculated the adsorption enthalpies and entropies to describe the temperature dependency of the adsorption. PMID:21495767

  16. Molecular dynamics predictions of the influence of graphite stacking arrangement on the thermal conductivity tensor

    NASA Astrophysics Data System (ADS)

    Khadem, Masoud H.; Wemhoff, Aaron P.

    2013-06-01

    The effect of stacking configuration on the phonon-based thermal conductivity of graphite is investigated using equilibrium molecular dynamics. Hexagonal (AAA), Bernal (ABA), and Rhombohedral (ABC) stacking forms are considered in a 5 × 5 nm domain. The intralayer thermal conductivity values are predicted to be 450-800 W/m K for both zigzag and armchair directions for different configurations, which are in agreement with previous results for graphite and few-layer graphene. The interlayer thermal conductivity values are calculated in the range of 9-55 W/m K. The intralayer thermal conductivity in the armchair direction appears to increase with increasing vertical alignment of carbon atoms in adjacent layers.

  17. Molecular dynamics prediction and experimental evidence for density of normal and metastable liquid zirconium

    NASA Astrophysics Data System (ADS)

    Wang, H. P.; Yang, S. J.; Hu, L.; Wei, B.

    2016-06-01

    The density of normal and metastable undercooled liquid zirconium was predicted by performing molecular dynamics calculation with a system consisting of 4000 atoms and measured by electrostatic levitation experiments. The results show that the density increases linearly with the descending of temperature, including a maximum undercooling of 928 K. The density is 6.00 g cm-3 at the melting temperature, which agrees well with the experimental result of 6.06 g cm-3. Furthermore, the atomic number is increased to 32,000 on the basis of 4000 atoms and there appears only 0.02% difference. Besides, the pair distribution function was applied to display the atomic structure, which indicates the liquid structure change occurs at the first neighbor distance.

  18. Deleterious Effects of Exact Exchange Functionals on Predictions of Molecular Conductance.

    PubMed

    Feng, Qingguo; Yamada, Atsushi; Baer, Roi; Dunietz, Barry D

    2016-08-01

    Kohn-Sham (KS) density functional theory (DFT) describes well the atomistic structure of molecular junctions and their coupling to the semi-infinite metallic electrodes but severely overestimates conductance due to the spuriously large density of charge-carrier states of the KS system. Previous works show that inclusion of appropriate amounts of nonlocal exchange in the functional can fix the problem and provide realistic conductance estimates. Here however we discover that nonlocal exchange can also lead to deleterious effects which artificially overestimate transmittance even beyond the KS-DFT prediction. The effect is a result of exchange coupling between nonoverlapping states of diradical character. We prescribe a practical recipe for eliminating such artifacts. PMID:27454778

  19. Prediction of static contact angles on the basis of molecular forces and adsorption data.

    PubMed

    Diaz, M Elena; Savage, Michael D; Cerro, Ramon L

    2016-08-01

    At a three-phase contact line, a liquid bulk phase is in contact with and coexists with a very thin layer of adsorbed molecules. This adsorbed film in the immediate vicinity of a liquid wedge modifies the balance of forces between the liquid and solid phases such that, when included in the balance of forces, a quantitative relationship emerges between the adsorbed film thickness and the static contact angle. This relationship permits the prediction of static contact angles from molecular forces and equilibrium adsorption data by means of quantities that are physically meaningful and measurable. For n-alkanes on polytetrafluoroethylene, for which there are experimental data available on adsorption and contact angles, our computations show remarkable agreement with the data. The results obtained are an improvement on previously published calculations-particularly for alkanes with a low number of carbon atoms, for which adsorption is significant. PMID:27627371

  20. Prediction of static contact angles on the basis of molecular forces and adsorption data

    NASA Astrophysics Data System (ADS)

    Diaz, M. Elena; Savage, Michael D.; Cerro, Ramon L.

    2016-08-01

    At a three-phase contact line, a liquid bulk phase is in contact with and coexists with a very thin layer of adsorbed molecules. This adsorbed film in the immediate vicinity of a liquid wedge modifies the balance of forces between the liquid and solid phases such that, when included in the balance of forces, a quantitative relationship emerges between the adsorbed film thickness and the static contact angle. This relationship permits the prediction of static contact angles from molecular forces and equilibrium adsorption data by means of quantities that are physically meaningful and measurable. For n-alkanes on polytetrafluoroethylene, for which there are experimental data available on adsorption and contact angles, our computations show remarkable agreement with the data. The results obtained are an improvement on previously published calculations—particularly for alkanes with a low number of carbon atoms, for which adsorption is significant.

  1. Zsyntax: A Formal Language for Molecular Biology with Projected Applications in Text Mining and Biological Prediction

    PubMed Central

    Boniolo, Giovanni; D'Agostino, Marcello; Di Fiore, Pier Paolo

    2010-01-01

    We propose a formal language that allows for transposing biological information precisely and rigorously into machine-readable information. This language, which we call Zsyntax (where Z stands for the Greek word ζωή, life), is grounded on a particular type of non-classical logic, and it can be used to write algorithms and computer programs. We present it as a first step towards a comprehensive formal language for molecular biology in which any biological process can be written and analyzed as a sort of logical “deduction”. Moreover, we illustrate the potential value of this language, both in the field of text mining and in that of biological prediction. PMID:20209084

  2. Prediction of static contact angles on the basis of molecular forces and adsorption data.

    PubMed

    Diaz, M Elena; Savage, Michael D; Cerro, Ramon L

    2016-08-01

    At a three-phase contact line, a liquid bulk phase is in contact with and coexists with a very thin layer of adsorbed molecules. This adsorbed film in the immediate vicinity of a liquid wedge modifies the balance of forces between the liquid and solid phases such that, when included in the balance of forces, a quantitative relationship emerges between the adsorbed film thickness and the static contact angle. This relationship permits the prediction of static contact angles from molecular forces and equilibrium adsorption data by means of quantities that are physically meaningful and measurable. For n-alkanes on polytetrafluoroethylene, for which there are experimental data available on adsorption and contact angles, our computations show remarkable agreement with the data. The results obtained are an improvement on previously published calculations-particularly for alkanes with a low number of carbon atoms, for which adsorption is significant.

  3. Spatial-Temporal [{sup 18}F]FDG-PET Features for Predicting Pathologic Response of Esophageal Cancer to Neoadjuvant Chemoradiation Therapy

    SciTech Connect

    Tan, Shan; Kligerman, Seth; Chen, Wengen; Lu, Minh; Kim, Grace; Feigenberg, Steven; D'Souza, Warren D.; Suntharalingam, Mohan; Lu, Wei

    2013-04-01

    Purpose: To extract and study comprehensive spatial-temporal {sup 18}F-labeled fluorodeoxyglucose ([{sup 18}F]FDG) positron emission tomography (PET) features for the prediction of pathologic tumor response to neoadjuvant chemoradiation therapy (CRT) in esophageal cancer. Methods and Materials: Twenty patients with esophageal cancer were treated with trimodal therapy (CRT plus surgery) and underwent [{sup 18}F]FDG-PET/CT scans both before (pre-CRT) and after (post-CRT) CRT. The 2 scans were rigidly registered. A tumor volume was semiautomatically delineated using a threshold standardized uptake value (SUV) of ≥2.5, followed by manual editing. Comprehensive features were extracted to characterize SUV intensity distribution, spatial patterns (texture), tumor geometry, and associated changes resulting from CRT. The usefulness of each feature in predicting pathologic tumor response to CRT was evaluated using the area under the receiver operating characteristic curve (AUC) value. Results: The best traditional response measure was decline in maximum SUV (SUV{sub max}; AUC, 0.76). Two new intensity features, decline in mean SUV (SUV{sub mean}) and skewness, and 3 texture features (inertia, correlation, and cluster prominence) were found to be significant predictors with AUC values ≥0.76. According to these features, a tumor was more likely to be a responder when the SUV{sub mean} decline was larger, when there were relatively fewer voxels with higher SUV values pre-CRT, or when [{sup 18}F]FDG uptake post-CRT was relatively homogeneous. All of the most accurate predictive features were extracted from the entire tumor rather than from the most active part of the tumor. For SUV intensity features and tumor size features, changes were more predictive than pre- or post-CRT assessment alone. Conclusion: Spatial-temporal [{sup 18}F]FDG-PET features were found to be useful predictors of pathologic tumor response to neoadjuvant CRT in esophageal cancer.

  4. Molecular and immunologic markers of kidney cancer-potential applications in predictive, preventive and personalized medicine.

    PubMed

    Mickley, Amanda; Kovaleva, Olga; Kzhyshkowska, Julia; Gratchev, Alexei

    2015-01-01

    Kidney cancer is one of the deadliest malignancies due to frequent late diagnosis (33 % or renal cell carcinoma are metastatic at diagnosis) and poor treatment options. There are two major subtypes of kidney cancer: renal cell carcinoma (RCC) and renal pelvis carcinoma. The risk factors for RCC, accounting for more than 90 % of all kidney cancers, are smoking, obesity, hypertension, misuse of pain medication, and some genetic diseases. The most common molecular markers of kidney cancer include mutations and epigenetic inactivation of von Hippel-Lindau (VHL) gene, genes of vascular endothelial growth factor (VEGF) pathway, and carbonic anhydrase IX (CIAX). The role of epigenetic pathways, including DNA methylation and chromatin structure remodeling, was also demonstrated. Immunologic properties of RCC enable this type of tumor to escape immune response effectively. An important role in this process is played by tumor-associated macrophages that demonstrate mixed M1/M2 phenotype. In this review, we discuss molecular and cellular aspects for RCC development and current state of knowledge allowing personalized approaches for diagnostics and prognostic prediction of this disease. A set of macrophage markers is suggested for the analysis of the association of macrophage phenotype and disease prognosis. PMID:26500709

  5. Protein-protein structure prediction by scoring molecular dynamics trajectories of putative poses.

    PubMed

    Sarti, Edoardo; Gladich, Ivan; Zamuner, Stefano; Correia, Bruno E; Laio, Alessandro

    2016-09-01

    The prediction of protein-protein interactions and their structural configuration remains a largely unsolved problem. Most of the algorithms aimed at finding the native conformation of a protein complex starting from the structure of its monomers are based on searching the structure corresponding to the global minimum of a suitable scoring function. However, protein complexes are often highly flexible, with mobile side chains and transient contacts due to thermal fluctuations. Flexibility can be neglected if one aims at finding quickly the approximate structure of the native complex, but may play a role in structure refinement, and in discriminating solutions characterized by similar scores. We here benchmark the capability of some state-of-the-art scoring functions (BACH-SixthSense, PIE/PISA and Rosetta) in discriminating finite-temperature ensembles of structures corresponding to the native state and to non-native configurations. We produce the ensembles by running thousands of molecular dynamics simulations in explicit solvent starting from poses generated by rigid docking and optimized in vacuum. We find that while Rosetta outperformed the other two scoring functions in scoring the structures in vacuum, BACH-SixthSense and PIE/PISA perform better in distinguishing near-native ensembles of structures generated by molecular dynamics in explicit solvent. Proteins 2016; 84:1312-1320. © 2016 Wiley Periodicals, Inc. PMID:27253756

  6. A new molecular signature method for prediction of driver cancer pathways from transcriptional data

    PubMed Central

    Rykunov, Dmitry; Beckmann, Noam D.; Li, Hui; Uzilov, Andrew; Schadt, Eric E.; Reva, Boris

    2016-01-01

    Assigning cancer patients to the most effective treatments requires an understanding of the molecular basis of their disease. While DNA-based molecular profiling approaches have flourished over the past several years to transform our understanding of driver pathways across a broad range of tumors, a systematic characterization of key driver pathways based on RNA data has not been undertaken. Here we introduce a new approach for predicting the status of driver cancer pathways based on signature functions derived from RNA sequencing data. To identify the driver cancer pathways of interest, we mined DNA variant data from TCGA and nominated driver alterations in seven major cancer pathways in breast, ovarian and colon cancer tumors. The activation status of these driver pathways were then characterized using RNA sequencing data by constructing classification signature functions in training datasets and then testing the accuracy of the signatures in test datasets. The signature functions differentiate well tumors with nominated pathway activation from tumors with no signs of activation: average AUC equals to 0.83. Our results confirm that driver genomic alterations are distinctively displayed at the transcriptional level and that the transcriptional signatures can generally provide an alternative to DNA sequencing methods in detecting specific driver pathways. PMID:27098033

  7. A new molecular signature method for prediction of driver cancer pathways from transcriptional data.

    PubMed

    Rykunov, Dmitry; Beckmann, Noam D; Li, Hui; Uzilov, Andrew; Schadt, Eric E; Reva, Boris

    2016-06-20

    Assigning cancer patients to the most effective treatments requires an understanding of the molecular basis of their disease. While DNA-based molecular profiling approaches have flourished over the past several years to transform our understanding of driver pathways across a broad range of tumors, a systematic characterization of key driver pathways based on RNA data has not been undertaken. Here we introduce a new approach for predicting the status of driver cancer pathways based on signature functions derived from RNA sequencing data. To identify the driver cancer pathways of interest, we mined DNA variant data from TCGA and nominated driver alterations in seven major cancer pathways in breast, ovarian and colon cancer tumors. The activation status of these driver pathways were then characterized using RNA sequencing data by constructing classification signature functions in training datasets and then testing the accuracy of the signatures in test datasets. The signature functions differentiate well tumors with nominated pathway activation from tumors with no signs of activation: average AUC equals to 0.83. Our results confirm that driver genomic alterations are distinctively displayed at the transcriptional level and that the transcriptional signatures can generally provide an alternative to DNA sequencing methods in detecting specific driver pathways. PMID:27098033

  8. Proteome-wide prediction of targets for aspirin: new insight into the molecular mechanism of aspirin

    PubMed Central

    Dai, Shao-Xing; Li, Wen-Xing

    2016-01-01

    Besides its anti-inflammatory, analgesic and anti-pyretic properties, aspirin is used for the prevention of cardiovascular disease and various types of cancer. The multiple activities of aspirin likely involve several molecular targets and pathways rather than a single target. Therefore, systematic identification of these targets of aspirin can help us understand the underlying mechanisms of the activities. In this study, we identified 23 putative targets of aspirin in the human proteome by using binding pocket similarity detecting tool combination with molecular docking, free energy calculation and pathway analysis. These targets have diverse folds and are derived from different protein family. However, they have similar aspirin-binding pockets. The binding free energy with aspirin for newly identified targets is comparable to that for the primary targets. Pathway analysis revealed that the targets were enriched in several pathways such as vascular endothelial growth factor (VEGF) signaling, Fc epsilon RI signaling and arachidonic acid metabolism, which are strongly involved in inflammation, cardiovascular disease and cancer. Therefore, the predicted target profile of aspirin suggests a new explanation for the disease prevention ability of aspirin. Our findings provide a new insight of aspirin and its efficacy of disease prevention in a systematic and global view. PMID:26989626

  9. Proteome-wide prediction of targets for aspirin: new insight into the molecular mechanism of aspirin.

    PubMed

    Dai, Shao-Xing; Li, Wen-Xing; Li, Gong-Hua; Huang, Jing-Fei

    2016-01-01

    Besides its anti-inflammatory, analgesic and anti-pyretic properties, aspirin is used for the prevention of cardiovascular disease and various types of cancer. The multiple activities of aspirin likely involve several molecular targets and pathways rather than a single target. Therefore, systematic identification of these targets of aspirin can help us understand the underlying mechanisms of the activities. In this study, we identified 23 putative targets of aspirin in the human proteome by using binding pocket similarity detecting tool combination with molecular docking, free energy calculation and pathway analysis. These targets have diverse folds and are derived from different protein family. However, they have similar aspirin-binding pockets. The binding free energy with aspirin for newly identified targets is comparable to that for the primary targets. Pathway analysis revealed that the targets were enriched in several pathways such as vascular endothelial growth factor (VEGF) signaling, Fc epsilon RI signaling and arachidonic acid metabolism, which are strongly involved in inflammation, cardiovascular disease and cancer. Therefore, the predicted target profile of aspirin suggests a new explanation for the disease prevention ability of aspirin. Our findings provide a new insight of aspirin and its efficacy of disease prevention in a systematic and global view. PMID:26989626

  10. Thermal vibration of a single-layered graphene with initial stress predicted by semiquantum molecular dynamics

    NASA Astrophysics Data System (ADS)

    Liu, Rumeng; Wang, Lifeng; Jiang, Jingnong

    2016-09-01

    Thermal vibration of a rectangular single-layered graphene sheet (RSLGS) with initial stress is investigated by a semiquantum molecular dynamics (SQMD) method on the basis of modified Langevin dynamics. The quantum effect in the thermal vibration of RSLGS is accounted by introducing a quantum thermal bath. The spectrum of the thermal vibration of RSLGSs is obtained both by SQMD and classical molecular dynamics (CMD). The RSLGS vibrates with the same frequencies via both the SQMD simulation and the CMD simulation. The root of mean squared (rms) amplitude obtained via the CMD is greater than that obtained via the SQMD. The energy in high order mode is frozen at very low temperature if quantum effect is taken into consideration. An elastic plate model with initial stress considering quantum effects is established to describe the thermal vibration of the RSLGS. The rms amplitude of RSLGS calculated by plate model with the law of energy equipartition and that obtained from the CMD coincide very well. The plate model considering the quantum effects provides accurate prediction of the rms amplitude of the RSLGS obtained from the SQMD. These results indicate that quantum effects cannot be neglected in the thermal vibration of the RSLGS at low temperature case.

  11. Clinical features and molecular characteristics of invasive community-acquired methicillin-resistant Staphylococcus aureus infections in Taiwanese children.

    PubMed

    Chen, Chih-Jung; Su, Lin-Hui; Chiu, Cheng-Hsun; Lin, Tzou-Yien; Wong, Kin-Sun; Chen, Yi-Ywan M; Huang, Yhu-Chering

    2007-11-01

    Highly virulent community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA) has been associated with morbidity and mortality in various countries of the world. We characterized the clinical and molecular features of pediatric invasive CA-MRSA infections in Taiwan. Between July 2000 and June 2005, 31 previously healthy children with invasive CA-MRSA infections were identified from 423 children with community-onset methicillin-resistant S. aureus infections. The medical records were reviewed. The clinical isolates, if available, were collected for molecular characterization. Sixteen (51.6%) patients were male, and the mean age was 5.7 years. Adolescents accounted for 9 (29%) cases. Eighteen children had bone and/or joint infections, 14 had deep-seated soft tissue infections, 11 had pneumonia, and 2 had central nervous system infections. Multiorgan involvement was identified in 8 of 20 bacteremic cases. Twenty-two patients (71%) required surgical interventions. The mean hospital stay was 27.4 days. All of the 15 available isolates were classified as sequence type (ST) 59 or its single locus variant and belonged to 2 previously reported community-associated clones containing staphylococcal cassette chromosome mec (SCCmec) type IV or type V(T) in Taiwan. Most of the isolates were multiresistant to clindamycin (94%) and erythromycin (97%). Eleven (73.3%) isolates carried pvl genes, and the strains harboring pvl genes were significantly associated with lung involvement. In conclusion, invasive CA-MRSA infections in pediatric population were not limited to young children. Surgical interventions were often required, and a prolonged course of antibiotic therapy was needed. A multiresistant CA-MRSA clone characterized as ST59 was identified from these children in Taiwan. PMID:17662565

  12. Clinical and Biologic Features Predictive of Survival After Relapse of Neuroblastoma: A Report From the International Neuroblastoma Risk Group Project

    PubMed Central

    London, Wendy B.; Castel, Victoria; Monclair, Tom; Ambros, Peter F.; Pearson, Andrew D.J.; Cohn, Susan L.; Berthold, Frank; Nakagawara, Akira; Ladenstein, Ruth L.; Iehara, Tomoko; Matthay, Katherine K.

    2011-01-01

    Purpose Survival after neuroblastoma relapse is poor. Understanding the relationship between clinical and biologic features and outcome after relapse may help in selection of optimal therapy. Our aim was to determine which factors were significantly predictive of postrelapse overall survival (OS) in patients with recurrent neuroblastoma—particularly whether time from diagnosis to first relapse (TTFR) was a significant predictor of OS. Patients and Methods Patients with first relapse/progression were identified in the International Neuroblastoma Risk Group (INRG) database. Time from study enrollment until first event and OS time starting from first event were calculated. Cox regression models were used to calculate the hazard ratio of increased death risk and perform survival tree regression. TTFR was tested in a multivariable Cox model with other factors. Results In the INRG database (N = 8,800), 2,266 patients experienced first progression/relapse. Median time to relapse was 13.2 months (range, 1 day to 11.4 years). Five-year OS from time of first event was 20% (SE, ± 1%). TTFR was statistically significantly associated with OS time in a nonlinear relationship; patients with TTFR of 36 months or longer had the lowest risk of death, followed by patients who relapsed in the period of 0 to less than 6 months or 18 to 36 months. Patients who relapsed between 6 and 18 months after diagnosis had the highest risk of death. TTFR, age, International Neuroblastoma Staging System stage, and MYCN copy number status were independently predictive of postrelapse OS in multivariable analysis. Conclusion Age, stage, MYCN status, and TTFR are significant prognostic factors for postrelapse survival and may help in the design of clinical trials evaluating novel agents. PMID:21768459

  13. Prediction of hot regions in protein-protein interaction by combining density-based incremental clustering with feature-based classification.

    PubMed

    Hu, Jing; Zhang, Xiaolong; Liu, Xiaoming; Tang, Jinshan

    2015-06-01

    Discovering hot regions in protein-protein interaction is important for drug and protein design, while experimental identification of hot regions is a time-consuming and labor-intensive effort; thus, the development of predictive models can be very helpful. In hot region prediction research, some models are based on structure information, and others are based on a protein interaction network. However, the prediction accuracy of these methods can still be improved. In this paper, a new method is proposed for hot region prediction, which combines density-based incremental clustering with feature-based classification. The method uses density-based incremental clustering to obtain rough hot regions, and uses feature-based classification to remove the non-hot spot residues from the rough hot regions. Experimental results show that the proposed method significantly improves the prediction performance of hot regions.

  14. Combining Molecular Docking and Molecular Dynamics to Predict the Binding Modes of Flavonoid Derivatives with the Neuraminidase of the 2009 H1N1 Influenza A Virus

    PubMed Central

    Lu, Shih-Jen; Chong, Fok-Ching

    2012-01-01

    Control of flavonoid derivatives inhibitors release through the inhibition of neuraminidase has been identified as a potential target for the treatment of H1N1 influenza disease. We have employed molecular dynamics simulation techniques to optimize the 2009 H1N1 influenza neuraminidase X-ray crystal structure. Molecular docking of the compounds revealed the possible binding mode. Our molecular dynamics simulations combined with the solvated interaction energies technique was applied to predict the docking models of the inhibitors in the binding pocket of the H1N1 influenza neuraminidase. In the simulations, the correlation of the predicted and experimental binding free energies of all 20 flavonoid derivatives inhibitors is satisfactory, as indicated by R2 = 0.75. PMID:22605992

  15. Features Predicting Weight Loss in Overweight or Obese Participants in a Web-Based Intervention: Randomized Trial

    PubMed Central

    Freyne, Jill; Saunders, Ian; Berkovsky, Shlomo; Smith, Greg; Noakes, Manny

    2012-01-01

    compared to the information-based site only at week 12 (P = .01). The average number of days that each site was used varied significantly (P = .02) and was higher for the supportive site at 5.96 (SD 11.36) and personalized-supportive site at 5.50 (SD 10.35), relative to the information-based site at 3.43 (SD 4.28). In total, 435 participants provided a valid final weight at the 12-week follow-up. Intention-to-treat analyses (using multiple imputations) revealed that there were no statistically significant differences in weight loss between sites (P = .42). On average, participants lost 2.76% (SE 0.32%) of their initial body weight, with 23.7% (SE 3.7%) losing 5% or more of their initial weight. Within supportive conditions, the level of use of the online weight tracker was predictive of weight loss (model estimate = 0.34, P < .001). Age (model estimate = 0.04, P < .001) and initial BMI (model estimate = -0.03, P < .002) were associated with frequency of use of the weight tracker. Conclusions Relative to a static control, inclusion of social networking features and personalized meal planning recommendations in a web-based weight loss program did not demonstrate additive effects for user weight loss or retention. These features did, however, increase the average number of days that a user engaged with the system. For users of the supportive websites, greater use of the weight tracker tool was associated with greater weight loss. PMID:23234759

  16. Predictions of the physicochemical properties of amino acid side chain analogs using molecular simulation.

    PubMed

    Ahmed, Alauddin; Sandler, Stanley I

    2016-03-01

    A candidate drug compound is released for clinical trails (in vivo activity) only if its physicochemical properties meet desirable bioavailability and partitioning criteria. Amino acid side chain analogs play vital role in the functionalities of protein and peptides and as such are important in drug discovery. We demonstrate here that the predictions of solvation free energies in water, in 1-octanol, and self-solvation free energies computed using force field-based expanded ensemble molecular dynamics simulation provide good accuracy compared to existing empirical and semi-empirical methods. These solvation free energies are then, as shown here, used for the prediction of a wide range of physicochemical properties important in the assessment of bioavailability and partitioning of compounds. In particular, we consider here the vapor pressure, the solubility in both water and 1-octanol, and the air-water, air-octanol, and octanol-water partition coefficients of amino acid side chain analogs computed from the solvation free energies. The calculated solvation free energies using different force fields are compared against each other and with available experimental data. The protocol here can also be used for a newly designed drug and other molecules where force field parameters and charges are obtained from density functional theory. PMID:26864716

  17. Predicted molecular signaling guiding photoreceptor cell migration following transplantation into damaged retina

    PubMed Central

    Unachukwu, Uchenna John; Warren, Alice; Li, Ze; Mishra, Shawn; Zhou, Jing; Sauane, Moira; Lim, Hyungsik; Vazquez, Maribel; Redenti, Stephen

    2016-01-01

    To replace photoreceptors lost to disease or trauma and restore vision, laboratories around the world are investigating photoreceptor replacement strategies using subretinal transplantation of photoreceptor precursor cells (PPCs) and retinal progenitor cells (RPCs). Significant obstacles to advancement of photoreceptor cell-replacement include low migration rates of transplanted cells into host retina and an absence of data describing chemotactic signaling guiding migration of transplanted cells in the damaged retinal microenvironment. To elucidate chemotactic signaling guiding transplanted cell migration, bioinformatics modeling of PPC transplantation into light-damaged retina was performed. The bioinformatics modeling analyzed whole-genome expression data and matched PPC chemotactic cell-surface receptors to cognate ligands expressed in the light-damaged retinal microenvironment. A library of significantly predicted chemotactic ligand-receptor pairs, as well as downstream signaling networks was generated. PPC and RPC migration in microfluidic ligand gradients were analyzed using a highly predicted ligand-receptor pair, SDF-1α – CXCR4, and both PPCs and RPCs exhibited significant chemotaxis. This work present a systems level model and begins to elucidate molecular mechanisms involved in PPC and RPC migration within the damaged retinal microenvironment. PMID:26935401

  18. Predicted molecular signaling guiding photoreceptor cell migration following transplantation into damaged retina.

    PubMed

    Unachukwu, Uchenna John; Warren, Alice; Li, Ze; Mishra, Shawn; Zhou, Jing; Sauane, Moira; Lim, Hyungsik; Vazquez, Maribel; Redenti, Stephen

    2016-01-01

    To replace photoreceptors lost to disease or trauma and restore vision, laboratories around the world are investigating photoreceptor replacement strategies using subretinal transplantation of photoreceptor precursor cells (PPCs) and retinal progenitor cells (RPCs). Significant obstacles to advancement of photoreceptor cell-replacement include low migration rates of transplanted cells into host retina and an absence of data describing chemotactic signaling guiding migration of transplanted cells in the damaged retinal microenvironment. To elucidate chemotactic signaling guiding transplanted cell migration, bioinformatics modeling of PPC transplantation into light-damaged retina was performed. The bioinformatics modeling analyzed whole-genome expression data and matched PPC chemotactic cell-surface receptors to cognate ligands expressed in the light-damaged retinal microenvironment. A library of significantly predicted chemotactic ligand-receptor pairs, as well as downstream signaling networks was generated. PPC and RPC migration in microfluidic ligand gradients were analyzed using a highly predicted ligand-receptor pair, SDF-1α - CXCR4, and both PPCs and RPCs exhibited significant chemotaxis. This work present a systems level model and begins to elucidate molecular mechanisms involved in PPC and RPC migration within the damaged retinal microenvironment. PMID:26935401

  19. Predicted molecular signaling guiding photoreceptor cell migration following transplantation into damaged retina

    NASA Astrophysics Data System (ADS)

    Unachukwu, Uchenna John; Warren, Alice; Li, Ze; Mishra, Shawn; Zhou, Jing; Sauane, Moira; Lim, Hyungsik; Vazquez, Maribel; Redenti, Stephen

    2016-03-01

    To replace photoreceptors lost to disease or trauma and restore vision, laboratories around the world are investigating photoreceptor replacement strategies using subretinal transplantation of photoreceptor precursor cells (PPCs) and retinal progenitor cells (RPCs). Significant obstacles to advancement of photoreceptor cell-replacement include low migration rates of transplanted cells into host retina and an absence of data describing chemotactic signaling guiding migration of transplanted cells in the damaged retinal microenvironment. To elucidate chemotactic signaling guiding transplanted cell migration, bioinformatics modeling of PPC transplantation into light-damaged retina was performed. The bioinformatics modeling analyzed whole-genome expression data and matched PPC chemotactic cell-surface receptors to cognate ligands expressed in the light-damaged retinal microenvironment. A library of significantly predicted chemotactic ligand-receptor pairs, as well as downstream signaling networks was generated. PPC and RPC migration in microfluidic ligand gradients were analyzed using a highly predicted ligand-receptor pair, SDF-1α – CXCR4, and both PPCs and RPCs exhibited significant chemotaxis. This work present a systems level model and begins to elucidate molecular mechanisms involved in PPC and RPC migration within the damaged retinal microenvironment.

  20. Predicted molecular signaling guiding photoreceptor cell migration following transplantation into damaged retina

    NASA Astrophysics Data System (ADS)

    Unachukwu, Uchenna John; Warren, Alice; Li, Ze; Mishra, Shawn; Zhou, Jing; Sauane, Moira; Lim, Hyungsik; Vazquez, Maribel; Redenti, Stephen

    2016-03-01

    To replace photoreceptors lost to disease or trauma and restore vision, laboratories around the world are investigating photoreceptor replacement strategies using subretinal transplantation of photoreceptor precursor cells (PPCs) and retinal progenitor cells (RPCs). Significant obstacles to advancement of photoreceptor cell-replacement include low migration rates of transplanted cells into host retina and an absence of data describing chemotactic signaling guiding migration of transplanted cells in the damaged retinal microenvironment. To elucidate chemotactic signaling guiding transplanted cell migration, bioinformatics modeling of PPC transplantation into light-damaged retina was performed. The bioinformatics modeling analyzed whole-genome expression data and matched PPC chemotactic cell-surface receptors to cognate ligands expressed in the light-damaged retinal microenvironment. A library of significantly predicted chemotactic ligand-receptor pairs, as well as downstream signaling networks was generated. PPC and RPC migration in microfluidic ligand gradients were analyzed using a highly predicted ligand-receptor pair, SDF-1α - CXCR4, and both PPCs and RPCs exhibited significant chemotaxis. This work present a systems level model and begins to elucidate molecular mechanisms involved in PPC and RPC migration within the damaged retinal microenvironment.

  1. Soil features and indoor radon concentration prediction: radon in soil gas, pedology, permeability and 226Ra content.

    PubMed

    Lara, E; Rocha, Z; Santos, T O; Rios, F J; Oliveira, A H

    2015-11-01

    This work aims at relating some physicochemical features of soils and their use as a tool for prediction of indoor radon concentrations of the Metropolitan Region of Belo Horizonte (RMBH), Minas Gerais, Brazil. The measurements of soil gas radon concentrations were performed by using an AlphaGUARD monitor. The (226)Ra content analysis was performed by gamma spectrometry (high pure germanium) and permeabilities were performed by using the RADON-JOK permeameter. The GEORP indicator and soil radon index (RI) were also calculated. Approximately 53 % of the Perferric Red Latosols measurement site could be classified as 'high risk' (Swedish criteria). The Litholic Neosols presented the lowest radon concentration mean in soil gas. The Perferric Red Latosols presented significantly high radon concentration mean in soil gas (60.6 ± 8.7 kBq m(-3)), high indoor radon concentration, high RI, (226)Ra content and GEORP. The preliminary results may indicate an influence of iron formations present very close to the Perferric Red Latosols in the retention of uranium minerals.

  2. Natural ligand motifs of closely related HLA-DR4 molecules predict features of rheumatoid arthritis associated peptides.

    PubMed

    Friede, T; Gnau, V; Jung, G; Keilholz, W; Stevanović, S; Rammensee, H G

    1996-06-01

    Rheumatoid arthritis (RA), one of the most common autoimmune disorders, is believed to be mediated via. T lymphocytes and genetic studies have shown that it is strongly associated with HLA-DR4. The DR4 subtypes DR4Dw4, DR4Dw14 and DR4Dw15 represent increased risk factors for RA, whereas DR4Dw10 is not associated with the disorder. Our study determines and compares the natural ligand motifs of these MHC class II molecules and identifies 60 natural ligands. At relative position 4 (P4), only the RA-associated DR4 molecules allow, or even prefer, negatively charged amino acids, but do not allow those which are positively charged (Arg, Lys). In the case of DR4Dw10 the preference for these amino acids is reversed. The results predict features of the putative RA-inducing peptide(s). A remarkable specificity, almost exclusively for negative charges (Asp, Glu), is found at P9 of the DR4Dw15 motif. This specificity can be ascribed to amino acid beta57 of the DR beta chain, and gives an important insight into the beta57-association of another autoimmune disease, insulin-dependent diabetes mellitus type I. PMID:8672555

  3. Soil features and indoor radon concentration prediction: radon in soil gas, pedology, permeability and 226Ra content.

    PubMed

    Lara, E; Rocha, Z; Santos, T O; Rios, F J; Oliveira, A H

    2015-11-01

    This work aims at relating some physicochemical features of soils and their use as a tool for prediction of indoor radon concentrations of the Metropolitan Region of Belo Horizonte (RMBH), Minas Gerais, Brazil. The measurements of soil gas radon concentrations were performed by using an AlphaGUARD monitor. The (226)Ra content analysis was performed by gamma spectrometry (high pure germanium) and permeabilities were performed by using the RADON-JOK permeameter. The GEORP indicator and soil radon index (RI) were also calculated. Approximately 53 % of the Perferric Red Latosols measurement site could be classified as 'high risk' (Swedish criteria). The Litholic Neosols presented the lowest radon concentration mean in soil gas. The Perferric Red Latosols presented significantly high radon concentration mean in soil gas (60.6 ± 8.7 kBq m(-3)), high indoor radon concentration, high RI, (226)Ra content and GEORP. The preliminary results may indicate an influence of iron formations present very close to the Perferric Red Latosols in the retention of uranium minerals. PMID:25920786

  4. Renal cell carcinoma with angioleiomyoma-like stroma: clinicopathological, immunohistochemical, and molecular features supporting classification as a distinct entity.

    PubMed

    Williamson, Sean R; Cheng, Liang; Eble, John N; True, Lawrence D; Gupta, Nilesh S; Wang, Mingsheng; Zhang, Shaobo; Grignon, David J

    2015-02-01

    , immunohistochemical, and molecular features, unrelated to clear cell renal cell carcinoma. The immunoprofile overlaps partly with that of clear cell papillary renal cell carcinoma, though morphology and reactivity for CD10 are points of contrast. PMID:25189644

  5. Current immunological and molecular tools for leptospirosis: diagnostics, vaccine design, and biomarkers for predicting severity.

    PubMed

    Rajapakse, Senaka; Rodrigo, Chaturaka; Handunnetti, Shiroma M; Fernando, Sumadhya Deepika

    2015-01-01

    Leptospirosis is a zoonotic spirochaetal illness that is endemic in many tropical countries. The research base on leptospirosis is not as strong as other tropical infections such as malaria. However, it is a lethal infection that can attack many vital organs in its severe form, leading to multi-organ dysfunction syndrome and death. There are many gaps in knowledge regarding the pathophysiology of leptospirosis and the role of host immunity in causing symptoms. This hinders essential steps in combating disease, such as developing a potential vaccine. Another major problem with leptospirosis is the lack of an easy to perform, accurate diagnostic tests. Many clinicians in resource limited settings resort to clinical judgment in diagnosing leptospirosis. This is unfortunate, as many other diseases such as dengue, hanta virus, rickettsial infections, and even severe bacterial sepsis, can mimic leptospirosis. Another interesting problem is the prediction of disease severity at the onset of the illness. The majority of patients recover from leptospirosis with only a mild febrile illness, while a few others have severe illness with multi-organ failure. Clinical features are poor predictors of potential severity of infection, and therefore the search is on for potential biomarkers that can serve as early warnings for severe disease. This review concentrates on these three important aspects of this neglected tropical disease: diagnostics, developing a vaccine, and potential biomarkers to predict disease severity.

  6. Molecular profiles of BRCA1-mutated and matched sporadic breast tumours: relation with clinico-pathological features

    PubMed Central

    Berns, E M J J; Staveren, I L van; Verhoog, L; Ouweland, A M W van de; Gelder, M Meijer-van; Meijers-Heijboer, H; Portengen, H; Foekens, J A; Dorssers, L C J; Klijn, J G M

    2001-01-01

    About 5–10% of breast cancers are hereditary; a genetically and clinically heterogeneous disease in which several susceptibility genes, including BRCA1, have been identified. While distinct tumour features can be used to estimate the likelihood that a breast tumour is caused by a BRCA1 germline mutation it is not yet possible to categorize a BRCA1 mutated tumour. The aim of the present study is to molecularly classify BRCA1 mutated breast cancers by resolving gene expression patterns of BRCA1 and matched sporadic surgical breast tumour specimens. The expression profiles of 6 frozen breast tumour tissues with a proven BRCA1 gene mutation were weighed against those from 12 patients without a known family history but who had similar clinico-pathological characteristics. In addition two fibroblast cultures, the breast cancer cell-line HCC1937 and its corresponding B-lymphoblastoid cell line (heterozygous for mutation BRCA1 5382insC) and an epithelial ovarian cancer cell line (A2780) were studied. Using a high density membrane based array for screening of RNA isolated from these samples and standard algorithms and software, we were able to distinguish subgroups of sporadic cases and a group consisting mainly of BRCA1-mutated breast tumours. Furthermore this pilot analysis revealed a gene cluster that differentially expressed genes related to cell substrate formation, adhesion, migration and cell organization in BRCA1-mutated tumours compared to sporadic breast tumours. © 2001 Cancer Research Campaign http://www.bjcancer.com PMID:11506493

  7. Molecular bases of K+ secretory cells in the inner ear: shared and distinct features between birds and mammals

    PubMed Central

    Wilms, Viviane; Köppl, Christine; Söffgen, Chris; Hartmann, Anna-Maria; Nothwang, Hans Gerd

    2016-01-01

    In the cochlea, mammals maintain a uniquely high endolymphatic potential (EP), which is not observed in other vertebrate groups. However, a high [K+] is always present in the inner ear endolymph. Here, we show that Kir4.1, which is required in the mammalian stria vascularis to generate the highly positive EP, is absent in the functionally equivalent avian tegmentum vasculosum. In contrast, the molecular repertoire required for K+ secretion, specifically NKCC1, KCNQ1, KCNE1, BSND and CLC-K, is shared between the tegmentum vasculosum, the vestibular dark cells and the marginal cells of the stria vascularis. We further show that in barn owls, the tegmentum vasculosum is enlarged and a higher EP (~+34 mV) maintained, compared to other birds. Our data suggest that both the tegmentum vasculosum and the stratified stria vascularis evolved from an ancestral vestibular epithelium that already featured the major cell types of the auditory epithelia. Genetic recruitment of Kir4.1 specifically to strial melanocytes was then a crucial step in mammalian evolution enabling an increase in the cochlear EP. An increased EP may be related to high-frequency hearing, as this is a hallmark of barn owls among birds and mammals among amniotes. PMID:27680950

  8. A Biphasic Pleural Tumor with Features of an Epithelioid and Small Cell Mesothelioma: Morphologic and Molecular Findings

    PubMed Central

    2016-01-01

    Malignant mesotheliomas are generally classified into epithelioid, sarcomatoid, desmoplastic, and biphasic types with rare reports of a small cell form. These small cell variants display some morphologic overlap with desmoplastic small round cell tumors (DSRCTs) which generally occur within the abdominal cavity of young males and are defined by a characteristic t(11;22)(p13;q12) translocation. However, there are rare reports of DSRCTs lacking this translocation. We present a 78-year-old man with a pleura-based biphasic neoplasm with features of both epithelioid mesothelioma and a small cell blastema-like neoplasm. The epithelioid portion showed IHC reactivity for pan cytokeratin, CK5/6, D2-40, and calretinin and the small cell portion marked with CD99, pan cytokeratin, WT1, FLI1, S100, CD200, MyoD1, and CD15. Fluorescence in situ hybridization testing for the t(11;22)(p13;q12) translocation disclosed loss of the EWSR1 gene in 94% of tumor cell nuclei, but there was no evidence of the classic translocation. Array based-comparative genomic hybridization (a-CGH) confirmed the tumor had numerous chromosome copy number losses, including 11p15.5-p11.12 and 22q12.1-q13.33, with loss of the EWSR1 and WT1 gene regions. Herein, we report novel complex CGH findings in a biphasic tumor and review the molecular genetic alterations in both mesothelioma and DSRCTs. PMID:27403364

  9. Clinical features, Outcomes and Molecular Profiles of Drug Resistance in Tuberculous Meningitis in non-HIV Patients

    PubMed Central

    Zhang, Jingya; Hu, Xuejiao; Hu, Xin; Ye, Yuanxin; Shang, Mengqiao; An, Yunfei; Gou, Haimei; Zhao, Zhenzhen; Peng, Wu; Song, Xingbo; Zhou, Yanhong; Kang, Mei; Xie, Yi; Chen, Xuerong; Lu, Xiaojun; Ying, Binwu; Wang, Lanlan

    2016-01-01

    Tuberculous meningitis continues to be a serious problem for physicians because it is difficult to make an early diagnosis and the consequences of delaying treatment are severe. The objective of this study is to provide data for the optimization of diagnostic and timely treatment of tuberculous meningitis. Of the 401 human immunodeficiency virus (HIV)-negative tuberculous meningitis patients in our study, 332 were found to have an impaired blood brain barrier (82.8%). Nearly 17.0% of patients failed to be timely diagnosed. Headache (53.6%) and fever (48.6%) were the most common features, and Computed Tomography/Magnetic Resonance Imaging (CT/MRI) detected 96 patients (23.9%) with abnormal meningeal imaging. Cerebrospinal fluid real-time polymerase chain reaction was positive in 73.8% of the tuberculous meningitis patients, whereas, smears and cultures detected only 6.7% and 5.2%, respectively. Further analysis identified striking differences between drug-resistant and drug-susceptible tuberculous meningitis. Patients with drug resistance correlated with grave prognosis. Tuberculous meningitis diagnosis should overall embody clinical symptoms, laboratory and cerebral imaging findings, and more sensitive diagnostic approaches are still warranted. Our data suggest cerebrospinal fluid polymerase chain reaction for mycobacterial DNA and molecular drug susceptibility testing as routine assays for suspected tuberculous meningitis patients, and observation of the blood brain barrier function could be performed for individual management. PMID:26738994

  10. [The alternative way of colorectal cancer developing. The histogenetic and molecular features of serrated lesions (review, continued)].

    PubMed

    Ageĭkina, N V; Duvanskiĭ, V A; Kniazev, M V; Mal'kov, P G; Danilova, N V; Kharlova, O A

    2014-01-01

    The occurrence of colorectal cancer can be traced in two ways: from conventional adenomas with the APC-gene mutation (model Fearon-Vogelstein) and the "serrated way", that has a unique genetic profile and morphological characteristics at the early stages. These neoplasms are determined from 7 to 9%. The risk of developing cancer of them is 7.5-15%. Precursors of epithelial neoplasia are aberrant crypts foci. About 20% of colorectal cancer demonstrated the common defects in DNA methylation (CIMP-positive profile), mutations BRAF (KRAS)--oncogenes, microsatellite instability (MSI). The serrated lesions may have these mutations. Serrated polyposis syndrome has specific genetic changes associated with biallelic mutation MUTYH also. Risk of colorectal cancer is very high in this syndrome and is more than 50%. Often the synchronous or metachronous cancers presence. They are usually accompanied by MSI-H and represented serrated morphology too. Understanding epigenetic ways and molecular features of serrated lesions gives an knowledge of their clinical significance and provides the evidence for the treatment and monitoring of patients with this disease. This review is devoted to these issues.

  11. Methyl Radical in Clathrate Silica Voids. The Peculiar Physisorption Features of the Guest-Host Molecular Dynamics Interaction.

    PubMed

    Dmitriev, Yurij A; Buscarino, Gianpiero; Benetis, Nikolas P

    2016-08-11

    EPR line shape simulations of CH3/SiO2 clathrates and comparison to CH3/N2O and CH3/SiO2 experiments reveal the motional conditions of the CH3 radical up to the unusual regime of its stability, the high-temperature diffusional regime, at 300 K. In the low-temperature region, the CH3 in clathrates is found to rotate around the in-plane axes even at as low temperatures as 3.8 K. However, nonrotating methyls performing only libration about the C2-axes as well as around the C3-axis are also found, proving the existence of special sites in the clathrate voids that begin to accumulate a significant fraction of methyl radicals at temperatures below approximately 7 K. A distinctive feature in the spectrum anisotropy and line width temperature profiles is found nearby 25 K, which is interpreted as the radical physisorption inside the voids that occurs with the sample temperature lowering. The unusual increase of the CH3/SiO2 clathrate EPR spectral width with temperature over approximately 120 K has its origin in repeated angular momentum vector alterations due to frequent collisions with the clathrate void walls between periodical free rotation periods. This relaxation mechanism resembles to spin-rotation interaction known only for small molecular species in nonviscous fluids but unknown earlier for methyl hosted in solids. PMID:27405003

  12. Predicting the Effect of Accelerated Fractionation in Postoperative Radiotherapy for Head and Neck Cancer Based on Molecular Marker Profiles: Data From a Randomized Clinical Trial

    SciTech Connect

    Suwinski, Rafal; Jaworska, Magdalena; Nikiel, Barbara; Grzegorz, Wozniak; Bankowska-Wozniak, Magdalena; Wojciech, Majewski; Krzysztof, Skladowski; Dariusz, Lange

    2010-06-01

    Purpose: To determine the prognostic and predictive values of molecular marker expression profiles based on data from a randomized clinical trial of postoperative conventional fractionation (p-CF) therapy versus 7-day-per-week postoperative continuous accelerated irradiation (p-CAIR) therapy for squamous cell cancer of the head and neck. Methods and Materials: Tumor samples from 148 patients (72 p-CF and 76 p-CAIR patients) were available for molecular studies. Immunohistochemistry was used to assess levels of EGFR, nm23, Ki-67, p-53, and cyclin D1 expression. To evaluate the effect of fractionation relative to the expression profiles, data for locoregional tumor control (LRC) were analyzed using the Cox proportional hazard regression model. Survival curves were compared using the Cox f test. Results: Patients who had tumors with low Ki-67, low p-53, and high EGFR expression levels and oral cavity/oropharyngeal primary cancer sites tended to benefit from p-CAIR. A joint score for the gain in LRC from p-CAIR based of these features was used to separate the patients into two groups: those who benefited significantly from p-CAIR with respect to LRC (n = 49 patients; 5-year LRC of 28% vs. 68%; p = 0.01) and those who did not benefit from p-CAIR (n = 99 patients; 5-year LRC of 72% vs. 66%; p = 0.38). The nm23 expression level appeared useful as a prognostic factor but not as a predictor of fractionation effect. Conclusions: These results support the studies that demonstrate the potential of molecular profiles to predict the benefit from accelerated radiotherapy. The molecular profile that favored accelerated treatment (low Ki-67, low p-53, and high EGFR expression) was in a good accordance with results provided by other investigators. Combining individual predictors in a joint score may improve their predictive potential.

  13. Distinct molecular features facilitating ice-binding mechanisms in hyperactive antifreeze proteins closely related to an Antarctic sea ice bacterium.

    PubMed

    Banerjee, Rachana; Chakraborti, Pratim; Bhowmick, Rupa; Mukhopadhyay, Subhasish

    2015-01-01

    Antifreeze proteins or ice-binding proteins (IBPs) facilitate the survival of certain cellular organisms in freezing environment by inhibiting the growth of ice crystals in solution. Present study identifies orthologs of the IBP of Colwellia sp. SLW05, which were obtained from a wide range of taxa. Phylogenetic analysis on the basis of conserved regions (predicted as the 'ice-binding domain' [IBD]) present in all the orthologs, separates the bacterial and archaeal orthologs from that of the eukaryotes'. Correspondence analysis pointed out that the bacterial and archaeal IBDs have relatively higher average hydrophobicity than the eukaryotic members. IBDs belonging to bacterial as well as archaeal AFPs contain comparatively more strands, and therefore are revealed to be under higher evolutionary selection pressure. Molecular docking studies prove that the ice crystals form more stable complex with the bacterial as well as archaeal proteins than the eukaryotic orthologs. Analysis of the docked structures have traced out the ice-binding sites (IBSs) in all the orthologs which continue to facilitate ice-binding activity even after getting mutated with respect to the well-studied IBSs of Typhula ishikariensis and notably, all these mutations performing ice-binding using 'anchored clathrate mechanism' have been found to prefer polar and hydrophilic amino acids. Horizontal gene transfer studies point toward a strong selection pressure favoring independent evolution of the IBPs in some polar organisms including prokaryotes as well as eukaryotes because these proteins facilitate the polar organisms to acclimatize to the adversities in their niche, thus safeguarding their existence.

  14. Molecular evolution patterns reveal life history features of mycoplasma-related endobacteria associated with arbuscular mycorrhizal fungi.

    PubMed

    Toomer, Kevin H; Chen, Xiuhua; Naito, Mizue; Mondo, Stephen J; den Bakker, Henk C; VanKuren, Nicholas W; Lekberg, Ylva; Morton, Joseph B; Pawlowska, Teresa E

    2015-07-01

    The mycoplasma-related endobacteria (MRE), representing a recently discovered lineage of Mollicutes, are widely distributed across arbuscular mycorrhizal fungi (AMF, Glomeromycota). AMF colonize roots of most terrestrial plants and improve plant mineral nutrient uptake in return for plant-assimilated carbon. The role of MRE in the biology of their fungal hosts is unknown. To start characterizing this association, we assessed partitioning of MRE genetic diversity within AMF individuals and across the AMF phylogeographic range. We further used molecular evolution patterns to make inferences about MRE codivergence with AMF, their lifestyle and antiquity of the Glomeromycota-MRE association. While we did not detect differentiation between MRE derived from different continents, high levels of diversity were apparent in MRE populations within AMF host individuals. MRE exhibited significant codiversification with AMF over ecological time and the absence of codivergence over evolutionary time. Moreover, genetic recombination was evident in MRE. These patterns indicate that, while MRE transmission is predominantly vertical, their complex intrahost populations are likely generated by horizontal transmission and recombination. Based on predictions of evolutionary theory, we interpreted these observations as a suggestion that MRE may be antagonists of AMF. Finally, we detected a marginally significant signature of codivergence of MRE with Glomeromycota and the Endogone lineage of Mucoromycotina, implying that the symbiosis between MRE and fungi may predate the divergence between these two groups of fungi.

  15. Predicting Pathological Features at Radical Prostatectomy in Patients with Prostate Cancer Eligible for Active Surveillance by Multiparametric Magnetic Resonance Imaging

    PubMed Central

    de Cobelli, Ottavio; Terracciano, Daniela; Tagliabue, Elena; Raimondi, Sara; Bottero, Danilo; Cioffi, Antonio; Jereczek-Fossa, Barbara; Petralia, Giuseppe; Cordima, Giovanni; Almeida, Gilberto Laurino; Lucarelli, Giuseppe; Buonerba, Carlo; Matei, Deliu Victor; Renne, Giuseppe; Di Lorenzo, Giuseppe; Ferro, Matteo

    2015-01-01

    Purpose The aim of this study was to investigate the prognostic performance of multiparametric magnetic resonance imaging (mpMRI) and Prostate Imaging Reporting and Data System (PIRADS) score in predicting pathologic features in a cohort of patients eligible for active surveillance who underwent radical prostatectomy. Methods A total of 223 patients who fulfilled the criteria for “Prostate Cancer Research International: Active Surveillance”, were included. Mp–1.5 Tesla MRI examination staging with endorectal coil was performed at least 6–8 weeks after TRUS-guided biopsy. In all patients, the likelihood of the presence of cancer was assigned using PIRADS score between 1 and 5. Outcomes of interest were: Gleason score upgrading, extra capsular extension (ECE), unfavorable prognosis (occurrence of both upgrading and ECE), large tumor volume (≥0.5ml), and seminal vesicle invasion (SVI). Receiver Operating Characteristic (ROC) curves and Decision Curve Analyses (DCA) were performed for models with and without inclusion of PIRADS score. Res