Comparison of accuracy of fibrosis degree classifications by liver biopsy and non-invasive tests in chronic hepatitis C.

PubMed

Boursier, Jérôme; Bertrais, Sandrine; Oberti, Frédéric; Gallois, Yves; Fouchard-Hubert, Isabelle; Rousselet, Marie-Christine; Zarski, Jean-Pierre; Calès, Paul

2011-11-30

Non-invasive tests have been constructed and evaluated mainly for binary diagnoses such as significant fibrosis. Recently, detailed fibrosis classifications for several non-invasive tests have been developed, but their accuracy has not been thoroughly evaluated in comparison to liver biopsy, especially in clinical practice and for Fibroscan. Therefore, the main aim of the present study was to evaluate the accuracy of detailed fibrosis classifications available for non-invasive tests and liver biopsy. The secondary aim was to validate these accuracies in independent populations. Four HCV populations provided 2,068 patients with liver biopsy, four different pathologist skill-levels and non-invasive tests. Results were expressed as percentages of correctly classified patients. In population #1 including 205 patients and comparing liver biopsy (reference: consensus reading by two experts) and blood tests, Metavir fibrosis (FM) stage accuracy was 64.4% in local pathologists vs. 82.2% (p < 10-3) in single expert pathologist. Significant discrepancy (≥ 2FM vs reference histological result) rates were: Fibrotest: 17.2%, FibroMeter2G: 5.6%, local pathologists: 4.9%, FibroMeter3G: 0.5%, expert pathologist: 0% (p < 10-3). In population #2 including 1,056 patients and comparing blood tests, the discrepancy scores, taking into account the error magnitude, of detailed fibrosis classification were significantly different between FibroMeter2G (0.30 ± 0.55) and FibroMeter3G (0.14 ± 0.37, p < 10-3) or Fibrotest (0.84 ± 0.80, p < 10-3). In population #3 (and #4) including 458 (359) patients and comparing blood tests and Fibroscan, accuracies of detailed fibrosis classification were, respectively: Fibrotest: 42.5% (33.5%), Fibroscan: 64.9% (50.7%), FibroMeter2G: 68.7% (68.2%), FibroMeter3G: 77.1% (83.4%), p < 10-3 (p < 10-3). Significant discrepancy (≥ 2 FM) rates were, respectively: Fibrotest: 21.3% (22.2%), Fibroscan: 12.9% (12.3%), FibroMeter2G: 5.7% (6.0%), FibroMeter3G: 0.9% (0.9%), p < 10-3 (p < 10-3). The accuracy in detailed fibrosis classification of the best-performing blood test outperforms liver biopsy read by a local pathologist, i.e., in clinical practice; however, the classification precision is apparently lesser. This detailed classification accuracy is much lower than that of significant fibrosis with Fibroscan and even Fibrotest but higher with FibroMeter3G. FibroMeter classification accuracy was significantly higher than those of other non-invasive tests. Finally, for hepatitis C evaluation in clinical practice, fibrosis degree can be evaluated using an accurate blood test.

Comparison of accuracy of fibrosis degree classifications by liver biopsy and non-invasive tests in chronic hepatitis C

PubMed Central

2011-01-01

Background Non-invasive tests have been constructed and evaluated mainly for binary diagnoses such as significant fibrosis. Recently, detailed fibrosis classifications for several non-invasive tests have been developed, but their accuracy has not been thoroughly evaluated in comparison to liver biopsy, especially in clinical practice and for Fibroscan. Therefore, the main aim of the present study was to evaluate the accuracy of detailed fibrosis classifications available for non-invasive tests and liver biopsy. The secondary aim was to validate these accuracies in independent populations. Methods Four HCV populations provided 2,068 patients with liver biopsy, four different pathologist skill-levels and non-invasive tests. Results were expressed as percentages of correctly classified patients. Results In population #1 including 205 patients and comparing liver biopsy (reference: consensus reading by two experts) and blood tests, Metavir fibrosis (FM) stage accuracy was 64.4% in local pathologists vs. 82.2% (p < 10-3) in single expert pathologist. Significant discrepancy (≥ 2FM vs reference histological result) rates were: Fibrotest: 17.2%, FibroMeter2G: 5.6%, local pathologists: 4.9%, FibroMeter3G: 0.5%, expert pathologist: 0% (p < 10-3). In population #2 including 1,056 patients and comparing blood tests, the discrepancy scores, taking into account the error magnitude, of detailed fibrosis classification were significantly different between FibroMeter2G (0.30 ± 0.55) and FibroMeter3G (0.14 ± 0.37, p < 10-3) or Fibrotest (0.84 ± 0.80, p < 10-3). In population #3 (and #4) including 458 (359) patients and comparing blood tests and Fibroscan, accuracies of detailed fibrosis classification were, respectively: Fibrotest: 42.5% (33.5%), Fibroscan: 64.9% (50.7%), FibroMeter2G: 68.7% (68.2%), FibroMeter3G: 77.1% (83.4%), p < 10-3 (p < 10-3). Significant discrepancy (≥ 2 FM) rates were, respectively: Fibrotest: 21.3% (22.2%), Fibroscan: 12.9% (12.3%), FibroMeter2G: 5.7% (6.0%), FibroMeter3G: 0.9% (0.9%), p < 10-3 (p < 10-3). Conclusions The accuracy in detailed fibrosis classification of the best-performing blood test outperforms liver biopsy read by a local pathologist, i.e., in clinical practice; however, the classification precision is apparently lesser. This detailed classification accuracy is much lower than that of significant fibrosis with Fibroscan and even Fibrotest but higher with FibroMeter3G. FibroMeter classification accuracy was significantly higher than those of other non-invasive tests. Finally, for hepatitis C evaluation in clinical practice, fibrosis degree can be evaluated using an accurate blood test. PMID:22129438

Validation of a selective ensemble-based classification scheme for myoelectric control using a three-dimensional Fitts' Law test.

PubMed

Scheme, Erik J; Englehart, Kevin B

2013-07-01

When controlling a powered upper limb prosthesis it is important not only to know how to move the device, but also when not to move. A novel approach to pattern recognition control, using a selective multiclass one-versus-one classification scheme has been shown to be capable of rejecting unintended motions. This method was shown to outperform other popular classification schemes when presented with muscle contractions that did not correspond to desired actions. In this work, a 3-D Fitts' Law test is proposed as a suitable alternative to using virtual limb environments for evaluating real-time myoelectric control performance. The test is used to compare the selective approach to a state-of-the-art linear discriminant analysis classification based scheme. The framework is shown to obey Fitts' Law for both control schemes, producing linear regression fittings with high coefficients of determination (R(2) > 0.936). Additional performance metrics focused on quality of control are discussed and incorporated in the evaluation. Using this framework the selective classification based scheme is shown to produce significantly higher efficiency and completion rates, and significantly lower overshoot and stopping distances, with no significant difference in throughput.

Overweight and Obesity Prevalence Among School-Aged Nunavik Inuit Children According to Three Body Mass Index Classification Systems.

PubMed

Medehouenou, Thierry Comlan Marc; Ayotte, Pierre; St-Jean, Audray; Meziou, Salma; Roy, Cynthia; Muckle, Gina; Lucas, Michel

2015-07-01

Little is known about the suitability of three commonly used body mass index (BMI) classification system for Indigenous children. This study aims to estimate overweight and obesity prevalence among school-aged Nunavik Inuit children according to International Obesity Task Force (IOTF), Centers for Disease Control and Prevention (CDC), and World Health Organization (WHO) BMI classification systems, to measure agreement between those classification systems, and to investigate whether BMI status as defined by these classification systems is associated with levels of metabolic and inflammatory biomarkers. Data were collected on 290 school-aged children (aged 8-14 years; 50.7% girls) from the Nunavik Child Development Study with data collected in 2005-2010. Anthropometric parameters were measured and blood sampled. Participants were classified as normal weight, overweight, and obese according to BMI classification systems. Weighted kappa (κw) statistics assessed agreement between different BMI classification systems, and multivariate analysis of variance ascertained their relationship with metabolic and inflammatory biomarkers. The combined prevalence rate of overweight/obesity was 26.9% (with 6.6% obesity) with IOTF, 24.1% (11.0%) with CDC, and 40.4% (12.8%) with WHO classification systems. Agreement was the highest between IOTF and CDC (κw = .87) classifications, and substantial for IOTF and WHO (κw = .69) and for CDC and WHO (κw = .73). Insulin and high-sensitivity C-reactive protein plasma levels were significantly higher from normal weight to obesity, regardless of classification system. Among obese subjects, higher insulin level was observed with IOTF. Compared with other systems, IOTF classification appears to be more specific to identify overweight and obesity in Inuit children. Copyright © 2015 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

HMM for hyperspectral spectrum representation and classification with endmember entropy vectors

NASA Astrophysics Data System (ADS)

Arabi, Samir Y. W.; Fernandes, David; Pizarro, Marco A.

2015-10-01

The Hyperspectral images due to its good spectral resolution are extensively used for classification, but its high number of bands requires a higher bandwidth in the transmission data, a higher data storage capability and a higher computational capability in processing systems. This work presents a new methodology for hyperspectral data classification that can work with a reduced number of spectral bands and achieve good results, comparable with processing methods that require all hyperspectral bands. The proposed method for hyperspectral spectra classification is based on the Hidden Markov Model (HMM) associated to each Endmember (EM) of a scene and the conditional probabilities of each EM belongs to each other EM. The EM conditional probability is transformed in EM vector entropy and those vectors are used as reference vectors for the classes in the scene. The conditional probability of a spectrum that will be classified is also transformed in a spectrum entropy vector, which is classified in a given class by the minimum ED (Euclidian Distance) among it and the EM entropy vectors. The methodology was tested with good results using AVIRIS spectra of a scene with 13 EM considering the full 209 bands and the reduced spectral bands of 128, 64 and 32. For the test area its show that can be used only 32 spectral bands instead of the original 209 bands, without significant loss in the classification process.

Efficacy of the Kyoto Classification of Gastritis in Identifying Patients at High Risk for Gastric Cancer.

PubMed

Sugimoto, Mitsushige; Ban, Hiromitsu; Ichikawa, Hitomi; Sahara, Shu; Otsuka, Taketo; Inatomi, Osamu; Bamba, Shigeki; Furuta, Takahisa; Andoh, Akira

2017-01-01

Objective The Kyoto gastritis classification categorizes the endoscopic characteristics of Helicobacter pylori (H. pylori) infection-associated gastritis and identifies patterns associated with a high risk of gastric cancer. We investigated its efficacy, comparing scores in patients with H. pylori-associated gastritis and with gastric cancer. Methods A total of 1,200 patients with H. pylori-positive gastritis alone (n=932), early-stage H. pylori-positive gastric cancer (n=189), and successfully treated H. pylori-negative cancer (n=79) were endoscopically graded according to the Kyoto gastritis classification for atrophy, intestinal metaplasia, fold hypertrophy, nodularity, and diffuse redness. Results The prevalence of O-II/O-III-type atrophy according to the Kimura-Takemoto classification in early-stage H. pylori-positive gastric cancer and successfully treated H. pylori-negative cancer groups was 45.1%, which was significantly higher than in subjects with gastritis alone (12.7%, p<0.001). Kyoto gastritis scores of atrophy and intestinal metaplasia in the H. pylori-positive cancer group were significantly higher than in subjects with gastritis alone (all p<0.001). No significant differences were noted in the rates of gastric fold hypertrophy or diffuse redness between the two groups. In a multivariate analysis, the risks for H. pylori-positive gastric cancer increased with intestinal metaplasia (odds ratio: 4.453, 95% confidence interval: 3.332-5.950, <0.001) and male sex (1.737, 1.102-2.739, p=0.017). Conclusion Making an appropriate diagnosis and detecting patients at high risk is crucial for achieving total eradication of gastric cancer. The scores of intestinal metaplasia and atrophy of the scoring system in the Kyoto gastritis classification may thus be useful for detecting these patients.

Efficacy of the Kyoto Classification of Gastritis in Identifying Patients at High Risk for Gastric Cancer

PubMed Central

Sugimoto, Mitsushige; Ban, Hiromitsu; Ichikawa, Hitomi; Sahara, Shu; Otsuka, Taketo; Inatomi, Osamu; Bamba, Shigeki; Furuta, Takahisa; Andoh, Akira

2017-01-01

Objective The Kyoto gastritis classification categorizes the endoscopic characteristics of Helicobacter pylori (H. pylori) infection-associated gastritis and identifies patterns associated with a high risk of gastric cancer. We investigated its efficacy, comparing scores in patients with H. pylori-associated gastritis and with gastric cancer. Methods A total of 1,200 patients with H. pylori-positive gastritis alone (n=932), early-stage H. pylori-positive gastric cancer (n=189), and successfully treated H. pylori-negative cancer (n=79) were endoscopically graded according to the Kyoto gastritis classification for atrophy, intestinal metaplasia, fold hypertrophy, nodularity, and diffuse redness. Results The prevalence of O-II/O-III-type atrophy according to the Kimura-Takemoto classification in early-stage H. pylori-positive gastric cancer and successfully treated H. pylori-negative cancer groups was 45.1%, which was significantly higher than in subjects with gastritis alone (12.7%, p<0.001). Kyoto gastritis scores of atrophy and intestinal metaplasia in the H. pylori-positive cancer group were significantly higher than in subjects with gastritis alone (all p<0.001). No significant differences were noted in the rates of gastric fold hypertrophy or diffuse redness between the two groups. In a multivariate analysis, the risks for H. pylori-positive gastric cancer increased with intestinal metaplasia (odds ratio: 4.453, 95% confidence interval: 3.332-5.950, <0.001) and male sex (1.737, 1.102-2.739, p=0.017). Conclusion Making an appropriate diagnosis and detecting patients at high risk is crucial for achieving total eradication of gastric cancer. The scores of intestinal metaplasia and atrophy of the scoring system in the Kyoto gastritis classification may thus be useful for detecting these patients. PMID:28321054

The Oxfordshire Community Stroke Project classification: correlation with imaging, associated complications, and prediction of outcome in acute ischemic stroke.

PubMed

Pittock, Sean J; Meldrum, Dara; Hardiman, Orla; Thornton, John; Brennan, Paul; Moroney, Joan T

2003-01-01

This preliminary study investigates the risk factor profile, post stroke complications, and outcome for four OCSP (Oxfordshire Community Stroke Project Classification) subtypes. One hundred seventeen consecutive ischemic stroke patients were clinically classified into 1 of 4 subtypes: total anterior (TACI), partial anterior (PACI), lacunar (LACI), and posterior (POCI) circulation infarcts. Study evaluations were performed at admission, 2 weeks, and 6 months. There was a good correlation between clinical classification and radiological diagnosis if a negative CT head was considered consistent with a lacunar infarction. No significant difference in risk factor profile was observed between subtypes. The TACI group had significantly higher mortality (P < .001), morbidity (P < .001, as per disability scales), length of hospital stay (P < .001), and complications (respiratory tract infection and seizures [P < .01]) as compared to the other three groups which were all similar at the different time points. The only significant difference found was the higher rate of stroke recurrence within the first 6 months in the POCI group (P < .001). The OCSP classification identifies two major groups (TACI and other 3 groups combined) who behave differently with respect to post stroke outcome. Further study with larger numbers of patients and thus greater power will be required to allow better discrimination of OCSP subtypes in respect of risk factors, complications, and outcomes if the OCSP is to be used to stratify patients in clinical trials.

The Carnegie Classification of Institutions of Higher Education. 2000 Edition. A Technical Report.

ERIC Educational Resources Information Center

Carnegie Foundation for the Advancement of Teaching, Menlo Park, CA.

The Carnegie Classification of Institutions of Higher Education is the framework in which institutional diversity in United States higher education is commonly described. Developed in 1971, the Classification was designed to support research in higher education by identifying categories of colleges and universities that would be homogeneous with…

Choice of probe tone and classification of trace patterns in tympanometry undertaken in early infancy.

PubMed

Baldwin, Margaret

2006-07-01

Tympanometry using 226 Hz, 678 Hz, and 1000 Hz probe tones was undertaken on two groups of babies, age 2 to 21 weeks. A group of 104 babies with normal ABR thresholds or TEOAEs were compared with a second group of 107 babies who had evidence of temporary conductive hearing loss based on the findings of a test battery, which included air and bone conduction ABR. The tympanograms were classified by Method 1, a simple visual classification system, and Method 2, adapted from a system described by Marchant et al (1986). The majority of tympanograms recorded in both groups using the 226 Hz probe tone were 'normal' Type A, with no significant difference in middle ear pressure or static admittance. However, both classification methods demonstrated significant differences between the two groups using the higher frequency probe tones, with Method 2 being the preferred system of classification. Tympanometry using 226 Hz is invalid below 21 weeks and 1000 Hz is the frequency of choice.

Higher Improvement in Patient-Reported Outcomes Can Be Achieved After Transforaminal Lumbar Interbody Fusion for Clinical and Radiographic Degenerative Spondylolisthesis Classification Type D Degenerative Lumbar Spondylolisthesis.

PubMed

Chen, Xi; Xu, Liang; Qiu, Yong; Chen, Zhong-Hui; Zhou, Qing-Shuang; Li, Song; Sun, Xu

2018-06-01

Clinical and radiographic degenerative spondylolisthesis (CARDS) classification defines a distinct subset of patients with kyphotic angulation at the involved segment (type D). Research using CARDS classification to investigate motion characteristics at involved segments or patient-related outcomes (PROs) following surgical intervention is sparse. We investigated the relationship between CARDS type D spondylolisthesis and dynamic instability and PROs in type D versus non-type D spondylolisthesis. We reviewed consecutive patients who received transforaminal lumbar interbody fusion for L4-5 spondylolisthesis between 2009 and 2015. Patients were assigned into type D and non-type D groups. Translational motion was determined by upright lumbar lateral radiography with supine sagittal magnetic resonance imaging or flexion and extension radiography. Demographics, radiographic parameters, and PROs were evaluated. Type D and non-type D groups comprised 34 and 163 patients, respectively. Compared with non-type D, type D group was characterized by lordotic angulation loss and higher degree of olisthesis on upright radiographs and demonstrated higher translational motion on upright lumbar lateral radiography with supine sagittal magnetic resonance imaging analysis. After surgery, mean reduction rate was significantly higher in type D group; type D had less slippage, but differences in slip angle and disc height were not significant. Preoperative Oswestry Disability Index and visual analog scale for back pain scores were higher in type D group. Type D spondylolisthesis and dynamic instability were associated with achieving minimal clinically important differences in PROs. CARDS type D spondylolisthesis is a distinct subset associated with dynamic instability and worse PROs. Higher improvement in PROs can be achieved in CARDS type D spondylolisthesis after surgery. Copyright © 2018 Elsevier Inc. All rights reserved.

Effect of e-learning program on risk assessment and pressure ulcer classification - A randomized study.

PubMed

Bredesen, Ida Marie; Bjøro, Karen; Gunningberg, Lena; Hofoss, Dag

2016-05-01

Pressure ulcers (PUs) are a problem in health care. Staff competency is paramount to PU prevention. Education is essential to increase skills in pressure ulcer classification and risk assessment. Currently, no pressure ulcer learning programs are available in Norwegian. Develop and test an e-learning program for assessment of pressure ulcer risk and pressure ulcer classification. Forty-four nurses working in acute care hospital wards or nursing homes participated and were assigned randomly into two groups: an e-learning program group (intervention) and a traditional classroom lecture group (control). Data was collected immediately before and after training, and again after three months. The study was conducted at one nursing home and two hospitals between May and December 2012. Accuracy of risk assessment (five patient cases) and pressure ulcer classification (40 photos [normal skin, pressure ulcer categories I-IV] split in two sets) were measured by comparing nurse evaluations in each of the two groups to a pre-established standard based on ratings by experts in pressure ulcer classification and risk assessment. Inter-rater reliability was measured by exact percent agreement and multi-rater Fleiss kappa. A Mann-Whitney U test was used for continuous sum score variables. An e-learning program did not improve Braden subscale scoring. For pressure ulcer classification, however, the intervention group scored significantly higher than the control group on several of the categories in post-test immediately after training. However, after three months there were no significant differences in classification skills between the groups. An e-learning program appears to have a greater effect on the accuracy of pressure ulcer classification than classroom teaching in the short term. For proficiency in Braden scoring, no significant effect of educational methods on learning results was detected. Copyright © 2016 Elsevier Ltd. All rights reserved.

LDA boost classification: boosting by topics

NASA Astrophysics Data System (ADS)

Lei, La; Qiao, Guo; Qimin, Cao; Qitao, Li

2012-12-01

AdaBoost is an efficacious classification algorithm especially in text categorization (TC) tasks. The methodology of setting up a classifier committee and voting on the documents for classification can achieve high categorization precision. However, traditional Vector Space Model can easily lead to the curse of dimensionality and feature sparsity problems; so it affects classification performance seriously. This article proposed a novel classification algorithm called LDABoost based on boosting ideology which uses Latent Dirichlet Allocation (LDA) to modeling the feature space. Instead of using words or phrase, LDABoost use latent topics as the features. In this way, the feature dimension is significantly reduced. Improved Naïve Bayes (NB) is designed as the weaker classifier which keeps the efficiency advantage of classic NB algorithm and has higher precision. Moreover, a two-stage iterative weighted method called Cute Integration in this article is proposed for improving the accuracy by integrating weak classifiers into strong classifier in a more rational way. Mutual Information is used as metrics of weights allocation. The voting information and the categorization decision made by basis classifiers are fully utilized for generating the strong classifier. Experimental results reveals LDABoost making categorization in a low-dimensional space, it has higher accuracy than traditional AdaBoost algorithms and many other classic classification algorithms. Moreover, its runtime consumption is lower than different versions of AdaBoost, TC algorithms based on support vector machine and Neural Networks.

Weight-elimination neural networks applied to coronary surgery mortality prediction.

PubMed

Ennett, Colleen M; Frize, Monique

2003-06-01

The objective was to assess the effectiveness of the weight-elimination cost function in improving classification performance of artificial neural networks (ANNs) and to observe how changing the a priori distribution of the training set affects network performance. Backpropagation feedforward ANNs with and without weight-elimination estimated mortality for coronary artery surgery patients. The ANNs were trained and tested on cases with 32 input variables describing the patient's medical history; the output variable was in-hospital mortality (mortality rates: training 3.7%, test 3.8%). Artificial training sets with mortality rates of 20%, 50%, and 80% were created to observe the impact of training with a higher-than-normal prevalence. When the results were averaged, weight-elimination networks achieved higher sensitivity rates than those without weight-elimination. Networks trained on higher-than-normal prevalence achieved higher sensitivity rates at the cost of lower specificity and correct classification. The weight-elimination cost function can improve the classification performance when the network is trained with a higher-than-normal prevalence. A network trained with a moderately high artificial mortality rate (artificial mortality rate of 20%) can improve the sensitivity of the model without significantly affecting other aspects of the model's performance. The ANN mortality model achieved comparable performance as additive and statistical models for coronary surgery mortality estimation in the literature.

A Ternary Hybrid EEG-NIRS Brain-Computer Interface for the Classification of Brain Activation Patterns during Mental Arithmetic, Motor Imagery, and Idle State.

PubMed

Shin, Jaeyoung; Kwon, Jinuk; Im, Chang-Hwan

2018-01-01

The performance of a brain-computer interface (BCI) can be enhanced by simultaneously using two or more modalities to record brain activity, which is generally referred to as a hybrid BCI. To date, many BCI researchers have tried to implement a hybrid BCI system by combining electroencephalography (EEG) and functional near-infrared spectroscopy (NIRS) to improve the overall accuracy of binary classification. However, since hybrid EEG-NIRS BCI, which will be denoted by hBCI in this paper, has not been applied to ternary classification problems, paradigms and classification strategies appropriate for ternary classification using hBCI are not well investigated. Here we propose the use of an hBCI for the classification of three brain activation patterns elicited by mental arithmetic, motor imagery, and idle state, with the aim to elevate the information transfer rate (ITR) of hBCI by increasing the number of classes while minimizing the loss of accuracy. EEG electrodes were placed over the prefrontal cortex and the central cortex, and NIRS optodes were placed only on the forehead. The ternary classification problem was decomposed into three binary classification problems using the "one-versus-one" (OVO) classification strategy to apply the filter-bank common spatial patterns filter to EEG data. A 10 × 10-fold cross validation was performed using shrinkage linear discriminant analysis (sLDA) to evaluate the average classification accuracies for EEG-BCI, NIRS-BCI, and hBCI when the meta-classification method was adopted to enhance classification accuracy. The ternary classification accuracies for EEG-BCI, NIRS-BCI, and hBCI were 76.1 ± 12.8, 64.1 ± 9.7, and 82.2 ± 10.2%, respectively. The classification accuracy of the proposed hBCI was thus significantly higher than those of the other BCIs ( p < 0.005). The average ITR for the proposed hBCI was calculated to be 4.70 ± 1.92 bits/minute, which was 34.3% higher than that reported for a previous binary hBCI study.

Estimating workload using EEG spectral power and ERPs in the n-back task

NASA Astrophysics Data System (ADS)

Brouwer, Anne-Marie; Hogervorst, Maarten A.; van Erp, Jan B. F.; Heffelaar, Tobias; Zimmerman, Patrick H.; Oostenveld, Robert

2012-08-01

Previous studies indicate that both electroencephalogram (EEG) spectral power (in particular the alpha and theta band) and event-related potentials (ERPs) (in particular the P300) can be used as a measure of mental work or memory load. We compare their ability to estimate workload level in a well-controlled task. In addition, we combine both types of measures in a single classification model to examine whether this results in higher classification accuracy than either one alone. Participants watched a sequence of visually presented letters and indicated whether or not the current letter was the same as the one (n instances) before. Workload was varied by varying n. We developed different classification models using ERP features, frequency power features or a combination (fusion). Training and testing of the models simulated an online workload estimation situation. All our ERP, power and fusion models provide classification accuracies between 80% and 90% when distinguishing between the highest and the lowest workload condition after 2 min. For 32 out of 35 participants, classification was significantly higher than chance level after 2.5 s (or one letter) as estimated by the fusion model. Differences between the models are rather small, though the fusion model performs better than the other models when only short data segments are available for estimating workload.

Carnegie's New Community Engagement Classification: Affirming Higher Education's Role in Community

ERIC Educational Resources Information Center

Driscoll, Amy

2009-01-01

In 2005, the Carnegie Foundation for the Advancement of Teaching (CFAT) stirred the higher education world with the announcement of a new classification for institutions that engage with community. The classification, community engagement, is the first in a set of planned classification schemes resulting from the foundation's reexamination of the…

Comparison of staging diagnosis by two magnifying endoscopy classification for superficial oesophageal cancer.

PubMed

Ebi, Masahide; Shimura, Takaya; Murakami, Kenji; Yamada, Tomonori; Hirata, Yoshikazu; Tsukamoto, Hironobu; Mizoshita, Tsutomu; Tanida, Satoshi; Kataoka, Hiromi; Kamiya, Takeshi; Joh, Takashi

2012-11-01

Due to the possibility of lymph node metastasis, surgical resection is indicated for superficial oesophageal cancer with invasion to a depth greater than the muscularis mucosa. Although two magnifying endoscopy classifications are currently used to diagnose the depth of invasion, which classification is more suitable remains controversial. To compare and evaluate the clinical outcomes of two classifications for superficial oesophageal squamous cell carcinoma. This cross-sectional study consists of 44 superficial oesophageal squamous cell carcinoma lesions with magnification image-enhanced endoscopy images. Only magnifying endoscopic images were displayed to two experienced endoscopists who independently diagnosed the depth of invasion according to both classifications. The sensitivity of invasion greater than the muscularis mucosa tended to be higher in Inoue's classification than Arima's classification (78.3±6.2% vs. 50.0±3.0%; P=0.144), whereas the specificity was significantly lower in Inoue's classification than in Arima's classification (61.9±0.0% vs. 97.6±3.4%; P=0.043). For both classifications, rates of concordance were 90.9% and 84.4%, and κ statistics were 0.81 and 0.66, respectively. Our results suggest that Arima's classification is suitable for general screening before treatment to avoid unnecessary surgery. Inoue's classification is appropriate for assessing wide lesion. Copyright © 2012 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

Predictive value of C-reactive protein/albumin ratio in acute pancreatitis.

PubMed

Kaplan, Mustafa; Ates, Ihsan; Akpinar, Muhammed Yener; Yuksel, Mahmut; Kuzu, Ufuk Baris; Kacar, Sabite; Coskun, Orhan; Kayacetin, Ertugrul

2017-08-15

Serum C-reactive protein (CRP) increases and albumin decreases in patients with inflammation and infection. However, their role in patients with acute pancreatitis is not clear. The present study was to investigate the predictive significance of the CRP/albumin ratio for the prognosis and mortality in acute pancreatitis patients. This study was performed retrospectively with 192 acute pancreatitis patients between January 2002 and June 2015. Ranson scores, Atlanta classification and CRP/albumin ratios of the patients were calculated. The CRP/albumin ratio was higher in deceased patients compared to survivors. The CRP/albumin ratio was positively correlated with Ranson score and Atlanta classification in particular and with important prognostic markers such as hospitalization time, CRP and erythrocyte sedimentation rate. In addition to the CRP/albumin ratio, necrotizing pancreatitis type, moderately severe and severe Atlanta classification, and total Ranson score were independent risk factors of mortality. It was found that an increase of 1 unit in the CRP/albumin ratio resulted in an increase of 1.52 times in mortality risk. A prediction value about CRP/albumin ratio >16.28 was found to be a significant marker in predicting mortality with 92.1% sensitivity and 58.0% specificity. It was seen that Ranson and Atlanta classification were higher in patients with CRP/albumin ratio >16.28 compared with those with CRP/albumin ratio ≤16.28. Patients with CRP/albumin ratio >16.28 had a 19.3 times higher chance of death. The CRP/albumin ratio is a novel but promising, easy-to-measure, repeatable, non-invasive inflammation-based prognostic score in acute pancreatitis. Copyright © 2017 The Editorial Board of Hepatobiliary & Pancreatic Diseases International. Published by Elsevier B.V. All rights reserved.

Cardiovascular risk factors in children.

PubMed

Fraporti, Marisete Inês; Scherer Adami, Fernanda; Dutra Rosolen, Michele

2017-10-01

Systemic hypertension is one of the main risk factors for cardiovascular disease (CVD). Early diagnosis and treatment of hypertension in childhood can potentially have a significant impact on future adverse outcomes. To investigate the relationship of diastolic (DBP) and systolic blood pressure (SBP) with anthropometric data and area of residence of children in municipalities of Rio Grande do Sul state, Brazil. This is a cross-sectional study of 709 children between six and nine years of age. Blood pressure, weight, height and waist circumference (WC) were measured. Statistical tests had a maximum significance level of 5% (p≤0.05) and the software used was SPSS version 13.0. Obesity was significantly associated with pre-hypertension, and stage 1 and 2 hypertension as assessed by DBP and SBP (≤0.05); high WC was significantly associated with a classification of pre-hypertension and stage 1 hypertension based on DBP and a classification of stage 1 and 2 hypertension based on SBP (≤0.01). Children living in urban areas had significantly higher mean SBP than those living in rural areas. Those with high WC presented higher SBP and DBP compared to children with normal WC. Obese children showed higher mean SBP and DBP compared to those who were overweight or normal weight and mean SBP and DBP also increased with older age and higher mean body mass index and WC. Copyright © 2017 Sociedade Portuguesa de Cardiologia. Publicado por Elsevier España, S.L.U. All rights reserved.

Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification.

PubMed

Liu, Da; Li, Jianxun

2016-12-16

Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches.

A neural network approach to cloud classification

NASA Technical Reports Server (NTRS)

Lee, Jonathan; Weger, Ronald C.; Sengupta, Sailes K.; Welch, Ronald M.

1990-01-01

It is shown that, using high-spatial-resolution data, very high cloud classification accuracies can be obtained with a neural network approach. A texture-based neural network classifier using only single-channel visible Landsat MSS imagery achieves an overall cloud identification accuracy of 93 percent. Cirrus can be distinguished from boundary layer cloudiness with an accuracy of 96 percent, without the use of an infrared channel. Stratocumulus is retrieved with an accuracy of 92 percent, cumulus at 90 percent. The use of the neural network does not improve cirrus classification accuracy. Rather, its main effect is in the improved separation between stratocumulus and cumulus cloudiness. While most cloud classification algorithms rely on linear parametric schemes, the present study is based on a nonlinear, nonparametric four-layer neural network approach. A three-layer neural network architecture, the nonparametric K-nearest neighbor approach, and the linear stepwise discriminant analysis procedure are compared. A significant finding is that significantly higher accuracies are attained with the nonparametric approaches using only 20 percent of the database as training data, compared to 67 percent of the database in the linear approach.

The Future of Classification in Wheelchair Sports; Can Data Science and Technological Advancement Offer an Alternative Point of View?

PubMed

van der Slikke, Rienk M A; Bregman, Daan J J; Berger, Monique A M; de Witte, Annemarie M H; Veeger, Dirk-Jan H E J

2017-11-01

Classification is a defining factor for competition in wheelchair sports, but it is a delicate and time-consuming process with often questionable validity. 1 New inertial sensor based measurement methods applied in match play and field tests, allow for more precise and objective estimates of the impairment effect on wheelchair mobility performance. It was evaluated if these measures could offer an alternative point of view for classification. Six standard wheelchair mobility performance outcomes of different classification groups were measured in match play (n=29), as well as best possible performance in a field test (n=47). In match-results a clear relationship between classification and performance level is shown, with increased performance outcomes in each adjacent higher classification group. Three outcomes differed significantly between the low and mid-class groups, and one between the mid and high-class groups. In best performance (field test), a split between the low and mid-class groups shows (5 out of 6 outcomes differed significantly) but hardly any difference between the mid and high-class groups. This observed split was confirmed by cluster analysis, revealing the existence of only two performance based clusters. The use of inertial sensor technology to get objective measures of wheelchair mobility performance, combined with a standardized field-test, brought alternative views for evidence based classification. The results of this approach provided arguments for a reduced number of classes in wheelchair basketball. Future use of inertial sensors in match play and in field testing could enhance evaluation of classification guidelines as well as individual athlete performance.

Morphometric differences of nasopalatine canal based on 3D classifications: descriptive analysis on CBCT.

PubMed

Fernández-Alonso, A; Suárez-Quintanilla, J A; Rapado-González, O; Suárez-Cunqueiro, María Mercedes

2015-09-01

This descriptive retrospective study analyzed differences among sagittal, coronal and axial NC groups based on the dimensions of nasopalatine canal (NC), buccal bone plate (BBP) and palatal bone plate (PBP) to canal. Measurements were made on 224 CBCTs for NC, BBP and PBP on the three anatomic planes at three levels: level 1, when the incisive foramen is completely closed on the axial plane; level 2, at the midpoint of NC length (NCL) on the sagittal plane; and level 3, at the foramina of Stenson on the sagittal plane. ANOVA tests with post hoc tests were used. The intraclass correlation coefficient and Kappa test were used for evaluating the intraobserver agreement. Regarding coronal classification, these significant differences were found: BBP length (BL)level 1 was lower for the two parallel canals group; PBP length (PL)level 1 was lower for single canal group; and NCL was lower for Y-type canal group. Regarding axial classification, these significant differences were found: LPlevel 1 was lower for 3.1-3 group; PBP width (PW)level 3 was the greatest for 3.1-3; and LPlevel 3 was lower for 1.1. Presurgical evaluation with CBCT in premaxillae region should include analysis on coronal and axial planes and not only on sagittal plane seeing as morphometric differences were found on coronal and axial planes. Following the morphological coronal classification, two parallel canals presented a higher NCL, a higher LP and a lower LV at inferior edge of alveolar ridge.

The Society for Vascular Surgery lower extremity threatened limb classification system based on Wound, Ischemia, and foot Infection (WIfI) correlates with risk of major amputation and time to wound healing.

PubMed

Zhan, Luke X; Branco, Bernardino C; Armstrong, David G; Mills, Joseph L

2015-04-01

The purpose of this study was to evaluate whether the new Society for Vascular Surgery (SVS) Wound, Ischemia, and foot Infection (WIfI) classification system correlates with important clinical outcomes for limb salvage and wound healing. A total of 201 consecutive patients with threatened limbs treated from 2010 to 2011 in an academic medical center were analyzed. These patients were stratified into clinical stages 1 to 4 on the basis of the SVS WIfI classification. The SVS objective performance goals of major amputation, 1-year amputation-free survival (AFS) rate, and wound healing time (WHT) according to WIfI clinical stages were compared. The mean age was 58 years (79% male, 93% with diabetes). Forty-two patients required major amputation (21%); 159 (78%) had limb salvage. The amputation group had a significantly higher prevalence of advanced stage 4 patients (P < .001), whereas the limb salvage group presented predominantly as stages 1 to 3. Patients in clinical stages 3 and 4 had a significantly higher incidence of amputation (P < .001), decreased AFS (P < .001), and delayed WHT (P < .002) compared with those in stages 1 and 2. Among patients presenting with stage 3, primarily as a result of wound and ischemia grades, revascularization resulted in accelerated WHT (P = .008). These data support the underlying concept of the SVS WIfI, that an appropriate classification system correlates with important clinical outcomes for limb salvage and wound healing. As the clinical stage progresses, the risk of major amputation increases, 1-year AFS declines, and WHT is prolonged. We further demonstrated benefit of revascularization to improve WHT in selected patients, especially those in stage 3. Future efforts are warranted to incorporate the SVS WIfI classification into clinical decision-making algorithms in conjunction with a comorbidity index and anatomic classification. Copyright © 2015 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Using discordance to improve classification in narrative clinical databases: an application to community-acquired pneumonia.

PubMed

Hripcsak, George; Knirsch, Charles; Zhou, Li; Wilcox, Adam; Melton, Genevieve B

2007-03-01

Data mining in electronic medical records may facilitate clinical research, but much of the structured data may be miscoded, incomplete, or non-specific. The exploitation of narrative data using natural language processing may help, although nesting, varying granularity, and repetition remain challenges. In a study of community-acquired pneumonia using electronic records, these issues led to poor classification. Limiting queries to accurate, complete records led to vastly reduced, possibly biased samples. We exploited knowledge latent in the electronic records to improve classification. A similarity metric was used to cluster cases. We defined discordance as the degree to which cases within a cluster give different answers for some query that addresses a classification task of interest. Cases with higher discordance are more likely to be incorrectly classified, and can be reviewed manually to adjust the classification, improve the query, or estimate the likely accuracy of the query. In a study of pneumonia--in which the ICD9-CM coding was found to be very poor--the discordance measure was statistically significantly correlated with classification correctness (.45; 95% CI .15-.62).

Estimating the Economic Impact of Higher Education: A Case Study of the Five Colleges in Berks County, Pennsylvania. Professional File Number 117, Summer 2010

ERIC Educational Resources Information Center

D'Allegro, Mary-Lou; Paff, Lolita A.

2010-01-01

Most economic impact studies are prepared by external consultants at significant cost to an individual college, a higher education state system, or a set of institutions with similar Carnegie Classifications. This case study provides a detailed framework that academic institutions may use to derive economic impact estimates without hiring external…

Object-based random forest classification of Landsat ETM+ and WorldView-2 satellite imagery for mapping lowland native grassland communities in Tasmania, Australia

NASA Astrophysics Data System (ADS)

Melville, Bethany; Lucieer, Arko; Aryal, Jagannath

2018-04-01

This paper presents a random forest classification approach for identifying and mapping three types of lowland native grassland communities found in the Tasmanian Midlands region. Due to the high conservation priority assigned to these communities, there has been an increasing need to identify appropriate datasets that can be used to derive accurate and frequently updateable maps of community extent. Therefore, this paper proposes a method employing repeat classification and statistical significance testing as a means of identifying the most appropriate dataset for mapping these communities. Two datasets were acquired and analysed; a Landsat ETM+ scene, and a WorldView-2 scene, both from 2010. Training and validation data were randomly subset using a k-fold (k = 50) approach from a pre-existing field dataset. Poa labillardierei, Themeda triandra and lowland native grassland complex communities were identified in addition to dry woodland and agriculture. For each subset of randomly allocated points, a random forest model was trained based on each dataset, and then used to classify the corresponding imagery. Validation was performed using the reciprocal points from the independent subset that had not been used to train the model. Final training and classification accuracies were reported as per class means for each satellite dataset. Analysis of Variance (ANOVA) was undertaken to determine whether classification accuracy differed between the two datasets, as well as between classifications. Results showed mean class accuracies between 54% and 87%. Class accuracy only differed significantly between datasets for the dry woodland and Themeda grassland classes, with the WorldView-2 dataset showing higher mean classification accuracies. The results of this study indicate that remote sensing is a viable method for the identification of lowland native grassland communities in the Tasmanian Midlands, and that repeat classification and statistical significant testing can be used to identify optimal datasets for vegetation community mapping.

Global Stress Classification System for Materials Used in Solar Energy Applications

NASA Astrophysics Data System (ADS)

Slamova, Karolina; Schill, Christian; Herrmann, Jan; Datta, Pawan; Chih Wang, Chien

2016-08-01

Depending on the geographical location, the individual or combined impact of environmental stress factors and corresponding performance losses for solar applications varies significantly. Therefore, as a strategy to reduce investment risks and operating and maintenance costs, it is necessary to adapt the materials and components of solar energy systems specifically to regional environmental conditions. The project «GloBe Solar» supports this strategy by focusing on the development of a global stress classification system for materials in solar energy applications. The aim of this classification system is to assist in the identification of the individual stress conditions for every location on the earth's surface. The stress classification system could serve as a decision support tool for the industry (manufacturers, investors, lenders and project developers) and help to improve knowledge and services that can provide higher confidence to solar power systems.

Object-based land cover classification based on fusion of multifrequency SAR data and THAICHOTE optical imagery

NASA Astrophysics Data System (ADS)

Sukawattanavijit, Chanika; Srestasathiern, Panu

2017-10-01

Land Use and Land Cover (LULC) information are significant to observe and evaluate environmental change. LULC classification applying remotely sensed data is a technique popularly employed on a global and local dimension particularly, in urban areas which have diverse land cover types. These are essential components of the urban terrain and ecosystem. In the present, object-based image analysis (OBIA) is becoming widely popular for land cover classification using the high-resolution image. COSMO-SkyMed SAR data was fused with THAICHOTE (namely, THEOS: Thailand Earth Observation Satellite) optical data for land cover classification using object-based. This paper indicates a comparison between object-based and pixel-based approaches in image fusion. The per-pixel method, support vector machines (SVM) was implemented to the fused image based on Principal Component Analysis (PCA). For the objectbased classification was applied to the fused images to separate land cover classes by using nearest neighbor (NN) classifier. Finally, the accuracy assessment was employed by comparing with the classification of land cover mapping generated from fused image dataset and THAICHOTE image. The object-based data fused COSMO-SkyMed with THAICHOTE images demonstrated the best classification accuracies, well over 85%. As the results, an object-based data fusion provides higher land cover classification accuracy than per-pixel data fusion.

Degree Classification and Recent Graduates' Ability: Is There Any Signalling Effect?

ERIC Educational Resources Information Center

Di Pietro, Giorgio

2017-01-01

Research across several countries has shown that degree classification (i.e. the final grade awarded to students successfully completing university) is an important determinant of graduates' first destination outcome. Graduates leaving university with higher degree classifications have better employment opportunities and a higher likelihood of…

The usefulness of the revised classification for chronic kidney disease by the KDIGO for determining the frequency of diabetic micro- and macroangiopathies in Japanese patients with type 2 diabetes mellitus.

PubMed

Ito, Hiroyuki; Oshikiri, Koshiro; Mifune, Mizuo; Abe, Mariko; Antoku, Shinichi; Takeuchi, Yuichiro; Togane, Michiko; Yukawa, Chizuko

2012-01-01

A new classification of chronic kidney disease (CKD) was proposed by the Kidney Disease: Improving Global Outcomes (KDIGO) in 2011. The major point of revision of this classification was the introduction of a two-dimensional staging of the CKD according to the level of albuminuria in addition to the GFR level. Furthermore, the previous CKD stage 3 was subdivided into two stages (G3a and G3b). We examined the prevalence of diabetic micro- and macroangiopathies in patients with type 2 diabetes mellitus based on the new classification. A cross-sectional study was performed in 2018 patients with type 2 diabetes mellitus. All of the diabetic micro- and macroangiopathies significantly more common in the later stages of both the GFR and albuminuria. The proportion of subjects with diabetic retinopathy, neuropathy, cerebrovascular disease and coronary heart disease was significantly higher in the G3b group than in the G3a group. The brachial-ankle pulse wave velocity, which is one of the surrogate markers for atherosclerosis, was also significantly greater in the G3b group compared to the G3a group. The subdivision of the G3 stage in the revised classification proposed by the KDIGO is useful to evaluate the risk for diabetic vascular complications. Copyright © 2012 Elsevier Inc. All rights reserved.

32 CFR 2103.12 - Level of original classification.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 32 National Defense 6 2010-07-01 2010-07-01 false Level of original classification. 2103.12... DECLASSIFIED Original Classification § 2103.12 Level of original classification. Unnecessary classification, and classification at a level higher than is necessary, shall be avoided. If there is reasonable doubt...

Classification and prediction of pilot weather encounters: A discriminant function analysis.

PubMed

O'Hare, David; Hunter, David R; Martinussen, Monica; Wiggins, Mark

2011-05-01

Flight into adverse weather continues to be a significant hazard for General Aviation (GA) pilots. Weather-related crashes have a significantly higher fatality rate than other GA crashes. Previous research has identified lack of situational awareness, risk perception, and risk tolerance as possible explanations for why pilots would continue into adverse weather. However, very little is known about the nature of these encounters or the differences between pilots who avoid adverse weather and those who do not. Visitors to a web site described an experience with adverse weather and completed a range of measures of personal characteristics. The resulting data from 364 pilots were carefully screened and subject to a discriminant function analysis. Two significant functions were found. The first, accounting for 69% of the variance, reflected measures of risk awareness and pilot judgment while the second differentiated pilots in terms of their experience levels. The variables measured in this study enabled us to correctly discriminate between the three groups of pilots considerably better (53% correct classifications) than would have been possible by chance (33% correct classifications). The implications of these findings for targeting safety interventions are discussed.

New workflow for classification of genetic variants' pathogenicity applied to hereditary recurrent fevers by the International Study Group for Systemic Autoinflammatory Diseases (INSAID).

PubMed

Van Gijn, Marielle E; Ceccherini, Isabella; Shinar, Yael; Carbo, Ellen C; Slofstra, Mariska; Arostegui, Juan I; Sarrabay, Guillaume; Rowczenio, Dorota; Omoyımnı, Ebun; Balci-Peynircioglu, Banu; Hoffman, Hal M; Milhavet, Florian; Swertz, Morris A; Touitou, Isabelle

2018-03-29

Hereditary recurrent fevers (HRFs) are rare inflammatory diseases sharing similar clinical symptoms and effectively treated with anti-inflammatory biological drugs. Accurate diagnosis of HRF relies heavily on genetic testing. This study aimed to obtain an experts' consensus on the clinical significance of gene variants in four well-known HRF genes: MEFV , TNFRSF1A , NLRP3 and MVK . We configured a MOLGENIS web platform to share and analyse pathogenicity classifications of the variants and to manage a consensus-based classification process. Four experts in HRF genetics submitted independent classifications of 858 variants. Classifications were driven to consensus by recruiting four more expert opinions and by targeting discordant classifications in five iterative rounds. Consensus classification was reached for 804/858 variants (94%). None of the unsolved variants (6%) remained with opposite classifications (eg, pathogenic vs benign). New mutational hotspots were found in all genes. We noted a lower pathogenic variant load and a higher fraction of variants with unknown or unsolved clinical significance in the MEFV gene. Applying a consensus-driven process on the pathogenicity assessment of experts yielded rapid classification of almost all variants of four HRF genes. The high-throughput database will profoundly assist clinicians and geneticists in the diagnosis of HRFs. The configured MOLGENIS platform and consensus evolution protocol are usable for assembly of other variant pathogenicity databases. The MOLGENIS software is available for reuse at http://github.com/molgenis/molgenis; the specific HRF configuration is available at http://molgenis.org/said/. The HRF pathogenicity classifications will be published on the INFEVERS database at https://fmf.igh.cnrs.fr/ISSAID/infevers/. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Integrating Interview Methodology to Analyze Inter-Institutional Comparisons of Service-Learning within the Carnegie Community Engagement Classification Framework

ERIC Educational Resources Information Center

Plante, Jarrad D.; Cox, Thomas D.

2016-01-01

Service-learning has a longstanding history in higher education in and includes three main tenets: academic learning, meaningful community service, and civic learning. The Carnegie Foundation for the Advancement of Teaching created an elective classification system called the Carnegie Community Engagement Classification for higher education…

Comprehensive decision tree models in bioinformatics.

PubMed

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics.

Comprehensive Decision Tree Models in Bioinformatics

PubMed Central

Stiglic, Gregor; Kocbek, Simon; Pernek, Igor; Kokol, Peter

2012-01-01

Purpose Classification is an important and widely used machine learning technique in bioinformatics. Researchers and other end-users of machine learning software often prefer to work with comprehensible models where knowledge extraction and explanation of reasoning behind the classification model are possible. Methods This paper presents an extension to an existing machine learning environment and a study on visual tuning of decision tree classifiers. The motivation for this research comes from the need to build effective and easily interpretable decision tree models by so called one-button data mining approach where no parameter tuning is needed. To avoid bias in classification, no classification performance measure is used during the tuning of the model that is constrained exclusively by the dimensions of the produced decision tree. Results The proposed visual tuning of decision trees was evaluated on 40 datasets containing classical machine learning problems and 31 datasets from the field of bioinformatics. Although we did not expected significant differences in classification performance, the results demonstrate a significant increase of accuracy in less complex visually tuned decision trees. In contrast to classical machine learning benchmarking datasets, we observe higher accuracy gains in bioinformatics datasets. Additionally, a user study was carried out to confirm the assumption that the tree tuning times are significantly lower for the proposed method in comparison to manual tuning of the decision tree. Conclusions The empirical results demonstrate that by building simple models constrained by predefined visual boundaries, one not only achieves good comprehensibility, but also very good classification performance that does not differ from usually more complex models built using default settings of the classical decision tree algorithm. In addition, our study demonstrates the suitability of visually tuned decision trees for datasets with binary class attributes and a high number of possibly redundant attributes that are very common in bioinformatics. PMID:22479449

68Ga-PSMA-11 PET/CT in Newly Diagnosed Carcinoma of the Prostate: Correlation of Intraprostatic PSMA Uptake with Several Clinical Parameters.

PubMed

Koerber, Stefan A; Utzinger, Maximilian T; Kratochwil, Clemens; Kesch, Claudia; Haefner, Matthias F; Katayama, Sonja; Mier, Walter; Iagaru, Andrei H; Herfarth, Klaus; Haberkorn, Uwe; Debus, Juergen; Giesel, Frederik L

2017-12-01

68 Ga-prostate-specific membrane antigen (PSMA) PET/CT is a promising diagnostic tool for patients with prostate cancer. Our study evaluates SUVs in benign prostate tissue and malignant, intraprostatic tumor lesions and correlates results with several clinical parameters. Methods: One hundred four men with newly diagnosed prostate carcinoma and no previous therapy were included in this study. SUV max was measured and correlated with biopsy findings and MRI. Afterward, data were compared with current prostate-specific antigen (PSA) values, Gleason score (GS), and d'Amico risk classification. Results: In this investigation a mean SUV max of 1.88 ± 0.44 in healthy prostate tissue compared with 10.77 ± 8.45 in malignant prostate lesions ( P < 0.001) was observed. Patients with higher PSA, higher GS, and higher d'Amico risk score had statistically significant higher PSMA uptake on PET/CT ( P < 0.001 each). Conclusion: PSMA PET/CT is well suited for detecting the intraprostatic malignant lesion in patients with newly diagnosed prostate cancer. Our findings indicate a significant correlation of PSMA uptake with PSA, GS, and risk classification according to the d'Amico scale. © 2017 by the Society of Nuclear Medicine and Molecular Imaging.

14 CFR 1203.203 - Degree of protection.

Code of Federal Regulations, 2011 CFR

2011-01-01

...) Authorized categories of classification. The three categories of classification, as authorized and defined in... be safeguarded as if it were classified pending a determination by an original classification... appropriate level of classification, it shall be safeguarded at the higher level of classification pending a...

[Evaluation of the appropriateness of hospital admissions using the iso-gravity classification systems APR-DRG and Disease Staging and the Italian version of Appropriateness Evaluation Protocol (AEP)].

PubMed

D'Andrea, G; Capalbo, G; Volpe, M; Marchetti, M; Vicentini, F; Capelli, G; Cambieri, A; Cicchetti, A; Ricciardi, G; Catananti, C

2006-01-01

Our main purpose was to evaluate the organizational appropriateness of admissions made in a university hospital, by comparing two iso-gravity classification systems, APR-DRG and Disease Staging, with the Italian version of AEP (PRUO). Our analysis focused on admissions made in 2001, related to specific Diagnosis Related Groups (DRGs), which, according an Italian Law, would be considered at high risk of inappropriateness, if treated as ordinary admissions. The results obtained by using the 2 classification systems did not show statistically significant differences with respect to the total number of admissions. On the other hand, some DRGs showed statistically significant differences due to different algorithms of attribution of the severity levels used by the two systems. For almost all of the DRGs studied, the AEP-based analysis of a sample of medical records showed an higher number of inappropriate admissions in comparison with the number expected by iso-gravity classification methods. The difference is possibly due to the percentage limits of tolerability fixed by the Law for each DRG. Therefore, the authors suggest an integrated use of the two methods to evaluate organizational appropriateness of hospital admissions.

The Adam Walsh Act: An Examination of Sex Offender Risk Classification Systems.

PubMed

Zgoba, Kristen M; Miner, Michael; Levenson, Jill; Knight, Raymond; Letourneau, Elizabeth; Thornton, David

2016-12-01

This study was designed to compare the Adam Walsh Act (AWA) classification tiers with actuarial risk assessment instruments and existing state classification schemes in their respective abilities to identify sex offenders at high risk to re-offend. Data from 1,789 adult sex offenders released from prison in four states were collected (Minnesota, New Jersey, Florida, and South Carolina). On average, the sexual recidivism rate was approximately 5% at 5 years and 10% at 10 years. AWA Tier 2 offenders had higher Static-99R scores and higher recidivism rates than Tier 3 offenders, and in Florida, these inverse correlations were statistically significant. Actuarial measures and existing state tier systems, in contrast, did a better job of identifying high-risk offenders and recidivists. As well, we examined the distribution of risk assessment scores within and across tier categories, finding that a majority of sex offenders fall into AWA Tier 3, but more than half score low or moderately low on the Static-99R. The results indicate that the AWA sex offender classification scheme is a poor indicator of relative risk and is likely to result in a system that is less effective in protecting the public than those currently implemented in the states studied. © The Author(s) 2015.

Transport on Riemannian manifold for functional connectivity-based classification.

PubMed

Ng, Bernard; Dressler, Martin; Varoquaux, Gaël; Poline, Jean Baptiste; Greicius, Michael; Thirion, Bertrand

2014-01-01

We present a Riemannian approach for classifying fMRI connectivity patterns before and after intervention in longitudinal studies. A fundamental difficulty with using connectivity as features is that covariance matrices live on the positive semi-definite cone, which renders their elements inter-related. The implicit independent feature assumption in most classifier learning algorithms is thus violated. In this paper, we propose a matrix whitening transport for projecting the covariance estimates onto a common tangent space to reduce the statistical dependencies between their elements. We show on real data that our approach provides significantly higher classification accuracy than directly using Pearson's correlation. We further propose a non-parametric scheme for identifying significantly discriminative connections from classifier weights. Using this scheme, a number of neuroanatomically meaningful connections are found, whereas no significant connections are detected with pure permutation testing.

An Active Learning Framework for Hyperspectral Image Classification Using Hierarchical Segmentation

NASA Technical Reports Server (NTRS)

Zhang, Zhou; Pasolli, Edoardo; Crawford, Melba M.; Tilton, James C.

2015-01-01

Augmenting spectral data with spatial information for image classification has recently gained significant attention, as classification accuracy can often be improved by extracting spatial information from neighboring pixels. In this paper, we propose a new framework in which active learning (AL) and hierarchical segmentation (HSeg) are combined for spectral-spatial classification of hyperspectral images. The spatial information is extracted from a best segmentation obtained by pruning the HSeg tree using a new supervised strategy. The best segmentation is updated at each iteration of the AL process, thus taking advantage of informative labeled samples provided by the user. The proposed strategy incorporates spatial information in two ways: 1) concatenating the extracted spatial features and the original spectral features into a stacked vector and 2) extending the training set using a self-learning-based semi-supervised learning (SSL) approach. Finally, the two strategies are combined within an AL framework. The proposed framework is validated with two benchmark hyperspectral datasets. Higher classification accuracies are obtained by the proposed framework with respect to five other state-of-the-art spectral-spatial classification approaches. Moreover, the effectiveness of the proposed pruning strategy is also demonstrated relative to the approaches based on a fixed segmentation.

Ground-based cloud classification by learning stable local binary patterns

NASA Astrophysics Data System (ADS)

Wang, Yu; Shi, Cunzhao; Wang, Chunheng; Xiao, Baihua

2018-07-01

Feature selection and extraction is the first step in implementing pattern classification. The same is true for ground-based cloud classification. Histogram features based on local binary patterns (LBPs) are widely used to classify texture images. However, the conventional uniform LBP approach cannot capture all the dominant patterns in cloud texture images, thereby resulting in low classification performance. In this study, a robust feature extraction method by learning stable LBPs is proposed based on the averaged ranks of the occurrence frequencies of all rotation invariant patterns defined in the LBPs of cloud images. The proposed method is validated with a ground-based cloud classification database comprising five cloud types. Experimental results demonstrate that the proposed method achieves significantly higher classification accuracy than the uniform LBP, local texture patterns (LTP), dominant LBP (DLBP), completed LBP (CLTP) and salient LBP (SaLBP) methods in this cloud image database and under different noise conditions. And the performance of the proposed method is comparable with that of the popular deep convolutional neural network (DCNN) method, but with less computation complexity. Furthermore, the proposed method also achieves superior performance on an independent test data set.

Improving mental task classification by adding high frequency band information.

PubMed

Zhang, Li; He, Wei; He, Chuanhong; Wang, Ping

2010-02-01

Features extracted from delta, theta, alpha, beta and gamma bands spanning low frequency range are commonly used to classify scalp-recorded electroencephalogram (EEG) for designing brain-computer interface (BCI) and higher frequencies are often neglected as noise. In this paper, we implemented an experimental validation to demonstrate that high frequency components could provide helpful information for improving the performance of the mental task based BCI. Electromyography (EMG) and electrooculography (EOG) artifacts were removed by using blind source separation (BSS) techniques. Frequency band powers and asymmetry ratios from the high frequency band (40-100 Hz) together with those from the lower frequency bands were used to represent EEG features. Finally, Fisher discriminant analysis (FDA) combining with Mahalanobis distance were used as the classifier. In this study, four types of classifications were performed using EEG signals recorded from four subjects during five mental tasks. We obtained significantly higher classification accuracy by adding the high frequency band features compared to using the low frequency bands alone, which demonstrated that the information in high frequency components from scalp-recorded EEG is valuable for the mental task based BCI.

Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition.

PubMed

Janousova, Eva; Schwarz, Daniel; Kasparek, Tomas

2015-06-30

We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance. We also showed that classifiers based on features calculated using more computation-intensive image preprocessing perform better; mMLDA with classification boundary calculated as weighted mean discriminative scores of the groups had improved sensitivity but similar accuracy compared to the original MLDA; reducing a number of eigenvectors during data reduction did not always lead to higher classification accuracy, since noise as well as the signal important for classification were removed. Our findings provide important information for schizophrenia research and may improve accuracy of computer-aided diagnostics of neuropsychiatric diseases. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Changes in the administrative prevalence of autism spectrum disorders: contribution of special education and health from 2002-2008.

PubMed

Pinborough-Zimmerman, Judith; Bakian, Amanda V; Fombonne, Eric; Bilder, Deborah; Taylor, Jocelyn; McMahon, William M

2012-04-01

This study examined changes in the administrative prevalence of autism spectrum disorders (ASD) in Utah children from 2002 to 2008 by record source (school and health), age (four, six, and eight), and special education classification. Prevalence increased 100% with 1 in 77 children aged eight identified with ASD by 2008. Across study years and age groups rates were higher when health and school data were combined with a greater proportion of cases ascertained from health. The proportion of children with both a health ASD diagnosis and a special education autism classification did not significantly change. Most children with an ASD health diagnosis did not have an autism special education classification. Findings highlight the growing health and educational impact of ASD.

Polarimetry based partial least square classification of ex vivo healthy and basal cell carcinoma human skin tissues.

PubMed

Ahmad, Iftikhar; Ahmad, Manzoor; Khan, Karim; Ikram, Masroor

2016-06-01

Optical polarimetry was employed for assessment of ex vivo healthy and basal cell carcinoma (BCC) tissue samples from human skin. Polarimetric analyses revealed that depolarization and retardance for healthy tissue group were significantly higher (p<0.001) compared to BCC tissue group. Histopathology indicated that these differences partially arise from BCC-related characteristic changes in tissue morphology. Wilks lambda statistics demonstrated the potential of all investigated polarimetric properties for computer assisted classification of the two tissue groups. Based on differences in polarimetric properties, partial least square (PLS) regression classified the samples with 100% accuracy, sensitivity and specificity. These findings indicate that optical polarimetry together with PLS statistics hold promise for automated pathology classification. Copyright © 2016 Elsevier B.V. All rights reserved.

Sea ice classification using fast learning neural networks

NASA Technical Reports Server (NTRS)

Dawson, M. S.; Fung, A. K.; Manry, M. T.

1992-01-01

A first learning neural network approach to the classification of sea ice is presented. The fast learning (FL) neural network and a multilayer perceptron (MLP) trained with backpropagation learning (BP network) were tested on simulated data sets based on the known dominant scattering characteristics of the target class. Four classes were used in the data simulation: open water, thick lossy saline ice, thin saline ice, and multiyear ice. The BP network was unable to consistently converge to less than 25 percent error while the FL method yielded an average error of approximately 1 percent on the first iteration of training. The fast learning method presented can significantly reduce the CPU time necessary to train a neural network as well as consistently yield higher classification accuracy than BP networks.

Biallelic and Triallelic 5-Hydroxytyramine Transporter Gene-Linked Polymorphic Region (5-HTTLPR) Polymorphisms and Their Relationship with Lifelong Premature Ejaculation: A Case-Control Study in a Chinese Population

PubMed Central

Huang, Yuanyuan; Zhang, Xiansheng; Gao, Jingjing; Tang, Dongdong; Gao, Pan; Li, Chao; Liu, Weiqun; Liang, Chaozhao

2016-01-01

Background This study aimed to explore the relationship between premature ejaculation (PE) and the serotonin transporter gene-linked polymorphic region (5-HTTLPR) with respect to the biallelic and triallelic classifications. Material/Methods A total of 115 outpatients who complained of ejaculating prematurely and who were diagnosed as having lifelong premature ejaculation (LPE) and 101 controls without PE complaint were recruited. All subjects completed a detailed questionnaire and were genotyped for 5-HTTLPR polymorphism using PCR-based technology. We evaluated the associations between 5-HTTLPR allelic and genotypic frequencies and their association with LPE, as well as the intravaginal ejaculation latency time (IELT) of different 5-HTTLPR genotypes among LPE patients. Results The patients and controls did not differ significantly in terms of any characteristic except age. The results showed no significant difference regarding biallelic 5-HTTLPR. According to the triallelic classification, no significant difference was found when comparing the genotypic distribution (P=0.091). However, the distribution of the S, LG, and LA alleles in the cases was significantly different from the controls (P=0.018). We found a significantly lower frequency of LA allele and higher frequency of LG allele in patients. Based on another classification by expression, we found a significantly lower frequency of the L’L’ genotype (OR=0.37; 95%CI=0.15–0.91, P=0.025) in patients with LPE. No significant association was detected between IELT of LPE and different genotypes. Conclusions Contrary to the general classification based on S/L alleles, triallelic 5-HTTLPR was associated with LPE. Triallelic 5-HTTLPR may be a promising field for genetic research in PE to avoid false-negative results in future studies. PMID:27311544

Branch classification: A new mechanism for improving branch predictor performance

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

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

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

AVHRR composite period selection for land cover classification

USGS Publications Warehouse

Maxwell, S.K.; Hoffer, R.M.; Chapman, P.L.

2002-01-01

Multitemporal satellite image datasets provide valuable information on the phenological characteristics of vegetation, thereby significantly increasing the accuracy of cover type classifications compared to single date classifications. However, the processing of these datasets can become very complex when dealing with multitemporal data combined with multispectral data. Advanced Very High Resolution Radiometer (AVHRR) biweekly composite data are commonly used to classify land cover over large regions. Selecting a subset of these biweekly composite periods may be required to reduce the complexity and cost of land cover mapping. The objective of our research was to evaluate the effect of reducing the number of composite periods and altering the spacing of those composite periods on classification accuracy. Because inter-annual variability can have a major impact on classification results, 5 years of AVHRR data were evaluated. AVHRR biweekly composite images for spectral channels 1-4 (visible, near-infrared and two thermal bands) covering the entire growing season were used to classify 14 cover types over the entire state of Colorado for each of five different years. A supervised classification method was applied to maintain consistent procedures for each case tested. Results indicate that the number of composite periods can be halved-reduced from 14 composite dates to seven composite dates-without significantly reducing overall classification accuracy (80.4% Kappa accuracy for the 14-composite data-set as compared to 80.0% for a seven-composite dataset). At least seven composite periods were required to ensure the classification accuracy was not affected by inter-annual variability due to climate fluctuations. Concentrating more composites near the beginning and end of the growing season, as compared to using evenly spaced time periods, consistently produced slightly higher classification values over the 5 years tested (average Kappa) of 80.3% for the heavy early/late case as compared to 79.0% for the alternate dataset case).

Application of Sensor Fusion to Improve Uav Image Classification

NASA Astrophysics Data System (ADS)

Jabari, S.; Fathollahi, F.; Zhang, Y.

2017-08-01

Image classification is one of the most important tasks of remote sensing projects including the ones that are based on using UAV images. Improving the quality of UAV images directly affects the classification results and can save a huge amount of time and effort in this area. In this study, we show that sensor fusion can improve image quality which results in increasing the accuracy of image classification. Here, we tested two sensor fusion configurations by using a Panchromatic (Pan) camera along with either a colour camera or a four-band multi-spectral (MS) camera. We use the Pan camera to benefit from its higher sensitivity and the colour or MS camera to benefit from its spectral properties. The resulting images are then compared to the ones acquired by a high resolution single Bayer-pattern colour camera (here referred to as HRC). We assessed the quality of the output images by performing image classification tests. The outputs prove that the proposed sensor fusion configurations can achieve higher accuracies compared to the images of the single Bayer-pattern colour camera. Therefore, incorporating a Pan camera on-board in the UAV missions and performing image fusion can help achieving higher quality images and accordingly higher accuracy classification results.

Classification of instability after reverse shoulder arthroplasty guides surgical management and outcomes.

PubMed

Abdelfattah, Adham; Otto, Randall J; Simon, Peter; Christmas, Kaitlyn N; Tanner, Gregory; LaMartina, Joey; Levy, Jonathan C; Cuff, Derek J; Mighell, Mark A; Frankle, Mark A

2018-04-01

Revision of unstable reverse shoulder arthroplasty (RSA) remains a significant challenge. The purpose of this study was to determine the reliability of a new treatment-guiding classification for instability after RSA, to describe the clinical outcomes of patients stabilized operatively, and to identify those with higher risk of recurrence. All patients undergoing revision for instability after RSA were identified at our institution. Demographic, clinical, radiographic, and intraoperative data were collected. A classification was developed using all identified causes of instability after RSA and allocating them to 1 of 3 defined treatment-guiding categories. Eight surgeons reviewed all data and applied the classification scheme to each case. Interobserver and intraobserver reliability was used to evaluate the classification scheme. Preoperative clinical outcomes were compared with final follow-up in stabilized shoulders. Forty-three revision cases in 34 patients met the inclusion for study. Five patients remained unstable after revision. Persistent instability most commonly occurred in persistent deltoid dysfunction and postoperative acromial fractures but also in 1 case of soft tissue impingement. Twenty-one patients remained stable at minimum 2 years of follow-up and had significant improvement of clinical outcome scores and range of motion. Reliability of the classification scheme showed substantial and almost perfect interobserver and intraobserver agreement among all the participants (κ = 0.699 and κ = 0.851, respectively). Instability after RSA can be successfully treated with revision surgery using the reliable treatment-guiding classification scheme presented herein. However, more understanding is needed for patients with greater risk of recurrent instability after revision surgery. Copyright © 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

Risk Factors for Failure of Male Slings and Artificial Urinary Sphincters: Results from a Large Middle European Cohort Study.

PubMed

Hüsch, Tanja; Kretschmer, Alexander; Thomsen, Frauke; Kronlachner, Dominik; Kurosch, Martin; Obaje, Alice; Anding, Ralf; Pottek, Tobias; Rose, Achim; Olianas, Roberto; Friedl, Alexander; Hübner, Wilhelm; Homberg, Roland; Pfitzenmaier, Jesco; Grein, Ulrich; Queissert, Fabian; Naumann, Carsten Maik; Schweiger, Josef; Wotzka, Carola; Nyarangi-Dix, Joanne; Hofmann, Torben; Ulm, Kurt; Bauer, Ricarda M; Haferkamp, Axel

2017-01-01

We analysed the impact of predefined risk factors: age, diabetes, history of pelvic irradiation, prior surgery for stress urinary incontinence (SUI), prior urethral stricture, additional procedure during SUI surgery, duration of incontinence, ASA-classification and cause for incontinence on failure and complications in male SUI surgery. We retrospectively identified 506 patients with an artificial urinary sphincter (AUS) and 513 patients with a male sling (MS) in a multicenter cohort study. Complication rates were correlated to the risk factors in univariate analysis. Subsequently, a multivariate logistic regression adjusted to the risk factors was performed. A p value <0.05 was considered statistically significant. A history of pelvic irradiation was an independent risk factor for explantation in AUS (p < 0.001) and MS (p = 0.018). Moreover, prior urethral stricture (p = 0.036) and higher ASA-classification (p = 0.039) were positively correlated with explantation in univariate analysis for AUS. Urethral erosion was correlated with prior urethral stricture (p < 0.001) and a history of pelvic irradiation (p < 0.001) in AUS. Furthermore, infection was correlated with additional procedures during SUI surgery in univariate analysis (p = 0.037) in MS. We first identified the correlation of higher ASA-classification and explantation in AUS. Nevertheless, only a few novel risk factors had a significant influence on the failure of MS or AUS. © 2016 S. Karger AG, Basel.

Classification of EEG Signals Based on Pattern Recognition Approach.

PubMed

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a "pattern recognition" approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90-7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11-89.63% and 91.60-81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy.

Classification of EEG Signals Based on Pattern Recognition Approach

PubMed Central

Amin, Hafeez Ullah; Mumtaz, Wajid; Subhani, Ahmad Rauf; Saad, Mohamad Naufal Mohamad; Malik, Aamir Saeed

2017-01-01

Feature extraction is an important step in the process of electroencephalogram (EEG) signal classification. The authors propose a “pattern recognition” approach that discriminates EEG signals recorded during different cognitive conditions. Wavelet based feature extraction such as, multi-resolution decompositions into detailed and approximate coefficients as well as relative wavelet energy were computed. Extracted relative wavelet energy features were normalized to zero mean and unit variance and then optimized using Fisher's discriminant ratio (FDR) and principal component analysis (PCA). A high density EEG dataset validated the proposed method (128-channels) by identifying two classifications: (1) EEG signals recorded during complex cognitive tasks using Raven's Advance Progressive Metric (RAPM) test; (2) EEG signals recorded during a baseline task (eyes open). Classifiers such as, K-nearest neighbors (KNN), Support Vector Machine (SVM), Multi-layer Perceptron (MLP), and Naïve Bayes (NB) were then employed. Outcomes yielded 99.11% accuracy via SVM classifier for coefficient approximations (A5) of low frequencies ranging from 0 to 3.90 Hz. Accuracy rates for detailed coefficients were 98.57 and 98.39% for SVM and KNN, respectively; and for detailed coefficients (D5) deriving from the sub-band range (3.90–7.81 Hz). Accuracy rates for MLP and NB classifiers were comparable at 97.11–89.63% and 91.60–81.07% for A5 and D5 coefficients, respectively. In addition, the proposed approach was also applied on public dataset for classification of two cognitive tasks and achieved comparable classification results, i.e., 93.33% accuracy with KNN. The proposed scheme yielded significantly higher classification performances using machine learning classifiers compared to extant quantitative feature extraction. These results suggest the proposed feature extraction method reliably classifies EEG signals recorded during cognitive tasks with a higher degree of accuracy. PMID:29209190

Simultaneous data pre-processing and SVM classification model selection based on a parallel genetic algorithm applied to spectroscopic data of olive oils.

PubMed

Devos, Olivier; Downey, Gerard; Duponchel, Ludovic

2014-04-01

Classification is an important task in chemometrics. For several years now, support vector machines (SVMs) have proven to be powerful for infrared spectral data classification. However such methods require optimisation of parameters in order to control the risk of overfitting and the complexity of the boundary. Furthermore, it is established that the prediction ability of classification models can be improved using pre-processing in order to remove unwanted variance in the spectra. In this paper we propose a new methodology based on genetic algorithm (GA) for the simultaneous optimisation of SVM parameters and pre-processing (GENOPT-SVM). The method has been tested for the discrimination of the geographical origin of Italian olive oil (Ligurian and non-Ligurian) on the basis of near infrared (NIR) or mid infrared (FTIR) spectra. Different classification models (PLS-DA, SVM with mean centre data, GENOPT-SVM) have been tested and statistically compared using McNemar's statistical test. For the two datasets, SVM with optimised pre-processing give models with higher accuracy than the one obtained with PLS-DA on pre-processed data. In the case of the NIR dataset, most of this accuracy improvement (86.3% compared with 82.8% for PLS-DA) occurred using only a single pre-processing step. For the FTIR dataset, three optimised pre-processing steps are required to obtain SVM model with significant accuracy improvement (82.2%) compared to the one obtained with PLS-DA (78.6%). Furthermore, this study demonstrates that even SVM models have to be developed on the basis of well-corrected spectral data in order to obtain higher classification rates. Copyright © 2013 Elsevier Ltd. All rights reserved.

Comparative analysis of distribution and abundance of West Nile and Eastern Equine Encephalomyelitis virus vectors in Suffolk County, New York, using human population density and land use/cover data

USGS Publications Warehouse

Rochlin, I.; Harding, K.; Ginsberg, H.S.; Campbell, S.R.

2008-01-01

Five years of CDC light trap data from Suffolk County, NY, were analyzed to compare the applicability of human population density (HPD) and land use/cover (LUC) classification systems to describe mosquito abundance and to determine whether certain mosquito species of medical importance tend to be more common in urban (defined by HPD) or residential (defined by LUC) areas. Eleven study sites were categorized as urban or rural using U.S. Census Bureau data and by LUC types using geographic information systems (GISs). Abundance and percent composition of nine mosquito taxa, all known or potential vectors of arboviruses, were analyzed to determine spatial patterns. By HPD definitions, three mosquito species, Aedes canadensis (Theobald), Coquillettidia perturbans (Walker), and Culiseta melanura (Coquillett), differed significantly between habitat types, with higher abundance and percent composition in rural areas. Abundance and percent composition of these three species also increased with freshwater wetland, natural vegetation areas, or a combination when using LUC definitions. Additionally, two species, Ae. canadensis and Cs. melanura, were negatively affected by increased residential area. One species, Aedes vexans (Meigen), had higher percent composition in urban areas. Two medically important taxa, Culex spp. and Aedes triseriatus (Say), were proportionally more prevalent in residential areas by LUC classification, as was Aedes trivittatus (Coquillett). Although HPD classification was readily available and had some predictive value, LUC classification resulted in higher spatial resolution and better ability to develop location specific predictive models.

Effect of Process-Oriented Guided-Inquiry Learning on Non-majors Biology Students' Understanding of Biological Classification

NASA Astrophysics Data System (ADS)

Wozniak, Breann M.

The purpose of this study was to examine the effect of process-oriented guided-inquiry learning (POGIL) on non-majors college biology students' understanding of biological classification. This study addressed an area of science instruction, POGIL in the non-majors college biology laboratory, which has yet to be qualitatively and quantitatively researched. A concurrent triangulation mixed methods approach was used. Students' understanding of biological classification was measured in two areas: scores on pre and posttests (consisting of 11 multiple choice questions), and conceptions of classification as elicited in pre and post interviews and instructor reflections. Participants were Minnesota State University, Mankato students enrolled in BIOL 100 Summer Session. One section was taught with the traditional curriculum (n = 6) and the other section in the POGIL curriculum (n = 10) developed by the researcher. Three students from each section were selected to take part in pre and post interviews. There were no significant differences within each teaching method (p < .05). There was a tendency of difference in the means. The POGIL group may have scored higher on the posttest (M = 8.830 +/- .477 vs. M = 7.330 +/- .330; z =-1.729, p = .084) and the traditional group may have scored higher on the pretest than the posttest (M = 8.333 +/- .333 vs M = 7.333 +/- .333; z = -1.650 , p = .099). Two themes emerged after the interviews and instructor reflections: 1) After instruction students had a more extensive understanding of classification in three areas: vocabulary terms, physical characteristics, and types of evidence used to classify. Both groups extended their understanding, but only POGIL students could explain how molecular evidence is used in classification. 2) The challenges preventing students from understanding classification were: familiar animal categories and aquatic habitats, unfamiliar organisms, combining and subdividing initial groupings, and the hierarchical nature of classification. The POGIL students were the only group to surpass these challenges after the teaching intervention. This study shows that POGIL is an effective technique at eliciting students' misconceptions, and addressing these misconceptions, leading to an increase in student understanding of biological classification.

Exploring the Impact of Target Eccentricity and Task Difficulty on Covert Visual Spatial Attention and Its Implications for Brain Computer Interfacing

PubMed Central

Roijendijk, Linsey; Farquhar, Jason; van Gerven, Marcel; Jensen, Ole; Gielen, Stan

2013-01-01

Objective Covert visual spatial attention is a relatively new task used in brain computer interfaces (BCIs) and little is known about the characteristics which may affect performance in BCI tasks. We investigated whether eccentricity and task difficulty affect alpha lateralization and BCI performance. Approach We conducted a magnetoencephalography study with 14 participants who performed a covert orientation discrimination task at an easy or difficult stimulus contrast at either a near (3.5°) or far (7°) eccentricity. Task difficulty was manipulated block wise and subjects were aware of the difficulty level of each block. Main Results Grand average analyses revealed a significantly larger hemispheric lateralization of posterior alpha power in the difficult condition than in the easy condition, while surprisingly no difference was found for eccentricity. The difference between task difficulty levels was significant in the interval between 1.85 s and 2.25 s after cue onset and originated from a stronger decrease in the contralateral hemisphere. No significant effect of eccentricity was found. Additionally, single-trial classification analysis revealed a higher classification rate in the difficult (65.9%) than in the easy task condition (61.1%). No effect of eccentricity was found in classification rate. Significance Our results indicate that manipulating the difficulty of a task gives rise to variations in alpha lateralization and that using a more difficult task improves covert visual spatial attention BCI performance. The variations in the alpha lateralization could be caused by different factors such as an increased mental effort or a higher visual attentional demand. Further research is necessary to discriminate between them. We did not discover any effect of eccentricity in contrast to results of previous research. PMID:24312477

Digital mammography: observer performance study of the effects of pixel size on radiologists' characterization of malignant and benign microcalcifications

NASA Astrophysics Data System (ADS)

Chan, Heang-Ping; Helvie, Mark A.; Petrick, Nicholas; Sahiner, Berkman; Adler, Dorit D.; Blane, Caroline E.; Joynt, Lynn K.; Paramagul, Chintana; Roubidoux, Marilyn A.; Wilson, Todd E.; Hadjiiski, Lubomir M.; Goodsitt, Mitchell M.

1999-05-01

A receiver operating characteristic (ROC) experiment was conducted to evaluate the effects of pixel size on the characterization of mammographic microcalcifications. Digital mammograms were obtained by digitizing screen-film mammograms with a laser film scanner. One hundred twelve two-view mammograms with biopsy-proven microcalcifications were digitized at a pixel size of 35 micrometer X 35 micrometer. A region of interest (ROI) containing the microcalcifications was extracted from each image. ROI images with pixel sizes of 70 micrometers, 105 micrometers, and 140 micrometers were derived from the ROI of 35 micrometer pixel size by averaging 2 X 2, 3 X 3, and 4 X 4 neighboring pixels, respectively. The ROI images were printed on film with a laser imager. Seven MQSA-approved radiologists participated as observers. The likelihood of malignancy of the microcalcifications was rated on a 10-point confidence rating scale and analyzed with ROC methodology. The classification accuracy was quantified by the area, Az, under the ROC curve. The statistical significance of the differences in the Az values for different pixel sizes was estimated with the Dorfman-Berbaum-Metz (DBM) method for multi-reader, multi-case ROC data. It was found that five of the seven radiologists demonstrated a higher classification accuracy with the 70 micrometer or 105 micrometer images. The average Az also showed a higher classification accuracy in the range of 70 to 105 micrometer pixel size. However, the differences in A(subscript z/ between different pixel sizes did not achieve statistical significance. The low specificity of image features of microcalcifications an the large interobserver and intraobserver variabilities may have contributed to the relatively weak dependence of classification accuracy on pixel size.

Clinical significance of laryngopharyngeal reflux in patients with chronic obstructive pulmonary disease.

PubMed

Jung, Young Ho; Lee, Doh Young; Kim, Dong Wook; Park, Sung Soo; Heo, Eun Young; Chung, Hee Soon; Kim, Deog Kyeom

2015-01-01

Although chronic obstructive pulmonary disease (COPD) is closely associated with gastroesophageal reflux disease (GERD), the clinical significance of laryngopharyngeal reflux (LPR) is not fully understood in COPD. Prospective cohorts were established among 118 patients with COPD from March 2013 to July 2014. Thirty-two age-matched and sex-matched normal controls, who had routine health check-ups during the study period, were included. Laryngopharyngeal reflux finding scores (RFS) and reflux symptom index (RSI) for LPR were subjected to association analysis with severity and acute exacerbation of COPD during the 1-year follow-up. The mean age of patients enrolled in the study was 69.2±8.8 years, with 93.2% being male. Positive RFS (>7) and RSI (>13) were observed in 51 (42.5%) and six patients (5.0%), respectively. RFS and RSI were significantly higher in patients with COPD than in normal, healthy patients (P<0.001). RFS was significantly correlated with residual volume/total lung capacity (%, P=0.048). Scores for diffuse laryngeal edema, erythema, and hyperemia were significantly higher in the high-risk group (Global Initiative for Chronic Obstructive Lung Disease classification C and D; P=0.025 and P=0.049, respectively), while RSI was significantly higher in the more symptomatic group (Global Initiative for Chronic Obstructive Lung Disease classification B and D; P=0.047). RSI and RFS were significant predictors for severe acute exacerbation of COPD (P=0.03 and P=0.047, respectively), while only RSI was associated with severity of dyspnea. Laryngeal examination and evaluation of laryngeal reflux symptom could be a surrogate clinical indicator related to severe acute exacerbation of COPD. Further studies of LPR in COPD patients should be considered.

Object-Based Random Forest Classification of Land Cover from Remotely Sensed Imagery for Industrial and Mining Reclamation

NASA Astrophysics Data System (ADS)

Chen, Y.; Luo, M.; Xu, L.; Zhou, X.; Ren, J.; Zhou, J.

2018-04-01

The RF method based on grid-search parameter optimization could achieve a classification accuracy of 88.16 % in the classification of images with multiple feature variables. This classification accuracy was higher than that of SVM and ANN under the same feature variables. In terms of efficiency, the RF classification method performs better than SVM and ANN, it is more capable of handling multidimensional feature variables. The RF method combined with object-based analysis approach could highlight the classification accuracy further. The multiresolution segmentation approach on the basis of ESP scale parameter optimization was used for obtaining six scales to execute image segmentation, when the segmentation scale was 49, the classification accuracy reached the highest value of 89.58 %. The classification accuracy of object-based RF classification was 1.42 % higher than that of pixel-based classification (88.16 %), and the classification accuracy was further improved. Therefore, the RF classification method combined with object-based analysis approach could achieve relatively high accuracy in the classification and extraction of land use information for industrial and mining reclamation areas. Moreover, the interpretation of remotely sensed imagery using the proposed method could provide technical support and theoretical reference for remotely sensed monitoring land reclamation.

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia

PubMed Central

Kim, Junghoe; Calhoun, Vince D.; Shim, Eunsoo; Lee, Jong-Hwan

2015-01-01

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns. PMID:25987366

Application of tripolar concentric electrodes and prefeature selection algorithm for brain-computer interface.

PubMed

Besio, Walter G; Cao, Hongbao; Zhou, Peng

2008-04-01

For persons with severe disabilities, a brain-computer interface (BCI) may be a viable means of communication. Lapalacian electroencephalogram (EEG) has been shown to improve classification in EEG recognition. In this work, the effectiveness of signals from tripolar concentric electrodes and disc electrodes were compared for use as a BCI. Two sets of left/right hand motor imagery EEG signals were acquired. An autoregressive (AR) model was developed for feature extraction with a Mahalanobis distance based linear classifier for classification. An exhaust selection algorithm was employed to analyze three factors before feature extraction. The factors analyzed were 1) length of data in each trial to be used, 2) start position of data, and 3) the order of the AR model. The results showed that tripolar concentric electrodes generated significantly higher classification accuracy than disc electrodes.

A new qualitative pattern classification of shear wave elastograghy for solid breast mass evaluation.

PubMed

Cong, Rui; Li, Jing; Guo, Song

2017-02-01

To examine the efficacy of qualitative shear wave elastography (SWE) in the classification and evaluation of solid breast masses, and to compare this method with conventional ultrasonograghy (US), quantitative SWE parameters and qualitative SWE classification proposed before. From April 2015 to March 2016, 314 consecutive females with 325 breast masses who decided to undergo core needle biopsy and/or surgical biopsy were enrolled. Conventional US and SWE were previously performed in all enrolled subjects. Each mass was classified by two different qualitative classifications. One was established in our study, herein named the Qual1. Qual1 could classify the SWE images into five color patterns by the visual evaluations: Color pattern 1 (homogeneous pattern); Color pattern 2 (comparative homogeneous pattern); Color pattern 3 (irregularly heterogeneous pattern); Color pattern 4 (intralesional echo pattern); and Color pattern 5 (the stiff rim sign pattern). The second qualitative classification was named Qual2 here, and included a four-color overlay pattern classification (Tozaki and Fukuma, Acta Radiologica, 2011). The Breast Imaging Reporting and Data System (BI-RADS) assessment and quantitative SWE parameters were recorded. Diagnostic performances of conventional US, SWE parameters, and combinations of US and SWE parameters were compared. With pathological results as the gold standard, of the 325 examined breast masses, 139 (42.77%) samples were malignant and 186 (57.23%) were benign. The Qual1 showed a higher Az value than the Qual2 and quantitative SWE parameters (all P<0.05). When applying Qual1=Color pattern 1 for downgrading and Qual1=Color pattern 5 for upgrading the BI-RADS categories, we obtained the highest Az value (0.951), and achieved a significantly higher specificity (86.56%, P=0.002) than that of the US (81.18%) with the same sensitivity (94.96%). The qualitative classification proposed in this study may be representative of SWE parameters and has potential to be relevant assistance in breast mass diagnoses. Copyright © 2016. Published by Elsevier B.V.

14 CFR 1203.203 - Degree of protection.

Code of Federal Regulations, 2012 CFR

2012-01-01

... be safeguarded as if it were classified pending a determination by an original classification... appropriate level of classification, it shall be safeguarded at the higher level of classification pending a determination by an original classification authority, who shall make this determination within 30 days. (b...

14 CFR § 1203.203 - Degree of protection.

Code of Federal Regulations, 2014 CFR

2014-01-01

... classification authority, who shall make this determination within 30 days. If there is reasonable doubt about the appropriate level of classification, it shall be safeguarded at the higher level of classification pending a determination by an original classification authority, who shall make this determination within...

14 CFR 1203.203 - Degree of protection.

Code of Federal Regulations, 2013 CFR

2013-01-01

... be safeguarded as if it were classified pending a determination by an original classification... appropriate level of classification, it shall be safeguarded at the higher level of classification pending a determination by an original classification authority, who shall make this determination within 30 days. (b...

Statistical sensor fusion of ECG data using automotive-grade sensors

NASA Astrophysics Data System (ADS)

Koenig, A.; Rehg, T.; Rasshofer, R.

2015-11-01

Driver states such as fatigue, stress, aggression, distraction or even medical emergencies continue to be yield to severe mistakes in driving and promote accidents. A pathway towards improving driver state assessment can be found in psycho-physiological measures to directly quantify the driver's state from physiological recordings. Although heart rate is a well-established physiological variable that reflects cognitive stress, obtaining heart rate contactless and reliably is a challenging task in an automotive environment. Our aim was to investigate, how sensory fusion of two automotive grade sensors would influence the accuracy of automatic classification of cognitive stress levels. We induced cognitive stress in subjects and estimated levels from their heart rate signals, acquired from automotive ready ECG sensors. Using signal quality indices and Kalman filters, we were able to decrease Root Mean Squared Error (RMSE) of heart rate recordings by 10 beats per minute. We then trained a neural network to classify the cognitive workload state of subjects from heart rate and compared classification performance for ground truth, the individual sensors and the fused heart rate signal. We obtained an increase of 5 % higher correct classification by fusing signals as compared to individual sensors, staying only 4 % below the maximally possible classification accuracy from ground truth. These results are a first step towards real world applications of psycho-physiological measurements in vehicle settings. Future implementations of driver state modeling will be able to draw from a larger pool of data sources, such as additional physiological values or vehicle related data, which can be expected to drive classification to significantly higher values.

Breast density characterization using texton distributions.

PubMed

Petroudi, Styliani; Brady, Michael

2011-01-01

Breast density has been shown to be one of the most significant risks for developing breast cancer, with women with dense breasts at four to six times higher risk. The Breast Imaging Reporting and Data System (BI-RADS) has a four class classification scheme that describes the different breast densities. However, there is great inter and intra observer variability among clinicians in reporting a mammogram's density class. This work presents a novel texture classification method and its application for the development of a completely automated breast density classification system. The new method represents the mammogram using textons, which can be thought of as the building blocks of texture under the operational definition of Leung and Malik as clustered filter responses. The new proposed method characterizes the mammographic appearance of the different density patterns by evaluating the texton spatial dependence matrix (TDSM) in the breast region's corresponding texton map. The TSDM is a texture model that captures both statistical and structural texture characteristics. The normalized TSDM matrices are evaluated for mammograms from the different density classes and corresponding texture models are established. Classification is achieved using a chi-square distance measure. The fully automated TSDM breast density classification method is quantitatively evaluated on mammograms from all density classes from the Oxford Mammogram Database. The incorporation of texton spatial dependencies allows for classification accuracy reaching over 82%. The breast density classification accuracy is better using texton TSDM compared to simple texton histograms.

Novel Strength Test Battery to Permit Evidence-Based Paralympic Classification

PubMed Central

Beckman, Emma M.; Newcombe, Peter; Vanlandewijck, Yves; Connick, Mark J.; Tweedy, Sean M.

2014-01-01

Abstract Ordinal-scale strength assessment methods currently used in Paralympic athletics classification prevent the development of evidence-based classification systems. This study evaluated a battery of 7, ratio-scale, isometric tests with the aim of facilitating the development of evidence-based methods of classification. This study aimed to report sex-specific normal performance ranges, evaluate test–retest reliability, and evaluate the relationship between the measures and body mass. Body mass and strength measures were obtained from 118 participants—63 males and 55 females—ages 23.2 years ± 3.7 (mean ± SD). Seventeen participants completed the battery twice to evaluate test–retest reliability. The body mass–strength relationship was evaluated using Pearson correlations and allometric exponents. Conventional patterns of force production were observed. Reliability was acceptable (mean intraclass correlation = 0.85). Eight measures had moderate significant correlations with body size (r = 0.30–61). Allometric exponents were higher in males than in females (mean 0.99 vs 0.30). Results indicate that this comprehensive and parsimonious battery is an important methodological advance because it has psychometric properties critical for the development of evidence-based classification. Measures were interrelated with body size, indicating further research is required to determine whether raw measures require normalization in order to be validly applied in classification. PMID:25068950

The morphology and classification of α ganglion cells in the rat retinae: a fractal analysis study.

PubMed

Jelinek, Herbert F; Ristanović, Dušan; Milošević, Nebojša T

2011-09-30

Rat retinal ganglion cells have been proposed to consist of a varying number of subtypes. Dendritic morphology is an essential aspect of classification and a necessary step toward understanding structure-function relationships of retinal ganglion cells. This study aimed at using a heuristic classification procedure in combination with the box-counting analysis to classify the alpha ganglion cells in the rat retinae based on the dendritic branching pattern and to investigate morphological changes with retinal eccentricity. The cells could be divided into two groups: cells with simple dendritic pattern (box dimension lower than 1.390) and cells with complex dendritic pattern (box dimension higher than 1.390) according to their dendritic branching pattern complexity. Both were further divided into two subtypes due to the stratification within the inner plexiform layer. In the present study we have shown that the alpha rat RCGs can be classified further by their dendritic branching complexity and thus extend those of previous reports that fractal analysis can be successfully used in neuronal classification, particularly that the fractal dimension represents a robust and sensitive tool for the classification of retinal ganglion cells. A hypothesis of possible functional significance of our classification scheme is also discussed. Copyright © 2011 Elsevier B.V. All rights reserved.

Word pair classification during imagined speech using direct brain recordings

NASA Astrophysics Data System (ADS)

Martin, Stephanie; Brunner, Peter; Iturrate, Iñaki; Millán, José Del R.; Schalk, Gerwin; Knight, Robert T.; Pasley, Brian N.

2016-05-01

People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70-150 Hz) time features with a support vector machine model to classify individual words from a pair of words. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the SVM kernel. Classification accuracy reached 88% in a two-class classification framework (50% chance level), and average classification accuracy across fifteen word-pairs was significant across five subjects (mean = 58% p < 0.05). We also compared classification accuracy between imagined speech, overt speech and listening. As predicted, higher classification accuracy was obtained in the listening and overt speech conditions (mean = 89% and 86%, respectively; p < 0.0001), where speech stimuli were directly presented. The results provide evidence for a neural representation for imagined words in the temporal lobe, frontal lobe and sensorimotor cortex, consistent with previous findings in speech perception and production. These data represent a proof of concept study for basic decoding of speech imagery, and delineate a number of key challenges to usage of speech imagery neural representations for clinical applications.

Word pair classification during imagined speech using direct brain recordings

PubMed Central

Martin, Stephanie; Brunner, Peter; Iturrate, Iñaki; Millán, José del R.; Schalk, Gerwin; Knight, Robert T.; Pasley, Brian N.

2016-01-01

People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used high gamma (70–150 Hz) time features with a support vector machine model to classify individual words from a pair of words. To account for temporal irregularities during speech production, we introduced a non-linear time alignment into the SVM kernel. Classification accuracy reached 88% in a two-class classification framework (50% chance level), and average classification accuracy across fifteen word-pairs was significant across five subjects (mean = 58%; p < 0.05). We also compared classification accuracy between imagined speech, overt speech and listening. As predicted, higher classification accuracy was obtained in the listening and overt speech conditions (mean = 89% and 86%, respectively; p < 0.0001), where speech stimuli were directly presented. The results provide evidence for a neural representation for imagined words in the temporal lobe, frontal lobe and sensorimotor cortex, consistent with previous findings in speech perception and production. These data represent a proof of concept study for basic decoding of speech imagery, and delineate a number of key challenges to usage of speech imagery neural representations for clinical applications. PMID:27165452

American College of Cardiology/American Heart Association/European Society of Cardiology/World Heart Federation universal definition of myocardial infarction classification system and the risk of cardiovascular death: observations from the TRITON-TIMI 38 trial (Trial to Assess Improvement in Therapeutic Outcomes by Optimizing Platelet Inhibition With Prasugrel-Thrombolysis in Myocardial Infarction 38).

PubMed

Bonaca, Marc P; Wiviott, Stephen D; Braunwald, Eugene; Murphy, Sabina A; Ruff, Christian T; Antman, Elliott M; Morrow, David A

2012-01-31

The availability of more sensitive biomarkers of myonecrosis and a new classification system from the universal definition of myocardial infarction (MI) have led to evolution of the classification of MI. The prognostic implications of MI defined in the current era have not been well described. We investigated the association between new or recurrent MI by subtype according to the European Society of Cardiology/American College of Cardiology/American Heart Association/World Health Federation Task Force for the Redefinition of MI Classification System and the risk of cardiovascular death among 13 608 patients with acute coronary syndrome in the Trial to Assess Improvement in Therapeutic Outcomes by Optimizing Platelet Inhibition with Prasugrel-Thrombolysis in Myocardial Infarction 38 (TRITON-TIMI 38). The adjusted risk of cardiovascular death was evaluated by landmark analysis starting at the time of the MI through 180 days after the event. Patients who experienced an MI during follow-up had a higher risk of cardiovascular death at 6 months than patients without an MI (6.5% versus 1.3%, P<0.001). This higher risk was present across all subtypes of MI, including type 4a (peri-percutaneous coronary intervention, 3.2%; P<0.001) and type 4b (stent thrombosis, 15.4%; P<0.001). After adjustment for important clinical covariates, the occurrence of any MI was associated with a 5-fold higher risk of death at 6 months (95% confidence interval 3.8-7.1), with similarly increased risk across subtypes. MI is associated with a significantly increased risk of cardiovascular death, with a consistent relationship across all types as defined by the universal classification system. These findings underscore the clinical relevance of these events and the importance of therapies aimed at preventing MI.

Towards a ternary NIRS-BCI: single-trial classification of verbal fluency task, Stroop task and unconstrained rest

NASA Astrophysics Data System (ADS)

Schudlo, Larissa C.; Chau, Tom

2015-12-01

Objective. The majority of near-infrared spectroscopy (NIRS) brain-computer interface (BCI) studies have investigated binary classification problems. Limited work has considered differentiation of more than two mental states, or multi-class differentiation of higher-level cognitive tasks using measurements outside of the anterior prefrontal cortex. Improvements in accuracies are needed to deliver effective communication with a multi-class NIRS system. We investigated the feasibility of a ternary NIRS-BCI that supports mental states corresponding to verbal fluency task (VFT) performance, Stroop task performance, and unconstrained rest using prefrontal and parietal measurements. Approach. Prefrontal and parietal NIRS signals were acquired from 11 able-bodied adults during rest and performance of the VFT or Stroop task. Classification was performed offline using bagging with a linear discriminant base classifier trained on a 10 dimensional feature set. Main results. VFT, Stroop task and rest were classified at an average accuracy of 71.7% ± 7.9%. The ternary classification system provided a statistically significant improvement in information transfer rate relative to a binary system controlled by either mental task (0.87 ± 0.35 bits/min versus 0.73 ± 0.24 bits/min). Significance. These results suggest that effective communication can be achieved with a ternary NIRS-BCI that supports VFT, Stroop task and rest via measurements from the frontal and parietal cortices. Further development of such a system is warranted. Accurate ternary classification can enhance communication rates offered by NIRS-BCIs, improving the practicality of this technology.

Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series

NASA Astrophysics Data System (ADS)

Nitze, Ingmar; Barrett, Brian; Cawkwell, Fiona

2015-02-01

The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types and land surface characteristics, the ability to discriminate land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may impede their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance (FI) measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and Grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland based on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalised Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification, the months December and January showed the highest, and July and August the lowest separability for both VIs over the entire nine-year period. This temporal separability was reflected in the classification accuracies, where the optimal choice of image dates outperformed the worst image date by 13% using NDVI and 5% using EVI on a mono-temporal analysis. With the addition of the next best image periods to the data input the classification accuracies converged quickly to their limit at around 8-10 images. The binary classification schemes, using two classes only, showed a stronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the NDVI for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors with a higher spatial resolution. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results.

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

PubMed

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

2014-07-04

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

Analysis of EMG Signals in Aggressive and Normal Activities by Using Higher-Order Spectra

PubMed Central

Sezgin, Necmettin

2012-01-01

The analysis and classification of electromyography (EMG) signals are very important in order to detect some symptoms of diseases, prosthetic arm/leg control, and so on. In this study, an EMG signal was analyzed using bispectrum, which belongs to a family of higher-order spectra. An EMG signal is the electrical potential difference of muscle cells. The EMG signals used in the present study are aggressive or normal actions. The EMG dataset was obtained from the machine learning repository. First, the aggressive and normal EMG activities were analyzed using bispectrum and the quadratic phase coupling of each EMG episode was determined. Next, the features of the analyzed EMG signals were fed into learning machines to separate the aggressive and normal actions. The best classification result was 99.75%, which is sufficient to significantly classify the aggressive and normal actions. PMID:23193379

Differences between conventional and glyphosate tolerant soybeans and moisture effect in their discrimination by near infrared spectroscopy.

PubMed

Esteve Agelet, Lidia; Armstrong, Paul R; Tallada, Jasper G; Hurburgh, Charles R

2013-12-01

Previous studies showed that Near Infrared Spectroscopy (NIRS) could distinguish between Roundup Ready® (RR) and conventional soybeans at the bulk and single seed sample level, but it was not clear which compounds drove the classification. In this research the varieties used did not show significant differences in major compounds between RR and conventional beans, but moisture content had a big impact on classification accuracies. Four of the five RR samples had slightly higher moistures and had a higher water uptake than their conventional counterparts. This could be linked with differences in their hulls, being either compositional or morphological. Because water absorption occurs in the same region as main compounds in hulls (mainly carbohydrates) and water causes physical changes from swelling, variations in moisture cause a complex interaction resulting in a large impact on discrimination accuracies. Copyright © 2013 Elsevier Ltd. All rights reserved.

Management of thoracolumbar spine trauma: An overview

PubMed Central

Rajasekaran, S; Kanna, Rishi Mugesh; Shetty, Ajoy Prasad

2015-01-01

Thoracolumbar spine fractures are common injuries that can result in significant disability, deformity and neurological deficit. Controversies exist regarding the appropriate radiological investigations, the indications for surgical management and the timing, approach and type of surgery. This review provides an overview of the epidemiology, biomechanical principles, radiological and clinical evaluation, classification and management principles. Literature review of all relevant articles published in PubMed covering thoracolumbar spine fractures with or without neurologic deficit was performed. The search terms used were thoracolumbar, thoracic, lumbar, fracture, trauma and management. All relevant articles and abstracts covering thoracolumbar spine fractures with and without neurologic deficit were reviewed. Biomechanically the thoracolumbar spine is predisposed to a higher incidence of spinal injuries. Computed tomography provides adequate bony detail for assessing spinal stability while magnetic resonance imaging shows injuries to soft tissues (posterior ligamentous complex [PLC]) and neurological structures. Different classification systems exist and the most recent is the AO spine knowledge forum classification of thoracolumbar trauma. Treatment includes both nonoperative and operative methods and selected based on the degree of bony injury, neurological involvement, presence of associated injuries and the integrity of the PLC. Significant advances in imaging have helped in the better understanding of thoracolumbar fractures, including information on canal morphology and injury to soft tissue structures. The ideal classification that is simple, comprehensive and guides management is still elusive. Involvement of three columns, progressive neurological deficit, significant kyphosis and canal compromise with neurological deficit are accepted indications for surgical stabilization through anterior, posterior or combined approaches. PMID:25593358

The evaluation of lumbar paraspinal muscle quantity and quality using the Goutallier classification and lumbar indentation value.

PubMed

Tamai, Koji; Chen, Jessica; Stone, Michael; Arakelyan, Anush; Paholpak, Permsak; Nakamura, Hiroaki; Buser, Zorica; Wang, Jeffrey C

2018-05-01

The cross-sectional area and fat infiltration are accepted as standard parameters for quantitative and qualitative evaluation of muscle degeneration. However, they are time-consuming, which prevents them from being used in a clinical setting. The aim of this study was to analyze the relationship between lumbar muscle degeneration and spinal degenerative disorders, using lumbar indentation value (LIV) as quantitative and Goutallier classification as qualitative measures. This is a retrospective analysis of kinematic magnetic resonance images (kMRI). Two-hundred and thirty patients with kMRIs taken in weight-bearing positions were selected randomly. The LIV and Goutallier classification were evaluated at L4-5. The correlation of these two parameters with patients' age, gender, lumbar lordosis (LL), range of motion, disc degeneration, disc height, and Modic change were analyzed. There was no significant trend of LIV among the different grades of Goutallier classification (p = 0.943). There was a significant increase in age with higher grades of Goutallier classification (p < 0.001). In contrast, there was no correlation between LIV and age (p = 0.799). The Goutallier classification positively correlated with LL (r = 0.377) and severe disc degeneration (r = 0.249). The LIV positively correlated with LL (r = 0.476) and degenerative spondylolisthesis (r = 0.184). Multinomial logistic regression analysis showed that age (p = 0.026), gender (p = 0.003), and LIV (p < 0.001) were significant predictors for patients with low LL (< 10°). Lumbar muscle quantity and quality showed specific correlation with age and spine disorders. Additionally, LL can be predicted by the muscle quantity, but not the quality. These time-saving evaluation tools potentially accelerate the study of lumbar muscles. These slides can be retrieved under Electronic Supplementary Material.

Object based image analysis for the classification of the growth stages of Avocado crop, in Michoacán State, Mexico

NASA Astrophysics Data System (ADS)

Gao, Yan; Marpu, Prashanth; Morales Manila, Luis M.

2014-11-01

This paper assesses the suitability of 8-band Worldview-2 (WV2) satellite data and object-based random forest algorithm for the classification of avocado growth stages in Mexico. We tested both pixel-based with minimum distance (MD) and maximum likelihood (MLC) and object-based with Random Forest (RF) algorithm for this task. Training samples and verification data were selected by visual interpreting the WV2 images for seven thematic classes: fully grown, middle stage, and early stage of avocado crops, bare land, two types of natural forests, and water body. To examine the contribution of the four new spectral bands of WV2 sensor, all the tested classifications were carried out with and without the four new spectral bands. Classification accuracy assessment results show that object-based classification with RF algorithm obtained higher overall higher accuracy (93.06%) than pixel-based MD (69.37%) and MLC (64.03%) method. For both pixel-based and object-based methods, the classifications with the four new spectral bands (overall accuracy obtained higher accuracy than those without: overall accuracy of object-based RF classification with vs without: 93.06% vs 83.59%, pixel-based MD: 69.37% vs 67.2%, pixel-based MLC: 64.03% vs 36.05%, suggesting that the four new spectral bands in WV2 sensor contributed to the increase of the classification accuracy.

Objected-oriented remote sensing image classification method based on geographic ontology model

NASA Astrophysics Data System (ADS)

Chu, Z.; Liu, Z. J.; Gu, H. Y.

2016-11-01

Nowadays, with the development of high resolution remote sensing image and the wide application of laser point cloud data, proceeding objected-oriented remote sensing classification based on the characteristic knowledge of multi-source spatial data has been an important trend on the field of remote sensing image classification, which gradually replaced the traditional method through improving algorithm to optimize image classification results. For this purpose, the paper puts forward a remote sensing image classification method that uses the he characteristic knowledge of multi-source spatial data to build the geographic ontology semantic network model, and carries out the objected-oriented classification experiment to implement urban features classification, the experiment uses protégé software which is developed by Stanford University in the United States, and intelligent image analysis software—eCognition software as the experiment platform, uses hyperspectral image and Lidar data that is obtained through flight in DaFeng City of JiangSu as the main data source, first of all, the experiment uses hyperspectral image to obtain feature knowledge of remote sensing image and related special index, the second, the experiment uses Lidar data to generate nDSM(Normalized DSM, Normalized Digital Surface Model),obtaining elevation information, the last, the experiment bases image feature knowledge, special index and elevation information to build the geographic ontology semantic network model that implement urban features classification, the experiment results show that, this method is significantly higher than the traditional classification algorithm on classification accuracy, especially it performs more evidently on the respect of building classification. The method not only considers the advantage of multi-source spatial data, for example, remote sensing image, Lidar data and so on, but also realizes multi-source spatial data knowledge integration and application of the knowledge to the field of remote sensing image classification, which provides an effective way for objected-oriented remote sensing image classification in the future.

Effect of higher frequency on the classification of steady-state visual evoked potentials

NASA Astrophysics Data System (ADS)

Won, Dong-Ok; Hwang, Han-Jeong; Dähne, Sven; Müller, Klaus-Robert; Lee, Seong-Whan

2016-02-01

Objective. Most existing brain-computer interface (BCI) designs based on steady-state visual evoked potentials (SSVEPs) primarily use low frequency visual stimuli (e.g., <20 Hz) to elicit relatively high SSVEP amplitudes. While low frequency stimuli could evoke photosensitivity-based epileptic seizures, high frequency stimuli generally show less visual fatigue and no stimulus-related seizures. The fundamental objective of this study was to investigate the effect of stimulation frequency and duty-cycle on the usability of an SSVEP-based BCI system. Approach. We developed an SSVEP-based BCI speller using multiple LEDs flickering with low frequencies (6-14.9 Hz) with a duty-cycle of 50%, or higher frequencies (26-34.7 Hz) with duty-cycles of 50%, 60%, and 70%. The four different experimental conditions were tested with 26 subjects in order to investigate the impact of stimulation frequency and duty-cycle on performance and visual fatigue, and evaluated with a questionnaire survey. Resting state alpha powers were utilized to interpret our results from the neurophysiological point of view. Main results. The stimulation method employing higher frequencies not only showed less visual fatigue, but it also showed higher and more stable classification performance compared to that employing relatively lower frequencies. Different duty-cycles in the higher frequency stimulation conditions did not significantly affect visual fatigue, but a duty-cycle of 50% was a better choice with respect to performance. The performance of the higher frequency stimulation method was also less susceptible to resting state alpha powers, while that of the lower frequency stimulation method was negatively correlated with alpha powers. Significance. These results suggest that the use of higher frequency visual stimuli is more beneficial for performance improvement and stability as time passes when developing practical SSVEP-based BCI applications.

A higher order conditional random field model for simultaneous classification of land cover and land use

NASA Astrophysics Data System (ADS)

Albert, Lena; Rottensteiner, Franz; Heipke, Christian

2017-08-01

We propose a new approach for the simultaneous classification of land cover and land use considering spatial as well as semantic context. We apply a Conditional Random Fields (CRF) consisting of a land cover and a land use layer. In the land cover layer of the CRF, the nodes represent super-pixels; in the land use layer, the nodes correspond to objects from a geospatial database. Intra-layer edges of the CRF model spatial dependencies between neighbouring image sites. All spatially overlapping sites in both layers are connected by inter-layer edges, which leads to higher order cliques modelling the semantic relation between all land cover and land use sites in the clique. A generic formulation of the higher order potential is proposed. In order to enable efficient inference in the two-layer higher order CRF, we propose an iterative inference procedure in which the two classification tasks mutually influence each other. We integrate contextual relations between land cover and land use in the classification process by using contextual features describing the complex dependencies of all nodes in a higher order clique. These features are incorporated in a discriminative classifier, which approximates the higher order potentials during the inference procedure. The approach is designed for input data based on aerial images. Experiments are carried out on two test sites to evaluate the performance of the proposed method. The experiments show that the classification results are improved compared to the results of a non-contextual classifier. For land cover classification, the result is much more homogeneous and the delineation of land cover segments is improved. For the land use classification, an improvement is mainly achieved for land use objects showing non-typical characteristics or similarities to other land use classes. Furthermore, we have shown that the size of the super-pixels has an influence on the level of detail of the classification result, but also on the degree of smoothing induced by the segmentation method, which is especially beneficial for land cover classes covering large, homogeneous areas.

Position Between Trunk and Pelvis During Gait Depending on the Gross Motor Function Classification System.

PubMed

Sanz-Mengibar, Jose Manuel; Altschuck, Natalie; Sanchez-de-Muniain, Paloma; Bauer, Christian; Santonja-Medina, Fernando

2017-04-01

To understand whether there is a trunk postural control threshold in the sagittal plane for the transition between the Gross Motor Function Classification System (GMFCS) levels measured with 3-dimensional gait analysis. Kinematics from 97 children with spastic bilateral cerebral palsy from spine angles according to Plug-In Gait model (Vicon) were plotted relative to their GMFCS level. Only average and minimum values of the lumbar spine segment correlated with GMFCS levels. Maximal values at loading response correlated independently with age at all functional levels. Average and minimum values were significant when analyzing age in combination with GMFCS level. There are specific postural control patterns in the average and minimum values for the position between trunk and pelvis in the sagittal plane during gait, for the transition among GMFCS I-III levels. Higher classifications of gross motor skills correlate with more extended spine angles.

Classifications of central solar domestic hot water systems

NASA Astrophysics Data System (ADS)

Guo, J. Y.; Hao, B.; Peng, C.; Wang, S. S.

2016-08-01

Currently, there are many means by which to classify solar domestic hot water systems, which are often categorized according to their scope of supply, solar collector positions, and type of heat storage tank. However, the lack of systematic and scientific classification as well as the general disregard of the thermal performance of the auxiliary heat source is important to DHW systems. Thus, the primary focus of this paper is to determine a classification system for solar domestic hot water systems based on the positions of the solar collector and auxiliary heating device, both respectively and in combination. Field-testing data regarding many central solar DHW systems demonstrates that the position of the auxiliary heat source clearly reflects the operational energy consumption. The consumption of collective auxiliary heating hot water system is much higher than individual auxiliary heating hot water system. In addition, costs are significantly reduced by the separation of the heat storage tank and the auxiliary heating device.

The neutrophil to lymphocyte ratios of our pediatric patients with Bell's palsy.

PubMed

Eryilmaz, Aylin; Basal, Yesim; Tosun, Ayse; Kurt Omurlu, Imran; Basak, Sema

2015-12-01

Neutrophil to Lymphocyte Ratio (NLR) is considered to be a reliable indicator in etiological investigation and identification of the disease severity in inflammatory disorders. There are numerous observations or evidences suggesting that Bell's palsy is an inflammatory disorder. Our aim was to investigate the presence of any clue which might suggest inflammatory etiology and also the presence of compliance between NLR elevation and inflammation severity in children. Patients younger than 18 years with Bell's palsy and who had not another inflammatory disorder in addition to Bell's palsy were included. A total of 25 patients and 25 healthy individuals were taken. The patient group and the control group were compared in terms of NLR, neutrophil and lymphocytes. The relationship of NLR with pre-treatment House-Brackmann classification was evaluated. The mean age was 9.86±5.07 in the patient group and 9.14±5.94 in the control group. In all members of the patient group, oral prednisolone (1 mg/kg/d) was administered for 7 days. The post-treatment House-Brackmann classification of all patients was determined as grade 1. The average neutrophil values were significantly higher in the patient group. In terms of average lymphocyte values, no statistically significant difference was found. The average NLR value was 1.78 (0.93-4.58) in the pediatric patient group and 1.1 (0.6-2.05) in the control group. NLR was significantly higher in the patient group. NLR and pre-treatment House-Brackmann classification showed no statistically significant correlation (r=0.173, p>0.05). When cut-off value was taken as 3 for NLR, no statistically significant difference was found between groups. High NLR values determined in pediatric patients with Bell's palsy support the inflammatory feature of this disease. NLR is recommended as a supportive parameter in the diagnosis of pediatric patients with Bell's palsy. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

32 CFR 2700.12 - Criteria for and level of original classification.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Criteria for and level of original classification. (a) General Policy. Documents or other material are to... authorized or shall have force. (d) Unnecessary classification, and classification at a level higher than is... 32 National Defense 6 2010-07-01 2010-07-01 false Criteria for and level of original...

The prognostic efficacy and improvements of the 7th edition Union for International Cancer Control tumor-node-metastasis classifications for Chinese patients with gastric cancer: Results based on a retrospective three-decade population study.

PubMed

Gu, Huizi; Li, Dongmei; Zhu, Haitao; Zhang, Hao; Yu, Ying; Qin, Dongxue; Yi, Mei; Li, Xiang; Lu, Ping

2017-03-01

This study aimed to evaluate survival trends for patients with gastric cancer in northeast China in the most recent three decades and analyze the applicability of the UICC tumor-node-metastasis (TNM) classification 7th edition for Chinese patients with gastric cancer. A review of all inpatient and outpatient records of patients with gastric cancer was conducted in the first hospital of China Medical University and the Liaoning Cancer Hospital and Institute. All patients who met the inclusion criteria and were seen from January 1980 through December 2009 were included in the study. The primary outcome was 5-year survival, which was analyzed according to decade of diagnosis and TNM classifications. From 1980 through 2009, the 5-year survival rates for patients with gastric cancer (n=2414) increased from 39.1% to 57.3%. Decade of diagnosis was significantly associated with patient survival (p = 0.013), and the 5-year survival rate in the 2000s was remarkably higher than that in the 1980s and 1990s (p = 0.004 and 0.049, respectively). When classified according to the UICC TNM classification of gastric cancer 7th edition, the prognoses of stage IIIA and stage IIIB patients were not significantly different (p = 0.077). However, if stage T4b and stage N0 patients were classified as stage IIIA, the prognoses of stage IIIA and stage IIIB patients were significantly different (p < 0.001). Hence, there was a significant difference in survival during the three time periods in Northeast China. Classifying stage T4b and stage N0 patients as stage IIIA according to the 7th edition of UICC gastric cancer TNM classifications better stratified Chinese patients and predicted prognoses.

Exploring the impact of target eccentricity and task difficulty on covert visual spatial attention and its implications for brain computer interfacing.

PubMed

Roijendijk, Linsey; Farquhar, Jason; van Gerven, Marcel; Jensen, Ole; Gielen, Stan

2013-01-01

Covert visual spatial attention is a relatively new task used in brain computer interfaces (BCIs) and little is known about the characteristics which may affect performance in BCI tasks. We investigated whether eccentricity and task difficulty affect alpha lateralization and BCI performance. We conducted a magnetoencephalography study with 14 participants who performed a covert orientation discrimination task at an easy or difficult stimulus contrast at either a near (3.5°) or far (7°) eccentricity. Task difficulty was manipulated block wise and subjects were aware of the difficulty level of each block. Grand average analyses revealed a significantly larger hemispheric lateralization of posterior alpha power in the difficult condition than in the easy condition, while surprisingly no difference was found for eccentricity. The difference between task difficulty levels was significant in the interval between 1.85 s and 2.25 s after cue onset and originated from a stronger decrease in the contralateral hemisphere. No significant effect of eccentricity was found. Additionally, single-trial classification analysis revealed a higher classification rate in the difficult (65.9%) than in the easy task condition (61.1%). No effect of eccentricity was found in classification rate. Our results indicate that manipulating the difficulty of a task gives rise to variations in alpha lateralization and that using a more difficult task improves covert visual spatial attention BCI performance. The variations in the alpha lateralization could be caused by different factors such as an increased mental effort or a higher visual attentional demand. Further research is necessary to discriminate between them. We did not discover any effect of eccentricity in contrast to results of previous research.

Drug related webpages classification using images and text information based on multi-kernel learning

NASA Astrophysics Data System (ADS)

Hu, Ruiguang; Xiao, Liping; Zheng, Wenjuan

2015-12-01

In this paper, multi-kernel learning(MKL) is used for drug-related webpages classification. First, body text and image-label text are extracted through HTML parsing, and valid images are chosen by the FOCARSS algorithm. Second, text based BOW model is used to generate text representation, and image-based BOW model is used to generate images representation. Last, text and images representation are fused with a few methods. Experimental results demonstrate that the classification accuracy of MKL is higher than those of all other fusion methods in decision level and feature level, and much higher than the accuracy of single-modal classification.

Tensor-based classification of an auditory mobile BCI without a subject-specific calibration phase

NASA Astrophysics Data System (ADS)

Zink, Rob; Hunyadi, Borbála; Van Huffel, Sabine; De Vos, Maarten

2016-04-01

Objective. One of the major drawbacks in EEG brain-computer interfaces (BCI) is the need for subject-specific training of the classifier. By removing the need for a supervised calibration phase, new users could potentially explore a BCI faster. In this work we aim to remove this subject-specific calibration phase and allow direct classification. Approach. We explore canonical polyadic decompositions and block term decompositions of the EEG. These methods exploit structure in higher dimensional data arrays called tensors. The BCI tensors are constructed by concatenating ERP templates from other subjects to a target and non-target trial and the inherent structure guides a decomposition that allows accurate classification. We illustrate the new method on data from a three-class auditory oddball paradigm. Main results. The presented approach leads to a fast and intuitive classification with accuracies competitive with a supervised and cross-validated LDA approach. Significance. The described methods are a promising new way of classifying BCI data with a forthright link to the original P300 ERP signal over the conventional and widely used supervised approaches.

Classification of pulmonary pathology from breath sounds using the wavelet packet transform and an extreme learning machine.

PubMed

Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian; Huliraj, N; Revadi, S S

2017-06-08

Auscultation is a medical procedure used for the initial diagnosis and assessment of lung and heart diseases. From this perspective, we propose assessing the performance of the extreme learning machine (ELM) classifiers for the diagnosis of pulmonary pathology using breath sounds. Energy and entropy features were extracted from the breath sound using the wavelet packet transform. The statistical significance of the extracted features was evaluated by one-way analysis of variance (ANOVA). The extracted features were inputted into the ELM classifier. The maximum classification accuracies obtained for the conventional validation (CV) of the energy and entropy features were 97.36% and 98.37%, respectively, whereas the accuracies obtained for the cross validation (CRV) of the energy and entropy features were 96.80% and 97.91%, respectively. In addition, maximum classification accuracies of 98.25% and 99.25% were obtained for the CV and CRV of the ensemble features, respectively. The results indicate that the classification accuracy obtained with the ensemble features was higher than those obtained with the energy and entropy features.

Excessive bodybuilding as pathology? A first neurophysiological classification.

PubMed

Maier, Moritz Julian; Haeussinger, Florian Benedikt; Hautzinger, Martin; Fallgatter, Andreas Jochen; Ehlis, Ann-Christine

2017-11-15

Excessive bodybuilding as a pathological syndrome has been classified based on two different theories: bodybuilding as dependency or as muscle dysmorphic disorder (MDD). This study is a first attempt to find psychophysiological data supporting one of these classifications. Twenty-four participants (bodybuilders vs healthy controls) were presented with pictures of bodies, exercise equipment or general reward stimuli in a control or experimental condition, and were measured with functional near-infrared spectroscopy (fNIRS). Higher activation in the dorsolateral prefrontal cortex (DLPFC) and the orbitofrontal cortex (OFC) while watching bodies and training equipment in the experimental condition (muscular bodies and bodybuilding-typical equipment) would be an indicator for the addiction theory. Higher activation in motion-related areas would be an indicator for the MDD theory. We found no task-related differences between the groups in the DLPFC and OFC, but a significantly higher activation in bodybuilders in the primary somatosensory cortex (PSC) and left-hemispheric supplementary motor area (SMA) while watching body pictures (across conditions) as compared to the control group. These neurophysiological results could be interpreted as a first evidence for the MDD theory of excessive bodybuilding.

Computer-aided analysis of Skylab multispectral scanner data in mountainous terrain for land use, forestry, water resource, and geologic applications

NASA Technical Reports Server (NTRS)

Hoffer, R. M. (Principal Investigator)

1975-01-01

The author has identified the following significant results. One of the most significant results of this Skylab research involved the geometric correction and overlay of the Skylab multispectral scanner data with the LANDSAT multispectral scanner data, and also with a set of topographic data, including elevation, slope, and aspect. The Skylab S192 multispectral scanner data had distinct differences in noise level of the data in the various wavelength bands. Results of the temporal evaluation of the SL-2 and SL-3 photography were found to be particularly important for proper interpretation of the computer-aided analysis of the SL-2 and SL-3 multispectral scanner data. There was a quality problem involving the ringing effect introduced by digital filtering. The modified clustering technique was found valuable when working with multispectral scanner data involving many wavelength bands and covering large geographic areas. Analysis of the SL-2 scanner data involved classification of major cover types and also forest cover types. Comparison of the results obtained wth Skylab MSS data and LANDSAT MSS data indicated that the improved spectral resolution of the Skylab scanner system enabled a higher classification accuracy to be obtained for forest cover types, although the classification performance for major cover types was not significantly different.

An examination of the identity development of African American undergraduate engineering students attending an HBCU

NASA Astrophysics Data System (ADS)

Taylor, Kenneth J.

This study examined the identity development for a sample of 90 African American undergraduate engineering male and female students attending an HBCU. Using the Student Development Task and Lifestyle Assessment (SDTLA), which is based on Chickering and Reisser's identity development theory, differences in identity development were examined with respect to gender, academic classification, and grade point average. Previous research has shown the need to look beyond academic factors to understand and influence the persistence of African American engineering students. Non-cognitive factors, including identity development have proven to be influential in predicting persistence, especially for African American engineering students. Results from the analysis revealed significant means for academic classification and five of the dependent variables to include career planning peer relations, emotional autonomy, educational involvement, and establishing and clarifying purpose. Post hoc analysis confirmed significant differences for four of those dependent variables. However, the analysis failed to confirm statistical significant differences in peer relations due to academic classification. The significant decline in the mean scores for development in these four areas, as students progressed from sophomore to senior year revealed strong implications for the need to provide programming and guidance for those students. Institutions of higher education should provide more attention to the non-cognitive areas of development as a means of understanding identity development and working toward creating support systems for students.

Fully Convolutional Networks for Ground Classification from LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

Rizaldy, A.; Persello, C.; Gevaert, C. M.; Oude Elberink, S. J.

2018-05-01

Deep Learning has been massively used for image classification in recent years. The use of deep learning for ground classification from LIDAR point clouds has also been recently studied. However, point clouds need to be converted into an image in order to use Convolutional Neural Networks (CNNs). In state-of-the-art techniques, this conversion is slow because each point is converted into a separate image. This approach leads to highly redundant computation during conversion and classification. The goal of this study is to design a more efficient data conversion and ground classification. This goal is achieved by first converting the whole point cloud into a single image. The classification is then performed by a Fully Convolutional Network (FCN), a modified version of CNN designed for pixel-wise image classification. The proposed method is significantly faster than state-of-the-art techniques. On the ISPRS Filter Test dataset, it is 78 times faster for conversion and 16 times faster for classification. Our experimental analysis on the same dataset shows that the proposed method results in 5.22 % of total error, 4.10 % of type I error, and 15.07 % of type II error. Compared to the previous CNN-based technique and LAStools software, the proposed method reduces the total error and type I error (while type II error is slightly higher). The method was also tested on a very high point density LIDAR point clouds resulting in 4.02 % of total error, 2.15 % of type I error and 6.14 % of type II error.

Integrative image segmentation optimization and machine learning approach for high quality land-use and land-cover mapping using multisource remote sensing data

NASA Astrophysics Data System (ADS)

Gibril, Mohamed Barakat A.; Idrees, Mohammed Oludare; Yao, Kouame; Shafri, Helmi Zulhaidi Mohd

2018-01-01

The growing use of optimization for geographic object-based image analysis and the possibility to derive a wide range of information about the image in textual form makes machine learning (data mining) a versatile tool for information extraction from multiple data sources. This paper presents application of data mining for land-cover classification by fusing SPOT-6, RADARSAT-2, and derived dataset. First, the images and other derived indices (normalized difference vegetation index, normalized difference water index, and soil adjusted vegetation index) were combined and subjected to segmentation process with optimal segmentation parameters obtained using combination of spatial and Taguchi statistical optimization. The image objects, which carry all the attributes of the input datasets, were extracted and related to the target land-cover classes through data mining algorithms (decision tree) for classification. To evaluate the performance, the result was compared with two nonparametric classifiers: support vector machine (SVM) and random forest (RF). Furthermore, the decision tree classification result was evaluated against six unoptimized trials segmented using arbitrary parameter combinations. The result shows that the optimized process produces better land-use land-cover classification with overall classification accuracy of 91.79%, 87.25%, and 88.69% for SVM and RF, respectively, while the results of the six unoptimized classifications yield overall accuracy between 84.44% and 88.08%. Higher accuracy of the optimized data mining classification approach compared to the unoptimized results indicates that the optimization process has significant impact on the classification quality.

Association of Krouse Classification for Sinonasal Inverted Papilloma With Recurrence: A Systematic Review and Meta-analysis.

PubMed

Lisan, Quentin; Moya-Plana, Antoine; Bonfils, Pierre

2017-11-01

The risk factors for the recurrence of sinonasal inverted papilloma are still unclear. To investigate the potential association between the Krouse classification and the recurrence rates of sinonasal inverted papilloma. The EMBASE and MEDLINE databases were searched for the period January 1, 1964, through September 30, 2016, using the following search strategy: (paranasal sinuses [Medical Subject Headings (MeSH) terms] OR sinonasal [all fields]) AND (inverted papilloma [MeSH terms] OR (inverted [all fields] AND papilloma [all fields]). The inclusion criteria were (1) studies including sinonasal inverted papilloma only and no other forms of papillomas, such as oncocytic papilloma; (2) minimum follow-up of 1 year after the surgery; and (3) clear report of cases (recurrence) and controls according to the Krouse classification system or deducible from the full-text article. Literature search was performed by 2 reviewers. Of the 625 articles retrieved in the literature, 97 full-text articles were reviewed. Observational cohort studies or randomized controlled trials were included, and the following variables were extracted from full-text articles: authors of the study, publication year, follow-up data, and number of cases (recurrence) and controls (no recurrence) in each of the 4 stages of the Krouse classification system. The Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines were followed. Odds ratios (ORs) and 95% CIs were estimated, and data of included studies were pooled using a random-effects model. The main outcome was recurrence after surgical removal of sinonasal inverted papilloma according to each stage of the Krouse classification system. Thirteen studies comprising 1787 patients were analyzed. A significant increased risk of recurrence (51%) was highlighted for Krouse stage T3 disease when compared with stage T2 (pooled OR, 1.51; 95% CI, 1.09-2.09). No significant difference in risk of recurrence was found between Krouse stages T1 and T2 disease (pooled OR, 1.14; 95% CI, 0.63-2.04) or between stages T3 and T4 (pooled OR, 1.27; 95% CI, 0.72-2.26). Inverted papillomas classified as stage T3 according to the Krouse classification system presented a 51% higher likelihood of recurrence. Head and neck surgeons must be aware of this higher likelihood of recurrence when planning and performing surgery for sinonasal inverted papilloma.

Application of classification tree and logistic regression for the management and health intervention plans in a community-based study.

PubMed

Teng, Ju-Hsi; Lin, Kuan-Chia; Ho, Bin-Shenq

2007-10-01

A community-based aboriginal study was conducted and analysed to explore the application of classification tree and logistic regression. A total of 1066 aboriginal residents in Yilan County were screened during 2003-2004. The independent variables include demographic characteristics, physical examinations, geographic location, health behaviours, dietary habits and family hereditary diseases history. Risk factors of cardiovascular diseases were selected as the dependent variables in further analysis. The completion rate for heath interview is 88.9%. The classification tree results find that if body mass index is higher than 25.72 kg m(-2) and the age is above 51 years, the predicted probability for number of cardiovascular risk factors > or =3 is 73.6% and the population is 322. If body mass index is higher than 26.35 kg m(-2) and geographical latitude of the village is lower than 24 degrees 22.8', the predicted probability for number of cardiovascular risk factors > or =4 is 60.8% and the population is 74. As the logistic regression results indicate that body mass index, drinking habit and menopause are the top three significant independent variables. The classification tree model specifically shows the discrimination paths and interactions between the risk groups. The logistic regression model presents and analyses the statistical independent factors of cardiovascular risks. Applying both models to specific situations will provide a different angle for the design and management of future health intervention plans after community-based study.

Concordance of obesity classification between body mass index and percent body fat among school children in Saudi Arabia.

PubMed

Al-Mohaimeed, Abdulrahman; Ahmed, Saifuddin; Dandash, Khadiga; Ismail, Mohammed Saleh; Saquib, Nazmus

2015-03-05

In Saudi Arabia, where childhood obesity is a major public health issue, it is important to identify the best tool for obesity classification. Hence, we compared two field methods for their usefulness in epidemiological studies. The sample consisted of 874 primary school (grade I-IV) children, aged 6-10 years, and was obtained through a multi-stage random sampling procedure. Weight and height were measured, and BMI (kg/m(2)) was calculated. Percent body fat was determined with a Futrex analyzer that uses near infrared reactance (NIR) technology. Method specific cut-off values were used for obesity classification. Sensitivity, specificity, positive and negative predictive values were determined for BMI, and the agreement between BMI and percent body fat was calculated. Compared to boys, the mean BMI was higher in girls whereas the mean percent body fat was lower (p-values < 0.0001). According to BMI, the prevalence of overweight or obesity was significantly higher in girls (34.3% vs. 17.3%); as oppose to percent body fat, which was similar between the sexes (6.6% vs. 7.0%). The sensitivity of BMI to classify overweight or obesity was high (boys = 93%, girls = 100%); and its false-positive detection rate was also high (boys = 63%, girls = 81%). The agreement rate was low between these two methods (boys = 0.48, girls =0.24). There is poor agreement in obesity classification between BMI and percent body fat, using NIR method, among Saudi school children.

Assessment of land use and land cover change using spatiotemporal analysis of landscape: case study in south of Tehran.

PubMed

Sabr, Abutaleb; Moeinaddini, Mazaher; Azarnivand, Hossein; Guinot, Benjamin

2016-12-01

In the recent years, dust storms originating from local abandoned agricultural lands have increasingly impacted Tehran and Karaj air quality. Designing and implementing mitigation plans are necessary to study land use/land cover change (LUCC). Land use/cover classification is particularly relevant in arid areas. This study aimed to map land use/cover by pixel- and object-based image classification methods, analyse landscape fragmentation and determine the effects of two different classification methods on landscape metrics. The same sets of ground data were used for both classification methods. Because accuracy of classification plays a key role in better understanding LUCC, both methods were employed. Land use/cover maps of the southwest area of Tehran city for the years 1985, 2000 and 2014 were obtained from Landsat digital images and classified into three categories: built-up, agricultural and barren lands. The results of our LUCC analysis showed that the most important changes in built-up agricultural land categories were observed in zone B (Shahriar, Robat Karim and Eslamshahr) between 1985 and 2014. The landscape metrics obtained for all categories pictured high landscape fragmentation in the study area. Despite no significant difference was evidenced between the two classification methods, the object-based classification led to an overall higher accuracy than using the pixel-based classification. In particular, the accuracy of the built-up category showed a marked increase. In addition, both methods showed similar trends in fragmentation metrics. One of the reasons is that the object-based classification is able to identify buildings, impervious surface and roads in dense urban areas, which produced more accurate maps.

Does semi-automatic bone-fragment segmentation improve the reproducibility of the Letournel acetabular fracture classification?

PubMed

Boudissa, M; Orfeuvre, B; Chabanas, M; Tonetti, J

2017-09-01

The Letournel classification of acetabular fracture shows poor reproducibility in inexperienced observers, despite the introduction of 3D imaging. We therefore developed a method of semi-automatic segmentation based on CT data. The present prospective study aimed to assess: (1) whether semi-automatic bone-fragment segmentation increased the rate of correct classification; (2) if so, in which fracture types; and (3) feasibility using the open-source itksnap 3.0 software package without incurring extra cost for users. Semi-automatic segmentation of acetabular fractures significantly increases the rate of correct classification by orthopedic surgery residents. Twelve orthopedic surgery residents classified 23 acetabular fractures. Six used conventional 3D reconstructions provided by the center's radiology department (conventional group) and 6 others used reconstructions obtained by semi-automatic segmentation using the open-source itksnap 3.0 software package (segmentation group). Bone fragments were identified by specific colors. Correct classification rates were compared between groups on Chi 2 test. Assessment was repeated 2 weeks later, to determine intra-observer reproducibility. Correct classification rates were significantly higher in the "segmentation" group: 114/138 (83%) versus 71/138 (52%); P<0.0001. The difference was greater for simple (36/36 (100%) versus 17/36 (47%); P<0.0001) than complex fractures (79/102 (77%) versus 54/102 (53%); P=0.0004). Mean segmentation time per fracture was 27±3min [range, 21-35min]. The segmentation group showed excellent intra-observer correlation coefficients, overall (ICC=0.88), and for simple (ICC=0.92) and complex fractures (ICC=0.84). Semi-automatic segmentation, identifying the various bone fragments, was effective in increasing the rate of correct acetabular fracture classification on the Letournel system by orthopedic surgery residents. It may be considered for routine use in education and training. III: prospective case-control study of a diagnostic procedure. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Three-dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours.

PubMed

Fetit, Ahmed E; Novak, Jan; Peet, Andrew C; Arvanitits, Theodoros N

2015-09-01

The aim of this study was to assess the efficacy of three-dimensional texture analysis (3D TA) of conventional MR images for the classification of childhood brain tumours in a quantitative manner. The dataset comprised pre-contrast T1 - and T2-weighted MRI series obtained from 48 children diagnosed with brain tumours (medulloblastoma, pilocytic astrocytoma and ependymoma). 3D and 2D TA were carried out on the images using first-, second- and higher order statistical methods. Six supervised classification algorithms were trained with the most influential 3D and 2D textural features, and their performances in the classification of tumour types, using the two feature sets, were compared. Model validation was carried out using the leave-one-out cross-validation (LOOCV) approach, as well as stratified 10-fold cross-validation, in order to provide additional reassurance. McNemar's test was used to test the statistical significance of any improvements demonstrated by 3D-trained classifiers. Supervised learning models trained with 3D textural features showed improved classification performances to those trained with conventional 2D features. For instance, a neural network classifier showed 12% improvement in area under the receiver operator characteristics curve (AUC) and 19% in overall classification accuracy. These improvements were statistically significant for four of the tested classifiers, as per McNemar's tests. This study shows that 3D textural features extracted from conventional T1 - and T2-weighted images can improve the diagnostic classification of childhood brain tumours. Long-term benefits of accurate, yet non-invasive, diagnostic aids include a reduction in surgical procedures, improvement in surgical and therapy planning, and support of discussions with patients' families. It remains necessary, however, to extend the analysis to a multicentre cohort in order to assess the scalability of the techniques used. Copyright © 2015 John Wiley & Sons, Ltd.

Building confidence and credibility into CAD with belief decision trees

NASA Astrophysics Data System (ADS)

Affenit, Rachael N.; Barns, Erik R.; Furst, Jacob D.; Rasin, Alexander; Raicu, Daniela S.

2017-03-01

Creating classifiers for computer-aided diagnosis in the absence of ground truth is a challenging problem. Using experts' opinions as reference truth is difficult because the variability in the experts' interpretations introduces uncertainty in the labeled diagnostic data. This uncertainty translates into noise, which can significantly affect the performance of any classifier on test data. To address this problem, we propose a new label set weighting approach to combine the experts' interpretations and their variability, as well as a selective iterative classification (SIC) approach that is based on conformal prediction. Using the NIH/NCI Lung Image Database Consortium (LIDC) dataset in which four radiologists interpreted the lung nodule characteristics, including the degree of malignancy, we illustrate the benefits of the proposed approach. Our results show that the proposed 2-label-weighted approach significantly outperforms the accuracy of the original 5- label and 2-label-unweighted classification approaches by 39.9% and 7.6%, respectively. We also found that the weighted 2-label models produce higher skewness values by 1.05 and 0.61 for non-SIC and SIC respectively on root mean square error (RMSE) distributions. When each approach was combined with selective iterative classification, this further improved the accuracy of classification for the 2-weighted-label by 7.5% over the original, and improved the skewness of the 5-label and 2-unweighted-label by 0.22 and 0.44 respectively.

Deep neural network with weight sparsity control and pre-training extracts hierarchical features and enhances classification performance: Evidence from whole-brain resting-state functional connectivity patterns of schizophrenia.

PubMed

Kim, Junghoe; Calhoun, Vince D; Shim, Eunsoo; Lee, Jong-Hwan

2016-01-01

Functional connectivity (FC) patterns obtained from resting-state functional magnetic resonance imaging data are commonly employed to study neuropsychiatric conditions by using pattern classifiers such as the support vector machine (SVM). Meanwhile, a deep neural network (DNN) with multiple hidden layers has shown its ability to systematically extract lower-to-higher level information of image and speech data from lower-to-higher hidden layers, markedly enhancing classification accuracy. The objective of this study was to adopt the DNN for whole-brain resting-state FC pattern classification of schizophrenia (SZ) patients vs. healthy controls (HCs) and identification of aberrant FC patterns associated with SZ. We hypothesized that the lower-to-higher level features learned via the DNN would significantly enhance the classification accuracy, and proposed an adaptive learning algorithm to explicitly control the weight sparsity in each hidden layer via L1-norm regularization. Furthermore, the weights were initialized via stacked autoencoder based pre-training to further improve the classification performance. Classification accuracy was systematically evaluated as a function of (1) the number of hidden layers/nodes, (2) the use of L1-norm regularization, (3) the use of the pre-training, (4) the use of framewise displacement (FD) removal, and (5) the use of anatomical/functional parcellation. Using FC patterns from anatomically parcellated regions without FD removal, an error rate of 14.2% was achieved by employing three hidden layers and 50 hidden nodes with both L1-norm regularization and pre-training, which was substantially lower than the error rate from the SVM (22.3%). Moreover, the trained DNN weights (i.e., the learned features) were found to represent the hierarchical organization of aberrant FC patterns in SZ compared with HC. Specifically, pairs of nodes extracted from the lower hidden layer represented sparse FC patterns implicated in SZ, which was quantified by using kurtosis/modularity measures and features from the higher hidden layer showed holistic/global FC patterns differentiating SZ from HC. Our proposed schemes and reported findings attained by using the DNN classifier and whole-brain FC data suggest that such approaches show improved ability to learn hidden patterns in brain imaging data, which may be useful for developing diagnostic tools for SZ and other neuropsychiatric disorders and identifying associated aberrant FC patterns. Copyright © 2015 Elsevier Inc. All rights reserved.

Association of Ki-67 Labelling Index and IL-17A with Pituitary Adenoma.

PubMed

Glebauskiene, Brigita; Liutkeviciene, Rasa; Vilkeviciute, Alvita; Gudinaviciene, Inga; Rocyte, Aurelija; Simonaviciute, Dovile; Mazetyte, Ruta; Kriauciuniene, Loresa; Zaliuniene, Dalia

2018-01-01

The aim of the present study was to determine if the Ki-67 labelling index reflects invasiveness of pituitary adenoma and to evaluate IL-17A concentration in blood serum of pituitary adenoma patients. The study was conducted in the Hospital of Lithuanian University of Health Sciences. All pituitary adenomas were analysed based on magnetic resonance imaging findings. The suprasellar extension and sphenoid sinus invasion by pituitary adenoma were classified according to Hardy classification modified by Wilson. Knosp classification system was used to quantify the invasion of the cavernous sinus. The Ki-67 labelling index was obtained by immunohistochemical analysis with the monoclonal antibody, and serum levels of IL-17A were determined by enzyme-linked immunosorbent assay (ELISA). Sixty-nine PA tissue samples were investigated. Serum levels of IL-17A were determined in 60 patients with PA and 64 control subjects. Analysis revealed statistically significantly higher Ki-67 labelling index in invasive compared to noninvasive pituitary adenomas. Median serum IL-17A level was higher in the pituitary adenoma patients than in the control group. Conclusion . IL-17A might be a significant marker for patients with pituitary adenoma and Ki-67 labelling index in case of invasive pituitary adenomas.

[Quantitative classification in catering trade and countermeasures of supervision and management in Hunan Province].

PubMed

Liu, Xiulan; Chen, Lizhang; He, Xiang

2012-02-01

To analyze the status quo of quantitative classification in Hunan Province catering industry, and to discuss the countermeasures in-depth. According to relevant laws and regulations, and after referring to Daily supervision and quantitative scoring sheet and consulting experts, a checklist of key supervision indicators was made. The implementation of quantitative classification in 10 cities in Hunan Province was studied, and the status quo was analyzed. All the 390 catering units implemented quantitative classified management. The larger the catering enterprise, the higher level of quantitative classification. In addition to cafeterias, the smaller the catering units, the higher point of deduction, and snack bars and beverage stores were the highest. For those quantified and classified as C and D, the point of deduction was higher in the procurement and storage of raw materials, operation processing and other aspects. The quantitative classification of Hunan Province has relatively wide coverage. There are hidden risks in food security in small catering units, snack bars, and beverage stores. The food hygienic condition of Hunan Province needs to be improved.

Anti-intellectualism and political ideology in a sample of undergraduate and graduate students.

PubMed

Laverghetta, Antonio; Stewart, Juliana; Weinstein, Lawrence

2007-12-01

To estimate correlations for scores on a student anti-intellectualism scale with scores on a measure of political conservatism, 235 students were given a survey containing a student anti-intellectualism scale, a political conservatism scale, and a demographics questionnaire identifying the participants' sex, college classification, ethnicity, political party affiliation, and self-described political ideology. The political conservatism scale contained two factors, Religiosity and Economic Conservatism, both of which were scored separately in addition to an overall Conservatism score. Students' Anti-intellectualism scores were correlated with Political Conservatism scores (r = .37, p < .01), with Religiosity scores (r = .42, p < .01), and with Economic Conservatism scores (r = .17, p < .05). An analysis of variance indicated a significant difference in students' Anti-intellectualism scores based on college classification (F4,233 = 2.27, p < .04). Specifically, freshman had significantly higher scores than graduate students.

Incremental Treatment Costs Attributable to Overweight and Obesity in Patients with Diabetes: Quantile Regression Approach.

PubMed

Lee, Seung-Mi; Choi, In-Sun; Han, Euna; Suh, David; Shin, Eun-Kyung; Je, Seyunghe; Lee, Sung Su; Suh, Dong-Churl

2018-01-01

This study aimed to estimate treatment costs attributable to overweight and obesity in patients with diabetes who were less than 65 years of age in the United States. This study used data from the Medical Expenditure Panel Survey from 2001 to 2013. Patients with diabetes were identified by using the International Classification of Diseases, Ninth Revision, Clinical Modification code (250), clinical classification codes (049 and 050), or self-reported physician diagnoses. Total treatment costs attributable to overweight and obesity were calculated as the differences in the adjusted costs compared with individuals with diabetes and normal weight. Adjusted costs were estimated by using generalized linear models or unconditional quantile regression models. The mean annual treatment costs attributable to obesity were $1,852 higher than those attributable to normal weight, while costs attributable to overweight were $133 higher. The unconditional quantile regression results indicated that the impact of obesity on total treatment costs gradually became more significant as treatment costs approached the upper quantile. Among patients with diabetes who were less than 65 years of age, patients with diabetes and obesity have significantly higher treatment costs than patients with diabetes and normal weight. The economic burden of diabetes to society will continue to increase unless more proactive preventive measures are taken to effectively treat patients with overweight or obesity. © 2017 The Obesity Society.

Classification of Microarray Data Using Kernel Fuzzy Inference System

PubMed Central

Kumar Rath, Santanu

2014-01-01

The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function. PMID:27433543

Forest tree species discrimination in western Himalaya using EO-1 Hyperion

NASA Astrophysics Data System (ADS)

George, Rajee; Padalia, Hitendra; Kushwaha, S. P. S.

2014-05-01

The information acquired in the narrow bands of hyperspectral remote sensing data has potential to capture plant species spectral variability, thereby improving forest tree species mapping. This study assessed the utility of spaceborne EO-1 Hyperion data in discrimination and classification of broadleaved evergreen and conifer forest tree species in western Himalaya. The pre-processing of 242 bands of Hyperion data resulted into 160 noise-free and vertical stripe corrected reflectance bands. Of these, 29 bands were selected through step-wise exclusion of bands (Wilk's Lambda). Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) algorithms were applied to the selected bands to assess their effectiveness in classification. SVM was also applied to broadband data (Landsat TM) to compare the variation in classification accuracy. All commonly occurring six gregarious tree species, viz., white oak, brown oak, chir pine, blue pine, cedar and fir in western Himalaya could be effectively discriminated. SVM produced a better species classification (overall accuracy 82.27%, kappa statistic 0.79) than SAM (overall accuracy 74.68%, kappa statistic 0.70). It was noticed that classification accuracy achieved with Hyperion bands was significantly higher than Landsat TM bands (overall accuracy 69.62%, kappa statistic 0.65). Study demonstrated the potential utility of narrow spectral bands of Hyperion data in discriminating tree species in a hilly terrain.

Comparison of Single and Multi-Scale Method for Leaf and Wood Points Classification from Terrestrial Laser Scanning Data

NASA Astrophysics Data System (ADS)

Wei, Hongqiang; Zhou, Guiyun; Zhou, Junjie

2018-04-01

The classification of leaf and wood points is an essential preprocessing step for extracting inventory measurements and canopy characterization of trees from the terrestrial laser scanning (TLS) data. The geometry-based approach is one of the widely used classification method. In the geometry-based method, it is common practice to extract salient features at one single scale before the features are used for classification. It remains unclear how different scale(s) used affect the classification accuracy and efficiency. To assess the scale effect on the classification accuracy and efficiency, we extracted the single-scale and multi-scale salient features from the point clouds of two oak trees of different sizes and conducted the classification on leaf and wood. Our experimental results show that the balanced accuracy of the multi-scale method is higher than the average balanced accuracy of the single-scale method by about 10 % for both trees. The average speed-up ratio of single scale classifiers over multi-scale classifier for each tree is higher than 30.

[Analysis of the factors contributing to diabetes insipidus after surgeries for craniopharyngiomas].

PubMed

Luo, Shi; Pan, Jun; Qi, Song-Tao; Fang, Lu-Xiong; Fan, Jun; Liu, Bao-Guo

2009-03-01

To analyze the factors contributing to the occurrence of diabetes insipidus after operations for craniopharyngiomas. A total of 121 cases of diabetes insipidus following surgeries for craniopharyngiomas were retrospectively analyzed and the factors associated with postoperative diabetes insipidus were analyzed. The incidence of diabetes insipidus was 27.3% (33/121 cases) before the operation, 89.9% (107/1119) early after the operation and 39.8%(37/93) in later stages after the operation. The occurrence of early postoperative diabetes insipidus showed a significant relation to the classification and calcification of the craniopharyngioma. Patients with supradiaphragmatic and extraventricular tumors had the lowest incidence of postoperative diabetes insipidus. Late postoperative diabetes insipidus was closely correlated to such factors as age, classification of craniopharyngioma, and intraoperative treatment of the pituitary stalk, but not to the scope of tumor resection or tumor calcification. Late diabetes insipidus was more frequent in children and patients with severed pituitary stalk. The incidence of late postoperative diabetes insipidus was significantly higher in patients with supradiaphragmatic and extra-intraventricular tumors than in those with tumors beneath the diaphragma sellae and extraventricular tumors. Postoperative diabetes insipidus following surgeries for craniopharyngiomas is closely related to the tumor classification, calcification and pituitary stalk protection.

Does the Spine Surgeon’s Experience Affect Fracture Classification, Assessment of Stability, and Treatment Plan in Thoracolumbar Injuries?

PubMed Central

Kanna, Rishi Mugesh; Schroeder, Gregory D.; Oner, Frank Cumhur; Vialle, Luiz; Chapman, Jens; Dvorak, Marcel; Fehlings, Michael; Shetty, Ajoy Prasad; Schnake, Klaus; Kandziora, Frank; Vaccaro, Alexander R.

2017-01-01

Study Design: Prospective survey-based study. Objectives: The AO Spine thoracolumbar injury classification has been shown to have good reproducibility among clinicians. However, the influence of spine surgeons’ clinical experience on fracture classification, stability assessment, and decision on management based on this classification has not been studied. Furthermore, the usefulness of varying imaging modalities including radiographs, computed tomography (CT) and magnetic resonance imaging (MRI) in the decision process was also studied. Methods: Forty-one spine surgeons from different regions, acquainted with the AOSpine classification system, were provided with 30 thoracolumbar fractures in a 3-step assessment: first radiographs, followed by CT and MRI. Surgeons classified the fracture, evaluated stability, chose management, and identified reasons for any changes. The surgeons were divided into 2 groups based on years of clinical experience as <10 years (n = 12) and >10 years (n = 29). Results: There were no significant differences between the 2 groups in correctly classifying A1, B2, and C type fractures. Surgeons with less experience had more correct diagnosis in classifying A3 (47.2% vs 38.5% in step 1, 73.6% vs 60.3% in step 2 and 77.8% vs 65.5% in step 3), A4 (16.7% vs 24.1% in step 1, 72.9% vs 57.8% in step 2 and 70.8% vs 56.0% in step3) and B1 injuries (31.9% vs 20.7% in step 1, 41.7% vs 36.8% in step 2 and 38.9% vs 33.9% in step 3). In the assessment of fracture stability and decision on treatment, the less and more experienced surgeons performed equally. The selection of a particular treatment plan varied in all subtypes except in A1 and C type injuries. Conclusion: Surgeons’ experience did not significantly affect overall fracture classification, evaluating stability and planning the treatment. Surgeons with less experience had a higher percentage of correct classification in A3 and A4 injuries. Despite variations between them in classification, the assessment of overall stability and management decisions were similar between the 2 groups. PMID:28815158

Does the Spine Surgeon's Experience Affect Fracture Classification, Assessment of Stability, and Treatment Plan in Thoracolumbar Injuries?

PubMed

Rajasekaran, Shanmuganathan; Kanna, Rishi Mugesh; Schroeder, Gregory D; Oner, Frank Cumhur; Vialle, Luiz; Chapman, Jens; Dvorak, Marcel; Fehlings, Michael; Shetty, Ajoy Prasad; Schnake, Klaus; Kandziora, Frank; Vaccaro, Alexander R

2017-06-01

Prospective survey-based study. The AO Spine thoracolumbar injury classification has been shown to have good reproducibility among clinicians. However, the influence of spine surgeons' clinical experience on fracture classification, stability assessment, and decision on management based on this classification has not been studied. Furthermore, the usefulness of varying imaging modalities including radiographs, computed tomography (CT) and magnetic resonance imaging (MRI) in the decision process was also studied. Forty-one spine surgeons from different regions, acquainted with the AOSpine classification system, were provided with 30 thoracolumbar fractures in a 3-step assessment: first radiographs, followed by CT and MRI. Surgeons classified the fracture, evaluated stability, chose management, and identified reasons for any changes. The surgeons were divided into 2 groups based on years of clinical experience as <10 years (n = 12) and >10 years (n = 29). There were no significant differences between the 2 groups in correctly classifying A1, B2, and C type fractures. Surgeons with less experience had more correct diagnosis in classifying A3 (47.2% vs 38.5% in step 1, 73.6% vs 60.3% in step 2 and 77.8% vs 65.5% in step 3), A4 (16.7% vs 24.1% in step 1, 72.9% vs 57.8% in step 2 and 70.8% vs 56.0% in step3) and B1 injuries (31.9% vs 20.7% in step 1, 41.7% vs 36.8% in step 2 and 38.9% vs 33.9% in step 3). In the assessment of fracture stability and decision on treatment, the less and more experienced surgeons performed equally. The selection of a particular treatment plan varied in all subtypes except in A1 and C type injuries. Surgeons' experience did not significantly affect overall fracture classification, evaluating stability and planning the treatment. Surgeons with less experience had a higher percentage of correct classification in A3 and A4 injuries. Despite variations between them in classification, the assessment of overall stability and management decisions were similar between the 2 groups.

Do urban environments increase the risk of anxiety, depression and psychosis? An epidemiological study.

PubMed

McKenzie, Karen; Murray, Aja; Booth, Tom

2013-09-25

The present study aimed to investigate whether there is an association between type of living environment (urban versus rural) and anxiety, depression and psychosis in the Scottish population. Data were obtained from the Scottish Neighbourhood Statistics database on Scottish Index of Multiple Deprivation and urban-rural classifications for 6505 data zones across Scotland. Multiple regression was used to test the association between prescriptions for psychotropic medication for anxiety, depression and psychosis, and type of living environment according to urban-rural classification, controlling for a range of socio-economic factors. Urban-rural classification significantly predicted poorer mental health both before (β=-.29) and after (β=-.20) controlling for a large number of socio-economic variables, with more urban areas having higher rates of prescription for psychotropic medication for anxiety, depression and psychosis. The current study focussed on macro-level variables and did not include individual level data. As such, the study did not include data on individual diagnoses, but instead used drug prescriptions for anxiety, depression and psychosis as a proxy for level of affective disorders within data zones. More urban living environments in Scotland are associated with higher rates of prescription for psychotropic medication for anxiety, depression and psychosis. © 2013 Elsevier B.V. All rights reserved.

High-resolution 3 T MRI of traumatic and degenerative triangular fibrocartilage complex (TFCC) abnormalities using Palmer and Outerbridge classifications.

PubMed

Nozaki, T; Rafijah, G; Yang, L; Ueno, T; Horiuchi, S; Hitt, D; Yoshioka, H

2017-10-01

To investigate the usefulness of high-resolution 3 T magnetic resonance imaging (MRI) for the evaluation of traumatic and degenerative triangular fibrocartilage complex (TFCC) abnormalities among three groups: patients presenting with wrist pain who were (a) younger than age 50 years or (b) age 50 or older (PT<50 and PT≥50, respectively), and (c) asymptomatic controls who were younger than age 50 years (AC). High-resolution 3 T MRI was evaluated retrospectively in 96 patients, including 47 PT<50, 38 PT≥50, and 11 AC. Two board-certified radiologists reviewed the MRI images independently. MRI features of TFCC injury were analysed according to the Palmer classification, and cartilage degeneration around the TFCC was evaluated using the Outerbridge classification. Differences in MRI findings among these groups were detected using chi-square test. Cohen's kappa was calculated to assess interobserver and intra-observer reliability. The incidence of Palmer class 1A, 1C and 1D traumatic TFCC injury was significantly (p<0.05) higher in PT≥50 than in PT<50 (class 1A: 47.4% versus 27.7%, class 1C: 31.6% versus 12.8%, and class 1D: 21.1% versus 2.1%). Likewise, MRI findings of TFCC degeneration were observed more frequently in PT≥50 than in PT<50 (p<0.01). Outerbridge grade 2 or higher cartilage degeneration was significantly (p<0.01) more frequently seen in PT≥50 than in PT<50 (55.3% versus 17% in the lunate, 28.9% versus 4.3% in the triquetrum, 73.7% versus 12.8% in the ulna). High-resolution wrist MRI at 3 T enables detailed evaluation of TFCC traumatic injury and degenerative changes using the Palmer and Outerbridge classifications, with good or excellent interobserver and intra-observer reliability. Copyright © 2017 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

A hybrid cost-sensitive ensemble for imbalanced breast thermogram classification.

PubMed

Krawczyk, Bartosz; Schaefer, Gerald; Woźniak, Michał

2015-11-01

Early recognition of breast cancer, the most commonly diagnosed form of cancer in women, is of crucial importance, given that it leads to significantly improved chances of survival. Medical thermography, which uses an infrared camera for thermal imaging, has been demonstrated as a particularly useful technique for early diagnosis, because it detects smaller tumors than the standard modality of mammography. In this paper, we analyse breast thermograms by extracting features describing bilateral symmetries between the two breast areas, and present a classification system for decision making. Clearly, the costs associated with missing a cancer case are much higher than those for mislabelling a benign case. At the same time, datasets contain significantly fewer malignant cases than benign ones. Standard classification approaches fail to consider either of these aspects. In this paper, we introduce a hybrid cost-sensitive classifier ensemble to address this challenging problem. Our approach entails a pool of cost-sensitive decision trees which assign a higher misclassification cost to the malignant class, thereby boosting its recognition rate. A genetic algorithm is employed for simultaneous feature selection and classifier fusion. As an optimisation criterion, we use a combination of misclassification cost and diversity to achieve both a high sensitivity and a heterogeneous ensemble. Furthermore, we prune our ensemble by discarding classifiers that contribute minimally to the decision making. For a challenging dataset of about 150 thermograms, our approach achieves an excellent sensitivity of 83.10%, while maintaining a high specificity of 89.44%. This not only signifies improved recognition of malignant cases, it also statistically outperforms other state-of-the-art algorithms designed for imbalanced classification, and hence provides an effective approach for analysing breast thermograms. Our proposed hybrid cost-sensitive ensemble can facilitate a highly accurate early diagnostic of breast cancer based on thermogram features. It overcomes the difficulties posed by the imbalanced distribution of patients in the two analysed groups. Copyright © 2015 Elsevier B.V. All rights reserved.

Glenoid deformity in the coronal plane correlates with humeral head changes in osteoarthritis: a radiographic analysis.

PubMed

Hawi, Nael; Magosch, Petra; Tauber, Mark; Lichtenberg, Sven; Martetschläger, Frank; Habermeyer, Peter

2017-02-01

A variety of measurements can be used to assess radiographic osteoarthritic changes of the shoulder. This study aimed to analyze the correlation between the radiographic humeral-sided Samilson and Prieto classification system and 3 different radiographic classifications describing the changes of the glenoid in the coronal plane. The study material included standardized radiographs of 50 patients with idiopathic osteoarthritis before anatomic shoulder replacement. On the basis of radiographic measurements, the cases were evaluated using the Samilson and Prieto grading system, angle β, inclination type, and critical shoulder angle by 2 independent observers. Classification measurements showed an excellent agreement between observers. Our results showed that the humeral-sided Samilson and Prieto grading system had a statistically significant good correlation with angle β (observer 1, r = 0.74; observer 2, r = 0.77; P < .05) and a statistically significant excellent correlation with the inclination type of the glenoid (observer 1, r  = 0.86; observer 2, r = 0.8; P < .05). A poor correlation to the critical shoulder angle was observed (r = -0.14, r = 0.03; P > .05). The grade of humeral-sided osteoarthritis according to Samilson and Prieto correlates with the glenoid-sided osteoarthritic changes of the glenoid in the coronal plane described by the angle β and by the inclination type of the glenoid. Higher glenoid-sided inclination is associated with higher grade of osteoarthritis in primary shoulder osteoarthritis. Copyright © 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

EEG source space analysis of the supervised factor analytic approach for the classification of multi-directional arm movement

NASA Astrophysics Data System (ADS)

Shenoy Handiru, Vikram; Vinod, A. P.; Guan, Cuntai

2017-08-01

Objective. In electroencephalography (EEG)-based brain-computer interface (BCI) systems for motor control tasks the conventional practice is to decode motor intentions by using scalp EEG. However, scalp EEG only reveals certain limited information about the complex tasks of movement with a higher degree of freedom. Therefore, our objective is to investigate the effectiveness of source-space EEG in extracting relevant features that discriminate arm movement in multiple directions. Approach. We have proposed a novel feature extraction algorithm based on supervised factor analysis that models the data from source-space EEG. To this end, we computed the features from the source dipoles confined to Brodmann areas of interest (BA4a, BA4p and BA6). Further, we embedded class-wise labels of multi-direction (multi-class) source-space EEG to an unsupervised factor analysis to make it into a supervised learning method. Main Results. Our approach provided an average decoding accuracy of 71% for the classification of hand movement in four orthogonal directions, that is significantly higher (>10%) than the classification accuracy obtained using state-of-the-art spatial pattern features in sensor space. Also, the group analysis on the spectral characteristics of source-space EEG indicates that the slow cortical potentials from a set of cortical source dipoles reveal discriminative information regarding the movement parameter, direction. Significance. This study presents evidence that low-frequency components in the source space play an important role in movement kinematics, and thus it may lead to new strategies for BCI-based neurorehabilitation.

Using Classification Trees to Predict Alumni Giving for Higher Education

ERIC Educational Resources Information Center

Weerts, David J.; Ronca, Justin M.

2009-01-01

As the relative level of public support for higher education declines, colleges and universities aim to maximize alumni-giving to keep their programs competitive. Anchored in a utility maximization framework, this study employs the classification and regression tree methodology to examine characteristics of alumni donors and non-donors at a…

Development of functional spaghetti enriched in bioactive compounds using barley coarse fraction obtained by air classification.

PubMed

Verardo, Vito; Gómez-Caravaca, Ana Maria; Messia, Maria Cristina; Marconi, Emanuele; Caboni, Maria Fiorenza

2011-09-14

Barley byproducts obtained by air classification have been used to produce a different barley functional spaghetti, which were compared to different commercial whole semolina samples. Total, insoluble, and soluble fiber and β-glucan contents of the barley spaghetti were found to be greater than those of commercial samples. Furthermore, it was proved that barley spaghetti reached the FDA requirements, which could allow these pastas to deserve the health claims "good source of dietary fiber" and "may reduce the risk of heart disease". When the barley coarse fraction was used, a flavan-3-ols enrichment and an increase of antioxidant activity were reported, while commercial samples showed the absence of flavan-3-ols and a higher presence of phenolic acids and tannins. Whole semolina commercial spaghetti had a significantly higher content of phenolic acids than semolina spaghetti samples. Besides, it was observed that when vital gluten was added to the spaghetti formulation, phenolic compounds were blocked in the gluten network and were partially released during the cooking process.

Acromioclavicular joint dislocations: radiological correlation between Rockwood classification system and injury patterns in human cadaver species.

PubMed

Eschler, Anica; Rösler, Klaus; Rotter, Robert; Gradl, Georg; Mittlmeier, Thomas; Gierer, Philip

2014-09-01

The classification system of Rockwood and Young is a commonly used classification for acromioclavicular joint separations subdividing types I-VI. This classification hypothesizes specific lesions to anatomical structures (acromioclavicular and coracoclavicular ligaments, capsule, attached muscles) leading to the injury. In recent literature, our understanding for anatomical correlates leading to the radiological-based Rockwood classification is questioned. The goal of this experimental-based investigation was to approve the correlation between the anatomical injury pattern and the Rockwood classification. In four human cadavers (seven shoulders), the acromioclavicular and coracoclavicular ligaments were transected stepwise. Radiological correlates were recorded (Zanca view) with 15-kg longitudinal tension applied at the wrist. The resulting acromio- and coracoclavicular distances were measured. Radiographs after acromioclavicular ligament transection showed joint space enlargement (8.6 ± 0.3 vs. 3.1 ± 0.5 mm, p < 0.05) and no significant change in coracoclavicular distance (10.4 ± 0.9 vs. 10.0 ± 0.8 mm). According to the Rockwood classification only type I and II lesions occurred. After additional coracoclavicular ligament cut, the acromioclavicular joint space width increased to 16.7 ± 2.7 vs. 8.6 ± 0.3 mm, p < 0.05. The mean coracoclavicular distance increased to 20.6 ± 2.1 mm resulting in type III-V lesions concerning the Rockwood classification. Trauma with intact coracoclavicular ligaments did not result in acromioclavicular joint lesions higher than Rockwood type I and II. The clinical consequence for reconstruction of low-grade injuries might be a solely surgical approach for the acromioclavicular ligaments or conservative treatment. High-grade injuries were always based on additional structural damage to the coracoclavicular ligaments. Rockwood type V lesions occurred while muscle attachments were intact.

A liver cirrhosis classification on B-mode ultrasound images by the use of higher order local autocorrelation features

NASA Astrophysics Data System (ADS)

Sasaki, Kenya; Mitani, Yoshihiro; Fujita, Yusuke; Hamamoto, Yoshihiko; Sakaida, Isao

2017-02-01

In this paper, in order to classify liver cirrhosis on regions of interest (ROIs) images from B-mode ultrasound images, we have proposed to use the higher order local autocorrelation (HLAC) features. In a previous study, we tried to classify liver cirrhosis by using a Gabor filter based approach. However, the classification performance of the Gabor feature was poor from our preliminary experimental results. In order accurately to classify liver cirrhosis, we examined to use the HLAC features for liver cirrhosis classification. The experimental results show the effectiveness of HLAC features compared with the Gabor feature. Furthermore, by using a binary image made by an adaptive thresholding method, the classification performance of HLAC features has improved.

Experience with International Neuroblastoma Staging System and Pathology Classification

PubMed Central

Ikeda, H; Iehara, T; Tsuchida, Y; Kaneko, M; Hata, J; Naito, H; Iwafuchi, M; Ohnuma, N; Mugishima, H; Toyoda, Y; Hamazaki, M; Mimaya, J; Kondo, S; Kawa, K; Okada, A; Hiyama, E; Suita, S; Takamatsu, H

2002-01-01

The International Neuroblastoma Staging System and Pathology Classification were proposed in 1988 and in 1999, respectively, but their clinical value has not yet been fully studied in new patients. Six hundred and forty-four patients with neuroblastoma treated between January 1995 and December 1999 were analysed by these classifications. The 4-year overall survival rate of patients <12 months of age with INSS stages 1, 2A, 2B, 3 and 4S disease was 98.5%, which was significantly higher than the 73.1% rate in stage 4 patients <12 months (P<0.0001). When patients were ⩾12 months, the 4-year overall survival rate of patients with neuroblastoma at 1, 2A, 2B and 3 stages was 100% and that of patients at stage 4 was 48.5% (P<0.0001). As to the International Neuroblastoma Pathology Classification histology, the 4-year overall survival rate was 98.8% in patients with favourable histology and 60.7% in those with unfavourable histology in the <12 months group (P<0.0001). In the ⩾12 months group, the 4-year oral survival of patients with favourable histology was 95.3% and that of patients with unfavourable histology was 50.6% (P<0.0001). Among biological factors, MYCN amplification, DNA diploidy and 1p deletions were significantly associated with poor prognosis in patients <12 months, as were MYCN amplification and DNA diploidy in patients ⩾12 months of age. Multivariate analysis showed that the INSS stage (stage 4 vs other stages) and International Neuroblastoma Pathology Classification histology (unfavourable vs favourable) were significantly and independently associated with the survival of patients undergoing treatment, stratified by age, stage and MYCN amplification (P=0.0002 and P=0.0051, respectively). British Journal of Cancer (2002) 86, 1110–1116. DOI: 10.1038/sj/bjc/6600231 www.bjcancer.com © 2002 Cancer Research UK PMID:11953858

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

PubMed Central

2014-01-01

Introduction Using genome-wide expression profiles of a prospective training cohort of breast cancer patients, ClinicoMolecular Triad Classification (CMTC) was recently developed to classify breast cancers into three clinically relevant groups to aid treatment decisions. CMTC was found to be both prognostic and predictive in a large external breast cancer cohort in that study. This study serves to validate the reproducibility of CMTC and its prognostic value using independent patient cohorts. Methods An independent internal cohort (n = 284) and a new external cohort (n = 2,181) were used to validate the association of CMTC between clinicopathological factors, 12 known gene signatures, two molecular subtype classifiers, and 19 oncogenic signalling pathway activities, and to reproduce the abilities of CMTC to predict clinical outcomes of breast cancer. In addition, we also updated the outcome data of the original training cohort (n = 147). Results The original training cohort reached a statistically significant difference (p < 0.05) in disease-free survivals between the three CMTC groups after an additional two years of follow-up (median = 55 months). The prognostic value of the triad classification was reproduced in the second independent internal cohort and the new external validation cohort. CMTC achieved even higher prognostic significance when all available patients were analyzed (n = 4,851). Oncogenic pathways Myc, E2F1, Ras and β-catenin were again implicated in the high-risk groups. Conclusions Both prospective internal cohorts and the independent external cohorts reproduced the triad classification of CMTC and its prognostic significance. CMTC is an independent prognostic predictor, and it outperformed 12 other known prognostic gene signatures, molecular subtype classifications, and all other standard prognostic clinicopathological factors. Our results support further development of CMTC portfolio into a guide for personalized breast cancer treatments. PMID:24996446

Efficacy of hidden markov model over support vector machine on multiclass classification of healthy and cancerous cervical tissues

NASA Astrophysics Data System (ADS)

Mukhopadhyay, Sabyasachi; Kurmi, Indrajit; Pratiher, Sawon; Mukherjee, Sukanya; Barman, Ritwik; Ghosh, Nirmalya; Panigrahi, Prasanta K.

2018-02-01

In this paper, a comparative study between SVM and HMM has been carried out for multiclass classification of cervical healthy and cancerous tissues. In our study, the HMM methodology is more promising to produce higher accuracy in classification.

Rational classification of portal vein thrombosis and its clinical significance.

PubMed

Ma, Jingqin; Yan, Zhiping; Luo, Jianjun; Liu, Qingxin; Wang, Jianhua; Qiu, Shijing

2014-01-01

Portal vein thrombosis (PVT) is commonly classified into acute (symptom duration <60 days and absence of portal carvernoma and portal hypertension) and chronic types. However, the rationality of this classification has received little attention. In this study, 60 patients (40 men and 20 women) with PVT were examined using contrast-enhanced computed tomography (CT). The percentage of vein occlusion, including portal vein (PV) and superior mesenteric vein (SMV), was measured on CT image. Of 60 patients, 17 (28.3%) met the criterion of acute PVT. Symptoms occurred more frequently in patients with superior mesenteric vein thrombosis (SMVT) compared to those without SMVT (p<0.001). However, there was no significant difference in PV occlusion between patients with and without symptoms. The frequency of cavernous transformation was significantly higher in patients with complete PVT than those with partial PVT (p<0.001). Complications of portal hypertension were significantly associated with cirrhosis (p<0.001) rather than with the severity of PVT and presence of cavernoma. These results suggest that the severity of PVT is only associated with the formation of portal cavernoma but unrelated to the onset of symptoms and the development of portal hypertension. We classified PVT into complete and partial types, and each was subclassified into with and without portal cavernoma. In conclusion, neither symptom duration nor cavernous transformation can clearly distinguish between acute and chronic PVT. The new classification system can determine the pathological alterations of PVT, patency of portal vein and outcome of treatment in a longitudinal study.

Rational Classification of Portal Vein Thrombosis and Its Clinical Significance

PubMed Central

Ma, Jingqin; Yan, Zhiping; Luo, Jianjun; Liu, Qingxin; Wang, Jianhua; Qiu, Shijing

2014-01-01

Portal vein thrombosis (PVT) is commonly classified into acute (symptom duration <60 days and absence of portal carvernoma and portal hypertension) and chronic types. However, the rationality of this classification has received little attention. In this study, 60 patients (40 men and 20 women) with PVT were examined using contrast-enhanced computed tomography (CT). The percentage of vein occlusion, including portal vein (PV) and superior mesenteric vein (SMV), was measured on CT image. Of 60 patients, 17 (28.3%) met the criterion of acute PVT. Symptoms occurred more frequently in patients with superior mesenteric vein thrombosis (SMVT) compared to those without SMVT (p<0.001). However, there was no significant difference in PV occlusion between patients with and without symptoms. The frequency of cavernous transformation was significantly higher in patients with complete PVT than those with partial PVT (p<0.001). Complications of portal hypertension were significantly associated with cirrhosis (p<0.001) rather than with the severity of PVT and presence of cavernoma. These results suggest that the severity of PVT is only associated with the formation of portal cavernoma but unrelated to the onset of symptoms and the development of portal hypertension. We classified PVT into complete and partial types, and each was subclassified into with and without portal cavernoma. In conclusion, neither symptom duration nor cavernous transformation can clearly distinguish between acute and chronic PVT. The new classification system can determine the pathological alterations of PVT, patency of portal vein and outcome of treatment in a longitudinal study. PMID:25393320

Interobserver Variability of Radiographic Assessment Using a Mobile Messaging Application as a Teleconsultation Tool

PubMed Central

Özkan, Sezai; Mellema, Jos J.; Ring, David; Chen, Neal C.

2017-01-01

Background: To examine whether interobserver reliability, decision-making, and confidence in decision-making in the treatment of distal radius fractures changes if radiographs are viewed on a messenger application on a mobile phone compared to a standard DICOM viewer. Methods: Radiographs of distal radius fractures were presented to surgeons on either a smart phone using a mobile messenger application or a laptop using a DICOM viewer application. Twenty observers participated: 10 (50%) were randomly assigned to the DICOM viewer group and 10 (50%) to the mobile messenger group. Each observer was asked to evaluate the cases and (1) classify the fracture type according to the AO classification, (2) recommend operative or conservative treatment and (3) rate their confidence about this decision. Results: There was no significant difference in interobserver reliability for AO classification and recommendation for surgery for distal radius fractures in both groups. The percentage of recommendation for surgery was significantly higher in the messenger application group compared to the DICOM viewer group (89% versus 78%, P=0.019) and the confidence for treatment decision was significantly higher in the mobile messenger group compared to the DICOM viewer group (8.9 versus 7.9, P=0.026). Conclusion: Messenger applications on mobile phones could facilitate remote decision-making for patients with distal radius fractures, but should be used with caution. PMID:29226202

Clinicopathological characteristics of head and neck Merkel cell carcinomas.

PubMed

Knopf, Andreas; Bas, Murat; Hofauer, Benedikt; Mansour, Naglaa; Stark, Thomas

2017-01-01

There are still controversies about the therapeutic strategies and subsequent outcome in head and neck Merkel cell carcinoma. Clinicopathological data of 23 Merkel cell carcinomas, 93 cutaneous head and neck squamous cell carcinomas (HNSCCs), 126 malignant melanomas, and 91 primary parotid gland carcinomas were comprehensively analyzed. Merkel cell carcinomas were cytokeratin 20 (CK20)/neuron-specific enolase (NSE)/chromogranin A (CgA)/synaptophysin (Syn)/thyroid transcription factor-1 (TTF-1)/MIB1 immunostained. All Merkel cell carcinomas underwent wide local excision. Parotidectomy/neck dissection was performed in 40%/33% cutaneous Merkel cell carcinoma and 100%/100% in parotid gland Merkel cell carcinoma. Five-year recurrence-free interval (RFI)/overall survival (OS) was significantly higher in malignant melanoma (81/80%) than in cutaneous Merkel cell carcinoma/HNSCC. Interestingly, 5-year RFI/OS was significantly higher in Merkel cell carcinoma (61%/79%) than in HNSCC (33%/65%; p < .0001) despite comparable TNM classifications and treatment regimens. There were neither differences of RFI/OS between parotid gland Merkel cell carcinoma and parotid gland carcinomas, nor in the immunohistochemical profile. Five-year RFI/OS was significantly better in cutaneous Merkel cell carcinoma when compared with TNM classification matched HNSCC. Five-year RFI/OS was comparable in parotid gland Merkel cell carcinoma and other primary parotid gland malignancies. © 2016 Wiley Periodicals, Inc. Head Neck 39: 92-97, 2017. © 2016 Wiley Periodicals, Inc.

Combining two open source tools for neural computation (BioPatRec and Netlab) improves movement classification for prosthetic control.

PubMed

Prahm, Cosima; Eckstein, Korbinian; Ortiz-Catalan, Max; Dorffner, Georg; Kaniusas, Eugenijus; Aszmann, Oskar C

2016-08-31

Controlling a myoelectric prosthesis for upper limbs is increasingly challenging for the user as more electrodes and joints become available. Motion classification based on pattern recognition with a multi-electrode array allows multiple joints to be controlled simultaneously. Previous pattern recognition studies are difficult to compare, because individual research groups use their own data sets. To resolve this shortcoming and to facilitate comparisons, open access data sets were analysed using components of BioPatRec and Netlab pattern recognition models. Performances of the artificial neural networks, linear models, and training program components were compared. Evaluation took place within the BioPatRec environment, a Matlab-based open source platform that provides feature extraction, processing and motion classification algorithms for prosthetic control. The algorithms were applied to myoelectric signals for individual and simultaneous classification of movements, with the aim of finding the best performing algorithm and network model. Evaluation criteria included classification accuracy and training time. Results in both the linear and the artificial neural network models demonstrated that Netlab's implementation using scaled conjugate training algorithm reached significantly higher accuracies than BioPatRec. It is concluded that the best movement classification performance would be achieved through integrating Netlab training algorithms in the BioPatRec environment so that future prosthesis training can be shortened and control made more reliable. Netlab was therefore included into the newest release of BioPatRec (v4.0).

Classification of non-Hodgkin lymphoma in Algeria according to the World Health Organization classification.

PubMed

Boudjerra, Nadia; Perry, Anamarija M; Audouin, Josée; Diebold, Jacques; Nathwani, Bharat N; MacLennan, Kenneth A; Müller-Hermelink, Hans K; Bast, Martin; Boilesen, Eugene; Armitage, James O; Weisenburger, Dennis D

2015-04-01

The relative distribution of non-Hodgkin lymphoma (NHL) subtypes differs markedly around the world. The aim of this study was to report this distribution in Algeria. A panel of four hematopathologists classified 197 consecutive cases according to the World Health Organization classification, including 87.3% B-cell and 12.7% T- or natural killer (NK)-cell NHLs. This series was compared with similar cohorts from Western Europe (WEU) and North America (NA). Algeria had a significantly higher frequency of diffuse large B-cell lymphoma (DLBCL: 52.8%) and a lower frequency of follicular lymphoma (FL: 13.2%) compared with WEU (DLBCL: 32.2%; FL: 20.0%) and NA (DLBCL: 29.3%; FL: 33.6%). The frequency of mantle cell lymphoma was lower in Algeria (2.5%) compared with WEU (8.3%). Smaller differences were also found among the NK/T-cell lymphomas. In conclusion, we found important differences between Algeria and Western countries, and further epidemiologic studies are needed to explain these differences.

Improving LUC estimation accuracy with multiple classification system for studying impact of urbanization on watershed flood

NASA Astrophysics Data System (ADS)

Dou, P.

2017-12-01

Guangzhou has experienced a rapid urbanization period called "small change in three years and big change in five years" since the reform of China, resulting in significant land use/cover changes(LUC). To overcome the disadvantages of single classifier for remote sensing image classification accuracy, a multiple classifier system (MCS) is proposed to improve the quality of remote sensing image classification. The new method combines advantages of different learning algorithms, and achieves higher accuracy (88.12%) than any single classifier did. With the proposed MCS, land use/cover (LUC) on Landsat images from 1987 to 2015 was obtained, and the LUCs were used on three watersheds (Shijing river, Chebei stream, and Shahe stream) to estimate the impact of urbanization on water flood. The results show that with the high accuracy LUC, the uncertainty in flood simulations are reduced effectively (for Shijing river, Chebei stream, and Shahe stream, the uncertainty reduced 15.5%, 17.3% and 19.8% respectively).

Extended census transform histogram for land-use scene classification

NASA Astrophysics Data System (ADS)

Yuan, Baohua; Li, Shijin

2017-04-01

With the popular use of high-resolution satellite images, more and more research efforts have been focused on land-use scene classification. In scene classification, effective visual features can significantly boost the final performance. As a typical texture descriptor, the census transform histogram (CENTRIST) has emerged as a very powerful tool due to its effective representation ability. However, the most prominent limitation of CENTRIST is its small spatial support area, which may not necessarily be adept at capturing the key texture characteristics. We propose an extended CENTRIST (eCENTRIST), which is made up of three subschemes in a greater neighborhood scale. The proposed eCENTRIST not only inherits the advantages of CENTRIST but also encodes the more useful information of local structures. Meanwhile, multichannel eCENTRIST, which can capture the interactions from multichannel images, is developed to obtain higher categorization accuracy rates. Experimental results demonstrate that the proposed method can achieve competitive performance when compared to state-of-the-art methods.

An alternative view of protein fold space.

PubMed

Shindyalov, I N; Bourne, P E

2000-02-15

Comparing and subsequently classifying protein structures information has received significant attention concurrent with the increase in the number of experimentally derived 3-dimensional structures. Classification schemes have focused on biological function found within protein domains and on structure classification based on topology. Here an alternative view is presented that groups substructures. Substructures are long (50-150 residue) highly repetitive near-contiguous pieces of polypeptide chain that occur frequently in a set of proteins from the PDB defined as structurally non-redundant over the complete polypeptide chain. The substructure classification is based on a previously reported Combinatorial Extension (CE) algorithm that provides a significantly different set of structure alignments than those previously described, having, for example, only a 40% overlap with FSSP. Qualitatively the algorithm provides longer contiguous aligned segments at the price of a slightly higher root-mean-square deviation (rmsd). Clustering these alignments gives a discreet and highly repetitive set of substructures not detectable by sequence similarity alone. In some cases different substructures represent all or different parts of well known folds indicative of the Russian doll effect--the continuity of protein fold space. In other cases they fall into different structure and functional classifications. It is too early to determine whether these newly classified substructures represent new insights into the evolution of a structural framework important to many proteins. What is apparent from on-going work is that these substructures have the potential to be useful probes in finding remote sequence homology and in structure prediction studies. The characteristics of the complete all-by-all comparison of the polypeptide chains present in the PDB and details of the filtering procedure by pair-wise structure alignment that led to the emergent substructure gallery are discussed. Substructure classification, alignments, and tools to analyze them are available at http://cl.sdsc.edu/ce.html.

Micro-Doppler Based Classification of Human Aquatic Activities via Transfer Learning of Convolutional Neural Networks.

PubMed

Park, Jinhee; Javier, Rios Jesus; Moon, Taesup; Kim, Youngwook

2016-11-24

Accurate classification of human aquatic activities using radar has a variety of potential applications such as rescue operations and border patrols. Nevertheless, the classification of activities on water using radar has not been extensively studied, unlike the case on dry ground, due to its unique challenge. Namely, not only is the radar cross section of a human on water small, but the micro-Doppler signatures are much noisier due to water drops and waves. In this paper, we first investigate whether discriminative signatures could be obtained for activities on water through a simulation study. Then, we show how we can effectively achieve high classification accuracy by applying deep convolutional neural networks (DCNN) directly to the spectrogram of real measurement data. From the five-fold cross-validation on our dataset, which consists of five aquatic activities, we report that the conventional feature-based scheme only achieves an accuracy of 45.1%. In contrast, the DCNN trained using only the collected data attains 66.7%, and the transfer learned DCNN, which takes a DCNN pre-trained on a RGB image dataset and fine-tunes the parameters using the collected data, achieves a much higher 80.3%, which is a significant performance boost.

Video event classification and image segmentation based on noncausal multidimensional hidden Markov models.

PubMed

Ma, Xiang; Schonfeld, Dan; Khokhar, Ashfaq A

2009-06-01

In this paper, we propose a novel solution to an arbitrary noncausal, multidimensional hidden Markov model (HMM) for image and video classification. First, we show that the noncausal model can be solved by splitting it into multiple causal HMMs and simultaneously solving each causal HMM using a fully synchronous distributed computing framework, therefore referred to as distributed HMMs. Next we present an approximate solution to the multiple causal HMMs that is based on an alternating updating scheme and assumes a realistic sequential computing framework. The parameters of the distributed causal HMMs are estimated by extending the classical 1-D training and classification algorithms to multiple dimensions. The proposed extension to arbitrary causal, multidimensional HMMs allows state transitions that are dependent on all causal neighbors. We, thus, extend three fundamental algorithms to multidimensional causal systems, i.e., 1) expectation-maximization (EM), 2) general forward-backward (GFB), and 3) Viterbi algorithms. In the simulations, we choose to limit ourselves to a noncausal 2-D model whose noncausality is along a single dimension, in order to significantly reduce the computational complexity. Simulation results demonstrate the superior performance, higher accuracy rate, and applicability of the proposed noncausal HMM framework to image and video classification.

A comparison of unsupervised classification procedures on LANDSAT MSS data for an area of complex surface conditions in Basilicata, Southern Italy

NASA Technical Reports Server (NTRS)

Justice, C.; Townshend, J. (Principal Investigator)

1981-01-01

Two unsupervised classification procedures were applied to ratioed and unratioed LANDSAT multispectral scanner data of an area of spatially complex vegetation and terrain. An objective accuracy assessment was undertaken on each classification and comparison was made of the classification accuracies. The two unsupervised procedures use the same clustering algorithm. By on procedure the entire area is clustered and by the other a representative sample of the area is clustered and the resulting statistics are extrapolated to the remaining area using a maximum likelihood classifier. Explanation is given of the major steps in the classification procedures including image preprocessing; classification; interpretation of cluster classes; and accuracy assessment. Of the four classifications undertaken, the monocluster block approach on the unratioed data gave the highest accuracy of 80% for five coarse cover classes. This accuracy was increased to 84% by applying a 3 x 3 contextual filter to the classified image. A detailed description and partial explanation is provided for the major misclassification. The classification of the unratioed data produced higher percentage accuracies than for the ratioed data and the monocluster block approach gave higher accuracies than clustering the entire area. The moncluster block approach was additionally the most economical in terms of computing time.

A statewide investigation of geographic lung cancer incidence patterns and radon exposure in a low-smoking population.

PubMed

Ou, Judy Y; Fowler, Brynn; Ding, Qian; Kirchhoff, Anne C; Pappas, Lisa; Boucher, Kenneth; Akerley, Wallace; Wu, Yelena; Kaphingst, Kimberly; Harding, Garrett; Kepka, Deanna

2018-01-31

Lung cancer is the leading cause of cancer-related mortality in Utah despite having the nation's lowest smoking rate. Radon exposure and differences in lung cancer incidence between nonmetropolitan and metropolitan areas may explain this phenomenon. We compared smoking-adjusted lung cancer incidence rates between nonmetropolitan and metropolitan counties by predicted indoor radon level, sex, and cancer stage. We also compared lung cancer incidence by county classification between Utah and all SEER sites. SEER*Stat provided annual age-adjusted rates per 100,000 from 1991 to 2010 for each Utah county and all other SEER sites. County classification, stage, and sex were obtained from SEER*Stat. Smoking was obtained from Environmental Public Health Tracking estimates by Ortega et al. EPA provided low (< 2 pCi/L), moderate (2-4 pCi/L), and high (> 4 pCi/L) indoor radon levels for each county. Poisson models calculated overall, cancer stage, and sex-specific rates and p-values for smoking-adjusted and unadjusted models. LOESS smoothed trend lines compared incidence rates between Utah and all SEER sites by county classification. All metropolitan counties had moderate radon levels; 12 (63%) of the 19 nonmetropolitan counties had moderate predicted radon levels and 7 (37%) had high predicted radon levels. Lung cancer incidence rates were higher in nonmetropolitan counties than metropolitan counties (34.8 vs 29.7 per 100,000, respectively). Incidence of distant stage cancers was significantly higher in nonmetropolitan counties after controlling for smoking (16.7 vs 15.4, p = 0.02*). Incidence rates in metropolitan, moderate radon and nonmetropolitan, moderate radon counties were similar. Nonmetropolitan, high radon counties had a significantly higher incidence of lung cancer compared to nonmetropolitan, moderate radon counties after adjustment for smoking (41.7 vs 29.2, p < 0.0001*). Lung cancer incidence patterns in Utah were opposite of metropolitan/nonmetropolitan trends in other SEER sites. Lung cancer incidence and distant stage incidence rates were consistently higher in nonmetropolitan Utah counties than metropolitan counties, suggesting that limited access to preventative screenings may play a role in this disparity. Smoking-adjusted incidence rates in nonmetropolitan, high radon counties were significantly higher than moderate radon counties, suggesting that radon was also major contributor to lung cancer in these regions. National studies should account for geographic and environmental factors when examining nonmetropolitan/metropolitan differences in lung cancer.

Does expert knowledge improve automatic probabilistic classification of gait joint motion patterns in children with cerebral palsy?

PubMed Central

Papageorgiou, Eirini; Nieuwenhuys, Angela; Desloovere, Kaat

2017-01-01

Background This study aimed to improve the automatic probabilistic classification of joint motion gait patterns in children with cerebral palsy by using the expert knowledge available via a recently developed Delphi-consensus study. To this end, this study applied both Naïve Bayes and Logistic Regression classification with varying degrees of usage of the expert knowledge (expert-defined and discretized features). A database of 356 patients and 1719 gait trials was used to validate the classification performance of eleven joint motions. Hypotheses Two main hypotheses stated that: (1) Joint motion patterns in children with CP, obtained through a Delphi-consensus study, can be automatically classified following a probabilistic approach, with an accuracy similar to clinical expert classification, and (2) The inclusion of clinical expert knowledge in the selection of relevant gait features and the discretization of continuous features increases the performance of automatic probabilistic joint motion classification. Findings This study provided objective evidence supporting the first hypothesis. Automatic probabilistic gait classification using the expert knowledge available from the Delphi-consensus study resulted in accuracy (91%) similar to that obtained with two expert raters (90%), and higher accuracy than that obtained with non-expert raters (78%). Regarding the second hypothesis, this study demonstrated that the use of more advanced machine learning techniques such as automatic feature selection and discretization instead of expert-defined and discretized features can result in slightly higher joint motion classification performance. However, the increase in performance is limited and does not outweigh the additional computational cost and the higher risk of loss of clinical interpretability, which threatens the clinical acceptance and applicability. PMID:28570616

Training sample selection based on self-training for liver cirrhosis classification using ultrasound images

NASA Astrophysics Data System (ADS)

Fujita, Yusuke; Mitani, Yoshihiro; Hamamoto, Yoshihiko; Segawa, Makoto; Terai, Shuji; Sakaida, Isao

2017-03-01

Ultrasound imaging is a popular and non-invasive tool used in the diagnoses of liver disease. Cirrhosis is a chronic liver disease and it can advance to liver cancer. Early detection and appropriate treatment are crucial to prevent liver cancer. However, ultrasound image analysis is very challenging, because of the low signal-to-noise ratio of ultrasound images. To achieve the higher classification performance, selection of training regions of interest (ROIs) is very important that effect to classification accuracy. The purpose of our study is cirrhosis detection with high accuracy using liver ultrasound images. In our previous works, training ROI selection by MILBoost and multiple-ROI classification based on the product rule had been proposed, to achieve high classification performance. In this article, we propose self-training method to select training ROIs effectively. Evaluation experiments were performed to evaluate effect of self-training, using manually selected ROIs and also automatically selected ROIs. Experimental results show that self-training for manually selected ROIs achieved higher classification performance than other approaches, including our conventional methods. The manually ROI definition and sample selection are important to improve classification accuracy in cirrhosis detection using ultrasound images.

Hierarchical Higher Order Crf for the Classification of Airborne LIDAR Point Clouds in Urban Areas

NASA Astrophysics Data System (ADS)

Niemeyer, J.; Rottensteiner, F.; Soergel, U.; Heipke, C.

2016-06-01

We propose a novel hierarchical approach for the classification of airborne 3D lidar points. Spatial and semantic context is incorporated via a two-layer Conditional Random Field (CRF). The first layer operates on a point level and utilises higher order cliques. Segments are generated from the labelling obtained in this way. They are the entities of the second layer, which incorporates larger scale context. The classification result of the segments is introduced as an energy term for the next iteration of the point-based layer. This framework iterates and mutually propagates context to improve the classification results. Potentially wrong decisions can be revised at later stages. The output is a labelled point cloud as well as segments roughly corresponding to object instances. Moreover, we present two new contextual features for the segment classification: the distance and the orientation of a segment with respect to the closest road. It is shown that the classification benefits from these features. In our experiments the hierarchical framework improve the overall accuracies by 2.3% on a point-based level and by 3.0% on a segment-based level, respectively, compared to a purely point-based classification.

Equipment management risk rating system based on engineering endpoints.

PubMed

James, P J

1999-01-01

The equipment management risk ratings system outlined here offers two significant departures from current practice: risk classifications are based on intrinsic device risks, and the risk rating system is based on engineering endpoints. Intrinsic device risks are categorized as physical, clinical and technical, and these flow from the incoming equipment assessment process. Engineering risk management is based on verification of engineering endpoints such as clinical measurements or energy delivery. This practice eliminates the ambiguity associated with ranking risk in terms of physiologic and higher-level outcome endpoints such as no significant hazards, low significance, injury, or mortality.

Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds.

PubMed

Cannon, Edward O; Amini, Ata; Bender, Andreas; Sternberg, Michael J E; Muggleton, Stephen H; Glen, Robert C; Mitchell, John B O

2007-05-01

We investigate the classification performance of circular fingerprints in combination with the Naive Bayes Classifier (MP2D), Inductive Logic Programming (ILP) and Support Vector Inductive Logic Programming (SVILP) on a standard molecular benchmark dataset comprising 11 activity classes and about 102,000 structures. The Naive Bayes Classifier treats features independently while ILP combines structural fragments, and then creates new features with higher predictive power. SVILP is a very recently presented method which adds a support vector machine after common ILP procedures. The performance of the methods is evaluated via a number of statistical measures, namely recall, specificity, precision, F-measure, Matthews Correlation Coefficient, area under the Receiver Operating Characteristic (ROC) curve and enrichment factor (EF). According to the F-measure, which takes both recall and precision into account, SVILP is for seven out of the 11 classes the superior method. The results show that the Bayes Classifier gives the best recall performance for eight of the 11 targets, but has a much lower precision, specificity and F-measure. The SVILP model on the other hand has the highest recall for only three of the 11 classes, but generally far superior specificity and precision. To evaluate the statistical significance of the SVILP superiority, we employ McNemar's test which shows that SVILP performs significantly (p < 5%) better than both other methods for six out of 11 activity classes, while being superior with less significance for three of the remaining classes. While previously the Bayes Classifier was shown to perform very well in molecular classification studies, these results suggest that SVILP is able to extract additional knowledge from the data, thus improving classification results further.

Hidden among Sea Anemones: The First Comprehensive Phylogenetic Reconstruction of the Order Actiniaria (Cnidaria, Anthozoa, Hexacorallia) Reveals a Novel Group of Hexacorals

PubMed Central

Rodríguez, Estefanía; Barbeitos, Marcos S.; Brugler, Mercer R.; Crowley, Louise M.; Grajales, Alejandro; Gusmão, Luciana; Häussermann, Verena; Reft, Abigail; Daly, Marymegan

2014-01-01

Sea anemones (order Actiniaria) are among the most diverse and successful members of the anthozoan subclass Hexacorallia, occupying benthic marine habitats across all depths and latitudes. Actiniaria comprises approximately 1,200 species of solitary and skeleton-less polyps and lacks any anatomical synapomorphy. Although monophyly is anticipated based on higher-level molecular phylogenies of Cnidaria, to date, monophyly has not been explicitly tested and at least some hypotheses on the diversification of Hexacorallia have suggested that actiniarians are para- or poly-phyletic. Published phylogenies have demonstrated the inadequacy of existing morphological-based classifications within Actiniaria. Superfamilial groups and most families and genera that have been rigorously studied are not monophyletic, indicating conflict with the current hierarchical classification. We test the monophyly of Actiniaria using two nuclear and three mitochondrial genes with multiple analytical methods. These analyses are the first to include representatives of all three currently-recognized suborders within Actiniaria. We do not recover Actiniaria as a monophyletic clade: the deep-sea anemone Boloceroides daphneae, previously included within the infraorder Boloceroidaria, is resolved outside of Actiniaria in several of the analyses. We erect a new genus and family for B. daphneae, and rank this taxon incerti ordinis. Based on our comprehensive phylogeny, we propose a new formal higher-level classification for Actiniaria composed of only two suborders, Anenthemonae and Enthemonae. Suborder Anenthemonae includes actiniarians with a unique arrangement of mesenteries (members of Edwardsiidae and former suborder Endocoelantheae). Suborder Enthemonae includes actiniarians with the typical arrangement of mesenteries for actiniarians (members of former suborders Protantheae, Ptychodacteae, and Nynantheae and subgroups therein). We also erect subgroups within these two newly-erected suborders. Although some relationships among these newly-defined groups are still ambiguous, morphological and molecular results are consistent enough to proceed with a new higher-level classification and to discuss the putative functional and evolutionary significance of several morphological attributes within Actiniaria. PMID:24806477

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification.

PubMed

Zhou, Tao; Li, Zhaofu; Pan, Jianjun

2018-01-27

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively.

Analysis of the Carnegie Classification of Community Engagement: Patterns and Impact on Institutions

ERIC Educational Resources Information Center

Driscoll, Amy

2014-01-01

This chapter describes the impact that participation in the Carnegie Classification for Community Engagement had on the institutions of higher learning that applied for the classification. This is described in terms of changes in direct community engagement, monitoring and reporting on community engagement, and levels of student and professor…

Mealybugs (Hemiptera: Coccomorpha: Pseudococcidae) with oral rim ducts; description of a new genus and species from Turkey, and discussion of their higher classification within the Pseudococcidae.

PubMed

Kaydan, Mehmet Bora; Szita, Éva

2017-02-03

A new monotypic mealybug genus with oral rim ducts, Bromusicoccus Kaydan gen. n. (Hemiptera: Pseudococcidae: Pseudococcinae), is described from Turkey. The higher classification of mealybug genera with oral rim tubular ducts worldwide is discussed and a key is provided to separate them.

Influence of leaching conditions for ecotoxicological classification of ash.

PubMed

Stiernström, S; Enell, A; Wik, O; Hemström, K; Breitholtz, M

2014-02-01

The Waste Framework Directive (WFD; 2008/98/EC) states that classification of hazardous ecotoxicological properties of wastes (i.e. criteria H-14), should be based on the Community legislation on chemicals (i.e. CLP Regulation 1272/2008). However, harmonizing the waste and chemical classification may involve drastic changes related to choice of leaching tests as compared to e.g. the current European standard for ecotoxic characterization of waste (CEN 14735). The primary aim of the present study was therefore to evaluate the influence of leaching conditions, i.e. pH (inherent pH (∼10), and 7), liquid to solid (L/S) ratio (10 and 1000 L/kg) and particle size (<4 mm, <1 mm, and <0.125 mm), for subsequent chemical analysis and ecotoxicity testing in relation to classification of municipal waste incineration bottom ash. The hazard potential, based on either comparisons between element levels in leachate and literature toxicity data or ecotoxicity testing of the leachates, was overall significantly higher at low particle size (<0.125 mm) as compared to particle fractions <1mm and <4mm, at pH 10 as compared to pH 7, and at L/S 10 as compared to L/S 1000. These results show that the choice of leaching conditions is crucial for H-14 classification of ash and must be carefully considered in deciding on future guidance procedures in Europe. Copyright © 2013 Elsevier Ltd. All rights reserved.

Cloud-Scale Genomic Signals Processing for Robust Large-Scale Cancer Genomic Microarray Data Analysis.

PubMed

Harvey, Benjamin Simeon; Ji, Soo-Yeon

2017-01-01

As microarray data available to scientists continues to increase in size and complexity, it has become overwhelmingly important to find multiple ways to bring forth oncological inference to the bioinformatics community through the analysis of large-scale cancer genomic (LSCG) DNA and mRNA microarray data that is useful to scientists. Though there have been many attempts to elucidate the issue of bringing forth biological interpretation by means of wavelet preprocessing and classification, there has not been a research effort that focuses on a cloud-scale distributed parallel (CSDP) separable 1-D wavelet decomposition technique for denoising through differential expression thresholding and classification of LSCG microarray data. This research presents a novel methodology that utilizes a CSDP separable 1-D method for wavelet-based transformation in order to initialize a threshold which will retain significantly expressed genes through the denoising process for robust classification of cancer patients. Additionally, the overall study was implemented and encompassed within CSDP environment. The utilization of cloud computing and wavelet-based thresholding for denoising was used for the classification of samples within the Global Cancer Map, Cancer Cell Line Encyclopedia, and The Cancer Genome Atlas. The results proved that separable 1-D parallel distributed wavelet denoising in the cloud and differential expression thresholding increased the computational performance and enabled the generation of higher quality LSCG microarray datasets, which led to more accurate classification results.

Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce.

PubMed

Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

2016-01-01

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network's initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data.

[Implementation of cytology images classification--the Bethesda 2001 System--in a group of screened women from Podlaskie region--effect evaluation].

PubMed

Zbroch, Tomasz; Knapp, Paweł Grzegorz; Knapp, Piotr Andrzej

2007-09-01

Increasing knowledge concerning carcinogenesis within cervical epithelium has forced us to make continues modifications of cytology classification of the cervical smears. Eventually, new descriptions of the submicroscopic cytomorphological abnormalities have enabled the implementation of Bethesda System which was meant to take place of the former Papanicolaou classification although temporarily both are sometimes used simultaneously. The aim of this study was to compare results of these two classification systems in the aspect of diagnostic accuracy verified by further tests of the diagnostic algorithm for the cervical lesion evaluation. The study was conducted in the group of women selected from general population, the criteria being the place of living and cervical cancer age risk group, in the consecutive periods of mass screening in Podlaski region. The performed diagnostic tests have been based on the commonly used algorithm, as well as identical laboratory and methodological conditions. Performed assessment revealed comparable diagnostic accuracy of both analyzing classifications, verified by histological examination, although with marked higher specificity for dysplastic lesions with decreased number of HSIL results and increased diagnosis of LSILs. Higher number of performed colposcopies and biopsies were an additional consequence of TBS classification. Results based on Bethesda System made it possible to find the sources and reasons of abnormalities with much greater precision, which enabled causing agent treatment. Two evaluated cytology classification systems, although not much different, depicted higher potential of TBS and better, more effective communication between cytology laboratory and gynecologist, making reasonable implementation of The Bethesda System in the daily cytology screening work.

Preparation of Underrepresented Males for Scientific Careers: A Study of the Dr. John H. Hopps Jr. Defense Research Scholars Program at Morehouse College

PubMed Central

Thompson, Rahmelle C.; Monroe-White, Thema; Xavier, Jeffrey; Howell, Courtney; Moore, Myisha Roberson; Haynes, J. K.

2016-01-01

Equal representation within higher education science, technology, engineering, and mathematics (STEM) fields and the STEM workforce in the United States across demographically diverse populations is a long-standing challenge. This study uses two-to-one nearest-neighbor matched-comparison group design to examine academic achievement, pursuit of graduate science degree, and classification of graduate institution attended by students participating in the Hopps Scholars Program (Hopps) at Morehouse College. Hopps is a highly structured enrichment program aimed at increasing participation of black males in STEM fields. Morehouse institutional records, Hopps Program records, and National Student Clearinghouse data were used to examine differences between Hopps and non-Hopps STEM graduates of Morehouse. Two-way sample t tests and chi-square tests revealed significant differences in academic achievement, likelihood of STEM degree pursuit, and the classification of graduate institutions attended by Hopps versus non-Hopps students. Hopps Scholars were significantly more likely than non-Hopps STEM graduates both to pursue STEM doctoral degrees and to attend doctoral-granting institutions with higher research activity. The Hopps Program’s approach to training black male students for scientific careers is a model of success for other programs committed to increasing the number of black males pursuing advanced degrees in STEM. PMID:27562959

Incidence of Gastrointestinal Bleeding After Percutaneous Coronary Intervention: A Single Center Experience.

PubMed

Aziz, Fahad

2014-02-01

Gastrointestinal (GI) bleeding is a hemorrhagic complication after percutaneous coronary intervention in patients with acute myocardial infarction. The purpose of the study is to determine predictors of GI bleeding and impact of GI bleeding on the patients undergoing percutaneous coronary intervention. GI bleeding occurred in 6 (7.1%) of 84 patients with STEMI/NSETMI (ST-segment elevated myocardial infarction/Non ST-segment elevated myocardial infarction) undergoing primary percutaneous coronary intervention. Univariate analysis demonstrates that patients with GI bleeding had a significantly higher previous GI bleeding (16.66% vs. 8.6%, P < 0.001). Higher Killip classification at presentation was associated with higher incidence of GI bleeding (61% vs. 18%, P < 0.01). The use of proton pump inhibitors did not reduce the risk of GI bleeding. The GI bleeding in these patients was associated with higher mortality and morbidity in the post percutaneous coronary intervention period. Although, GI bleeding in patients with MI significantly increases mortality and morbidity, previous GI bleeding and higher Killip class are associated with higher incidence of GI bleeding. High-risk patients for GI bleeding can be identified at presentation.

Trifactorial classification system for osteotome sinus floor elevation based on an observational retrospective analysis of 926 implants followed up to 10 years.

PubMed

French, David; Nadji, Nabil; Liu, Shawn X; Larjava, Hannu

2015-06-01

A novel osteotome trifactorial classification system is proposed for transcrestal osteotome-mediated sinus floor elevation (OSFE) sites that includes residual bone height (RBH), sinus floor anatomy (contour), and multiple versus single sites OSFE (tenting). An analysis of RBH, contour, and tenting was retrospectively applied to a cohort of 926 implants placed using OSFE without added bone graft and followed up to 10 years. RBH was divided into three groups: high (RBH > 6 mm), mid (RBH = 4.1 to 6 mm), and low (RBH = 2 to 4 mm). The sinus "contour" was divided into four groups: flat, concave, angle, and septa. For "tenting", single versus multiple adjacent OSFE sites were compared. The prevalence of flat sinus floors increased as RBH decreased. RBH was a significant predictor of failure with rates as follows: low- RBH = 5.1%, mid-RBH = 1.5%, and high-RBH = 0.4%. Flat sinus floors and single sites as compared to multiple sites had higher observed failure rates but neither achieved statistical significance; however, the power of the study was limited by low numbers of failures. The osteotome trifactorial classification system as proposed can assist planning OSFE cases and may allow better comparison of future OSFE studies.

Proposal of a new classification of postoperative ileus based on its clinical impact-results of a global survey and preliminary evaluation in colorectal surgery.

PubMed

Venara, Aurélien; Slim, Karem; Regimbeau, Jean-Marc; Ortega-Deballon, Pablo; Vielle, Bruno; Lermite, Emilie; Meurette, Guillaume; Hamy, Antoine

2017-06-01

There is no consensual definition of postoperative ileus (POI), which leads to a lack of reproducibility. The aims of this study were (i) to propose and evaluate a classification of postoperative ileus based on its consequences and (ii) to assess the reproducibility of the classification. A national global survey was carried out according to the DELPHI method in order to create a classification of primary POI. The classification was subsequently tested on a cohort of patients who underwent colorectal surgery. Finally, a reproducibility test was performed in five teaching hospitals with junior and senior surgeons. A five-stage classification was proposed: grade A (least) to grade E (worst). For better differentiation, subcategories (D1/D2) were included. Overall, 173 patients were included who underwent colorectal surgery. Forty of them experienced primary postoperative ileus (23.1%). Grade A occurred in 10 cases, grade B in 10 cases, grade C in 14 cases, grade D1 in 2 cases, and grade D2 in 2 cases. POI-related death (grade E) occurred in 2 cases. Patients with grade A POI recovered their gastrointestinal function significantly faster than those with higher grades (p = 0.01), and were more likely to undergo laparoscopic surgery (p = 0.04). The Intraclass Correlation Coefficient (ICC) was 0.83 in the overall population, and 0.83 and 0.82 respectively in the junior and senior surgeon populations. This classification is easy to both use and reproduce. It will improve the reproducibility, evaluation, and assessment of POI. These preliminary results should be confirmed in a multi-centric international study.

Comparison Between Spectral, Spatial and Polarimetric Classification of Urban and Periurban Landcover Using Temporal Sentinel - 1 Images

NASA Astrophysics Data System (ADS)

Roychowdhury, K.

2016-06-01

Landcover is the easiest detectable indicator of human interventions on land. Urban and peri-urban areas present a complex combination of landcover, which makes classification challenging. This paper assesses the different methods of classifying landcover using dual polarimetric Sentinel-1 data collected during monsoon (July) and winter (December) months of 2015. Four broad landcover classes such as built up areas, water bodies and wetlands, vegetation and open spaces of Kolkata and its surrounding regions were identified. Polarimetric analyses were conducted on Single Look Complex (SLC) data of the region while ground range detected (GRD) data were used for spectral and spatial classification. Unsupervised classification by means of K-Means clustering used backscatter values and was able to identify homogenous landcovers over the study area. The results produced an overall accuracy of less than 50% for both the seasons. Higher classification accuracy (around 70%) was achieved by adding texture variables as inputs along with the backscatter values. However, the accuracy of classification increased significantly with polarimetric analyses. The overall accuracy was around 80% in Wishart H-A-Alpha unsupervised classification. The method was useful in identifying urban areas due to their double-bounce scattering and vegetated areas, which have more random scattering. Normalized Difference Built-up index (NDBI) and Normalized Difference Vegetation Index (NDVI) obtained from Landsat 8 data over the study area were used to verify vegetation and urban classes. The study compares the accuracies of different methods of classifying landcover using medium resolution SAR data in a complex urban area and suggests that polarimetric analyses present the most accurate results for urban and suburban areas.

Impact of the revised International Prognostic Scoring System, cytogenetics and monosomal karyotype on outcome after allogeneic stem cell transplantation for myelodysplastic syndromes and secondary acute myeloid leukemia evolving from myelodysplastic syndromes: a retrospective multicenter study of the European Society of Blood and Marrow Transplantation

PubMed Central

Koenecke, Christian; Göhring, Gudrun; de Wreede, Liesbeth C.; van Biezen, Anja; Scheid, Christof; Volin, Liisa; Maertens, Johan; Finke, Jürgen; Schaap, Nicolaas; Robin, Marie; Passweg, Jakob; Cornelissen, Jan; Beelen, Dietrich; Heuser, Michael; de Witte, Theo; Kröger, Nicolaus

2015-01-01

The aim of this study was to determine the impact of the revised 5-group International Prognostic Scoring System cytogenetic classification on outcome after allogeneic stem cell transplantation in patients with myelodysplastic syndromes or secondary acute myeloid leukemia who were reported to the European Society for Blood and Marrow Transplantation database. A total of 903 patients had sufficient cytogenetic information available at stem cell transplantation to be classified according to the 5-group classification. Poor and very poor risk according to this classification was an independent predictor of shorter relapse-free survival (hazard ratio 1.40 and 2.14), overall survival (hazard ratio 1.38 and 2.14), and significantly higher cumulative incidence of relapse (hazard ratio 1.64 and 2.76), compared to patients with very good, good or intermediate risk. When comparing the predictive performance of a series of Cox models both for relapse-free survival and for overall survival, a model with simplified 5-group cytogenetics (merging very good, good and intermediate cytogenetics) performed best. Furthermore, monosomal karyotype is an additional negative predictor for outcome within patients of the poor, but not the very poor risk group of the 5-group classification. The revised International Prognostic Scoring System cytogenetic classification allows patients with myelodysplastic syndromes to be separated into three groups with clearly different outcomes after stem cell transplantation. Poor and very poor risk cytogenetics were strong predictors of poor patient outcome. The new cytogenetic classification added value to prediction of patient outcome compared to prediction models using only traditional risk factors or the 3-group International Prognostic Scoring System cytogenetic classification. PMID:25552702

A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform

PubMed Central

Yu, Xiao; Ding, Enjie; Chen, Chunxu; Liu, Xiaoming; Li, Li

2015-01-01

Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC) to select salient features from the marginal spectrum of vibration signals by Hilbert–Huang Transform (HHT). In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS) into window spectrums, following which Rand Index (RI) criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs). Next, a hybrid REBs fault diagnosis is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines). The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU). The said test results evidence three major advantages of the novel method. First, the fault classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, fault classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500–800 and a m range of 50–300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR) = 5. Experimental results indicate that the proposed WMSC method yields a high REBs fault classification accuracy and a good performance in Gauss white noise reduction. PMID:26540059

A Novel Characteristic Frequency Bands Extraction Method for Automatic Bearing Fault Diagnosis Based on Hilbert Huang Transform.

PubMed

Yu, Xiao; Ding, Enjie; Chen, Chunxu; Liu, Xiaoming; Li, Li

2015-11-03

Because roller element bearings (REBs) failures cause unexpected machinery breakdowns, their fault diagnosis has attracted considerable research attention. Established fault feature extraction methods focus on statistical characteristics of the vibration signal, which is an approach that loses sight of the continuous waveform features. Considering this weakness, this article proposes a novel feature extraction method for frequency bands, named Window Marginal Spectrum Clustering (WMSC) to select salient features from the marginal spectrum of vibration signals by Hilbert-Huang Transform (HHT). In WMSC, a sliding window is used to divide an entire HHT marginal spectrum (HMS) into window spectrums, following which Rand Index (RI) criterion of clustering method is used to evaluate each window. The windows returning higher RI values are selected to construct characteristic frequency bands (CFBs). Next, a hybrid REBs fault diagnosis is constructed, termed by its elements, HHT-WMSC-SVM (support vector machines). The effectiveness of HHT-WMSC-SVM is validated by running series of experiments on REBs defect datasets from the Bearing Data Center of Case Western Reserve University (CWRU). The said test results evidence three major advantages of the novel method. First, the fault classification accuracy of the HHT-WMSC-SVM model is higher than that of HHT-SVM and ST-SVM, which is a method that combines statistical characteristics with SVM. Second, with Gauss white noise added to the original REBs defect dataset, the HHT-WMSC-SVM model maintains high classification accuracy, while the classification accuracy of ST-SVM and HHT-SVM models are significantly reduced. Third, fault classification accuracy by HHT-WMSC-SVM can exceed 95% under a Pmin range of 500-800 and a m range of 50-300 for REBs defect dataset, adding Gauss white noise at Signal Noise Ratio (SNR) = 5. Experimental results indicate that the proposed WMSC method yields a high REBs fault classification accuracy and a good performance in Gauss white noise reduction.

Variation in Men's Dietary Intake Between Occupations, Based on Data From the Japan Environment and Children's Study.

PubMed

Tanaka, Rie; Tsuji, Mayumi; Asakura, Keiko; Senju, Ayako; Shibata, Eiji; Kusuhara, Koichi; Morokuma, Seiichi; Sanefuji, Masafumi; Kawamoto, Toshihiro

2018-06-01

There has been increasing interest in dietary health promotion in the workplace. Although many previous studies have focused on dietary habits in specific occupations, variation between occupational groups requires clarification. The present study aimed to examine differences in food and nutrient intake between occupational groups, using detailed classification. A cross-sectional study was conducted using data from the Japan Environment and Children's Study. The study included 38,721 employed Japanese expectant fathers aged between 20 and 65 years. Dietary intake was assessed using a food frequency questionnaire. Occupations were categorized into 11 categories according to the Japan Standard Occupational Classification. Analysis of variance and analysis of covariance were performed to compare dietary intake of occupational groups. Logistic regression analysis was performed to examine the differences in adherence to dietary recommendations across occupations. Dietary intake differed significantly between occupations. Specific dietary intake was observed in security and agricultural workers, who tended to exhibit higher consumption levels for numerous foods and nutrients. In addition, relative to other workers, security workers showed higher intake of dairy products and calcium, and agricultural workers consumed larger amounts of pickles and salt. The study categorized occupations into detailed categories using the Japan Standard Occupational Classification, which facilitated the clarification of overall dietary trends across occupations and identification of specific dietary characteristics in individual occupations. The findings could aid in workplace health promotion.

Accurate crop classification using hierarchical genetic fuzzy rule-based systems

NASA Astrophysics Data System (ADS)

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

2014-10-01

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

A Classification of Remote Sensing Image Based on Improved Compound Kernels of Svm

NASA Astrophysics Data System (ADS)

Zhao, Jianing; Gao, Wanlin; Liu, Zili; Mou, Guifen; Lu, Lin; Yu, Lina

The accuracy of RS classification based on SVM which is developed from statistical learning theory is high under small number of train samples, which results in satisfaction of classification on RS using SVM methods. The traditional RS classification method combines visual interpretation with computer classification. The accuracy of the RS classification, however, is improved a lot based on SVM method, because it saves much labor and time which is used to interpret images and collect training samples. Kernel functions play an important part in the SVM algorithm. It uses improved compound kernel function and therefore has a higher accuracy of classification on RS images. Moreover, compound kernel improves the generalization and learning ability of the kernel.

Variability in Standard Outcomes of Posterior Lumbar Fusion Determined by National Databases.

PubMed

Joseph, Jacob R; Smith, Brandon W; Park, Paul

2017-01-01

National databases are used with increasing frequency in spine surgery literature to evaluate patient outcomes. The differences between individual databases in relationship to outcomes of lumbar fusion are not known. We evaluated the variability in standard outcomes of posterior lumbar fusion between the University HealthSystem Consortium (UHC) database and the Healthcare Cost and Utilization Project National Inpatient Sample (NIS). NIS and UHC databases were queried for all posterior lumbar fusions (International Classification of Diseases, Ninth Revision code 81.07) performed in 2012. Patient demographics, comorbidities (including obesity), length of stay (LOS), in-hospital mortality, and complications such as urinary tract infection, deep venous thrombosis, pulmonary embolism, myocardial infarction, durotomy, and surgical site infection were collected using specific International Classification of Diseases, Ninth Revision codes. Analysis included 21,470 patients from the NIS database and 14,898 patients from the UHC database. Demographic data were not significantly different between databases. Obesity was more prevalent in UHC (P = 0.001). Mean LOS was 3.8 days in NIS and 4.55 in UHC (P < 0.0001). Complications were significantly higher in UHC, including urinary tract infection, deep venous thrombosis, pulmonary embolism, myocardial infarction, surgical site infection, and durotomy. In-hospital mortality was similar between databases. NIS and UHC databases had similar demographic patient populations undergoing posterior lumbar fusion. However, the UHC database reported significantly higher complication rate and longer LOS. This difference may reflect academic institutions treating higher-risk patients; however, a definitive reason for the variability between databases is unknown. The inability to precisely determine the basis of the variability between databases highlights the limitations of using administrative databases for spinal outcome analysis. Copyright © 2016 Elsevier Inc. All rights reserved.

Does the rise in eating disorders lead to increasing risk of orthorexia nervosa? Correlations with gender, education, and body mass index.

PubMed

Sanlier, Nevin; Yassibas, Emine; Bilici, Saniye; Sahin, Gulsah; Celik, Bülent

2016-01-01

Investigating eating disorders and orthorexia nervosa, especially in the young population, is an important step in taking protective precautions and identifying disease. This study was carried out to determine the relationship of eating disorders and orthorexia nervosa to gender, BMI, and field of study in a population of university students in Turkey. In all, 900 university students aged 17-23 years participated in this study. EAT-40 and ORTO-15, which are validated instruments for the screening of participants with anormal eating behaviors and orthorexia nervosa, respectively, were used. There was not a significant difference in EAT-40 scores according to gender and BMI classification. However, EAT-40 scores were high among the students in social science. The number of orthorectic participants among women is higher than that among men, and ORTO-15 scores were not associated with BMI classification and field of study. A significant negative correlation was found between EAT-40 and ORTO-15 scores.

Forensic Applicability of Femur Subtrochanteric Shape to Ancestry Assessment in Thai and White American Males.

PubMed

Tallman, Sean D; Winburn, Allysha P

2015-09-01

Ancestry assessment from the postcranial skeleton presents a significant challenge to forensic anthropologists. However, metric dimensions of the femur subtrochanteric region are believed to distinguish between individuals of Asian and non-Asian descent. This study tests the discriminatory power of subtrochanteric shape using modern samples of 128 Thai and 77 White American males. Results indicate that the samples' platymeric index distributions are significantly different (p≤0.001), with the Thai platymeric index range generally lower and the White American range generally higher. While the application of ancestry assessment methods developed from Native American subtrochanteric data results in low correct classification rates for the Thai sample (50.8-57.8%), adapting these methods to the current samples leads to better classification. The Thai data may be more useful in forensic analysis than previously published subtrochanteric data derived from Native American samples. Adapting methods to include appropriate geographic and contemporaneous populations increases the accuracy of femur subtrochanteric ancestry methods. © 2015 American Academy of Forensic Sciences.

Application of remote sensing in South Dakota to provide accurate inventories of agricultural crops, enhance contrast in photographic products, monitor rangeland habitat loss, map Aspen, and prepare hydrogeologic surveys

NASA Technical Reports Server (NTRS)

Myers, V. I. (Principal Investigator); Dalsted, K. J.; Best, R. G.; Smith, J. R.; Eidenshink, J. C.; Schmer, F. A.; Andrawis, A. S.; Rahn, P. H.

1977-01-01

The author has identified the following significant results. Digital analysis of LANDSAT CCT's indicated that two discrete spectral background zones occurred among the five soil zone. K-CLASS classification of corn revealed that accuracy increased when two background zones were used, compared to the classification of corn stratified by five soil zones. Selectively varying film type developer and development time produces higher contract in reprocessed imagery. Interpretation of rangeland and cropped land data from 1968 aerial photography and 1976 LANDSAT imagery indicated losses in rangeland habitat. Thermal imagery was useful in locating potential sources of sub-surface water and geothermal energy, estimating evapotranspiration, and inventorying the land.

Assessing the statistical significance of the achieved classification error of classifiers constructed using serum peptide profiles, and a prescription for random sampling repeated studies for massive high-throughput genomic and proteomic studies.

PubMed

Lyons-Weiler, James; Pelikan, Richard; Zeh, Herbert J; Whitcomb, David C; Malehorn, David E; Bigbee, William L; Hauskrecht, Milos

2005-01-01

Peptide profiles generated using SELDI/MALDI time of flight mass spectrometry provide a promising source of patient-specific information with high potential impact on the early detection and classification of cancer and other diseases. The new profiling technology comes, however, with numerous challenges and concerns. Particularly important are concerns of reproducibility of classification results and their significance. In this work we describe a computational validation framework, called PACE (Permutation-Achieved Classification Error), that lets us assess, for a given classification model, the significance of the Achieved Classification Error (ACE) on the profile data. The framework compares the performance statistic of the classifier on true data samples and checks if these are consistent with the behavior of the classifier on the same data with randomly reassigned class labels. A statistically significant ACE increases our belief that a discriminative signal was found in the data. The advantage of PACE analysis is that it can be easily combined with any classification model and is relatively easy to interpret. PACE analysis does not protect researchers against confounding in the experimental design, or other sources of systematic or random error. We use PACE analysis to assess significance of classification results we have achieved on a number of published data sets. The results show that many of these datasets indeed possess a signal that leads to a statistically significant ACE.

The reliability and validity of the Saliba Postural Classification System

PubMed Central

Collins, Cristiana Kahl; Johnson, Vicky Saliba; Godwin, Ellen M.; Pappas, Evangelos

2016-01-01

Objectives To determine the reliability and validity of the Saliba Postural Classification System (SPCS). Methods Two physical therapists classified pictures of 100 volunteer participants standing in their habitual posture for inter and intra-tester reliability. For validity, 54 participants stood on a force plate in a habitual and a corrected posture, while a vertical force was applied through the shoulders until the clinician felt a postural give. Data were extracted at the time the give was felt and at a time in the corrected posture that matched the peak vertical ground reaction force (VGRF) in the habitual posture. Results Inter-tester reliability demonstrated 75% agreement with a Kappa = 0.64 (95% CI = 0.524–0.756, SE = 0.059). Intra-tester reliability demonstrated 87% agreement with a Kappa = 0.8, (95% CI = 0.702–0.898, SE = 0.05) and 80% agreement with a Kappa = 0.706, (95% CI = 0.594–0818, SE = 0.057). The examiner applied a significantly higher (p < 0.001) peak vertical force in the corrected posture prior to a postural give when compared to the habitual posture. Within the corrected posture, the %VGRF was higher when the test was ongoing vs. when a postural give was felt (p < 0.001). The %VGRF was not different between the two postures when comparing the peaks (p = 0.214). Discussion The SPCS has substantial agreement for inter- and intra-tester reliability and is largely a valid postural classification system as determined by the larger vertical forces in the corrected postures. Further studies on the correlation between the SPCS and diagnostic classifications are indicated. PMID:27559288

The reliability and validity of the Saliba Postural Classification System.

PubMed

Collins, Cristiana Kahl; Johnson, Vicky Saliba; Godwin, Ellen M; Pappas, Evangelos

2016-07-01

To determine the reliability and validity of the Saliba Postural Classification System (SPCS). Two physical therapists classified pictures of 100 volunteer participants standing in their habitual posture for inter and intra-tester reliability. For validity, 54 participants stood on a force plate in a habitual and a corrected posture, while a vertical force was applied through the shoulders until the clinician felt a postural give. Data were extracted at the time the give was felt and at a time in the corrected posture that matched the peak vertical ground reaction force (VGRF) in the habitual posture. Inter-tester reliability demonstrated 75% agreement with a Kappa = 0.64 (95% CI = 0.524-0.756, SE = 0.059). Intra-tester reliability demonstrated 87% agreement with a Kappa = 0.8, (95% CI = 0.702-0.898, SE = 0.05) and 80% agreement with a Kappa = 0.706, (95% CI = 0.594-0818, SE = 0.057). The examiner applied a significantly higher (p < 0.001) peak vertical force in the corrected posture prior to a postural give when compared to the habitual posture. Within the corrected posture, the %VGRF was higher when the test was ongoing vs. when a postural give was felt (p < 0.001). The %VGRF was not different between the two postures when comparing the peaks (p = 0.214). The SPCS has substantial agreement for inter- and intra-tester reliability and is largely a valid postural classification system as determined by the larger vertical forces in the corrected postures. Further studies on the correlation between the SPCS and diagnostic classifications are indicated.

From genus to phylum: large-subunit and internal transcribed spacer rRNA operon regions show similar classification accuracies influenced by database composition.

PubMed

Porras-Alfaro, Andrea; Liu, Kuan-Liang; Kuske, Cheryl R; Xie, Gary

2014-02-01

We compared the classification accuracy of two sections of the fungal internal transcribed spacer (ITS) region, individually and combined, and the 5' section (about 600 bp) of the large-subunit rRNA (LSU), using a naive Bayesian classifier and BLASTN. A hand-curated ITS-LSU training set of 1,091 sequences and a larger training set of 8,967 ITS region sequences were used. Of the factors evaluated, database composition and quality had the largest effect on classification accuracy, followed by fragment size and use of a bootstrap cutoff to improve classification confidence. The naive Bayesian classifier and BLASTN gave similar results at higher taxonomic levels, but the classifier was faster and more accurate at the genus level when a bootstrap cutoff was used. All of the ITS and LSU sections performed well (>97.7% accuracy) at higher taxonomic ranks from kingdom to family, and differences between them were small at the genus level (within 0.66 to 1.23%). When full-length sequence sections were used, the LSU outperformed the ITS1 and ITS2 fragments at the genus level, but the ITS1 and ITS2 showed higher accuracy when smaller fragment sizes of the same length and a 50% bootstrap cutoff were used. In a comparison using the larger ITS training set, ITS1 and ITS2 had very similar accuracy classification for fragments between 100 and 200 bp. Collectively, the results show that any of the ITS or LSU sections we tested provided comparable classification accuracy to the genus level and underscore the need for larger and more diverse classification training sets.

From Genus to Phylum: Large-Subunit and Internal Transcribed Spacer rRNA Operon Regions Show Similar Classification Accuracies Influenced by Database Composition

PubMed Central

Liu, Kuan-Liang; Kuske, Cheryl R.

2014-01-01

We compared the classification accuracy of two sections of the fungal internal transcribed spacer (ITS) region, individually and combined, and the 5′ section (about 600 bp) of the large-subunit rRNA (LSU), using a naive Bayesian classifier and BLASTN. A hand-curated ITS-LSU training set of 1,091 sequences and a larger training set of 8,967 ITS region sequences were used. Of the factors evaluated, database composition and quality had the largest effect on classification accuracy, followed by fragment size and use of a bootstrap cutoff to improve classification confidence. The naive Bayesian classifier and BLASTN gave similar results at higher taxonomic levels, but the classifier was faster and more accurate at the genus level when a bootstrap cutoff was used. All of the ITS and LSU sections performed well (>97.7% accuracy) at higher taxonomic ranks from kingdom to family, and differences between them were small at the genus level (within 0.66 to 1.23%). When full-length sequence sections were used, the LSU outperformed the ITS1 and ITS2 fragments at the genus level, but the ITS1 and ITS2 showed higher accuracy when smaller fragment sizes of the same length and a 50% bootstrap cutoff were used. In a comparison using the larger ITS training set, ITS1 and ITS2 had very similar accuracy classification for fragments between 100 and 200 bp. Collectively, the results show that any of the ITS or LSU sections we tested provided comparable classification accuracy to the genus level and underscore the need for larger and more diverse classification training sets. PMID:24242255

Relationship between Plasma Triglyceride Level and Severity of Hypertriglyceridemic Pancreatitis.

PubMed

Wang, Sheng-Huei; Chou, Yu-Ching; Shangkuan, Wei-Chuan; Wei, Kuang-Yu; Pan, Yu-Han; Lin, Hung-Che

2016-01-01

Hypertriglyceridemia is the third most common cause of acute pancreatitis, but whether the level of triglyceride (TG) is related to severity of pancreatitis is unclear. To evaluate the effect of TG level on the severity of hypertriglyceridemic pancreatitis (HTGP). Retrospective cohort study. We reviewed the records of 144 patients with HTGP from 1999 to 2013 at Tri-Service General Hospital. Patients with possible etiology of pancreatitis, such as gallstones, those consuming alcohol or drugs, or those with infections were excluded. The classification of severity of pancreatitis was based on the revised Atlanta classification. We allocated the patients into high-TG and low-TG groups based on the optimal cut-off value (2648 mg/dL), which was derived from the receiver operating characteristic (ROC) curve between TG level and severity of HTGP. We then compared the clinical characteristics, pancreatitis severity, and mortality rates of the groups. There were 66 patients in the low-TG group and 78 patients in the high-TG group. There was no significant difference in the age, sex ratio, body mass index, and comorbidity between the 2 groups. The high-TG group had significantly higher levels of glucose (P = 0.022), total cholesterol (P = 0.002), and blood urea nitrogen (P = 0.037), and lower levels of sodium (P = 0.003) and bicarbonate (P = 0.002) than the low-TG group. The incidences of local complication (P = 0.002) and severe and moderate form of pancreatitis (P = 0.004) were significantly higher in the high-TG group than in the low-TG group. The mortality rate was higher in the high-TG group than in the low-TG group (P = 0.07). Higher TG level in patients with HTGP may be associated with adverse prognosis, but randomized and prospective studies are needed in the future verify this relationship.

A graduated food addiction classifications approach significantly differentiates depression, anxiety and stress among people with type 2 diabetes.

PubMed

Raymond, Karren-Lee; Kannis-Dymand, Lee; Lovell, Geoff P

2017-10-01

To examine differences in depression, anxiety, and stress across people with type 2 diabetes mellitus (t2d) classified according to a four level processed food addiction (PFA) severity indicator dichotomy. Four hundred and eight participants with a t2d diagnoses completed an online survey including the Yale Food Addiction Scale (YFAS) and the DASS-21. Based on YFAS symptom counts participants were classified as either: non-PFA; mild-PFA; moderate-PFA; or severe-PFA. Multivariate, λ=0.422, F(9,978.51)=46.286, p<0.001, n p 2 =0.250, and univariate analyses of variance demonstrated that depression F(3,408)=159.891, p<0.001, n p 2 =0.543, anxiety F(3,408)=127.419, p<0.001, n p 2 =0.486, and stress scores F(3,408)=129.714, p<0.001, n p 2 =0.491, significantly and meaningfully increased from one PFA classification level to the next. Furthermore, the proportion of participants with more severe classifications of depression χ 2 (12)=297.820, p<0.001, anxiety χ 2 (12)=271.805, p<0.001, and stress χ 2 (12)=240.875, p<0.001, were significantly higher in the more severe PFA groupings. For people with t2d, PFA is an important and meaningful associate of depression, anxiety, and stress, and that the adopted four level PFA severity indicator dichotomy is valid and useful. Copyright © 2017 Elsevier B.V. All rights reserved.

Influence of P300 latency jitter on event related potential-based brain-computer interface performance

NASA Astrophysics Data System (ADS)

Aricò, P.; Aloise, F.; Schettini, F.; Salinari, S.; Mattia, D.; Cincotti, F.

2014-06-01

Objective. Several ERP-based brain-computer interfaces (BCIs) that can be controlled even without eye movements (covert attention) have been recently proposed. However, when compared to similar systems based on overt attention, they displayed significantly lower accuracy. In the current interpretation, this is ascribed to the absence of the contribution of short-latency visual evoked potentials (VEPs) in the tasks performed in the covert attention modality. This study aims to investigate if this decrement (i) is fully explained by the lack of VEP contribution to the classification accuracy; (ii) correlates with lower temporal stability of the single-trial P300 potentials elicited in the covert attention modality. Approach. We evaluated the latency jitter of P300 evoked potentials in three BCI interfaces exploiting either overt or covert attention modalities in 20 healthy subjects. The effect of attention modality on the P300 jitter, and the relative contribution of VEPs and P300 jitter to the classification accuracy have been analyzed. Main results. The P300 jitter is higher when the BCI is controlled in covert attention. Classification accuracy negatively correlates with jitter. Even disregarding short-latency VEPs, overt-attention BCI yields better accuracy than covert. When the latency jitter is compensated offline, the difference between accuracies is not significant. Significance. The lower temporal stability of the P300 evoked potential generated during the tasks performed in covert attention modality should be regarded as the main contributing explanation of lower accuracy of covert-attention ERP-based BCIs.

Evaluation of feature selection algorithms for classification in temporal lobe epilepsy based on MR images

NASA Astrophysics Data System (ADS)

Lai, Chunren; Guo, Shengwen; Cheng, Lina; Wang, Wensheng; Wu, Kai

2017-02-01

It's very important to differentiate the temporal lobe epilepsy (TLE) patients from healthy people and localize the abnormal brain regions of the TLE patients. The cortical features and changes can reveal the unique anatomical patterns of brain regions from the structural MR images. In this study, structural MR images from 28 normal controls (NC), 18 left TLE (LTLE), and 21 right TLE (RTLE) were acquired, and four types of cortical feature, namely cortical thickness (CTh), cortical surface area (CSA), gray matter volume (GMV), and mean curvature (MCu), were explored for discriminative analysis. Three feature selection methods, the independent sample t-test filtering, the sparse-constrained dimensionality reduction model (SCDRM), and the support vector machine-recursive feature elimination (SVM-RFE), were investigated to extract dominant regions with significant differences among the compared groups for classification using the SVM classifier. The results showed that the SVM-REF achieved the highest performance (most classifications with more than 92% accuracy), followed by the SCDRM, and the t-test. Especially, the surface area and gray volume matter exhibited prominent discriminative ability, and the performance of the SVM was improved significantly when the four cortical features were combined. Additionally, the dominant regions with higher classification weights were mainly located in temporal and frontal lobe, including the inferior temporal, entorhinal cortex, fusiform, parahippocampal cortex, middle frontal and frontal pole. It was demonstrated that the cortical features provided effective information to determine the abnormal anatomical pattern and the proposed method has the potential to improve the clinical diagnosis of the TLE.

Research on the transfer learning of the vehicle logo recognition

NASA Astrophysics Data System (ADS)

Zhao, Wei

2017-08-01

The Convolutional Neural Network of Deep Learning has been a huge success in the field of image intelligent transportation system can effectively solve the traffic safety, congestion, vehicle management and other problems of traffic in the city. Vehicle identification is a vital part of intelligent transportation, and the effective information in vehicles is of great significance to vehicle identification. With the traffic system on the vehicle identification technology requirements are getting higher and higher, the vehicle as an important type of vehicle information, because it should not be removed, difficult to change and other features for vehicle identification provides an important method. The current vehicle identification recognition (VLR) is mostly used to extract the characteristics of the method of classification, which for complex classification of its generalization ability to be some constraints, if the use of depth learning technology, you need a lot of training samples. In this paper, the method of convolution neural network based on transfer learning can solve this problem effectively, and it has important practical application value in the task of vehicle mark recognition.

Angiogenesis extent and macrophage density increase simultaneously with pathological progression in B-cell non-Hodgkin's lymphomas

PubMed Central

Vacca, A; Ribatti, D; Ruco, L; Giacchetta, F; Nico, B; Quondamatteo, F; Ria, R; Iurlaro, M; Dammacco, F

1999-01-01

Node biopsies of 30 benign lymphadenopathies and 71 B-cell non-Hodgkin's lymphomas (B-NHLs) were investigated for microvessel and macrophage counts using immunohistochemistry and morphometric analysis. Both counts were significantly higher in B-NHL. Moreover, when these were grouped into low-grade and high-grade lymphomas, according to the Kiel classification and Working Formulation (WF), statistically significant higher counts were found in the high-grade tumours. Immunohistochemistry and electron microscopy revealed a close spatial association between microvessels and macrophages. Overall, the results suggest that, in analogy to what has already been shown in solid tumours, angiogenesis occurring in B-NHLs increases with tumour progression, and that macrophages promote the induction of angiogenesis via the release of their angiogenic factors. © 1999 Cancer Research Campaign PMID:10070898

Relationship between risk classifications used to organize the demand for oral health in a small city of São Paulo, Brazil.

PubMed

Peres, João; Mendes, Karine Laura Cortellazzi; Wada, Ronaldo Seichi; Sousa, Maria da Luz Rosario de

2017-06-01

Oral health teams can work with both information of the people related to the family context as individual epidemiological through risk ratings, considering equity and service organization. The purpose of our study was to evaluate the association between tools that classify individual and family risk. The study group consisted of students from the age group of 5-6 years and 11-12 years who were classified regarding risk of caries and whether their parents had periodontal disease, in addition to the family risk. There was an association between the risk rating for decay in children (n = 128) and family risk classification with Coef C = 0.338 and p = 0.01, indicating that the higher the family's risk, the higher the risk of caries. Similarly, the association between the risk classification for periodontal disease in parents and family risk classification with Coef C = 0.5503 and p = 0.03 indicated that the higher the family risk, the higher the risk of periodontal disease. It can be concluded that the use of family risk rating tool is indicated as a possibility of ordering actions of the dental service, organizing their demand with greater equity, in this access door.

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

NASA Technical Reports Server (NTRS)

Smith, Aaron; Evans, Michael; Downey, Joseph

2017-01-01

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

The Usefulness of the TOAST Classification and Prognostic Significance of Pyramidal Symptoms During the Acute Phase of Cerebellar Ischemic Stroke.

PubMed

Dziadkowiak, Edyta; Chojdak-Łukasiewicz, Justyna; Guziński, Maciej; Noga, Leszek; Paradowski, Bogusław

2016-04-01

Cerebellar stroke is a rare condition with very nonspecific clinical features. The symptoms in the acute phase could imitate acute peripheral vestibular disorders or a brainstem lesion. The aim of this study was to assess the usefulness of the Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification in cerebellar stroke and the impact of clinical features on the prognosis. We retrospectively analyzed 107 patients with diagnosed ischemic cerebellar infarction. We studied the clinical features and compared them based on the location of the ischemic lesion and its distribution in the posterior interior cerebellar artery (PICA), superior cerebellar artery (SCA), and anterior inferior cerebellar artery (AICA) territories. According to the TOAST classification, stroke was more prevalent in atrial fibrillation (26/107) and when the lesion was in the PICA territory (39/107). Pyramidal signs occurred in 29/107 of patients and were more prevalent when the lesion was distributed in more than two vascular regions (p = 0.00640). Mortality was higher among patients with ischemic lesion caused by cardiac sources (p = 0.00094) and with pyramidal signs (p = 0.00640). The TOAST classification is less useful in assessing supratentorial ischemic infarcts. Cardioembolic etiology, location of the ischemic lesion, and pyramidal signs support a negative prognosis.

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

NASA Technical Reports Server (NTRS)

Fagan, Matthew E.; Defries, Ruth S.; Sesnie, Steven E.; Arroyo-Mora, J. Pablo; Soto, Carlomagno; Singh, Aditya; Townsend, Philip A.; Chazdon, Robin L.

2015-01-01

An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p less than 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer's accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes.

Multi-Feature Classification of Multi-Sensor Satellite Imagery Based on Dual-Polarimetric Sentinel-1A, Landsat-8 OLI, and Hyperion Images for Urban Land-Cover Classification

PubMed Central

Pan, Jianjun

2018-01-01

This paper focuses on evaluating the ability and contribution of using backscatter intensity, texture, coherence, and color features extracted from Sentinel-1A data for urban land cover classification and comparing different multi-sensor land cover mapping methods to improve classification accuracy. Both Landsat-8 OLI and Hyperion images were also acquired, in combination with Sentinel-1A data, to explore the potential of different multi-sensor urban land cover mapping methods to improve classification accuracy. The classification was performed using a random forest (RF) method. The results showed that the optimal window size of the combination of all texture features was 9 × 9, and the optimal window size was different for each individual texture feature. For the four different feature types, the texture features contributed the most to the classification, followed by the coherence and backscatter intensity features; and the color features had the least impact on the urban land cover classification. Satisfactory classification results can be obtained using only the combination of texture and coherence features, with an overall accuracy up to 91.55% and a kappa coefficient up to 0.8935, respectively. Among all combinations of Sentinel-1A-derived features, the combination of the four features had the best classification result. Multi-sensor urban land cover mapping obtained higher classification accuracy. The combination of Sentinel-1A and Hyperion data achieved higher classification accuracy compared to the combination of Sentinel-1A and Landsat-8 OLI images, with an overall accuracy of up to 99.12% and a kappa coefficient up to 0.9889. When Sentinel-1A data was added to Hyperion images, the overall accuracy and kappa coefficient were increased by 4.01% and 0.0519, respectively. PMID:29382073

Increased body mass index is associated with improved overall survival in extranodal natural killer/T-cell lymphoma, nasal type.

PubMed

Li, Ya-Jun; Yi, Ping-Yong; Li, Ji-Wei; Liu, Xian-Ling; Liu, Xi-Yu; Zhou, Fang; OuYang, Zhou; Sun, Zhong-Yi; Huang, Li-Jun; He, Jun-Qiao; Yao, Yuan; Fan, Zhou; Tang, Tian; Jiang, Wen-Qi

2017-01-17

The role of body mass index (BMI) in lymphoma survival outcomes is controversial. The prognostic significance of BMI in extranodal natural killer (NK)/T-cell lymphoma (ENKTL) is unclear. We evaluated the prognostic role of BMI in patients with ENKTL. We retrospectively analyzed 742 patients with newly diagnosed ENKTL. The prognostic value of BMI was compared between patients with low BMIs (< 20.0 kg/m2) and patients with high BMIs (≥ 20.0 kg/m2). The prognostic value of the International Prognostic Index (IPI) and the Korean Prognostic Index (KPI) was also evaluated and compared with that of the BMI classification. Patients with low BMIs tended to exhibit higher Eastern Cooperative Oncology Group performance status (ECOG PS) scores (≥ 2) (P = 0.001), more frequent B symptoms (P < 0.001), lower albumin levels (P < 0.001), higher KPI scores (P = 0.03), and lower rates of complete remission (P < 0.001) than patients with high BMIs, as well as inferior progression-free survival (PFS, P = 0.003), and inferior overall survival (OS, P = 0.001). Multivariate analysis demonstrated that age > 60 years, mass > 5 cm, stage III/IV, elevated LDH levels, albumin levels < 35 g/L and low BMIs were independent adverse predictors of OS. The BMI classification was found to be superior to the IPI with respect to predicting patient outcomes among low-risk patients and the KPI with respect to distinguishing between intermediate-low- and high-intermediate-risk patients. Higher BMI at the time of diagnosis is associated with improved overall survival in ENKTL. Using the BMI classification may improve the IPI and KPI prognostic models.

Increased body mass index is associated with improved overall survival in extranodal natural killer/T-cell lymphoma, nasal type

PubMed Central

Li, Ya-Jun; Yi, Ping-Yong; Li, Ji-Wei; Liu, Xian-Ling; Liu, Xi-Yu; Zhou, Fang; OuYang, Zhou; Sun, Zhong-Yi; Huang, Li-Jun; He, Jun-Qiao; Yao, Yuan; Fan, Zhou; Tang, Tian; Jiang, Wen-Qi

2017-01-01

Objectives: The role of body mass index (BMI) in lymphoma survival outcomes is controversial. The prognostic significance of BMI in extranodal natural killer (NK)/T-cell lymphoma (ENKTL) is unclear. We evaluated the prognostic role of BMI in patients with ENKTL. Methods: We retrospectively analyzed 742 patients with newly diagnosed ENKTL. The prognostic value of BMI was compared between patients with low BMIs (< 20.0 kg/m2) and patients with high BMIs (≥ 20.0 kg/m2). The prognostic value of the International Prognostic Index (IPI) and the Korean Prognostic Index (KPI) was also evaluated and compared with that of the BMI classification. Results: Patients with low BMIs tended to exhibit higher Eastern Cooperative Oncology Group performance status (ECOG PS) scores (≥ 2) (P = 0.001), more frequent B symptoms (P < 0.001), lower albumin levels (P < 0.001), higher KPI scores (P = 0.03), and lower rates of complete remission (P < 0.001) than patients with high BMIs, as well as inferior progression-free survival (PFS, P = 0.003), and inferior overall survival (OS, P = 0.001). Multivariate analysis demonstrated that age > 60 years, mass > 5 cm, stage III/IV, elevated LDH levels, albumin levels < 35 g/L and low BMIs were independent adverse predictors of OS. The BMI classification was found to be superior to the IPI with respect to predicting patient outcomes among low-risk patients and the KPI with respect to distinguishing between intermediate-low- and high-intermediate-risk patients. Conclusions: Higher BMI at the time of diagnosis is associated with improved overall survival in ENKTL. Using the BMI classification may improve the IPI and KPI prognostic models. PMID:28002803

Comparison of remote sensing image processing techniques to identify tornado damage areas from Landsat TM data

USGS Publications Warehouse

Myint, S.W.; Yuan, M.; Cerveny, R.S.; Giri, C.P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and objectoriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. ?? 2008 by MDPI.

Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data

PubMed Central

Myint, Soe W.; Yuan, May; Cerveny, Randall S.; Giri, Chandra P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. PMID:27879757

Automatic Cataract Hardness Classification Ex Vivo by Ultrasound Techniques.

PubMed

Caixinha, Miguel; Santos, Mário; Santos, Jaime

2016-04-01

To demonstrate the feasibility of a new methodology for cataract hardness characterization and automatic classification using ultrasound techniques, different cataract degrees were induced in 210 porcine lenses. A 25-MHz ultrasound transducer was used to obtain acoustical parameters (velocity and attenuation) and backscattering signals. B-Scan and parametric Nakagami images were constructed. Ninety-seven parameters were extracted and subjected to a Principal Component Analysis. Bayes, K-Nearest-Neighbours, Fisher Linear Discriminant and Support Vector Machine (SVM) classifiers were used to automatically classify the different cataract severities. Statistically significant increases with cataract formation were found for velocity, attenuation, mean brightness intensity of the B-Scan images and mean Nakagami m parameter (p < 0.01). The four classifiers showed a good performance for healthy versus cataractous lenses (F-measure ≥ 92.68%), while for initial versus severe cataracts the SVM classifier showed the higher performance (90.62%). The results showed that ultrasound techniques can be used for non-invasive cataract hardness characterization and automatic classification. Copyright © 2016 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Multi-class biological tissue classification based on a multi-classifier: Preliminary study of an automatic output power control for ultrasonic surgical units.

PubMed

Youn, Su Hyun; Sim, Taeyong; Choi, Ahnryul; Song, Jinsung; Shin, Ki Young; Lee, Il Kwon; Heo, Hyun Mu; Lee, Daeweon; Mun, Joung Hwan

2015-06-01

Ultrasonic surgical units (USUs) have the advantage of minimizing tissue damage during surgeries that require tissue dissection by reducing problems such as coagulation and unwanted carbonization, but the disadvantage of requiring manual adjustment of power output according to the target tissue. In order to overcome this limitation, it is necessary to determine the properties of in vivo tissues automatically. We propose a multi-classifier that can accurately classify tissues based on the unique impedance of each tissue. For this purpose, a multi-classifier was built based on single classifiers with high classification rates, and the classification accuracy of the proposed model was compared with that of single classifiers for various electrode types (Type-I: 6 mm invasive; Type-II: 3 mm invasive; Type-III: surface). The sensitivity and positive predictive value (PPV) of the multi-classifier by cross checks were determined. According to the 10-fold cross validation results, the classification accuracy of the proposed model was significantly higher (p<0.05 or <0.01) than that of existing single classifiers for all electrode types. In particular, the classification accuracy of the proposed model was highest when the 3mm invasive electrode (Type-II) was used (sensitivity=97.33-100.00%; PPV=96.71-100.00%). The results of this study are an important contribution to achieving automatic optimal output power adjustment of USUs according to the properties of individual tissues. Copyright © 2015 Elsevier Ltd. All rights reserved.

Semi-supervised SVM for individual tree crown species classification

NASA Astrophysics Data System (ADS)

Dalponte, Michele; Ene, Liviu Theodor; Marconcini, Mattia; Gobakken, Terje; Næsset, Erik

2015-12-01

In this paper a novel semi-supervised SVM classifier is presented, specifically developed for tree species classification at individual tree crown (ITC) level. In ITC tree species classification, all the pixels belonging to an ITC should have the same label. This assumption is used in the learning of the proposed semi-supervised SVM classifier (ITC-S3VM). This method exploits the information contained in the unlabeled ITC samples in order to improve the classification accuracy of a standard SVM. The ITC-S3VM method can be easily implemented using freely available software libraries. The datasets used in this study include hyperspectral imagery and laser scanning data acquired over two boreal forest areas characterized by the presence of three information classes (Pine, Spruce, and Broadleaves). The experimental results quantify the effectiveness of the proposed approach, which provides classification accuracies significantly higher (from 2% to above 27%) than those obtained by the standard supervised SVM and by a state-of-the-art semi-supervised SVM (S3VM). Particularly, by reducing the number of training samples (i.e. from 100% to 25%, and from 100% to 5% for the two datasets, respectively) the proposed method still exhibits results comparable to the ones of a supervised SVM trained with the full available training set. This property of the method makes it particularly suitable for practical forest inventory applications in which collection of in situ information can be very expensive both in terms of cost and time.

Big Data: A Parallel Particle Swarm Optimization-Back-Propagation Neural Network Algorithm Based on MapReduce

PubMed Central

Cao, Jianfang; Cui, Hongyan; Shi, Hao; Jiao, Lijuan

2016-01-01

A back-propagation (BP) neural network can solve complicated random nonlinear mapping problems; therefore, it can be applied to a wide range of problems. However, as the sample size increases, the time required to train BP neural networks becomes lengthy. Moreover, the classification accuracy decreases as well. To improve the classification accuracy and runtime efficiency of the BP neural network algorithm, we proposed a parallel design and realization method for a particle swarm optimization (PSO)-optimized BP neural network based on MapReduce on the Hadoop platform using both the PSO algorithm and a parallel design. The PSO algorithm was used to optimize the BP neural network’s initial weights and thresholds and improve the accuracy of the classification algorithm. The MapReduce parallel programming model was utilized to achieve parallel processing of the BP algorithm, thereby solving the problems of hardware and communication overhead when the BP neural network addresses big data. Datasets on 5 different scales were constructed using the scene image library from the SUN Database. The classification accuracy of the parallel PSO-BP neural network algorithm is approximately 92%, and the system efficiency is approximately 0.85, which presents obvious advantages when processing big data. The algorithm proposed in this study demonstrated both higher classification accuracy and improved time efficiency, which represents a significant improvement obtained from applying parallel processing to an intelligent algorithm on big data. PMID:27304987

Regionalization of Chinese Material Medical Quality Based on Maximum Entropy Model: A case study of Atractylodes lancea

NASA Astrophysics Data System (ADS)

Shoudong, Zhu; Huasheng, Peng; Lanping, Guo; Tongren, Xu; Yan, Zhang; Meilan, Chen; Qingxiu, Hao; Liping, Kang; Luqi, Huang

2017-02-01

Atractylodes is an East-Asiatic endemic genera that distributed in China, Japan and Russian Far Eastern. As an important resource of medicinal plant, atractylodes has long been used as herbal medicine. To example the significant features in its trueborn quality and geographical distribution, we explored the relationships between medicine quality and habitat suitability in two classifications-lower atractylodin content than the standard of Chinese Pharmacopoeia (2010) and the other has higher content. We found that the atractylodin content is negatively related to the habitat suitability for atractylodes with lower atractylodin, while the atractylodin content is positively related to the habitat suitability for those with higher atractylodin. By analyzing the distribution of atractylodeswith lower atractylodin content than the standard of Pharmacopeia, we discovered that the main ecological factors that could inhibit the accumulation of atractylodin were soil type (39.7%), soil clay content (26.7%), mean temperature in December (22.3%), Cation-exchange capacity (6%), etc. And these ecological factors promoted the accumulation of atractylodin for the atractylodes with higher atractylodin. By integrating the two classifications, we finally predicted the distribution of atractylodin content in China.Our results realized the query of atractylodes quality in arbitrary coordinates, and satisfied the actually cultivation demands of “Planting area based on atractylodin quality”.

Improved wavelet packet classification algorithm for vibrational intrusions in distributed fiber-optic monitoring systems

NASA Astrophysics Data System (ADS)

Wang, Bingjie; Pi, Shaohua; Sun, Qi; Jia, Bo

2015-05-01

An improved classification algorithm that considers multiscale wavelet packet Shannon entropy is proposed. Decomposition coefficients at all levels are obtained to build the initial Shannon entropy feature vector. After subtracting the Shannon entropy map of the background signal, components of the strongest discriminating power in the initial feature vector are picked out to rebuild the Shannon entropy feature vector, which is transferred to radial basis function (RBF) neural network for classification. Four types of man-made vibrational intrusion signals are recorded based on a modified Sagnac interferometer. The performance of the improved classification algorithm has been evaluated by the classification experiments via RBF neural network under different diffusion coefficients. An 85% classification accuracy rate is achieved, which is higher than the other common algorithms. The classification results show that this improved classification algorithm can be used to classify vibrational intrusion signals in an automatic real-time monitoring system.

Blob-level active-passive data fusion for Benthic classification

NASA Astrophysics Data System (ADS)

Park, Joong Yong; Kalluri, Hemanth; Mathur, Abhinav; Ramnath, Vinod; Kim, Minsu; Aitken, Jennifer; Tuell, Grady

2012-06-01

We extend the data fusion pixel level to the more semantically meaningful blob level, using the mean-shift algorithm to form labeled blobs having high similarity in the feature domain, and connectivity in the spatial domain. We have also developed Bhattacharyya Distance (BD) and rule-based classifiers, and have implemented these higher-level data fusion algorithms into the CZMIL Data Processing System. Applying these new algorithms to recent SHOALS and CASI data at Plymouth Harbor, Massachusetts, we achieved improved benthic classification accuracies over those produced with either single sensor, or pixel-level fusion strategies. These results appear to validate the hypothesis that classification accuracy may be generally improved by adopting higher spatial and semantic levels of fusion.

New FIGO and Swedish intrapartum cardiotocography classification systems incorporated in the fetal ECG ST analysis (STAN) interpretation algorithm: agreements and discrepancies in cardiotocography classification and evaluation of significant ST events.

PubMed

Olofsson, Per; Norén, Håkan; Carlsson, Ann

2018-02-01

The updated intrapartum cardiotocography (CTG) classification system by FIGO in 2015 (FIGO2015) and the FIGO2015-approached classification by the Swedish Society of Obstetricians and Gynecologist in 2017 (SSOG2017) are not harmonized with the fetal ECG ST analysis (STAN) algorithm from 2007 (STAN2007). The study aimed to reveal homogeneity and agreement between the systems in classifying CTG and ST events, and relate them to maternal and perinatal outcomes. Among CTG traces with ST events, 100 traces originally classified as normal, 100 as suspicious and 100 as pathological were randomly selected from a STAN database and classified by two experts in consensus. Homogeneity and agreement statistics between the CTG classifications were performed. Maternal and perinatal outcomes were evaluated in cases with clinically hidden ST data (n = 151). A two-tailed p < 0.05 was regarded as significant. For CTG classes, the heterogeneity was significant between the old and new systems, and agreements were moderate to strong (proportion of agreement, kappa index 0.70-0.86). Between the new classifications, heterogeneity was significant and agreements strong (0.90, 0.92). For significant ST events, heterogeneities were significant and agreements moderate to almost perfect (STAN2007 vs. FIGO2015 0.86, 0.72; STAN2007 vs. SSOG2017 0.92, 0.84; FIGO2015 vs. SSOG2017 0.94, 0.87). Significant ST events occurred more often combined with STAN2007 than with FIGO2015 classification, but not with SSOG2017; correct identification of adverse outcomes was not significantly different between the systems. There are discrepancies in the classification of CTG patterns and significant ST events between the old and new systems. The clinical relevance of the findings remains to be shown. © 2017 The Authors. Acta Obstetricia et Gynecologica Scandinavica published by John Wiley & Sons Ltd on behalf of Nordic Federation of Societies of Obstetrics and Gynecology (NFOG).

Power quality analysis based on spatial correlation

NASA Astrophysics Data System (ADS)

Li, Jiangtao; Zhao, Gang; Liu, Haibo; Li, Fenghou; Liu, Xiaoli

2018-03-01

With the industrialization and urbanization, the status of electricity in the production and life is getting higher and higher. So the prediction of power quality is the more potential significance. Traditional power quality analysis methods include: power quality data compression, disturbance event pattern classification, disturbance parameter calculation. Under certain conditions, these methods can predict power quality. This paper analyses the temporal variation of power quality of one provincial power grid in China from time angle. The distribution of power quality was analyzed based on spatial autocorrelation. This paper tries to prove that the research idea of geography is effective for mining the potential information of power quality.

The addition of entropy-based regularity parameters improves sleep stage classification based on heart rate variability.

PubMed

Aktaruzzaman, M; Migliorini, M; Tenhunen, M; Himanen, S L; Bianchi, A M; Sassi, R

2015-05-01

The work considers automatic sleep stage classification, based on heart rate variability (HRV) analysis, with a focus on the distinction of wakefulness (WAKE) from sleep and rapid eye movement (REM) from non-REM (NREM) sleep. A set of 20 automatically annotated one-night polysomnographic recordings was considered, and artificial neural networks were selected for classification. For each inter-heartbeat (RR) series, beside features previously presented in literature, we introduced a set of four parameters related to signal regularity. RR series of three different lengths were considered (corresponding to 2, 6, and 10 successive epochs, 30 s each, in the same sleep stage). Two sets of only four features captured 99 % of the data variance in each classification problem, and both of them contained one of the new regularity features proposed. The accuracy of classification for REM versus NREM (68.4 %, 2 epochs; 83.8 %, 10 epochs) was higher than when distinguishing WAKE versus SLEEP (67.6 %, 2 epochs; 71.3 %, 10 epochs). Also, the reliability parameter (Cohens's Kappa) was higher (0.68 and 0.45, respectively). Sleep staging classification based on HRV was still less precise than other staging methods, employing a larger variety of signals collected during polysomnographic studies. However, cheap and unobtrusive HRV-only sleep classification proved sufficiently precise for a wide range of applications.

[A clinical study on the relationship of the tail femur distance and the lag screw migration or cutting-out after the third generation of Gamma nail fixation of intertrochanteric fracture].

PubMed

Hou, Yu; Yao, Qi; Zhang, Gen'ai; Ding, Lixiang

2018-01-01

To confirm the association between tail femur distance (TFD) and lag screw migration or cutting-out in the treatment of intertrochanteric fracture with the third generation of Gamma nail (TGN). The clinical data of 124 cases of intertrochanteric fracture treated with TGN internal fixation and followed up more than 18 months between January 2012 and December 2015 were reviewed and analyzed. There were 52 males and 72 females, with an age of 46-93 years (mean, 78.5 years). According to AO/Association for the Study of Internal Fixation (AO/ASIF) classification, 43 cases were type 31-A1, 69 cases were type 31-A2, and 12 cases were type 31-A3. The time from injury to operation was 1-10 days (mean, 2.9 days). According to the fracture healing of the patients, the patients were divided into the healing group and failure group. The age, gender, height, bone mineral density (BMD), fracture AO/ASIF classification, the time from injury to operation, and the TFD value at 1 day after operation were recorded and compared. The risk factors for the migration or cutting-out of lag screw were analyzed by logistic regression. There were 111 cases in healing group, the healing time was 80-110 days (mean, 95.5 days). There were 13 cases in failure group, including 2 cases of lag screw cutting-out and 11 cases of significant migration. Except for the TFD value at 1 day after operation in failure group was significantly higher than that in the healing group( t =5.14, P =0.00), there was no significant difference in gender, age, height, BMD, fracture of AO/ASIF classification, and the time from injury to operation ( P >0.05) between 2 groups. logistic regression analysis showed that TFD value was a risk factor for the migration or cutting-out of lag screw (B=1.22, standardized coefficient=0.32, Wald χ 2 =14.66, P =0.00, OR=3.37). The patients with higher TFD value had higher risk of postoperative lag screw migration or cutting-out. This result indicates that the appropriate length of the lag screw is helpful to reduce TFD value and prevent postoperative lag screw migration or cutting-out.

Use of the serum anti-Müllerian hormone assay as a surrogate for polycystic ovarian morphology: impact on diagnosis and phenotypic classification of polycystic ovary syndrome.

PubMed

Fraissinet, Alice; Robin, Geoffroy; Pigny, Pascal; Lefebvre, Tiphaine; Catteau-Jonard, Sophie; Dewailly, Didier

2017-08-01

Does the use of the serum anti-Müllerian hormone (AMH) assay to replace or complement ultrasound (U/S) affect the diagnosis or phenotypic distribution of polycystic ovary syndrome (PCOS)? Combining U/S and the serum AMH assay to define polycystic ovarian morphology (PCOM) diagnoses PCOS (according to the Rotterdam classification) in more patients than definitions using one or the other of these indicators exclusively. Since 2003, PCOM, as defined by U/S, is one of the three diagnostic criteria for PCOS. As it is closely correlated with follicle excess seen at U/S, an excessive serum AMH level could be used as a surrogate for PCOM. Single-center retrospective study from a database of prospectively collected clinical, laboratory and ultrasound data from patients referred for oligo-anovulation (OA) and/or hyperandrogenism (HA) between January 2009 and January 2016. The standard Rotterdam classification for PCOS was tested against two modified versions that defined PCOM by either excessive serum AMH level alone (AMH-only) or a combination (i.e. 'and/or') of the latter and U/S. The PCOS phenotypes were defined as A (full phenotype, OA+HA+PCOM), B (OA+HA), C (HA+PCOM) and D (OA+PCOM). PCOS was more frequently diagnosed when PCOM was defined as the combination 'positive U/S' and/or 'positive AMH' (n = 639) than by either only U/S-only (standard definition, n = 612) or by AMH-only (n = 601). With this combination, PCOM was recognized in 637 of the 639 cases that met the Rotterdam classification, and phenotype B practically disappeared. In this population, U/S and AMH markers were discordant for PCOM in 103 (16.1%) cases (9% U/S-only, 7.1% AMH-only, P = 0.159). The markers used had no other significant impact on the phenotypic distribution (except for phenotype B). However, the percentage of cases positive by U/S-only was significantly higher in phenotype D than in phenotype A (14.1% vs. 5.8%, P < 0.05). Furthermore, in the discordant cases, plasma LH levels were significantly higher in the AMH-only group than in the concordant cases, and fasting insulin serum levels tended to be higher in the U/S-only group. This is a retrospective study. A referral bias explains the relatively high proportion of patients with phenotype D (28%). PCOM was defined by in-house thresholds. The AMH assay used is no longer commercially available. Our results suggest that ideally both U/S data and serum AMH level should be integrated to define PCOM in the Rotterdam classification. In a cost-effectiveness approach, the choice of one or the other has little impact on the diagnosis and the phenotyping of PCOS. No external funding. The authors have no conflict of interest to declare. © The Author 2017. Published by Oxford University Press on behalf of the European Society of Human Reproduction and Embryology. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Etiological classifications of transient ischemic attacks: subtype classification by TOAST, CCS and ASCO--a pilot study.

PubMed

Amort, Margareth; Fluri, Felix; Weisskopf, Florian; Gensicke, Henrik; Bonati, Leo H; Lyrer, Philippe A; Engelter, Stefan T

2012-01-01

In patients with transient ischemic attacks (TIA), etiological classification systems are not well studied. The Trial of ORG 10172 in Acute Stroke Treatment (TOAST), the Causative Classification System (CCS), and the Atherosclerosis Small Vessel Disease Cardiac Source Other Cause (ASCO) classification may be useful to determine the underlying etiology. We aimed at testing the feasibility of each of the 3 systems. Furthermore, we studied and compared their prognostic usefulness. In a single-center TIA registry prospectively ascertained over 2 years, we applied 3 etiological classification systems. We compared the distribution of underlying etiologies, the rates of patients with determined versus undetermined etiology, and studied whether etiological subtyping distinguished TIA patients with versus without subsequent stroke or TIA within 3 months. The 3 systems were applicable in all 248 patients. A determined etiology with the highest level of causality was assigned similarly often with TOAST (35.9%), CCS (34.3%), and ASCO (38.7%). However, the frequency of undetermined causes differed significantly between the classification systems and was lowest for ASCO (TOAST: 46.4%; CCS: 37.5%; ASCO: 18.5%; p < 0.001). In TOAST, CCS, and ASCO, cardioembolism (19.4/14.5/18.5%) was the most common etiology, followed by atherosclerosis (11.7/12.9/14.5%). At 3 months, 33 patients (13.3%, 95% confidence interval 9.3-18.2%) had recurrent cerebral ischemic events. These were strokes in 13 patients (5.2%; 95% confidence interval 2.8-8.8%) and TIAs in 20 patients (8.1%, 95% confidence interval 5.0-12.2%). Patients with a determined etiology (high level of causality) had higher rates of subsequent strokes than those without a determined etiology [TOAST: 6.7% (95% confidence interval 2.5-14.1%) vs. 4.4% (95% confidence interval 1.8-8.9%); CSS: 9.3% (95% confidence interval 4.1-17.5%) vs. 3.1% (95% confidence interval 1.0-7.1%); ASCO: 9.4% (95% confidence interval 4.4-17.1%) vs. 2.6% (95% confidence interval 0.7-6.6%)]. However, this difference was only significant in the ASCO classification (p = 0.036). Using ASCO, there was neither an increase in risk of subsequent stroke among patients with incomplete diagnostic workup (at least one subtype scored 9) compared with patients with adequate workup (no subtype scored 9), nor among patients with multiple causes compared with patients with a single cause. In TIA patients, all etiological classification systems provided a similar distribution of underlying etiologies. The increase in stroke risk in TIA patients with determined versus undetermined etiology was most evident using the ASCO classification. Copyright © 2012 S. Karger AG, Basel.

Stromal cell markers are differentially expressed in the synovial tissue of patients with early arthritis.

PubMed

Choi, Ivy Y; Karpus, Olga N; Turner, Jason D; Hardie, Debbie; Marshall, Jennifer L; de Hair, Maria J H; Maijer, Karen I; Tak, Paul P; Raza, Karim; Hamann, Jörg; Buckley, Christopher D; Gerlag, Danielle M; Filer, Andrew

2017-01-01

Previous studies have shown increased expression of stromal markers in synovial tissue (ST) of patients with established rheumatoid arthritis (RA). Here, ST expression of stromal markers in early arthritis in relationship to diagnosis and prognostic outcome was studied. ST from 56 patients included in two different early arthritis cohorts and 7 non-inflammatory controls was analysed using immunofluorescence to detect stromal markers CD55, CD248, fibroblast activation protein (FAP) and podoplanin. Diagnostic classification (gout, psoriatic arthritis, unclassified arthritis (UA), parvovirus associated arthritis, reactive arthritis and RA), disease outcome (resolving vs persistent) and clinical variables were determined at baseline and after follow-up, and related to the expression of stromal markers. We observed expression of all stromal markers in ST of early arthritis patients, independent of diagnosis or prognostic outcome. Synovial expression of FAP was significantly higher in patients developing early RA compared to other diagnostic groups and non-inflammatory controls. In RA FAP protein was expressed in both lining and sublining layers. Podoplanin expression was higher in all early inflammatory arthritis patients than controls, but did not differentiate diagnostic outcomes. Stromal marker expression was not associated with prognostic outcomes of disease persistence or resolution. There was no association with clinical or sonographic variables. Stromal cell markers CD55, CD248, FAP and podoplanin are expressed in ST in the earliest stage of arthritis. Baseline expression of FAP is higher in early synovitis patients who fulfil classification criteria for RA over time. These results suggest that significant fibroblast activation occurs in RA in the early window of disease.

C-reactive protein and serum creatinine, but not haemoglobin A1c, are independent predictors of coronary heart disease risk in non-diabetic Chinese.

PubMed

Salim, Agus; Tai, E Shyong; Tan, Vincent Y; Welsh, Alan H; Liew, Reginald; Naidoo, Nasheen; Wu, Yi; Yuan, Jian-Min; Koh, Woon P; van Dam, Rob M

2016-08-01

In western populations, high-sensitivity C-reactive protein (hsCRP), and to a lesser degree serum creatinine and haemoglobin A1c, predict risk of coronary heart disease (CHD). However, data on Asian populations that are increasingly affected by CHD are sparse and it is not clear whether these biomarkers can be used to improve CHD risk classification. We conducted a nested case-control study within the Singapore Chinese Health Study cohort, with incident 'hard' CHD (myocardial infarction or CHD death) as an outcome. We used data from 965 men (298 cases, 667 controls) and 528 women (143 cases, 385 controls) to examine the utility of hsCRP, serum creatinine and haemoglobin A1c in improving the prediction of CHD risk over and above traditional risk factors for CHD included in the ATP III model. For each sex, the performance of models with only traditional risk factors used in the ATP III model was compared with models with the biomarkers added using weighted Cox proportional hazards analysis. The impact of adding these biomarkers was assessed using the net reclassification improvement index. For men, loge hsCRP (hazard ratio 1.25, 95% confidence interval: 1.05; 1.49) and loge serum creatinine (hazard ratio 4.82, 95% confidence interval: 2.10; 11.04) showed statistically significantly associations with CHD risk when added to the ATP III model. We did not observe a significant association between loge haemoglobin A1c and CHD risk (hazard ratio 1.83, 95% confidence interval: 0.21; 16.06). Adding hsCRP and serum creatinine to the ATP III model improved risk classification in men with a net gain of 6.3% of cases (p-value = 0.001) being reclassified to a higher risk category, while it did not significantly reduce the accuracy of classification for non-cases. For women, squared hsCRP was borderline significantly (hazard ratio 1.01, 95% confidence interval: 1.00; 1.03) and squared serum creatinine was significantly (hazard ratio 1.81, 95% confidence interval: 1.49; 2.21) associated with CHD risk. However, the association between squared haemoglobin A1c and CHD risk was not significant (hazard ratio 1.05, 95% confidence interval: 0.99; 1.12). The addition of hsCRP and serum creatinine to the ATP III model resulted in 3.7% of future cases being reclassified to a higher risk category (p-value = 0.025), while it did not significantly reduce the accuracy of classification for non-cases. Adding hsCRP and serum creatinine, but not haemoglobin A1c, to traditional risk factors improved CHD risk prediction among non-diabetic Singaporean Chinese. The improved risk estimates will allow better identification of individuals at high risk of CHD than existing risk calculators such as the ATP III model. © The European Society of Cardiology 2016.

Multidate mapping of mosquito habitat. [Nebraska, South Dakota

NASA Technical Reports Server (NTRS)

Woodzick, T. L.; Maxwell, E. L.

1977-01-01

LANDSAT data from three overpasses formed the data base for a multidate classification of 15 ground cover categories in the margins of Lewis and Clark Lake, a fresh water impoundment between South Dakota and Nebraska. When scaled to match topographic maps of the area, the ground cover classification maps were used as a general indicator of potential mosquito-breeding habitat by distinguishing productive wetlands areas from nonproductive nonwetlands areas. The 12 channel multidate classification was found to have an accuracy 23% higher than the average of the three single date 4 channel classifications.

Privacy-Preserving Evaluation of Generalization Error and Its Application to Model and Attribute Selection

NASA Astrophysics Data System (ADS)

Sakuma, Jun; Wright, Rebecca N.

Privacy-preserving classification is the task of learning or training a classifier on the union of privately distributed datasets without sharing the datasets. The emphasis of existing studies in privacy-preserving classification has primarily been put on the design of privacy-preserving versions of particular data mining algorithms, However, in classification problems, preprocessing and postprocessing— such as model selection or attribute selection—play a prominent role in achieving higher classification accuracy. In this paper, we show generalization error of classifiers in privacy-preserving classification can be securely evaluated without sharing prediction results. Our main technical contribution is a new generalized Hamming distance protocol that is universally applicable to preprocessing and postprocessing of various privacy-preserving classification problems, such as model selection in support vector machine and attribute selection in naive Bayes classification.

Effect of higher frequency on the classification of steady-state visual evoked potentials.

PubMed

Won, Dong-Ok; Hwang, Han-Jeong; Dähne, Sven; Müller, Klaus-Robert; Lee, Seong-Whan

2016-02-01

Most existing brain-computer interface (BCI) designs based on steady-state visual evoked potentials (SSVEPs) primarily use low frequency visual stimuli (e.g., <20 Hz) to elicit relatively high SSVEP amplitudes. While low frequency stimuli could evoke photosensitivity-based epileptic seizures, high frequency stimuli generally show less visual fatigue and no stimulus-related seizures. The fundamental objective of this study was to investigate the effect of stimulation frequency and duty-cycle on the usability of an SSVEP-based BCI system. We developed an SSVEP-based BCI speller using multiple LEDs flickering with low frequencies (6-14.9 Hz) with a duty-cycle of 50%, or higher frequencies (26-34.7 Hz) with duty-cycles of 50%, 60%, and 70%. The four different experimental conditions were tested with 26 subjects in order to investigate the impact of stimulation frequency and duty-cycle on performance and visual fatigue, and evaluated with a questionnaire survey. Resting state alpha powers were utilized to interpret our results from the neurophysiological point of view. The stimulation method employing higher frequencies not only showed less visual fatigue, but it also showed higher and more stable classification performance compared to that employing relatively lower frequencies. Different duty-cycles in the higher frequency stimulation conditions did not significantly affect visual fatigue, but a duty-cycle of 50% was a better choice with respect to performance. The performance of the higher frequency stimulation method was also less susceptible to resting state alpha powers, while that of the lower frequency stimulation method was negatively correlated with alpha powers. These results suggest that the use of higher frequency visual stimuli is more beneficial for performance improvement and stability as time passes when developing practical SSVEP-based BCI applications.

Ling classification describes endoscopic progressive process of achalasia and successful peroral endoscopy myotomy prevents endoscopic progression of achalasia.

PubMed

Zhang, Wen-Gang; Linghu, En-Qiang; Chai, Ning-Li; Li, Hui-Kai

2017-05-14

To verify the hypothesis that the Ling classification describes the endoscopic progressive process of achalasia and determine the ability of successful peroral endoscopic myotomy (POEM) to prevent endoscopic progression of achalasia. We retrospectively reviewed the endoscopic findings, symptom duration, and manometric data in patients with achalasia. A total of 359 patients (197 women, 162 men) with a mean age of 42.1 years (range, 12-75 years) were evaluated. Symptom duration ranged from 2 to 360 mo, with a median of 36 mo. Patients were classified with Ling type I ( n = 119), IIa ( n = 106), IIb ( n = 60), IIc ( n = 60), or III ( n = 14), according to the Ling classification. Of the 359 patients, 349 underwent POEM, among whom 21 had an endoscopic follow-up for more than 2 years. Pre-treatment and post-treatment Ling classifications of these 21 patients were compared. Symptom duration increased significantly with increasing Ling classification (from I to III) ( P < 0.05), whereas lower esophageal sphincter pressure decreased with increasing Ling type (from I to III) ( P < 0.05). There was no difference in sex ratio or onset age among the Ling types, although the age at time of diagnosis was higher in Ling types IIc and III than in Ling types I, IIa, and IIb. Of the 21 patients, 19 underwent high-resolution manometry both before and after treatment. The mean preoperative and postoperative lower esophageal sphincter pressure were 34.6 mmHg (range, 15.3-59.4 mmHg) and 15.0 mmHg (range, 2.1-21.6 mmHg), respectively, indicating a statistically significant decrease after POEM. All of the 21 patients were treated successfully by POEM (postoperative Eckardt score ≤ 3) and still had the same Ling type during a mean follow-up period of 37.8 mo (range, 24-51 mo). The Ling classification represents the endoscopic progressive process of achalasia and may be able to serve as an endoscopic assessment criterion for achalasia. Successful POEM (Eckardt score ≤ 3) seems to have the ability to prevent endoscopic evolvement of achalasia. However, studies with larger populations are warranted to confirm our findings.

Plasma lipophilic antioxidants and malondialdehyde in congestive heart failure patients: relationship to disease severity.

PubMed

Polidori, Maria Cristina; Savino, Ketty; Alunni, Gianfranco; Freddio, Michela; Senin, Umberto; Sies, Helmut; Stahl, Wilhelm; Mecocci, Patrizia

2002-01-15

Plasma levels of malondialdehyde (MDA), vitamin A, and of antioxidant micronutrients including vitamin E, lutein, zeaxanthin, beta-cryptoxanthin, lycopene, and alpha- and beta-carotene were measured in 30 patients with class II and III congestive heart failure (CHF) according to the New York Heart Association (NYHA) classification and in 55 controls. Ejection fraction was evaluated by echocardiography in all patients as a measure of the emptying capacity of the heart. Plasma levels of all measured compounds were significantly lower and MDA significantly higher in patients compared to controls (p <.001). Class II NYHA patients showed significantly lower MDA levels and significantly higher levels of vitamin A, vitamin E, lutein, and lycopene than class III patients. Ejection fraction was inversely correlated with MDA levels and directly correlated with vitamin A, vitamin E, lutein, and lycopene levels in patients. The present study supports the concept that an increased consumption of vitamin-rich fruits and vegetables might help in achieving cardiovascular health.

Exploring human error in military aviation flight safety events using post-incident classification systems.

PubMed

Hooper, Brionny J; O'Hare, David P A

2013-08-01

Human error classification systems theoretically allow researchers to analyze postaccident data in an objective and consistent manner. The Human Factors Analysis and Classification System (HFACS) framework is one such practical analysis tool that has been widely used to classify human error in aviation. The Cognitive Error Taxonomy (CET) is another. It has been postulated that the focus on interrelationships within HFACS can facilitate the identification of the underlying causes of pilot error. The CET provides increased granularity at the level of unsafe acts. The aim was to analyze the influence of factors at higher organizational levels on the unsafe acts of front-line operators and to compare the errors of fixed-wing and rotary-wing operations. This study analyzed 288 aircraft incidents involving human error from an Australasian military organization occurring between 2001 and 2008. Action errors accounted for almost twice (44%) the proportion of rotary wing compared to fixed wing (23%) incidents. Both classificatory systems showed significant relationships between precursor factors such as the physical environment, mental and physiological states, crew resource management, training and personal readiness, and skill-based, but not decision-based, acts. The CET analysis showed different predisposing factors for different aspects of skill-based behaviors. Skill-based errors in military operations are more prevalent in rotary wing incidents and are related to higher level supervisory processes in the organization. The Cognitive Error Taxonomy provides increased granularity to HFACS analyses of unsafe acts.

Adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique algorithm for tackling binary imbalanced datasets in biomedical data classification.

PubMed

Li, Jinyan; Fong, Simon; Sung, Yunsick; Cho, Kyungeun; Wong, Raymond; Wong, Kelvin K L

2016-01-01

An imbalanced dataset is defined as a training dataset that has imbalanced proportions of data in both interesting and uninteresting classes. Often in biomedical applications, samples from the stimulating class are rare in a population, such as medical anomalies, positive clinical tests, and particular diseases. Although the target samples in the primitive dataset are small in number, the induction of a classification model over such training data leads to poor prediction performance due to insufficient training from the minority class. In this paper, we use a novel class-balancing method named adaptive swarm cluster-based dynamic multi-objective synthetic minority oversampling technique (ASCB_DmSMOTE) to solve this imbalanced dataset problem, which is common in biomedical applications. The proposed method combines under-sampling and over-sampling into a swarm optimisation algorithm. It adaptively selects suitable parameters for the rebalancing algorithm to find the best solution. Compared with the other versions of the SMOTE algorithm, significant improvements, which include higher accuracy and credibility, are observed with ASCB_DmSMOTE. Our proposed method tactfully combines two rebalancing techniques together. It reasonably re-allocates the majority class in the details and dynamically optimises the two parameters of SMOTE to synthesise a reasonable scale of minority class for each clustered sub-imbalanced dataset. The proposed methods ultimately overcome other conventional methods and attains higher credibility with even greater accuracy of the classification model.

Multi-phenology WorldView-2 imagery improves remote sensing of savannah tree species

NASA Astrophysics Data System (ADS)

Madonsela, Sabelo; Cho, Moses Azong; Mathieu, Renaud; Mutanga, Onisimo; Ramoelo, Abel; Kaszta, Żaneta; Kerchove, Ruben Van De; Wolff, Eléonore

2017-06-01

Biodiversity mapping in African savannah is important for monitoring changes and ensuring sustainable use of ecosystem resources. Biodiversity mapping can benefit from multi-spectral instruments such as WorldView-2 with very high spatial resolution and a spectral configuration encompassing important spectral regions not previously available for vegetation mapping. This study investigated i) the benefits of the eight-band WorldView-2 (WV-2) spectral configuration for discriminating tree species in Southern African savannah and ii) if multiple-images acquired at key points of the typical phenological development of savannahs (peak productivity, transition to senescence) improve on tree species classifications. We first assessed the discriminatory power of WV-2 bands using interspecies-Spectral Angle Mapper (SAM) via Band Add-On procedure and tested the spectral capability of WorldView-2 against simulated IKONOS for tree species classification. The results from interspecies-SAM procedure identified the yellow and red bands as the most statistically significant bands (p = 0.000251 and p = 0.000039 respectively) in the discriminatory power of WV-2 during the transition from wet to dry season (April). Using Random Forest classifier, the classification scenarios investigated showed that i) the 8-bands of the WV-2 sensor achieved higher classification accuracy for the April date (transition from wet to dry season, senescence) compared to the March date (peak productivity season) ii) the WV-2 spectral configuration systematically outperformed the IKONOS sensor spectral configuration and iii) the multi-temporal approach (March and April combined) improved the discrimination of tress species and produced the highest overall accuracy results at 80.4%. Consistent with the interspecies-SAM procedure, the yellow (605 nm) band also showed a statistically significant contribution in the improved classification accuracy from WV-2. These results highlight the mapping opportunities presented by WV-2 data for monitoring the distribution status of e.g. species often harvested by local communities (e.g. Sclerocharya birrea), encroaching species, or species-specific tree losses induced by elephants.

A review of supervised object-based land-cover image classification

NASA Astrophysics Data System (ADS)

Ma, Lei; Li, Manchun; Ma, Xiaoxue; Cheng, Liang; Du, Peijun; Liu, Yongxue

2017-08-01

Object-based image classification for land-cover mapping purposes using remote-sensing imagery has attracted significant attention in recent years. Numerous studies conducted over the past decade have investigated a broad array of sensors, feature selection, classifiers, and other factors of interest. However, these research results have not yet been synthesized to provide coherent guidance on the effect of different supervised object-based land-cover classification processes. In this study, we first construct a database with 28 fields using qualitative and quantitative information extracted from 254 experimental cases described in 173 scientific papers. Second, the results of the meta-analysis are reported, including general characteristics of the studies (e.g., the geographic range of relevant institutes, preferred journals) and the relationships between factors of interest (e.g., spatial resolution and study area or optimal segmentation scale, accuracy and number of targeted classes), especially with respect to the classification accuracy of different sensors, segmentation scale, training set size, supervised classifiers, and land-cover types. Third, useful data on supervised object-based image classification are determined from the meta-analysis. For example, we find that supervised object-based classification is currently experiencing rapid advances, while development of the fuzzy technique is limited in the object-based framework. Furthermore, spatial resolution correlates with the optimal segmentation scale and study area, and Random Forest (RF) shows the best performance in object-based classification. The area-based accuracy assessment method can obtain stable classification performance, and indicates a strong correlation between accuracy and training set size, while the accuracy of the point-based method is likely to be unstable due to mixed objects. In addition, the overall accuracy benefits from higher spatial resolution images (e.g., unmanned aerial vehicle) or agricultural sites where it also correlates with the number of targeted classes. More than 95.6% of studies involve an area less than 300 ha, and the spatial resolution of images is predominantly between 0 and 2 m. Furthermore, we identify some methods that may advance supervised object-based image classification. For example, deep learning and type-2 fuzzy techniques may further improve classification accuracy. Lastly, scientists are strongly encouraged to report results of uncertainty studies to further explore the effects of varied factors on supervised object-based image classification.

An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment.

PubMed

Yao, Dongren; Calhoun, Vince D; Fu, Zening; Du, Yuhui; Sui, Jing

2018-05-15

Discriminating Alzheimer's disease (AD) from its prodromal form, mild cognitive impairment (MCI), is a significant clinical problem that may facilitate early diagnosis and intervention, in which a more challenging issue is to classify MCI subtypes, i.e., those who eventually convert to AD (cMCI) versus those who do not (MCI). To solve this difficult 4-way classification problem (AD, MCI, cMCI and healthy controls), a competition was hosted by Kaggle to invite the scientific community to apply their machine learning approaches on pre-processed sets of T1-weighted magnetic resonance images (MRI) data and the demographic information from the international Alzheimer's disease neuroimaging initiative (ADNI) database. This paper summarizes our competition results. We first proposed a hierarchical process by turning the 4-way classification into five binary classification problems. A new feature selection technology based on relative importance was also proposed, aiming to identify a more informative and concise subset from 426 sMRI morphometric and 3 demographic features, to ensure each binary classifier to achieve its highest accuracy. As a result, about 2% of the original features were selected to build a new feature space, which can achieve the final four-way classification with a 54.38% accuracy on testing data through hierarchical grouping, higher than several alternative methods in comparison. More importantly, the selected discriminative features such as hippocampal volume, parahippocampal surface area, and medial orbitofrontal thickness, etc. as well as the MMSE score, are reasonable and consistent with those reported in AD/MCI deficits. In summary, the proposed method provides a new framework for multi-way classification using hierarchical grouping and precise feature selection. Copyright © 2018 Elsevier B.V. All rights reserved.

Clinical and Pathological Staging Validation in the Eighth Edition of the TNM Classification for Lung Cancer: Correlation between Solid Size on Thin-Section Computed Tomography and Invasive Size in Pathological Findings in the New T Classification.

PubMed

Aokage, Keiju; Miyoshi, Tomohiro; Ishii, Genichiro; Kusumoto, Masahiro; Nomura, Shogo; Katsumata, Shinya; Sekihara, Keigo; Hishida, Tomoyuki; Tsuboi, Masahiro

2017-09-01

The aim of this study was to validate the new eighth edition of the TNM classification and to elucidate whether radiological solid size corresponds to pathological invasive size incorporated in this T factor. We analyzed the data on 1792 patients who underwent complete resection from 2003 to 2011 at the National Cancer Center Hospital East, Japan. We reevaluated preoperative thin-section computed tomography (TSCT) to determine solid size and pathological invasive size using the fourth edition of the WHO classification and reclassified them according to the new TNM classification. The discriminative power of survival curves by the seventh edition was compared with that by the eighth edition by using concordance probability estimates and Akaike's information criteria calculated using a univariable Cox regression model. Pearson's correlation coefficient was calculated to elucidate the correlation between radiological solid size using TSCT and pathological invasive size. The overall survival curves in the eighth edition were well distinct at each clinical and pathological stage. The 5-year survival rates of patients with clinical and pathological stage 0 newly defined were both 100%. The concordance probability estimate and Akaike's information criterion values of the eighth edition were higher than those of the seventh edition in discriminatory power for overall survival. Solid size on TSCT scan and pathological invasive size showed a positive linear relationship, and Pearson's correlation coefficient was calculated as 0.83, which indicated strong correlation. This TNM classification will be feasible regarding patient survival, and radiological solid size correlates significantly with pathological invasive size as a new T factor. Copyright © 2017 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

Foliar and woody materials discriminated using terrestrial LiDAR in a mixed natural forest

NASA Astrophysics Data System (ADS)

Zhu, Xi; Skidmore, Andrew K.; Darvishzadeh, Roshanak; Niemann, K. Olaf; Liu, Jing; Shi, Yifang; Wang, Tiejun

2018-02-01

Separation of foliar and woody materials using remotely sensed data is crucial for the accurate estimation of leaf area index (LAI) and woody biomass across forest stands. In this paper, we present a new method to accurately separate foliar and woody materials using terrestrial LiDAR point clouds obtained from ten test sites in a mixed forest in Bavarian Forest National Park, Germany. Firstly, we applied and compared an adaptive radius near-neighbor search algorithm with a fixed radius near-neighbor search method in order to obtain both radiometric and geometric features derived from terrestrial LiDAR point clouds. Secondly, we used a random forest machine learning algorithm to classify foliar and woody materials and examined the impact of understory and slope on the classification accuracy. An average overall accuracy of 84.4% (Kappa = 0.75) was achieved across all experimental plots. The adaptive radius near-neighbor search method outperformed the fixed radius near-neighbor search method. The classification accuracy was significantly higher when the combination of both radiometric and geometric features was utilized. The analysis showed that increasing slope and understory coverage had a significant negative effect on the overall classification accuracy. Our results suggest that the utilization of the adaptive radius near-neighbor search method coupling both radiometric and geometric features has the potential to accurately discriminate foliar and woody materials from terrestrial LiDAR data in a mixed natural forest.

Realizing parameterless automatic classification of remote sensing imagery using ontology engineering and cyberinfrastructure techniques

NASA Astrophysics Data System (ADS)

Sun, Ziheng; Fang, Hui; Di, Liping; Yue, Peng

2016-09-01

It was an untouchable dream for remote sensing experts to realize total automatic image classification without inputting any parameter values. Experts usually spend hours and hours on tuning the input parameters of classification algorithms in order to obtain the best results. With the rapid development of knowledge engineering and cyberinfrastructure, a lot of data processing and knowledge reasoning capabilities become online accessible, shareable and interoperable. Based on these recent improvements, this paper presents an idea of parameterless automatic classification which only requires an image and automatically outputs a labeled vector. No parameters and operations are needed from endpoint consumers. An approach is proposed to realize the idea. It adopts an ontology database to store the experiences of tuning values for classifiers. A sample database is used to record training samples of image segments. Geoprocessing Web services are used as functionality blocks to finish basic classification steps. Workflow technology is involved to turn the overall image classification into a total automatic process. A Web-based prototypical system named PACS (Parameterless Automatic Classification System) is implemented. A number of images are fed into the system for evaluation purposes. The results show that the approach could automatically classify remote sensing images and have a fairly good average accuracy. It is indicated that the classified results will be more accurate if the two databases have higher quality. Once the experiences and samples in the databases are accumulated as many as an expert has, the approach should be able to get the results with similar quality to that a human expert can get. Since the approach is total automatic and parameterless, it can not only relieve remote sensing workers from the heavy and time-consuming parameter tuning work, but also significantly shorten the waiting time for consumers and facilitate them to engage in image classification activities. Currently, the approach is used only on high resolution optical three-band remote sensing imagery. The feasibility using the approach on other kinds of remote sensing images or involving additional bands in classification will be studied in future.

Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error.

PubMed

Beheshti, Iman; Demirel, Hasan; Farokhian, Farnaz; Yang, Chunlan; Matsuda, Hiroshi

2016-12-01

This paper presents an automatic computer-aided diagnosis (CAD) system based on feature ranking for detection of Alzheimer's disease (AD) using structural magnetic resonance imaging (sMRI) data. The proposed CAD system is composed of four systematic stages. First, global and local differences in the gray matter (GM) of AD patients compared to the GM of healthy controls (HCs) are analyzed using a voxel-based morphometry technique. The aim is to identify significant local differences in the volume of GM as volumes of interests (VOIs). Second, the voxel intensity values of the VOIs are extracted as raw features. Third, the raw features are ranked using a seven-feature ranking method, namely, statistical dependency (SD), mutual information (MI), information gain (IG), Pearson's correlation coefficient (PCC), t-test score (TS), Fisher's criterion (FC), and the Gini index (GI). The features with higher scores are more discriminative. To determine the number of top features, the estimated classification error based on training set made up of the AD and HC groups is calculated, with the vector size that minimized this error selected as the top discriminative feature. Fourth, the classification is performed using a support vector machine (SVM). In addition, a data fusion approach among feature ranking methods is introduced to improve the classification performance. The proposed method is evaluated using a data-set from ADNI (130 AD and 130 HC) with 10-fold cross-validation. The classification accuracy of the proposed automatic system for the diagnosis of AD is up to 92.48% using the sMRI data. An automatic CAD system for the classification of AD based on feature-ranking method and classification errors is proposed. In this regard, seven-feature ranking methods (i.e., SD, MI, IG, PCC, TS, FC, and GI) are evaluated. The optimal size of top discriminative features is determined by the classification error estimation in the training phase. The experimental results indicate that the performance of the proposed system is comparative to that of state-of-the-art classification models. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

Mainzer, A.; Masiero, J.; Bauer, J.

We have combined the NEOWISE and Sloan Digital Sky Survey data to study the albedos of 24,353 asteroids with candidate taxonomic classifications derived using Sloan photometry. We find a wide range of moderate to high albedos for candidate S-type asteroids that are analogous to the S complex defined by previous spectrophotometrically based taxonomic systems. The candidate C-type asteroids, while generally very dark, have a tail of higher albedos that overlaps the S types. The albedo distribution for asteroids with a photometrically derived Q classification is extremely similar to those of the S types. Asteroids with similar colors to (4) Vestamore » have higher albedos than the S types, and most have orbital elements similar to known Vesta family members. Finally, we show that the relative reflectance at 3.4 and 4.6 {mu}m is higher for D-type asteroids and suggest that their red visible and near-infrared spectral slope extends out to these wavelengths. Understanding the relationship between size, albedo, and taxonomic classification is complicated by the fact that the objects with classifications were selected from the visible/near-infrared Sloan Moving Object Catalog, which is biased against fainter asteroids, including those with lower albedos.« less

Stacked sparse autoencoder in hyperspectral data classification using spectral-spatial, higher order statistics and multifractal spectrum features

NASA Astrophysics Data System (ADS)

Wan, Xiaoqing; Zhao, Chunhui; Wang, Yanchun; Liu, Wu

2017-11-01

This paper proposes a novel classification paradigm for hyperspectral image (HSI) using feature-level fusion and deep learning-based methodologies. Operation is carried out in three main steps. First, during a pre-processing stage, wave atoms are introduced into bilateral filter to smooth HSI, and this strategy can effectively attenuate noise and restore texture information. Meanwhile, high quality spectral-spatial features can be extracted from HSI by taking geometric closeness and photometric similarity among pixels into consideration simultaneously. Second, higher order statistics techniques are firstly introduced into hyperspectral data classification to characterize the phase correlations of spectral curves. Third, multifractal spectrum features are extracted to characterize the singularities and self-similarities of spectra shapes. To this end, a feature-level fusion is applied to the extracted spectral-spatial features along with higher order statistics and multifractal spectrum features. Finally, stacked sparse autoencoder is utilized to learn more abstract and invariant high-level features from the multiple feature sets, and then random forest classifier is employed to perform supervised fine-tuning and classification. Experimental results on two real hyperspectral data sets demonstrate that the proposed method outperforms some traditional alternatives.

14 CFR 1203.406 - Additional classification factors.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Section 1203.406 Aeronautics and Space NATIONAL AERONAUTICS AND SPACE ADMINISTRATION INFORMATION SECURITY... other Government departments and agencies should be considered. Classification of official information... exists elsewhere for the information under consideration which would make it necessary to assign a higher...

Identification of asteroid dynamical families

NASA Technical Reports Server (NTRS)

Valsecchi, G. B.; Carusi, A.; Knezevic, Z.; Kresak, L.; Williams, J. G.

1989-01-01

Problems involved in the identification of asteroid dynamical families are discussed, and some methodological guidelines are presented. Asteroid family classifications are reviewed, and differences in the existing classifications are examined with special attention given to the effects of observational selection on the classification of family membership. The paper also discusses various theories of secular perturbations, including the classical linear theory, the theory of Williams (1969), and the higher order/degree theory of Yuasa (1973).

Average Likelihood Methods for Code Division Multiple Access (CDMA)

DTIC Science & Technology

2014-05-01

lengths in the range of 22 to 213 and possibly higher. Keywords: DS / CDMA signals, classification, balanced CDMA load, synchronous CDMA , decision...likelihood ratio test (ALRT). We begin this classification problem by finding the size of the spreading matrix that generated the DS - CDMA signal. As...Theoretical Background The classification of DS / CDMA signals should not be confused with the problem of multiuser detection. The multiuser detection deals

Classification bias in commercial business lists for retail food stores in the U.S.

PubMed

Han, Euna; Powell, Lisa M; Zenk, Shannon N; Rimkus, Leah; Ohri-Vachaspati, Punam; Chaloupka, Frank J

2012-04-18

Aspects of the food environment such as the availability of different types of food stores have recently emerged as key modifiable factors that may contribute to the increased prevalence of obesity. Given that many of these studies have derived their results based on secondary datasets and the relationship of food stores with individual weight outcomes has been reported to vary by store type, it is important to understand the extent to which often-used secondary data correctly classify food stores. We evaluated the classification bias of food stores in Dun & Bradstreet (D&B) and InfoUSA commercial business lists. We performed a full census in 274 randomly selected census tracts in the Chicago metropolitan area and collected detailed store attributes inside stores for classification. Store attributes were compared by classification match status and store type. Systematic classification bias by census tract characteristics was assessed in multivariate regression. D&B had a higher classification match rate than InfoUSA for supermarkets and grocery stores, while InfoUSA was higher for convenience stores. Both lists were more likely to correctly classify large supermarkets, grocery stores, and convenience stores with more cash registers and different types of service counters (supermarkets and grocery stores only). The likelihood of a correct classification match for supermarkets and grocery stores did not vary systemically by tract characteristics whereas convenience stores were more likely to be misclassified in predominately Black tracts. Researches can rely on classification of food stores in commercial datasets for supermarkets and grocery stores whereas classifications for convenience and specialty food stores are subject to some systematic bias by neighborhood racial/ethnic composition.

Classification bias in commercial business lists for retail food stores in the U.S.

PubMed Central

2012-01-01

Background Aspects of the food environment such as the availability of different types of food stores have recently emerged as key modifiable factors that may contribute to the increased prevalence of obesity. Given that many of these studies have derived their results based on secondary datasets and the relationship of food stores with individual weight outcomes has been reported to vary by store type, it is important to understand the extent to which often-used secondary data correctly classify food stores. We evaluated the classification bias of food stores in Dun & Bradstreet (D&B) and InfoUSA commercial business lists. Methods We performed a full census in 274 randomly selected census tracts in the Chicago metropolitan area and collected detailed store attributes inside stores for classification. Store attributes were compared by classification match status and store type. Systematic classification bias by census tract characteristics was assessed in multivariate regression. Results D&B had a higher classification match rate than InfoUSA for supermarkets and grocery stores, while InfoUSA was higher for convenience stores. Both lists were more likely to correctly classify large supermarkets, grocery stores, and convenience stores with more cash registers and different types of service counters (supermarkets and grocery stores only). The likelihood of a correct classification match for supermarkets and grocery stores did not vary systemically by tract characteristics whereas convenience stores were more likely to be misclassified in predominately Black tracts. Conclusion Researches can rely on classification of food stores in commercial datasets for supermarkets and grocery stores whereas classifications for convenience and specialty food stores are subject to some systematic bias by neighborhood racial/ethnic composition. PMID:22512874

Inter-observer reliability of radiographic classifications and measurements in the assessment of Perthes' disease.

PubMed

Wiig, Ola; Terjesen, Terje; Svenningsen, Svein

2002-10-01

We evaluated the inter-observer agreement of radiographic methods when evaluating patients with Perthes' disease. The radiographs were assessed at the time of diagnosis and at the 1-year follow-up by local orthopaedic surgeons (O) and 2 experienced pediatric orthopedic surgeons (TT and SS). The Catterall, Salter-Thompson, and Herring lateral pillar classifications were compared, and the femoral head coverage (FHC), center-edge angle (CE-angle), and articulo-trochanteric distance (ATD) were measured in the affected and normal hips. On the primary evaluation, the lateral pillar and Salter-Thompson classifications had a higher level of agreement among the observers than the Catterall classification, but none of the classifications showed good agreement (weighted kappa values between O and SS 0.56, 0.54, 0.49, respectively). Combining Catterall groups 1 and 2 into one group, and groups 3 and 4 into another resulted in better agreement (kappa 0.55) than with the original 4-group system. The agreement was also better (kappa 0.62-0.70) between experienced than between less experienced examiners for all classifications. The femoral head coverage was a more reliable and accurate measure than the CE-angle for quantifying the acetabular covering of the femoral head, as indicated by higher intraclass correlation coefficients (ICC) and smaller inter-observer differences. The ATD showed good agreement in all comparisons and had low interobserver differences. We conclude that all classifications of femoral head involvement are adequate in clinical work if the radiographic assessment is done by experienced examiners. When they are less experienced examiners, a 2-group classification or the lateral pillar classification is more reliable. For evaluation of containment of the femoral head, FHC is more appropriate than the CE-angle.

12 CFR 1777.20 - Capital classifications.

Code of Federal Regulations, 2010 CFR

2010-01-01

... notice of proposed capital classification, holds core capital equaling or exceeding the minimum capital... classification, holds core capital equaling or exceeding the minimum capital level. (3) Significantly... the date specified in the notice of proposed capital classification, holds core capital less than the...

A classification scheme for edge-localized modes based on their probability distributions

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

Shabbir, A., E-mail: aqsa.shabbir@ugent.be; Max Planck Institute for Plasma Physics, D-85748 Garching; Hornung, G.

We present here an automated classification scheme which is particularly well suited to scenarios where the parameters have significant uncertainties or are stochastic quantities. To this end, the parameters are modeled with probability distributions in a metric space and classification is conducted using the notion of nearest neighbors. The presented framework is then applied to the classification of type I and type III edge-localized modes (ELMs) from a set of carbon-wall plasmas at JET. This provides a fast, standardized classification of ELM types which is expected to significantly reduce the effort of ELM experts in identifying ELM types. Further, themore » classification scheme is general and can be applied to various other plasma phenomena as well.« less

Influence of stuttering variation on talker group classification in preschool children: Preliminary findings

PubMed Central

Johnson, Kia N.; Karrass, Jan; Conture, Edward G.; Walden, Tedra

2010-01-01

The purpose of this study was to investigate whether variations in disfluencies of young children who do (CWS) and do not stutter (CWNS) significantly change their talker group classification or diagnosis from stutterer to nonstutterer, and vice versa. Participants consisted of 17 3- to 5-year-old CWS and 9 3- to 5-year-old CWNS, with no statistically significant between-group difference in chronological age (CWS: M = 45.53 months, SD = 8.32; CWNS: M = 47.67 months, SD = 6.69). All participants had speech, language, and hearing development within normal limits, with the exception of stuttering for CWS. Both talker groups participated in a series of speaking samples that varied by: (a) conversational partner [parent and clinician], (b) location [home and clinic], and (c) context [conversation and narrative]. The primary dependent measures for this study were the number of stuttering-like disfluencies (SLD) per total number of spoken words [%SLD] and the ratio of SLD to total disfluencies (TD) [SLD/TD]. Results indicated that significant variability of stuttering did not exist as a result of conversational partner or location. Changes in context, however, did impact the CWS, who demonstrated higher SLD/TD in the conversation sample versus a narrative sample. Consistent with hypotheses, CWS and CWNS were accurately identified as stutterers and nonstutterers, respectively, regardless of changes to conversational partner, location or context for the overall participant sample. Present findings were taken to suggest that during assessment, variations in stuttering frequency resulting from changes in conversational partner, location or context do not significantly influence the diagnosis of stuttering, especially for children not on the talker group classification borderline between CWS and CWNS. PMID:19167719

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

DOE PAGES

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang; ...

2017-04-03

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validationmore » results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.« less

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

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

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang

Here, the feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validationmore » results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.« less

Application of visible and near-infrared spectroscopy to classification of Miscanthus species.

PubMed

Jin, Xiaoli; Chen, Xiaoling; Xiao, Liang; Shi, Chunhai; Chen, Liang; Yu, Bin; Yi, Zili; Yoo, Ji Hye; Heo, Kweon; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J; Peng, Junhua

2017-01-01

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species.

Application of visible and near-infrared spectroscopy to classification of Miscanthus species

PubMed Central

Shi, Chunhai; Chen, Liang; Yu, Bin; Yi, Zili; Yoo, Ji Hye; Heo, Kweon; Yu, Chang Yeon; Yamada, Toshihiko; Sacks, Erik J.; Peng, Junhua

2017-01-01

The feasibility of visible and near infrared (NIR) spectroscopy as tool to classify Miscanthus samples was explored in this study. Three types of Miscanthus plants, namely, M. sinensis, M. sacchariflorus and M. fIoridulus, were analyzed using a NIR spectrophotometer. Several classification models based on the NIR spectra data were developed using line discriminated analysis (LDA), partial least squares (PLS), least squares support vector machine regression (LSSVR), radial basis function (RBF) and neural network (NN). The principal component analysis (PCA) presented rough classification with overlapping samples, while the models of Line_LSSVR, RBF_LSSVR and RBF_NN presented almost same calibration and validation results. Due to the higher speed of Line_LSSVR than RBF_LSSVR and RBF_NN, we selected the line_LSSVR model as a representative. In our study, the model based on line_LSSVR showed higher accuracy than LDA and PLS models. The total correct classification rates of 87.79 and 96.51% were observed based on LDA and PLS model in the testing set, respectively, while the line_LSSVR showed 99.42% of total correct classification rate. Meanwhile, the lin_LSSVR model in the testing set showed correct classification rate of 100, 100 and 96.77% for M. sinensis, M. sacchariflorus and M. fIoridulus, respectively. The lin_LSSVR model assigned 99.42% of samples to the right groups, except one M. fIoridulus sample. The results demonstrated that NIR spectra combined with a preliminary morphological classification could be an effective and reliable procedure for the classification of Miscanthus species. PMID:28369059

Driver behavior profiling: An investigation with different smartphone sensors and machine learning

PubMed Central

Ferreira, Jair; Carvalho, Eduardo; Ferreira, Bruno V.; de Souza, Cleidson; Suhara, Yoshihiko; Pentland, Alex

2017-01-01

Driver behavior impacts traffic safety, fuel/energy consumption and gas emissions. Driver behavior profiling tries to understand and positively impact driver behavior. Usually driver behavior profiling tasks involve automated collection of driving data and application of computer models to generate a classification that characterizes the driver aggressiveness profile. Different sensors and classification methods have been employed in this task, however, low-cost solutions and high performance are still research targets. This paper presents an investigation with different Android smartphone sensors, and classification algorithms in order to assess which sensor/method assembly enables classification with higher performance. The results show that specific combinations of sensors and intelligent methods allow classification performance improvement. PMID:28394925

Expression of SLP-2 gene and CCBE1 are associated with prognosis of rectal cancer.

PubMed

Zhang, L; Liu, F-J

2017-03-01

This study aims to investigate the clinical significance of SLP-2 gene for patients with rectal cancer. To analyze the effect of CCBE1 (Collagen and calcium-binding EGF domain-containing protein 1) on rectal cancer tissue and lymph vessels of para-carcinoma tissue. A total of 50 samples of rectal cancer tissues were enrolled in the experimental group, confirmed by pathological examination. 50 samples of para-carcinoma normal tissues were collected as control group. Protein expression of SLP-2 and CCBE1 was examined with immunohistochemical staining. mRNA expression of SLP-2 was examined with RT-PCR. Lymphatic vessel density (LVD) was evaluated with LYVE-1 immunohistochemical staining. Correlation analysis was performed to assess the relationship between patient survival data and clinical pathological features of rectal cancer. Immunohistochemical staining showed that, compared with the control group, a positive expression rate of SLP-2 in the experimental group was significantly higher (68.0% vs. 24.0%, p<0.05), and mRNA of SLP-2 was also significantly increased (p<0.05). Compared with the control group, protein expression of CCBE1 in the experimental group was significantly higher (p<0.05). Moreover, the expression level of SLP-2 was remarkably associated with TNM classification and lymphatic metastasis. Further analysis demonstrated that a positive expression of CCBE1 was associated with lymphatic metastasis, LVD and Ducks classification, and had a negative correlation with survival rate. Increased expression of SLP-2 promoted the formation of lymph vessels and exacerbated lymphatic metastasis of rectal cancer via up-regulating CCBE1. As a risk factor related to lymphatic metastasis, CCBE1 could be a novel biomarker for diagnosis and prognosis of rectal cancer.

Summer syncope syndrome.

PubMed

Huang, Jennifer Juxiang; Sharda, Natasha; Riaz, Irbaz Bin; Alpert, Joseph S

2014-08-01

Antihypertensive therapy is associated with significant relative risk reductions in the incidence of heart failure, myocardial infarction, and stroke. However, a common adverse reaction to antihypertensive therapy is orthostatic hypotension, dehydration, and syncope. We propose that continued use of antihypertensive medications at the same dosage during the dry summer months in patients living in the Sonoran desert leads to an increase in syncopal episodes. All hypertensive patients who were treated with medications and admitted with International Classification of Diseases, 9th Revision code diagnosis of syncope were included. They were defined as "cases" if they presented during the summer months (May to September 2012) and "controls" if they presented during the winter months (November 2012 to March 2013). The primary outcome measure was the presence of clinical dehydration. The statistical significance was determined using the 2-sided Fisher exact test. A total of 496 patients with an International Classification of Diseases, 9th Revision code diagnosis of syncope were screened, and 179 patients were included in the final analysis. In patients taking antihypertensive medications, there were a significantly higher number of cases of syncope secondary to dehydration or orthostatic hypotension during the summer months (45%) compared with the winter months (26%) (P = .01). The incidence of syncope was significantly higher in older patients (63%) compared with younger individuals (37%) during the summer months. The incidence of syncope increases during the summer months among people who reside in a dry desert climate and who are taking antihypertensive medications. On the basis of our findings, we describe an easily preventable condition that we define as the "Summer Syncope Syndrome." We recommend judicious reduction of antihypertensive therapy in patients residing in a hot and dry climate, particularly during the summer months. Copyright © 2014 Elsevier Inc. All rights reserved.

Perinatal mortality classification: an analysis of 112 cases of stillbirth.

PubMed

Reis, Ana Paula; Rocha, Ana; Lebre, Andrea; Ramos, Umbelina; Cunha, Ana

2017-10-01

This was a retrospective cohort analysis of stillbirths that occurred from January 2004 to December 2013 in our institution. We compared Tulip and Wigglesworth classification systems on a cohort of stillbirths and analysed the main differences between these two classifications. In this period, there were 112 stillbirths of a total of 31,758 births (stillbirth rate of 3.5 per 1000 births). There were 99 antepartum deaths and 13 intrapartum deaths. Foetal autopsy was performed in 99 cases and placental histopathological examination in all of the cases. The Wigglesworth found 'unknown' causes in 47 cases and the Tulip classification allocated 33 of these. Fourteen cases remained in the group of 'unknown' causes. Therefore, the Wigglesworth classification of stillbirths results in a higher proportion of unexplained stillbirths. We suggest that the traditional Wigglesworth classification should be substituted by a classification that manages the available information.

Effects of the body mass index on menopausal symptoms among Asian American midlife women using two different classification systems.

PubMed

Chang, Sun Ju; Chee, Wonshik; Im, Eun-Ok

2014-01-01

To explore the effects of the body mass index (BMI) on menopausal symptoms among Asian American midlife women using two different classification systems: the international classification and the BMI classification for public health action among Asian populations. Secondary analysis using data from two large Internet survey studies. Communities and groups of midlife women on the Internet. A total of 223 Asian American midlife women who were recruited over the Internet. The Midlife Women's Symptom Index and self-reports of height and weight were used to collect data. The data were analyzed using multiple analyses of covariance. No significant differences in the prevalence and severity scores among three subscales and total menopausal symptoms according to the international classification were found. When the BMI classification for public health action among Asian populations was used as an independent variable, significant differences were found in the severity scores of three subscales and total menopausal symptoms. Results of the post-hoc analyses showed that Asian American midlife women who were in the BMI classification for high risk had significantly more severe menopausal symptoms than those who were in the BMI classification for increased risk. For Asian American women, BMI categorized using the BMI classification for Asian populations is more closely related to the severity of menopausal symptoms than BMI categorized using the international classification. Nurses need to consider the BMI classification for Asian populations when they develop interventions to prevent and alleviate menopausal symptoms among Asian American midlife women. © 2013 AWHONN, the Association of Women's Health, Obstetric and Neonatal Nurses.

Increased Risk of Death for Patients on the Waitlist for Liver Transplant Residing at Greater Distance From Specialized Liver Transplant Centers in the United States.

PubMed

Cicalese, Luca; Shirafkan, Ali; Jennings, Kristofer; Zorzi, Daria; Rastellini, Cristiana

2016-10-01

We have previously shown that patients listed for orthotopic liver transplantation (OLT) in United Network for Organ Sharing Region 4 (Texas and Oklahoma) have higher waitlist mortality rates when residing more than 30 miles from specialized liver transplant centers (LTC). Considering that findings might only be exclusive for this region with its peculiarities in terms of having the highest land surface extensions, lowest population densities, and largest rural populations. We investigated the entire OLT patient population in the United States to assess if our previous regional findings are nationally validated and if a rural, micropolitan, or metropolitan residence location affects outcome of waitlisted OLT patients in the nation. Patients waiting for OLT in the United States from 2002 to 2012 were stratified by distance from the patients' residence to LTC and by Rural Urban Commuting Area (RUCA) codes classification. Statistical analyses were performed to evaluate risk of mortality on the waitlist and the likelihood to receive an OLT using a Cox proportional hazards model and a generalized additive model with a logistic link. Survival time and probability of death while on the waitlist for OLT using distance to LTC showed significant increased risk with the distance (P = 0.001 and P < 0.0001, respectively). At the same time, using RUCA classification as the variable did not show significance (P = 0.14 and P = 0.73, respectively). Distance from an LTC is a risk factor of mortality on the waitlist for OLT, whereas RUCA classification is not a significant factor.

Juvenile idiopathic arthritis in adulthood: fulfilment of classification criteria for adult rheumatic diseases, long-term outcomes and predictors of inactive disease, functional status and damage.

PubMed

Oliveira-Ramos, Filipa; Eusébio, Mónica; M Martins, Fernando; Mourão, Ana Filipa; Furtado, Carolina; Campanilho-Marques, Raquel; Cordeiro, Inês; Ferreira, Joana; Cerqueira, Marcos; Figueira, Ricardo; Brito, Iva; Canhão, Helena; Santos, Maria José; Melo-Gomes, José A; Fonseca, João Eurico

2016-01-01

To determine how adult juvenile idiopathic arthritis (JIA) patients fulfil classification criteria for adult rheumatic diseases, evaluate their outcomes and determine clinical predictors of inactive disease, functional status and damage. Patients with JIA registered on the Rheumatic Diseases Portuguese Register (Reuma.pt) older than 18 years and with more than 5 years of disease duration were included. Data regarding sociodemographic features, fulfilment of adult classification criteria, Health Assessment Questionnaire, Juvenile Arthritis Damage Index-articular (JADI-A) and Juvenile Arthritis Damage Index-extra-articular (JADI-E) damage index and disease activity were analysed. 426 patients were included. Most of patients with systemic JIA fulfilled criteria for Adult Still's disease. 95.6% of the patients with rheumatoid factor (RF)-positive polyarthritis and 57.1% of the patients with RF-negative polyarthritis matched criteria for rheumatoid arthritis (RA). 38.9% of the patients with extended oligoarthritis were classified as RA while 34.8% of the patients with persistent oligoarthritis were classified as spondyloarthritis. Patients with enthesitis-related arthritis fulfilled criteria for spondyloarthritis in 94.7%. Patients with psoriatic arthritis maintained this classification. Patients with inactive disease had lower disease duration, lower diagnosis delay and corticosteroids exposure. Longer disease duration was associated with higher HAQ, JADI-A and JADI-E. Higher JADI-A was also associated with biological treatment and retirement due to JIA disability and higher JADI-E with corticosteroids exposure. Younger age at disease onset was predictive of higher HAQ, JADI-A and JADI-E and decreased the chance of inactive disease. Most of the included patients fulfilled classification criteria for adult rheumatic diseases, maintain active disease and have functional impairment. Younger age at disease onset was predictive of higher disability and decreased the chance of inactive disease.

European validation of The Comprehensive International Classification of Functioning, Disability and Health Core Set for Osteoarthritis from the perspective of patients with osteoarthritis of the knee or hip.

PubMed

Weigl, Martin; Wild, Heike

2017-09-15

To validate the International Classification of Functioning, Disability and Health Comprehensive Core Set for Osteoarthritis from the patient perspective in Europe. This multicenter cross-sectional study involved 375 patients with knee or hip osteoarthritis. Trained health professionals completed the Comprehensive Core Set, and patients completed the Short-Form 36 questionnaire. Content validity was evaluated by calculating prevalences of impairments in body function and structures, limitations in activities and participation and environmental factors, which were either barriers or facilitators. Convergent construct validity was evaluated by correlating the International Classification of Functioning, Disability and Health categories with the Short-Form 36 Physical Component Score and the SF-36 Mental Component Score in a subgroup of 259 patients. The prevalences of all body function, body structure and activities and participation categories were >40%, >32% and >20%, respectively, and all environmental factors were relevant for >16% of patients. Few categories showed relevant differences between knee and hip osteoarthritis. All body function categories and all but two activities and participation categories showed significant correlations with the Physical Component Score. Body functions from the ICF chapter Mental Functions showed higher correlations with the Mental Component Score than with the Physical Component Score. This study supports the validity of the International Classification of Functioning, Disability and Health Comprehensive Core Set for Osteoarthritis. Implications for Rehabilitation Comprehensive International Classification of Functioning, Disability and Health Core Sets were developed as practical tools for application in multidisciplinary assessments. The validity of the Comprehensive International Classification of Functioning, Disability and Health Core Set for Osteoarthritis in this study supports its application in European patients with osteoarthritis. The differences in results between this Europe validation study and a previous Singaporean validation study underscore the need to validate the International Classification of Functioning, Disability and Health Core Sets in different regions of the world.

Injury severity in ice skating: an epidemiologic analysis using a standardised injury classification system.

PubMed

Ostermann, Roman C; Hofbauer, Marcus; Tiefenböck, Thomas M; Pumberger, Matthias; Tiefenböck, Michael; Platzer, Patrick; Aldrian, Silke

2015-01-01

Although injuries sustained during ice skating have been reported to be more serious than other forms of skating, the potential injury risks are often underestimated by skating participants. The purpose of this study was to give a descriptive overview of injury patterns occurring during ice skating. Special emphasis was put on injury severity by using a standardised injury classification system. Over a six month period, all patients treated with ice-skating-related injuries at Europe's largest hospital were included. Patient demographics were collected and all injuries categorised according to the Abbreviated Injury Scale (AIS) 2005. A descriptive statistic and logistic regression analysis was performed. Three hundred and forty-one patients (134 M, 207 F) were included in this study. Statistical analysis revealed that age had a significant influence on injury severity. People > 50 years had a higher risk of sustaining a more severe injury according to the AIS compared with younger skaters. Furthermore, the risk of head injury was significantly lower for people aged between 18 and 50 years than for people < 18 years (p = 0.0007) and significantly higher for people > 50 years than for people aged between 18 and 50 years (p = 0.04). The severity of ice-skating injuries is associated with the patient's age, showing more severe injuries in older patients. Awareness should be raised among the public and physicians about the risks associated with this activity in order to promote further educational interventions and the use of protective gear.

Comparison of Segmental Versus Longitudinal Intravascular Ultrasound Analysis for Pediatric Cardiac Allograft Vasculopathy.

PubMed

Kuhn, M A; Burch, M; Chinnock, R E; Fenton, M J

2017-10-01

Intravascular ultrasound (IVUS) has been routinely used in some centers to investigate cardiac allograft vasculopathy in pediatric heart transplant recipients. We present an alternative method using more sophisticated imaging software. This study presents a comparison of this method with an established standard method. All patients who had IVUS performed in 2014 were retrospectively evaluated. The standard technique consisted of analysis of 10 operator-selected segments along the vessel. Each study was re-evaluated using a longitudinal technique, taken at every third cardiac cycle, along the entire vessel. Semiautomatic edge detection software was used to detect vessel imaging planes. Measurements included outer and inner diameter, total and luminal area, maximal intimal thickness (MIT), and intimal index. Each IVUS was graded for severity using the Stanford classification. All results were given as mean ± standard deviation (SD). Groups were compared using Student t test. A P value <.05 was considered significant. There were 59 IVUS studies performed on 58 patients. There was no statistically significant difference between outer diameter, inner diameter, or total area. In the longitudinal group, there was a significantly smaller luminal area, higher MIT, and higher intimal index. Using the longitudinal technique, there was an increase in Stanford classification in 20 patients. The longitudinal technique appeared more sensitive in assessing the degree of cardiac allograft vasculopathy and may play a role in the increase in the degree of thickening seen. It may offer an alternative way of grading severity of cardiac allograft vasculopathy in pediatric heart transplant recipients. Copyright © 2017 Elsevier Inc. All rights reserved.

Vertical and Horizontal Jump Capacity in International Cerebral Palsy Football Players.

PubMed

Reina, Raúl; Iturricastillo, Aitor; Sabido, Rafael; Campayo-Piernas, Maria; Yanci, Javier

2018-05-01

To evaluate the reliability and validity of vertical and horizontal jump tests in football players with cerebral palsy (FPCP) and to analyze the jump performance differences between current International Federation for Cerebral Palsy Football functional classes (ie, FT5-FT8). A total of 132 international parafootballers (25.8 [6.7] y; 70.0 [9.1] kg; 175.7 [7.3] cm; 22.8 [2.8] kg·m -2 ; and 10.7 [7.5] y training experience) participated in the study. The participants were classified according to the International Federation for Cerebral Palsy Football classification rules, and a group of 39 players without cerebral palsy was included in the study as a control group. Football players' vertical and horizontal jump performance was assessed. All the tests showed good to excellent relative intrasession reliability scores, both in FPCP and in the control group (intraclass correlation = .78-.97, SEM < 10.5%). Significant between-groups differences (P < .001) were obtained in the countermovement jump, standing broad jump, 4 bounds for distance, and triple hop for distance dominant leg and nondominant leg. The control group performed higher/farther jumps with regard to all the FPCP classes, obtaining significant differences and moderate to large effect sizes (ESs) (.85 < ES < 5.54, P < .01). Players in FT8 class (less severe impairments) had significantly higher scores in all the jump tests than players in the lower classes (ES = moderate to large, P < .01). The vertical and horizontal jump tests performed in this study could be applied to the classification procedures and protocols for FPCP.

Addiction and Engagement: An Explorative Study Toward Classification Criteria for Internet Gaming Disorder.

PubMed

Lehenbauer-Baum, Mario; Klaps, Armin; Kovacovsky, Zuzana; Witzmann, Karolin; Zahlbruckner, Raphaela; Stetina, Birgit U

2015-06-01

The DSM-5 introduced Internet gaming disorder (IGD) as a condition needing more research. Proposed criteria include tolerance, preoccupation, deceiving, or continued excess despite psychosocial problems. However, studies suggest differences between addicted and engaged players. Therefore, this study investigated differences between engagement and addiction in a German-speaking sample of expert World of Warcraft players. Using an online-based questionnaire, 682 participants were surveyed (Mage=23.26 years; 84.9% male) from German-speaking areas. An adapted version of the "Asheron's call" questionnaire (which covers six addiction criteria, including salience, euphoria, and tolerance), the WHOQOL-BREF, the Gaming Motivation Scale, the BDI, the SPIN, and a brief version of the personality questionnaire BFI-10 were used. The average gamer in the sample played on level 87.93 and had been playing for 5.42 years. Addicted players had higher scores on the BDI and SPIN and significantly lower scores in all dimensions of quality of life. Addicted gamers played for 39.25 hours per week (engaged players: 11.93 hours per week) with significantly higher scores in items tapping achievement and immersion. There were differences regarding the BFI-10 in terms of "agreeableness," "conscientiousness," and "neuroticism." The results suggest that factors such as achievement and immersion set engaged and addicted users apart. Addiction seems to be significantly more connected to other psychopathologies such as depression and social anxiety. The results suggest that euphoria, tolerance, and cognitive salience should be handled with caution when it comes to a classification of IGD similar to (behavioral) addiction.

Global terrain classification using Multiple-Error-Removed Improved-Terrain (MERIT) to address susceptibility of landslides and other geohazards

NASA Astrophysics Data System (ADS)

Iwahashi, J.; Yamazaki, D.; Matsuoka, M.; Thamarux, P.; Herrick, J.; Yong, A.; Mital, U.

2017-12-01

A seamless model of landform classifications with regional accuracy will be a powerful platform for geophysical studies that forecast geologic hazards. Spatial variability as a function of landform on a global scale was captured in the automated classifications of Iwahashi and Pike (2007) and additional developments are presented here that incorporate more accurate depictions using higher-resolution elevation data than the original 1-km scale Shuttle Radar Topography Mission digital elevation model (DEM). We create polygon-based terrain classifications globally by using the 280-m DEM interpolated from the Multi-Error-Removed Improved-Terrain DEM (MERIT; Yamazaki et al., 2017). The multi-scale pixel-image analysis method, known as Multi-resolution Segmentation (Baatz and Schäpe, 2000), is first used to classify the terrains based on geometric signatures (slope and local convexity) calculated from the 280-m DEM. Next, we apply the machine learning method of "k-means clustering" to prepare the polygon-based classification at the globe-scale using slope, local convexity and surface texture. We then group the divisions with similar properties by hierarchical clustering and other statistical analyses using geological and geomorphological data of the area where landslides and earthquakes are frequent (e.g. Japan and California). We find the 280-m DEM resolution is only partially sufficient for classifying plains. We nevertheless observe that the categories correspond to reported landslide and liquefaction features at the global scale, suggesting that our model is an appropriate platform to forecast ground failure. To predict seismic amplification, we estimate site conditions using the time-averaged shear-wave velocity in the upper 30-m (VS30) measurements compiled by Yong et al. (2016) and the terrain model developed by Yong (2016; Y16). We plan to test our method on finer resolution DEMs and report our findings to obtain a more globally consistent terrain model as there are known errors in DEM derivatives at higher-resolutions. We expect the improvement in DEM resolution (4 times greater detail) and the combination of regional and global coverage will yield a consistent dataset of polygons that have the potential to improve relations to the Y16 estimates significantly.

Pretreatment risk stratification of feeding tube use in patients treated with intensity-modulated radiotherapy for head and neck cancer.

PubMed

Anderson, Nigel J; Jackson, James E; Smith, Jennifer G; Wada, Morikatsu; Schneider, Michal; Poulsen, Michael; Rolfo, Maureen; Fahandej, Maziar; Gan, Hui; Joon, Daryl Lim; Khoo, Vincent

2018-05-13

The purpose of this study was to establish a risk stratification model for feeding tube use in patients who undergo intensity-modulated radiotherapy (IMRT) for head and neck cancers. One hundred thirty-nine patients treated with definitive IMRT (+/- concurrent chemotherapy) for head and neck mucosal cancers were included in this study. Patients were recommended a prophylactic feeding tube and followed up by a dietician for at least 8 weeks postradiotherapy (post-RT). Potential prognostic factors were analyzed for risk and duration of feeding tube use for at least 25% of dietary requirements. Many variables had significant effects on risk and/or duration of feeding tube use in univariate analyses. Subsequent multivariable analysis showed that T classification ≥3 and level 2 lymphadenopathy were the best independent significant predictors of higher risk and duration of feeding tube use, respectively, in oral cavity, pharyngeal, and supraglottic primaries. In patients treated with definitive IMRT, T classification ≥3 and level 2 lymphadenopathy can potentially stratify patients into 4 risk groups for developing severe dysphagia requiring feeding tube use. © 2018 Wiley Periodicals, Inc.

Does the Aged Care Funding Instrument provide increased funding in residential care? Comparisons with the Residential Classification Scale.

PubMed

Chan, Geoffrey Z P; Chin, Collin K L; McKitrick, Douglas J; Warne, Roger W

2014-06-01

To determine whether the Aged Care Funding Instrument (ACFI) provides more funding than the Residential Classification Scale (RCS) for residents in the Hellenic Residential Care Facility. All residents within the care facility were assessed over a six 6-month period using ACFI, RCS and Clifton Assessment Procedures for the Elderly (CAPE) scores. Differences in funding levels were calculated using ACFI and RCS instruments against a standardised CAPE score. CAPE dependency RCS funding per resident per day varied from $32.20 for grade A to $116.20 for grade E4 residents. CAPE ACFI funding varied from $20.20 for grade A to $127.50 for grade E4. There was no significant difference in mean overall funding between the two scales (ACFI $92.50 vs RCS $90.35, P = 0.76). The ACFI does provide a small but not significant increase in funding to residents in residential care. It redirects funding to higher dependency residents. © 2013 The Authors. Australasian Journal on Ageing © 2013 ACOTA.

Physicochemical properties of honey from Marche, Central Italy: classification of unifloral and multifloral honeys by multivariate analysis.

PubMed

Truzzi, Cristina; Illuminati, Silvia; Annibaldia, Anna; Finale, Carolina; Rossetti, Monica; Scarponi, Giuseppe

2014-11-01

The purpose of this study was the physicochemical characterization and classification of Italian honey from Marche Region with a chemometric approach. A total of 135 honeys of different botanical origins [acacia (Robinia pseudoacacia L.), chestnut (Castanea sativa), coriander (Coriandrum sativum L.), lime (Tilia spp.), sunflower (Helianthus annuus L.), Metcalfa honeydew and multifloral honey] were considered. The average results of electrical conductivity (0.14-1.45 mS cm(-1)), pH (3.89-5.42), free acidity (10.9-39.0 meq(NaOH) kg(-1)), lactones (2.4-4.5 meq(NaOH) kg(-1)), total acidity (14.5-40.9 meq(NaOH) kg(-1)), proline (229-665 mg kg(-1)) and 5-(hydroxy-methyl)-2-furaldehyde (0.6-3.9 mg kg(-1)) content show wide variability among the analysed honey types, with statistically significant differences between the different honey types. Pattern recognition methods such as principal component analysis and discriminant analysis were performed in order to find a relationship between variables and types of honey and to classify honey on the basis of its physicochemical properties. The variables of electrical conductivity, acidity (free, lactones), pH and proline content exhibited higher discriminant power and provided enough information for the classification and distinction of unifloral honey types, but not for the classification of multifloral honey (100% and 85% of samples correctly classified, respectively).

40 CFR 51.900 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... higher or lower, classifications are ranked from lowest to highest as follows: classification under... National Ambient Air Quality Standard § 51.900 Definitions. The following definitions apply for purposes of... 42 U.S.C. 7401-7671q (2003). (f) Applicable requirements means for an area the following requirements...

40 CFR 51.900 - Definitions.

Code of Federal Regulations, 2010 CFR

2010-07-01

... higher or lower, classifications are ranked from lowest to highest as follows: classification under... National Ambient Air Quality Standard § 51.900 Definitions. The following definitions apply for purposes of... 42 U.S.C. 7401-7671q (2003). (f) Applicable requirements means for an area the following requirements...

The 'third class' of medications: Sales and purchasing behavior are associated with pharmacist only and pharmacy medicine classifications in Australia.

PubMed

Emmerton, Lynne

2009-01-01

Nonprescription (over-the-counter) medications in Australia are classified as Pharmacist Only Medicines, Pharmacy Medicines, or unscheduled medications. This report characterizes these medication classifications using key sales and purchasing behavior variables. Descriptive, nonexperimental, cross-sectional study. 15 pharmacies in southeast Queensland, Australia, with data recorded over 36 hours per pharmacy in mid-August, 2006. Eligible purchasers (n = 3,470 medication purchases) of all nonprescription medications (including nutritional supplements). Researchers documented details of all observed nonprescription medication sales and interviewed all available patients following the transaction. Incidence of product-related consultation, products purchased (brand, dosage form, classification), and purchasing behavior data (including previous purchase, intended use, intended user, and intention to purchase a particular brand). More restrictive classification of the purchased medication was significantly (P < 0.01) associated with younger purchasers, purchase of a single nonprescription medication, intent to self-use the medication, intent to purchase a particular brand, repeat purchase, brand-switching interventions by pharmacy staff, pharmacy staff influence on first-time purchases, and observed consultation by pharmacists. Legislative compliance issues were identified: Pharmacists consulted in only 54.7% of Pharmacist Only Medicine sales and 13 cases (1.7% of observed sales) occurred in which Pharmacist Only and Pharmacy Medicines had been sourced from publicly accessible areas of the store. Pharmacist Only Medicines received greater levels of professional involvement during their sale than Pharmacy Medicines and unscheduled medications, despite higher levels of product familiarity among patients. To optimize the benefits of this classification system, emphasis on professional guidelines is recommended.

Classification of oral cancers using Raman spectroscopy of serum

NASA Astrophysics Data System (ADS)

Sahu, Aditi; Talathi, Sneha; Sawant, Sharada; Krishna, C. Murali

2014-03-01

Oral cancers are the sixth most common malignancy worldwide, with low 5-year disease free survival rates, attributable to late detection due to lack of reliable screening modalities. Our in vivo Raman spectroscopy studies have demonstrated classification of normal and tumor as well as cancer field effects (CFE), the earliest events in oral cancers. In view of limitations such as requirement of on-site instrumentation and stringent experimental conditions of this approach, feasibility of classification of normal and cancer using serum was explored using 532 nm excitation. In this study, strong resonance features of β-carotenes, present differentially in normal and pathological conditions, were observed. In the present study, Raman spectra of sera of 36 buccal mucosa, 33 tongue cancers and 17 healthy subjects were recorded using Raman microprobe coupled with 40X objective using 785 nm excitation, a known source of excitation for biomedical applications. To eliminate heterogeneity, average of 3 spectra recorded from each sample was subjected to PC-LDA followed by leave-one-out-cross-validation. Findings indicate average classification efficiency of ~70% for normal and cancer. Buccal mucosa and tongue cancer serum could also be classified with an efficiency of ~68%. Of the two cancers, buccal mucosa cancer and normal could be classified with a higher efficiency. Findings of the study are quite comparable to that of our earlier study, which suggest that there exist significant differences, other than β- carotenes, between normal and cancerous samples which can be exploited for the classification. Prospectively, extensive validation studies will be undertaken to confirm the findings.

Histogram analysis of apparent diffusion coefficient maps for assessing thymic epithelial tumours: correlation with world health organization classification and clinical staging.

PubMed

Kong, Ling-Yan; Zhang, Wei; Zhou, Yue; Xu, Hai; Shi, Hai-Bin; Feng, Qing; Xu, Xiao-Quan; Yu, Tong-Fu

2018-04-01

To investigate the value of apparent diffusion coefficients (ADCs) histogram analysis for assessing World Health Organization (WHO) pathological classification and Masaoka clinical stages of thymic epithelial tumours. 37 patients with histologically confirmed thymic epithelial tumours were enrolled. ADC measurements were performed using hot-spot ROI (ADC HS-ROI ) and histogram-based approach. ADC histogram parameters included mean ADC (ADC mean ), median ADC (ADC median ), 10 and 90 percentile of ADC (ADC 10 and ADC 90 ), kurtosis and skewness. One-way ANOVA, independent-sample t-test, and receiver operating characteristic were used for statistical analyses. There were significant differences in ADC mean , ADC median , ADC 10 , ADC 90 and ADC HS-ROI among low-risk thymoma (type A, AB, B1; n = 14), high-risk thymoma (type B2, B3; n = 9) and thymic carcinoma (type C, n = 14) groups (all p-values <0.05), while no significant difference in skewness (p = 0.181) and kurtosis (p = 0.088). ADC 10 showed best differentiating ability (cut-off value, ≤0.689 × 10 -3 mm 2 s -1 ; AUC, 0.957; sensitivity, 95.65%; specificity, 92.86%) for discriminating low-risk thymoma from high-risk thymoma and thymic carcinoma. Advanced Masaoka stages (Stage III and IV; n = 24) tumours showed significant lower ADC parameters and higher kurtosis than early Masaoka stage (Stage I and II; n = 13) tumours (all p-values <0.05), while no significant difference on skewness (p = 0.063). ADC 10 showed best differentiating ability (cut-off value, ≤0.689 × 10 -3 mm 2 s -1 ; AUC, 0.913; sensitivity, 91.30%; specificity, 85.71%) for discriminating advanced and early Masaoka stage epithelial tumours. ADC histogram analysis may assist in assessing the WHO pathological classification and Masaoka clinical stages of thymic epithelial tumours. Advances in knowledge: 1. ADC histogram analysis could help to assess WHO pathological classification of thymic epithelial tumours. 2. ADC histogram analysis could help to evaluate Masaoka clinical stages of thymic epithelial tumours. 3. ADC 10 might be a promising imaging biomarker for assessing and characterizing thymic epithelial tumours.

Polsar Land Cover Classification Based on Hidden Polarimetric Features in Rotation Domain and Svm Classifier

NASA Astrophysics Data System (ADS)

Tao, C.-S.; Chen, S.-W.; Li, Y.-Z.; Xiao, S.-P.

2017-09-01

Land cover classification is an important application for polarimetric synthetic aperture radar (PolSAR) data utilization. Rollinvariant polarimetric features such as H / Ani / α / Span are commonly adopted in PolSAR land cover classification. However, target orientation diversity effect makes PolSAR images understanding and interpretation difficult. Only using the roll-invariant polarimetric features may introduce ambiguity in the interpretation of targets' scattering mechanisms and limit the followed classification accuracy. To address this problem, this work firstly focuses on hidden polarimetric feature mining in the rotation domain along the radar line of sight using the recently reported uniform polarimetric matrix rotation theory and the visualization and characterization tool of polarimetric coherence pattern. The former rotates the acquired polarimetric matrix along the radar line of sight and fully describes the rotation characteristics of each entry of the matrix. Sets of new polarimetric features are derived to describe the hidden scattering information of the target in the rotation domain. The latter extends the traditional polarimetric coherence at a given rotation angle to the rotation domain for complete interpretation. A visualization and characterization tool is established to derive new polarimetric features for hidden information exploration. Then, a classification scheme is developed combing both the selected new hidden polarimetric features in rotation domain and the commonly used roll-invariant polarimetric features with a support vector machine (SVM) classifier. Comparison experiments based on AIRSAR and multi-temporal UAVSAR data demonstrate that compared with the conventional classification scheme which only uses the roll-invariant polarimetric features, the proposed classification scheme achieves both higher classification accuracy and better robustness. For AIRSAR data, the overall classification accuracy with the proposed classification scheme is 94.91 %, while that with the conventional classification scheme is 93.70 %. Moreover, for multi-temporal UAVSAR data, the averaged overall classification accuracy with the proposed classification scheme is up to 97.08 %, which is much higher than the 87.79 % from the conventional classification scheme. Furthermore, for multitemporal PolSAR data, the proposed classification scheme can achieve better robustness. The comparison studies also clearly demonstrate that mining and utilization of hidden polarimetric features and information in the rotation domain can gain the added benefits for PolSAR land cover classification and provide a new vision for PolSAR image interpretation and application.

Spot counting on fluorescence in situ hybridization in suspension images using Gaussian mixture model

NASA Astrophysics Data System (ADS)

Liu, Sijia; Sa, Ruhan; Maguire, Orla; Minderman, Hans; Chaudhary, Vipin

2015-03-01

Cytogenetic abnormalities are important diagnostic and prognostic criteria for acute myeloid leukemia (AML). A flow cytometry-based imaging approach for FISH in suspension (FISH-IS) was established that enables the automated analysis of several log-magnitude higher number of cells compared to the microscopy-based approaches. The rotational positioning can occur leading to discordance between spot count. As a solution of counting error from overlapping spots, in this study, a Gaussian Mixture Model based classification method is proposed. The Akaike information criterion (AIC) and Bayesian information criterion (BIC) of GMM are used as global image features of this classification method. Via Random Forest classifier, the result shows that the proposed method is able to detect closely overlapping spots which cannot be separated by existing image segmentation based spot detection methods. The experiment results show that by the proposed method we can obtain a significant improvement in spot counting accuracy.

Brucella taxonomy and evolution

PubMed Central

Ficht, Thomas

2010-01-01

Taxonomy and nomenclature represent man-made systems designed to enhance understanding of the relationship between organisms by comparison of discrete sets of properties. Initial efforts at bacterial taxonomy were flawed as a result of the previous use of nonsystematic approaches including common names resulting in confusing and inaccurate nomenclature. A decision was made to start afresh with bacterial nomenclature and to avoid the hazards experienced in the taxonomic classification of higher organisms. This was achieved by developing new rules designed to simplify classification and avoid unnecessary and confusing changes. This article reviews the work of a number of scientists attempting to reconcile new molecular data describing the phylogenetic relationship between Brucella organisms and a broader family of organisms with widely variant phenotypes that include human virulence and host range against a backdrop of strict regulatory requirements that fail to recognize significant differences between organisms with similar nomenclature. PMID:20521932

Clinical review: Bleeding - a notable complication of treatment in patients with acute coronary syndromes: incidence, predictors, classification, impact on prognosis, and management

PubMed Central

2013-01-01

This article focuses on the incidence, predictors, classification, impact on prognosis, and management of bleeding associated with the treatment of acute coronary syndrome. The issue of bleeding complications is related to the continual improvement of ischemic heart disease treatment, which involves mainly (a) the widespread use of coronary angiography, (b) developments in percutaneous coronary interventions, and (c) the introduction of new antithrombotics. Bleeding has become an important health and economic problem and has an incidence of 2.0% to 17%. Bleeding significantly influences both the short- and long-term prognoses. If a group of patients at higher risk of bleeding complications can be identified according to known risk factors and a risk scoring system can be developed, we may focus more on preventive measures that should help us to reduce the incidence of bleeding. PMID:24093465

Image quality classification for DR screening using deep learning.

PubMed

FengLi Yu; Jing Sun; Annan Li; Jun Cheng; Cheng Wan; Jiang Liu

2017-07-01

The quality of input images significantly affects the outcome of automated diabetic retinopathy (DR) screening systems. Unlike the previous methods that only consider simple low-level features such as hand-crafted geometric and structural features, in this paper we propose a novel method for retinal image quality classification (IQC) that performs computational algorithms imitating the working of the human visual system. The proposed algorithm combines unsupervised features from saliency map and supervised features coming from convolutional neural networks (CNN), which are fed to an SVM to automatically detect high quality vs poor quality retinal fundus images. We demonstrate the superior performance of our proposed algorithm on a large retinal fundus image dataset and the method could achieve higher accuracy than other methods. Although retinal images are used in this study, the methodology is applicable to the image quality assessment and enhancement of other types of medical images.

Implementation of Multispectral Image Classification on a Remote Adaptive Computer

NASA Technical Reports Server (NTRS)

Figueiredo, Marco A.; Gloster, Clay S.; Stephens, Mark; Graves, Corey A.; Nakkar, Mouna

1999-01-01

As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms its justified. Field Programmable Gate Arrays enable the implementation of algorithms at the hardware gate level, leading to orders of m a,gnitude performance increase over microprocessor based systems. The automatic classification of spaceborne multispectral images is an example of a computation intensive application, that, can benefit from implementation on an FPGA - based custom computing machine (adaptive or reconfigurable computer). A probabilistic neural network is used here to classify pixels of of a multispectral LANDSAT-2 image. The implementation described utilizes Java client/server application programs to access the adaptive computer from a remote site. Results verify that a remote hardware version of the algorithm (implemented on an adaptive computer) is significantly faster than a local software version of the same algorithm implemented on a typical general - purpose computer).

The impact of endorsing Spitzer's proposed criteria for PTSD in the forthcoming DSM-V on male and female Veterans.

PubMed

Miller, Lyndsey N; Chard, Kathleen M; Schumm, Jeremiah A; O'Brien, Carol

2011-06-01

This study explored differences between Spitzer's proposed model of posttraumatic stress disorder (PTSD) and the current DSM-IV diagnostic classification scheme in 353 Veterans. The majority of Veterans (89%) diagnosed with PTSD as specified in the DSM-IV also met Spitzer's proposed criteria. Veterans who met both DSM-IV and Spitzer's proposed criteria had significantly higher Clinician Administered PTSD Scale severity scores than Veterans only meeting DSM-IV criteria. Logistic regression indicated that being African American and having no comorbid diagnosis of major depressive disorder or history of a substance use disorder were found to predict those Veterans who met current, but not proposed criteria. These findings have important implications regarding proposed changes to the diagnostic classification criteria for PTSD in the forthcoming DSM-V. Copyright © 2011 Elsevier Ltd. All rights reserved.

Spatial-spectral blood cell classification with microscopic hyperspectral imagery

NASA Astrophysics Data System (ADS)

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

2017-10-01

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

The Importance of Temporal and Spatial Vegetation Structure Information in Biotope Mapping Schemes: A Case Study in Helsingborg, Sweden

NASA Astrophysics Data System (ADS)

Gao, Tian; Qiu, Ling; Hammer, Mårten; Gunnarsson, Allan

2012-02-01

Temporal and spatial vegetation structure has impact on biodiversity qualities. Yet, current schemes of biotope mapping do only to a limited extend incorporate these factors in the mapping. The purpose of this study is to evaluate the application of a modified biotope mapping scheme that includes temporal and spatial vegetation structure. A refined scheme was developed based on a biotope classification, and applied to a green structure system in Helsingborg city in southern Sweden. It includes four parameters of vegetation structure: continuity of forest cover, age of dominant trees, horizontal structure, and vertical structure. The major green structure sites were determined by interpretation of panchromatic aerial photographs assisted with a field survey. A set of biotope maps was constructed on the basis of each level of modified classification. An evaluation of the scheme included two aspects in particular: comparison of species richness between long-continuity and short-continuity forests based on identification of woodland continuity using ancient woodland indicators (AWI) species and related historical documents, and spatial distribution of animals in the green space in relation to vegetation structure. The results indicate that (1) the relationship between forest continuity: according to verification of historical documents, the richness of AWI species was higher in long-continuity forests; Simpson's diversity was significantly different between long- and short-continuity forests; the total species richness and Shannon's diversity were much higher in long-continuity forests shown a very significant difference. (2) The spatial vegetation structure and age of stands influence the richness and abundance of the avian fauna and rabbits, and distance to the nearest tree and shrub was a strong determinant of presence for these animal groups. It is concluded that continuity of forest cover, age of dominant trees, horizontal and vertical structures of vegetation should now be included in urban biotope classifications.

Clinical outcome and long-term survival of 150 consecutive patients with pancreatic neuroendocrine tumors: A comprehensive analysis by the World Health Organization 2010 grading classification.

PubMed

Deng, Ben-Yuan; Liu, Fei; Yin, Si-Neng; Chen, An-Ping; Xu, Lin; Li, Bo

2018-06-01

The World Health Organization (WHO) has revised its grading system for pancreatic neuroendocrine tumors (PNETs) in 2010 into three main group, which has not been widely and comprehensively evaluated. We aimed to validate the clinical valve of this system associated with the clinical outcome and long-term survival when applied to PNETs, which were rare and heterogeneous. We retrospectively collected and analyzed the data of 150 consecutive patients with PNETs who underwent a resection. Sixty-four males and 86 females with PNETs were enrolled in our study. The clinical stage from I to IV by European Neuroendocrine Tumor Society were respectively defined in 53, 60, 19 and 18 patients. Seventy-two patients were pathologically diagnosed as neuroendocrine tumor G1 (NET G1), 48 as neuroendocrine tumor G2 (NET G2) and 30 as neuroendocrine carcinoma G3 (NEC G3). Patients with a radical resection obtained a notably higher overall survival (OS) than that of patients who underwent a palliative surgery (P=0.001). The 5-year OS of patients with NET G1 was significantly higher than that of patients with NET G2 (P=0.015) and NEC G3 (P<0.001); the comparison of OS for patients with NET G2 and NEC G3 was also statistically significant (P=0.005). In both univariate and multivariate analysis, clinical staging by ENETS (stage I and II vs. stage III and IV), resection (radical vs. palliative) and WHO 2010 grading classification (NET G1 and G2 vs. NEC G3) were validated to be independent predictors for the survivals of PNETs. The newly-updated WHO 2010 grading classification was prognostic for the OS of PNETs and could be widely adopted in clinical practice. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

46 CFR 503.59 - Safeguarding classified information.

Code of Federal Regulations, 2014 CFR

2014-10-01

... classification. (b) Whenever classified material is removed from a storage facility, such material shall not be... classification of the information; and (2) The prospective recipient requires access to the information in order... documents that have been destroyed. (k) An inventory of all documents classified higher than confidential...

46 CFR 503.59 - Safeguarding classified information.

Code of Federal Regulations, 2013 CFR

2013-10-01

... classification. (b) Whenever classified material is removed from a storage facility, such material shall not be... classification of the information; and (2) The prospective recipient requires access to the information in order... documents that have been destroyed. (k) An inventory of all documents classified higher than confidential...

46 CFR 503.59 - Safeguarding classified information.

Code of Federal Regulations, 2012 CFR

2012-10-01

... classification. (b) Whenever classified material is removed from a storage facility, such material shall not be... classification of the information; and (2) The prospective recipient requires access to the information in order... documents that have been destroyed. (k) An inventory of all documents classified higher than confidential...

Displaced aggression predicts switching deficits in people with temporal lobe epilepsy.

PubMed

Gul, Amara; Ahmad, Hira

2014-12-01

This study examined the relationship between task-switching abilities and displaced aggression in people with temporal lobe epilepsy (PWE). Participants (35 PWE and 35 healthy controls) performed emotion and gender classification switching tasks. People with temporal lobe epilepsy showed larger switch costs than controls. This result reflected task-switching deficits in PWE. People with temporal lobe epilepsy reported higher anger rumination, revenge planning, and behavioral displaced aggression compared with controls. Displaced aggression was a significant predictor of the task switch costs. It is suggested that displaced aggression is a significant marker of task-switching deficits. Copyright © 2014 Elsevier Inc. All rights reserved.

Information analysis of a spatial database for ecological land classification

NASA Technical Reports Server (NTRS)

Davis, Frank W.; Dozier, Jeff

1990-01-01

An ecological land classification was developed for a complex region in southern California using geographic information system techniques of map overlay and contingency table analysis. Land classes were identified by mutual information analysis of vegetation pattern in relation to other mapped environmental variables. The analysis was weakened by map errors, especially errors in the digital elevation data. Nevertheless, the resulting land classification was ecologically reasonable and performed well when tested with higher quality data from the region.

Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands

PubMed Central

Atzori, Manfredo; Cognolato, Matteo; Müller, Henning

2016-01-01

Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too. PMID:27656140

Discrimination of different sub-basins on Tajo River based on water influence factor

NASA Astrophysics Data System (ADS)

Bermudez, R.; Gascó, J. M.; Tarquis, A. M.; Saa-Requejo, A.

2009-04-01

Numeric taxonomy has been applied to classify Tajo basin water (Spain) till Portugal border. Several stations, a total of 52, that estimate 15 water variables have been used in this study. The different groups have been obtained applying a Euclidean distance among stations (distance classification) and a Euclidean distance between each station and the centroid estimated among them (centroid classification), varying the number of parameters and with or without variable typification. In order to compare the classification a log-log relation has been established, between number of groups created and distances, to select the best one. It has been observed that centroid classification is more appropriate following in a more logic way the natural constrictions than the minimum distance among stations. Variable typification doesn't improve the classification except when the centroid method is applied. Taking in consideration the ions and the sum of them as variables, the classification improved. Stations are grouped based on electric conductivity (CE), total anions (TA), total cations (TC) and ions ratio (Na/Ca and Mg/Ca). For a given classification and comparing the different groups created a certain variation in ions concentration and ions ratio are observed. However, the variation in each ion among groups is different depending on the case. For the last group, regardless the classification, the increase in all ions is general. Comparing the dendrograms, and groups that originated, Tajo river basin can be sub dived in five sub-basins differentiated by the main influence on water: 1. With a higher ombrogenic influence (rain fed). 2. With ombrogenic and pedogenic influence (rain and groundwater fed). 3. With pedogenic influence. 4. With lithogenic influence (geological bedrock). 5. With a higher ombrogenic and lithogenic influence added.

Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands.

PubMed

Atzori, Manfredo; Cognolato, Matteo; Müller, Henning

2016-01-01

Natural control methods based on surface electromyography (sEMG) and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications, and commercial prostheses are capable of offering natural control for only a few movements. In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its methods for natural control of robotic hands via sEMG using a large number of intact subjects and amputees. We tested convolutional networks for the classification of an average of 50 hand movements in 67 intact subjects and 11 transradial amputees. The simple architecture of the neural network allowed to make several tests in order to evaluate the effect of pre-processing, layer architecture, data augmentation and optimization. The classification results are compared with a set of classical classification methods applied on the same datasets. The classification accuracy obtained with convolutional neural networks using the proposed architecture is higher than the average results obtained with the classical classification methods, but lower than the results obtained with the best reference methods in our tests. The results show that convolutional neural networks with a very simple architecture can produce accurate results comparable to the average classical classification methods. They show that several factors (including pre-processing, the architecture of the net and the optimization parameters) can be fundamental for the analysis of sEMG data. Larger networks can achieve higher accuracy on computer vision and object recognition tasks. This fact suggests that it may be interesting to evaluate if larger networks can increase sEMG classification accuracy too.

Influence of history of head trauma and epilepsy on delinquents in a juvenile classification home.

PubMed

Miura, Hideki; Fujiki, Masumi; Shibata, Arihiro; Ishikawa, Kenji

2005-12-01

Juvenile delinquents often show poor impulse control and cognitive abnormalities, which may be related to disturbances in brain development due to head trauma and/or epilepsy. The aim of the present study was to examine the influence of head trauma and/or epilepsy on delinquent behavior. We examined 1,336 juvenile delinquents (1,151 males and 185 females) who had been admitted to the Nagoya Juvenile Classification Home, Aichi, Japan. Among them, 52 subjects with a history of epilepsy, convulsion or loss of consciousness, head injury requiring neurological assessment and/or treatment, or neurosurgical operation (head trauma/epilepsy group), were examined by electroencephalography and compared to subjects without these histories (control group) with respect to types of crime, history of amphetamine use, psychiatric treatment, child abuse, and family history. Among the 52 subjects, 43 (82.7%) showed abnormal findings. The head trauma/epilepsy group had significantly higher rates of psychiatric treatment (P<0.0001, OR=16.852, 95% CI=8.068-35.199) and family history of drug abuse (P<0.05, OR=2.303, 95% CI=1.003-5.290). Furthermore, the percentage of members who were sent to juvenile training school by the family court was significantly higher in the head trauma/epilepsy group (72.5%) than in the control group (38.9%, P<0.0001). The juvenile delinquents who had a history of head trauma and/or epilepsy showed a high prevalence of electroencephalograph abnormality, and higher rates of psychiatric treatment and family history of drug abuse, and were more likely to be sent to juvenile training school by the family court.

Literature classification for semi-automated updating of biological knowledgebases

PubMed Central

2013-01-01

Background As the output of biological assays increase in resolution and volume, the body of specialized biological data, such as functional annotations of gene and protein sequences, enables extraction of higher-level knowledge needed for practical application in bioinformatics. Whereas common types of biological data, such as sequence data, are extensively stored in biological databases, functional annotations, such as immunological epitopes, are found primarily in semi-structured formats or free text embedded in primary scientific literature. Results We defined and applied a machine learning approach for literature classification to support updating of TANTIGEN, a knowledgebase of tumor T-cell antigens. Abstracts from PubMed were downloaded and classified as either "relevant" or "irrelevant" for database update. Training and five-fold cross-validation of a k-NN classifier on 310 abstracts yielded classification accuracy of 0.95, thus showing significant value in support of data extraction from the literature. Conclusion We here propose a conceptual framework for semi-automated extraction of epitope data embedded in scientific literature using principles from text mining and machine learning. The addition of such data will aid in the transition of biological databases to knowledgebases. PMID:24564403

Can single classifiers be as useful as model ensembles to produce benthic seabed substratum maps?

NASA Astrophysics Data System (ADS)

Turner, Joseph A.; Babcock, Russell C.; Hovey, Renae; Kendrick, Gary A.

2018-05-01

Numerous machine-learning classifiers are available for benthic habitat map production, which can lead to different results. This study highlights the performance of the Random Forest (RF) classifier, which was significantly better than Classification Trees (CT), Naïve Bayes (NB), and a multi-model ensemble in terms of overall accuracy, Balanced Error Rate (BER), Kappa, and area under the curve (AUC) values. RF accuracy was often higher than 90% for each substratum class, even at the most detailed level of the substratum classification and AUC values also indicated excellent performance (0.8-1). Total agreement between classifiers was high at the broadest level of classification (75-80%) when differentiating between hard and soft substratum. However, this sharply declined as the number of substratum categories increased (19-45%) including a mix of rock, gravel, pebbles, and sand. The model ensemble, produced from the results of all three classifiers by majority voting, did not show any increase in predictive performance when compared to the single RF classifier. This study shows how a single classifier may be sufficient to produce benthic seabed maps and model ensembles of multiple classifiers.

Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods.

PubMed

Polat, Huseyin; Danaei Mehr, Homay; Cetin, Aydin

2017-04-01

As Chronic Kidney Disease progresses slowly, early detection and effective treatment are the only cure to reduce the mortality rate. Machine learning techniques are gaining significance in medical diagnosis because of their classification ability with high accuracy rates. The accuracy of classification algorithms depend on the use of correct feature selection algorithms to reduce the dimension of datasets. In this study, Support Vector Machine classification algorithm was used to diagnose Chronic Kidney Disease. To diagnose the Chronic Kidney Disease, two essential types of feature selection methods namely, wrapper and filter approaches were chosen to reduce the dimension of Chronic Kidney Disease dataset. In wrapper approach, classifier subset evaluator with greedy stepwise search engine and wrapper subset evaluator with the Best First search engine were used. In filter approach, correlation feature selection subset evaluator with greedy stepwise search engine and filtered subset evaluator with the Best First search engine were used. The results showed that the Support Vector Machine classifier by using filtered subset evaluator with the Best First search engine feature selection method has higher accuracy rate (98.5%) in the diagnosis of Chronic Kidney Disease compared to other selected methods.

LMD Based Features for the Automatic Seizure Detection of EEG Signals Using SVM.

PubMed

Zhang, Tao; Chen, Wanzhong

2017-08-01

Achieving the goal of detecting seizure activity automatically using electroencephalogram (EEG) signals is of great importance and significance for the treatment of epileptic seizures. To realize this aim, a newly-developed time-frequency analytical algorithm, namely local mean decomposition (LMD), is employed in the presented study. LMD is able to decompose an arbitrary signal into a series of product functions (PFs). Primarily, the raw EEG signal is decomposed into several PFs, and then the temporal statistical and non-linear features of the first five PFs are calculated. The features of each PF are fed into five classifiers, including back propagation neural network (BPNN), K-nearest neighbor (KNN), linear discriminant analysis (LDA), un-optimized support vector machine (SVM) and SVM optimized by genetic algorithm (GA-SVM), for five classification cases, respectively. Confluent features of all PFs and raw EEG are further passed into the high-performance GA-SVM for the same classification tasks. Experimental results on the international public Bonn epilepsy EEG dataset show that the average classification accuracy of the presented approach are equal to or higher than 98.10% in all the five cases, and this indicates the effectiveness of the proposed approach for automated seizure detection.

Targeting an efficient target-to-target interval for P300 speller brain–computer interfaces

PubMed Central

Sellers, Eric W.; Wang, Xingyu

2013-01-01

Longer target-to-target intervals (TTI) produce greater P300 event-related potential amplitude, which can increase brain–computer interface (BCI) classification accuracy and decrease the number of flashes needed for accurate character classification. However, longer TTIs requires more time for each trial, which will decrease the information transfer rate of BCI. In this paper, a P300 BCI using a 7 × 12 matrix explored new flash patterns (16-, 18- and 21-flash pattern) with different TTIs to assess the effects of TTI on P300 BCI performance. The new flash patterns were designed to minimize TTI, decrease repetition blindness, and examine the temporal relationship between each flash of a given stimulus by placing a minimum of one (16-flash pattern), two (18-flash pattern), or three (21-flash pattern) non-target flashes between each target flashes. Online results showed that the 16-flash pattern yielded the lowest classification accuracy among the three patterns. The results also showed that the 18-flash pattern provides a significantly higher information transfer rate (ITR) than the 21-flash pattern; both patterns provide high ITR and high accuracy for all subjects. PMID:22350331

EEG alpha spindles and prolonged brake reaction times during auditory distraction in an on-road driving study.

PubMed

Sonnleitner, Andreas; Treder, Matthias Sebastian; Simon, Michael; Willmann, Sven; Ewald, Arne; Buchner, Axel; Schrauf, Michael

2014-01-01

Driver distraction is responsible for a substantial number of traffic accidents. This paper describes the impact of an auditory secondary task on drivers' mental states during a primary driving task. N=20 participants performed the test procedure in a car following task with repeated forced braking on a non-public test track. Performance measures (provoked reaction time to brake lights) and brain activity (EEG alpha spindles) were analyzed to describe distracted drivers. Further, a classification approach was used to investigate whether alpha spindles can predict drivers' mental states. Results show that reaction times and alpha spindle rate increased with time-on-task. Moreover, brake reaction times and alpha spindle rate were significantly higher while driving with auditory secondary task opposed to driving only. In single-trial classification, a combination of spindle parameters yielded a median classification error of about 8% in discriminating the distracted from the alert driving. Reduced driving performance (i.e., prolonged brake reaction times) during increased cognitive load is assumed to be indicated by EEG alpha spindles, enabling the quantification of driver distraction in experiments on public roads without verbally assessing the drivers' mental states. Copyright © 2013 Elsevier Ltd. All rights reserved.

Combined EEG-fNIRS decoding of motor attempt and imagery for brain switch control: an offline study in patients with tetraplegia.

PubMed

Blokland, Yvonne; Spyrou, Loukianos; Thijssen, Dick; Eijsvogels, Thijs; Colier, Willy; Floor-Westerdijk, Marianne; Vlek, Rutger; Bruhn, Jorgen; Farquhar, Jason

2014-03-01

Combining electrophysiological and hemodynamic features is a novel approach for improving current performance of brain switches based on sensorimotor rhythms (SMR). This study was conducted with a dual purpose: to test the feasibility of using a combined electroencephalogram/functional near-infrared spectroscopy (EEG-fNIRS) SMR-based brain switch in patients with tetraplegia, and to examine the performance difference between motor imagery and motor attempt for this user group. A general improvement was found when using both EEG and fNIRS features for classification as compared to using the single-modality EEG classifier, with average classification rates of 79% for attempted movement and 70% for imagined movement. For the control group, rates of 87% and 79% were obtained, respectively, where the "attempted movement" condition was replaced with "actual movement." A combined EEG-fNIRS system might be especially beneficial for users who lack sufficient control of current EEG-based brain switches. The average classification performance in the patient group for attempted movement was significantly higher than for imagined movement using the EEG-only as well as the combined classifier, arguing for the case of a paradigm shift in current brain switch research.

Real-time classification of auditory sentences using evoked cortical activity in humans

NASA Astrophysics Data System (ADS)

Moses, David A.; Leonard, Matthew K.; Chang, Edward F.

2018-06-01

Objective. Recent research has characterized the anatomical and functional basis of speech perception in the human auditory cortex. These advances have made it possible to decode speech information from activity in brain regions like the superior temporal gyrus, but no published work has demonstrated this ability in real-time, which is necessary for neuroprosthetic brain-computer interfaces. Approach. Here, we introduce a real-time neural speech recognition (rtNSR) software package, which was used to classify spoken input from high-resolution electrocorticography signals in real-time. We tested the system with two human subjects implanted with electrode arrays over the lateral brain surface. Subjects listened to multiple repetitions of ten sentences, and rtNSR classified what was heard in real-time from neural activity patterns using direct sentence-level and HMM-based phoneme-level classification schemes. Main results. We observed single-trial sentence classification accuracies of 90% or higher for each subject with less than 7 minutes of training data, demonstrating the ability of rtNSR to use cortical recordings to perform accurate real-time speech decoding in a limited vocabulary setting. Significance. Further development and testing of the package with different speech paradigms could influence the design of future speech neuroprosthetic applications.

Computer-aided interpretation approach for optical tomographic images

NASA Astrophysics Data System (ADS)

Klose, Christian D.; Klose, Alexander D.; Netz, Uwe J.; Scheel, Alexander K.; Beuthan, Jürgen; Hielscher, Andreas H.

2010-11-01

A computer-aided interpretation approach is proposed to detect rheumatic arthritis (RA) in human finger joints using optical tomographic images. The image interpretation method employs a classification algorithm that makes use of a so-called self-organizing mapping scheme to classify fingers as either affected or unaffected by RA. Unlike in previous studies, this allows for combining multiple image features, such as minimum and maximum values of the absorption coefficient for identifying affected and not affected joints. Classification performances obtained by the proposed method were evaluated in terms of sensitivity, specificity, Youden index, and mutual information. Different methods (i.e., clinical diagnostics, ultrasound imaging, magnet resonance imaging, and inspection of optical tomographic images), were used to produce ground truth benchmarks to determine the performance of image interpretations. Using data from 100 finger joints, findings suggest that some parameter combinations lead to higher sensitivities, while others to higher specificities when compared to single parameter classifications employed in previous studies. Maximum performances are reached when combining the minimum/maximum ratio of the absorption coefficient and image variance. In this case, sensitivities and specificities over 0.9 can be achieved. These values are much higher than values obtained when only single parameter classifications were used, where sensitivities and specificities remained well below 0.8.

Three-column classification and Schatzker classification: a three- and two-dimensional computed tomography characterisation and analysis of tibial plateau fractures.

PubMed

Patange Subba Rao, Sheethal Prasad; Lewis, James; Haddad, Ziad; Paringe, Vishal; Mohanty, Khitish

2014-10-01

The aim of the study was to evaluate inter-observer reliability and intra-observer reproducibility between the three-column classification and Schatzker classification systems using 2D and 3D CT models. Fifty-two consecutive patients with tibial plateau fractures were evaluated by five orthopaedic surgeons. All patients were classified into Schatzker and three-column classification systems using x-rays and 2D and 3D CT images. The inter-observer reliability was evaluated in the first round and the intra-observer reliability was determined during the second round 2 weeks later. The average intra-observer reproducibility for the three-column classification was from substantial to excellent in all sub classifications, as compared with Schatzker classification. The inter-observer kappa values increased from substantial to excellent in three-column classification and to moderate in Schatzker classification The average values for three-column classification for all the categories are as follows: (I-III) k2D = 0.718, 95% CI 0.554-0.864, p < 0.0001 and average 3D = 0.874, 95% CI 0.754-0.890, p < 0.0001. For Schatzker classification system, the average values for all six categories are as follows: (I-VI) k2D = 0.536, 95% CI 0.365-0.685, p < 0.0001 and average k3D = 0.552 95% CI 0.405-0.700, p < 0.0001. The values are statistically significant. Statistically significant inter-observer values in both rounds were noted with the three-column classification, making it statistically an excellent agreement. The intra-observer reproducibility for the three-column classification improved as compared with the Schatzker classification. The three-column classification seems to be an effective way to characterise and classify fractures of tibial plateau.

Application of Interactive Classification System in University Study Course Comparison

ERIC Educational Resources Information Center

Birzniece, Ilze; Rudzajs, Peteris; Kalibatiene, Diana; Vasilecas, Olegas; Rencis, Edgars

2015-01-01

The growing amount of information in the world has increased the need for computerized classification of different objects. This situation is present in higher education as well where the possibility of effortless detection of similarity between different study courses would give the opportunity to organize student exchange programmes effectively…

A revised family-level classification of the Polyporales (Basidiomycota)

Treesearch

Alfredo Justo; Otto Miettinen; Dimitrios Floudas; Beatriz Ortiz-Santana; Elisabet Sjökvist; Daniel Lindner; Karen Nakasone; Tuomo Niemelä; Karl-Henrik Larsson; Leif Ryvarden; David S. Hibbett

2017-01-01

Polyporales is strongly supported as a clade of Agaricomycetes, but the lack of a consensus higher-level classification within the group is a barrier to further taxonomic revision. We amplified nrLSU, nrITS, and rpb1 genes across the Polyporales, with a special focus on the latter. We...

An Illustration of Diagnostic Classification Modeling in Student Learning Outcomes Assessment

ERIC Educational Resources Information Center

Jurich, Daniel P.; Bradshaw, Laine P.

2014-01-01

The assessment of higher-education student learning outcomes is an important component in understanding the strengths and weaknesses of academic and general education programs. This study illustrates the application of diagnostic classification models, a burgeoning set of statistical models, in assessing student learning outcomes. To facilitate…

Automated breast tissue density assessment using high order regional texture descriptors in mammography

NASA Astrophysics Data System (ADS)

Law, Yan Nei; Lieng, Monica Keiko; Li, Jingmei; Khoo, David Aik-Aun

2014-03-01

Breast cancer is the most common cancer and second leading cause of cancer death among women in the US. The relative survival rate is lower among women with a more advanced stage at diagnosis. Early detection through screening is vital. Mammography is the most widely used and only proven screening method for reliably and effectively detecting abnormal breast tissues. In particular, mammographic density is one of the strongest breast cancer risk factors, after age and gender, and can be used to assess the future risk of disease before individuals become symptomatic. A reliable method for automatic density assessment would be beneficial and could assist radiologists in the evaluation of mammograms. To address this problem, we propose a density classification method which uses statistical features from different parts of the breast. Our method is composed of three parts: breast region identification, feature extraction and building ensemble classifiers for density assessment. It explores the potential of the features extracted from second and higher order statistical information for mammographic density classification. We further investigate the registration of bilateral pairs and time-series of mammograms. The experimental results on 322 mammograms demonstrate that (1) a classifier using features from dense regions has higher discriminative power than a classifier using only features from the whole breast region; (2) these high-order features can be effectively combined to boost the classification accuracy; (3) a classifier using these statistical features from dense regions achieves 75% accuracy, which is a significant improvement from 70% accuracy obtained by the existing approaches.

Prefrontal gray matter volume mediates genetic risks for obesity.

PubMed

Opel, N; Redlich, R; Kaehler, C; Grotegerd, D; Dohm, K; Heindel, W; Kugel, H; Thalamuthu, A; Koutsouleris, N; Arolt, V; Teuber, A; Wersching, H; Baune, B T; Berger, K; Dannlowski, U

2017-05-01

Genetic and neuroimaging research has identified neurobiological correlates of obesity. However, evidence for an integrated model of genetic risk and brain structural alterations in the pathophysiology of obesity is still absent. Here we investigated the relationship between polygenic risk for obesity, gray matter structure and body mass index (BMI) by the use of univariate and multivariate analyses in two large, independent cohorts (n=330 and n=347). Higher BMI and higher polygenic risk for obesity were significantly associated with medial prefrontal gray matter decrease, and prefrontal gray matter was further shown to significantly mediate the effect of polygenic risk for obesity on BMI in both samples. Building on this, the successful individualized prediction of BMI by means of multivariate pattern classification algorithms trained on whole-brain imaging data and external validations in the second cohort points to potential clinical applications of this imaging trait marker.

Lenke and King classification systems for adolescent idiopathic scoliosis: interobserver agreement and postoperative results.

PubMed

Hosseinpour-Feizi, Hojjat; Soleimanpour, Jafar; Sales, Jafar Ganjpour; Arzroumchilar, Ali

2011-01-01

The aim of this study was to investigate the interobserver agreement of the Lenke and King classifications for adolescent idiopathic scoliosis, and to compare the results of surgery performed based on classification of the scoliosis according to each of these classification systems. The study was conducted in Shohada Hospital in Tabriz, Iran, between 2009 and 2010. First, a reliability assessment was undertaken to assess interobserver agreement of the Lenke and King classifications for adolescent idiopathic scoliosis. Second, postoperative efficacy and safety of surgery performed based on the Lenke and King classifications were compared. Kappa coefficients of agreement were calculated to assess the agreement. Outcomes were compared using bivariate tests and repeated measures analysis of variance. A low to moderate interobserver agreement was observed for the King classification; the Lenke classification yielded mostly high agreement coefficients. The outcome of surgery was not found to be substantially different between the two systems. Based on the results, the Lenke classification method seems advantageous. This takes into consideration the Lenke classification's priority in providing details of curvatures in different anatomical surfaces to explain precise intensity of scoliosis, that it has higher interobserver agreement scores, and also that it leads to noninferior postoperative results compared with the King classification method.

The Japanese Histologic Classification and T-score in the Oxford Classification system could predict renal outcome in Japanese IgA nephropathy patients.

PubMed

Kaihan, Ahmad Baseer; Yasuda, Yoshinari; Katsuno, Takayuki; Kato, Sawako; Imaizumi, Takahiro; Ozeki, Takaya; Hishida, Manabu; Nagata, Takanobu; Ando, Masahiko; Tsuboi, Naotake; Maruyama, Shoichi

2017-12-01

The Oxford Classification is utilized globally, but has not been fully validated. In this study, we conducted a comparative analysis between the Oxford Classification and Japanese Histologic Classification (JHC) to predict renal outcome in Japanese patients with IgA nephropathy (IgAN). A retrospective cohort study including 86 adult IgAN patients was conducted. The Oxford Classification and the JHC were evaluated by 7 independent specialists. The JHC, MEST score in the Oxford Classification, and crescents were analyzed in association with renal outcome, defined as a 50% increase in serum creatinine. In multivariate analysis without the JHC, only the T score was significantly associated with renal outcome. While, a significant association was revealed only in the JHC on multivariate analysis with JHC. The JHC and T score in the Oxford Classification were associated with renal outcome among Japanese patients with IgAN. Superiority of the JHC as a predictive index should be validated with larger study population and cohort studies in different ethnicities.

Multidimensional poverty measure and analysis: a case study from Hechi City, China.

PubMed

Wang, Yanhui; Wang, Baixue

2016-01-01

Aiming at the anti-poverty outline of China and the human-environment sustainable development, we propose a multidimensional poverty measure and analysis methodology for measuring the poverty-stricken counties and their contributing factors. We build a set of multidimensional poverty indicators with Chinese characteristics, integrating A-F double cutoffs, dimensional aggregation and decomposition approach, and GIS spatial analysis to evaluate the poor's multidimensional poverty characteristics under different geographic and socioeconomic conditions. The case study from 11 counties of Hechi City shows that, firstly, each county existed at least four respects of poverty, and overall the poverty level showed the spatial pattern of surrounding higher versus middle lower. Secondly, three main poverty contributing factors were unsafe housing, family health and adults' illiteracy, while the secondary factors include fuel type and children enrollment rate, etc., generally demonstrating strong autocorrelation; in terms of poverty degree, the western of the research area shows a significant aggregation effect, whereas the central and the eastern represent significant spatial heterogeneous distribution. Thirdly, under three kinds of socioeconomic classifications, the intra-classification diversities of H, A, and MPI are greater than their inter-classification ones, while each of the three indexes has a positive correlation with both the rocky desertification degree and topographic fragmentation degree, respectively. This study could help policymakers better understand the local poverty by identifying the poor, locating them and describing their characteristics, so as to take differentiated poverty alleviation measures according to specific conditions of each county.

Grasp movement decoding from premotor and parietal cortex.

PubMed

Townsend, Benjamin R; Subasi, Erk; Scherberger, Hansjörg

2011-10-05

Despite recent advances in harnessing cortical motor-related activity to control computer cursors and robotic devices, the ability to decode and execute different grasping patterns remains a major obstacle. Here we demonstrate a simple Bayesian decoder for real-time classification of grip type and wrist orientation in macaque monkeys that uses higher-order planning signals from anterior intraparietal cortex (AIP) and ventral premotor cortex (area F5). Real-time decoding was based on multiunit signals, which had similar tuning properties to cells in previous single-unit recording studies. Maximum decoding accuracy for two grasp types (power and precision grip) and five wrist orientations was 63% (chance level, 10%). Analysis of decoder performance showed that grip type decoding was highly accurate (90.6%), with most errors occurring during orientation classification. In a subsequent off-line analysis, we found small but significant performance improvements (mean, 6.25 percentage points) when using an optimized spike-sorting method (superparamagnetic clustering). Furthermore, we observed significant differences in the contributions of F5 and AIP for grasp decoding, with F5 being better suited for classification of the grip type and AIP contributing more toward decoding of object orientation. However, optimum decoding performance was maximal when using neural activity simultaneously from both areas. Overall, these results highlight quantitative differences in the functional representation of grasp movements in AIP and F5 and represent a first step toward using these signals for developing functional neural interfaces for hand grasping.

Variation of Care Time Between Nursing Units in Classification-Based Nurse-to-Resident Ratios: A Multilevel Analysis

PubMed Central

Planer, Katarina; Hagel, Anja

2018-01-01

A validity test was conducted to determine how care level–based nurse-to-resident ratios compare with actual daily care times per resident in Germany. Stability across different long-term care facilities was tested. Care level–based nurse-to-resident ratios were compared with the standard minimum nurse-to-resident ratios. Levels of care are determined by classification authorities in long-term care insurance programs and are used to distribute resources. Care levels are a powerful tool for classifying authorities in long-term care insurance. We used observer-based measurement of assignable direct and indirect care time in 68 nursing units for 2028 residents across 2 working days. Organizational data were collected at the end of the quarter in which the observation was made. Data were collected from January to March, 2012. We used a null multilevel model with random intercepts and multilevel models with fixed and random slopes to analyze data at both the organization and resident levels. A total of 14% of the variance in total care time per day was explained by membership in nursing units. The impact of care levels on care time differed significantly between nursing units. Forty percent of residents at the lowest care level received less than the standard minimum registered nursing time per day. For facilities that have been significantly disadvantaged in the current staffing system, a higher minimum standard will function more effectively than a complex classification system without scientific controls. PMID:29442533

Variation of Care Time Between Nursing Units in Classification-Based Nurse-to-Resident Ratios: A Multilevel Analysis.

PubMed

Brühl, Albert; Planer, Katarina; Hagel, Anja

2018-01-01

A validity test was conducted to determine how care level-based nurse-to-resident ratios compare with actual daily care times per resident in Germany. Stability across different long-term care facilities was tested. Care level-based nurse-to-resident ratios were compared with the standard minimum nurse-to-resident ratios. Levels of care are determined by classification authorities in long-term care insurance programs and are used to distribute resources. Care levels are a powerful tool for classifying authorities in long-term care insurance. We used observer-based measurement of assignable direct and indirect care time in 68 nursing units for 2028 residents across 2 working days. Organizational data were collected at the end of the quarter in which the observation was made. Data were collected from January to March, 2012. We used a null multilevel model with random intercepts and multilevel models with fixed and random slopes to analyze data at both the organization and resident levels. A total of 14% of the variance in total care time per day was explained by membership in nursing units. The impact of care levels on care time differed significantly between nursing units. Forty percent of residents at the lowest care level received less than the standard minimum registered nursing time per day. For facilities that have been significantly disadvantaged in the current staffing system, a higher minimum standard will function more effectively than a complex classification system without scientific controls.

Effects of different dietary inclusion levels of macadamia oil cake on growth performance and carcass characteristics in South African mutton merino lambs.

PubMed

Acheampong-Boateng, Owoahene; Bakare, Archibold G; Nkosi, Douglas B; Mbatha, Khanyisile R

2017-04-01

Growth performance and carcass characteristics of South African mutton merino fed graded levels of macadamia oil cake were assessed. A total of 60 South African mutton merino lambs were used in the experiment (initial live weight 25.0 ± 0.45 kg). Five diets with different inclusion levels of macadamia oil cake (MOC) were formulated: T1 (0% MOC, control), T2 (5% MOC), T3 (10% MOC), T4 (15% MOC) and T5 (20% MOC). Effects of inclusion level of MOC on average daily gain (ADG) and average daily feed intake (ADFI) were not significant (P > 0.05). Effects of inclusion levels of MOC on feed conversion ratio (FCR) of sheep were significant (P < 0.05). Highest proportion (71.2%) of sheep in the study had a carcass fat classification of 2, followed by a proportion of 17.3% sheep with a carcass fat classification of 3 and lastly 11.5% sheep had carcass fat classification of 4. Warm and cold carcass mass, chest circumference, carcass length and dressing percentage were higher in sheep fed on 5% MOC compared to other treatment diets (0, 10, 15 and 20% MOC) (P < 0.05). Fat rib eye had a greater area in sheep fed on 5% MOC (P < 0.05). It was concluded that 5% MOC provided the best results in terms of carcass characteristic measurements in sheep.

Classification of electroencephalograph signals using time-frequency decomposition and linear discriminant analysis

NASA Astrophysics Data System (ADS)

Szuflitowska, B.; Orlowski, P.

2017-08-01

Automated detection system consists of two key steps: extraction of features from EEG signals and classification for detection of pathology activity. The EEG sequences were analyzed using Short-Time Fourier Transform and the classification was performed using Linear Discriminant Analysis. The accuracy of the technique was tested on three sets of EEG signals: epilepsy, healthy and Alzheimer's Disease. The classification error below 10% has been considered a success. The higher accuracy are obtained for new data of unknown classes than testing data. The methodology can be helpful in differentiation epilepsy seizure and disturbances in the EEG signal in Alzheimer's Disease.

Mortality risk factor analysis in colonic perforation: would retroperitoneal contamination increase mortality in colonic perforation?

PubMed

Yoo, Ri Na; Kye, Bong-Hyeon; Kim, Gun; Kim, Hyung Jin; Cho, Hyeon-Min

2017-10-01

Colonic perforation is a lethal condition presenting high morbidity and mortality in spite of urgent surgical treatment. This study investigated the surgical outcome of patients with colonic perforation associated with retroperitoneal contamination. Retrospective analysis was performed for 30 patients diagnosed with colonic perforation caused by either inflammation or ischemia who underwent urgent surgical treatment in our facility from January 2005 to December 2014. Patient characteristics were analyzed to find risk factors correlated with increased postoperative mortality. Using the Physiological and Operative Severity Score for the Enumeration of Mortality and Morbidity (POSSUM) audit system, the mortality and morbidity rates were estimated to verify the surgical outcomes. Patients with retroperitoneal contamination, defined by the presence of retroperitoneal air in the preoperative abdominopelvic CT, were compared to those without retroperitoneal contamination. Eight out of 30 patients (26.7%) with colonic perforation had died after urgent surgical treatment. Factors associated with mortality included age, American Society of Anesthesiologists (ASA) physical status classification, and the ischemic cause of colonic perforation. Three out of 6 patients (50%) who presented retroperitoneal contamination were deceased. Although the patients with retroperitoneal contamination did not show significant increase in the mortality rate, they showed significantly higher ASA physical status classification than those without retroperitoneal contamination. The mortality rate predicted from Portsmouth POSSUM was higher in the patients with retroperitoneal contamination. Patients presenting colonic perforation along with retroperitoneal contamination demonstrated severe comorbidity. However, retroperitoneal contamination was not found to be correlated with the mortality rate.

Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval.

PubMed

Ferreira, José Raniery; de Azevedo-Marques, Paulo Mazzoncini; Oliveira, Marcelo Costa

2017-03-01

Lung cancer is the leading cause of cancer-related deaths in the world. Its diagnosis is a challenge task to specialists due to several aspects on the classification of lung nodules. Therefore, it is important to integrate content-based image retrieval methods on the lung nodule classification process, since they are capable of retrieving similar cases from databases that were previously diagnosed. However, this mechanism depends on extracting relevant image features in order to obtain high efficiency. The goal of this paper is to perform the selection of 3D image features of margin sharpness and texture that can be relevant on the retrieval of similar cancerous and benign lung nodules. A total of 48 3D image attributes were extracted from the nodule volume. Border sharpness features were extracted from perpendicular lines drawn over the lesion boundary. Second-order texture features were extracted from a cooccurrence matrix. Relevant features were selected by a correlation-based method and a statistical significance analysis. Retrieval performance was assessed according to the nodule's potential malignancy on the 10 most similar cases and by the parameters of precision and recall. Statistical significant features reduced retrieval performance. Correlation-based method selected 2 margin sharpness attributes and 6 texture attributes and obtained higher precision compared to all 48 extracted features on similar nodule retrieval. Feature space dimensionality reduction of 83 % obtained higher retrieval performance and presented to be a computationaly low cost method of retrieving similar nodules for the diagnosis of lung cancer.

Regularised extreme learning machine with misclassification cost and rejection cost for gene expression data classification.

PubMed

Lu, Huijuan; Wei, Shasha; Zhou, Zili; Miao, Yanzi; Lu, Yi

2015-01-01

The main purpose of traditional classification algorithms on bioinformatics application is to acquire better classification accuracy. However, these algorithms cannot meet the requirement that minimises the average misclassification cost. In this paper, a new algorithm of cost-sensitive regularised extreme learning machine (CS-RELM) was proposed by using probability estimation and misclassification cost to reconstruct the classification results. By improving the classification accuracy of a group of small sample which higher misclassification cost, the new CS-RELM can minimise the classification cost. The 'rejection cost' was integrated into CS-RELM algorithm to further reduce the average misclassification cost. By using Colon Tumour dataset and SRBCT (Small Round Blue Cells Tumour) dataset, CS-RELM was compared with other cost-sensitive algorithms such as extreme learning machine (ELM), cost-sensitive extreme learning machine, regularised extreme learning machine, cost-sensitive support vector machine (SVM). The results of experiments show that CS-RELM with embedded rejection cost could reduce the average cost of misclassification and made more credible classification decision than others.

Derivation of an artificial gene to improve classification accuracy upon gene selection.

PubMed

Seo, Minseok; Oh, Sejong

2012-02-01

Classification analysis has been developed continuously since 1936. This research field has advanced as a result of development of classifiers such as KNN, ANN, and SVM, as well as through data preprocessing areas. Feature (gene) selection is required for very high dimensional data such as microarray before classification work. The goal of feature selection is to choose a subset of informative features that reduces processing time and provides higher classification accuracy. In this study, we devised a method of artificial gene making (AGM) for microarray data to improve classification accuracy. Our artificial gene was derived from a whole microarray dataset, and combined with a result of gene selection for classification analysis. We experimentally confirmed a clear improvement of classification accuracy after inserting artificial gene. Our artificial gene worked well for popular feature (gene) selection algorithms and classifiers. The proposed approach can be applied to any type of high dimensional dataset. Copyright © 2011 Elsevier Ltd. All rights reserved.

Image Classification Using Biomimetic Pattern Recognition with Convolutional Neural Networks Features

PubMed Central

Huo, Guanying

2017-01-01

As a typical deep-learning model, Convolutional Neural Networks (CNNs) can be exploited to automatically extract features from images using the hierarchical structure inspired by mammalian visual system. For image classification tasks, traditional CNN models employ the softmax function for classification. However, owing to the limited capacity of the softmax function, there are some shortcomings of traditional CNN models in image classification. To deal with this problem, a new method combining Biomimetic Pattern Recognition (BPR) with CNNs is proposed for image classification. BPR performs class recognition by a union of geometrical cover sets in a high-dimensional feature space and therefore can overcome some disadvantages of traditional pattern recognition. The proposed method is evaluated on three famous image classification benchmarks, that is, MNIST, AR, and CIFAR-10. The classification accuracies of the proposed method for the three datasets are 99.01%, 98.40%, and 87.11%, respectively, which are much higher in comparison with the other four methods in most cases. PMID:28316614

Classification versus inference learning contrasted with real-world categories.

PubMed

Jones, Erin L; Ross, Brian H

2011-07-01

Categories are learned and used in a variety of ways, but the research focus has been on classification learning. Recent work contrasting classification with inference learning of categories found important later differences in category performance. However, theoretical accounts differ on whether this is due to an inherent difference between the tasks or to the implementation decisions. The inherent-difference explanation argues that inference learners focus on the internal structure of the categories--what each category is like--while classification learners focus on diagnostic information to predict category membership. In two experiments, using real-world categories and controlling for earlier methodological differences, inference learners learned more about what each category was like than did classification learners, as evidenced by higher performance on a novel classification test. These results suggest that there is an inherent difference between learning new categories by classifying an item versus inferring a feature.

Examining the validity and utility of two secondary sources of food environment data against street audits in England.

PubMed

Wilkins, Emma L; Radley, Duncan; Morris, Michelle A; Griffiths, Claire

2017-12-20

Secondary data containing the locations of food outlets is increasingly used in nutrition and obesity research and policy. However, evidence evaluating these data is limited. This study validates two sources of secondary food environment data: Ordnance Survey Points of Interest data (POI) and food hygiene data from the Food Standards Agency (FSA), against street audits in England and appraises the utility of these data. Audits were conducted across 52 Lower Super Output Areas in England. All streets within each Lower Super Output Area were covered to identify the name and street address of all food outlets therein. Audit-identified outlets were matched to outlets in the POI and FSA data to identify true positives (TP: outlets in both the audits and the POI/FSA data), false positives (FP: outlets in the POI/FSA data only) and false negatives (FN: outlets in the audits only). Agreement was assessed using positive predictive values (PPV: TP/(TP + FP)) and sensitivities (TP/(TP + FN)). Variations in sensitivities and PPVs across environment and outlet types were assessed using multi-level logistic regression. Proprietary classifications within the POI data were additionally used to classify outlets, and agreement between audit-derived and POI-derived classifications was assessed. Street audits identified 1172 outlets, compared to 1100 and 1082 for POI and FSA respectively. PPVs were statistically significantly higher for FSA (0.91, CI: 0.89-0.93) than for POI (0.86, CI: 0.84-0.88). However, sensitivity values were not different between the two datasets. Sensitivity and PPVs varied across outlet types for both datasets. Without accounting for this, POI had statistically significantly better PPVs in rural and affluent areas. After accounting for variability across outlet types, FSA had statistically significantly better sensitivity in rural areas and worse sensitivity in rural middle affluence areas (relative to deprived). Audit-derived and POI-derived classifications exhibited substantial agreement (p < 0.001; Kappa = 0.66, CI: 0.63-0.70). POI and FSA data have good agreement with street audits; although both datasets had geographic biases which may need to be accounted for in analyses. Use of POI proprietary classifications is an accurate method for classifying outlets, providing time savings compared to manual classification of outlets.

Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method.

PubMed

Guo, Xinyu; Dominick, Kelli C; Minai, Ali A; Li, Hailong; Erickson, Craig A; Lu, Long J

2017-01-01

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t -test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t -test p < 0.05) while 19 of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided.

Brain-computer interfacing under distraction: an evaluation study

NASA Astrophysics Data System (ADS)

Brandl, Stephanie; Frølich, Laura; Höhne, Johannes; Müller, Klaus-Robert; Samek, Wojciech

2016-10-01

Objective. While motor-imagery based brain-computer interfaces (BCIs) have been studied over many years by now, most of these studies have taken place in controlled lab settings. Bringing BCI technology into everyday life is still one of the main challenges in this field of research. Approach. This paper systematically investigates BCI performance under 6 types of distractions that mimic out-of-lab environments. Main results. We report results of 16 participants and show that the performance of the standard common spatial patterns (CSP) + regularized linear discriminant analysis classification pipeline drops significantly in this ‘simulated’ out-of-lab setting. We then investigate three methods for improving the performance: (1) artifact removal, (2) ensemble classification, and (3) a 2-step classification approach. While artifact removal does not enhance the BCI performance significantly, both ensemble classification and the 2-step classification combined with CSP significantly improve the performance compared to the standard procedure. Significance. Systematically analyzing out-of-lab scenarios is crucial when bringing BCI into everyday life. Algorithms must be adapted to overcome nonstationary environments in order to tackle real-world challenges.

Evaluation of several schemes for classification of remotely sensed data: Their parameters and performance. [Foster County, North Dakota; Grant County, Kansas; Iroquois County, Illinois, Tippecanoe County, Indiana; and Pottawattamie and Shelby Counties, Iowa

NASA Technical Reports Server (NTRS)

Scholz, D.; Fuhs, N.; Hixson, M.; Akiyama, T. (Principal Investigator)

1979-01-01

The author has identified the following significant results. Data sets for corn, soybeans, winter wheat, and spring wheat were used to evaluate the following schemes for crop identification: (1) per point Gaussian maximum classifier; (2) per point sum of normal densities classifiers; (3) per point linear classifier; (4) per point Gaussian maximum likelihood decision tree classifiers; and (5) texture sensitive per field Gaussian maximum likelihood classifier. Test site location and classifier both had significant effects on classification accuracy of small grains; classifiers did not differ significantly in overall accuracy, with the majority of the difference among classifiers being attributed to training method rather than to the classification algorithm applied. The complexity of use and computer costs for the classifiers varied significantly. A linear classification rule which assigns each pixel to the class whose mean is closest in Euclidean distance was the easiest for the analyst and cost the least per classification.

Significance of clustering and classification applications in digital and physical libraries

NASA Astrophysics Data System (ADS)

Triantafyllou, Ioannis; Koulouris, Alexandros; Zervos, Spiros; Dendrinos, Markos; Giannakopoulos, Georgios

2015-02-01

Applications of clustering and classification techniques can be proved very significant in both digital and physical (paper-based) libraries. The most essential application, document classification and clustering, is crucial for the content that is produced and maintained in digital libraries, repositories, databases, social media, blogs etc., based on various tags and ontology elements, transcending the traditional library-oriented classification schemes. Other applications with very useful and beneficial role in the new digital library environment involve document routing, summarization and query expansion. Paper-based libraries can benefit as well since classification combined with advanced material characterization techniques such as FTIR (Fourier Transform InfraRed spectroscopy) can be vital for the study and prevention of material deterioration. An improved two-level self-organizing clustering architecture is proposed in order to enhance the discrimination capacity of the learning space, prior to classification, yielding promising results when applied to the above mentioned library tasks.

Occult pneumothoraces in Chinese patients with significant blunt chest trauma: radiological classification and proposed clinical significance.

PubMed

Lee, Ryan K L; Graham, Colin A; Yeung, Janice H H; Ahuja, Anil T; Rainer, Timothy H

2012-12-01

An occult pneumothorax (OP) is a pneumothorax not seen on a supine chest X-ray (CXR) but detected on abdominal or thoracic computed tomography (CT) scanning. With the increasing use of CT in the management of significantly injured trauma patients, more OPs are being detected. The aim of this study was to classify OPs diagnosed on thoracic CT (TCT) and correlate them with their clinical significance. Retrospective analysis of prospectively collected trauma registry data. Total 36 (N=36) consecutive significantly injured trauma patients admitted through the emergency department (ED) who sustained blunt chest trauma and underwent TCT between 1 January 2007 and 31 December 2008 were included. OP was defined as the identification (by a consultant radiologist) of a pneumothorax on TCT that had not been detected on supine CXR. OPs were classified by laterality (unilateral/bilateral) and location (apical, basal, non apical/basal). The size of pneumothoraces, severity of injury [including number of associated thoracic injuries and injury severity score (ISS)], length of hospital stay and mortality were compared between groups. The need for tube thoracostomy and clinical outcome were also analysed. Patients with bilateral OPs (N=8) had significantly more associated thoracic injuries (median: 2 vs. 1, p=0.01), higher ISS (median: 35 vs. 23, p=0.02) and longer hospital stay (median: 20 days vs. 11 days, p=0.01) than those with a unilateral OP (N=28). Basal OPs (N=7) were significantly larger than apical (N=10) and non-apical/basal Ops (N=11). Basal OPs were associated with significantly more associated thoracic injuries (median: 2 vs. 1, p=0.01), higher ISS (median: 35 vs. 25, p=0.04) and longer hospital stays (median: 23 days vs. 17 days, p=0.02) than apical Ops, which had higher ISS (median: 35 vs. 25, p=0.04) and longer hospital stays (median: 23 days vs. 15 days, p=0.02) than non-apical/basal OPs. Non-apical/basal OPs were associated with more related injuries (median: 2 vs. 1, p=0.02) than apical OPs. All apical and non-apical/basal OPs were successfully managed expectantly without associated mortality. This TCT classification of OP is proposed to help clinicians to decide on subsequent management of the OP. Basal OPs are significantly larger in size, and both basal and bilateral OPs are associated with higher severity of injury and longer hospital stay. These groups of patient may benefit from prophylactic tube thoracostomy instead of conservative treatment. On the other hand, apical and non-apical/basal groups is smaller in size, less severely injured and thus can be successfully managed expectantly. Copyright © 2012 Elsevier Ltd. All rights reserved.

Real-Time Classification of Exercise Exertion Levels Using Discriminant Analysis of HRV Data.

PubMed

Jeong, In Cheol; Finkelstein, Joseph

2015-01-01

Heart rate variability (HRV) was shown to reflect activation of sympathetic nervous system however it is not clear which set of HRV parameters is optimal for real-time classification of exercise exertion levels. There is no studies that compared potential of two types of HRV parameters (time-domain and frequency-domain) in predicting exercise exertion level using discriminant analysis. The main goal of this study was to compare potential of HRV time-domain parameters versus HRV frequency-domain parameters in classifying exercise exertion level. Rest, exercise, and recovery categories were used in classification models. Overall 79.5% classification agreement by the time-domain parameters as compared to overall 52.8% classification agreement by frequency-domain parameters demonstrated that the time-domain parameters had higher potential in classifying exercise exertion levels.

Hyperspectral feature mapping classification based on mathematical morphology

NASA Astrophysics Data System (ADS)

Liu, Chang; Li, Junwei; Wang, Guangping; Wu, Jingli

2016-03-01

This paper proposed a hyperspectral feature mapping classification algorithm based on mathematical morphology. Without the priori information such as spectral library etc., the spectral and spatial information can be used to realize the hyperspectral feature mapping classification. The mathematical morphological erosion and dilation operations are performed respectively to extract endmembers. The spectral feature mapping algorithm is used to carry on hyperspectral image classification. The hyperspectral image collected by AVIRIS is applied to evaluate the proposed algorithm. The proposed algorithm is compared with minimum Euclidean distance mapping algorithm, minimum Mahalanobis distance mapping algorithm, SAM algorithm and binary encoding mapping algorithm. From the results of the experiments, it is illuminated that the proposed algorithm's performance is better than that of the other algorithms under the same condition and has higher classification accuracy.

Comparative analysis of the 2016 ACR-EULAR and the 2002 AECG classification criteria for Sjögren's syndrome: Findings from the NIH cohort.

PubMed

Billings, M; Amin Hadavand, M; Alevizos, I

2018-03-01

The introduction of new classification criteria for Sjögren's syndrome, known as the 2016 American College of Rheumatology/European League against Rheumatism Classification Criteria (ACR-EULAR), created a need for the evaluation of its performance in an external cohort. The purpose of this study was to compare the performance of the 2016 ACR-EULAR classification set with the widely used American-European Consensus Group Classification criteria (AECG) in the cohort at the National Institutes of Health, USA, and to compare the performance of the sets in classifying both primary and secondary Sjögren's syndrome (pSS and sSS). The study cohort at the NIH (N = 1,303) was enrolled for clinical suspicion of SS. Participants were classified as SS, pSS, and sSS according to both classification sets. Performance of 2016 ACR-EULAR and AECG sets was compared holding each as gold standard to the other. Statistical analysis of test diagnostics and agreement between the two sets were undertaken. By the AECG set, 701 were classified as having SS (627 pSS, 74 sSS) and 714 were classified with SS (647 pSS, 67 sSS) by the 2016 ACR-EULAR set. Sensitivity and specificity of the two sets were comparable in classifying SS, pSS, and sSS. There was high agreement between the two sets for classifying SS (κ = 0.79), pSS (κ = 0.81), and sSS (κ = 0.87). The specificity of the 2016 ACR-EULAR set was significantly higher for classifying sSS than pSS, while the sensitivity was similar for the two disease groups. However, this pattern was also exhibited by the AECG set. There was high agreement between the two classification sets with comparable performance diagnostics. There was no evidence of superior performance value by the new 2016 ACR-EULAR set over the AECG set, and the two sets were found to be equivalent. Findings from our cohort indicate that 2016 ACR-EULAR classification could be extended to classification of sSS. Published 2018. This article is a U.S. Government work and is in the public domain in the USA.

Using texture analysis to improve per-pixel classification of very high resolution images for mapping plastic greenhouses

NASA Astrophysics Data System (ADS)

Agüera, Francisco; Aguilar, Fernando J.; Aguilar, Manuel A.

The area occupied by plastic-covered greenhouses has undergone rapid growth in recent years, currently exceeding 500,000 ha worldwide. Due to the vast amount of input (water, fertilisers, fuel, etc.) required, and output of different agricultural wastes (vegetable, plastic, chemical, etc.), the environmental impact of this type of production system can be serious if not accompanied by sound and sustainable territorial planning. For this, the new generation of satellites which provide very high resolution imagery, such as QuickBird and IKONOS can be useful. In this study, one QuickBird and one IKONOS satellite image have been used to cover the same area under similar circumstances. The aim of this work was an exhaustive comparison of QuickBird vs. IKONOS images in land-cover detection. In terms of plastic greenhouse mapping, comparative tests were designed and implemented, each with separate objectives. Firstly, the Maximum Likelihood Classification (MLC) was applied using five different approaches combining R, G, B, NIR, and panchromatic bands. The combinations of the bands used, significantly influenced some of the indexes used to classify quality in this work. Furthermore, the quality classification of the QuickBird image was higher in all cases than that of the IKONOS image. Secondly, texture features derived from the panchromatic images at different window sizes and with different grey levels were added as a fifth band to the R, G, B, NIR images to carry out the MLC. The inclusion of texture information in the classification did not improve the classification quality. For classifications with texture information, the best accuracies were found in both images for mean and angular second moment texture parameters. The optimum window size in these texture parameters was 3×3 for IK images, while for QB images it depended on the quality index studied, but the optimum window size was around 15×15. With regard to the grey level, the optimum was 128. Thus, the optimum texture parameter depended on the main objective of the image classification. If the main classification goal is to minimize the number of pixels wrongly classified, the mean texture parameter should be used, whereas if the main classification goal is to minimize the unclassified pixels the angular second moment texture parameter should be used. On the whole, both QuickBird and IKONOS images offered promising results in classifying plastic greenhouses.

Grouping patients for masseter muscle genotype-phenotype studies.

PubMed

Moawad, Hadwah Abdelmatloub; Sinanan, Andrea C M; Lewis, Mark P; Hunt, Nigel P

2012-03-01

To use various facial classifications, including either/both vertical and horizontal facial criteria, to assess their effects on the interpretation of masseter muscle (MM) gene expression. Fresh MM biopsies were obtained from 29 patients (age, 16-36 years) with various facial phenotypes. Based on clinical and cephalometric analysis, patients were grouped using three different classifications: (1) basic vertical, (2) basic horizontal, and (3) combined vertical and horizontal. Gene expression levels of the myosin heavy chain genes MYH1, MYH2, MYH3, MYH6, MYH7, and MYH8 were recorded using quantitative reverse transcriptase polymerase chain reaction (RT-PCR) and were related to the various classifications. The significance level for statistical analysis was set at P ≤ .05. Using classification 1, none of the MYH genes were found to be significantly different between long face (LF) patients and the average vertical group. Using classification 2, MYH3, MYH6, and MYH7 genes were found to be significantly upregulated in retrognathic patients compared with prognathic and average horizontal groups. Using classification 3, only the MYH7 gene was found to be significantly upregulated in retrognathic LF compared with prognathic LF, prognathic average vertical faces, and average vertical and horizontal groups. The use of basic vertical or basic horizontal facial classifications may not be sufficient for genetics-based studies of facial phenotypes. Prognathic and retrognathic facial phenotypes have different MM gene expressions; therefore, it is not recommended to combine them into one single group, even though they may have a similar vertical facial phenotype.

[Evaluation of the diagnosis of subclinical endometritis in dairy cattle using ultrasound].

PubMed

Lenz, Mirjam; Drillich, Marc; Heuwieser, Wolfgang

2007-01-01

The aim of this study was to determine signs of subclinical endometritis found by ultrasound that are associated with reduced fertility in dairy cows. The maximum diameter of the uterine lumen was determined by ultrasound in 324 cows without clinical signs of endometritis after evaluation of the genital tract 21 to 27 days postpartum. Cows were classified into healthy or with subclinical endometritis by three threshold values for the maximum uterine lumen diameter of 0.2 cm, 0.5 cm or 0.8 cm. Examinations by rectal palpation and ultrasound as well as classifications were repeated 14 days later. In addition, ovaries were scanned by ultrasound to determine the stage of the estrous cycle. In a subgroup of 103 cows the echotexture of the uterus and its contents was evaluated. In these cows the diagnosis of subclinical endometritis was performed by a scoring system. The diameter of the uterine lumen was significantly affected by stage of the estrous cycle at the time of examination. However, no effects were found for the stage of the cycle at the time of examination on subsequent reproductive performance. A uterine lumen with a maximum diameter of more than 0.2 cm showed a significant negative association with conception rate and proportion of cows pregnant. Classification based on higher threshold values did not reveal an association with reproductive performance. Echogenic content in the uterus also decreased reproductive performance. A classification based on the echotexture of the uterus and its contents revealed significant differences between healthy cows and cows with subclinical endometritis regarding the proportion of cows inseminated and pregnant. The results of this study showed that the diagnostic of bovine endometritis should be broadend by ultrasonography. The definition of subclinical endometritis diagnosed by means of ultrasonography has to be evaluated in further studies.

Classification of collective behavior: a comparison of tracking and machine learning methods to study the effect of ambient light on fish shoaling.

PubMed

Butail, Sachit; Salerno, Philip; Bollt, Erik M; Porfiri, Maurizio

2015-12-01

Traditional approaches for the analysis of collective behavior entail digitizing the position of each individual, followed by evaluation of pertinent group observables, such as cohesion and polarization. Machine learning may enable considerable advancements in this area by affording the classification of these observables directly from images. While such methods have been successfully implemented in the classification of individual behavior, their potential in the study collective behavior is largely untested. In this paper, we compare three methods for the analysis of collective behavior: simple tracking (ST) without resolving occlusions, machine learning with real data (MLR), and machine learning with synthetic data (MLS). These methods are evaluated on videos recorded from an experiment studying the effect of ambient light on the shoaling tendency of Giant danios. In particular, we compute average nearest-neighbor distance (ANND) and polarization using the three methods and compare the values with manually-verified ground-truth data. To further assess possible dependence on sampling rate for computing ANND, the comparison is also performed at a low frame rate. Results show that while ST is the most accurate at higher frame rate for both ANND and polarization, at low frame rate for ANND there is no significant difference in accuracy between the three methods. In terms of computational speed, MLR and MLS take significantly less time to process an image, with MLS better addressing constraints related to generation of training data. Finally, all methods are able to successfully detect a significant difference in ANND as the ambient light intensity is varied irrespective of the direction of intensity change.

Molecular classification of liver cirrhosis in a rat model by proteomics and bioinformatics.

PubMed

Xu, Xiu-Qin; Leow, Chon K; Lu, Xin; Zhang, Xuegong; Liu, Jun S; Wong, Wing-Hung; Asperger, Arndt; Deininger, Sören; Eastwood Leung, Hon-Chiu

2004-10-01

Liver cirrhosis is a worldwide health problem. Reliable, noninvasive methods for early detection of liver cirrhosis are not available. Using a three-step approach, we classified sera from rats with liver cirrhosis following different treatment insults. The approach consisted of: (i) protein profiling using surface-enhanced laser desorption/ionization (SELDI) technology; (ii) selection of a statistically significant serum biomarker set using machine learning algorithms; and (iii) identification of selected serum biomarkers by peptide sequencing. We generated serum protein profiles from three groups of rats: (i) normal (n=8), (ii) thioacetamide-induced liver cirrhosis (n=22), and (iii) bile duct ligation-induced liver fibrosis (n=5) using a weak cation exchanger surface. Profiling data were further analyzed by a recursive support vector machine algorithm to select a panel of statistically significant biomarkers for class prediction. Sensitivity and specificity of classification using the selected protein marker set were higher than 92%. A consistently down-regulated 3495 Da protein in cirrhosis samples was one of the selected significant biomarkers. This 3495 Da protein was purified on-chip and trypsin digested. Further structural characterization of this biomarkers candidate was done by using cross-platform matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) peptide mass fingerprinting (PMF) and matrix-assisted laser desorption/ionization time of flight/time of flight (MALDI-TOF/TOF) tandem mass spectrometry (MS/MS). Combined data from PMF and MS/MS spectra of two tryptic peptides suggested that this 3495 Da protein shared homology to a histidine-rich glycoprotein. These results demonstrated a novel approach to discovery of new biomarkers for early detection of liver cirrhosis and classification of liver diseases.

Test of spectral/spatial classifier

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator); Kast, J. L.; Davis, B. J.

1977-01-01

The author has identified the following significant results. The supervised ECHO processor (which utilizes class statistics for object identification) successfully exploits the redundancy of states characteristic of sampled imagery of ground scenes to achieve better classification accuracy, reduce the number of classifications required, and reduce the variability of classification results. The nonsupervised ECHO processor (which identifies objects without the benefit of class statistics) successfully reduces the number of classifications required and the variability of the classification results.

Revealing Significant Relations between Chemical/Biological Features and Activity: Associative Classification Mining for Drug Discovery

ERIC Educational Resources Information Center

Yu, Pulan

2012-01-01

Classification, clustering and association mining are major tasks of data mining and have been widely used for knowledge discovery. Associative classification mining, the combination of both association rule mining and classification, has emerged as an indispensable way to support decision making and scientific research. In particular, it offers a…

Multivariate evaluation of Thyroid Imaging Reporting and Data System (TI-RADS) in diagnosis malignant thyroid nodule: application to PCA and PLS-DA analysis.

PubMed

Zhang, Tan; Li, Fangxuan; Mu, Jiali; Liu, Juntian; Zhang, Sheng

2017-06-01

To explore the significance of ultrasonic features in differential diagnosis of thyroid nodules via combining the thyroid imaging reporting and data system (TI-RADS) and multivariate statistical analysis. Patients who received surgical treatment and was diagnosed with single thyroid nodule by postoperative pathology and preoperative ultrasound were enrolled in this study. Multivariate analysis was applied to assess the significant ultrasonic features which correlated with identifying benign or malignance and grading the TI-RADS classification of thyroid nodule. There were significant differences in the nodule size, aspect ratio, internal, echogenicity, boundary, presence or absence of calcifications, calcification type and CDFI between benign and malignant thyroid nodules. Multivariate analysis showed clear-cut distinction both between benign and malignance and among different TI-RADS categories of malignancy nodules. The shape and calcification of the nodule were important factors for distinguish the benign and malignance. Height of the nodule, aspect and calcification was important factors for grading TI-RADS categories of malignancy thyroid nodules. Ill-defined boundary, irregular shape and presence of calcification related with highly malignant risk for thyroid nodule. The larger height and aspect and presence of calcification related with higher TI-RADS classification of malignancy thyroid nodule.

Incorporating advanced language models into the P300 speller using particle filtering

NASA Astrophysics Data System (ADS)

Speier, W.; Arnold, C. W.; Deshpande, A.; Knall, J.; Pouratian, N.

2015-08-01

Objective. The P300 speller is a common brain-computer interface (BCI) application designed to communicate language by detecting event related potentials in a subject’s electroencephalogram signal. Information about the structure of natural language can be valuable for BCI communication, but attempts to use this information have thus far been limited to rudimentary n-gram models. While more sophisticated language models are prevalent in natural language processing literature, current BCI analysis methods based on dynamic programming cannot handle their complexity. Approach. Sampling methods can overcome this complexity by estimating the posterior distribution without searching the entire state space of the model. In this study, we implement sequential importance resampling, a commonly used particle filtering (PF) algorithm, to integrate a probabilistic automaton language model. Main result. This method was first evaluated offline on a dataset of 15 healthy subjects, which showed significant increases in speed and accuracy when compared to standard classification methods as well as a recently published approach using a hidden Markov model (HMM). An online pilot study verified these results as the average speed and accuracy achieved using the PF method was significantly higher than that using the HMM method. Significance. These findings strongly support the integration of domain-specific knowledge into BCI classification to improve system performance.

Racial and Ethnic Disparities in the Incidence and Trends of Soft Tissue Sarcoma Among Adolescents and Young Adults in the United States, 1995-2008.

PubMed

Hsieh, Mei-Chin; Wu, Xiao-Cheng; Andrews, Patricia A; Chen, Vivien W

2013-09-01

The aim of this study was to examine racial/ethnic disparities in the incidence rates and trends of soft tissue sarcoma (STS) by gender, age, and histological type among adolescents and young adults (AYAs) aged 15-29 years. The 1995-2008 incidence data from 25 population-based cancer registries, covering 64% of the United States population, were obtained from the North American Association of Central Cancer Registries. The Surveillance, Epidemiology and End Results AYA site recode and International Classification of Diseases for Oncology, 3rd Edition, were adopted to categorize STS histological types and anatomic groups. Age-adjusted incidence rates and average annual percent change (AAPC) were calculated. The incidence of all STSs combined was 34% higher in males than females (95% CI: 1.28, 1.39), 60% higher among blacks than whites (95% CI: 1.52, 1.68), and slightly higher among Hispanics than whites. Compared with whites, blacks had significantly higher incidence of fibromatous neoplasms, and Hispanics had significantly higher incidence of liposarcoma. Whites were more likely to be diagnosed with synovial sarcoma than blacks. Black and Hispanic males had significantly higher Kaposi sarcoma incidence than white males. The AAPC of all STSs combined showed a significant decrease from 1995 to 2008 (AAPC=-2.1%; 95% CI: -3.2%, -1.0%). However, after excluding Kaposi sarcoma, there was no significant trend. The incidence rates of STS histological types in AYAs vary among racial/ethnic groups. The declining trends of STS are due mainly to decreasing incidence of Kaposi sarcoma in all races/ethnicities. Research to identify factors associated with racial/ethnic disparities in AYA STS is necessary.

Use of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) for improving the accuracy of the risk classification of type 1 diabetes.

PubMed

Sosenko, Jay M; Skyler, Jay S; Mahon, Jeffrey; Krischer, Jeffrey P; Greenbaum, Carla J; Rafkin, Lisa E; Beam, Craig A; Boulware, David C; Matheson, Della; Cuthbertson, David; Herold, Kevan C; Eisenbarth, George; Palmer, Jerry P

2014-04-01

OBJECTIVE We studied the utility of the Diabetes Prevention Trial-Type 1 Risk Score (DPTRS) for improving the accuracy of type 1 diabetes (T1D) risk classification in TrialNet Natural History Study (TNNHS) participants. RESEARCH DESIGN AND METHODS The cumulative incidence of T1D was compared between normoglycemic individuals with DPTRS values >7.00 and dysglycemic individuals in the TNNHS (n = 991). It was also compared between individuals with DPTRS values <7.00 or >7.00 among those with dysglycemia and those with multiple autoantibodies in the TNNHS. DPTRS values >7.00 were compared with dysglycemia for characterizing risk in Diabetes Prevention Trial-Type 1 (DPT-1) (n = 670) and TNNHS participants. The reliability of DPTRS values >7.00 was compared with dysglycemia in the TNNHS. RESULTS The cumulative incidence of T1D for normoglycemic TNNHS participants with DPTRS values >7.00 was comparable to those with dysglycemia. Among those with dysglycemia, the cumulative incidence was much higher (P < 0.001) for those with DPTRS values >7.00 than for those with values <7.00 (3-year risks: 0.16 for <7.00 and 0.46 for >7.00). Dysglycemic individuals in DPT-1 were at much higher risk for T1D than those with dysglycemia in the TNNHS (P < 0.001); there was no significant difference in risk between the studies among those with DPTRS values >7.00. The proportion in the TNNHS reverting from dysglycemia to normoglycemia at the next visit was higher than the proportion reverting from DPTRS values >7.00 to values <7.00 (36 vs. 23%). CONCLUSIONS DPTRS thresholds can improve T1D risk classification accuracy by identifying high-risk normoglycemic and low-risk dysglycemic individuals. The 7.00 DPTRS threshold characterizes risk more consistently between populations and has greater reliability than dysglycemia.

Joint Standing Committee on Education: Update on Higher Education Personnel Study

ERIC Educational Resources Information Center

West Virginia Higher Education Policy Commission, 2006

2006-01-01

The following topics are included in this update: (1) Comparison of West Virginia classification and compensation systems to those of the University of Michigan, the University system of Maryland, and the University of North Carolina; (2) Classification and Compensation System Training, including an agenda and summary of a two-day seminar devoted…

How Factor Analysis Can Be Used in Classification.

ERIC Educational Resources Information Center

Harman, Harry H.

This is a methodological study that suggests a taxometric technique for objective classification of yeasts. It makes use of the minres method of factor analysis and groups strains of yeast according to their factor profiles. The similarities are judged in the higher-dimensional space determined by the factor analysis, but otherwise rely on the…

Microbubble signal and trial of org in acute stroke treatment (TOAST) classification in ischemic stroke.

PubMed

Lee, Chan-Hyuk; Kang, Hyun Goo; Lee, Ji Sung; Ryu, Han Uk; Jeong, Seul-Ki

2018-07-15

Right-to-left shunt (RLS) through a patent foramen ovale (PFO) is likely associated with ischemic stroke. Many studies have attempted to demonstrate the association between RLS and ischemic stroke. However, information on the association between the degree of RLS and the subtypes of ischemic stroke categorized by the Trial of ORG 10172 in Acute Stroke Treatment (TOAST) classification is lacking. This was a retrospective study involving 508 patients with ischemic stroke who underwent a transcranial Doppler (TCD) microbubble test between 2013 and 2015. The degree of RLS was divided into 4 grades according to the microbubble signal (MBS) as follows: no MBS, grade 1; MBS < 20, grade 2; MBS > 20, grade 3; curtain sign, grade 4. The degree of RLS and the type of ischemic stroke as classified by TOAST were analyzed and compared with other clinical information and laboratory findings. The higher RLS grade was associated with the cardioembolism (CE) and stroke of undetermined etiology (SUE), and the microbubble signals were inversely related with small vessel disease (SVD). An MBS higher than grade 3 showed a 2.95-fold higher association with SUE than large artery atherosclerosis (LAA), while grade 4 MBS revealed an approximately 8-fold higher association with SUE than LAA. RLS identified by the TCD microbubble test was significantly and independently associated with cryptogenic ischemic stroke (negative evaluation). Subsequent studies are needed to determine the biologic relationship between RLS and ischemic stroke, particularly the cryptogenic subtype of ischemic stroke. Copyright © 2018 Elsevier B.V. All rights reserved.

The Society for Vascular Surgery Wound, Ischemia, and foot Infection (WIfI) classification system correlates with cost of care for diabetic foot ulcers treated in a multidisciplinary setting.

PubMed

Hicks, Caitlin W; Canner, Joseph K; Karagozlu, Hikmet; Mathioudakis, Nestoras; Sherman, Ronald L; Black, James H; Abularrage, Christopher J

2018-05-01

We have previously demonstrated that the Society for Vascular Surgery Wound, Ischemia, and foot Infection (WIfI) classification correlates with wound healing time in patients with diabetic foot ulcers (DFUs) treated in a multidisciplinary setting. Our aim was to assess whether the charges and costs associated with DFU care increase with higher WIfI stages. All patients presenting to our multidisciplinary diabetic limb preservation service from June 2012 to June 2016 were enrolled in a prospective database. Inpatient and outpatient charges, costs, and total revenue from initial visit until complete wound healing were compared for wounds stratified by WIfI classification. A total of 319 wound episodes in 248 patients were captured, including 31% WIfI stage 1, 16% stage 2, 30% stage 3, and 24% stage 4 wounds. Limb salvage at 1 year was 95% ± 2%, and wound healing was achieved in 85% ± 2%. The mean number of overall inpatient admissions (stage 1, 2.07 ± 0.48 vs stage 4, 3.40 ± 0.27; P < .001), procedure-related admissions (stage 1, 1.86 ± 0.45 vs stage 4, 2.28 ± 0.24; P < .001), and inpatient vascular interventions (stage 1, 0.14 ± 0.10 vs stage 4, 0.80 ± 0.12; P < .001) increased significantly with increasing WIfI stage. There were no significant differences in mean number of inpatient podiatric interventions or outpatient procedures between groups (P ≥ .10). The total cost of care per wound episode increased progressively from stage 1 ($3995 ± $1047) to stage 4 ($50,546 ± $4887) wounds (P < .001). Inpatient costs were significantly higher for advanced stage wounds (stage 1, $21,296 ± $4445 vs stage 4, $54,513 ± $5001; P < .001), whereas outpatient procedure costs were not significantly different between groups (P = .72). Overall, hospital total revenue increased with increasing WIfI stage (stage 1, $4182 ± $1185 vs stage 4, $55,790 ± $5540; P < .002). Increasing WIfI stage is associated with a prolonged wound healing time, a higher number of surgical procedures, and an increased cost of care. While limb salvage outcomes are excellent, the overall cost of DFU care from presentation to healing is substantial, especially for patients with advanced (WIfI stage 3/4) disease treated in a multidisciplinary setting. Copyright © 2017 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.

Pattern Classifications Using Grover's and Ventura's Algorithms in a Two-qubits System

NASA Astrophysics Data System (ADS)

Singh, Manu Pratap; Radhey, Kishori; Rajput, B. S.

2018-03-01

Carrying out the classification of patterns in a two-qubit system by separately using Grover's and Ventura's algorithms on different possible superposition, it has been shown that the exclusion superposition and the phase-invariance superposition are the most suitable search states obtained from two-pattern start-states and one-pattern start-states, respectively, for the simultaneous classifications of patterns. The higher effectiveness of Grover's algorithm for large search states has been verified but the higher effectiveness of Ventura's algorithm for smaller data base has been contradicted in two-qubit systems and it has been demonstrated that the unknown patterns (not present in the concerned data-base) are classified more efficiently than the known ones (present in the data-base) in both the algorithms. It has also been demonstrated that different states of Singh-Rajput MES obtained from the corresponding self-single- pattern start states are the most suitable search states for the classification of patterns |00>,|01 >, |10> and |11> respectively on the second iteration of Grover's method or the first operation of Ventura's algorithm.

Molecular systematics of the barklouse family Psocidae (Insecta: Psocodea: 'Psocoptera') and implications for morphological and behavioral evolution.

PubMed

Yoshizawa, Kazunori; Johnson, Kevin P

2008-02-01

We evaluated the higher level classification within the family Psocidae (Insecta: Psocodea: 'Psocoptera') based on combined analyses of nuclear 18S, Histone 3, wingless and mitochondrial 12S, 16S and COI gene sequences. Various analyses (inclusion/exclusion of incomplete taxa and/or rapidly evolving genes, data partitioning, and analytical method selection) all provided similar results, which were generally concordant with relationships inferred using morphological observations. Based on the phylogenetic trees estimated for Psocidae, we propose a revised higher level classification of this family, although uncertainty still exists regarding some aspects of this classification. This classification includes a basal division into two subfamilies, 'Amphigerontiinae' (possibly paraphyletic) and Psocinae. The Amphigerontiinae is divided into the tribes Kaindipsocini (new tribe), Blastini, Amphigerontini, and Stylatopsocini. Psocinae is divided into the tribes 'Ptyctini' (probably paraphyletic), Psocini, Atrichadenotecnini (new tribe), Sigmatoneurini, Metylophorini, and Thyrsophorini (the latter includes the taxon previously recognized as Cerastipsocini). We examined the evolution of symmetric/asymmetric male genitalia over this tree and found this character to be quite homoplasious.

Assessing key cost drivers associated with caring for chronic kidney disease patients.

PubMed

Damien, Paul; Lanham, Holly J; Parthasarathy, Murali; Shah, Nikhil L

2016-12-28

To examine key factors influencing chronic kidney disease (CKD) patients' total expenditure and offer recommendations on how to reduce total cost of CKD care without compromising quality. Using the 2002-2011 Medical Expenditure Panel Survey (MEPS) data, our cross-sectional study analyzed 197 patient records-79 patients with one record and 59 with two entries per patient (138 unique patients). We used three patient groups, based on international statistical classification of diseases version 9 code for condition (ICD9CODX) classification, to focus inference from the analysis: (a) non-dialysis dependent CKD, (b) dialysis and (c) transplant. Covariate information included region, demographic, co-morbid conditions and types of services. We used descriptive methods and multivariate generalized linear models to understand the impact of cost drivers. We compared actual and predicted CKD cost of care data using a hold-out sample of nine, randomly selected patients to validate the models. Total costs were significantly affected by treatment type, with dialysis being significantly higher than non-dialysis and transplant groups. Costs were highest in the West region of the U.S. Average costs for patients with public insurance were significantly higher than patients with private insurance (p < .0743), and likewise, for patients with co-morbid conditions over those without co-morbid conditions (p < .001). Managing CKD patients both before and after the onset of dialysis treatment and managing co-morbid conditions in individuals with CKD are potential sources of substantial cost savings in the care of CKD patients. Comparing total costs pre and post the United States Affordable Care Act could provide invaluable insights into managing the cost-quality tradeoff in CKD care.

Assessing behavioural changes in ALS: cross-validation of ALS-specific measures.

PubMed

Pinto-Grau, Marta; Costello, Emmet; O'Connor, Sarah; Elamin, Marwa; Burke, Tom; Heverin, Mark; Pender, Niall; Hardiman, Orla

2017-07-01

The Beaumont Behavioural Inventory (BBI) is a behavioural proxy report for the assessment of behavioural changes in ALS. This tool has been validated against the FrSBe, a non-ALS-specific behavioural assessment, and further comparison of the BBI against a disease-specific tool was considered. This study cross-validates the BBI against the ALS-FTD-Q. Sixty ALS patients, 8% also meeting criteria for FTD, were recruited. All patients were evaluated using the BBI and the ALS-FTD-Q, completed by a carer. Correlational analysis was performed to assess construct validity. Precision, sensitivity, specificity, and overall accuracy of the BBI when compared to the ALS-FTD-Q, were obtained. The mean score of the whole sample on the BBI was 11.45 ± 13.06. ALS-FTD patients scored significantly higher than non-demented ALS patients (31.6 ± 14.64, 9.62 ± 11.38; p < 0.0001). A significant large positive correlation between the BBI and the ALS-FTD-Q was observed (r = 0.807, p < 0.0001), and no significant correlations between the BBI and other clinical/demographic characteristics indicate good convergent and discriminant validity, respectively. 72% of overall concordance was observed. Precision, sensitivity, and specificity for the classification of severely impaired patients were adequate. However, lower concordance in the classification of mild behavioural changes was observed, with higher sensitivity using the BBI, most likely secondary to BBI items which endorsed behavioural aspects not measured by the ALS-FTD-Q. Good construct validity has been further confirmed when the BBI is compared to an ALS-specific tool. Furthermore, the BBI is a more comprehensive behavioural assessment for ALS, as it measures the whole behavioural spectrum in this condition.

The role of T1 perfusion-based classification in magnetic resonance-guided high-intensity focused ultrasound ablation of uterine fibroids.

PubMed

Keserci, Bilgin; Duc, Nguyen Minh

2017-12-01

To comparatively evaluate the role of magnetic resonance (MR) T1 perfusion-based time-signal intensity (SI) curves of fibroid tissue and the myometrium in classification of fibroids for predicting treatment outcomes of high-intensity focused ultrasound (HIFU) treatment. The fibroids of 74 women who underwent MR-HIFU treatment were classified into group A (time-SI curve of fibroid lower than that of the myometrium) and group B (time-SI curve of fibroid equal to or higher than that of the myometrium). Non-perfused volume (NPV) ratios immediately after treatment and fibroid volume reduction ratios and symptom severity scores (SSS) at the 6-month follow-up were retrospectively assessed. The immediate NPV ratios in groups A and B were 95.3 ± 6.3% (n = 62) and 63.8 ± 11% (n = 12), respectively. At the 6-month follow-up, the fibroid volume reduction ratios in groups A and B were 0.52 ± 0.14 (n = 50) and 0.07 ± 0.14 (n = 11), with the corresponding improvement in mean transformed SSS being 0.86 ± 0.14 and 0.19 ± 0.3, respectively. No serious adverse effects were reported. Our novel classification method could play an important role in classifying fibroids for predicting the immediate outcomes of HIFU treatment. • MRI is an important modality for outcome prediction in HIFU treatment • Patient selection is a significant factor for achieving high NPV ratio • NPV ratio is very strongly correlated with T1 perfusion-based classification • T1 perfusion-based classification is a strong predictor of treatment outcome.

The Analysis of Object-Based Change Detection in Mining Area: a Case Study with Pingshuo Coal Mine

NASA Astrophysics Data System (ADS)

Zhang, M.; Zhou, W.; Li, Y.

2017-09-01

Accurate information on mining land use and land cover change are crucial for monitoring and environmental change studies. In this paper, RapidEye Remote Sensing Image (Map 2012) and SPOT7 Remote Sensing Image (Map 2015) in Pingshuo Mining Area are selected to monitor changes combined with object-based classification and change vector analysis method, we also used R in highresolution remote sensing image for mining land classification, and found the feasibility and the flexibility of open source software. The results show that (1) the classification of reclaimed mining land has higher precision, the overall accuracy and kappa coefficient of the classification of the change region map were 86.67 % and 89.44 %. It's obvious that object-based classification and change vector analysis which has a great significance to improve the monitoring accuracy can be used to monitor mining land, especially reclaiming mining land; (2) the vegetation area changed from 46 % to 40 % accounted for the proportion of the total area from 2012 to 2015, and most of them were transformed into the arable land. The sum of arable land and vegetation area increased from 51 % to 70 %; meanwhile, build-up land has a certain degree of increase, part of the water area was transformed into arable land, but the extent of the two changes is not obvious. The result illustrated the transformation of reclaimed mining area, at the same time, there is still some land convert to mining land, and it shows the mine is still operating, mining land use and land cover are the dynamic procedure.

Lenke and King classification systems for adolescent idiopathic scoliosis: interobserver agreement and postoperative results

PubMed Central

Hosseinpour-Feizi, Hojjat; Soleimanpour, Jafar; Sales, Jafar Ganjpour; Arzroumchilar, Ali

2011-01-01

Purpose The aim of this study was to investigate the interobserver agreement of the Lenke and King classifications for adolescent idiopathic scoliosis, and to compare the results of surgery performed based on classification of the scoliosis according to each of these classification systems. Methods The study was conducted in Shohada Hospital in Tabriz, Iran, between 2009 and 2010. First, a reliability assessment was undertaken to assess interobserver agreement of the Lenke and King classifications for adolescent idiopathic scoliosis. Second, postoperative efficacy and safety of surgery performed based on the Lenke and King classifications were compared. Kappa coefficients of agreement were calculated to assess the agreement. Outcomes were compared using bivariate tests and repeated measures analysis of variance. Results A low to moderate interobserver agreement was observed for the King classification; the Lenke classification yielded mostly high agreement coefficients. The outcome of surgery was not found to be substantially different between the two systems. Conclusion Based on the results, the Lenke classification method seems advantageous. This takes into consideration the Lenke classification’s priority in providing details of curvatures in different anatomical surfaces to explain precise intensity of scoliosis, that it has higher interobserver agreement scores, and also that it leads to noninferior postoperative results compared with the King classification method. PMID:22267934

The Evaluation of Interstitial Abnormalities in Group B of the 2011 Global Initiative for Chronic Obstructive Lung Disease (GOLD) Classification of Chronic Obstructive Pulmonary Disease (COPD).

PubMed

Ohgiya, Masahiro; Matsui, Hirotoshi; Tamura, Atsuhisa; Kato, Takafumi; Akagawa, Shinobu; Ohta, Ken

2017-10-15

Objective In 2011, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification categorized chronic obstructive pulmonary disease (COPD) patients into 4 groups. A report demonstrated that the mortality in Group B was higher than that in Group C. Ischemic heart disease and cancer were suggested to be the cause. The aim of the present study was to test the hypothesis that interstitial lung abnormalities (ILAs) are more prevalent in Group B than Group C and that they may be responsible for the higher mortality in Group B. Methods Patients were selected based on their pulmonary function test results. The inclusion criterion was a forced expiratory volume in 1 second (FEV 1 )/forced vital capacity (FVC) of <70% after the inhalation of a bronchodilator. Patients without a smoking history or computed tomography (CT) scan were excluded. The medical records of the patients were retrospectively reviewed, and the selected patients were categorized into Groups A to D. High-resolution CT scans were used to investigate the presence of ILAs and determine the low attenuation area (LAA). Results Among the 349 COPD patients, ILAs were detected in 10.3% of the patients in Group A, 22.5% of the patients in Group B, 5.6% of the patients in Group C, and 23.1% of the patients in Group D. In Group B, the frequency of ILAs was significantly higher and the area affected by the ILAs was significantly greater in comparison to Group C. Among the patterns of interstitial abnormalities, the area of honeycombing in Group B was significantly greater than that in Group C. Furthermore, among the patients in Group B, the LAA in the ILA-positive patients was significantly greater than that in the ILA-negative patients. Conclusion In Group B, the area occupied by ILAs-especially honeycombing-was greater than that in Group C. This contributed to the preserved %FEV 1 and possibly to the poorer prognosis of the patients in Group B.

Intestinal permeability study of minoxidil: assessment of minoxidil as a high permeability reference drug for biopharmaceutics classification.

PubMed

Ozawa, Makoto; Tsume, Yasuhiro; Zur, Moran; Dahan, Arik; Amidon, Gordon L

2015-01-05

The purpose of this study was to evaluate minoxidil as a high permeability reference drug for Biopharmaceutics Classification System (BCS). The permeability of minoxidil was determined in in situ intestinal perfusion studies in rodents and permeability studies across Caco-2 cell monolayers. The permeability of minoxidil was compared with that of metoprolol, an FDA reference drug for BCS classification. In rat perfusion studies, the permeability of minoxidil was somewhat higher than that of metoprolol in the jejunum, while minoxidil showed lower permeability than metoprolol in the ileum. The permeability of minoxidil was independent of intestinal segment, while the permeability of metoprolol was region-dependent. Similarly, in mouse perfusion study, the jejunal permeability of minoxidil was 2.5-fold higher than that of metoprolol. Minoxidil and metoprolol showed similar permeability in Caco-2 study at apical pH of 6.5 and basolateral pH of 7.4. The permeability of minoxidil was independent of pH, while metoprolol showed pH-dependent transport in Caco-2 study. Minoxidil exhibited similar permeability in the absorptive direction (AP-BL) in comparison with secretory direction (BL-AP), while metoprolol had higher efflux ratio (ER > 2) at apical pH of 6.5 and basolateral pH of 7.4. No concentration-dependent transport was observed for either minoxidil or metoprolol transport in Caco-2 study. Verapamil did not alter the transport of either compounds across Caco-2 cell monolayers. The permeability of minoxidil was independent of both pH and intestinal segment in intestinal perfusion studies and Caco-2 studies. Caco-2 studies also showed no involvement of carrier mediated transport in the absorption process of minoxidil. These results suggest that minoxidil may be an acceptable reference drug for BCS high permeability classification. However, minoxidil exhibited higher jejunal permeability than metoprolol and thus to use minoxidil as a reference drug would raise the permeability criteria for BCS high permeability classification.

Quality assurance of chemical ingredient classification for the National Drug File - Reference Terminology.

PubMed

Zheng, Ling; Yumak, Hasan; Chen, Ling; Ochs, Christopher; Geller, James; Kapusnik-Uner, Joan; Perl, Yehoshua

2017-09-01

The National Drug File - Reference Terminology (NDF-RT) is a large and complex drug terminology consisting of several classification hierarchies on top of an extensive collection of drug concepts. These hierarchies provide important information about clinical drugs, e.g., their chemical ingredients, mechanisms of action, dosage form and physiological effects. Within NDF-RT such information is represented using tens of thousands of roles connecting drugs to classifications. In previous studies, we have introduced various kinds of Abstraction Networks to summarize the content and structure of terminologies in order to facilitate their visual comprehension, and support quality assurance of terminologies. However, these previous kinds of Abstraction Networks are not appropriate for summarizing the NDF-RT classification hierarchies, due to its unique structure. In this paper, we present the novel Ingredient Abstraction Network (IAbN) to summarize, visualize and support the audit of NDF-RT's Chemical Ingredients hierarchy and its associated drugs. A common theme in our quality assurance framework is to use characterizations of sets of concepts, revealed by the Abstraction Network structure, to capture concepts, the modeling of which is more complex than for other concepts. For the IAbN, we characterize drug ingredient concepts as more complex if they belong to IAbN groups with multiple parent groups. We show that such concepts have a statistically significantly higher rate of errors than a control sample and identify two especially common patterns of errors. Copyright © 2017 Elsevier Inc. All rights reserved.

Improvement in defect classification efficiency by grouping disposition for reticle inspection

NASA Astrophysics Data System (ADS)

Lai, Rick; Hsu, Luke T. H.; Chang, Peter; Ho, C. H.; Tsai, Frankie; Long, Garrett; Yu, Paul; Miller, John; Hsu, Vincent; Chen, Ellison

2005-11-01

As the lithography design rule of IC manufacturing continues to migrate toward more advanced technology nodes, the mask error enhancement factor (MEEF) increases and necessitates the use of aggressive OPC features. These aggressive OPC features pose challenges to reticle inspection due to high false detection, which is time-consuming for defect classification and impacts the throughput of mask manufacturing. Moreover, higher MEEF leads to stricter mask defect capture criteria so that new generation reticle inspection tool is equipped with better detection capability. Hence, mask process induced defects, which were once undetectable, are now detected and results in the increase of total defect count. Therefore, how to review and characterize reticle defects efficiently is becoming more significant. A new defect review system called ReviewSmart has been developed based on the concept of defect grouping disposition. The review system intelligently bins repeating or similar defects into defect groups and thus allows operators to review massive defects more efficiently. Compared to the conventional defect review method, ReviewSmart not only reduces defect classification time and human judgment error, but also eliminates desensitization that is formerly inevitable. In this study, we attempt to explore the most efficient use of ReviewSmart by evaluating various defect binning conditions. The optimal binning conditions are obtained and have been verified for fidelity qualification through inspection reports (IRs) of production masks. The experiment results help to achieve the best defect classification efficiency when using ReviewSmart in the mask manufacturing and development.

Bag-of-features approach for improvement of lung tissue classification in diffuse lung disease

NASA Astrophysics Data System (ADS)

Kato, Noriji; Fukui, Motofumi; Isozaki, Takashi

2009-02-01

Many automated techniques have been proposed to classify diffuse lung disease patterns. Most of the techniques utilize texture analysis approaches with second and higher order statistics, and show successful classification result among various lung tissue patterns. However, the approaches do not work well for the patterns with inhomogeneous texture distribution within a region of interest (ROI), such as reticular and honeycombing patterns, because the statistics can only capture averaged feature over the ROI. In this work, we have introduced the bag-of-features approach to overcome this difficulty. In the approach, texture images are represented as histograms or distributions of a few basic primitives, which are obtained by clustering local image features. The intensity descriptor and the Scale Invariant Feature Transformation (SIFT) descriptor are utilized to extract the local features, which have significant discriminatory power due to their specificity to a particular image class. In contrast, the drawback of the local features is lack of invariance under translation and rotation. We improved the invariance by sampling many local regions so that the distribution of the local features is unchanged. We evaluated the performance of our system in the classification task with 5 image classes (ground glass, reticular, honeycombing, emphysema, and normal) using 1109 ROIs from 211 patients. Our system achieved high classification accuracy of 92.8%, which is superior to that of the conventional system with the gray level co-occurrence matrix (GLCM) feature especially for inhomogeneous texture patterns.

Evaluation of host and viral factors associated with severe dengue based on the 2009 WHO classification.

PubMed

Pozo-Aguilar, Jorge O; Monroy-Martínez, Verónica; Díaz, Daniel; Barrios-Palacios, Jacqueline; Ramos, Celso; Ulloa-García, Armando; García-Pillado, Janet; Ruiz-Ordaz, Blanca H

2014-12-11

Dengue fever (DF) is the most prevalent arthropod-borne viral disease affecting humans. The World Health Organization (WHO) proposed a revised classification in 2009 to enable the more effective identification of cases of severe dengue (SD). This was designed primarily as a clinical tool, but it also enables cases of SD to be differentiated into three specific subcategories (severe vascular leakage, severe bleeding, and severe organ dysfunction). However, no study has addressed whether this classification has advantage in estimating factors associated with the progression of disease severity or dengue pathogenesis. We evaluate in a dengue outbreak associated risk factors that could contribute to the development of SD according to the 2009 WHO classification. A prospective cross-sectional study was performed during an epidemic of dengue in 2009 in Chiapas, Mexico. Data were analyzed for host and viral factors associated with dengue cases, using the 1997 and 2009 WHO classifications. The cost-benefit ratio (CBR) was also estimated. The sensitivity in the 1997 WHO classification for determining SD was 75%, and the specificity was 97.7%. For the 2009 scheme, these were 100% and 81.1%, respectively. The 2009 classification showed a higher benefit (537%) with a lower cost (10.2%) than the 1997 WHO scheme. A secondary antibody response was strongly associated with SD. Early viral load was higher in cases of SD than in those with DF. Logistic regression analysis identified predictive SD factors (secondary infection, disease phase, viral load) within the 2009 classification. However, within the 1997 scheme it was not possible to differentiate risk factors between DF and dengue hemorrhagic fever or dengue shock syndrome. The critical clinical stage for determining SD progression was the transition from fever to defervescence in which plasma leakage can occur. The clinical phenotype of SD is influenced by the host (secondary response) and viral factors (viral load). The 2009 WHO classification showed greater sensitivity to identify SD in real time. Timely identification of SD enables accurate early decisions, allowing proper management of health resources for the benefit of patients at risk for SD. This is possible based on the 2009 WHO classification.

The Classification Ability with Naked Eyes According to the Understanding Level about Rocks of Pre-service Science Teachers

NASA Astrophysics Data System (ADS)

Seong, Cho Kyu; Ho, Chung Duk; Pyo, Hong Deok; Kyeong Jin, Park

2016-04-01

This study aimed to investigate the classification ability with naked eyes according to the understanding level about rocks of pre-service science teachers. We developed a questionnaire concerning misconception about minerals and rocks. The participant were 132 pre-service science teachers. Data were analyzed using Rasch model. Participants were divided into a master group and a novice group according to their understanding level. Seventeen rocks samples (6 igneous, 5 sedimentary, and 6 metamorphic rocks) were presented to pre-service science teachers to examine their classification ability, and they classified the rocks according to the criteria we provided. The study revealed three major findings. First, the pre-service science teachers mainly classified rocks according to textures, color, and grain size. Second, while they relatively easily classified igneous rocks, participants were confused when distinguishing sedimentary and metamorphic rocks from one another by using the same classification criteria. On the other hand, the understanding level of rocks has shown a statistically significant correlation with the classification ability in terms of the formation mechanism of rocks, whereas there was no statically significant relationship found with determination of correct name of rocks. However, this study found that there was a statistically significant relationship between the classification ability with regard the formation mechanism of rocks and the determination of correct name of rocks Keywords : Pre-service science teacher, Understanding level, Rock classification ability, Formation mechanism, Criterion of classification

[Extracting black soil border in Heilongjiang province based on spectral angle match method].

PubMed

Zhang, Xin-Le; Zhang, Shu-Wen; Li, Ying; Liu, Huan-Jun

2009-04-01

As soils are generally covered by vegetation most time of a year, the spectral reflectance collected by remote sensing technique is from the mixture of soil and vegetation, so the classification precision based on remote sensing (RS) technique is unsatisfied. Under RS and geographic information systems (GIS) environment and with the help of buffer and overlay analysis methods, land use and soil maps were used to derive regions of interest (ROI) for RS supervised classification, which plus MODIS reflectance products were chosen to extract black soil border, with methods including spectral single match. The results showed that the black soil border in Heilongjiang province can be extracted with soil remote sensing method based on MODIS reflectance products, especially in the north part of black soil zone; the classification precision of spectral angel mapping method is the highest, but the classifying accuracy of other soils can not meet the need, because of vegetation covering and similar spectral characteristics; even for the same soil, black soil, the classifying accuracy has obvious spatial heterogeneity, in the north part of black soil zone in Heilongjiang province it is higher than in the south, which is because of spectral differences; as soil uncovering period in Northeastern China is relatively longer, high temporal resolution make MODIS images get the advantage over soil remote sensing classification; with the help of GIS, extracting ROIs by making the best of auxiliary data can improve the precision of soil classification; with the help of auxiliary information, such as topography and climate, the classification accuracy was enhanced significantly. As there are five main factors determining soil classes, much data of different types, such as DEM, terrain factors, climate (temperature, precipitation, etc.), parent material, vegetation map, and remote sensing images, were introduced to classify soils, so how to choose some of the data and quantify the weights of different data layers needs further study.

Social Media Mining for Toxicovigilance: Automatic Monitoring of Prescription Medication Abuse from Twitter.

PubMed

Sarker, Abeed; O'Connor, Karen; Ginn, Rachel; Scotch, Matthew; Smith, Karen; Malone, Dan; Gonzalez, Graciela

2016-03-01

Prescription medication overdose is the fastest growing drug-related problem in the USA. The growing nature of this problem necessitates the implementation of improved monitoring strategies for investigating the prevalence and patterns of abuse of specific medications. Our primary aims were to assess the possibility of utilizing social media as a resource for automatic monitoring of prescription medication abuse and to devise an automatic classification technique that can identify potentially abuse-indicating user posts. We collected Twitter user posts (tweets) associated with three commonly abused medications (Adderall(®), oxycodone, and quetiapine). We manually annotated 6400 tweets mentioning these three medications and a control medication (metformin) that is not the subject of abuse due to its mechanism of action. We performed quantitative and qualitative analyses of the annotated data to determine whether posts on Twitter contain signals of prescription medication abuse. Finally, we designed an automatic supervised classification technique to distinguish posts containing signals of medication abuse from those that do not and assessed the utility of Twitter in investigating patterns of abuse over time. Our analyses show that clear signals of medication abuse can be drawn from Twitter posts and the percentage of tweets containing abuse signals are significantly higher for the three case medications (Adderall(®): 23 %, quetiapine: 5.0 %, oxycodone: 12 %) than the proportion for the control medication (metformin: 0.3 %). Our automatic classification approach achieves 82 % accuracy overall (medication abuse class recall: 0.51, precision: 0.41, F measure: 0.46). To illustrate the utility of automatic classification, we show how the classification data can be used to analyze abuse patterns over time. Our study indicates that social media can be a crucial resource for obtaining abuse-related information for medications, and that automatic approaches involving supervised classification and natural language processing hold promises for essential future monitoring and intervention tasks.

[Research progress in molecular classification of gastric cancer].

PubMed

Zhou, Menglong; Li, Guichao; Zhang, Zhen

2016-09-25

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

Drug-induced sedation endoscopy (DISE) classification systems: a systematic review and meta-analysis.

PubMed

Dijemeni, Esuabom; D'Amone, Gabriele; Gbati, Israel

2017-12-01

Drug-induced sedation endoscopy (DISE) classification systems have been used to assess anatomical findings on upper airway obstruction, and decide and plan surgical treatments and act as a predictor for surgical treatment outcome for obstructive sleep apnoea management. The first objective is to identify if there is a universally accepted DISE grading and classification system for analysing DISE findings. The second objective is to identify if there is one DISE grading and classification treatment planning framework for deciding appropriate surgical treatment for obstructive sleep apnoea (OSA). The third objective is to identify if there is one DISE grading and classification treatment outcome framework for determining the likelihood of success for a given OSA surgical intervention. A systematic review was performed to identify new and significantly modified DISE classification systems: concept, advantages and disadvantages. Fourteen studies proposing a new DISE classification system and three studies proposing a significantly modified DISE classification were identified. None of the studies were based on randomised control trials. DISE is an objective method for visualising upper airway obstruction. The classification and assessment of clinical findings based on DISE is highly subjective due to the increasing number of DISE classification systems. Hence, this creates a growing divergence in surgical treatment planning and treatment outcome. Further research on a universally accepted objective DISE assessment is critically needed.

From landscape to domain: Soils role in landscape classifications

USDA-ARS?s Scientific Manuscript database

Soil landscape classifications are designed to divide landscapes into units with significance for the provisioning and regulating of ecosystem services and the development of conservation plans for natural resources. More specifically, such classifications serve as the basis for stratifying manageme...

The impact of occupation according to income on depressive symptoms in South Korean individuals: Findings from the Korean Welfare Panel Study.

PubMed

Kim, Woorim; Park, Eun-Cheol; Lee, Tae-Hoon; Ju, Yeong Jun; Shin, Jaeyong; Lee, Sang Gyu

2016-05-01

In South Korea, societal perceptions on occupation are distinct, with people favouring white collar jobs. Hence both occupation type and income can have mental health effects. To examine the relationship between occupational classification and depression, along with the combined effect of occupational classification and household income. Data were from the Korean Welfare Panel Study (KOWEPS), 2010-2013. A total of 4,694 economically active participants at baseline were followed. Association between occupational classification and depression, measured using the Center for Epidemiological Studies Depression (CES-D) scale 11, was investigated using the linear mixed effects model. Blue collar (β: 0.3871, p = .0109) and sales and service worker groups (β: 0.3418, p = .0307) showed higher depression scores than the white collar group. Compared to the white collar high-income group, white collar low income, blue collar middle income, blue collar middle-low income, blue collar low income, sales and service middle-high income, sales and service middle-low income and sales and service low-income groups had higher depression scores. Occupational classification is associated with increasing depression scores. Excluding the highest income group, blue collar and sales and service worker groups exhibit higher depression scores than their white collar counterparts, implying the importance of addressing these groups. © The Author(s) 2016.

Evaluation of change detection techniques for monitoring coastal zone environments

NASA Technical Reports Server (NTRS)

Weismiller, R. A. (Principal Investigator); Kristof, S. J.; Scholz, D. K.; Anuta, P. E.; Momin, S. M.

1977-01-01

The author has identified the following significant results. Four change detection techniques were designed and implemented for evaluation: (1) post classification comparison change detection, (2) delta data change detection, (3) spectral/temporal change classification, and (4) layered spectral/temporal change classification. The post classification comparison technique reliably identified areas of change and was used as the standard for qualitatively evaluating the other three techniques. The layered spectral/temporal change classification and the delta data change detection results generally agreed with the post classification comparison technique results; however, many small areas of change were not identified. Major discrepancies existed between the post classification comparison and spectral/temporal change detection results.

Discrimination of natural and cultivated vegetation using Thematic Mapper spectral data

NASA Technical Reports Server (NTRS)

Degloria, Stephen D.; Bernstein, Ralph; Dizenzo, Silvano

1986-01-01

The availability of high quality spectral data from the current suite of earth observation satellite systems offers significant improvements in the ability to survey and monitor food and fiber production on both a local and global basis. Current research results indicate that Landsat TM data when used in either digital or analog formats achieve higher land-cover classification accuracies than MSS data using either comparable or improved spectral bands and spatial resolution. A review of these quantitative results is presented for both natural and cultivated vegetation.

Clinical limitations of the International Federation of Gynecology and Obstetrics (FIGO) classification of uterine fibroids.

PubMed

Laughlin-Tommaso, Shannon K; Hesley, Gina K; Hopkins, Matthew R; Brandt, Kathleen R; Zhu, Yunxiao; Stewart, Elizabeth A

2017-11-01

To determine the reproducibility of classifying uterine fibroids using the 2011 International Federation of Gynecology and Obstetrics (FIGO) staging system. The present retrospective cohort study included patients presenting for the treatment of symptomatic uterine fibroids at the Gynecology Fibroid Clinic at Mayo Clinic, Rochester, USA, between April 1, 2013 and April 1, 2014. Magnetic resonance imaging of fibroid uteri was performed and the images were independently reviewed by two academic gynecologists and two radiologists specializing in fibroid care. Fibroid classifications assigned by each physician were compared and the significance of the variations was graded by whether they would affect surgical planning. There were 42 fibroids from 23 patients; only 6 (14%) fibroids had unanimous classification agreement. The majority (36 [86%]) had at least two unique answers and 4 (10%) fibroids had four unique classifications. Variations in classification were not associated with physician specialty. More than one-third of the classification discrepancies would have impacted surgical planning. FIGO fibroid classification was not consistent among four fibroid specialists. The variation was clinically significant for 36% of the fibroids. Additional validation of the FIGO fibroid classification system is needed. © 2017 International Federation of Gynecology and Obstetrics.

ANALYSIS OF A CLASSIFICATION ERROR MATRIX USING CATEGORICAL DATA TECHNIQUES.

USGS Publications Warehouse

Rosenfield, George H.; Fitzpatrick-Lins, Katherine

1984-01-01

Summary form only given. A classification error matrix typically contains tabulation results of an accuracy evaluation of a thematic classification, such as that of a land use and land cover map. The diagonal elements of the matrix represent the counts corrected, and the usual designation of classification accuracy has been the total percent correct. The nondiagonal elements of the matrix have usually been neglected. The classification error matrix is known in statistical terms as a contingency table of categorical data. As an example, an application of these methodologies to a problem of remotely sensed data concerning two photointerpreters and four categories of classification indicated that there is no significant difference in the interpretation between the two photointerpreters, and that there are significant differences among the interpreted category classifications. However, two categories, oak and cottonwood, are not separable in classification in this experiment at the 0. 51 percent probability. A coefficient of agreement is determined for the interpreted map as a whole, and individually for each of the interpreted categories. A conditional coefficient of agreement for the individual categories is compared to other methods for expressing category accuracy which have already been presented in the remote sensing literature.

Dietary fiber and flavan-3-ols in shortbread biscuits enriched with barley flours co-products.

PubMed

Verardo, Vito; Riciputi, Ylenia; Messia, Maria Cristina; Vallicelli, Melania; Falasca, Luisa; Marconi, Emanuele; Caboni, Maria Fiorenza

2011-05-01

The coarse fraction obtained by air classification of barley flour, rich in dietary fiber and flavan-3-ols, was utilized to develop functional biscuits. The flavan-3-ol content, antioxidant activity and oxidative stability of biscuits were measured during storage under retail conditions for 1 year. The replacement of 60% (w/w) refined wheat flour with barley coarse fraction increased the ash, fiber and flavan-3-ol contents significantly. Biscuit samples enriched with barley coarse fraction had a significantly higher amount of fiber compared with the control sample (six times higher). The β-glucan content in enriched samples was 15 times higher than control samples. The flavan-3-ol loss in biscuits after baking was about 67%. The initial content of flavan-3-ols increased from 0.6 to 4.3 mg/100 g in biscuits formulated with barley coarse fraction and showed improved antioxidant properties. Lipid oxidation increased during the shelf-life; the enriched biscuit showed the higher lipid oxidation status, but the level reached during the shelf-life was lower than the limit of acceptance reported for bakery products and, for this reason, does not compromise the safety.

Analysis of swallowing sounds using hidden Markov models.

PubMed

Aboofazeli, Mohammad; Moussavi, Zahra

2008-04-01

In recent years, acoustical analysis of the swallowing mechanism has received considerable attention due to its diagnostic potentials. This paper presents a hidden Markov model (HMM) based method for the swallowing sound segmentation and classification. Swallowing sound signals of 15 healthy and 11 dysphagic subjects were studied. The signals were divided into sequences of 25 ms segments each of which were represented by seven features. The sequences of features were modeled by HMMs. Trained HMMs were used for segmentation of the swallowing sounds into three distinct phases, i.e., initial quiet period, initial discrete sounds (IDS) and bolus transit sounds (BTS). Among the seven features, accuracy of segmentation by the HMM based on multi-scale product of wavelet coefficients was higher than that of the other HMMs and the linear prediction coefficient (LPC)-based HMM showed the weakest performance. In addition, HMMs were used for classification of the swallowing sounds of healthy subjects and dysphagic patients. Classification accuracy of different HMM configurations was investigated. When we increased the number of states of the HMMs from 4 to 8, the classification error gradually decreased. In most cases, classification error for N=9 was higher than that of N=8. Among the seven features used, root mean square (RMS) and waveform fractal dimension (WFD) showed the best performance in the HMM-based classification of swallowing sounds. When the sequences of the features of IDS segment were modeled separately, the accuracy reached up to 85.5%. As a second stage classification, a screening algorithm was used which correctly classified all the subjects but one healthy subject when RMS was used as characteristic feature of the swallowing sounds and the number of states was set to N=8.

Endometrial cancer surgery costs: robot vs laparoscopy.

PubMed

Holtz, David O; Miroshnichenko, Gennady; Finnegan, Mark O; Chernick, Michael; Dunton, Charles J

2010-01-01

To compare surgical costs for endometrial cancer staging between robotic-assisted and traditional laparoscopic methods. Retrospective chart review from November 2005 to July 2006 (Canadian Task Force classification II-3). Non-university-affiliated teaching hospital. Thirty-three women with diagnosed endometrial cancer undergoing hysterectomy, bilateral salpingo-oophorectomy, and pelvic and paraaortic lymph node resection. Patients underwent either robotic or traditional laparoscopic surgery without randomization. Hospital cost data were obtained for operating room time, instrument use, and disposable items from hospital billing records and provided by the finance department. Separate overall hospital stay costs were also obtained. Mean operative costs were higher for robotic procedures ($3323 vs $2029; p<.001), due in part to longer operating room time ($1549 vs $1335; p=.03). The more significant cost difference was due to disposable instrumentation ($1755 vs $672; p<.001). Total hospital costs were also higher for robotic-assisted procedures ($5084 vs $ 3615; p=.002). Robotic surgery costs were significantly higher than traditional laparoscopy costs for staging of endometrial cancer in this small cohort of patients. Copyright (c) 2010 AAGL. Published by Elsevier Inc. All rights reserved.

A Unified Classification Framework for FP, DP and CP Data at X-Band in Southern China

NASA Astrophysics Data System (ADS)

Xie, Lei; Zhang, Hong; Li, Hhongzhong; Wang, Chao

2015-04-01

The main objective of this paper is to introduce an unified framework for crop classification in Southern China using data in fully polarimetric (FP), dual-pol (DP) and compact polarimetric (CP) modes. The TerraSAR-X data acquired over the Leizhou Peninsula, South China are used in our experiments. The study site involves four main crops (rice, banana, sugarcane eucalyptus). Through exploring the similarities between data in these three modes, a knowledge-based characteristic space is created and the unified framework is presented. The overall classification accuracies for data in the FP, coherent HH/VV are about 95%, and is about 91% in CP modes, which suggests that the proposed classification scheme is effective and promising. Compared with the Wishart Maximum Likelihood (ML) classifier, the proposed method exhibits higher classification accuracy.

Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification

NASA Astrophysics Data System (ADS)

Chiarelli, Antonio Maria; Croce, Pierpaolo; Merla, Arcangelo; Zappasodi, Filippo

2018-06-01

Objective. Brain–computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures. Approach. We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1 s and overall experiment length of 10 min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers. Main results. At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect. Significance. BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.

Relations of meeting national public health recommendations for muscular strengthening activities with strength, body composition, and obesity: the Women's Injury Study.

PubMed

Trudelle-Jackson, Elaine; Jackson, Allen W; Morrow, James R

2011-10-01

We examined the relations of meeting or not meeting the 2008 Physical Activity Guidelines for Americans recommendations for muscular strengthening activities with percentage of body fat, body mass index (BMI; defined as weight in kilograms divided by height in meters, squared), muscular strength, and obesity classification in women. We analyzed data on 918 women aged 20 to 83 years in the Women's Injury Study from 2007 to 2009. A baseline orthopedic examination included measurement of height, body weight, skinfolds, and muscle strength. Women who met muscle strengthening activity recommendations had significantly lower BMI and percentage of body fat and higher muscle strength. Women not meeting those recommendations were more likely to be obese (BMI ≥ 30) compared with women who met the recommendations after we adjusted for age, race, and aerobic physical activity (odds ratio = 2.28; 95% confidence interval = 1.61, 3.23). There was a small but significant positive association between meeting muscle strengthening activity recommendations and muscular strength, a moderate inverse association with body fat percentage, and a strong inverse association with obesity classification, providing preliminary support for the muscle strengthening activity recommendation for women.

Drain placement can safely be omitted for open partial nephrectomy: Results from a prospective randomized trial.

PubMed

Kriegmair, Maximilian C; Mandel, Philipp; Krombach, Patrick; Dönmez, Hasan; John, Axel; Häcker, Axel; Michel, Maurice S

2016-05-01

To examine the benefit of drain placement during open partial nephrectomy. Overall, 106 patients treated with open partial nephrectomy were enrolled in a prospective randomized trial. Based on the randomization, a drain was placed or omitted. Complications were assessed according to the Clavien classification. Pain level and requirement for analgesics was evaluated according to a customized pattern. There was no significant difference in the two groups regarding age, body mass index, American Society of Anesthesiologists score, tumor size and nephrometry (preoperative aspects and dimensions used for an anatomical classification). In terms of overall and drain-related complications, no advantage of placing a drain could be proven (P = 0.249). Patients with a drain suffered from a significantly higher pain level (P = 0.01) and showed prolonged mobilization (P < 0.001). There was no difference in bowel movements and requirement of additional analgesics (P = 0.347 and 0.11). The results of the study suggest that drain placement during open partial nephrectomy can safely be omitted, even in cases with violation of the collecting system. © 2016 The Japanese Urological Association.

Transoral laser microsurgery for oral squamous cell carcinoma: Oncologic outcomes and prognostic factors

PubMed Central

Sinha, Parul; Hackman, Trevor; Nussenbaum, Brian; Wu, Ningying; Lewis, James S.; Haughey, Bruce H.

2014-01-01

Background Modest survival rates are published for treatment of oral squamous cell carcinoma (OSCC) using conventional approaches. Few cohort studies are available for transoral resection of OSCC. Methods Analysis for recurrence, survival, and prognosis of patients with OSCC treated with transoral laser microsurgery (TLM) ± neck dissection was obtained from a prospective database. Results Ninety-five patients (71 patients had stages T1–T2 and 24 had stages T3–T4 disease) with minimum follow-up of 24 months met criteria and demonstrated negative margins in 95%. Five-year local control (LC) and disease-specific survival (DSS) were 78% and 76%, respectively. Surgical salvage achieved an absolute final locoregional control of 92%. Immune compromise and final margins were prognostic for LC, whereas T classification, N classification, TNM stage, comorbidity, and perineural invasion were also significant for DSS. Conclusion We document a large series of patients with OSCC treated with TLM, incorporating T1 to T4 primaries. A significant proportion of stage III/IV cases demonstrates feasibility of TLM in higher stages, with final margin positivity of 5%, LC greater than 90%, and comparable survival outcomes. PMID:23729304

Early developed ASD (adjacent segmental disease) in patients after surgical treatment of the spine due to cancer metastases.

PubMed

Guzik, Grzegorz

2017-05-12

The causes of ASD are still relatively unknown. Correlation between clinical status of patients and radiological MRI findings is of primary importance. The radiological classifications proposed by Pfirmann and Oner are most commonly used to assess intradiscal degenerative changes. The aim of the study was to assess the influence of the extension of spine fixation on the risk of developing ASD in a short time after surgery. A total of 332 patients with spinal tumors were treated in our hospital between 2010 and 2013. Of these patients, 287 underwent surgeries. A follow-up MRI examination was performed 12 months after surgical treatment. The study population comprised of 194 patients. Among metastases, breast cancer was predominant (29%); neurological deficits were detected in 76 patients. Metastases were seen in the thoracic (45%) and lumbar (30%) spine; in 25% of cases, they were of multisegmental character. Pathological fractures concerned 88% of the patients. Statistical calculations were made using the χ2 test. Statistical analysis was done using the Statistica v. 10 software. A p value <0.05 was accepted as statistically significant. The study population was divided on seven groups according to applied treatment. Clinical signs of ASD were noted in only seven patients. Two patients had symptoms of nerve root irritation in the lumbar spine. Twenty-two patients (11%) were diagnosed with ASD according to the MRI classifications by Oner, Rijt, and Ramos, while the more sensitive Pfirmann classification allowed to detect the disease in 46 patients (24%). Healthy or almost healthy discs of Oner type I correlated with the criteria of Pfirmann types II and III. The percentage of the incidence of ASD diagnosed 1 year after the surgery using the Pfirmann classifications was significantly higher than diagnosed according to the clinical examination. The incidence of ASD in patients after spine surgeries due to cancer metastases does not differ between the study groups. ASD detectability based on clinical signs is significantly lower than ASD detectability based on MR images according to the system by Pfirrmann et.al. ASD risk increase among patients with multilevel fixation.

The New Higher Level Classification of Eukaryotes with Emphasis on the Taxonomy of Protists

Treesearch

SINA M. ADL; ALASTAIR G. B. SIMPSON; MARK A. FARMER; ROBERT A. ANDERSEN; O. ROGER ANDERSON; JOHN R. BARTA; SAMUEL S. BOWSER; GUY BRUGEROLLE; ROBERT A. FENSOME; SUZANNE FREDERICQ; TIMOTHY Y. JAMES; SERGEI KARPOV; PAUL KUGRENS; JOHN KRUG; CHRISTOPHER E. LANE; LOUISE A. LEWIS; JEAN LODGE; DENIS H. LYNN; DAVID G. MANN; RICHARD M. MCCOURT; LEONEL MENDOZA; ØJVIND MOESTRUP; SHARON E. MOZLEY-STANDRIDGE; THOMAS A. NERAD; CAROL A. SHEARER; ALEXEY V. SMIRNOV; FREDERICK W. SPIEGEL; MAX F.J.R. TAYLOR

2005-01-01

This revision of the classification of unicellular eukaryotes updates that of Levine et al. (1980) for the protozoa and expands it to include other protists. Whereas the previous revision was primarily to incorporate the results of ultrastructural studies, this revision incorporates results from both ultrastructural research since 1980 and molecular phylogenetic...

The new higher level classification of eukaryotes with emphasis on the taxonomy of protists

Treesearch

Sina M. Adl; Alastair G.B. Simpson; Mark A. Farmer; Robert A. Andersen; O. Roger Anderson; John R. Barta; Samuel S. Bowser; Guy Brugerolle; Robert A. Fensome; Suzanne Fredericq; Timothy Y. James; Sergei Karpov; Paul Kugrens; John Krug; Christopher E. Lane; Louise A. Lewis; Jean Lodge; Denis H. Lynn; David G. Mann; Richard M. McCourt; Leonel Mendoza; Ojvind Moestrup; Sharon E. Mozley-Standridge; Thomas A. Nerad; Carol A. Shearer; Alexey V. Smirnov; Frederick W. Speigel; Max F.J.R. Taylor

2005-01-01

This revision of the classification of unicellular eukaryotes updates that of Levine et al. (1980) for the protozoa and expands it to include other protists. Whereas the previous revision was primarily to incorporate the results of ultrastructural studies, this revision incorporates results from both ultrastructural research since 1980 and molecular phylogenetic...

Effects of two classification strategies on a Benthic Community Index for streams in the Northern Lakes and Forests Ecoregion

USGS Publications Warehouse

Butcher, Jason T.; Stewart, Paul M.; Simon, Thomas P.

2003-01-01

Ninety-four sites were used to analyze the effects of two different classification strategies on the Benthic Community Index (BCI). The first, a priori classification, reflected the wetland status of the streams; the second, a posteriori classification, used a bio-environmental analysis to select classification variables. Both classifications were examined by measuring classification strength and testing differences in metric values with respect to group membership. The a priori (wetland) classification strength (83.3%) was greater than the a posteriori (bio-environmental) classification strength (76.8%). Both classifications found one metric that had significant differences between groups. The original index was modified to reflect the wetland classification by re-calibrating the scoring criteria for percent Crustacea and Mollusca. A proposed refinement to the original Benthic Community Index is suggested. This study shows the importance of using hypothesis-driven classifications, as well as exploratory statistical analysis, to evaluate alternative ways to reveal environmental variability in biological assessment tools.

Feature extraction based on extended multi-attribute profiles and sparse autoencoder for remote sensing image classification

NASA Astrophysics Data System (ADS)

Teffahi, Hanane; Yao, Hongxun; Belabid, Nasreddine; Chaib, Souleyman

2018-02-01

The satellite images with very high spatial resolution have been recently widely used in image classification topic as it has become challenging task in remote sensing field. Due to a number of limitations such as the redundancy of features and the high dimensionality of the data, different classification methods have been proposed for remote sensing images classification particularly the methods using feature extraction techniques. This paper propose a simple efficient method exploiting the capability of extended multi-attribute profiles (EMAP) with sparse autoencoder (SAE) for remote sensing image classification. The proposed method is used to classify various remote sensing datasets including hyperspectral and multispectral images by extracting spatial and spectral features based on the combination of EMAP and SAE by linking them to kernel support vector machine (SVM) for classification. Experiments on new hyperspectral image "Huston data" and multispectral image "Washington DC data" shows that this new scheme can achieve better performance of feature learning than the primitive features, traditional classifiers and ordinary autoencoder and has huge potential to achieve higher accuracy for classification in short running time.

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

PubMed

Li, Linyi; Xu, Tingbao; Chen, Yun

2017-01-01

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

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

PubMed Central

Xu, Tingbao; Chen, Yun

2017-01-01

In recent years the spatial resolutions of remote sensing images have been improved greatly. However, a higher spatial resolution image does not always lead to a better result of automatic scene classification. Visual attention is an important characteristic of the human visual system, which can effectively help to classify remote sensing scenes. In this study, a novel visual attention feature extraction algorithm was proposed, which extracted visual attention features through a multiscale process. And a fuzzy classification method using visual attention features (FC-VAF) was developed to perform high resolution remote sensing scene classification. FC-VAF was evaluated by using remote sensing scenes from widely used high resolution remote sensing images, including IKONOS, QuickBird, and ZY-3 images. FC-VAF achieved more accurate classification results than the others according to the quantitative accuracy evaluation indices. We also discussed the role and impacts of different decomposition levels and different wavelets on the classification accuracy. FC-VAF improves the accuracy of high resolution scene classification and therefore advances the research of digital image analysis and the applications of high resolution remote sensing images. PMID:28761440

Automated Feature Identification and Classification Using Automated Feature Weighted Self Organizing Map (FWSOM)

NASA Astrophysics Data System (ADS)

Starkey, Andrew; Usman Ahmad, Aliyu; Hamdoun, Hassan

2017-10-01

This paper investigates the application of a novel method for classification called Feature Weighted Self Organizing Map (FWSOM) that analyses the topology information of a converged standard Self Organizing Map (SOM) to automatically guide the selection of important inputs during training for improved classification of data with redundant inputs, examined against two traditional approaches namely neural networks and Support Vector Machines (SVM) for the classification of EEG data as presented in previous work. In particular, the novel method looks to identify the features that are important for classification automatically, and in this way the important features can be used to improve the diagnostic ability of any of the above methods. The paper presents the results and shows how the automated identification of the important features successfully identified the important features in the dataset and how this results in an improvement of the classification results for all methods apart from linear discriminatory methods which cannot separate the underlying nonlinear relationship in the data. The FWSOM in addition to achieving higher classification accuracy has given insights into what features are important in the classification of each class (left and right-hand movements), and these are corroborated by already published work in this area.

Applications of remote sensing, volume 1

NASA Technical Reports Server (NTRS)

Landgrebe, D. A. (Principal Investigator)

1977-01-01

The author has identified the following significant results. ECHO successfully exploits the redundancy of states characteristics of sampled imagery of ground scenes to achieve better classification accuracy, reduce the number of classifications required, and reduce the variability of classification results. The information required to produce ECHO classifications are cell size, cell homogeneity, cell-to-field annexation parameters, input data, and a class conditional marginal density statistics deck.

Decoding English Alphabet Letters Using EEG Phase Information

PubMed Central

Wang, YiYan; Wang, Pingxiao; Yu, Yuguo

2018-01-01

Increasing evidence indicates that the phase pattern and power of the low frequency oscillations of brain electroencephalograms (EEG) contain significant information during the human cognition of sensory signals such as auditory and visual stimuli. Here, we investigate whether and how the letters of the alphabet can be directly decoded from EEG phase and power data. In addition, we investigate how different band oscillations contribute to the classification and determine the critical time periods. An English letter recognition task was assigned, and statistical analyses were conducted to decode the EEG signal corresponding to each letter visualized on a computer screen. We applied support vector machine (SVM) with gradient descent method to learn the potential features for classification. It was observed that the EEG phase signals have a higher decoding accuracy than the oscillation power information. Low-frequency theta and alpha oscillations have phase information with higher accuracy than do other bands. The decoding performance was best when the analysis period began from 180 to 380 ms after stimulus presentation, especially in the lateral occipital and posterior temporal scalp regions (PO7 and PO8). These results may provide a new approach for brain-computer interface techniques (BCI) and may deepen our understanding of EEG oscillations in cognition. PMID:29467615

[Study on the classification of dominant pathogens related to febrile respiratory syndrome, based on the method of Bayes discriminant analysis].

PubMed

Li, X C; Li, J S; Meng, L; Bai, Y N; Yu, D S; Liu, X N; Liu, X F; Jiang, X J; Ren, X W; Yang, X T; Shen, X P; Zhang, J W

2017-08-10

Objective: To understand the dominant pathogens of febrile respiratory syndrome (FRS) patients in Gansu province and to establish the Bayes discriminant function in order to identify the patients infected with the dominant pathogens. Methods: FRS patients were collected in various sentinel hospitals of Gansu province from 2009 to 2015 and the dominant pathogens were determined by describing the composition of pathogenic profile. Significant clinical variables were selected by stepwise discriminant analysis to establish the Bayes discriminant function. Results: In the detection of pathogens for FRS, both influenza virus and rhinovirus showed higher positive rates than those caused by other viruses (13.79%, 8.63%), that accounting for 54.38%, 13.73% of total viral positive patients. Most frequently detected bacteria would include Streptococcus pneumoniae , and haemophilus influenza (44.41%, 18.07%) that accounting for 66.21% and 24.55% among the bacterial positive patients. The original-validated rate of discriminant function, established by 11 clinical variables, was 73.1%, with the cross-validated rate as 70.6%. Conclusion: Influenza virus, Rhinovirus, Streptococcus pneumoniae and Haemophilus influenzae were the dominant pathogens of FRS in Gansu province. Results from the Bayes discriminant analysis showed both higher accuracy in the classification of dominant pathogens, and applicative value for FRS.

Brain-actuated gait trainer with visual and proprioceptive feedback

NASA Astrophysics Data System (ADS)

Liu, Dong; Chen, Weihai; Lee, Kyuhwa; Chavarriaga, Ricardo; Bouri, Mohamed; Pei, Zhongcai; Millán, José del R.

2017-10-01

Objective. Brain-machine interfaces (BMIs) have been proposed in closed-loop applications for neuromodulation and neurorehabilitation. This study describes the impact of different feedback modalities on the performance of an EEG-based BMI that decodes motor imagery (MI) of leg flexion and extension. Approach. We executed experiments in a lower-limb gait trainer (the legoPress) where nine able-bodied subjects participated in three consecutive sessions based on a crossover design. A random forest classifier was trained from the offline session and tested online with visual and proprioceptive feedback, respectively. Post-hoc classification was conducted to assess the impact of feedback modalities and learning effect (an improvement over time) on the simulated trial-based performance. Finally, we performed feature analysis to investigate the discriminant power and brain pattern modulations across the subjects. Main results. (i) For real-time classification, the average accuracy was 62.33 +/- 4.95 % and 63.89 +/- 6.41 % for the two online sessions. The results were significantly higher than chance level, demonstrating the feasibility to distinguish between MI of leg extension and flexion. (ii) For post-hoc classification, the performance with proprioceptive feedback (69.45 +/- 9.95 %) was significantly better than with visual feedback (62.89 +/- 9.20 %), while there was no significant learning effect. (iii) We reported individual discriminate features and brain patterns associated to each feedback modality, which exhibited differences between the two modalities although no general conclusion can be drawn. Significance. The study reported a closed-loop brain-controlled gait trainer, as a proof of concept for neurorehabilitation devices. We reported the feasibility of decoding lower-limb movement in an intuitive and natural way. As far as we know, this is the first online study discussing the role of feedback modalities in lower-limb MI decoding. Our results suggest that proprioceptive feedback has an advantage over visual feedback, which could be used to improve robot-assisted strategies for motor training and functional recovery.

Comparison of Xenon-Enhanced Area-Detector CT and Krypton Ventilation SPECT/CT for Assessment of Pulmonary Functional Loss and Disease Severity in Smokers.

PubMed

Ohno, Yoshiharu; Fujisawa, Yasuko; Takenaka, Daisuke; Kaminaga, Shigeo; Seki, Shinichiro; Sugihara, Naoki; Yoshikawa, Takeshi

2018-02-01

The objective of this study was to compare the capability of xenon-enhanced area-detector CT (ADCT) performed with a subtraction technique and coregistered 81m Kr-ventilation SPECT/CT for the assessment of pulmonary functional loss and disease severity in smokers. Forty-six consecutive smokers (32 men and 14 women; mean age, 67.0 years) underwent prospective unenhanced and xenon-enhanced ADCT, 81m Kr-ventilation SPECT/CT, and pulmonary function tests. Disease severity was evaluated according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification. CT-based functional lung volume (FLV), the percentage of wall area to total airway area (WA%), and ventilated FLV on xenon-enhanced ADCT and SPECT/CT were calculated for each smoker. All indexes were correlated with percentage of forced expiratory volume in 1 second (%FEV 1 ) using step-wise regression analyses, and univariate and multivariate logistic regression analyses were performed. In addition, the diagnostic accuracy of the proposed model was compared with that of each radiologic index by means of McNemar analysis. Multivariate logistic regression showed that %FEV 1 was significantly affected (r = 0.77, r 2 = 0.59) by two factors: the first factor, ventilated FLV on xenon-enhanced ADCT (p < 0.0001); and the second factor, WA% (p = 0.004). Univariate logistic regression analyses indicated that all indexes significantly affected GOLD classification (p < 0.05). Multivariate logistic regression analyses revealed that ventilated FLV on xenon-enhanced ADCT and CT-based FLV significantly influenced GOLD classification (p < 0.0001). The diagnostic accuracy of the proposed model was significantly higher than that of ventilated FLV on SPECT/CT (p = 0.03) and WA% (p = 0.008). Xenon-enhanced ADCT is more effective than 81m Kr-ventilation SPECT/CT for the assessment of pulmonary functional loss and disease severity.

Prognostic value of somatosensory-evoked potentials in the surgical management of cervical spondylotic myelopathy.

PubMed

Hu, Yong; Ding, Yu; Ruan, Dike; Wong, Y W; Cheung, Kenneth M C; Luk, Keith D K

2008-05-01

Preoperative somatosensory-evoked potentials (SEPs) were retrospectively analyzed and classified, and compared with surgical outcome. To evaluate the value of the preoperative SEP waveform in predicting the clinical outcome after surgical management of cervical spondylotic myelopathy (CSM). SEPs have played an important role in spinal surgery. However, the value of SEPs in predicting the outcome of surgery for CSM remains controversial. This study enrolled 76 CSM patients who underwent surgical intervention. Median nerve SEPs were recorded before surgery. The Japanese Orthopedic Association (JOA) scoring system was used to evaluate the neurologic function before surgery and at postoperative follow-up at 1, 3, 6, 12, and 24 months. Patients were divided into 5 groups according to the classification of their preoperative SEP waveforms. Group I patients had normal SEPs, group IIa had normal latency and abnormal amplitude, group IIb had abnormal latency and normal amplitude, group III had abnormal latency and amplitude, and group IV had immeasurable waveforms. The myelopathic disability scores and surgical outcomes in different groups were compared by the Kruskal-Wallis test. The SEP classification was found to be significantly associated with the JOA score (Pearson's chi test, chi = 53.9, P < 0.05). There were no significant differences in JOA score recovery at different follow-up times within any SEP group. At 24 months after surgery, there was no significant difference in the recovery ratio between groups I and IIa, or between groups IIb and III (Kruskal-Wallis test, P > 0.05). However, the recovery ratio was significantly higher in groups I and IIa than in all the other groups (Kruskal-Wallis test, P < 0.05), and in groups IIb and III than in group IV (Kruskal-Wallis test, P < 0.05). SEP classification correlates well with CSM disability and postoperative recovery ratio. Median nerve SEP recordings would be a valuable and practical tool for the diagnosis and prognosis of myelopathy.

A graduated food addiction classification approach significantly differentiates obesity among people with type 2 diabetes.

PubMed

Raymond, Karren-Lee; Kannis-Dymand, Lee; Lovell, Geoff P

2016-10-01

This study examined a graduated severity level approach to food addiction classification against associations with World Health Organization obesity classifications (body mass index, kg/m 2 ) among 408 people with type 2 diabetes. A survey including the Yale Food Addiction Scale and several demographic questions demonstrated four distinct Yale Food Addiction Scale symptom severity groups (in line with Diagnostic and Statistical Manual of Mental Disorders (5th ed.) severity indicators): non-food addiction, mild food addiction, moderate food addiction and severe food addiction. Analysis of variance with post hoc tests demonstrated each severity classification group was significantly different in body mass index, with each grouping being associated with increased World Health Organization obesity classifications. These findings have implications for diagnosing food addiction and implementing treatment and prevention methodologies of obesity among people with type 2 diabetes.

Land Cover Classification in a Complex Urban-Rural Landscape with Quickbird Imagery

PubMed Central

Moran, Emilio Federico.

2010-01-01

High spatial resolution images have been increasingly used for urban land use/cover classification, but the high spectral variation within the same land cover, the spectral confusion among different land covers, and the shadow problem often lead to poor classification performance based on the traditional per-pixel spectral-based classification methods. This paper explores approaches to improve urban land cover classification with Quickbird imagery. Traditional per-pixel spectral-based supervised classification, incorporation of textural images and multispectral images, spectral-spatial classifier, and segmentation-based classification are examined in a relatively new developing urban landscape, Lucas do Rio Verde in Mato Grosso State, Brazil. This research shows that use of spatial information during the image classification procedure, either through the integrated use of textural and spectral images or through the use of segmentation-based classification method, can significantly improve land cover classification performance. PMID:21643433

41 CFR 105-62.101 - Security classification categories.

Code of Federal Regulations, 2013 CFR

2013-07-01

... 41 Public Contracts and Property Management 3 2013-07-01 2013-07-01 false Security classification... classification categories. As set forth in Executive Order 12065, official information or material which requires... three categories: Namely, Top Secret, Secret, or Confidential, depending on its degree of significance...

41 CFR 105-62.101 - Security classification categories.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 41 Public Contracts and Property Management 3 2012-01-01 2012-01-01 false Security classification... classification categories. As set forth in Executive Order 12065, official information or material which requires... three categories: Namely, Top Secret, Secret, or Confidential, depending on its degree of significance...

41 CFR 105-62.101 - Security classification categories.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 41 Public Contracts and Property Management 3 2014-01-01 2014-01-01 false Security classification... classification categories. As set forth in Executive Order 12065, official information or material which requires... three categories: Namely, Top Secret, Secret, or Confidential, depending on its degree of significance...

Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech☆

PubMed Central

Cao, Houwei; Verma, Ragini; Nenkova, Ani

2014-01-01

We introduce a ranking approach for emotion recognition which naturally incorporates information about the general expressivity of speakers. We demonstrate that our approach leads to substantial gains in accuracy compared to conventional approaches. We train ranking SVMs for individual emotions, treating the data from each speaker as a separate query, and combine the predictions from all rankers to perform multi-class prediction. The ranking method provides two natural benefits. It captures speaker specific information even in speaker-independent training/testing conditions. It also incorporates the intuition that each utterance can express a mix of possible emotion and that considering the degree to which each emotion is expressed can be productively exploited to identify the dominant emotion. We compare the performance of the rankers and their combination to standard SVM classification approaches on two publicly available datasets of acted emotional speech, Berlin and LDC, as well as on spontaneous emotional data from the FAU Aibo dataset. On acted data, ranking approaches exhibit significantly better performance compared to SVM classification both in distinguishing a specific emotion from all others and in multi-class prediction. On the spontaneous data, which contains mostly neutral utterances with a relatively small portion of less intense emotional utterances, ranking-based classifiers again achieve much higher precision in identifying emotional utterances than conventional SVM classifiers. In addition, we discuss the complementarity of conventional SVM and ranking-based classifiers. On all three datasets we find dramatically higher accuracy for the test items on whose prediction the two methods agree compared to the accuracy of individual methods. Furthermore on the spontaneous data the ranking and standard classification are complementary and we obtain marked improvement when we combine the two classifiers by late-stage fusion. PMID:25422534

Fractures of the Tibial Plateau Involve Similar Energies as the Tibial Pilon but Greater Articular Surface Involvement

PubMed Central

Dibbern, Kevin; Kempton, Laurence B.; Higgins, Thomas F.; Morshed, Saam; McKinley, Todd O.; Marsh, J. Lawrence; Anderson, Donald D.

2016-01-01

Patients with tibial pilon fractures have a higher incidence of post-traumatic osteoarthritis than those with fractures of the tibial plateau. This may indicate that pilon fractures present a greater mechanical insult to the joint than do plateau fractures. We tested the hypothesis that fracture energy and articular fracture edge length, two independent indicators of severity, are higher in pilon than plateau fractures. We also evaluated if clinical fracture classification systems accurately reflect severity. Seventy-five tibial plateau fractures and fifty-two tibial pilon fractures from a multi-institutional study were selected to span the spectrum of severity. Fracture severity measures were calculated using objective CT-based image analysis methods. The ranges of fracture energies measured for tibial plateau and pilon fractures were 3.2 to 33.2 Joules (J) and 3.6 to 32.2 J, respectively, and articular fracture edge lengths were 68.0 to 493.0 mm and 56.1 to 288.6 mm, respectively. There were no differences in the fracture energies between the two fracture types, but plateau fractures had greater articular fracture edge lengths (p<0.001). The clinical fracture classifications generally reflected severity, but there was substantial overlap of fracture severity measures between different classes. Clinical Significance Similar fracture energies with different degrees of articular surface involvement suggest a possible explanation for dissimilar rates of post-traumatic osteoarthritis for fractures of the tibial plateau compared to the tibial pilon. The substantial overlap of severity measures between different fracture classes may well have confounded prior clinical studies relying on fracture classification as a surrogate for severity. PMID:27381653

Characteristics and outcomes of ventilated patients according to time to liberation from mechanical ventilation.

PubMed

Peñuelas, Oscar; Frutos-Vivar, Fernando; Fernández, Cristina; Anzueto, Antonio; Epstein, Scott K; Apezteguía, Carlos; González, Marco; Nin, Nicholas; Raymondos, Konstantinos; Tomicic, Vinko; Desmery, Pablo; Arabi, Yaseen; Pelosi, Paolo; Kuiper, Michael; Jibaja, Manuel; Matamis, Dimitros; Ferguson, Niall D; Esteban, Andrés

2011-08-15

A new classification of patients based on the duration of liberation of mechanical ventilation has been proposed. To analyze outcomes based on the new weaning classification in a cohort of mechanically ventilated patients. Secondary analysis included 2,714 patients who were weaned and underwent scheduled extubation from a cohort of 4,968 adult patients mechanically ventilated for more than 12 hours. Patients were classified according to a new weaning classification: 1,502 patients (55%) as simple weaning,1,058 patients (39%) as difficult weaning, and 154 (6%) as prolonged weaning.Variables associated with prolonged weaning(.7d)were: severity at admission (odds ratio [OR] per unit of Simplified Acute Physiology Score II, 1.01; 95% confidence interval [CI], 1.001–1.02), duration of mechanical ventilation before first attempt of weaning (OR per day, 1.10; 95% CI, 1.06–1.13), chronic pulmonary disease other than chronic obstructive pulmonary disease (OR,13.23; 95% CI, 3.44–51.05), pneumonia as the reason to start mechanical ventilation (OR, 1.82; 95% CI, 1.07–3.08), and level of positive end-expiratory pressure applied before weaning (OR per unit,1.09; 95% CI, 1.04–1.14). The prolonged weaning group had a nonsignificant trend toward a higher rate of reintubation (P ¼ 0.08),tracheostomy (P ¼ 0.15), and significantly longer length of stay and higher mortality in the intensive care unit (OR for death, 1.97;95%CI, 1.17–3.31). The adjusted probability of death remained constant until Day 7, at which point it increased to 12.1%.

Speaker-sensitive emotion recognition via ranking: Studies on acted and spontaneous speech☆

PubMed

Cao, Houwei; Verma, Ragini; Nenkova, Ani

2015-01-01

We introduce a ranking approach for emotion recognition which naturally incorporates information about the general expressivity of speakers. We demonstrate that our approach leads to substantial gains in accuracy compared to conventional approaches. We train ranking SVMs for individual emotions, treating the data from each speaker as a separate query, and combine the predictions from all rankers to perform multi-class prediction. The ranking method provides two natural benefits. It captures speaker specific information even in speaker-independent training/testing conditions. It also incorporates the intuition that each utterance can express a mix of possible emotion and that considering the degree to which each emotion is expressed can be productively exploited to identify the dominant emotion. We compare the performance of the rankers and their combination to standard SVM classification approaches on two publicly available datasets of acted emotional speech, Berlin and LDC, as well as on spontaneous emotional data from the FAU Aibo dataset. On acted data, ranking approaches exhibit significantly better performance compared to SVM classification both in distinguishing a specific emotion from all others and in multi-class prediction. On the spontaneous data, which contains mostly neutral utterances with a relatively small portion of less intense emotional utterances, ranking-based classifiers again achieve much higher precision in identifying emotional utterances than conventional SVM classifiers. In addition, we discuss the complementarity of conventional SVM and ranking-based classifiers. On all three datasets we find dramatically higher accuracy for the test items on whose prediction the two methods agree compared to the accuracy of individual methods. Furthermore on the spontaneous data the ranking and standard classification are complementary and we obtain marked improvement when we combine the two classifiers by late-stage fusion.

Classification of materials using nuclear magnetic resonance dispersion and/or x-ray absorption

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

Espy, Michelle A.; Matlashov, Andrei N.; Schultz, Larry J.

Methods for determining the identity of a substance are provided. A classification parameter set is defined to allow identification of substances that previously could not be identified or to allow identification of substances with a higher degree of confidence. The classification parameter set may include at least one of relative nuclear susceptibility (RNS) or an x-ray linear attenuation coefficient (LAC). RNS represents the density of hydrogen nuclei present in a substance relative to the density of hydrogen nuclei present in water. The extended classification parameter set may include T.sub.1, T.sub.2, and/or T.sub.1.rho. as well as at least one additional classificationmore » parameter comprising one of RNS or LAC. Values obtained for additional classification parameters as well as values obtained for T.sub.1, T.sub.2, and T.sub.1.rho. can be compared to known classification parameter values to determine whether a particular substance is a known material.« less

Psychological Features and Their Relationship to Movement-Based Subgroups in People Living With Low Back Pain.

PubMed

Karayannis, Nicholas V; Jull, Gwendolen A; Nicholas, Michael K; Hodges, Paul W

2018-01-01

To determine the distribution of higher psychological risk features within movement-based subgroups for people with low back pain (LBP). Cross-sectional observational study. Participants were recruited from physiotherapy clinics and community advertisements. Measures were collected at a university outpatient-based physiotherapy clinic. People (N=102) seeking treatment for LBP. Participants were subgrouped according to 3 classification schemes: Mechanical Diagnosis and Treatment (MDT), Treatment-Based Classification (TBC), and O'Sullivan Classification (OSC). Questionnaires were used to categorize low-, medium-, and high-risk features based on depression, anxiety, and stress (Depression, Anxiety, and Stress Scale-21 Items); fear avoidance (Fear-Avoidance Beliefs Questionnaire); catastrophizing and coping (Pain-Related Self-Symptoms Scale); and self-efficacy (Pain Self-Efficacy Questionnaire). Psychological risk profiles were compared between movement-based subgroups within each scheme. Scores across all questionnaires revealed that most patients had low psychological risk profiles, but there were instances of higher (range, 1%-25%) risk profiles within questionnaire components. The small proportion of individuals with higher psychological risk scores were distributed between subgroups across TBC, MDT, and OSC schemes. Movement-based subgrouping alone cannot inform on individuals with higher psychological risk features. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Closed-loop adaptation of neurofeedback based on mental effort facilitates reinforcement learning of brain self-regulation.

PubMed

Bauer, Robert; Fels, Meike; Royter, Vladislav; Raco, Valerio; Gharabaghi, Alireza

2016-09-01

Considering self-rated mental effort during neurofeedback may improve training of brain self-regulation. Twenty-one healthy, right-handed subjects performed kinesthetic motor imagery of opening their left hand, while threshold-based classification of beta-band desynchronization resulted in proprioceptive robotic feedback. The experiment consisted of two blocks in a cross-over design. The participants rated their perceived mental effort nine times per block. In the adaptive block, the threshold was adjusted on the basis of these ratings whereas adjustments were carried out at random in the other block. Electroencephalography was used to examine the cortical activation patterns during the training sessions. The perceived mental effort was correlated with the difficulty threshold of neurofeedback training. Adaptive threshold-setting reduced mental effort and increased the classification accuracy and positive predictive value. This was paralleled by an inter-hemispheric cortical activation pattern in low frequency bands connecting the right frontal and left parietal areas. Optimal balance of mental effort was achieved at thresholds significantly higher than maximum classification accuracy. Rating of mental effort is a feasible approach for effective threshold-adaptation during neurofeedback training. Closed-loop adaptation of the neurofeedback difficulty level facilitates reinforcement learning of brain self-regulation. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Overweight and obesity prevalence among Cree youth of Eeyou Istchee according to three body mass index classification systems.

PubMed

St-Jean, Audray; Meziou, Salma; Ayotte, Pierre; Lucas, Michel

2017-11-22

Little is known about the suitability of three commonly used body mass index (BMI) classification systems for Indigenous youth. We estimated overweight and obesity prevalence among Cree youth of Eeyou Istchee according to three BMI classification systems, assessed the level of agreement between them, and evaluated their accuracy through body fat and cardiometabolic risk factors. Data on 288 youth (aged 8-17 years) were collected. Overweight and obesity prevalence were estimated with Centers for Disease Control and Prevention (CDC), International Obesity Task Force (IOTF) and World Health Organization (WHO) criteria. Agreement was measured with weighted kappa (κw). Associations with body fat and cardiometabolic risk factors were evaluated by analysis of variance. Obesity prevalence was 42.7% with IOTF, 47.2% with CDC, and 49.3% with WHO criteria. Agreement was almost perfect between IOTF and CDC (κw = 0.93), IOTF and WHO (κw = 0.91), and WHO and CDC (κw = 0.94). Means of body fat and cardiometabolic risk factors were significantly higher (P trend  < 0.001) from normal weight to obesity, regardless of the system used. Youth considered overweight by IOTF but obese by CDC or WHO exhibited less severe clinical obesity. IOTF seems to be more accurate in identifying obesity in Cree youth.

The Effect of Sub-Aperture in DRIA Framework Applied on Multi-Aspect PolSAR Data

NASA Astrophysics Data System (ADS)

Xue, Feiteng; Yin, Qiang; Lin, Yun; Hong, Wen

2016-08-01

Multi-aspect SAR is a new remote sensing technology, achieves consecutive data in large look angle as platform moves. Multi- aspect observation brings higher resolution and SNR to SAR picture. Multi-aspect PolSAR data can increase the accuracy of target identify and classification because it contains the 3-D polarimetric scattering properties.DRIA(detecting-removing-incoherent-adding)framework is a multi-aspect PolSAR data processing method. In this method, the anisotropic and isotropic scattering is separated by maximum- likelihood ratio test. The anisotropic scattering is removed to gain a removal series. The isotropic scattering is incoherent added to gain a high resolution picture. The removal series describes the anisotropic scattering property and is used in features extraction and classification.This article focuses on the effect brought by difference of sub-aperture numbers in anisotropic scattering detection and removal. The more sub-apertures are, the less look angle is. Artificial target has anisotropic scattering because of Bragg resonances. The increase of sub-aperture number brings more accurate observation in azimuth though the quality of each single image may loss. The accuracy of classification in agricultural fields is affected by the anisotropic scattering brought by Bragg resonances. The size of the sub-aperture has a significant effect in the removal result of Bragg resonances.

Component analysis of somatosensory evoked potentials for identifying spinal cord injury location.

PubMed

Wang, Yazhou; Li, Guangsheng; Luk, Keith D K; Hu, Yong

2017-05-24

This study aims to determine whether the time-frequency components (TFCs) of somatosensory evoked potentials (SEPs) can be used to identify the specific location of a compressive spinal cord injury using a classification technique. Waveforms of SEPs after compressive injuries at various locations (C4, C5 and C6) in rat spinal cords were decomposed into a series of TFCs using a high-resolution time-frequency analysis method. A classification method based on support vector machine (SVM) was applied to the distributions of these TFCs among different pathological locations. The difference among injury locations manifests itself in different categories of SEP TFCs. High-energy TFCs of normal-state SEPs have significantly higher power and frequency than those of injury-state SEPs. The location of C5 is characterized by a unique distribution pattern of middle-energy TFCs. The difference between C4 and C6 is evidenced by the distribution pattern of low-energy TFCs. The proposed classification method based on SEP TFCs offers a discrimination accuracy of 80.2%. In this study, meaningful information contained in various SEP components was investigated and used to propose a new application of SEPs for identification of the location of pathological changes in the cervical spinal cord.

A Classification of Institutions of Higher Education. 1987 Edition.

ERIC Educational Resources Information Center

Carnegie Foundation for the Advancement of Teaching, Princeton, NJ.

Statistics classifying American colleges and universities according to their educational functions and missions are included. Rather than creating a hierarchy, this information groups institutions by shared characteristics. Changes in higher education are portrayed, and a continued growth in institutions of higher education is noted. There are…

Comparison of the current AJCC-TNM numeric-based with a new anatomical location-based lymph node staging system for gastric cancer: A western experience.

PubMed

Galizia, Gennaro; Lieto, Eva; Auricchio, Annamaria; Cardella, Francesca; Mabilia, Andrea; Diana, Anna; Castellano, Paolo; De Vita, Ferdinando; Orditura, Michele

2017-01-01

In gastric cancer, the current AJCC numeric-based lymph node staging does not provide information on the anatomical extent of the disease and lymphadenectomy. A new anatomical location-based node staging, proposed by Choi, has shown better prognostic performance, thus soliciting Western world validation. Data from 284 gastric cancers undergoing radical surgery at the Second University of Naples from 2000 to 2014 were reviewed. The lymph nodes were reclassified into three groups (lesser and greater curvature, and extraperigastric nodes); presence of any metastatic lymph node in a given group was considered positive, prompting a new N and TNM stage classification. Receiver-operating-characteristic (ROC) curves for censored survival data and bootstrap methods were used to compare the capability of the two models to predict tumor recurrence. More than one third of node positive patients were reclassified into different N and TNM stages by the new system. Compared to the current staging system, the new classification significantly correlated with tumor recurrence rates and displayed improved indices of prognostic performance, such as the Bayesian information criterion and the Harrell C-index. Higher values at survival ROC analysis demonstrated a significantly better stratification of patients by the new system, mostly in the early phase of the follow-up, with a worse prognosis in more advanced new N stages, despite the same current N stage. This study suggests that the anatomical location-based classification of lymph node metastasis may be an important tool for gastric cancer prognosis and should be considered for future revision of the TNM staging system.

Comparison and optimization of machine learning methods for automated classification of circulating tumor cells.

PubMed

Lannin, Timothy B; Thege, Fredrik I; Kirby, Brian J

2016-10-01

Advances in rare cell capture technology have made possible the interrogation of circulating tumor cells (CTCs) captured from whole patient blood. However, locating captured cells in the device by manual counting bottlenecks data processing by being tedious (hours per sample) and compromises the results by being inconsistent and prone to user bias. Some recent work has been done to automate the cell location and classification process to address these problems, employing image processing and machine learning (ML) algorithms to locate and classify cells in fluorescent microscope images. However, the type of machine learning method used is a part of the design space that has not been thoroughly explored. Thus, we have trained four ML algorithms on three different datasets. The trained ML algorithms locate and classify thousands of possible cells in a few minutes rather than a few hours, representing an order of magnitude increase in processing speed. Furthermore, some algorithms have a significantly (P < 0.05) higher area under the receiver operating characteristic curve than do other algorithms. Additionally, significant (P < 0.05) losses to performance occur when training on cell lines and testing on CTCs (and vice versa), indicating the need to train on a system that is representative of future unlabeled data. Optimal algorithm selection depends on the peculiarities of the individual dataset, indicating the need of a careful comparison and optimization of algorithms for individual image classification tasks. © 2016 International Society for Advancement of Cytometry. © 2016 International Society for Advancement of Cytometry.

Use of signal analysis of heart sounds and murmurs to assess severity of mitral valve regurgitation attributable to myxomatous mitral valve disease in dogs.

PubMed

Ljungvall, Ingrid; Ahlstrom, Christer; Höglund, Katja; Hult, Peter; Kvart, Clarence; Borgarelli, Michele; Ask, Per; Häggström, Jens

2009-05-01

To investigate use of signal analysis of heart sounds and murmurs in assessing severity of mitral valve regurgitation (mitral regurgitation [MR]) in dogs with myxomatous mitral valve disease (MMVD). 77 client-owned dogs. Cardiac sounds were recorded from dogs evaluated by use of auscultatory and echocardiographic classification systems. Signal analysis techniques were developed to extract 7 sound variables (first frequency peak, murmur energy ratio, murmur duration > 200 Hz, sample entropy and first minimum of the auto mutual information function of the murmurs, and energy ratios of the first heart sound [S1] and second heart sound [S2]). Significant associations were detected between severity of MR and all sound variables, except the energy ratio of S1. An increase in severity of MR resulted in greater contribution of higher frequencies, increased signal irregularity, and decreased energy ratio of S2. The optimal combination of variables for distinguishing dogs with high-intensity murmurs from other dogs was energy ratio of S2 and murmur duration > 200 Hz (sensitivity, 79%; specificity, 71%) by use of the auscultatory classification. By use of the echocardiographic classification, corresponding variables were auto mutual information, first frequency peak, and energy ratio of S2 (sensitivity, 88%; specificity, 82%). Most of the investigated sound variables were significantly associated with severity of MR, which indicated a powerful diagnostic potential for monitoring MMVD. Signal analysis techniques could be valuable for clinicians when performing risk assessment or determining whether special care and more extensive examinations are required.

Feeding and gastrointestinal problems in children with cerebral palsy.

PubMed

Erkin, Gulten; Culha, Canan; Ozel, Sumru; Kirbiyik, Eylem Gulsen

2010-09-01

The aim of our study was to identify feeding and gastrointestinal system (GIS) problems in children with cerebral palsy (CP), and to evaluate the relationship between these problems and the severity of CP. A total of 120 children with CP were enrolled consecutively into the study (67 males, 53 females; mean age: 6.0±2.4 years; range: 2-12 years). The children were classified according to the Swedish classification as diplegic, hemiplegic, or quadriplegic. Severity of CP was classified based on the Gross Motor Function Classification System. The amount of time that the caregiver allocated to mealtimes, modifications of the food, as well as feeding and GIS problems was evaluated. Feeding dysfunction was classified as mild, moderate, or severe. Comparisons of GIS and feeding disorders and the severity of CP were carried out using χ test. The results indicated lack of appetite in 46 of the 120 children (38.3%), sialorrhea in 37 (30.8%), constipation in 30 (25%), difficulty in swallowing in 23 (19.2%), and feeding dysfunction in 26 (21.7%). On the basis of the Gross Motor Function Classification System (GMFCS), the incidence of GIS problems and feeding dysfunction was found to be significantly higher in the children classified in the severe group. The time taken to consume meals was significantly longer among children with feeding dysfunction. Feeding and GIS problems are frequent in children with CP, and more marked in those with severe CP. Approximately one fourth of children with CP suffer from feeding dysfunction, and more time has to be allocated to consume meals.

Changes in classification of genetic variants in BRCA1 and BRCA2.

PubMed

Kast, Karin; Wimberger, Pauline; Arnold, Norbert

2018-02-01

Classification of variants of unknown significance (VUS) in the breast cancer genes BRCA1 and BRCA2 changes with accumulating evidence for clinical relevance. In most cases down-staging towards neutral variants without clinical significance is possible. We searched the database of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) for changes in classification of genetic variants as an update to our earlier publication on genetic variants in the Centre of Dresden. Changes between 2015 and 2017 were recorded. In the group of variants of unclassified significance (VUS, Class 3, uncertain), only changes of classification towards neutral genetic variants were noted. In BRCA1, 25% of the Class 3 variants (n = 2/8) changed to Class 2 (likely benign) and Class 1 (benign). In BRCA2, in 50% of the Class 3 variants (n = 16/32), a change to Class 2 (n = 10/16) or Class 1 (n = 6/16) was observed. No change in classification was noted in Class 4 (likely pathogenic) and Class 5 (pathogenic) genetic variants in both genes. No up-staging from Class 1, Class 2 or Class 3 to more clinical significance was observed. All variants with a change in classification in our cohort were down-staged towards no clinical significance by a panel of experts of the German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC). Prevention in families with Class 3 variants should be based on pedigree based risks and should not be guided by the presence of a VUS.

Assessment of the correlation between serum prolidase and alpha-fetoprotein levels in patients with hepatocellular carcinoma

PubMed Central

Uygun Ilikhan, Sevil; Bilici, Muammer; Sahin, Hatice; Demir Akca, Ayşe Semra; Can, Murat; Oz, Ibrahim Ilker; Guven, Berrak; Buyukuysal, M Cagatay; Ustundag, Yucel

2015-01-01

AIM: To determine the predictive value of increased prolidase activity that reflects increased collagen turnover in patients with hepatocellular carcinoma (HCC). METHODS: Sixty-eight patients with HCC (mean age of 69.1 ± 10.1), 31 cirrhosis patients (mean age of 59.3 ± 6.3) and 33 healthy volunteers (mean age of 51.4 ± 12.6) were enrolled in this study. Univariate and multivariate analysis were used to evaluate the association of serum α-fetoprotein (AFP) values with HCC clinicopathological features, such as tumor size, number and presence of vascular and macrovascular invasion. The patients with HCC were divided into groups according to tumor size, number and presence of vascular invasion (diameters; ≤ 3 cm, 3-5 cm and ≥ 5 cm, number; 1, 2 and ≥ 3, macrovascular invasion; yes/no). Barcelona-clinic liver cancer (BCLC) criteria were used to stage HCC patients. Serum samples for measurement of prolidase and alpha-fetoprotein levels were kept at -80 °C until use. Prolidase levels were measured spectrophotometrically and AFP concentrations were determined by a chemiluminescence immunometric commercial diagnostic assay. RESULTS: In patients with HCC, prolidase and AFP values were evaluated according to tumor size, number, presence of macrovascular invasion and BCLC staging classification. Prolidase values were significantly higher in patients with HCC compared with controls (P < 0.001). Prolidase levels were significantly associated with tumor size and number (P < 0.001, P = 0.002, respectively). Prolidase levels also differed in patients in terms of BCLC staging classification (P < 0.001). Furthermore the prolidase levels in HCC patients showed a significant difference compared with patients with cirrhosis (P < 0.001). In HCC patients grouped according to tumor size, number and BCLC staging classification, AFP values differed separately (P = 0.032, P = 0.038, P = 0.015, respectively). In patients with HCC, there was a significant correlation (r = 0.616; P < 0.001) between prolidase and AFP values in terms of tumor size, number and BCLC staging classification, whereas the presence of macrovascular invasion did not show a positive association with serum prolidase and AFP levels. CONCLUSION: Considering the levels of both serum prolidase and AFP could contribute to the early diagnosing of hepatocellular carcinoma. PMID:26078578

Assessment of the correlation between serum prolidase and alpha-fetoprotein levels in patients with hepatocellular carcinoma.

PubMed

Ilikhan, Sevil Uygun; Bilici, Muammer; Sahin, Hatice; Akca, Ayşe Semra Demir; Can, Murat; Oz, Ibrahim Ilker; Guven, Berrak; Buyukuysal, M Cagatay; Ustundag, Yucel

2015-06-14

To determine the predictive value of increased prolidase activity that reflects increased collagen turnover in patients with hepatocellular carcinoma (HCC). Sixty-eight patients with HCC (mean age of 69.1 ± 10.1), 31 cirrhosis patients (mean age of 59.3 ± 6.3) and 33 healthy volunteers (mean age of 51.4 ± 12.6) were enrolled in this study. Univariate and multivariate analysis were used to evaluate the association of serum α-fetoprotein (AFP) values with HCC clinicopathological features, such as tumor size, number and presence of vascular and macrovascular invasion. The patients with HCC were divided into groups according to tumor size, number and presence of vascular invasion (diameters; ≤ 3 cm, 3-5 cm and ≥ 5 cm, number; 1, 2 and ≥ 3, macrovascular invasion; yes/no). Barcelona-clinic liver cancer (BCLC) criteria were used to stage HCC patients. Serum samples for measurement of prolidase and alpha-fetoprotein levels were kept at -80 °C until use. Prolidase levels were measured spectrophotometrically and AFP concentrations were determined by a chemiluminescence immunometric commercial diagnostic assay. In patients with HCC, prolidase and AFP values were evaluated according to tumor size, number, presence of macrovascular invasion and BCLC staging classification. Prolidase values were significantly higher in patients with HCC compared with controls (P < 0.001). Prolidase levels were significantly associated with tumor size and number (P < 0.001, P = 0.002, respectively). Prolidase levels also differed in patients in terms of BCLC staging classification (P < 0.001). Furthermore the prolidase levels in HCC patients showed a significant difference compared with patients with cirrhosis (P < 0.001). In HCC patients grouped according to tumor size, number and BCLC staging classification, AFP values differed separately (P = 0.032, P = 0.038, P = 0.015, respectively). In patients with HCC, there was a significant correlation (r = 0.616; P < 0.001) between prolidase and AFP values in terms of tumor size, number and BCLC staging classification, whereas the presence of macrovascular invasion did not show a positive association with serum prolidase and AFP levels. Considering the levels of both serum prolidase and AFP could contribute to the early diagnosing of hepatocellular carcinoma.

Diagnostic discrepancies in retinopathy of prematurity classification

PubMed Central

Campbell, J. Peter; Ryan, Michael C.; Lore, Emily; Tian, Peng; Ostmo, Susan; Jonas, Karyn; Chan, R.V. Paul; Chiang, Michael F.

2016-01-01

Objective To identify the most common areas for discrepancy in retinopathy of prematurity (ROP) classification between experts. Design Prospective cohort study. Subjects, Participants, and/or Controls 281 infants were identified as part of a multi-center, prospective, ROP cohort study from 7 participating centers. Each site had participating ophthalmologists who provided the clinical classification after routine examination using binocular indirect ophthalmoscopy (BIO), and obtained wide-angle retinal images, which were independently classified by two study experts. Methods Wide-angle retinal images (RetCam; Clarity Medical Systems, Pleasanton, CA) were obtained from study subjects, and two experts evaluated each image using a secure web-based module. Image-based classifications for zone, stage, plus disease, overall disease category (no ROP, mild ROP, Type II or pre-plus, and Type I) were compared between the two experts, and to the clinical classification obtained by BIO. Main Outcome Measures Inter-expert image-based agreement and image-based vs. ophthalmoscopic diagnostic agreement using absolute agreement and weighted kappa statistic. Results 1553 study eye examinations from 281 infants were included in the study. Experts disagreed on the stage classification in 620/1553 (40%) of comparisons, plus disease classification (including pre-plus) in 287/1553 (18%), zone in 117/1553 (8%), and overall ROP category in 618/1553 (40%). However, agreement for presence vs. absence of type 1 disease was >95%. There were no differences between image-based and clinical classification except for zone III disease. Conclusions The most common area of discrepancy in ROP classification is stage, although inter-expert agreement for clinically-significant disease such as presence vs. absence of type 1 and type 2 disease is high. There were no differences between image-based grading and the clinical exam in the ability to detect clinically-significant disease. This study provides additional evidence that image-based classification of ROP reliably detects clinically significant levels of ROP with high accuracy compared to the clinical exam. PMID:27238376

Knowledge categorization affects popularity and quality of Wikipedia articles

PubMed Central

Lomi, Alessandro

2018-01-01

The existence of a shared classification system is essential to knowledge production, transfer, and sharing. Studies of knowledge classification, however, rarely consider the fact that knowledge categories exist within hierarchical information systems designed to facilitate knowledge search and discovery. This neglect is problematic whenever information about categorical membership is itself used to evaluate the quality of the items that the category contains. The main objective of this paper is to show that the effects of category membership depend on the position that a category occupies in the hierarchical knowledge classification system of Wikipedia—an open knowledge production and sharing platform taking the form of a freely accessible on-line encyclopedia. Using data on all English-language Wikipedia articles, we examine how the position that a category occupies in the classification hierarchy affects the attention that articles in that category attract from Wikipedia editors, and their evaluation of quality of the Wikipedia articles. Specifically, we show that Wikipedia articles assigned to coarse-grained categories (i. e., categories that occupy higher positions in the hierarchical knowledge classification system) garner more attention from Wikipedia editors (i. e., attract a higher volume of text editing activity), but receive lower evaluations (i. e., they are considered to be of lower quality). The negative relation between attention and quality implied by this result is consistent with current theories of social categorization, but it also goes beyond available results by showing that the effects of categorization on evaluation depend on the position that a category occupies in a hierarchical knowledge classification system. PMID:29293627

Knowledge categorization affects popularity and quality of Wikipedia articles.

PubMed

Lerner, Jürgen; Lomi, Alessandro

2018-01-01

The existence of a shared classification system is essential to knowledge production, transfer, and sharing. Studies of knowledge classification, however, rarely consider the fact that knowledge categories exist within hierarchical information systems designed to facilitate knowledge search and discovery. This neglect is problematic whenever information about categorical membership is itself used to evaluate the quality of the items that the category contains. The main objective of this paper is to show that the effects of category membership depend on the position that a category occupies in the hierarchical knowledge classification system of Wikipedia-an open knowledge production and sharing platform taking the form of a freely accessible on-line encyclopedia. Using data on all English-language Wikipedia articles, we examine how the position that a category occupies in the classification hierarchy affects the attention that articles in that category attract from Wikipedia editors, and their evaluation of quality of the Wikipedia articles. Specifically, we show that Wikipedia articles assigned to coarse-grained categories (i. e., categories that occupy higher positions in the hierarchical knowledge classification system) garner more attention from Wikipedia editors (i. e., attract a higher volume of text editing activity), but receive lower evaluations (i. e., they are considered to be of lower quality). The negative relation between attention and quality implied by this result is consistent with current theories of social categorization, but it also goes beyond available results by showing that the effects of categorization on evaluation depend on the position that a category occupies in a hierarchical knowledge classification system.

Classifying four-category visual objects using multiple ERP components in single-trial ERP.

PubMed

Qin, Yu; Zhan, Yu; Wang, Changming; Zhang, Jiacai; Yao, Li; Guo, Xiaojuan; Wu, Xia; Hu, Bin

2016-08-01

Object categorization using single-trial electroencephalography (EEG) data measured while participants view images has been studied intensively. In previous studies, multiple event-related potential (ERP) components (e.g., P1, N1, P2, and P3) were used to improve the performance of object categorization of visual stimuli. In this study, we introduce a novel method that uses multiple-kernel support vector machine to fuse multiple ERP component features. We investigate whether fusing the potential complementary information of different ERP components (e.g., P1, N1, P2a, and P2b) can improve the performance of four-category visual object classification in single-trial EEGs. We also compare the classification accuracy of different ERP component fusion methods. Our experimental results indicate that the classification accuracy increases through multiple ERP fusion. Additional comparative analyses indicate that the multiple-kernel fusion method can achieve a mean classification accuracy higher than 72 %, which is substantially better than that achieved with any single ERP component feature (55.07 % for the best single ERP component, N1). We compare the classification results with those of other fusion methods and determine that the accuracy of the multiple-kernel fusion method is 5.47, 4.06, and 16.90 % higher than those of feature concatenation, feature extraction, and decision fusion, respectively. Our study shows that our multiple-kernel fusion method outperforms other fusion methods and thus provides a means to improve the classification performance of single-trial ERPs in brain-computer interface research.

Open vs Laparoscopic Simple Prostatectomy: A Comparison of Initial Outcomes and Cost.

PubMed

Demir, Aslan; Günseren, Kadir Ömür; Kordan, Yakup; Yavaşçaoğlu, İsmet; Vuruşkan, Berna Aytaç; Vuruşkan, Hakan

2016-08-01

We compared the cost-effectiveness of laparoscopic simple prostatectomy (LSP) vs open prostatectomy (OP). A total of 73 men treated for benign prostatic hyperplasia were enrolled for OP and LSP in groups 1 and 2, respectively. The findings were recorded perioperative, including operation time (OT), blood lost, transfusion rate, conversion to the open surgery, and the complications according to the Clavien Classification. The postoperative findings, including catheterization and drainage time, the amount of analgesic used, hospitalization time, postoperative complications, international prostate symptom score (IPSS) and International Index of Erectile Function (IIEF) scores, the extracted prostate weight, the uroflowmeter, as well as postvoiding residual (PVR) and quality of life (QoL) score at the postoperative third month, were analyzed. The cost of both techniques was also compared statistically. No statistical differences were found in the preoperative parameters, including age, IPSS and QoL score, maximum flow rate (Qmax), PVR, IIEF score, and prostate volumes, as measured by transabdominal ultrasonography. No statistical differences were established in terms of the OT and the weight of the extracted prostate. No differences were established with regard to complications according to Clavien's classification in groups. However, the bleeding rate was significantly lower in group 2. The drainage, catheterization, and hospitalization times and the amount of analgesics were significantly lower in the second group. The postoperative third month findings were not different statistically. Only the Qmax values were significantly greater in group 2. While there was only a $52 difference between groups with regard to operation cost, this difference was significantly different. The use of LSP for the prostates over 80 g is more effective than the OP in terms of OT, bleeding amount, transfusion rates, catheterization time, drain removal time, hospitalization time, consumed analgesic amount, and Qmax values. On the other hand, the mean cost of the LSP is higher than OP. Better effectiveness comes with higher cost.

Toward diagnostic and phenotype markers for genetically transmitted speech delay.

PubMed

Shriberg, Lawrence D; Lewis, Barbara A; Tomblin, J Bruce; McSweeny, Jane L; Karlsson, Heather B; Scheer, Alison R

2005-08-01

Converging evidence supports the hypothesis that the most common subtype of childhood speech sound disorder (SSD) of currently unknown origin is genetically transmitted. We report the first findings toward a set of diagnostic markers to differentiate this proposed etiological subtype (provisionally termed speech delay-genetic) from other proposed subtypes of SSD of unknown origin. Conversational speech samples from 72 preschool children with speech delay of unknown origin from 3 research centers were selected from an audio archive. Participants differed on the number of biological, nuclear family members (0 or 2+) classified as positive for current and/or prior speech-language disorder. Although participants in the 2 groups were found to have similar speech competence, as indexed by their Percentage of Consonants Correct scores, their speech error patterns differed significantly in 3 ways. Compared with children who may have reduced genetic load for speech delay (no affected nuclear family members), children with possibly higher genetic load (2+ affected members) had (a) a significantly higher proportion of relative omission errors on the Late-8 consonants; (b) a significantly lower proportion of relative distortion errors on these consonants, particularly on the sibilant fricatives /s/, /z/, and //; and (c) a significantly lower proportion of backed /s/ distortions, as assessed by both perceptual and acoustic methods. Machine learning routines identified a 3-part classification rule that included differential weightings of these variables. The classification rule had diagnostic accuracy value of 0.83 (95% confidence limits = 0.74-0.92), with positive and negative likelihood ratios of 9.6 (95% confidence limits = 3.1-29.9) and 0.40 (95% confidence limits = 0.24-0.68), respectively. The diagnostic accuracy findings are viewed as promising. The error pattern for this proposed subtype of SSD is viewed as consistent with the cognitive-linguistic processing deficits that have been reported for genetically transmitted verbal disorders.

Does Psychological Profile Influence Third Molar Extraction and Postoperative Pain?

PubMed

González-Martínez, Raquel; Jovani-Sancho, María Del Mar; Cortell-Ballester, Isidoro

2017-03-01

Our purposes were to determine the influence of psychological profile on hemodynamic changes in patients who undergo surgical removal of the third molars under intravenous sedation and to evaluate the effect on patients' anxiety and postoperative recovery. We performed a prospective study of 100 patients (American Society of Anesthesiologists classes I and II; aged ≥18 years) seen in the CIMIVClinic (Department of Oral Surgery, Casa de Salud University Hospital, Valencia, Spain) who underwent extractions of all third molars under intravenous sedation. All patients were administered the Symptom Checklist 90 Revised (SCL-90-R). The following parameters were monitored at different times during the surgical interventions: systolic blood pressure, diastolic blood pressure, oxygen saturation, and heart rate. Position and depth of impaction of the tooth (Pell and Gregory classification and Winter classification), surgery duration, and surgical technique also were recorded. Finally, the degree of pain experienced the week after the surgical intervention was measured using a visual analog scale. Patients' anxiety levels preoperatively were significantly higher in patients with psychological distress (P = .023). Postoperative pain significantly decreased from the first day to the seventh day in healthy patients but not in patients with altered psychological conditions (P < .05). Nevertheless, the hemodynamic changes were not correlated with the psychological impairment. Intravenous sedation enables the control of hemodynamic changes in all patients independently of their psychological profile. Patients with psychological distress present with higher levels of dental anxiety and postoperative pain. Future studies are needed to further clarify this interaction. Copyright © 2016 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

The writer independent online handwriting recognition system frog on hand and cluster generative statistical dynamic time warping.

PubMed

Bahlmann, Claus; Burkhardt, Hans

2004-03-01

In this paper, we give a comprehensive description of our writer-independent online handwriting recognition system frog on hand. The focus of this work concerns the presentation of the classification/training approach, which we call cluster generative statistical dynamic time warping (CSDTW). CSDTW is a general, scalable, HMM-based method for variable-sized, sequential data that holistically combines cluster analysis and statistical sequence modeling. It can handle general classification problems that rely on this sequential type of data, e.g., speech recognition, genome processing, robotics, etc. Contrary to previous attempts, clustering and statistical sequence modeling are embedded in a single feature space and use a closely related distance measure. We show character recognition experiments of frog on hand using CSDTW on the UNIPEN online handwriting database. The recognition accuracy is significantly higher than reported results of other handwriting recognition systems. Finally, we describe the real-time implementation of frog on hand on a Linux Compaq iPAQ embedded device.

Effects of autocorrelation upon LANDSAT classification accuracy. [Richmond, Virginia and Denver, Colorado

NASA Technical Reports Server (NTRS)

Craig, R. G. (Principal Investigator)

1983-01-01

Richmond, Virginia and Denver, Colorado were study sites in an effort to determine the effect of autocorrelation on the accuracy of a parallelopiped classifier of LANDSAT digital data. The autocorrelation was assumed to decay to insignificant levels when sampled at distances of at least ten pixels. Spectral themes developed using blocks of adjacent pixels, and using groups of pixels spaced at least 10 pixels apart were used. Effects of geometric distortions were minimized by using only pixels from the interiors of land cover sections. Accuracy was evaluated for three classes; agriculture, residential and "all other"; both type 1 and type 2 errors were evaluated by means of overall classification accuracy. All classes give comparable results. Accuracy is approximately the same in both techniques; however, the variance in accuracy is significantly higher using the themes developed from autocorrelated data. The vectors of mean spectral response were nearly identical regardless of sampling method used. The estimated variances were much larger when using autocorrelated pixels.

Multi-task feature selection in microarray data by binary integer programming.

PubMed

Lan, Liang; Vucetic, Slobodan

2013-12-20

A major challenge in microarray classification is that the number of features is typically orders of magnitude larger than the number of examples. In this paper, we propose a novel feature filter algorithm to select the feature subset with maximal discriminative power and minimal redundancy by solving a quadratic objective function with binary integer constraints. To improve the computational efficiency, the binary integer constraints are relaxed and a low-rank approximation to the quadratic term is applied. The proposed feature selection algorithm was extended to solve multi-task microarray classification problems. We compared the single-task version of the proposed feature selection algorithm with 9 existing feature selection methods on 4 benchmark microarray data sets. The empirical results show that the proposed method achieved the most accurate predictions overall. We also evaluated the multi-task version of the proposed algorithm on 8 multi-task microarray datasets. The multi-task feature selection algorithm resulted in significantly higher accuracy than when using the single-task feature selection methods.

Automatically classifying sentences in full-text biomedical articles into Introduction, Methods, Results and Discussion.

PubMed

Agarwal, Shashank; Yu, Hong

2009-12-01

Biomedical texts can be typically represented by four rhetorical categories: Introduction, Methods, Results and Discussion (IMRAD). Classifying sentences into these categories can benefit many other text-mining tasks. Although many studies have applied different approaches for automatically classifying sentences in MEDLINE abstracts into the IMRAD categories, few have explored the classification of sentences that appear in full-text biomedical articles. We first evaluated whether sentences in full-text biomedical articles could be reliably annotated into the IMRAD format and then explored different approaches for automatically classifying these sentences into the IMRAD categories. Our results show an overall annotation agreement of 82.14% with a Kappa score of 0.756. The best classification system is a multinomial naïve Bayes classifier trained on manually annotated data that achieved 91.95% accuracy and an average F-score of 91.55%, which is significantly higher than baseline systems. A web version of this system is available online at-http://wood.ims.uwm.edu/full_text_classifier/.

Automatic Identification & Classification of Surgical Margin Status from Pathology Reports Following Prostate Cancer Surgery

PubMed Central

D’Avolio, Leonard W.; Litwin, Mark S.; Rogers, Selwyn O.; Bui, Alex A. T.

2007-01-01

Prostate cancer removal surgeries that result in tumor found at the surgical margin, otherwise known as a positive surgical margin, have a significantly higher chance of biochemical recurrence and clinical progression. To support clinical outcomes assessment a system was designed to automatically identify, extract, and classify key phrases from pathology reports describing this outcome. Heuristics and boundary detection were used to extract phrases. Phrases were then classified using support vector machines into one of three classes: ‘positive (involved) margins,’ ‘negative (uninvolved) margins,’ and ‘not-applicable or definitive.’ A total of 851 key phrases were extracted from a sample of 782 reports produced between 1996 and 2006 from two major hospitals. Despite differences in reporting style, at least 1 sentence containing a diagnosis was extracted from 780 of the 782 reports (99.74%). Of the 851 sentences extracted, 97.3% contained diagnoses. Overall accuracy of automated classification of extracted sentences into the three categories was 97.18%. PMID:18693818

[Association of serum decoy receptor 3 protein level with the clinicopathologic features of bladder transitional cell carcinoma].

PubMed

Wang, Dong; Wang, Jian; Chen, Guojun

2013-12-01

To investigate the association of serum levels of decoy receptor 3(DcR3) protein and the clinicopathologic features of bladder transitional cell carcinoma. Enzyme-linked immunosorbent assay was used to examine the serum levels of DcR3 in patients with bladder transitional cell carcinoma for analysis of its association with the patients' age, gender, clinical stages and pathological classification. The patients with bladder transitional cell carcinoma showed a significantly elevated serum level of DcR3 (183.43 ∓78.45 pg/m1) compared with the normal level (116.65∓97.43 pg/m1, P<0.05). The serum level of DcR3 in the patients showed close correlations with the TNM stage and pathological classification of the tumor (P<0.05) but not with the patients' age or gender (P>0.05). In patients with bladder transitional cell carcinoma, a high serum level of DcR3 suggests a higher malignancy of the tumor.

Gas Classification Using Deep Convolutional Neural Networks.

PubMed

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-08

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

Gas Classification Using Deep Convolutional Neural Networks

PubMed Central

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-01

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723

Identification of extremely premature infants at high risk of rehospitalization.

PubMed

Ambalavanan, Namasivayam; Carlo, Waldemar A; McDonald, Scott A; Yao, Qing; Das, Abhik; Higgins, Rosemary D

2011-11-01

Extremely low birth weight infants often require rehospitalization during infancy. Our objective was to identify at the time of discharge which extremely low birth weight infants are at higher risk for rehospitalization. Data from extremely low birth weight infants in Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network centers from 2002-2005 were analyzed. The primary outcome was rehospitalization by the 18- to 22-month follow-up, and secondary outcome was rehospitalization for respiratory causes in the first year. Using variables and odds ratios identified by stepwise logistic regression, scoring systems were developed with scores proportional to odds ratios. Classification and regression-tree analysis was performed by recursive partitioning and automatic selection of optimal cutoff points of variables. A total of 3787 infants were evaluated (mean ± SD birth weight: 787 ± 136 g; gestational age: 26 ± 2 weeks; 48% male, 42% black). Forty-five percent of the infants were rehospitalized by 18 to 22 months; 14.7% were rehospitalized for respiratory causes in the first year. Both regression models (area under the curve: 0.63) and classification and regression-tree models (mean misclassification rate: 40%-42%) were moderately accurate. Predictors for the primary outcome by regression were shunt surgery for hydrocephalus, hospital stay of >120 days for pulmonary reasons, necrotizing enterocolitis stage II or higher or spontaneous gastrointestinal perforation, higher fraction of inspired oxygen at 36 weeks, and male gender. By classification and regression-tree analysis, infants with hospital stays of >120 days for pulmonary reasons had a 66% rehospitalization rate compared with 42% without such a stay. The scoring systems and classification and regression-tree analysis models identified infants at higher risk of rehospitalization and might assist planning for care after discharge.

Identification of Extremely Premature Infants at High Risk of Rehospitalization

PubMed Central

Carlo, Waldemar A.; McDonald, Scott A.; Yao, Qing; Das, Abhik; Higgins, Rosemary D.

2011-01-01

OBJECTIVE: Extremely low birth weight infants often require rehospitalization during infancy. Our objective was to identify at the time of discharge which extremely low birth weight infants are at higher risk for rehospitalization. METHODS: Data from extremely low birth weight infants in Eunice Kennedy Shriver National Institute of Child Health and Human Development Neonatal Research Network centers from 2002–2005 were analyzed. The primary outcome was rehospitalization by the 18- to 22-month follow-up, and secondary outcome was rehospitalization for respiratory causes in the first year. Using variables and odds ratios identified by stepwise logistic regression, scoring systems were developed with scores proportional to odds ratios. Classification and regression-tree analysis was performed by recursive partitioning and automatic selection of optimal cutoff points of variables. RESULTS: A total of 3787 infants were evaluated (mean ± SD birth weight: 787 ± 136 g; gestational age: 26 ± 2 weeks; 48% male, 42% black). Forty-five percent of the infants were rehospitalized by 18 to 22 months; 14.7% were rehospitalized for respiratory causes in the first year. Both regression models (area under the curve: 0.63) and classification and regression-tree models (mean misclassification rate: 40%–42%) were moderately accurate. Predictors for the primary outcome by regression were shunt surgery for hydrocephalus, hospital stay of >120 days for pulmonary reasons, necrotizing enterocolitis stage II or higher or spontaneous gastrointestinal perforation, higher fraction of inspired oxygen at 36 weeks, and male gender. By classification and regression-tree analysis, infants with hospital stays of >120 days for pulmonary reasons had a 66% rehospitalization rate compared with 42% without such a stay. CONCLUSIONS: The scoring systems and classification and regression-tree analysis models identified infants at higher risk of rehospitalization and might assist planning for care after discharge. PMID:22007016

Association between the clinical classification of hypothyroidism and reduced TSH in LT4 supplemental replacement treatment for pregnancy in China.

PubMed

Zhang, Lyu; Zhang, Zhaoyun; Ye, Hongying; Zhu, Xiaoming; Li, Yiming

2016-01-01

The study was aimed to evaluate the effects of levothyroxine (LT4) supplemental replacement treatment for pregnancy and analyze the associations between the clinical classification of hypothyroidism and reduced thyroid-stimulating hormone (TSH) in LT4 therapy. Totally, 195 pregnant women with hypothyroidism receiving routine prenatal care were enrolled. They were categorized into three groups: overt hypothyroidism (OH), subclinical hypothyroidism (SCH) with negative thyroperoxidase antibody (TPOAb), and SCH with positive TPOAb. The association between the clinical classification and reduced TSH in LT4 supplemental replacement treatment was assessed. The results indicated that reduced TSH was significantly different among the groups according to the clinical classifications (p = 0.043). The result was also significantly different between patients with OH and patients with SCH and negative TPOAb (p = 0.036). Similar result was reported for the comparison between patients with OH and patients with SCH and positive TPOAb (p = 0.016). Multiple variable analyses showed that LT4 supplementation, gestational age and the variable of clinical classifications were associated with reduced TSH independently. Our data suggested that the therapeutic effect of substitutive treatment with LT4 was significantly associated with different clinical classifications of hypothyroidism in pregnancy and the treatment should begin as soon as possible after diagnosis.

The new Epstein gleason score classification significantly reduces upgrading in prostate cancer patients.

PubMed

De Nunzio, Cosimo; Pastore, Antonio Luigi; Lombardo, Riccardo; Simone, Giuseppe; Leonardo, Costantino; Mastroianni, Riccardo; Collura, Devis; Muto, Giovanni; Gallucci, Michele; Carbone, Antonio; Fuschi, Andrea; Dutto, Lorenzo; Witt, Joern Heinrich; De Dominicis, Carlo; Tubaro, Andrea

2018-06-01

To evaluate the differences between the old and the new Gleason score classification systems in upgrading and downgrading rates. Between 2012 and 2015, we identified 9703 patients treated with retropubic radical prostatectomy (RP) in four tertiary centers. Biopsy specimens as well as radical prostatectomy specimens were graded according to both 2005 Gleason and 2014 ISUP five-tier Gleason grading system (five-tier GG system). Upgrading and downgrading rates on radical prostatectomy were first recorded for both classifications and then compared. The accuracy of the biopsy for each histological classification was determined by using the kappa coefficient of agreement and by assessing sensitivity, specificity, positive and negative predictive value. The five-tier GG system presented a lower clinically significant upgrading rate (1895/9703: 19,5% vs 2332/9703:24.0%; p = .001) and a similar clinically significant downgrading rate (756/9703: 7,7% vs 779/9703: 8%; p = .267) when compared to the 2005 ISUP classification. When evaluating their accuracy, the new five-tier GG system presented a better specificity (91% vs 83%) and a better negative predictive value (78% vs 60%). The kappa-statistics measures of agreement between needle biopsy and radical prostatectomy specimens were poor and good respectively for the five-tier GG system and for the 2005 Gleason score (k = 0.360 ± 0.007 vs k = 0.426 ± 0.007). The new Epstein classification significantly reduces upgrading events. The implementation of this new classification could better define prostate cancer aggressiveness with important clinical implications, particularly in prostate cancer management. Copyright © 2018 Elsevier Ltd, BASO ~ The Association for Cancer Surgery, and the European Society of Surgical Oncology. All rights reserved.

Comparison of the results from simple radiography, from before to after Salter osteotomy, in patients with Legg-Calvé-Perthes disease☆☆☆

PubMed Central

Toma, Hugo Futoshi; de Almeida Oliveira Felippe Viana, Thiago; Meireles, Rostanda Mart; Borelli, Isabel Moreira; Blumetti, Francesco Camara; Takimoto, Eduardo Shoiti; Dobashi, Eiffel Tsuyoshi

2014-01-01

Objectives To determine whether the clinical variables and preoperative classification of patients with Legg-Calvé-Perthes disease (LCPD) who undergo Salter osteotomy correlate with the radiographic result at the time of skeletal maturity. Methods In this retrospective cohort study, 47 individuals with LCPD who were treated using Salter osteotomy (1984–2004) were evaluated. The patients were evaluated according to sex, skin color, side affected and age at which osteotomy was performed. The preoperative radiographs were analyzed in accordance with the classifications of Waldenström, Catterall, Laredo and Herring. The radiographs obtained at the time of skeletal maturity were classified using the Stulberg method. Results The mean age at the time of surgical treatment was 82.87 months (6.9 years). The age presented a statistically significant correlation with the Stulberg grades at skeletal maturity (p < 0.001). Patients over the age of 6.12 years tended to present less favorable results. The variables of sex, skin color and side affected did not present any statistically significant correlation with the prognosis (p = 0.425; p = 0.467; p = 0.551, respectively). Only the Laredo classification presented a statistically significant correlation with the final result given by the Stulberg classification (p = 0.001). The other classifications used (Waldenström, Catterall and Herring) did not present any correlation between the time at which surgery was indicated and the postoperative result. Conclusions The age at which the patients underwent surgical treatment and the Laredo classification groups were the only variables that presented significant correlations with the Stulberg classification. PMID:26229850

The Influence of Hindu Epistemology on Ranganathan's Colon Classification.

ERIC Educational Resources Information Center

Maurer, Bradley Gerald

This study attempted to determine the influence of Hindu epistemology on Ranganathan's Colon Classification. Only the epistemological schools of Hindu philosophy and the Idea Plane element of Colon Classification were included. A literature search revealed that, although there is significant literature on each side of the problem, no bridges exist…

IMPROVING THE ACCURACY OF HISTORIC SATELLITE IMAGE CLASSIFICATION BY COMBINING LOW-RESOLUTION MULTISPECTRAL DATA WITH HIGH-RESOLUTION PANCHROMATIC DATA

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

Getman, Daniel J

2008-01-01

Many attempts to observe changes in terrestrial systems over time would be significantly enhanced if it were possible to improve the accuracy of classifications of low-resolution historic satellite data. In an effort to examine improving the accuracy of historic satellite image classification by combining satellite and air photo data, two experiments were undertaken in which low-resolution multispectral data and high-resolution panchromatic data were combined and then classified using the ECHO spectral-spatial image classification algorithm and the Maximum Likelihood technique. The multispectral data consisted of 6 multispectral channels (30-meter pixel resolution) from Landsat 7. These data were augmented with panchromatic datamore » (15m pixel resolution) from Landsat 7 in the first experiment, and with a mosaic of digital aerial photography (1m pixel resolution) in the second. The addition of the Landsat 7 panchromatic data provided a significant improvement in the accuracy of classifications made using the ECHO algorithm. Although the inclusion of aerial photography provided an improvement in accuracy, this improvement was only statistically significant at a 40-60% level. These results suggest that once error levels associated with combining aerial photography and multispectral satellite data are reduced, this approach has the potential to significantly enhance the precision and accuracy of classifications made using historic remotely sensed data, as a way to extend the time range of efforts to track temporal changes in terrestrial systems.« less

Pathological and Molecular Evaluation of Pancreatic Neoplasms

PubMed Central

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

2015-01-01

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

Monitoring nanotechnology using patent classifications: an overview and comparison of nanotechnology classification schemes

NASA Astrophysics Data System (ADS)

Jürgens, Björn; Herrero-Solana, Victor

2017-04-01

Patents are an essential information source used to monitor, track, and analyze nanotechnology. When it comes to search nanotechnology-related patents, a keyword search is often incomplete and struggles to cover such an interdisciplinary discipline. Patent classification schemes can reveal far better results since they are assigned by experts who classify the patent documents according to their technology. In this paper, we present the most important classifications to search nanotechnology patents and analyze how nanotechnology is covered in the main patent classification systems used in search systems nowadays: the International Patent Classification (IPC), the United States Patent Classification (USPC), and the Cooperative Patent Classification (CPC). We conclude that nanotechnology has a significantly better patent coverage in the CPC since considerable more nanotechnology documents were retrieved than by using other classifications, and thus, recommend its use for all professionals involved in nanotechnology patent searches.

[LiLa classification for paediatric long bone fractures. Intraobserver and interobserver reliability].

PubMed

Kamphaus, A; Rapp, M; Wessel, L M; Buchholz, M; Massalme, E; Schneidmüller, D; Roeder, C; Kaiser, M M

2015-04-01

There are two child-specific fracture classification systems for long bone fractures: the AO classification of pediatric long-bone fractures (PCCF) and the LiLa classification of pediatric fractures of long bones (LiLa classification). Both are still not widely established in comparison to the adult AO classification for long bone fractures. During a period of 12 months all long bone fractures in children were documented and classified according to the LiLa classification by experts and non-experts. Intraobserver and interobserver reliability were calculated according to Cohen (kappa). A total of 408 fractures were classified. The intraobserver reliability for location in the skeletal and bone segment showed an almost perfect agreement (K = 0.91-0.95) and also the morphology (joint/shaft fracture) (K = 0.87-0.93). Due to different judgment of the fracture displacement in the second classification round, the intraobserver reliability of the whole classification revealed moderate agreement (K = 0.53-0.58). Interobserver reliability showed moderate agreement (K = 0.55) often due to the low quality of the X-rays. Further differences occurred due to difficulties in assigning the precise transition from metaphysis to diaphysis. The LiLa classification is suitable and in most cases user-friendly for classifying long bone fractures in children. Reliability is higher than in established fracture specific classifications and comparable to the AO classification of pediatric long bone fractures. Some mistakes were due to a low quality of the X-rays and some due to difficulties to classify the fractures themselves. Improvements include a more precise definition of the metaphysis and the kind of displacement. Overall the LiLa classification should still be considered as an alternative for classifying pediatric long bone fractures.

Active learning for clinical text classification: is it better than random sampling?

PubMed

Figueroa, Rosa L; Zeng-Treitler, Qing; Ngo, Long H; Goryachev, Sergey; Wiechmann, Eduardo P

2012-01-01

This study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks. Three existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to classify text from five datasets. The performance of these algorithms was compared to that of passive learning on the five datasets. We then conducted a novel investigation of the interaction between dataset characteristics and the performance results. Classification accuracy and area under receiver operating characteristics (ROC) curves for each algorithm at different sample sizes were generated. The performance of active learning algorithms was compared with that of passive learning using a weighted mean of paired differences. To determine why the performance varies on different datasets, we measured the diversity and uncertainty of each dataset using relative entropy and correlated the results with the performance differences. The DIST and CMB algorithms performed better than passive learning. With a statistical significance level set at 0.05, DIST outperformed passive learning in all five datasets, while CMB was found to be better than passive learning in four datasets. We found strong correlations between the dataset diversity and the DIV performance, as well as the dataset uncertainty and the performance of the DIST algorithm. For medical text classification, appropriate active learning algorithms can yield performance comparable to that of passive learning with considerably smaller training sets. In particular, our results suggest that DIV performs better on data with higher diversity and DIST on data with lower uncertainty.

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

PubMed Central

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

2012-01-01

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

A new Fourier transform based CBIR scheme for mammographic mass classification: a preliminary invariance assessment

NASA Astrophysics Data System (ADS)

Gundreddy, Rohith Reddy; Tan, Maxine; Qui, Yuchen; Zheng, Bin

2015-03-01

The purpose of this study is to develop and test a new content-based image retrieval (CBIR) scheme that enables to achieve higher reproducibility when it is implemented in an interactive computer-aided diagnosis (CAD) system without significantly reducing lesion classification performance. This is a new Fourier transform based CBIR algorithm that determines image similarity of two regions of interest (ROI) based on the difference of average regional image pixel value distribution in two Fourier transform mapped images under comparison. A reference image database involving 227 ROIs depicting the verified soft-tissue breast lesions was used. For each testing ROI, the queried lesion center was systematically shifted from 10 to 50 pixels to simulate inter-user variation of querying suspicious lesion center when using an interactive CAD system. The lesion classification performance and reproducibility as the queried lesion center shift were assessed and compared among the three CBIR schemes based on Fourier transform, mutual information and Pearson correlation. Each CBIR scheme retrieved 10 most similar reference ROIs and computed a likelihood score of the queried ROI depicting a malignant lesion. The experimental results shown that three CBIR schemes yielded very comparable lesion classification performance as measured by the areas under ROC curves with the p-value greater than 0.498. However, the CBIR scheme using Fourier transform yielded the highest invariance to both queried lesion center shift and lesion size change. This study demonstrated the feasibility of improving robustness of the interactive CAD systems by adding a new Fourier transform based image feature to CBIR schemes.

The Rural Inpatient Mortality Study: Does Urban-Rural County Classification Predict Hospital Mortality in California?

PubMed

Linnen, Daniel T; Kornak, John; Stephens, Caroline

2018-03-28

Evidence suggests an association between rurality and decreased life expectancy. To determine whether rural hospitals have higher hospital mortality, given that very sick patients may be transferred to regional hospitals. In this ecologic study, we combined Medicare hospital mortality ratings (N = 1267) with US census data, critical access hospital classification, and National Center for Health Statistics urban-rural county classifications. Ratings included mortality for coronary artery bypass grafting, stroke, chronic obstructive pulmonary disease, heart attack, heart failure, and pneumonia across 277 California hospitals between July 2011 and June 2014. We used generalized estimating equations to evaluate the association of urban-rural county classifications on mortality ratings. Unfavorable Medicare hospital mortality rating "worse than the national rate" compared with "better" or "same." Compared with large central "metro" (metropolitan) counties, hospitals in medium-sized metro counties had 6.4 times the odds of rating "worse than the national rate" for hospital mortality (95% confidence interval = 2.8-14.8, p < 0.001). For hospitals in small metro counties, the odds of having such a rating were 3.7 times greater (95% confidence interval = 0.7-23.4, p = 0.12), although not statistically significant. Few ratings were provided for rural counties, and analysis of rural counties was underpowered. Hospitals in medium-sized metro counties are associated with unfavorable Medicare mortality ratings, but current methods to assign mortality ratings may hinder fair comparisons. Patient transfers from rural locations to regional medical centers may contribute to these results, a potential factor that future research should examine.

Machine learning on brain MRI data for differential diagnosis of Parkinson's disease and Progressive Supranuclear Palsy.

PubMed

Salvatore, C; Cerasa, A; Castiglioni, I; Gallivanone, F; Augimeri, A; Lopez, M; Arabia, G; Morelli, M; Gilardi, M C; Quattrone, A

2014-01-30

Supervised machine learning has been proposed as a revolutionary approach for identifying sensitive medical image biomarkers (or combination of them) allowing for automatic diagnosis of individual subjects. The aim of this work was to assess the feasibility of a supervised machine learning algorithm for the assisted diagnosis of patients with clinically diagnosed Parkinson's disease (PD) and Progressive Supranuclear Palsy (PSP). Morphological T1-weighted Magnetic Resonance Images (MRIs) of PD patients (28), PSP patients (28) and healthy control subjects (28) were used by a supervised machine learning algorithm based on the combination of Principal Components Analysis as feature extraction technique and on Support Vector Machines as classification algorithm. The algorithm was able to obtain voxel-based morphological biomarkers of PD and PSP. The algorithm allowed individual diagnosis of PD versus controls, PSP versus controls and PSP versus PD with an Accuracy, Specificity and Sensitivity>90%. Voxels influencing classification between PD and PSP patients involved midbrain, pons, corpus callosum and thalamus, four critical regions known to be strongly involved in the pathophysiological mechanisms of PSP. Classification accuracy of individual PSP patients was consistent with previous manual morphological metrics and with other supervised machine learning application to MRI data, whereas accuracy in the detection of individual PD patients was significantly higher with our classification method. The algorithm provides excellent discrimination of PD patients from PSP patients at an individual level, thus encouraging the application of computer-based diagnosis in clinical practice. Copyright © 2013 Elsevier B.V. All rights reserved.

Active learning for clinical text classification: is it better than random sampling?

PubMed Central

Figueroa, Rosa L; Ngo, Long H; Goryachev, Sergey; Wiechmann, Eduardo P

2012-01-01

Objective This study explores active learning algorithms as a way to reduce the requirements for large training sets in medical text classification tasks. Design Three existing active learning algorithms (distance-based (DIST), diversity-based (DIV), and a combination of both (CMB)) were used to classify text from five datasets. The performance of these algorithms was compared to that of passive learning on the five datasets. We then conducted a novel investigation of the interaction between dataset characteristics and the performance results. Measurements Classification accuracy and area under receiver operating characteristics (ROC) curves for each algorithm at different sample sizes were generated. The performance of active learning algorithms was compared with that of passive learning using a weighted mean of paired differences. To determine why the performance varies on different datasets, we measured the diversity and uncertainty of each dataset using relative entropy and correlated the results with the performance differences. Results The DIST and CMB algorithms performed better than passive learning. With a statistical significance level set at 0.05, DIST outperformed passive learning in all five datasets, while CMB was found to be better than passive learning in four datasets. We found strong correlations between the dataset diversity and the DIV performance, as well as the dataset uncertainty and the performance of the DIST algorithm. Conclusion For medical text classification, appropriate active learning algorithms can yield performance comparable to that of passive learning with considerably smaller training sets. In particular, our results suggest that DIV performs better on data with higher diversity and DIST on data with lower uncertainty. PMID:22707743

Evaluation of serum neutrophil gelatinase-associated lipocalin as a novel biomarker of cardiorenal syndrome in dogs.

PubMed

Jung, Han-Byeol; Kang, Min-Hee; Park, Hee-Myung

2018-05-01

Worsening renal function and azotemia in patients with heart failure (HF) are strongly associated with disease severity and poor prognosis. Increasing interest in this correlation led to the description and classification of cardiorenal syndrome (CRS). We evaluated the role of neutrophil gelatinase-associated lipocalin (NGAL) in the early detection of CRS in dogs with HF. Ten healthy dogs and 31 dogs admitted with HF were included in our study. NGAL and troponin-I were measured on samples collected on the day of admission; creatinine was measured on admission and again on day 7. The CRS group was defined as subsequently developing renal azotemia. Of 31 dogs with HF, 20 were included in the HF group, and 11 were included in the CRS group. The admission NGAL concentrations of the CRS group were significantly higher than those of other groups ( p < 0.001). The severity of HF evaluation based on the modified New York Heart Association classification showed significant correlation with NGAL ( p < 0.001) and troponin-I ( p = 0.009) concentration. However, only serum NGAL concentration at admission was significantly associated with the development of CRS in dogs with HF ( p = 0.021). The admission serum NGAL ≥ 16.0 ng/mL (optimal cutoff value) had a sensitivity of 90.9% and specificity of 90.0% in predicting the development of CRS.

[Evaluation of scientific production in different subareas of Public Health: limits of the current model and contributions to the debate].

PubMed

Iriart, Jorge Alberto Bernstein; Deslandes, Suely Ferreira; Martin, Denise; Camargo, Kenneth Rochel de; Carvalho, Marilia Sá; Coeli, Cláudia Medina

2015-10-01

The aim of this study was to discuss the limits of the quantitative evaluation model for scientific production in Public Health. An analysis of the scientific production of professors from the various subareas of Public Health was performed for 2010-2012. Distributions of the mean annual score for professors were compared according to subareas. The study estimated the likelihood that 60% of the professors in the graduate studies programs scored P50 (Very Good) or higher in their area. Professors of Epidemiology showed a significantly higher median annual score. Graduate studies programs whose faculty included at least 60% of Epidemiology professors and fewer than 10% from the subarea Social and Human Sciences in Health were significantly more likely to achieve a "Very Good" classification. The observed inequalities in scientific production between different subareas of Public Health point to the need to rethink their evaluation in order to avoid reproducing iniquities that have harmful consequences for the field's diversity.

Racial Disparities in Access and Outcomes of Cholecystectomy in the United States.

PubMed

Gahagan, John V; Hanna, Mark H; Whealon, Matthew D; Maximus, Steven; Phelan, Michael J; Lekawa, Michael; Barrios, Cristobal; Bernal, Nicole P

2016-10-01

Disparities in access to health care between white and minority patients are well described. We aimed to analyze the trends and outcomes of cholecystectomy based on racial classification. The Nationwide Inpatient Sample database was reviewed for all patients undergoing cholecystectomy from 2009 to 2012. Patients were stratified as white or non-white. A total of 243,536 patients were analyzed: 159,901 white and 83,635 non-white. Non-white patients had significantly higher proportions of Medicaid (25% vs 9.3%), self-pay (14% vs 7.1%), and no-charge (1.8% vs 0.64%). Non-white patients had significantly higher rates of emergent admission (84% vs 78%) compared with the white patients. Multivariate analysis revealed that non-whites had a significantly longer length of stay [mean difference of 0.14 days, 95% confidence interval (CI) 0.08-0.20] and higher total hospital charges (mean difference of $6748.00, 95% CI 5994.19-7501.81) than whites, despite a lower morbidity (odds ratio 0.94, 95% CI 0.90-0.98). Use of laparoscopy and mortality were not different. These differences persisted on subgroup analysis by insurance type. These findings suggest a gap in access to and outcomes of cholecystectomy in the minority population nationwide.

Relationships between ambient geochemistry, watershed land-use and trace metal concentrations in aquatic invertebrates living in stormwater treatment ponds

USGS Publications Warehouse

Karouna-Renier, N.K.; Sparling, D.W.

2001-01-01

Stormwater treatment ponds receive elevated levels of metals from urban runoff, but the effects of these pollutants on organisms residing in the ponds are unknown. We investigated the accumulation of Cu, Zn, and Pb by macroinvertebrates collected from stormwater treatment ponds in Maryland serving commercial, highway, residential and open-space watersheds, and determined whether watershed land-use classification influences metal concentrations in macroinvertebrates, sediments, and water. Three types of invertebrate samples were analyzed for molluscs, odonates, and composite. Zn concentrations in odonates from ponds draining watersheds with commercial development (mean=113.82 ug/g) were significantly higher than concentrations in the other land-use categories. Similarly, Cu levels in odonates from commercial ponds (mean=27.12 ug/g) were significantly higher than from highway (mean=20.23 ug/g) and open space (mean=17.79 ug/g) ponds. However, metal concentrations in sediments and water did not differ significantly among land-uses. The results suggest that despite the high variation in ambient metal concentrations within each land-use category, macroinvertebrates in ponds serving commercial watersheds accumulate higher levels of Cu and Zn. The levels of Cu, Zn, and Pb in invertebrates from all ponds were less than dietary concentrations considered toxic to fish.

Objectification of Orthodontic Treatment Needs: Does the Classification of Malocclusions or a History of Orthodontic Treatment Matter?

PubMed

Kozanecka, Anna; Sarul, Michał; Kawala, Beata; Antoszewska-Smith, Joanna

2016-01-01

Orthodontic classifications make it possible to give an accurate diagnosis but do not indicate an objective orthodontic treatment need. In order to evaluate the need for treatment, it is necessary to use such indicators as the IOTN. The aim of the study was to find (i) relationships between individual diagnosis and objective recommendations for treatment and (ii) an answer to the question whether and which occlusal anomalies play an important role in the objectification of treatment needs. Two hundred three 18-year-old adolescents (104 girls, 99 boys) were examined. In order to recognize occlusal anomalies, the classifications proposed by Orlik-Grzybowska and Ackerman-Proffit were used. The occlusal anomalies were divided into three categories: belonging to both classifications, typical of OrlikGrzybowska classification and typical of Ackerman-Proffit classification. In order to determine the objective need for orthodontic treatment, the Dental Health Component (DHC) of the IOTN was used. The occurrence of the following malocclusions covered by both classifications, namely abnormal overjet, crossbite and Angle's class, had a statistically significant (p < 0.05) impact on an increase of treatment needs in the subjects (DHC > 3). As for the classification by Orlik-Grzybowska, dental malpositions and canine class significantly affected the need for orthodontic treatment, while in the case of the Ackerman-Proffit scheme, it was asymmetry and crowding. There was no statistically significant correlation between past orthodontic treatment and current orthodontic treatment need. IOTN may be affected by a greater number of occlusal anomalies than it was assumed. Orthodontic treatment received in the past slightly reduces the need for treatment in 18-year-olds.

Expression and significance of CHIP in canine mammary gland tumors

PubMed Central

WANG, Huanan; YANG, Xu; JIN, Yipeng; PEI, Shimin; ZHANG, Di; MA, Wen; HUANG, Jian; QIU, Hengbin; ZHANG, Xinke; JIANG, Qiuyue; SUN, Weidong; ZHANG, Hong; LIN, Degui

2015-01-01

CHIP (Carboxy terminus of Hsc70 Interacting Protein) is an E3 ubiquitin ligase that can induce ubiquitination and degradation of several oncogenic proteins. The expression of CHIP is frequently lower in human breast cancer than in normal breast tissue. However, the expression and role of CHIP in the canine mammary gland tumor (CMGT) remain unclear. We investigated the potential correlation between CHIP expression and mammary gland tumor prognosis in female dogs. CHIP expression was measured in 54 dogs by immunohistochemistry and real-time RT-PCR. CHIP protein expression was significantly correlated with the histopathological diagnosis, outcome of disease and tumor classification. The transcriptional level of CHIP was significantly higher in normal tissues (P=0.001) and benign tumors (P=0.009) than it in malignant tumors. CHIP protein expression was significantly correlated with the transcriptional level of CHIP (P=0.0102). The log-rank test survival curves indicated that patients with low expression of CHIP had shorter overall periods of survival than those with higher CHIP protein expression (P=0.050). Our data suggest that CHIP may play an important role in the formation and development of CMGTs and serve as a valuable prognostic marker and potential target for genetic therapy. PMID:26156079

The Effect of Race in Patients with Achalasia Diagnosed With High-Resolution Esophageal Manometry.

PubMed

Chedid, Victor; Rosenblatt, Elizabeth; Gandhi, Kunjal Komal; Dhalla, Sameer; Nandwani, Monica C; Stein, Ellen M; Clarke, John O

2018-02-01

The advent of the Chicago Classification for esophageal motility disorders allowed for clinically reproducible subgrouping of patients with achalasia based on manometric phenotype. However, there are limited data with regards to racial variation using high-resolution esophageal manometry (HREM). The aim of our study was to evaluate the racial differences in patients with achalasia diagnosed with HREM using the Chicago Classification. We evaluated the clinical presentation, treatment decisions and outcomes between blacks and non-blacks with achalasia to identify potential racial disparities. We performed a retrospective review of consecutive patients referred for HREM at a single tertiary referral center from June 2008 through October 2012. All patients diagnosed with achalasia on HREM according to the Chicago Classification were included. Demographic, clinical and manometric data were abstracted. All studies interpreted before the Chicago Classification was in widespread use were reanalyzed. Race was defined as black or non-black. Patients who had missing data were excluded. Proportions were compared using chi-squared analysis and means were compared using the Student's t-test. A total of 1,268 patients underwent HREM during the study period, and 105 (8.3%) were manometrically diagnosed with achalasia (53% female, mean age: 53.8 ± 17.0 years) and also met the aforementioned inclusion and exclusion criteria. A higher percentage of women presented with achalasia in blacks as compared to whites or other races (P < 0.001). Non-blacks were more likely to present with reflux than blacks (P = 0.01), while blacks were more likely to be treated on the inpatient service than non-blacks (P < 0.001). There were no other significant differences noted in clinical presentation, treatment decisions and treatment outcomes among blacks and non-blacks. Our study highlights possible racial differences between blacks and non-blacks, including a higher proportion of black women diagnosed with achalasia and most blacks presenting with dysphagia. There is possibly a meaningful interaction of race and sex in the development of achalasia that might represent genetic differences in its pathophysiology. Further prospective studies are required to identify such differences. Copyright © 2018 Southern Society for Clinical Investigation. Published by Elsevier Inc. All rights reserved.

Positron Emission Tomography/Computed Tomography Assessment After Immunochemotherapy and Irradiation Using the Lugano Classification Criteria in the IELSG-26 Study of Primary Mediastinal B-Cell Lymphoma

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

Ceriani, Luca, E-mail: luca.ceriani@eoc.ch; Martelli, Maurizio; Gospodarowicz, Maria K.

Purpose: To assess the predictive value of {sup 18}F-fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) for disease recurrence after immunochemotherapy (R-CHT) and mediastinal irradiation (RT), using the recently published criteria of the Lugano classification to predict outcomes for patients with primary mediastinal large B-cell lymphoma. Methods and Materials: Among 125 patients prospectively enrolled in the IELSG-26 study, 88 were eligible for central review of PET/CT scans after completion of RT. Responses were evaluated using the 5-point Deauville scale at the end of induction R-CHT and after consolidation RT. According to the Lugano classification, a complete metabolic response (CMR) was defined bymore » a Deauville score (DS) ≤3. Results: The CMR (DS1, -2, or -3) rate increased from 74% (65 patients) after R-CHT to 89% (78 patients) after consolidation RT. Among the 10 patients (11%) with persistently positive scans, the residual uptake after RT was slightly higher than the liver uptake in 6 patients (DS4; 7%) and markedly higher in 4 patients (DS5; 4%): these patients had a significantly poorer 5-year progression-free survival and overall survival. At a median follow-up of 60 months (range, 35-107 months), no patients with a CMR after RT have relapsed. Among the 10 patients who did not reach a CMR, 3 of the 4 patients (positive predictive value, 75%) with DS5 after RT had subsequent disease progression (within the RT volume in all cases) and died. All patients with DS4 had good outcomes without recurrence. Conclusions: All the patients obtaining a CMR defined as DS ≤3 remained progression-free at 5 years, confirming the excellent negative predictive value of the Lugano classification criteria in primary mediastinal large B-cell lymphoma patients. The few patients with DS4 also had an excellent outcome, suggesting that they do not necessarily require additional therapy, because the residual {sup 18}F-fluorodeoxyglucose uptake may not reflect persistent lymphoma.« less

Diagnosing Autism Spectrum Disorder from Brain Resting-State Functional Connectivity Patterns Using a Deep Neural Network with a Novel Feature Selection Method

PubMed Central

Guo, Xinyu; Dominick, Kelli C.; Minai, Ali A.; Li, Hailong; Erickson, Craig A.; Lu, Long J.

2017-01-01

The whole-brain functional connectivity (FC) pattern obtained from resting-state functional magnetic resonance imaging data are commonly applied to study neuropsychiatric conditions such as autism spectrum disorder (ASD) by using different machine learning models. Recent studies indicate that both hyper- and hypo- aberrant ASD-associated FCs were widely distributed throughout the entire brain rather than only in some specific brain regions. Deep neural networks (DNN) with multiple hidden layers have shown the ability to systematically extract lower-to-higher level information from high dimensional data across a series of neural hidden layers, significantly improving classification accuracy for such data. In this study, a DNN with a novel feature selection method (DNN-FS) is developed for the high dimensional whole-brain resting-state FC pattern classification of ASD patients vs. typical development (TD) controls. The feature selection method is able to help the DNN generate low dimensional high-quality representations of the whole-brain FC patterns by selecting features with high discriminating power from multiple trained sparse auto-encoders. For the comparison, a DNN without the feature selection method (DNN-woFS) is developed, and both of them are tested with different architectures (i.e., with different numbers of hidden layers/nodes). Results show that the best classification accuracy of 86.36% is generated by the DNN-FS approach with 3 hidden layers and 150 hidden nodes (3/150). Remarkably, DNN-FS outperforms DNN-woFS for all architectures studied. The most significant accuracy improvement was 9.09% with the 3/150 architecture. The method also outperforms other feature selection methods, e.g., two sample t-test and elastic net. In addition to improving the classification accuracy, a Fisher's score-based biomarker identification method based on the DNN is also developed, and used to identify 32 FCs related to ASD. These FCs come from or cross different pre-defined brain networks including the default-mode, cingulo-opercular, frontal-parietal, and cerebellum. Thirteen of them are statically significant between ASD and TD groups (two sample t-test p < 0.05) while 19 of them are not. The relationship between the statically significant FCs and the corresponding ASD behavior symptoms is discussed based on the literature and clinician's expert knowledge. Meanwhile, the potential reason of obtaining 19 FCs which are not statistically significant is also provided. PMID:28871217

Semi-supervised classification tool for DubaiSat-2 multispectral imagery

NASA Astrophysics Data System (ADS)

Al-Mansoori, Saeed

2015-10-01

This paper addresses a semi-supervised classification tool based on a pixel-based approach of the multi-spectral satellite imagery. There are not many studies demonstrating such algorithm for the multispectral images, especially when the image consists of 4 bands (Red, Green, Blue and Near Infrared) as in DubaiSat-2 satellite images. The proposed approach utilizes both unsupervised and supervised classification schemes sequentially to identify four classes in the image, namely, water bodies, vegetation, land (developed and undeveloped areas) and paved areas (i.e. roads). The unsupervised classification concept is applied to identify two classes; water bodies and vegetation, based on a well-known index that uses the distinct wavelengths of visible and near-infrared sunlight that is absorbed and reflected by the plants to identify the classes; this index parameter is called "Normalized Difference Vegetation Index (NDVI)". Afterward, the supervised classification is performed by selecting training homogenous samples for roads and land areas. Here, a precise selection of training samples plays a vital role in the classification accuracy. Post classification is finally performed to enhance the classification accuracy, where the classified image is sieved, clumped and filtered before producing final output. Overall, the supervised classification approach produced higher accuracy than the unsupervised method. This paper shows some current preliminary research results which point out the effectiveness of the proposed technique in a virtual perspective.

Systemic sclerosis and localized scleroderma in childhood.

PubMed

Zulian, Francesco

2008-02-01

Juvenile scleroderma syndromes, including the systemic and the localized varieties, represent the third most frequent chronic rheumatic conditions in pediatric rheumatology practice. In children, systemic sclerosis shows a significantly less frequent involvement of all organs, a higher prevalence of arthritis and myositis, and a better outcome than in adults. Recently, new classification criteria were proposed, which help improve patient care by enabling earlier, more definite diagnoses and by standardizing the conduct of clinical trials. Localized scleroderma is the more frequent subtype of scleroderma in childhood. It comprises a group of distinct conditions that involve mainly the skin and subcutaneous tissues. They range from small plaques of fibrosis involving only the skin to diseases causing significant functional deformity with various extracutaneous features.

[CT morphometry for calcaneal fractures and comparison of the Zwipp and Sanders classifications].

PubMed

Andermahr, J; Jesch, A B; Helling, H J; Jubel, A; Fischbach, R; Rehm, K E

2002-01-01

The aim of the study is to correlate the CT-morphological changes of fractured calcaneus and the classifications of Zwipp and Sanders with the clinical outcome. In a retrospective clinical study, the preoperative CT scans of 75 calcaneal fractures were analysed. The morphometry of the fractures was determined by measuring height, length diameter and calcaneo-cuboidal angle in comparison to the intact contralateral side. At a mean of 38 months after trauma 44 patients were clinically followed-up. The data of CT image morphometry were correlated with the severity of fracture classified by Zwipp or Sanders as well as with the functional outcome. There was a good correlation between the fracture classifications and the morphometric data. Both fracture classifying systems have a predictive impact for functional outcome. The more exacting and accurate Zwipp classification considers the most important cofactors like involvement of the calcaneo-cuboidal joint, soft tissue damage, additional fractures etc. The Sanders classification is easier to use during clinical routine. The Zwipp classification includes more relevant cofactors (fracture of the calcaneo-cuboidal-joint, soft tissue swelling, etc.) and presents a higher correlation to the choice of therapy. Both classification systems present a prognostic impact concerning the clinical outcome.

A hybrid sensing approach for pure and adulterated honey classification.

PubMed

Subari, Norazian; Mohamad Saleh, Junita; Md Shakaff, Ali Yeon; Zakaria, Ammar

2012-10-17

This paper presents a comparison between data from single modality and fusion methods to classify Tualang honey as pure or adulterated using Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA) statistical classification approaches. Ten different brands of certified pure Tualang honey were obtained throughout peninsular Malaysia and Sumatera, Indonesia. Various concentrations of two types of sugar solution (beet and cane sugar) were used in this investigation to create honey samples of 20%, 40%, 60% and 80% adulteration concentrations. Honey data extracted from an electronic nose (e-nose) and Fourier Transform Infrared Spectroscopy (FTIR) were gathered, analyzed and compared based on fusion methods. Visual observation of classification plots revealed that the PCA approach able to distinct pure and adulterated honey samples better than the LDA technique. Overall, the validated classification results based on FTIR data (88.0%) gave higher classification accuracy than e-nose data (76.5%) using the LDA technique. Honey classification based on normalized low-level and intermediate-level FTIR and e-nose fusion data scored classification accuracies of 92.2% and 88.7%, respectively using the Stepwise LDA method. The results suggested that pure and adulterated honey samples were better classified using FTIR and e-nose fusion data than single modality data.

A discrete wavelet based feature extraction and hybrid classification technique for microarray data analysis.

PubMed

Bennet, Jaison; Ganaprakasam, Chilambuchelvan Arul; Arputharaj, Kannan

2014-01-01

Cancer classification by doctors and radiologists was based on morphological and clinical features and had limited diagnostic ability in olden days. The recent arrival of DNA microarray technology has led to the concurrent monitoring of thousands of gene expressions in a single chip which stimulates the progress in cancer classification. In this paper, we have proposed a hybrid approach for microarray data classification based on nearest neighbor (KNN), naive Bayes, and support vector machine (SVM). Feature selection prior to classification plays a vital role and a feature selection technique which combines discrete wavelet transform (DWT) and moving window technique (MWT) is used. The performance of the proposed method is compared with the conventional classifiers like support vector machine, nearest neighbor, and naive Bayes. Experiments have been conducted on both real and benchmark datasets and the results indicate that the ensemble approach produces higher classification accuracy than conventional classifiers. This paper serves as an automated system for the classification of cancer and can be applied by doctors in real cases which serve as a boon to the medical community. This work further reduces the misclassification of cancers which is highly not allowed in cancer detection.

Significance of perceptually relevant image decolorization for scene classification

NASA Astrophysics Data System (ADS)

Viswanathan, Sowmya; Divakaran, Govind; Soman, Kutti Padanyl

2017-11-01

Color images contain luminance and chrominance components representing the intensity and color information, respectively. The objective of this paper is to show the significance of incorporating chrominance information to the task of scene classification. An improved color-to-grayscale image conversion algorithm that effectively incorporates chrominance information is proposed using the color-to-gray structure similarity index and singular value decomposition to improve the perceptual quality of the converted grayscale images. The experimental results based on an image quality assessment for image decolorization and its success rate (using the Cadik and COLOR250 datasets) show that the proposed image decolorization technique performs better than eight existing benchmark algorithms for image decolorization. In the second part of the paper, the effectiveness of incorporating the chrominance component for scene classification tasks is demonstrated using a deep belief network-based image classification system developed using dense scale-invariant feature transforms. The amount of chrominance information incorporated into the proposed image decolorization technique is confirmed with the improvement to the overall scene classification accuracy. Moreover, the overall scene classification performance improved by combining the models obtained using the proposed method and conventional decolorization methods.

Land Cover Changes between 1974 and 2008 in Ulaanbaatar, Mongolia

NASA Astrophysics Data System (ADS)

Bagan, H.; Kinoshita, T.; Yamagata, Y.

2009-12-01

In the past 35 years, a combination of human actions and natural causes has led to a significant decline in land quality in Ulaanbaatar, the capital city of Mongolia. Human causes include changes in conventional livestock husbandry, overgrazing, and exploitation for traditional uses. Natural causes include a harsh, dry climate, short growing seasons, and thin soils. Since 1995, many herders left the countryside to come to the city in search of new opportunities, the Ger areas (wooden houses and Ger) have expended, resulting in urban sprawl. Since urbanization usually advance in an uncontrolled or unorganized way in Mongolia, they have destructive effects on the environment, particularly on basic ecosystems, wildlife habitat, and pollution of natural resources (e.g. air and water). Land use and land cover changes occurred in the region are investigated using satellite images acquired in 1974 (Landsat MSS), 1990 (Landsat TM), 2000 (ASTER), 2006 (IKONOS), and 2008 (ALOS). Pre-processing of all data included orthorectification and registration to precisely geolocated imagery. In the detection of changes, classification approaches were employed using a self-organizing map (SOM) neural network classifier (Fig. 1a) and new developed subspace classification method (Fig. 1b). From the time-series classified remote sensing images, we extract the land cover and land cover temporal changes from 1974 to 2008. The results show some important findings regarding the size and nature of the change occurred in the study area. A significant amount of steppe and forest lands have been destroyed or replaced by residential areas; as a result, the total area of urban region doubled in the 35-year period with a higher urbanization rate between 2000 and 2008. Key words: Environment; Land Cover; Urban; Change detection; Classification. References Chinbat,B., Bayantur,M., & Amarsaikhan.D. (2006). Investigation of the internal structure changes of ulaanbaatar city using RS and GIS. ISPRS Commission VII Mid-term Symposium “Remote Sensing: From Pixels to Processes”, Enschede, the Netherlands, 8-11 May 2006. 511-516. Bagan, H., Wang, Q., Watanabe, M., Karneyarna, S., & Bao, Y. (2008). Land-cover classification using ASTER multi-band combinations based on wavelet fusion and SOM neural network. Photogrammetric Engineering and Remote Sensing, 74, 333-342. Bagan, H., Yasuoka, Y., Endo, T., Wang, X., & Feng, Z. (2008). Classification of airborne hyperspectral data based on the average learning subspace method. IEEE Geoscience and Remote Sensing Letters, 5, 368-372. Figure 1. The self-organizing map (SOM) neural network classifier (a) and the subspace classification method (b).

Study of the atmospheric effects on the radiation detected by the sensor aboard orbiting platforms (ERTS/LANDSAT). M.S. Thesis - October 1978; [Ribeirao Preto and Brasilia, Brazil

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Morimoto, T.

1980-01-01

The author has identified the following significant results. Multispectral scanner data for Brasilia was corrected for atmospheric interference using the LOWTRAN-3 computer program and the analytical solution of the radiative transfer equation. This improved the contrast between two natural targets and the corrected images of two different dates were more similar than the original ones. Corrected images of MSS data for Ribeirao Preto gave a classification accuracy for sugar cane about 10% higher as compared to the original images.

Vegetation zones in changing climate

NASA Astrophysics Data System (ADS)

Belda, Michal; Holtanova, Eva; Halenka, Tomas; Kalvova, Jaroslava

2017-04-01

Climate patterns analysis can be performed for individual climate variables separately or the data can be aggregated using e.g. some kind of climate classification. These classifications usually correspond to vegetation distribution in the sense that each climate type is dominated by one vegetation zone or eco-region. Thus, the Köppen-Trewartha classification provides integrated assessment of temperature and precipitation together with their annual cycle as well. This way climate classifications also can be used as a convenient tool for the assessment and validation of climate models and for the analysis of simulated future climate changes. The Köppen-Trewartha classification is applied on full CMIP5 family of more than 40 GCM simulations and CRU dataset for comparison. This evaluation provides insight on the GCM performance and errors for simulations of the 20th century climate. Common regions are identified, such as Australia or Amazonia, where many state-of-the-art models perform inadequately. Moreover, the analysis of the CMIP5 ensemble for future under RCP 4.5 and RCP 8.5 is performed to assess the climate change for future. There are significant changes for some types in most models e.g. increase of savanna and decrease of tundra for the future climate. For some types significant shifts in latitude can be seen when studying their geographical location in selected continental areas, e.g. toward higher latitudes for boreal climate. Quite significant uncertainty can be seen for some types. For Europe, EuroCORDEX results for both 0.11 and 0.44 degree resolution are validated using Köppen-Trewartha types in comparison to E-OBS based classification. ERA-Interim driven simulations are compared to both present conditions of CMIP5 models as well as their downscaling by EuroCORDEX RCMs. Finally, the climate change signal assessment is provided using the individual climate types. In addition to the changes assessed similarly as for GCMs analysis in terms of the area of individual types, in the continental scale some shifts of boundaries between the selected types can be studied as well providing the information on climate change signal. The shift of the boundary between the boreal zone and continental temperate zone to the north is clearly seen in most simulations as well as eastern move of the boundary of the maritime and continental type of temperate zone. However, there can be quite clear problem with model biases in climate types association. When analysing climate types in Europe and their shifts under climate change using Köppen-Trewartha classification (KTC), for the temperate climate type there are subtypes defined following the continentality patterns, and we can see their generally meridionally located divide across Europe shifted to the east. There is a question whether this is realistic or rather due to the simplistic condition in KTC following the winter minimum temperature, while other continentality indices consider rather the amplitude of temperature during the year. This leads us to connect our analysis of climate change effects using climate classification to the more detailed analysis of continentality patterns development in Europe to provide better insight on the climate regimes and to verify the continentality conditions, their definitions and climate change effects on them. The comparison of several selected continentality indices is shown.

A method for classification of multisource data using interval-valued probabilities and its application to HIRIS data

NASA Technical Reports Server (NTRS)

Kim, H.; Swain, P. H.

1991-01-01

A method of classifying multisource data in remote sensing is presented. The proposed method considers each data source as an information source providing a body of evidence, represents statistical evidence by interval-valued probabilities, and uses Dempster's rule to integrate information based on multiple data source. The method is applied to the problems of ground-cover classification of multispectral data combined with digital terrain data such as elevation, slope, and aspect. Then this method is applied to simulated 201-band High Resolution Imaging Spectrometer (HIRIS) data by dividing the dimensionally huge data source into smaller and more manageable pieces based on the global statistical correlation information. It produces higher classification accuracy than the Maximum Likelihood (ML) classification method when the Hughes phenomenon is apparent.

A Wavelet Polarization Decomposition Net Model for Polarimetric SAR Image Classification

NASA Astrophysics Data System (ADS)

He, Chu; Ou, Dan; Yang, Teng; Wu, Kun; Liao, Mingsheng; Chen, Erxue

2014-11-01

In this paper, a deep model based on wavelet texture has been proposed for Polarimetric Synthetic Aperture Radar (PolSAR) image classification inspired by recent successful deep learning method. Our model is supposed to learn powerful and informative representations to improve the generalization ability for the complex scene classification tasks. Given the influence of speckle noise in Polarimetric SAR image, wavelet polarization decomposition is applied first to obtain basic and discriminative texture features which are then embedded into a Deep Neural Network (DNN) in order to compose multi-layer higher representations. We demonstrate that the model can produce a powerful representation which can capture some untraceable information from Polarimetric SAR images and show a promising achievement in comparison with other traditional SAR image classification methods for the SAR image dataset.

An efficient classification method based on principal component and sparse representation.

PubMed

Zhai, Lin; Fu, Shujun; Zhang, Caiming; Liu, Yunxian; Wang, Lu; Liu, Guohua; Yang, Mingqiang

2016-01-01

As an important application in optical imaging, palmprint recognition is interfered by many unfavorable factors. An effective fusion of blockwise bi-directional two-dimensional principal component analysis and grouping sparse classification is presented. The dimension reduction and normalizing are implemented by the blockwise bi-directional two-dimensional principal component analysis for palmprint images to extract feature matrixes, which are assembled into an overcomplete dictionary in sparse classification. A subspace orthogonal matching pursuit algorithm is designed to solve the grouping sparse representation. Finally, the classification result is gained by comparing the residual between testing and reconstructed images. Experiments are carried out on a palmprint database, and the results show that this method has better robustness against position and illumination changes of palmprint images, and can get higher rate of palmprint recognition.

Classifying the Colleges of the Forgotten Americans: A Geographically-Based Classification of Public Master's Colleges and Universities

ERIC Educational Resources Information Center

Katsinas, Stephen G.; Kinkead, J. Clint

2011-01-01

At the 2009 meeting of the Association for the Study of Higher Education's Council on Public Policy in Higher Education, Pat Callan, President of the National Center for Public Policy and Higher Education, asserted that Master's Colleges and Universities (MCUs) are the most understudied sector of American higher education. This paper described how…

A comparative agreement evaluation of two subaxial cervical spine injury classification systems: the AOSpine and the Allen and Ferguson schemes.

PubMed

Urrutia, Julio; Zamora, Tomas; Campos, Mauricio; Yurac, Ratko; Palma, Joaquin; Mobarec, Sebastian; Prada, Carlos

2016-07-01

We performed an agreement study using two subaxial cervical spine classification systems: the AOSpine and the Allen and Ferguson (A&F) classifications. We sought to determine which scheme allows better agreement by different evaluators and by the same evaluator on different occasions. Complete imaging studies of 65 patients with subaxial cervical spine injuries were classified by six evaluators (three spine sub-specialists and three senior orthopaedic surgery residents) using the AOSpine subaxial cervical spine classification system and the A&F scheme. The cases were displayed in a random sequence after a 6-week interval for repeat evaluation. The Kappa coefficient (κ) was used to determine inter- and intra-observer agreement. Inter-observer: considering the main AO injury types, the agreement was substantial for the AOSpine classification [κ = 0.61 (0.57-0.64)]; using AO sub-types, the agreement was moderate [κ = 0.57 (0.54-0.60)]. For the A&F classification, the agreement [κ = 0.46 (0.42-0.49)] was significantly lower than using the AOSpine scheme. Intra-observer: the agreement was substantial considering injury types [κ = 0.68 (0.62-0.74)] and considering sub-types [κ = 0.62 (0.57-0.66)]. Using the A&F classification, the agreement was also substantial [κ = 0.66 (0.61-0.71)]. No significant differences were observed between spine surgeons and orthopaedic residents in the overall inter- and intra-observer agreement, or in the inter- and intra-observer agreement of specific type of injuries. The AOSpine classification (using the four main injury types or at the sub-types level) allows a significantly better agreement than the A&F classification. The A&F scheme does not allow reliable communication between medical professionals.

Sequential Schooling or Lifelong Learning? International Frameworks through the Lens of English Higher Professional and Vocational Education

ERIC Educational Resources Information Center

Lester, Stan

2018-01-01

Purpose: The purpose of this paper is to review three international frameworks, including the International Standard Classification of Education (ISCED), in relation to one country's higher professional and vocational education system. Design/methodology/approach: The frameworks were examined in the context of English higher work-related…

Inter-rater reliability of a modified version of Delitto et al.’s classification-based system for low back pain: a pilot study

PubMed Central

Apeldoorn, Adri T.; van Helvoirt, Hans; Ostelo, Raymond W.; Meihuizen, Hanneke; Kamper, Steven J.; van Tulder, Maurits W.; de Vet, Henrica C. W.

2016-01-01

Study design Observational inter-rater reliability study. Objectives To examine: (1) the inter-rater reliability of a modified version of Delitto et al.’s classification-based algorithm for patients with low back pain; (2) the influence of different levels of familiarity with the system; and (3) the inter-rater reliability of algorithm decisions in patients who clearly fit into a subgroup (clear classifications) and those who do not (unclear classifications). Methods Patients were examined twice on the same day by two of three participating physical therapists with different levels of familiarity with the system. Patients were classified into one of four classification groups. Raters were blind to the others’ classification decision. In order to quantify the inter-rater reliability, percentages of agreement and Cohen’s Kappa were calculated. Results A total of 36 patients were included (clear classification n = 23; unclear classification n = 13). The overall rate of agreement was 53% and the Kappa value was 0·34 [95% confidence interval (CI): 0·11–0·57], which indicated only fair inter-rater reliability. Inter-rater reliability for patients with a clear classification (agreement 52%, Kappa value 0·29) was not higher than for patients with an unclear classification (agreement 54%, Kappa value 0·33). Familiarity with the system (i.e. trained with written instructions and previous research experience with the algorithm) did not improve the inter-rater reliability. Conclusion Our pilot study challenges the inter-rater reliability of the classification procedure in clinical practice. Therefore, more knowledge is needed about factors that affect the inter-rater reliability, in order to improve the clinical applicability of the classification scheme. PMID:27559279

An application to pulmonary emphysema classification based on model of texton learning by sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Min; Zhou, Xiangrong; Goshima, Satoshi; Chen, Huayue; Muramatsu, Chisako; Hara, Takeshi; Yokoyama, Ryojiro; Kanematsu, Masayuki; Fujita, Hiroshi

2012-03-01

We aim at using a new texton based texture classification method in the classification of pulmonary emphysema in computed tomography (CT) images of the lungs. Different from conventional computer-aided diagnosis (CAD) pulmonary emphysema classification methods, in this paper, firstly, the dictionary of texton is learned via applying sparse representation(SR) to image patches in the training dataset. Then the SR coefficients of the test images over the dictionary are used to construct the histograms for texture presentations. Finally, classification is performed by using a nearest neighbor classifier with a histogram dissimilarity measure as distance. The proposed approach is tested on 3840 annotated regions of interest consisting of normal tissue and mild, moderate and severe pulmonary emphysema of three subtypes. The performance of the proposed system, with an accuracy of about 88%, is comparably higher than state of the art method based on the basic rotation invariant local binary pattern histograms and the texture classification method based on texton learning by k-means, which performs almost the best among other approaches in the literature.

Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification.

PubMed

Chiarelli, Antonio Maria; Croce, Pierpaolo; Merla, Arcangelo; Zappasodi, Filippo

2018-06-01

Brain-computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures. We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1 s and overall experiment length of 10 min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers. At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect. BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.

The natural history of cystic echinococcosis in untreated and albendazole-treated patients.

PubMed

Solomon, N; Kachani, M; Zeyhle, E; Macpherson, C N L

2017-07-01

The World Health Organization (WHO) treatment protocols for cystic echinococcosis (CE) are based on the standardized ultrasound (US) classification. This study examined whether the classification reflected the natural history of CE in untreated and albendazole-treated patients. Data were collected during mass US screenings in CE endemic regions among transhumant populations, the Turkana and Berber peoples of Kenya and Morocco. Cysts were classified using the WHO classification. Patient records occurring prior to treatment, and after albendazole administration, were selected. 852 paired before/after observations of 360 cysts from 257 patients were analyzed. A McNemar-Bowker χ 2 test for symmetry was significant (p<0.0001). 744 observations (87.3%) maintained the same class, and 101 (11.9%) progressed, consistent with the classification. Regression to CE3B occurred in seven of 116 CE4 cyst observations (6.0%). A McNemar-Bowker χ 2 test of 1414 paired before/after observations of 288 cysts from 157 albendazole-treated patients was significant (p<0.0001). 1236 observations (87.4%) maintained the same class, and 149 (10.5%) progressed, consistent with the classification. Regression to CE3B occurred in 29 of 206 CE4 observations (14.1%). Significant asymmetry confirms the WHO classification's applicability to the natural history of CE and albendazole-induced changes. Regressions may reflect the stability of CE3B cysts. Copyright © 2017. Published by Elsevier B.V.

Multiple Spectral-Spatial Classification Approach for Hyperspectral Data

NASA Technical Reports Server (NTRS)

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

2010-01-01

A .new multiple classifier approach for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation methods based on dissimilar principles lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification -driven marker and forms a region in the spectral -spatial classification: map. Experimental results are presented for two hyperspectral airborne images. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques.

A parallel process growth mixture model of conduct problems and substance use with risky sexual behavior.

PubMed

Wu, Johnny; Witkiewitz, Katie; McMahon, Robert J; Dodge, Kenneth A

2010-10-01

Conduct problems, substance use, and risky sexual behavior have been shown to coexist among adolescents, which may lead to significant health problems. The current study was designed to examine relations among these problem behaviors in a community sample of children at high risk for conduct disorder. A latent growth model of childhood conduct problems showed a decreasing trend from grades K to 5. During adolescence, four concurrent conduct problem and substance use trajectory classes were identified (high conduct problems and high substance use, increasing conduct problems and increasing substance use, minimal conduct problems and increasing substance use, and minimal conduct problems and minimal substance use) using a parallel process growth mixture model. Across all substances (tobacco, binge drinking, and marijuana use), higher levels of childhood conduct problems during kindergarten predicted a greater probability of classification into more problematic adolescent trajectory classes relative to less problematic classes. For tobacco and binge drinking models, increases in childhood conduct problems over time also predicted a greater probability of classification into more problematic classes. For all models, individuals classified into more problematic classes showed higher proportions of early sexual intercourse, infrequent condom use, receiving money for sexual services, and ever contracting an STD. Specifically, tobacco use and binge drinking during early adolescence predicted higher levels of sexual risk taking into late adolescence. Results highlight the importance of studying the conjoint relations among conduct problems, substance use, and risky sexual behavior in a unified model. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Analysis of obstruction site in obstructive sleep apnea syndrome patients by drug induced sleep endoscopy.

PubMed

Koo, Soo Kweon; Choi, Jang Won; Myung, Nam Suk; Lee, Hyoung Ju; Kim, Yang Jae; Kim, Young Joong

2013-01-01

We analyzed site, pattern and degree of obstruction in Korean male obstructive sleep apnea syndrome (OSAS) patients by drug-induced sleep endoscopy (DISE). We also investigated possible links between BMI, AHI and DISE findings. Sixty-nine male patients underwent DISE. DISE findings were reported using our classification system in which modified 'VOTE classification' - obstruction type, site of obstruction, degree of obstruction and anatomical site contributing obstruction - was reported. Associations were analyzed among the results of the polysomnography, patients' characteristics and DISE finding. Multilevel airway obstruction was found in 84.06% of patients and 15.94% had a unilevel obstruction. Among those with unilevel obstruction, 90.90% had retropalatal level obstruction and 9.10% had retrolingual level obstruction. Palate with lateral pharyngeal wall obstruction (49.28%) is the most common obstruction type of the retropalatal level and tongue with lateral pharyngeal wall (37.68%) is the most common obstruction type of the retrolingual level. Examining the relation between obstruction site according to body mass index (BMI) and severity of OSAS (apnea hypopnea index, AHI), the lateral pharyngeal wall had an increasing tendency associated with higher BMI and higher AHI. But the lateral pharyngeal wall of both levels was statistically significant associated with higher AHI. The majority of the Korean male OSAS patients have multilevel obstruction and according to BMI and AHI, the DISE findings indicate that the lateral pharyngeal wall is the most important anatomical site contributing to obstruction regardless of the level at which the obstruction lies. © 2013 Elsevier Inc. All rights reserved.

Prognostic Performance and Reproducibility of the 1973 and 2004/2016 World Health Organization Grading Classification Systems in Non-muscle-invasive Bladder Cancer: A European Association of Urology Non-muscle Invasive Bladder Cancer Guidelines Panel Systematic Review.

PubMed

Soukup, Viktor; Čapoun, Otakar; Cohen, Daniel; Hernández, Virginia; Babjuk, Marek; Burger, Max; Compérat, Eva; Gontero, Paolo; Lam, Thomas; MacLennan, Steven; Mostafid, A Hugh; Palou, Joan; van Rhijn, Bas W G; Rouprêt, Morgan; Shariat, Shahrokh F; Sylvester, Richard; Yuan, Yuhong; Zigeuner, Richard

2017-11-01

Tumour grade is an important prognostic indicator in non-muscle-invasive bladder cancer (NMIBC). Histopathological classifications are limited by interobserver variability (reproducibility), which may have prognostic implications. European Association of Urology NMIBC guidelines suggest concurrent use of both 1973 and 2004/2016 World Health Organization (WHO) classifications. To compare the prognostic performance and reproducibility of the 1973 and 2004/2016 WHO grading systems for NMIBC. A systematic literature search was undertaken incorporating Medline, Embase, and the Cochrane Library. Studies were critically appraised for risk of bias (QUIPS). For prognosis, the primary outcome was progression to muscle-invasive or metastatic disease. Secondary outcomes were disease recurrence, and overall and cancer-specific survival. For reproducibility, the primary outcome was interobserver variability between pathologists. Secondary outcome was intraobserver variability (repeatability) by the same pathologist. Of 3593 articles identified, 20 were included in the prognostic review; three were eligible for the reproducibility review. Increasing tumour grade in both classifications was associated with higher disease progression and recurrence rates. Progression rates in grade 1 patients were similar to those in low-grade patients; progression rates in grade 3 patients were higher than those in high-grade patients. Survival data were limited. Reproducibility of the 2004/2016 system was marginally better than that of the 1973 system. Two studies on repeatability showed conflicting results. Most studies had a moderate to high risk of bias. Current grading classifications in NMIBC are suboptimal. The 1973 system identifies more aggressive tumours. Intra- and interobserver variability was slightly less in the 2004/2016 classification. We could not confirm that the 2004/2016 classification outperforms the 1973 classification in prediction of recurrence and progression. This article summarises the utility of two different grading systems for non-muscle-invasive bladder cancer. Both systems predict progression and recurrence, although pathologists vary in their reporting; suggestions for further improvements are made. Copyright © 2017 European Association of Urology. Published by Elsevier B.V. All rights reserved.

Geospatial analysis of spaceborne remote sensing data for assessing disaster impacts and modeling surface runoff in the built-environment

NASA Astrophysics Data System (ADS)

Wodajo, Bikila Teklu

Every year, coastal disasters such as hurricanes and floods claim hundreds of lives and severely damage homes, businesses, and lifeline infrastructure. This research was motivated by the 2005 Hurricane Katrina disaster, which devastated the Mississippi and Louisiana Gulf Coast. The primary objective was to develop a geospatial decision-support system for extracting built-up surfaces and estimating disaster impacts using spaceborne remote sensing satellite imagery. Pre-Katrina 1-m Ikonos imagery of a 5km x 10km area of Gulfport, Mississippi, was used as source data to develop the built-up area and natural surfaces or BANS classification methodology. Autocorrelation of 0.6 or higher values related to spectral reflectance values of groundtruth pixels were used to select spectral bands and establish the BANS decision criteria of unique ranges of reflectance values. Surface classification results using GeoMedia Pro geospatial analysis for Gulfport sample areas, based on BANS criteria and manually drawn polygons, were within +/-7% of the groundtruth. The difference between the BANS results and the groundtruth was statistically not significant. BANS is a significant improvement over other supervised classification methods, which showed only 50% correctly classified pixels. The storm debris and erosion estimation or SDE methodology was developed from analysis of pre- and post-Katrina surface classification results of Gulfport samples. The SDE severity level criteria considered hurricane and flood damages and vulnerability of inhabited built-environment. A linear regression model, with +0.93 Pearson R-value, was developed for predicting SDE as a function of pre-disaster percent built-up area. SDE predictions for Gulfport sample areas, used for validation, were within +/-4% of calculated values. The damage cost model considered maintenance, rehabilitation and reconstruction costs related to infrastructure damage and community impacts of Hurricane Katrina. The developed models were implemented for a study area along I-10 considering the predominantly flood-induced damages in New Orleans. The BANS methodology was calibrated for 0.6-m QuickBird2 multispectral imagery of Karachi Port area in Pakistan. The results were accurate within +/-6% of the groundtruth. Due to its computational simplicity, the unit hydrograph method is recommended for geospatial visualization of surface runoff in the built-environment using BANS surface classification maps and elevations data. Key words. geospatial analysis, satellite imagery, built-environment, hurricane, disaster impacts, runoff.

Does ASA classification impact success rates of endovascular aneurysm repairs?

PubMed

Conners, Michael S; Tonnessen, Britt H; Sternbergh, W Charles; Carter, Glen; Yoselevitz, Moises; Money, Samuel R

2002-09-01

The purpose of this study was to evaluate the technical success, clinical success, postoperative complication rate, need for a secondary procedure, and mortality rate with endovascular aneurysm repair (EAR), based on the physical status classification scheme advocated by the American Society of Anesthesiologists (ASA). At a single institution 167 patients underwent attempted EAR. Query of a prospectively maintained database supplemented with a retrospective review of medical records was used to gather statistics pertaining to patient demographics and outcome. In patients selected for EAR on the basis of acceptable anatomy, technical and clinical success rates were not significantly different among the different ASA classifications. Importantly, postoperative complication and 30-day mortality rates do not appear to significantly differ among the different ASA classifications in this patient population.

Combination of preoperative NLR, PLR and CEA could increase the diagnostic efficacy for I-III stage CRC.

PubMed

Peng, Hong-Xin; Yang, Lin; He, Bang-Shun; Pan, Yu-Qin; Ying, Hou-Qun; Sun, Hui-Ling; Lin, Kang; Hu, Xiu-Xiu; Xu, Tao; Wang, Shu-Kui

2017-09-01

Inflammation plays an important role in the development and progression of CRC. The members of inflammatory biomarkers, preoperative NLR and PLR, have been proved by numerous studies to be promising prognostic biomarkers for CRC. However, the diagnostic value of the two biomarkers in CRC remains unknown, and no study reported the combined diagnostic efficacy of NLR, PLR and CEA. Five hundred and fifty-nine patients with I-III stage CRC undergoing surgical resection and 559 gender- and age-matched healthy controls were enrolled in this retrospective study. NLR and PLR were calculated from preoperative peripheral blood cell count detected using white blood cell five classification by Sysmex XT-1800i Automated Hematology System and serum CEA were measured by electrochemiluminescence by ELECSYS 2010. The diagnostic performance of NLR, PLR and CEA for CRC was evaluated by ROC curve. Levels of NLR and PLR in the cases were significantly higher than them in the healthy controls. ROC curves comparison analyses showed that the diagnostic efficacy of NLR (AUC=.755, 95%CI=.728-.780) alone for CRC was significantly higher than PLR (AUC=.723, 95%CI=.696-.749, P=.037) and CEA (AUC=.690, 95%CI=.662-.717, P=.002) alone. In addition, the diagnostic efficacy of the combination of NLR, PLR and CEA(AUC=.831, 95%CI=.807-.852)for CRC was not only significantly higher than NLR alone but also higher than any combinations of the two of these three biomarkers (P<.05). Moreover, the NLR and PLR in the patients with TNM stage I/II was higher than that in the healthy controls, and patients with stage III had a higher NLR and PLR than those with stage I/II, but no significant difference was observed. Our study indicated that preoperative NLR could be a CRC diagnostic biomarker, even for early stage CRC, and the combination of NLR, PLR and CEA could significantly improve the diagnostic efficacy. © 2016 Wiley Periodicals, Inc.

Gene expression overlap affects karyotype prediction in pediatric acute lymphoblastic leukemia

DOE PAGES

Martin, S. B.; Mosquera-Caro, M. P.; Potter, J. W.; ...

2007-04-05

Leukemia is the most common childhood malignancy in the United States. Acute lymphoblastic leukemia (ALL) accounts for 75% of new leukemia cases in children. Although the outcome for children with ALL has improved dramatically over the past three decades, 25% of children with ALL still develop recurrent disease. Current risk classification schemes in pediatric ALL use clinical and laboratory parameters such as age and initial white blood cell count, as well as the presence of specific ALL-associated cytogenetic or molecular genetic abnormalities. Stratification based on cytogenetic analysis and molecular genetic detection consider B precursor ALL translocations such as t(12;21)(TEL-AML1), t(1;19)(E2A-PBX1)more » and t(9;22)(BCR-ABL), as well as numerical imbalances such as hyperdiploidy, specific chromosome trisomies or hypodiploidy. Despite such efforts, current diagnosis and risk classification schemes in pediatric ALL remain imprecise. In particular, it is likely that a significant number of higher-risk children are currently overtreated and could be cured with less intensive regimens, resulting in fewer toxicities and long-term side effects. Finally and conversely, a significant number of children in lower-risk categories still relapse and precise means to prospectively identify them have remained elusive.« less

Fuzzy logic modeling of bioaccumulation pattern of metals in coastal biota of Ondo State, Nigeria.

PubMed

Agunbiade, Foluso O; Olu-Owolabi, Bamidele I; Adebowale, Kayode O

2012-01-01

The accumulation patterns of ten metals in tissues of plant, Eichornia crassipes, and fishes, Hydrocynus forskahlii and Oreochromis mossambicus, were modeled with simple fuzzy classification (SFC) to assess toxic effects of anthropogenic activities on the coastal biota. The plant sample was separated into root, stem, and leaves and the fishes into bones, internal tissues, and muscles. They were analyzed for As, Cd, Cr, Cu, Ni, Pb, V, Fe, Mn, and Zn after wet oxidation of their dried samples. The results were converted into membership functions of five accumulation classes and aggregated with SFC. The classification results showed that there was no metal accumulation in the plant parts while the fishes were classified into low accumulation category. The internal tissues of the fishes had higher metal accumulation than the other parts. Generally, Fe and Mn had highest concentrations in the biota but are natural to the area and may not constitute significant risk. Cr had the highest transfer and accumulation from the coastal water into the aquatic lives and may be indicative of risk prone system being a toxic metal. Metal contaminations in the zone had not significantly accumulated in the biota making them less prone to risk associated with metal accumulation.

Classification scheme for sedimentary and igneous rocks in Gale crater, Mars

NASA Astrophysics Data System (ADS)

Mangold, N.; Schmidt, M. E.; Fisk, M. R.; Forni, O.; McLennan, S. M.; Ming, D. W.; Sautter, V.; Sumner, D.; Williams, A. J.; Clegg, S. M.; Cousin, A.; Gasnault, O.; Gellert, R.; Grotzinger, J. P.; Wiens, R. C.

2017-03-01

Rocks analyzed by the Curiosity rover in Gale crater include a variety of clastic sedimentary rocks and igneous float rocks transported by fluvial and impact processes. To facilitate the discussion of the range of lithologies, we present in this article a petrological classification framework adapting terrestrial classification schemes to Mars compositions (such as Fe abundances typically higher than for comparable lithologies on Earth), to specific Curiosity observations (such as common alkali-rich rocks), and to the capabilities of the rover instruments. Mineralogy was acquired only locally for a few drilled rocks, and so it does not suffice as a systematic classification tool, in contrast to classical terrestrial rock classification. The core of this classification involves (1) the characterization of rock texture as sedimentary, igneous or undefined according to grain/crystal sizes and shapes using imaging from the ChemCam Remote Micro-Imager (RMI), Mars Hand Lens Imager (MAHLI) and Mastcam instruments, and (2) the assignment of geochemical modifiers based on the abundances of Fe, Si, alkali, and S determined by the Alpha Particle X-ray Spectrometer (APXS) and ChemCam instruments. The aims are to help understand Gale crater geology by highlighting the various categories of rocks analyzed by the rover. Several implications are proposed from the cross-comparisons of rocks of various texture and composition, for instance between in place outcrops and float rocks. All outcrops analyzed by the rover are sedimentary; no igneous outcrops have been observed. However, some igneous rocks are clasts in conglomerates, suggesting that part of them are derived from the crater rim. The compositions of in-place sedimentary rocks contrast significantly with the compositions of igneous float rocks. While some of the differences between sedimentary rocks and igneous floats may be related to physical sorting and diagenesis of the sediments, some of the sedimentary rocks (e.g., potassic rocks) cannot be paired with any igneous rocks analyzed so far. In contrast, many float rocks, which cannot be classified from their poorly defined texture, plot on chemistry diagrams close to float rocks defined as igneous from their textures, potentially constraining their nature.

Classification scheme for sedimentary and igneous rocks in Gale crater, Mars

DOE PAGES

Mangold, Nicolas; Schmidt, Mariek E.; Fisk, Martin R.; ...

2016-11-05

Rocks analyzed by the Curiosity rover in Gale crater include a variety of clastic sedimentary rocks and igneous float rocks transported by fluvial and impact processes. Here, to facilitate the discussion of the range of lithologies, we present in this article a petrological classification framework adapting terrestrial classification schemes to Mars compositions (such as Fe abundances typically higher than for comparable lithologies on Earth), to specific Curiosity observations (such as common alkali-rich rocks), and to the capabilities of the rover instruments. Mineralogy was acquired only locally for a few drilled rocks, and so it does not suffice as a systematicmore » classification tool, in contrast to classical terrestrial rock classification. The core of this classification involves (1) the characterization of rock texture as sedimentary, igneous or undefined according to grain/crystal sizes and shapes using imaging from the ChemCam Remote Micro-Imager (RMI), Mars Hand Lens Imager (MAHLI) and Mastcam instruments, and (2) the assignment of geochemical modifiers based on the abundances of Fe, Si, alkali, and S determined by the Alpha Particle X-ray Spectrometer (APXS) and ChemCam instruments. The aims are to help understand Gale crater geology by highlighting the various categories of rocks analyzed by the rover. Several implications are proposed from the cross-comparisons of rocks of various texture and composition, for instance between in place outcrops and float rocks. All outcrops analyzed by the rover are sedimentary; no igneous outcrops have been observed. However, some igneous rocks are clasts in conglomerates, suggesting that part of them are derived from the crater rim. The compositions of in-place sedimentary rocks contrast significantly with the compositions of igneous float rocks. While some of the differences between sedimentary rocks and igneous floats may be related to physical sorting and diagenesis of the sediments, some of the sedimentary rocks (e.g., potassic rocks) cannot be paired with any igneous rocks analyzed so far. Finally, in contrast, many float rocks, which cannot be classified from their poorly defined texture, plot on chemistry diagrams close to float rocks defined as igneous from their textures, potentially constraining their nature.« less

Feature extraction and classification algorithms for high dimensional data

NASA Technical Reports Server (NTRS)

Lee, Chulhee; Landgrebe, David

1993-01-01

Feature extraction and classification algorithms for high dimensional data are investigated. Developments with regard to sensors for Earth observation are moving in the direction of providing much higher dimensional multispectral imagery than is now possible. In analyzing such high dimensional data, processing time becomes an important factor. With large increases in dimensionality and the number of classes, processing time will increase significantly. To address this problem, a multistage classification scheme is proposed which reduces the processing time substantially by eliminating unlikely classes from further consideration at each stage. Several truncation criteria are developed and the relationship between thresholds and the error caused by the truncation is investigated. Next an approach to feature extraction for classification is proposed based directly on the decision boundaries. It is shown that all the features needed for classification can be extracted from decision boundaries. A characteristic of the proposed method arises by noting that only a portion of the decision boundary is effective in discriminating between classes, and the concept of the effective decision boundary is introduced. The proposed feature extraction algorithm has several desirable properties: it predicts the minimum number of features necessary to achieve the same classification accuracy as in the original space for a given pattern recognition problem; and it finds the necessary feature vectors. The proposed algorithm does not deteriorate under the circumstances of equal means or equal covariances as some previous algorithms do. In addition, the decision boundary feature extraction algorithm can be used both for parametric and non-parametric classifiers. Finally, some problems encountered in analyzing high dimensional data are studied and possible solutions are proposed. First, the increased importance of the second order statistics in analyzing high dimensional data is recognized. By investigating the characteristics of high dimensional data, the reason why the second order statistics must be taken into account in high dimensional data is suggested. Recognizing the importance of the second order statistics, there is a need to represent the second order statistics. A method to visualize statistics using a color code is proposed. By representing statistics using color coding, one can easily extract and compare the first and the second statistics.

Classification scheme for sedimentary and igneous rocks in Gale crater, Mars

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

Mangold, Nicolas; Schmidt, Mariek E.; Fisk, Martin R.

Rocks analyzed by the Curiosity rover in Gale crater include a variety of clastic sedimentary rocks and igneous float rocks transported by fluvial and impact processes. Here, to facilitate the discussion of the range of lithologies, we present in this article a petrological classification framework adapting terrestrial classification schemes to Mars compositions (such as Fe abundances typically higher than for comparable lithologies on Earth), to specific Curiosity observations (such as common alkali-rich rocks), and to the capabilities of the rover instruments. Mineralogy was acquired only locally for a few drilled rocks, and so it does not suffice as a systematicmore » classification tool, in contrast to classical terrestrial rock classification. The core of this classification involves (1) the characterization of rock texture as sedimentary, igneous or undefined according to grain/crystal sizes and shapes using imaging from the ChemCam Remote Micro-Imager (RMI), Mars Hand Lens Imager (MAHLI) and Mastcam instruments, and (2) the assignment of geochemical modifiers based on the abundances of Fe, Si, alkali, and S determined by the Alpha Particle X-ray Spectrometer (APXS) and ChemCam instruments. The aims are to help understand Gale crater geology by highlighting the various categories of rocks analyzed by the rover. Several implications are proposed from the cross-comparisons of rocks of various texture and composition, for instance between in place outcrops and float rocks. All outcrops analyzed by the rover are sedimentary; no igneous outcrops have been observed. However, some igneous rocks are clasts in conglomerates, suggesting that part of them are derived from the crater rim. The compositions of in-place sedimentary rocks contrast significantly with the compositions of igneous float rocks. While some of the differences between sedimentary rocks and igneous floats may be related to physical sorting and diagenesis of the sediments, some of the sedimentary rocks (e.g., potassic rocks) cannot be paired with any igneous rocks analyzed so far. Finally, in contrast, many float rocks, which cannot be classified from their poorly defined texture, plot on chemistry diagrams close to float rocks defined as igneous from their textures, potentially constraining their nature.« less

Expression and clinicopathological significance of microRNA-21 and programmed cell death 4 in malignant melanoma.

PubMed

Jiao, Jian; Fan, Yu; Zhang, Yan

2015-10-01

To measure levels of microRNA (miR)-21 and its target gene, programmed cell death 4 (PDCD4), in samples of human cutaneous malignant melanoma and normal non-malignant control skin. Relative levels of miR-21 and PDCD4 mRNA were measured using a quantitative real-time reverse transcription-polymerase chain reaction. Correlations between the levels of the two molecules and the clinicopathological characteristics of malignant melanoma were analysed. A total of 67 cases of human cutaneous malignant melanoma were analysed and compared with 67 samples of normal nonmalignant control skin. Compared with normal skin samples, the relative level of miR-21 was significantly higher and the relative level of PDCD4 mRNA was significantly lower in the melanoma specimens. A significant negative correlation between PDCD4 mRNA and miR-21 was demonstrated in malignant melanoma (r = -0.602). Elevated miR-21 and reduced PDCD4 mRNA levels were both significantly correlated with increased tumour size, a higher Clark classification level and the presence of lymph node metastases in malignant melanoma. These findings suggest that miR-21 and PDCD4 might be potential biomarkers for malignant melanoma and might provide treatment targets in the future. © The Author(s) 2015.

Comparison of the current AJCC-TNM numeric-based with a new anatomical location-based lymph node staging system for gastric cancer: A western experience

PubMed Central

Auricchio, Annamaria; Cardella, Francesca; Mabilia, Andrea; Diana, Anna; Castellano, Paolo; De Vita, Ferdinando; Orditura, Michele

2017-01-01

Background In gastric cancer, the current AJCC numeric-based lymph node staging does not provide information on the anatomical extent of the disease and lymphadenectomy. A new anatomical location-based node staging, proposed by Choi, has shown better prognostic performance, thus soliciting Western world validation. Study design Data from 284 gastric cancers undergoing radical surgery at the Second University of Naples from 2000 to 2014 were reviewed. The lymph nodes were reclassified into three groups (lesser and greater curvature, and extraperigastric nodes); presence of any metastatic lymph node in a given group was considered positive, prompting a new N and TNM stage classification. Receiver-operating-characteristic (ROC) curves for censored survival data and bootstrap methods were used to compare the capability of the two models to predict tumor recurrence. Results More than one third of node positive patients were reclassified into different N and TNM stages by the new system. Compared to the current staging system, the new classification significantly correlated with tumor recurrence rates and displayed improved indices of prognostic performance, such as the Bayesian information criterion and the Harrell C-index. Higher values at survival ROC analysis demonstrated a significantly better stratification of patients by the new system, mostly in the early phase of the follow-up, with a worse prognosis in more advanced new N stages, despite the same current N stage. Conclusions This study suggests that the anatomical location-based classification of lymph node metastasis may be an important tool for gastric cancer prognosis and should be considered for future revision of the TNM staging system. PMID:28380037

FAST TRACK COMMUNICATION Algebraic classification of the Weyl tensor in higher dimensions based on its 'superenergy' tensor

NASA Astrophysics Data System (ADS)

Senovilla, José M. M.

2010-11-01

The algebraic classification of the Weyl tensor in the arbitrary dimension n is recovered by means of the principal directions of its 'superenergy' tensor. This point of view can be helpful in order to compute the Weyl aligned null directions explicitly, and permits one to obtain the algebraic type of the Weyl tensor by computing the principal eigenvalue of rank-2 symmetric future tensors. The algebraic types compatible with states of intrinsic gravitational radiation can then be explored. The underlying ideas are general, so that a classification of arbitrary tensors in the general dimension can be achieved.

Cooperative Learning for Distributed In-Network Traffic Classification

NASA Astrophysics Data System (ADS)

Joseph, S. B.; Loo, H. R.; Ismail, I.; Andromeda, T.; Marsono, M. N.

2017-04-01

Inspired by the concept of autonomic distributed/decentralized network management schemes, we consider the issue of information exchange among distributed network nodes to network performance and promote scalability for in-network monitoring. In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification. The results show that network nodes with sharing capability perform better with a higher average accuracy of 89.21% (sharing data) and 88.37% (sharing clusters) compared to 88.06% for nodes without cooperative learning capability. The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.

[Research of electroencephalography representational emotion recognition based on deep belief networks].

PubMed

Yang, Hao; Zhang, Junran; Jiang, Xiaomei; Liu, Fei

2018-04-01

In recent years, with the rapid development of machine learning techniques,the deep learning algorithm has been widely used in one-dimensional physiological signal processing. In this paper we used electroencephalography (EEG) signals based on deep belief network (DBN) model in open source frameworks of deep learning to identify emotional state (positive, negative and neutrals), then the results of DBN were compared with support vector machine (SVM). The EEG signals were collected from the subjects who were under different emotional stimuli, and DBN and SVM were adopted to identify the EEG signals with changes of different characteristics and different frequency bands. We found that the average accuracy of differential entropy (DE) feature by DBN is 89.12%±6.54%, which has a better performance than previous research based on the same data set. At the same time, the classification effects of DBN are better than the results from traditional SVM (the average classification accuracy of 84.2%±9.24%) and its accuracy and stability have a better trend. In three experiments with different time points, single subject can achieve the consistent results of classification by using DBN (the mean standard deviation is1.44%), and the experimental results show that the system has steady performance and good repeatability. According to our research, the characteristic of DE has a better classification result than other characteristics. Furthermore, the Beta band and the Gamma band in the emotional recognition model have higher classification accuracy. To sum up, the performances of classifiers have a promotion by using the deep learning algorithm, which has a reference for establishing a more accurate system of emotional recognition. Meanwhile, we can trace through the results of recognition to find out the brain regions and frequency band that are related to the emotions, which can help us to understand the emotional mechanism better. This study has a high academic value and practical significance, so further investigation still needs to be done.

A comprehensive laboratory-based program for classification of variants of uncertain significance in hereditary cancer genes.

PubMed

Eggington, J M; Bowles, K R; Moyes, K; Manley, S; Esterling, L; Sizemore, S; Rosenthal, E; Theisen, A; Saam, J; Arnell, C; Pruss, D; Bennett, J; Burbidge, L A; Roa, B; Wenstrup, R J

2014-09-01

Genetic testing has the potential to guide the prevention and treatment of disease in a variety of settings, and recent technical advances have greatly increased our ability to acquire large amounts of genetic data. The interpretation of this data remains challenging, as the clinical significance of genetic variation detected in the laboratory is not always clear. Although regulatory agencies and professional societies provide some guidance regarding the classification, reporting, and long-term follow-up of variants, few protocols for the implementation of these guidelines have been described. Because the primary aim of clinical testing is to provide results to inform medical management, a variant classification program that offers timely, accurate, confident and cost-effective interpretation of variants should be an integral component of the laboratory process. Here we describe the components of our laboratory's current variant classification program (VCP), based on 20 years of experience and over one million samples tested, using the BRCA1/2 genes as a model. Our VCP has lowered the percentage of tests in which one or more BRCA1/2 variants of uncertain significance (VUSs) are detected to 2.1% in the absence of a pathogenic mutation, demonstrating how the coordinated application of resources toward classification and reclassification significantly impacts the clinical utility of testing. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Classification of different degrees of adiposity in sedentary rats.

PubMed

Leopoldo, A S; Lima-Leopoldo, A P; Nascimento, A F; Luvizotto, R A M; Sugizaki, M M; Campos, D H S; da Silva, D C T; Padovani, C R; Cicogna, A C

2016-01-01

In experimental studies, several parameters, such as body weight, body mass index, adiposity index, and dual-energy X-ray absorptiometry, have commonly been used to demonstrate increased adiposity and investigate the mechanisms underlying obesity and sedentary lifestyles. However, these investigations have not classified the degree of adiposity nor defined adiposity categories for rats, such as normal, overweight, and obese. The aim of the study was to characterize the degree of adiposity in rats fed a high-fat diet using cluster analysis and to create adiposity intervals in an experimental model of obesity. Thirty-day-old male Wistar rats were fed a normal (n=41) or a high-fat (n=43) diet for 15 weeks. Obesity was defined based on the adiposity index; and the degree of adiposity was evaluated using cluster analysis. Cluster analysis allowed the rats to be classified into two groups (overweight and obese). The obese group displayed significantly higher total body fat and a higher adiposity index compared with those of the overweight group. No differences in systolic blood pressure or nonesterified fatty acid, glucose, total cholesterol, or triglyceride levels were observed between the obese and overweight groups. The adiposity index of the obese group was positively correlated with final body weight, total body fat, and leptin levels. Despite the classification of sedentary rats into overweight and obese groups, it was not possible to identify differences in the comorbidities between the two groups.

[Prevalence of hearing impairment in northwestern Germany. Results of an epidemiological study on hearing status (HÖRSTAT)].

PubMed

von Gablenz, P; Holube, I

2015-03-01

A pure-tone average of 0.5, 1, 2, and 4 kHz in the better ear (PTA-4) is the international standard criterion set by the World Health Organization (WHO) to describe hearing loss. Presently, there are no epidemiological data on hearing loss in Germany based on this criterion. A representative sample of adults from Oldenburg and Emden were invited for a hearing assessment. This article analyzes the association between hearing loss and age, sex, noise, occupation, and educational level. Age- and sex-specific prevalence rates following the WHO classification are compared with international findings. According to the WHO classification, the prevalence of hearing impairment in the study cohort (n=1,866) is approx. 16%. In men, who more commonly work in noisy jobs, a higher prevalence rate is observed than in women of the same age. Nevertheless, sex differences in the present study are smaller than those reported in most international studies. PTA-4 is approximately the same for men and women when effects of occupational noise are controlled, but differences in prevalence between occupational areas are still significant. Compared with international findings, age-specific prevalence rates in HÖRSTAT are low. In the synopsis of epidemiological studies of the past 25 years, a trend toward decreasing prevalence in middle and higher age groups can be observed.