stepwise linear discriminant: Topics by Science.gov

Sample records for stepwise linear discriminant

Selecting predictors for discriminant analysis of species performance: an example from an amphibious softwater plant.

PubMed

Vanderhaeghe, F; Smolders, A J P; Roelofs, J G M; Hoffmann, M

2012-03-01

Selecting an appropriate variable subset in linear multivariate methods is an important methodological issue for ecologists. Interest often exists in obtaining general predictive capacity or in finding causal inferences from predictor variables. Because of a lack of solid knowledge on a studied phenomenon, scientists explore predictor variables in order to find the most meaningful (i.e. discriminating) ones. As an example, we modelled the response of the amphibious softwater plant Eleocharis multicaulis using canonical discriminant function analysis. We asked how variables can be selected through comparison of several methods: univariate Pearson chi-square screening, principal components analysis (PCA) and step-wise analysis, as well as combinations of some methods. We expected PCA to perform best. The selected methods were evaluated through fit and stability of the resulting discriminant functions and through correlations between these functions and the predictor variables. The chi-square subset, at P < 0.05, followed by a step-wise sub-selection, gave the best results. In contrast to expectations, PCA performed poorly, as so did step-wise analysis. The different chi-square subset methods all yielded ecologically meaningful variables, while probable noise variables were also selected by PCA and step-wise analysis. We advise against the simple use of PCA or step-wise discriminant analysis to obtain an ecologically meaningful variable subset; the former because it does not take into account the response variable, the latter because noise variables are likely to be selected. We suggest that univariate screening techniques are a worthwhile alternative for variable selection in ecology. © 2011 German Botanical Society and The Royal Botanical Society of the Netherlands.
Discrimination of Geographical Origin of Asian Garlic Using Isotopic and Chemical Datasets under Stepwise Principal Component Analysis.

PubMed

Liu, Tsang-Sen; Lin, Jhen-Nan; Peng, Tsung-Ren

2018-01-16

Isotopic compositions of δ 2 H, δ 18 O, δ 13 C, and δ 15 N and concentrations of 22 trace elements from garlic samples were analyzed and processed with stepwise principal component analysis (PCA) to discriminate garlic's country of origin among Asian regions including South Korea, Vietnam, Taiwan, and China. Results indicate that there is no single trace-element concentration or isotopic composition that can accomplish the study's purpose and the stepwise PCA approach proposed does allow for discrimination between countries on a regional basis. Sequentially, Step-1 PCA distinguishes garlic's country of origin among Taiwanese, South Korean, and Vietnamese samples; Step-2 PCA discriminates Chinese garlic from South Korean garlic; and Step-3 and Step-4 PCA, Chinese garlic from Vietnamese garlic. In model tests, countries of origin of all audit samples were correctly discriminated by stepwise PCA. Consequently, this study demonstrates that stepwise PCA as applied is a simple and effective approach to discriminating country of origin among Asian garlics. © 2018 American Academy of Forensic Sciences.
Application of ERTS-1 imagery to the study of caribou movements and winter dispersal in relation to prevailing snowcover

NASA Technical Reports Server (NTRS)

Lent, P. C. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Step-wise discriminate analysis has demonstrated the feasibility of feature identification using linear discriminate functions of ERTS-1 MSS band densities and their ratios. The analysis indicated that features such as small streams can be detected even when they are in dark mountain shadow. The potential utility of this and similar analytic techniques appears considerable, and the limits it can be applied to analysis of ERTS-1 imagery are not yet fully known.
Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine

NASA Astrophysics Data System (ADS)

Santoso, Noviyanti; Wibowo, Wahyu

2018-03-01

A financial difficulty is the early stages before the bankruptcy. Bankruptcies caused by the financial distress can be seen from the financial statements of the company. The ability to predict financial distress became an important research topic because it can provide early warning for the company. In addition, predicting financial distress is also beneficial for investors and creditors. This research will be made the prediction model of financial distress at industrial companies in Indonesia by comparing the performance of Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) combined with variable selection technique. The result of this research is prediction model based on hybrid Stepwise-SVM obtains better balance among fitting ability, generalization ability and model stability than the other models.
Variation of facial features among three African populations: Body height match analyses.

PubMed

Taura, M G; Adamu, L H; Gudaji, A

2017-01-01

Body height is one of the variables that show a correlation with facial craniometry. Here we seek to discriminate the three populations (Nigerians, Ugandans and Kenyans) using facial craniometry based on different categories of body height of adult males. A total of 513 individuals comprising 234 Nigerians, 169 Ugandans and 110 Kenyans with mean age of 25.27, s=5.13 (18-40 years) participated. Paired and unpaired facial features were measured using direct craniometry. Multivariate and stepwise discriminate function analyses were used for differentiation of the three populations. The result showed significant overall facial differences among the three populations in all the body height categories. Skull height, total facial height, outer canthal distance, exophthalmometry, right ear width and nasal length were significantly different among the three different populations irrespective of body height categories. Other variables were sensitive to body height. Stepwise discriminant function analyses included maximum of six variables for better discrimination between the three populations. The single best discriminator of the groups was total facial height, however, for body height >1.70m the single best discriminator was nasal length. Most of the variables were better used with function 1, hence, better discrimination than function 2. In conclusion, adult body height in addition to other factors such as age, sex, and ethnicity should be considered in making decision on facial craniometry. However, not all the facial linear dimensions were sensitive to body height. Copyright © 2016 Elsevier GmbH. All rights reserved.
Feature selection and recognition from nonspecific volatile profiles for discrimination of apple juices according to variety and geographical origin.

PubMed

Guo, Jing; Yue, Tianli; Yuan, Yahong

2012-10-01

Apple juice is a complex mixture of volatile and nonvolatile components. To develop discrimination models on the basis of the volatile composition for an efficient classification of apple juices according to apple variety and geographical origin, chromatography volatile profiles of 50 apple juice samples belonging to 6 varieties and from 5 counties of Shaanxi (China) were obtained by headspace solid-phase microextraction coupled with gas chromatography. The volatile profiles were processed as continuous and nonspecific signals through multivariate analysis techniques. Different preprocessing methods were applied to raw chromatographic data. The blind chemometric analysis of the preprocessed chromatographic profiles was carried out. Stepwise linear discriminant analysis (SLDA) revealed satisfactory discriminations of apple juices according to variety and geographical origin, provided respectively 100% and 89.8% success rate in terms of prediction ability. Finally, the discriminant volatile compounds selected by SLDA were identified by gas chromatography-mass spectrometry. The proposed strategy was able to verify the variety and geographical origin of apple juices involving only a reduced number of discriminate retention times selected by the stepwise procedure. This result encourages the similar procedures to be considered in quality control of apple juices. This work presented a method for an efficient discrimination of apple juices according to apple variety and geographical origin using HS-SPME-GC-MS together with chemometric tools. Discrimination models developed could help to achieve greater control over the quality of the juice and to detect possible adulteration of the product. © 2012 Institute of Food Technologists®
Discrimination of nuclear explosions and earthquakes from teleseismic distances with a local network of short period seismic stations using artificial neural networks

NASA Astrophysics Data System (ADS)

Tiira, Timo

1996-10-01

Seismic discrimination capability of artificial neural networks (ANNs) was studied using earthquakes and nuclear explosions from teleseismic distances. The events were selected from two areas, which were analyzed separately. First, 23 nuclear explosions from Semipalatinsk and Lop Nor test sites were compared with 46 earthquakes from adjacent areas. Second, 39 explosions from Nevada test site were compared with 27 earthquakes from close-by areas. The basic discriminants were complexity, spectral ratio and third moment of frequency. The spectral discriminants were computed in five different ways to obtain all the information embedded in the signals, some of which were relatively weak. The discriminants were computed using data from six short period stations in Central and southern Finland. The spectral contents of the signals of both classes varied considerably between the stations. The 66 discriminants were formed into 65 optimum subsets of different sizes by using stepwise linear regression. A type of ANN called multilayer perceptron (MLP) was applied to each of the subsets. As a comparison the classification was repeated using linear discrimination analysis (LDA). Since the number of events was small the testing was made with the leave-one-out method. The ANN gave significantly better results than LDA. As a final tool for discrimination a combination of the ten neural nets with the best performance were used. All events from Central Asia were clearly discriminated and over 90% of the events from Nevada region were confidently discriminated. The better performance of ANNs was attributed to its ability to form complex decision regions between the groups and to its highly non-linear nature.
Multivariate calibration on NIR data: development of a model for the rapid evaluation of ethanol content in bakery products.

PubMed

Bello, Alessandra; Bianchi, Federica; Careri, Maria; Giannetto, Marco; Mori, Giovanni; Musci, Marilena

2007-11-05

A new NIR method based on multivariate calibration for determination of ethanol in industrially packed wholemeal bread was developed and validated. GC-FID was used as reference method for the determination of actual ethanol concentration of different samples of wholemeal bread with proper content of added ethanol, ranging from 0 to 3.5% (w/w). Stepwise discriminant analysis was carried out on the NIR dataset, in order to reduce the number of original variables by selecting those that were able to discriminate between the samples of different ethanol concentrations. With the so selected variables a multivariate calibration model was then obtained by multiple linear regression. The prediction power of the linear model was optimized by a new "leave one out" method, so that the number of original variables resulted further reduced.
Novel methods of time-resolved fluorescence data analysis for in-vivo tissue characterization: application to atherosclerosis.

PubMed

Jo, J A; Fang, Q; Papaioannou, T; Qiao, J H; Fishbein, M C; Dorafshar, A; Reil, T; Baker, D; Freischlag, J; Marcu, L

2004-01-01

This study investigates the ability of new analytical methods of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) data to characterize tissue in-vivo, such as the composition of atherosclerotic vulnerable plaques. A total of 73 TR-LIFS measurements were taken in-vivo from the aorta of 8 rabbits, and subsequently analyzed using the Laguerre deconvolution technique. The investigated spots were classified as normal aorta, thin or thick lesions, and lesions rich in either collagen or macrophages/foam-cells. Different linear and nonlinear classification algorithms (linear discriminant analysis, stepwise linear discriminant analysis, principal component analysis, and feedforward neural networks) were developed using spectral and TR features (ratios of intensity values and Laguerre expansion coefficients, respectively). Normal intima and thin lesions were discriminated from thick lesions (sensitivity >90%, specificity 100%) using only spectral features. However, both spectral and time-resolved features were necessary to discriminate thick lesions rich in collagen from thick lesions rich in foam cells (sensitivity >85%, specificity >93%), and thin lesions rich in foam cells from normal aorta and thin lesions rich in collagen (sensitivity >85%, specificity >94%). Based on these findings, we believe that TR-LIFS information derived from the Laguerre expansion coefficients can provide a valuable additional dimension for in-vivo tissue characterization.
Linear discriminant analysis of dermoscopic parameters for the differentiation of early melanomas from Clark naevi.

PubMed

Oka, Hiroshi; Tanaka, Masaru; Kobayashi, Seiichiro; Argenziano, Giuseppe; Soyer, H Peter; Nishikawa, Takeji

2004-04-01

As a first step to develop a screening system for pigmented skin lesions, we performed digital discriminant analyses between early melanomas and Clark naevi. A total of 59 cases of melanoma, including 23 melanoma in situ and 36 thin invasive melanomas (Breslow thickness < or =0.75 mm), and 188 clinically equivocal, histopathologically diagnosed Clark naevi were used in our study. After calculating 62 mathematical variables related to the colour, texture, asymmetry and circularity based on the dermoscopic findings of the pigmented skin lesions, we performed multivariate stepwise discriminant analysis using these variables to differentiate melanomas from naevi. The sensitivities and specificities of our model were 94.4 and 98.4%, respectively, for discriminating between melanomas (Breslow thickness < or =0.75 mm) and Clark naevi, and 73.9 and 85.6%, respectively, for discriminating between melanoma in situ and Clark naevi. Our algorithm accurately discriminated invasive melanomas from Clark naevi, but not melanomas in situ from Clark naevi.
Sex assessment using measurements of the first lumbar vertebra.

PubMed

Zheng, Wen Xu; Cheng, Fu Bo; Cheng, Kai Liang; Tian, Yong; Lai, Ying; Zhang, Wen Song; Zheng, Ya Juan; Li, You Qiong

2012-06-10

Sex determination is a vital part of the medico-legal system but can be difficult in cases where the integrity of the body has been compromised. The purpose of this study was to develop a technique for sex assessment from measurements of the first lumber vertebrate. Twenty-nine linear measurements and five ratios were collected from 113 Chinese adult males and 97 Chinese adult females using digital three-dimensional anthropometry methods. By using discriminant analysis, we found that 23 linear measurements and two ratios identified sexual dimorphism (P<0.01), with predictive accuracy ranging from 57.1% to 86.6%. Using a stepwise method of discriminant function analysis, we found three dimensions predicted sex with 88.6% accuracy: (a) upper end-plate width (EPWu), (b) left pedicle height (PHl), and (c) middle end-plate depth (EPDm). This study shows that a single first lumber vertebra can be used for this purpose, and that the discriminant equation will help forensic determination of sex in the Chinese population. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.
Multi-class ERP-based BCI data analysis using a discriminant space self-organizing map.

PubMed

Onishi, Akinari; Natsume, Kiyohisa

2014-01-01

Emotional or non-emotional image stimulus is recently applied to event-related potential (ERP) based brain computer interfaces (BCI). Though the classification performance is over 80% in a single trial, a discrimination between those ERPs has not been considered. In this research we tried to clarify the discriminability of four-class ERP-based BCI target data elicited by desk, seal, spider images and letter intensifications. A conventional self organizing map (SOM) and newly proposed discriminant space SOM (ds-SOM) were applied, then the discriminabilites were visualized. We also classify all pairs of those ERPs by stepwise linear discriminant analysis (SWLDA) and verify the visualization of discriminabilities. As a result, the ds-SOM showed understandable visualization of the data with a shorter computational time than the traditional SOM. We also confirmed the clear boundary between the letter cluster and the other clusters. The result was coherent with the classification performances by SWLDA. The method might be helpful not only for developing a new BCI paradigm, but also for the big data analysis.
determination of sex in south african blacks by discriminant function analysis of mandibular linear dimensions : A preliminary investigation using the zulu local population.

PubMed

Franklin, Daniel; O'Higgins, Paul; Oxnard, Charles E; Dadour, Ian

2006-12-01

The determination of sex is a critical component in forensic anthropological investigation. The literature attests to numerous metrical standards, each utilizing diffetent skeletal elements, for sex determination in South A frican Blacks. Metrical standards are popular because they provide a high degree of expected accuracy and are less error-prone than subjective nonmetric visual techniques. We note, however, that there appears to be no established metric mandible discriminant function standards for sex determination in this population.We report here on a preliminary investigation designed to evaluate whether the mandible is a practical element for sex determination in South African Blacks. The sample analyzed comprises 40 nonpathological Zulu individuals drawn from the R.A. Dart Collection. Ten linear measurements, obtained from mathematically trans-formed three-dimensional landmark data, are analyzed using basic univariate statistics and discriminant function analyses. Seven of the 10 measurements examined are found to be sexually dimorphic; the dimensions of the ramus are most dimorphic. The sex classification accuracy of the discriminant functions ranged from 72.5 to 87.5% for the univariate method, 92.5% for the stepwise method, and 57.5 to 95% for the direct method. We conclude that the mandible is an extremely useful element for sex determination in this population.
Benign-malignant mass classification in mammogram using edge weighted local texture features

NASA Astrophysics Data System (ADS)

Rabidas, Rinku; Midya, Abhishek; Sadhu, Anup; Chakraborty, Jayasree

2016-03-01

This paper introduces novel Discriminative Robust Local Binary Pattern (DRLBP) and Discriminative Robust Local Ternary Pattern (DRLTP) for the classification of mammographic masses as benign or malignant. Mass is one of the common, however, challenging evidence of breast cancer in mammography and diagnosis of masses is a difficult task. Since DRLBP and DRLTP overcome the drawbacks of Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) by discriminating a brighter object against the dark background and vice-versa, in addition to the preservation of the edge information along with the texture information, several edge-preserving texture features are extracted, in this study, from DRLBP and DRLTP. Finally, a Fisher Linear Discriminant Analysis method is incorporated with discriminating features, selected by stepwise logistic regression method, for the classification of benign and malignant masses. The performance characteristics of DRLBP and DRLTP features are evaluated using a ten-fold cross-validation technique with 58 masses from the mini-MIAS database, and the best result is observed with DRLBP having an area under the receiver operating characteristic curve of 0.982.
Artificial nose, NIR and UV-visible spectroscopy for the characterisation of the PDO Chianti Classico olive oil.

PubMed

Forina, M; Oliveri, P; Bagnasco, L; Simonetti, R; Casolino, M C; Nizzi Grifi, F; Casale, M

2015-11-01

An authentication study of the Italian PDO (Protected Designation of Origin) olive oil Chianti Classico, based on artificial nose, near-infrared and UV-visible spectroscopy, with a set of samples representative of the whole Chianti Classico production area and a considerable number of samples from other Italian PDO regions was performed. The signals provided by the three analytical techniques were used both individually and jointly, after fusion of the respective variables, in order to build a model for the Chianti Classico PDO olive oil. Different signal pre-treatments were performed in order to investigate their importance and their effects in enhancing and extracting information from experimental data, correcting backgrounds or removing baseline variations. Stepwise-Linear Discriminant Analysis (STEP-LDA) was used as a feature selection technique and, afterward, Linear Discriminant Analysis (LDA) and the class-modelling technique Quadratic Discriminant Analysis-UNEQual dispersed classes (QDA-UNEQ) were applied to sub-sets of selected variables, in order to obtain efficient models capable of characterising the extra virgin olive oils produced in the Chianti Classico PDO area. Copyright © 2015 Elsevier B.V. All rights reserved.
Cloud field classification based upon high spatial resolution textural features. I - Gray level co-occurrence matrix approach

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Chen, D. W.

1988-01-01

Stratocumulus, cumulus, and cirrus clouds were identified on the basis of cloud textural features which were derived from a single high-resolution Landsat MSS NIR channel using a stepwise linear discriminant analysis. It is shown that, using this method, it is possible to distinguish high cirrus clouds from low clouds with high accuracy on the basis of spatial brightness patterns. The largest probability of misclassification is associated with confusion between the stratocumulus breakup regions and the fair-weather cumulus.
Effects of Cerebral Blood Flow and Vessel Conditions on Speech Recognition in Patients With Postlingual Adult Cochlear Implant: Predictable Factors for the Efficacy of Cochlear Implant.

PubMed

Ishino, Takashi; Ragaee, Mahmoud Ali; Maruhashi, Tatsuya; Kajikawa, Masato; Higashi, Yukihito; Sonoyama, Toru; Takeno, Sachio; Hirakawa, Katsuhiro

Cochlear implantation (CI) has been the most successful procedure for restoring hearing in a patient with severe and profound hearing loss. However, possibly owing to the variable brain functions of each patient, its performance and the associated patient satisfaction are widely variable. The authors hypothesize that peripheral and cerebral circulation can be assessed by noninvasive and globally available methods, yielding superior presurgical predictive factors of the performance of CI in adult patients with postlingual hearing loss who are scheduled to undergo CI. Twenty-two adult patients with cochlear implants for postlingual hearing loss were evaluated using Doppler sonography measurement of the cervical arteries (reflecting cerebral blood flow), flow-mediated dilation (FMD; reflecting the condition of cerebral arteries), and their pre-/post-CI best score on a monosyllabic discrimination test (pre-/post-CI best monosyllabic discrimination [BMD] score). Correlations between post-CI BMD score and the other factors were examined using univariate analysis and stepwise multiple linear regression analysis. The prediction factors were calculated by examining the receiver-operating characteristic curve between post-CI BMD score and the significantly positively correlated factors. Age and duration of deafness had a moderately negative correlation. The mean velocity of the internal carotid arteries and FMD had a moderate-to-strong positive correlation with the post-CI BMD score in univariate analysis. Stepwise multiple linear regression analysis revealed that only FMD was significantly positively correlated with post-CI BMD score. Analysis of the receiver-operating characteristic curve showed that a FMD cutoff score of 1.8 significantly predicted post-CI BMD score. These data suggest that FMD is a convenient, noninvasive, and widely available tool for predicting the efficacy of cochlear implants. An FMD cutoff score of 1.8 could be a good index for determining whether patients will hear well with cochlear implants. It could also be used to predict whether cochlear implants will provide good speech recognition benefits to candidates, even if their speech discrimination is poor. This FMD index could become a useful predictive tool for candidates with poor speech discrimination to determine the efficacy of CI before surgery.
Selecting risk factors: a comparison of discriminant analysis, logistic regression and Cox's regression model using data from the Tromsø Heart Study.

PubMed

Brenn, T; Arnesen, E

1985-01-01

For comparative evaluation, discriminant analysis, logistic regression and Cox's model were used to select risk factors for total and coronary deaths among 6595 men aged 20-49 followed for 9 years. Groups with mortality between 5 and 93 per 1000 were considered. Discriminant analysis selected variable sets only marginally different from the logistic and Cox methods which always selected the same sets. A time-saving option, offered for both the logistic and Cox selection, showed no advantage compared with discriminant analysis. Analysing more than 3800 subjects, the logistic and Cox methods consumed, respectively, 80 and 10 times more computer time than discriminant analysis. When including the same set of variables in non-stepwise analyses, all methods estimated coefficients that in most cases were almost identical. In conclusion, discriminant analysis is advocated for preliminary or stepwise analysis, otherwise Cox's method should be used.
Differentiation of Chinese rice wines from different wineries based on mineral elemental fingerprinting.

PubMed

Shen, Fei; Wu, Jian; Ying, Yibin; Li, Bobin; Jiang, Tao

2013-12-15

Discrimination of Chinese rice wines from three well-known wineries ("Guyuelongshan", "Kuaijishan", and "Pagoda") in China has been carried out according to mineral element contents in this study. Nineteen macro and trace mineral elements (Na, Mg, Al, K, Ca, Mn, Fe, Cu, Zn, V, Cr, Co, Ni, As, Se, Mo, Cd, Ba and Pb) were determined by inductively coupled plasma mass spectrometry (ICP-MS) in 117 samples. Then the experimental data were subjected to analysis of variance (ANOVA) and principal component analysis (PCA) to reveal significant differences and potential patterns between samples. Stepwise linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA) were applied to develop classification models and achieved correct classified rates of 100% and 97.4% for the prediction sample set, respectively. The discrimination could be attributed to different raw materials (mainly water) and elaboration processes employed. The results indicate that the element compositions combined with multivariate analysis can be used as fingerprinting techniques to protect prestigious wineries and enable the authenticity of Chinese rice wine. Copyright © 2013 Elsevier Ltd. All rights reserved.
Chemometric classification of apple juices according to variety and geographical origin based on polyphenolic profiles.

PubMed

Guo, Jing; Yue, Tianli; Yuan, Yahong; Wang, Yutang

2013-07-17

To characterize and classify apple juices according to apple variety and geographical origin on the basis of their polyphenol composition, the polyphenolic profiles of 58 apple juice samples belonging to 5 apple varieties and from 6 regions in Shaanxi province of China were assessed. Fifty-one of the samples were from protected designation of origin (PDO) districts. Polyphenols were determined by high-performance liquid chromatography coupled to photodiode array detection (HPLC-PDA) and to a Q Exactive quadrupole-Orbitrap mass spectrometer. Chemometric techniques including principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were carried out on polyphenolic profiles of the samples to develop discrimination models. SLDA achieved satisfactory discriminations of apple juices according to variety and geographical origin, providing respectively 98.3 and 91.2% success rate in terms of prediction ability. This result demonstrated that polyphenols could served as characteristic indices to verify the variety and geographical origin of apple juices.

Prediction of health levels by remote sensing

NASA Technical Reports Server (NTRS)

Rush, M.; Vernon, S.

1975-01-01

Measures of the environment derived from remote sensing were compared to census population/housing measures in their ability to discriminate among health status areas in two urban communities. Three hypotheses were developed to explore the relationships between environmental and health data. Univariate and multiple step-wise linear regression analyses were performed on data from two sample areas in Houston and Galveston, Texas. Environmental data gathered by remote sensing were found to equal or surpass census data in predicting rates of health outcomes. Remote sensing offers the advantages of data collection for any chosen area or time interval, flexibilities not allowed by the decennial census.
Virtual Assessment of Sex: Linear and Angular Traits of the Mandibular Ramus Using Three-Dimensional Computed Tomography.

PubMed

Inci, Ercan; Ekizoglu, Oguzhan; Turkay, Rustu; Aksoy, Sema; Can, Ismail Ozgur; Solmaz, Dilek; Sayin, Ibrahim

2016-10-01

Morphometric analysis of the mandibular ramus (MR) provides highly accurate data to discriminate sex. The objective of this study was to demonstrate the utility and accuracy of MR morphometric analysis for sex identification in a Turkish population.Four hundred fifteen Turkish patients (18-60 y; 201 male and 214 female) who had previously had multidetector computed tomography scans of the cranium were included in the study. Multidetector computed tomography images were obtained using three-dimensional reconstructions and a volume-rendering technique, and 8 linear and 3 angular values were measured. Univariate, bivariate, and multivariate discriminant analyses were performed, and the accuracy rates for determining sex were calculated.Mandibular ramus values produced high accuracy rates of 51% to 95.6%. Upper ramus vertical height had the highest rate at 95.6%, and bivariate analysis showed 89.7% to 98.6% accuracy rates with the highest ratios of mandibular flexure upper border and maximum ramus breadth. Stepwise discrimination analysis gave a 99% accuracy rate for all MR variables.Our study showed that the MR, in particular morphometric measures of the upper part of the ramus, can provide valuable data to determine sex in a Turkish population. The method combines both anthropological and radiologic studies.
Remote Sensing as a Landscape Epidemiologic Tool to Identify Villages at High Risk for Malaria Transmission

NASA Technical Reports Server (NTRS)

Beck, Louisa R.; Rodriquez, Mario H.; Dister, Sheri W.; Rodriquez, Americo D.; Rejmankova, Eliska; Ulloa, Armando; Meza, Rosa A.; Roberts, Donald R.; Paris, Jack F.; Spanner, Michael A.;

1994-01-01

A landscape approach using remote sensing and Geographic Information System (GIS) technologies was developed to discriminate between villages at high and low risk for malaria transmission, as defined by adult Anopheles albimanus abundance. Satellite data for an area in southern Chiapas, Mexico were digitally processed to generate a map of landscape elements. The GIS processes were used to determine the proportion of mapped landscape elements surrounding 40 villages where An. albimanus data had been collected. The relationships between vector abundance and landscape element proportions were investigated using stepwise discriminant analysis and stepwise linear regression. Both analyses indicated that the most important landscape elements in terms of explaining vector abundance were transitional swamp and unmanaged pasture. Discriminant functions generated for these two elements were able to correctly distinguish between villages with high ind low vector abundance, with an overall accuracy of 90%. Regression results found both transitional swamp and unmanaged pasture proportions to be predictive of vector abundance during the mid-to-late wet season. This approach, which integrates remotely sensed data and GIS capabilities to identify villages with high vector-human contact risk, provides a promising tool for malaria surveillance programs that depend on labor-intensive field techniques. This is particularly relevant in areas where the lack of accurate surveillance capabilities may result in no malaria control action when, in fact, directed action is necessary. In general, this landscape approach could be applied to other vector-borne diseases in areas where: 1. the landscape elements critical to vector survival are known and 2. these elements can be detected at remote sensing scales.

An electroglottographical analysis-based discriminant function model differentiating multiple sclerosis patients from healthy controls.

PubMed

Vavougios, George D; Doskas, Triantafyllos; Konstantopoulos, Kostas

2018-05-01

Dysarthrophonia is a predominant symptom in many neurological diseases, affecting the quality of life of the patients. In this study, we produced a discriminant function equation that can differentiate MS patients from healthy controls, using electroglottographic variables not analyzed in a previous study. We applied stepwise linear discriminant function analysis in order to produce a function and score derived from electroglottographic variables extracted from a previous study. The derived discriminant function's statistical significance was determined via Wilk's λ test (and the associated p value). Finally, a 2 × 2 confusion matrix was used to determine the function's predictive accuracy, whereas the cross-validated predictive accuracy is estimated via the "leave-one-out" classification process. Discriminant function analysis (DFA) was used to create a linear function of continuous predictors. DFA produced the following model (Wilk's λ = 0.043, χ2 = 388.588, p < 0.0001, Tables 3 and 4): D (MS vs controls) = 0.728*DQx1 mean monologue + 0.325*CQx monologue + 0.298*DFx1 90% range monologue + 0.443*DQx1 90% range reading - 1.490*DQx1 90% range monologue. The derived discriminant score (S1) was used subsequently in order to form the coordinates of a ROC curve. Thus, a cutoff score of - 0.788 for S1 corresponded to a perfect classification (100% sensitivity and 100% specificity, p = 1.67e -22 ). Consistent with previous findings, electroglottographic evaluation represents an easy to implement and potentially important assessment in MS patients, achieving adequate classification accuracy. Further evaluation is needed to determine its use as a biomarker.
Optical system for tablet variety discrimination using visible/near-infrared spectroscopy

NASA Astrophysics Data System (ADS)

Shao, Yongni; He, Yong; Hu, Xingyue

2007-12-01

An optical system based on visible/near-infrared spectroscopy (Vis/NIRS) for variety discrimination of ginkgo (Ginkgo biloba L.) tablets was developed. This system consisted of a light source, beam splitter system, sample chamber, optical detector (diffuse reflection detector), and data collection. The tablet varieties used in the research include Da na kang, Xin bang, Tian bao ning, Yi kang, Hua na xing, Dou le, Lv yuan, Hai wang, and Ji yao. All samples (n=270) were scanned in the Vis/NIR region between 325 and 1075 nm using a spectrograph. The chemometrics method of principal component artificial neural network (PC-ANN) was used to establish discrimination models of them. In PC-ANN models, the scores of the principal components were chosen as the input nodes for the input layer of ANN, and the best discrimination rate of 91.1% was reached. Principal component analysis was also executed to select several optimal wavelengths based on loading values. Wavelengths at 481, 458, 466, 570, 1000, 662, and 400 nm were then used as the input data of stepwise multiple linear regression, the regression equation of ginkgo tablets was obtained, and the discrimination rate was researched 84.4%. The results indicated that this optical system could be applied to discriminating ginkgo (Ginkgo biloba L.) tablets, and it supplied a new method for fast ginkgo tablet variety discrimination.
Snow mapping and land use studies in Switzerland

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1977-01-01

The author has identified the following significant results. A system was developed for operational snow and land use mapping, based on a supervised classification method using various classification algorithms and representation of the results in maplike form on color film with a photomation system. Land use mapping, under European conditions, was achieved with a stepwise linear discriminant analysis by using additional ratio variables. On fall images, signatures of built-up areas were often not separable from wetlands. Two different methods were tested to correlate the size of settlements and the population with an accuracy for the densely populated Swiss Plateau between +2 or -12%.
Natural Resources Inventory and Land Evaluation in Switzerland

NASA Technical Reports Server (NTRS)

Haefner, H. (Principal Investigator)

1975-01-01

The author has identified the following significant results. A system was developed to operationally map and measure the areal extent of various land use categories for updating existing and producing new and actual thematic maps showing the latest state of rural and urban landscapes and its changes. The processing system includes: (1) preprocessing steps for radiometric and geometric corrections; (2) classification of the data by a multivariate procedure, using a stepwise linear discriminant analysis based on carefully selected training cells; and (3) output in form of color maps by printing black and white theme overlays of a selected scale with photomation system and its coloring and combination into a color composite.
Laguerre-based method for analysis of time-resolved fluorescence data: application to in-vivo characterization and diagnosis of atherosclerotic lesions.

PubMed

Jo, Javier A; Fang, Qiyin; Papaioannou, Thanassis; Baker, J Dennis; Dorafshar, Amir H; Reil, Todd; Qiao, Jian-Hua; Fishbein, Michael C; Freischlag, Julie A; Marcu, Laura

2006-01-01

We report the application of the Laguerre deconvolution technique (LDT) to the analysis of in-vivo time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) data and the diagnosis of atherosclerotic plaques. TR-LIFS measurements were obtained in vivo from normal and atherosclerotic aortas (eight rabbits, 73 areas), and subsequently analyzed using LDT. Spectral and time-resolved features were used to develop four classification algorithms: linear discriminant analysis (LDA), stepwise LDA (SLDA), principal component analysis (PCA), and artificial neural network (ANN). Accurate deconvolution of TR-LIFS in-vivo measurements from normal and atherosclerotic arteries was provided by LDT. The derived Laguerre expansion coefficients reflected changes in the arterial biochemical composition, and provided a means to discriminate lesions rich in macrophages with high sensitivity (>85%) and specificity (>95%). Classification algorithms (SLDA and PCA) using a selected number of features with maximum discriminating power provided the best performance. This study demonstrates the potential of the LDT for in-vivo tissue diagnosis, and specifically for the detection of macrophages infiltration in atherosclerotic lesions, a key marker of plaque vulnerability.
Application of the laguerre deconvolution method for time-resolved fluorescence spectroscopy to the characterization of atherosclerotic plaques.

PubMed

Jo, J A; Fang, Q; Papaioannou, T; Qiao, J H; Fishbein, M C; Beseth, B; Dorafshar, A H; Reil, T; Baker, D; Freischlag, J; Marcu, L

2005-01-01

This study investigates the ability of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) to detect inflammation in atherosclerotic lesion, a key feature of plaque vulnerability. A total of 348 TR-LIFS measurements were taken from carotid plaques of 30 patients, and subsequently analyzed using the Laguerre deconvolution technique. The investigated spots were classified as Early, Fibrotic/Calcified or Inflamed lesions. A stepwise linear discriminant analysis algorithm was developed using spectral and TR features (normalized intensity values and Laguerre expansion coefficients at discrete emission wavelengths, respectively). Features from only three emission wavelengths (390, 450 and 500 nm) were used in the classifier. The Inflamed lesions were discriminated with sensitivity > 80% and specificity > 90 %, when the Laguerre expansion coefficients were included in the feature space. These results indicate that TR-LIFS information derived from the Laguerre expansion coefficients at few selected emission wavelengths can discriminate inflammation in atherosclerotic plaques. We believe that TR-LIFS derived Laguerre expansion coefficients can provide a valuable additional dimension for the detection of vulnerable plaques.
Laguerre-based method for analysis of time-resolved fluorescence data: application to in-vivo characterization and diagnosis of atherosclerotic lesions

NASA Astrophysics Data System (ADS)

Jo, Javier A.; Fang, Qiyin; Papaioannou, Thanassis; Baker, J. Dennis; Dorafshar, Amir; Reil, Todd; Qiao, Jianhua; Fishbein, Michael C.; Freischlag, Julie A.; Marcu, Laura

2006-03-01

We report the application of the Laguerre deconvolution technique (LDT) to the analysis of in-vivo time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) data and the diagnosis of atherosclerotic plaques. TR-LIFS measurements were obtained in vivo from normal and atherosclerotic aortas (eight rabbits, 73 areas), and subsequently analyzed using LDT. Spectral and time-resolved features were used to develop four classification algorithms: linear discriminant analysis (LDA), stepwise LDA (SLDA), principal component analysis (PCA), and artificial neural network (ANN). Accurate deconvolution of TR-LIFS in-vivo measurements from normal and atherosclerotic arteries was provided by LDT. The derived Laguerre expansion coefficients reflected changes in the arterial biochemical composition, and provided a means to discriminate lesions rich in macrophages with high sensitivity (>85%) and specificity (>95%). Classification algorithms (SLDA and PCA) using a selected number of features with maximum discriminating power provided the best performance. This study demonstrates the potential of the LDT for in-vivo tissue diagnosis, and specifically for the detection of macrophages infiltration in atherosclerotic lesions, a key marker of plaque vulnerability.
Laguerre-based method for analysis of time-resolved fluorescence data: application to in-vivo characterization and diagnosis of atherosclerotic lesions

PubMed Central

Jo, Javier A.; Fang, Qiyin; Papaioannou, Thanassis; Baker, J. Dennis; Dorafshar, Amir H.; Reil, Todd; Qiao, Jian-Hua; Fishbein, Michael C.; Freischlag, Julie A.; Marcu, Laura

2007-01-01

We report the application of the Laguerre deconvolution technique (LDT) to the analysis of in-vivo time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) data and the diagnosis of atherosclerotic plaques. TR-LIFS measurements were obtained in vivo from normal and atherosclerotic aortas (eight rabbits, 73 areas), and subsequently analyzed using LDT. Spectral and time-resolved features were used to develop four classification algorithms: linear discriminant analysis (LDA), stepwise LDA (SLDA), principal component analysis (PCA), and artificial neural network (ANN). Accurate deconvolution of TR-LIFS in-vivo measurements from normal and atherosclerotic arteries was provided by LDT. The derived Laguerre expansion coefficients reflected changes in the arterial biochemical composition, and provided a means to discriminate lesions rich in macrophages with high sensitivity (>85%) and specificity (>95%). Classification algorithms (SLDA and PCA) using a selected number of features with maximum discriminating power provided the best performance. This study demonstrates the potential of the LDT for in-vivo tissue diagnosis, and specifically for the detection of macrophages infiltration in atherosclerotic lesions, a key marker of plaque vulnerability. PMID:16674179
Cognitive and Linguistic Sources of Variance in 2-Year-Olds’ Speech-Sound Discrimination: A Preliminary Investigation

PubMed Central

Lalonde, Kaylah; Holt, Rachael Frush

2017-01-01

Purpose This preliminary investigation explored potential cognitive and linguistic sources of variance in 2-year-olds’ speech-sound discrimination by using the toddler change/no-change procedure and examined whether modifications would result in a procedure that can be used consistently with younger 2-year-olds. Method Twenty typically developing 2-year-olds completed the newly modified toddler change/no-change procedure. Behavioral tests and parent report questionnaires were used to measure several cognitive and linguistic constructs. Stepwise linear regression was used to relate discrimination sensitivity to the cognitive and linguistic measures. In addition, discrimination results from the current experiment were compared with those from 2-year-old children tested in a previous experiment. Results Receptive vocabulary and working memory explained 56.6% of variance in discrimination performance. Performance was not different on the modified toddler change/no-change procedure used in the current experiment from in a previous investigation, which used the original version of the procedure. Conclusions The relationship between speech discrimination and receptive vocabulary and working memory provides further evidence that the procedure is sensitive to the strength of perceptual representations. The role for working memory might also suggest that there are specific subject-related, nonsensory factors limiting the applicability of the procedure to children who have not reached the necessary levels of cognitive and linguistic development. PMID:24023371
Cognitive and linguistic sources of variance in 2-year-olds’ speech-sound discrimination: a preliminary investigation.

PubMed

Lalonde, Kaylah; Holt, Rachael Frush

2014-02-01

This preliminary investigation explored potential cognitive and linguistic sources of variance in 2-year-olds’ speech-sound discrimination by using the toddler change/ no-change procedure and examined whether modifications would result in a procedure that can be used consistently with younger 2-year-olds. Twenty typically developing 2-year-olds completed the newly modified toddler change/no-change procedure. Behavioral tests and parent report questionnaires were used to measure several cognitive and linguistic constructs. Stepwise linear regression was used to relate discrimination sensitivity to the cognitive and linguistic measures. In addition, discrimination results from the current experiment were compared with those from 2-year-old children tested in a previous experiment. Receptive vocabulary and working memory explained 56.6% of variance in discrimination performance. Performance was not different on the modified toddler change/no-change procedure used in the current experiment from in a previous investigation, which used the original version of the procedure. The relationship between speech discrimination and receptive vocabulary and working memory provides further evidence that the procedure is sensitive to the strength of perceptual representations. The role for working memory might also suggest that there are specific subject-related, nonsensory factors limiting the applicability of the procedure to children who have not reached the necessary levels of cognitive and linguistic development.
Sex estimation from sternal measurements using multidetector computed tomography.

PubMed

Ekizoglu, Oguzhan; Hocaoglu, Elif; Inci, Ercan; Bilgili, Mustafa Gokhan; Solmaz, Dilek; Erdil, Irem; Can, Ismail Ozgur

2014-12-01

We aimed to show the utility and reliability of sternal morphometric analysis for sex estimation.Sex estimation is a very important step in forensic identification. Skeletal surveys are main methods for sex estimation studies. Morphometric analysis of sternum may provide high accuracy rated data in sex discrimination. In this study, morphometric analysis of sternum was evaluated in 1 mm chest computed tomography scans for sex estimation. Four hundred forty 3 subjects (202 female, 241 male, mean age: 44 ± 8.1 [distribution: 30-60 year old]) were included the study. Manubrium length (ML), mesosternum length (2L), Sternebra 1 (S1W), and Sternebra 3 (S3W) width were measured and also sternal index (SI) was calculated. Differences between genders were evaluated by student t-test. Predictive factors of sex were determined by discrimination analysis and receiver operating characteristic (ROC) analysis. Male sternal measurement values are significantly higher than females (P < 0.001) while SI is significantly low in males (P < 0.001). In discrimination analysis, MSL has high accuracy rate with 80.2% in females and 80.9% in males. MSL also has the best sensitivity (75.9%) and specificity (87.6%) values. Accuracy rates were above 80% in 3 stepwise discrimination analysis for both sexes. Stepwise 1 (ML, MSL, S1W, S3W) has the highest accuracy rate in stepwise discrimination analysis with 86.1% in females and 83.8% in males. Our study showed that morphometric computed tomography analysis of sternum might provide important information for sex estimation.
The Relationship of Selected Supply- and Demand-Side Factors to Forms of Perceived Discrimination among Adults with Multiple Sclerosis

ERIC Educational Resources Information Center

Roessler, Richard T.; Neath, Jeanne; McMahon, Brian T.; Rumrill, Phillip D.

2007-01-01

Single-predictor and stepwise multinomial logistic regression analyses and an external validation were completed on 3,082 allegations of employment discrimination by adults with multiple sclerosis. Women filed two thirds of the allegations, and individuals between 31 and 50 made the vast majority of discrimination charges (73%). Allegations…
[The research of establishing discriminant function for patients with angina pectoris by stepwise analysis based on serum inflammatory factors].

PubMed

Chen, Zhi-bin; Liang, Yan-bing; Tang, Hao; Wang, Zhong-hua; Zeng, Li-jin; Wu, Jing-guo; Li, Zhen-yu; Ma, Zhong-fu

2012-12-01

To improve cost-efficiency, discriminant functions in stepwise method was founded for the differential diagnosis of angina pectoris by detecting the serum level of high-sensitivity C-reactive protein (hs-CRP), macrophage migration inhibitory factor (MIF), interleukin-4 (IL-4) and interleukin-10 (IL-10) in patients with stable angina pectoris (SAP) and unstable angina pectoris (UAP). Thirty-nine SAP patients and 47 UAP patients were enrolled into the study, while 39 healthy volunteers were enrolled into the controlled group forming the entire set of training samples. The serum levels of hs-CRP, MIF, IL-4 and IL-10 were measured by enzyme linked immunosorbent assay (ELISA). Data was analyzed by software to define discriminant functions in the ways of "entering" and "stepwise". Both functions were evaluated by the results of validation. By the way of "enter independent together", the following discriminant functions were defined based on the data of training samples' age, hs-CRP, MIF, IL-4, IL-10: healthy control group =-129.858 + 2.869×age -2.451×hs-CRP + 1.393×MIF + 6.001×IL-4 + 4.848×IL-10; SAP group=-161.037 + 2.896×age-2.022×hs-CRP + 1.662×MIF + 6.703×IL-4 + 6.287×IL-10; UAP group=-199.087 + 2.468×age-1.440×hs-CRP + 3.404×MIF-13.875×IL-4 + 7.752×IL-10. Retrospective validation showed 4.8% of total miss-grouping, while cross-validation showed 5.6% of total miss-grouping. By the way of "stepwise", the above data was screened by software and training samples' age, MIF and IL-10 were suggested to define the following functions: healthy control group = - 125.218 + 2.659 × age + 0.599×MIF + 5.040 × IL-10; SAP group=-157.864 + 2.721×age + 1.008×MIF + 6.468×IL-10; UAP group=- 197.327 + 2.360×age + 2.932×MIF + 7.640×IL-10. Both retrospective and cross validation showed 6.4% of total miss-grouping. Both sets of discriminant functions had the same efficiency (100%) for differential diagnosis of SAP and UAP. The discriminant functions based on samples' age, MIF and IL-10, which were screened and suggested by stepwise method, may contribute to the differential diagnosis of atypical SAP and UAP, and therefore demonstrate better cost-efficiency.
Diagnostic power of optic disc morphology, peripapillary retinal nerve fiber layer thickness, and macular inner retinal layer thickness in glaucoma diagnosis with fourier-domain optical coherence tomography.

PubMed

Huang, Jehn-Yu; Pekmezci, Melike; Mesiwala, Nisreen; Kao, Andrew; Lin, Shan

2011-02-01

To evaluate the capability of the optic disc, peripapillary retinal nerve fiber layer (P-RNFL), macular inner retinal layer (M-IRL) parameters, and their combination obtained by Fourier-domain optical coherent tomography (OCT) in differentiating a glaucoma suspect from perimetric glaucoma. Two hundred and twenty eyes from 220 patients were enrolled in this study. The optic disc morphology, P-RNFL, and M-IRL were assessed by the Fourier-domain OCT (RTVue OCT, Model RT100, Optovue, Fremont, CA). A linear discriminant function was generated by stepwise linear discriminant analysis on the basis of OCT parameters and demographic factors. The diagnostic power of these parameters was evaluated with receiver operating characteristic (ROC) curve analysis. The diagnostic power in the clinically relevant range (specificity ≥ 80%) was presented as the partial area under the ROC curve (partial AROC). The individual OCT parameter with the largest AROC and partial AROC in the high specificity (≥ 80%) range were cup/disc vertical ratio (AROC = 0.854 and partial AROC = 0.142) for the optic disc parameters, average thickness (AROC = 0.919 and partial AROC = 0.147) for P-RNFL parameters, inferior hemisphere thickness (AROC = 0.871 and partial AROC = 0.138) for M-IRL parameters, respectively. The linear discriminant function further enhanced the ability in detecting perimetric glaucoma (AROC = 0.970 and partial AROC = 0.172). Average P-RNFL thickness is the optimal individual OCT parameter to detect perimetric glaucoma. Simultaneous evaluation on disc morphology, P-RNFL, and M-IRL thickness can improve the diagnostic accuracy in diagnosing glaucoma.
Sex Estimation From Sternal Measurements Using Multidetector Computed Tomography

PubMed Central

Ekizoglu, Oguzhan; Hocaoglu, Elif; Inci, Ercan; Bilgili, Mustafa Gokhan; Solmaz, Dilek; Erdil, Irem; Can, Ismail Ozgur

2014-01-01

Abstract We aimed to show the utility and reliability of sternal morphometric analysis for sex estimation. Sex estimation is a very important step in forensic identification. Skeletal surveys are main methods for sex estimation studies. Morphometric analysis of sternum may provide high accuracy rated data in sex discrimination. In this study, morphometric analysis of sternum was evaluated in 1 mm chest computed tomography scans for sex estimation. Four hundred forty 3 subjects (202 female, 241 male, mean age: 44 ± 8.1 [distribution: 30–60 year old]) were included the study. Manubrium length (ML), mesosternum length (2L), Sternebra 1 (S1W), and Sternebra 3 (S3W) width were measured and also sternal index (SI) was calculated. Differences between genders were evaluated by student t-test. Predictive factors of sex were determined by discrimination analysis and receiver operating characteristic (ROC) analysis. Male sternal measurement values are significantly higher than females (P < 0.001) while SI is significantly low in males (P < 0.001). In discrimination analysis, MSL has high accuracy rate with 80.2% in females and 80.9% in males. MSL also has the best sensitivity (75.9%) and specificity (87.6%) values. Accuracy rates were above 80% in 3 stepwise discrimination analysis for both sexes. Stepwise 1 (ML, MSL, S1W, S3W) has the highest accuracy rate in stepwise discrimination analysis with 86.1% in females and 83.8% in males. Our study showed that morphometric computed tomography analysis of sternum might provide important information for sex estimation. PMID:25501090
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.
Relationships between vegetation and terrain variables in southeastern Arizona. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Mouat, D. A. (Principal Investigator)

1974-01-01

The author has identified the following significant results. Relationships were established between eight terrain variables and plant species and 31 vegetation types. Certain plant species are better than others for differentiating or discriminating groups of specified terrain variables. Certain terrain variables are better than others for differentiating or discriminating groups of vegetation types. Stepwise discriminant analysis was shown to be a useful tool in plant ecological studies.

CORRELATION PURSUIT: FORWARD STEPWISE VARIABLE SELECTION FOR INDEX MODELS

PubMed Central

Zhong, Wenxuan; Zhang, Tingting; Zhu, Yu; Liu, Jun S.

2012-01-01

In this article, a stepwise procedure, correlation pursuit (COP), is developed for variable selection under the sufficient dimension reduction framework, in which the response variable Y is influenced by the predictors X1, X2, …, Xp through an unknown function of a few linear combinations of them. Unlike linear stepwise regression, COP does not impose a special form of relationship (such as linear) between the response variable and the predictor variables. The COP procedure selects variables that attain the maximum correlation between the transformed response and the linear combination of the variables. Various asymptotic properties of the COP procedure are established, and in particular, its variable selection performance under diverging number of predictors and sample size has been investigated. The excellent empirical performance of the COP procedure in comparison with existing methods are demonstrated by both extensive simulation studies and a real example in functional genomics. PMID:23243388
Inter- and intraspecific diversity in Cistus L. (Cistaceae) seeds, analysed with computer vision techniques.

PubMed

Lo Bianco, M; Grillo, O; Cañadas, E; Venora, G; Bacchetta, G

2017-03-01

This work aims to discriminate among different species of the genus Cistus, using seed parameters and following the scientific plant names included as accepted in The Plant List. Also, the intraspecific phenotypic differentiation of C. creticus, through comparison with three subspecies (C. creticus subsp. creticus, C. c. subsp. eriocephalus and C. c. subsp. corsicus), as well as the interpopulation variability among five C. creticus subsp. eriocephalus populations was evaluated. Seed mean weight and 137 morphocolorimetric quantitative variables, describing shape, size, colour and textural seed traits, were measured using image analysis techniques. Measured data were analysed applying step-wise linear discriminant analysis. An overall cross-validated classification performance of 80.6% was recorded at species level. With regard to C. creticus, as case study, percentages of correct discrimination of 96.7% and 99.6% were achieved at intraspecific and interpopulation levels, respectively. In this classification model, the relevance of the colorimetric and textural descriptive features was highlighted, as well as the seed mean weight, which was the most discriminant feature at specific and intraspecific level. These achievements proved the ability of the image analysis system as highly diagnostic for systematic purposes and confirm that seeds in the genus Cistus have important diagnostic value. © 2016 German Botanical Society and The Royal Botanical Society of the Netherlands.
Overlapped Partitioning for Ensemble Classifiers of P300-Based Brain-Computer Interfaces

PubMed Central

Onishi, Akinari; Natsume, Kiyohisa

2014-01-01

A P300-based brain-computer interface (BCI) enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300). In this study, we evaluated ensemble linear discriminant analysis (LDA) classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA), or none. The results show that an ensemble stepwise LDA (SWLDA) classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance. PMID:24695550
Overlapped partitioning for ensemble classifiers of P300-based brain-computer interfaces.

PubMed

Onishi, Akinari; Natsume, Kiyohisa

2014-01-01

A P300-based brain-computer interface (BCI) enables a wide range of people to control devices that improve their quality of life. Ensemble classifiers with naive partitioning were recently applied to the P300-based BCI and these classification performances were assessed. However, they were usually trained on a large amount of training data (e.g., 15300). In this study, we evaluated ensemble linear discriminant analysis (LDA) classifiers with a newly proposed overlapped partitioning method using 900 training data. In addition, the classification performances of the ensemble classifier with naive partitioning and a single LDA classifier were compared. One of three conditions for dimension reduction was applied: the stepwise method, principal component analysis (PCA), or none. The results show that an ensemble stepwise LDA (SWLDA) classifier with overlapped partitioning achieved a better performance than the commonly used single SWLDA classifier and an ensemble SWLDA classifier with naive partitioning. This result implies that the performance of the SWLDA is improved by overlapped partitioning and the ensemble classifier with overlapped partitioning requires less training data than that with naive partitioning. This study contributes towards reducing the required amount of training data and achieving better classification performance.
Hormonally active agents in the environment and children's behavior: assessing effects on children's gender-dimorphic outcomes.

PubMed

Sandberg, David E; Vena, John E; Weiner, John; Beehler, Gregory P; Swanson, Mya; Meyer-Bahlburg, Heino F L

2003-03-01

Early sex hormone exposure contributes to gender-dimorphic behavioral development in mammals, including humans. Environmental toxicants concentrated in contaminated sport fish can interfere with the actions of sex steroids. This study developed an outcome variable by combining gender-dimorphic behaviors that differentiates boys and girls. Offspring of participants in the New York State Angler Cohort Study (NYSACS) were targeted in a parent-report postal survey. Instruments were selected based on findings of gender differences in the general population. A linear discriminant function model incorporating three gender behavior scales correctly classified the sex of 97.7% of children (252 boys and 234 girls) from a random NYSACS sample. The discriminant function was cross-validated by correctly classifying the sex of 98.4% of children (457 boys and 425 girls) from the remaining NYSACS cases and 97.6% of children (154 boys and 142 girls) from an independent school sample. Within-sex stepwise multiple regression analyses revealed that masculine behavior increased among boys with age and with the number of years of maternal sport fish consumption. In girls, older age and previous live-born siblings were associated with more masculine behavior, whereas feminine behavior increased with the duration of breast feeding. These associations were replicated in an independent sample. A linear discriminant function effectively transformed the binary classification of sex (male-female) to a bipolar continuum of gender (masculinity-femininity). Findings from this study are consistent with the hypothesis that environmental contaminants contribute to shifts in gender-role behavior. Future investigations will need to account for competing explanations of this effect.
[Identification of Dendrobium varieties by infrared spectroscopy].

PubMed

Liu, Fei; Wang, Yuan-Zhong; Yang, Chun-Yan; Jin, Hang

2014-11-01

The difference of Dendrobium varieties were analyzed by Fourier transform infrared (FTIR) spectroscopy. The infrared spectra of 206 stems from 30 Dendrobium varieties were obtained, and showed that polysaccharides, especially fiber, were the main components in Dendrobium plants. FTIR combined with Wilks' Lambda stepwise discriminative analysis was used to identify Dendrobium varieties. The effects of spectral range and number of training samples on the discrimination results were also analysed. Two hundred eighty seven variables in the spectral range of 1 800-1 250 cm(-1) were studied, and showed that the return discrimination is 100% correct when the training samples number of each species was 2, 3, 4, 5, and 6, respectively, whereas for the remaining samples the correct rates of identification were equal to 79.4%, 91.3%, 93.0%, 98.2%, and 100%, respectively. The same discriminative analyses on five different training samples in the spectral range of 1 800-1 500, 1 500-1 250, 1 250-600, 1 250-950 and 950-650 cm(-1) were compared, which showed that the variables in the range of 1 800-1 250, 1 800-1 500 and 950-600 cm(-1) were more suitable for variety identification, and one can obtain the satisfactory result for discriminative analysis when the training sample is more than 3. Our results indicate that FTIR combined with stepwise discriminative analysis is an effective way to distinguish different Dendrobium varieties.
Single-Trial Classification of Multi-User P300-Based Brain-Computer Interface Using Riemannian Geometry.

PubMed

Korczowski, L; Congedo, M; Jutten, C

2015-08-01

The classification of electroencephalographic (EEG) data recorded from multiple users simultaneously is an important challenge in the field of Brain-Computer Interface (BCI). In this paper we compare different approaches for classification of single-trials Event-Related Potential (ERP) on two subjects playing a collaborative BCI game. The minimum distance to mean (MDM) classifier in a Riemannian framework is extended to use the diversity of the inter-subjects spatio-temporal statistics (MDM-hyper) or to merge multiple classifiers (MDM-multi). We show that both these classifiers outperform significantly the mean performance of the two users and analogous classifiers based on the step-wise linear discriminant analysis. More importantly, the MDM-multi outperforms the performance of the best player within the pair.
Local connected fractal dimension analysis in gill of fish experimentally exposed to toxicants.

PubMed

Manera, Maurizio; Giari, Luisa; De Pasquale, Joseph A; Sayyaf Dezfuli, Bahram

2016-06-01

An operator-neutral method was implemented to objectively assess European seabass, Dicentrarchus labrax (Linnaeus, 1758) gill pathology after experimental exposure to cadmium (Cd) and terbuthylazine (TBA) for 24 and 48h. An algorithm-derived local connected fractal dimension (LCFD) frequency measure was used in this comparative analysis. Canonical variates (CVA) and linear discriminant analysis (LDA) were used to evaluate the discrimination power of the method among exposure classes (unexposed, Cd exposed, TBA exposed). Misclassification, sensitivity and specificity, both with original and cross-validated cases, were determined. LCFDs frequencies enhanced the differences among classes which were visually selected after their means, respective variances and the differences between Cd and TBA exposed means, with respect to unexposed mean, were analyzed by scatter plots. Selected frequencies were then scanned by means of LDA, stepwise analysis, and Mahalanobis distance to detect the most discriminative frequencies out of ten originally selected. Discrimination resulted in 91.7% of cross-validated cases correctly classified (22 out of 24 total cases), with sensitivity and specificity, respectively, of 95.5% (1 false negative with respect to 21 really positive cases) and 75% (1 false positive with respect to 3 really negative cases). CVA with convex hull polygons ensured prompt, visually intuitive discrimination among exposure classes and graphically supported the false positive case. The combined use of semithin sections, which enhanced the visual evaluation of the overall lamellar structure; of LCFD analysis, which objectively detected local variation in complexity, without the possible bias connected to human personnel; and of CVA/LDA, could be an objective, sensitive and specific approach to study fish gill lamellar pathology. Furthermore this approach enabled discrimination with sufficient confidence between exposure classes or pathological states and avoided misdiagnosis. Copyright © 2016 Elsevier B.V. All rights reserved.
Sexual dimorphism of the mandible in a contemporary Chinese Han population.

PubMed

Dong, Hongmei; Deng, Mohong; Wang, WenPeng; Zhang, Ji; Mu, Jiao; Zhu, Guanghui

2015-10-01

A present limitation of forensic anthropology practice in China is the lack of population-specific criteria on contemporary human skeletons. In this study, a sample of 203 maxillofacial Cone beam computed tomography (CBCT) images, including 96 male and 107 female cases (20-65 years old), was analyzed to explore mandible sexual dimorphism in a population of contemporary adult Han Chinese to investigate the potential use of the mandible as sex indicator. A three-dimensional image from mandible CBCT scans was reconstructed using the SimPlant Pro 11.40 software. Nine linear and two angular parameters were measured. Discriminant function analysis (DFA) and logistic regression analysis (LRA) were used to develop the mathematics models for sex determination. All of the linear measurements studied and one angular measurement were found to be sexually dimorphic, with the maximum mandibular length and bi-condylar breadth being the most dimorphic by univariate DFA and LRA respectively. The cross-validated sex allocation accuracies on multivariate were ranged from 84.2% (direct DFA), 83.5% (direct LRA), 83.3% (stepwise DFA) to 80.5% (stepwise LRA). In general, multivariate DFA yielded a higher accuracy and LRA obtained a lower sex bias, and therefore both DFA and LRA had their own advantages for sex determination by the mandible in this sample. These results suggest that the mandible expresses sexual dimorphism in the contemporary adult Han Chinese population, indicating an excellent sexual discriminatory ability. Cone beam computed tomography scanning can be used as alternative source for contemporary osteometric techniques. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
Sex determination of the Acadian Flycatcher using discriminant analysis

USGS Publications Warehouse

Wilson, R.R.

1999-01-01

I used five morphometric variables from 114 individuals captured in Arkansas to develop a discriminant model to predict the sex of Acadian Flycatchers (Empidonax virescens). Stepwise discriminant function analyses selected wing chord and tail length as the most parsimonious subset of variables for discriminating sex. This two-variable model correctly classified 80% of females and 97% of males used to develop the model. Validation of the model using 19 individuals from Louisiana and Virginia resulted in 100% correct classification of males and females. This model provides criteria for sexing monomorphic Acadian Flycatchers during the breeding season and possibly during the winter.
Stepwise photochromism of bisnaphthopyrans exhibiting an excitation intensity-dependent color change.

PubMed

Inagaki, Yuki; Mutoh, Katsuya; Abe, Jiro

2018-06-07

Non-linear photoresponses against excitation light intensity are important for the development of attractive photofunctional materials exhibiting high spatial selective photoswitching that is not affected by weak background light. Biphotochromic systems composed of two fast photochromic units have the potential to show a stepwise two-photon absorption process in which the optical properties can be non-linearly controlled by changing the excitation light conditions. Herein, we designed and synthesized novel bisnaphthopyran derivatives containing fast photoswitchable naphthopyran units. The bisnaphthopyran derivatives show a stepwise two-photon-induced photochromic reaction upon UV light irradiation accompanied by a drastic color change due to a large change in the molecular structure between the one-photon product and the two-photon product. Consequently, the color of the bisnaphthopyran derivatives can be non-linearly controlled by changing the excitation intensity. This characteristic photochromic property of the biphotochromic system provides important insight into advanced photoresponsive materials.
Discriminant Analysis as a Tool for Admission Selection to Special Academic Programs. AIR 1986 Annual Forum Paper.

ERIC Educational Resources Information Center

Kissel, Mary Ann

The use of stepwise discriminant analysis as a means to select entering students who would benefit from a special program for the disadvantaged was studied. In fall 1984, 278 full-time black students were admitted as first-time students to a large urban university. Of the total, 200 entered a special program for the disadvantaged and 78 entered…
Multielement fingerprinting as a tool in origin authentication of PGI food products: Tropea red onion.

PubMed

Furia, Emilia; Naccarato, Attilio; Sindona, Giovanni; Stabile, Gaetano; Tagarelli, Antonio

2011-08-10

Tropea red onion ( Allium cepa L. var. Tropea) is among the most highly appreciated Italian products. It is cultivated in specific areas of Calabria and, due to its characteristics, was recently awarded with the protected geographical indications (PGI) certification from the European Union. A reliable classification of onion samples in groups corresponding to "Tropea" and "non-Tropea" categories is now available to the producers. This important goal has been achieved through the evaluation of three supervised chemometric approaches. Onion samples with PGI brand (120) and onion samples not cultivated following the production regulations (80) were digested by a closed-vessel microwave oven system. ICP-MS equipped with a dynamic reaction cell was used to determine the concentrations of 25 elements (Al, Ba, Ca, Cd, Ce, Cr, Dy, Eu, Fe, Ga, Gd, Ho, La, Mg, Mn, Na, Nd, Ni, Pr, Rb, Sm, Sr, Tl, Y, and Zn). The multielement fingerprint was processed using linear discriminant analysis (LDA) (standard and stepwise), soft independent modeling of class analogy (SIMCA), and back-propagation artificial neural network (BP-ANN). The cross-validation procedure has shown good results in terms of the prediction ability for all of the chemometric models: standard LDA, 94.0%; stepwise LDA, 94.5%; SIMCA, 95.5%; and BP-ANN, 91.5%.
Gender-Related Differences in Pelvic Morphometrics of the Retriever Dog Breed.

PubMed

Nganvongpanit, K; Pitakarnnop, T; Buddhachat, K; Phatsara, M

2017-02-01

This study presents the results from a morphometric analysis of 52 dry Retriever dog pelvic bones (30 male, 22 female). A total of 20 parameters were measured using an osteometric board and digital vernier caliper. Six parameters were found to be significantly higher (P < 0.05) in males than in females, while one parameter was significantly higher (P < 0.05) in females than in males. However, none of the measured parameters demonstrated clear cut-off values with no intersect between males and females. Therefore, we generated a stepwise discriminant analysis from all 20 parameters in order to develop a possible working equation to discriminate gender from a dog pelvic bone. Stepwise discriminant analysis was used to create a discrimination function: Y = [82.1*PS/AII] - [50.72*LIS/LI] - [23.09*OTD/SP] + [7.69*SP/IE] + [6.52*IC/OW] + [7.67*ISA/OW] + [20.77*AII/PS] + [504.71*OW/ISA] - [90.84*PS/ISA] - [148.95], which showed an accuracy rate of 86.27%. This is the first study presenting an equation/function for use in discriminating gender from a dog's pelvic measurements. The results can be used in veterinary forensic anthropology and also show that a dog's pelvis presents sexual dimorphisms, as in humans. © 2016 Blackwell Verlag GmbH.
Color Trails Test: normative data and criterion validity for the greek adult population.

PubMed

Messinis, Lambros; Malegiannaki, Amaryllis-Chryssi; Christodoulou, Tessa; Panagiotopoulos, Vassillis; Papathanasopoulos, Panagiotis

2011-06-01

The Color Trails Test (CTT) was developed as a culturally fair analog of the Trail Making Test. In the present study, normative data for the CTT were developed for the Greek adult population and further the criterion validity of the CTT was examined in two clinical groups (29 Parkinson's disease [PD] and 25 acute stroke patients). The instrument was applied to 163 healthy participants, aged 19-75. Stepwise linear regression analyses revealed a significant influence of age and education level on completion time in both parts of the CTT (increased age and decreased educational level contributed to slower completion times for both parts), whereas gender did not influence time to completion of part B. Further, the CTT appears to discriminate adequately between the performance of PD and acute stroke patients and matched healthy controls.
A Statistical Discrimination Experiment for Eurasian Events Using a Twenty-Seven-Station Network

DTIC Science & Technology

1980-07-08

to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...to test the effectiveness of a multivariate method of analysis for distinguishing earthquakes from explosions. The data base for the experiment...the weight assigned to each variable whenever a new one is added. Jennrich, R. I. (1977). Stepwise discriminant analysis , in Statistical Methods for
Multifactorial Analysis of a Biomarker Pool for Alzheimer Disease Risk in a North Indian Population.

PubMed

Talwar, Puneet; Grover, Sandeep; Sinha, Juhi; Chandna, Puneet; Agarwal, Rachna; Kushwaha, Suman; Kukreti, Ritushree

2017-01-01

Alzheimer disease (AD) is a progressive neurodegenerative disease with a complex multifactorial etiology. Here, we aim to identify a biomarker pool comprised of genetic variants and blood biomarkers as predictor of AD risk. We performed a case-control study involving 108 cases and 159 non-demented healthy controls to examine the association of multiple biomarkers with AD risk. The APOE genotyping revealed that ε4 allele frequency was significantly high (p value = 0.0001, OR = 2.66, 95% CI 1.58-4.46) in AD as compared to controls, whereas ε2 (p = 0.0430, OR = 0.29, CI 0.07-1.10) was overrepresented in controls. In biochemical assays, significant differences in levels of total copper, free copper, zinc, copper/zinc ratio, iron, epidermal growth factor receptor (EGFR), leptin, and albumin were also observed. The AD risk score (ADRS) as a linear combination of 6 candidate markers involving age, education status, APOE ε4 allele, levels of iron, Cu/Zn ratio, and EGFR was created using stepwise linear discriminant analysis. The area under the ROC curve of the ADRS panel for predicting AD risk was significantly high (AUC = 0.84, p < 0.0001, 95% CI 0.78-0.89, sensitivity = 70.0%, specificity = 83.8%) compared to individual parameters. These findings support the multifactorial etiology of AD and demonstrate the ability of a panel involving 6 biomarkers to discriminate AD cases from non-demented healthy controls. © 2017 S. Karger AG, Basel.
A pilot evaluation of a computer-based psychometric test battery designed to detect impairment in patients with cirrhosis.

PubMed

Cook, Nicola A; Kim, Jin Un; Pasha, Yasmin; Crossey, Mary Me; Schembri, Adrian J; Harel, Brian T; Kimhofer, Torben; Taylor-Robinson, Simon D

2017-01-01

Psychometric testing is used to identify patients with cirrhosis who have developed hepatic encephalopathy (HE). Most batteries consist of a series of paper-and-pencil tests, which are cumbersome for most clinicians. A modern, easy-to-use, computer-based battery would be a helpful clinical tool, given that in its minimal form, HE has an impact on both patients' quality of life and the ability to drive and operate machinery (with societal consequences). We compared the Cogstate™ computer battery testing with the Psychometric Hepatic Encephalopathy Score (PHES) tests, with a view to simplify the diagnosis. This was a prospective study of 27 patients with histologically proven cirrhosis. An analysis of psychometric testing was performed using accuracy of task performance and speed of completion as primary variables to create a correlation matrix. A stepwise linear regression analysis was performed with backward elimination, using analysis of variance. Strong correlations were found between the international shopping list, international shopping list delayed recall of Cogstate and the PHES digit symbol test. The Shopping List Tasks were the only tasks that consistently had P values of <0.05 in the linear regression analysis. Subtests of the Cogstate battery correlated very strongly with the digit symbol component of PHES in discriminating severity of HE. These findings would indicate that components of the current PHES battery with the international shopping list tasks of Cogstate would be discriminant and have the potential to be used easily in clinical practice.
Authentication of animal fats using direct analysis in real time (DART) ionization-mass spectrometry and chemometric tools.

PubMed

Vaclavik, Lukas; Hrbek, Vojtech; Cajka, Tomas; Rohlik, Bo-Anne; Pipek, Petr; Hajslova, Jana

2011-06-08

A combination of direct analysis in real time (DART) ionization coupled to time-of-flight mass spectrometry (TOFMS) and chemometrics was used for animal fat (lard and beef tallow) authentication. This novel instrumentation was employed for rapid profiling of triacylglycerols (TAGs) and polar compounds present in fat samples and their mixtures. Additionally, fat isolated from pork, beef, and pork/beef admixtures was analyzed. Mass spectral records were processed by principal component analysis (PCA) and stepwise linear discriminant analysis (LDA). DART-TOFMS profiles of TAGs were found to be more suitable for the purpose of discrimination among the examined fat types as compared to profiles of polar compounds. The LDA model developed using TAG data enabled not only reliable classification of samples representing neat fats but also detection of admixed lard and tallow at adulteration levels of 5 and 10% (w/w), respectively. The presented approach was also successfully applied to minced meat prepared from pork and beef with comparable fat content. Using the DART-TOFMS TAG profiles of fat isolated from meat mixtures, detection of 10% pork added to beef and vice versa was possible.
Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis.

PubMed

Ren, Y Y; Zhou, L C; Yang, L; Liu, P Y; Zhao, B W; Liu, H X

2016-09-01

The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages. Model predictive ability was validated both internally and externally. The applicability domain was checked by the leverage approach to verify prediction reliability. The obtained models are simple and easy to interpret. In general, LR performs much better than LDA and seems to be more attractive for the prediction of the more toxic compounds, i.e. compounds that exhibit excess toxicity versus non-polar narcotic compounds and more reactive compounds versus less reactive compounds. In addition, model fit and regression diagnostics was done through the influence plot which reflects the hat-values, studentized residuals, and Cook's distance statistics of each sample. Overdispersion was also checked for the LR model. The relationships between the descriptors and the aquatic toxic behaviour of compounds are also discussed.

Assessment of craniometric traits in South Indian dry skulls for sex determination.

PubMed

Ramamoorthy, Balakrishnan; Pai, Mangala M; Prabhu, Latha V; Muralimanju, B V; Rai, Rajalakshmi

2016-01-01

The skeleton plays an important role in sex determination in forensic anthropology. The skull bone is considered as the second best after the pelvic bone in sex determination due to its better retention of morphological features. Different populations have varying skeletal characteristics, making population specific analysis for sex determination essential. Hence the objective of this investigation is to obtain the accuracy of sex determination using cranial parameters of adult skulls to the highest percentage in South Indian population and to provide a baseline data for sex determination in South India. Seventy adult preserved human skulls were taken and based on the morphological traits were classified into 43 male skulls and 27 female skulls. A total of 26 craniometric parameters were studied. The data were analyzed by using the SPSS discriminant function. The analysis of stepwise, multivariate, and univariate discriminant function gave an accuracy of 77.1%, 85.7%, and 72.9% respectively. Multivariate direct discriminant function analysis classified skull bones into male and female with highest levels of accuracy. Using stepwise discriminant function analysis, the most dimorphic variable to determine sex of the skull, was biauricular breadth followed by weight. Subjecting the best dimorphic variables to univariate discriminant analysis, high levels of accuracy of sexual dimorphism was obtained. Percentage classification of high accuracies were obtained in this study indicating high level of sexual dimorphism in the crania, setting specific discriminant equations for the gender determination in South Indian people. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.
Multivariate statistical analysis of the polyphenolic constituents in kiwifruit juices to trace fruit varieties and geographical origins.

PubMed

Guo, Jing; Yuan, Yahong; Dou, Pei; Yue, Tianli

2017-10-01

Fifty-one kiwifruit juice samples of seven kiwifruit varieties from five regions in China were analyzed to determine their polyphenols contents and to trace fruit varieties and geographical origins by multivariate statistical analysis. Twenty-one polyphenols belonging to four compound classes were determined by ultra-high-performance liquid chromatography coupled with ultra-high-resolution TOF mass spectrometry. (-)-Epicatechin, (+)-catechin, procyanidin B1 and caffeic acid derivatives were the predominant phenolic compounds in the juices. Principal component analysis (PCA) allowed a clear separation of the juices according to kiwifruit varieties. Stepwise linear discriminant analysis (SLDA) yielded satisfactory categorization of samples, provided 100% success rate according to kiwifruit varieties and 92.2% success rate according to geographical origins. The result showed that polyphenolic profiles of kiwifruit juices contain enough information to trace fruit varieties and geographical origins. Copyright © 2017 Elsevier Ltd. All rights reserved.
Differentiation of Commercial PDO Wines Produced in Istria (Croatia) According to Variety and Harvest Year Based on HS-SPME-GC/MS Volatile Aroma Compound Profiling.

PubMed

Lukić, Igor; Horvat, Ivana

2017-03-01

To differentiate monovarietal wines made from native and introduced varieties in Istria (Croatia), samples of Malvazija istarska, Chardonnay and Muscat yellow from two harvest years (2013 and 2014) were subjected to headspace solid-phase microextraction and gas chromatographic/mass spectrometric analysis (HS-SPME-GC/MS) of volatile aroma compounds. Significant effects of variety and harvest year were determined, but their interaction complicated the differentiation. Particular compounds were consistent as markers of variety in both years: nerol for Malvazija, ethyl cinnamate and a tentatively identified isomer of dimethylbenzaldehyde for Chardonnay, and terpenes for Muscat yellow. Wines from 2013 contained higher concentrations of the majority of important volatiles. A 100% correct differentiation of Malvazija istarska and Chardonnay wines according to both variety and harvest year was achieved by stepwise linear discriminant analysis.
A linear spectral matching technique for retrieving equivalent water thickness and biochemical constituents of green vegetation

NASA Technical Reports Server (NTRS)

Gao, Bo-Cai; Goetz, Alexander F. H.

1992-01-01

Over the last decade, technological advances in airborne imaging spectrometers, having spectral resolution comparable with laboratory spectrometers, have made it possible to estimate biochemical constituents of vegetation canopies. Wessman estimated lignin concentration from data acquired with NASA's Airborne Imaging Spectrometer (AIS) over Blackhawk Island in Wisconsin. A stepwise linear regression technique was used to determine the single spectral channel or channels in the AIS data that best correlated with measured lignin contents using chemical methods. The regression technique does not take advantage of the spectral shape of the lignin reflectance feature as a diagnostic tool nor the increased discrimination among other leaf components with overlapping spectral features. A nonlinear least squares spectral matching technique was recently reported for deriving both the equivalent water thicknesses of surface vegetation and the amounts of water vapor in the atmosphere from contiguous spectra measured with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). The same technique was applied to a laboratory reflectance spectrum of fresh, green leaves. The result demonstrates that the fresh leaf spectrum in the 1.0-2.5 microns region consists of spectral components of dry leaves and the spectral component of liquid water. A linear least squares spectral matching technique for retrieving equivalent water thickness and biochemical components of green vegetation is described.
Unambiguous discrimination between linearly dependent equidistant states with multiple copies

NASA Astrophysics Data System (ADS)

Zhang, Wen-Hai; Ren, Gang

2018-07-01

Linearly independent quantum states can be unambiguously discriminated, but linearly dependent ones cannot. For linearly dependent quantum states, however, if C copies of the single states are available, then they may form linearly independent states, and can be unambiguously discriminated. We consider unambiguous discrimination among N = D + 1 linearly dependent states given that C copies are available and that the single copies span a D-dimensional space with equal inner products. The maximum unambiguous discrimination probability is derived for all C with equal a priori probabilities. For this classification of the linearly dependent equidistant states, our result shows that if C is even then adding a further copy fails to increase the maximum discrimination probability.
Classification of lung cancer patients and controls by chromatography of modified nucleosides in serum

USGS Publications Warehouse

McEntire, John E.; Kuo, Kenneth C.; Smith, Mark E.; Stalling, David L.; Richens, Jack W.; Zumwalt, Robert W.; Gehrke, Charles W.; Papermaster, Ben W.

1989-01-01

A wide spectrum of modified nucleosides has been quantified by high-performance liquid chromatography in serum of 49 male lung cancer patients, 35 patients with other cancers, and 48 patients hospitalized for nonneoplastic diseases. Data for 29 modified nucleoside peaks were normalized to an internal standard and analyzed by discriminant analysis and stepwise discriminant analysis. A model based on peaks selected by a stepwise discriminant procedure correctly classified 79% of the cancer and 75% of the noncancer subjects. It also demonstrated 84% sensitivity and 79% specificity when comparing lung cancer to noncancer subjects, and 80% sensitivity and 55% specificity in comparing lung cancer to other cancers. The nucleoside peaks having the greatest influence on the models varied dependent on the subgroups compared, confirming the importance of quantifying a wide array of nucleosides. These data support and expand previous studies which reported the utility of measuring modified nucleoside levels in serum and show that precise measurement of an array of 29 modified nucleosides in serum by high-performance liquid chromatography with UV scanning with subsequent data modeling may provide a clinically useful approach to patient classification in diagnosis and subsequent therapeutic monitoring.
Sex estimation from measurements of the first rib in a contemporary Polish population.

PubMed

Kubicka, Anna Maria; Piontek, Janusz

2016-01-01

The aim of this study was to evaluate the accuracy of sex assessment using measurements of the first rib from computed tomography (CT) to develop a discriminant formula. Four discriminant formulae were derived based on CT imaging of the right first rib of 85 female and 91 male Polish patients of known age and sex. In direct discriminant analysis, the first equation consisted of all first rib variables; the second included measurements of the rib body; the third comprised only two measurements of the sternal end of the first rib. The stepwise method selected the four best variables from all measurements. The discriminant function equation was then tested on a cross-validated group consisting of 23 females and 24 males. The direct discriminant analysis showed that sex assessment was possible in 81.5% of cases in the first group and in 91.5% in the cross-validated group when all variables for the first rib were included. The average accuracy for the original group for rib body and sternal end was 80.9 and 67.9%, respectively. The percentages of correctly assigned individuals for the functions based on the rib body and sternal end in the cross-validated group were 76.6 and 85.0%, respectively. Higher average accuracies were obtained for stepwise discriminant analysis: 83.1% for the original group and 91.2% for the cross-validated group. The exterior edge, anterior-posterior of the sternal end, and depth of the arc were the most reliable parameters. Our results suggest that the first rib is dimorphic and that the described method can be used for sex assessment.
Sex determination based on a thoracic vertebra and ribs evaluation using clinical chest radiography.

PubMed

Tsubaki, Shun; Morishita, Junji; Usumoto, Yosuke; Sakaguchi, Kyoko; Matsunobu, Yusuke; Kawazoe, Yusuke; Okumura, Miki; Ikeda, Noriaki

2017-07-01

Our aim was to investigate whether sex can be determined from a combination of geometric features obtained from the 10th thoracic vertebra, 6th rib, and 7th rib. Six hundred chest radiographs (300 males and 300 females) were randomly selected to include patients of six age groups (20s, 30s, 40s, 50s, 60s, and 70s). Each group included 100 images (50 males and 50 females). A total of 14 features, including 7 lengths, 5 indices for the vertebra, and 2 types of widths for ribs, were utilized and analyzed for sex determination. Dominant features contributing to sex determination were selected by stepwise discriminant analysis after checking the variance inflation factors for multicollinearity. The accuracy of sex determination using a combination of the vertebra and ribs was evaluated from the selected features by the stepwise discriminant analysis. The accuracies in each age group were also evaluated in this study. The accuracy of sex determination based on a combination of features of the vertebra and ribs was 88.8% (533/600). This performance was superior to that of the vertebra or ribs only. Moreover, sex determination of subjects in their 20s demonstrated the highest accuracy (96.0%, 96/100). The features selected in the stepwise discriminant analysis included some features in both the vertebra and ribs. These results indicate the usefulness of combined information obtained from the vertebra and ribs for sex determination. We conclude that a combination of geometric characteristics obtained from the vertebra and ribs could be useful for determining sex. Copyright © 2017 Elsevier B.V. All rights reserved.
Normative data and discriminant validity of Rey's Verbal Learning Test for the Greek adult population.

PubMed

Messinis, Lambros; Tsakona, Ioanna; Malefaki, Sonia; Papathanasopoulos, Panagiotis

2007-08-01

The present study sought to establish normative and discriminant validity data for Rey's Auditory Verbal Learning Test [Rey, A. (1964). L 'examen clinique en psychologie [Clinical tests in psychology]. Paris: Presses Universitaires de France; Schmidt, M. (1996). Rey auditory verbal learning test: A handbook. Los Angeles, CA: Western Psychological Services] using newly adapted learning lists for the Greek adult population. Applying the procedure suggested by Geffen et al. [Geffen, G., Moar, K. J., O'Hanlon, A. P., Clark, C. R., & Geffen, L. N. (1990). Performance measures of 16-86-year-old males and females on the auditory verbal learning test. The Clinical Neuropsychologist, 4, 45-63] we administered the test to 205 healthy participants, aged 18-78 years and two adult patient groups (long-term cannabis users and HIV symptomatic patients). Stepwise linear regression analyses showed that the variables age, education and gender contributed significantly to most trials of the RAVLT. Performance decreased in an age-dependent manner from young adulthood. Women, young adults and higher educated participants outperformed men, older adults and less educated individuals. The test appears to discriminate adequately between the performance of long-term heavy cannabis users and HIV seropositive symptomatic patients and matched healthy controls, as both patient groups performed more poorly than their respective control group. Normative data stratified by age, gender and education for the Greek adult population is presented for use in research and clinical settings.
Differentiating major depressive disorder in youths with attention deficit hyperactivity disorder.

PubMed

Diler, Rasim Somer; Daviss, W Burleson; Lopez, Adriana; Axelson, David; Iyengar, Satish; Birmaher, Boris

2007-09-01

Youths with attention deficit hyperactivity disorders (ADHD) frequently have comorbid major depressive disorders (MDD) sharing overlapping symptoms. Our objective was to examine which depressive symptoms best discriminate MDD among youths with ADHD. One-hundred-eleven youths with ADHD (5.2-17.8 years old) and their parents completed interviews with the K-SADS-PL and respective versions of the child or the parent Mood and Feelings Questionnaire (MFQ-C, MFQ-P). Controlling for group differences, logistic regression was used to calculate odds ratios reflecting the accuracy with which various depressive symptoms on the MFQ-C or MFQ-P discriminated MDD. Stepwise logistic regression then identified depressive symptoms that best discriminated the groups with and without MDD, using cross-validated misclassification rate as the criterion. Symptoms that discriminated youths with MDD (n=18) from those without MDD (n=93) were 4 of 6 mood/anhedonia symptoms, all 14 depressed cognition symptoms, and only 3 of 11 physical/vegetative symptoms. Mild irritability, miserable/unhappy moods, and symptoms related to sleep, appetite, energy levels and concentration did not discriminate MDD. A stepwise logistic regression correctly classified 89% of the comorbid MDD subjects, with only age, anhedonia at school, thoughts about killing self, thoughts that bad things would happen, and talking more slowly remaining in the final model. Results of this study may not generalize to community samples because subjects were drawn largely from a university-based outpatient psychiatric clinic. These findings stress the importance of social withdrawal, anhedonia, depressive cognitions, suicidal thoughts, and psychomotor retardation when trying to identify MDD among ADHD youths.
A pilot evaluation of a computer-based psychometric test battery designed to detect impairment in patients with cirrhosis

PubMed Central

Cook, Nicola A; Kim, Jin Un; Pasha, Yasmin; Crossey, Mary ME; Schembri, Adrian J; Harel, Brian T; Kimhofer, Torben; Taylor-Robinson, Simon D

2017-01-01

Background Psychometric testing is used to identify patients with cirrhosis who have developed hepatic encephalopathy (HE). Most batteries consist of a series of paper-and-pencil tests, which are cumbersome for most clinicians. A modern, easy-to-use, computer-based battery would be a helpful clinical tool, given that in its minimal form, HE has an impact on both patients’ quality of life and the ability to drive and operate machinery (with societal consequences). Aim We compared the Cogstate™ computer battery testing with the Psychometric Hepatic Encephalopathy Score (PHES) tests, with a view to simplify the diagnosis. Methods This was a prospective study of 27 patients with histologically proven cirrhosis. An analysis of psychometric testing was performed using accuracy of task performance and speed of completion as primary variables to create a correlation matrix. A stepwise linear regression analysis was performed with backward elimination, using analysis of variance. Results Strong correlations were found between the international shopping list, international shopping list delayed recall of Cogstate and the PHES digit symbol test. The Shopping List Tasks were the only tasks that consistently had P values of <0.05 in the linear regression analysis. Conclusion Subtests of the Cogstate battery correlated very strongly with the digit symbol component of PHES in discriminating severity of HE. These findings would indicate that components of the current PHES battery with the international shopping list tasks of Cogstate would be discriminant and have the potential to be used easily in clinical practice. PMID:28919805
A simple randomisation procedure for validating discriminant analysis: a methodological note.

PubMed

Wastell, D G

1987-04-01

Because the goal of discriminant analysis (DA) is to optimise classification, it designedly exaggerates between-group differences. This bias complicates validation of DA. Jack-knifing has been used for validation but is inappropriate when stepwise selection (SWDA) is employed. A simple randomisation test is presented which is shown to give correct decisions for SWDA. The general superiority of randomisation tests over orthodox significance tests is discussed. Current work on non-parametric methods of estimating the error rates of prediction rules is briefly reviewed.
Reproductive Status of Females in the Eusocial Wasp Polistes ferreri Saussure (Hymenoptera: Vespidae).

PubMed

Soares, E R P; Torres, V O; Antonialli-Junior, W F

2014-12-01

In the subfamily Polistinae, caste dimorphism is not pronounced and differences among females are primarily physiological and behavioral. We investigated factors that indicate the reproductive status in females of Polistes ferreri Saussure. We analyzed females from nine colonies and evaluated morphometric parameters, ovarian development, occurrence of insemination, relative age, and cuticular chemical profile. The colony females showed three kinds of ovarian development: type A, filamentous ovarioles; type B, ovarioles containing partially developed oocytes; and type C, long and well-developed ovarioles containing two or more mature oocytes. The stepwise discriminant analysis of the cuticular chemical profile showed that it was possible to distinguish the three groups of females: workers 1, workers 2, and queens. However, the stepwise discriminant analysis of the morphological differences did not show significant differences among these groups. The queens were among the older females in the colony and were always inseminated, while the age of the workers varied according to the stage of colony development.
Statistical analysis of Thematic Mapper Simulator data for the geobotanical discrimination of rock types in southwest Oregon

NASA Technical Reports Server (NTRS)

Morrissey, L. A.; Weinstock, K. J.; Mouat, D. A.; Card, D. H.

1984-01-01

An evaluation of Thematic Mapper Simulator (TMS) data for the geobotanical discrimination of rock types based on vegetative cover characteristics is addressed in this research. A methodology for accomplishing this evaluation utilizing univariate and multivariate techniques is presented. TMS data acquired with a Daedalus DEI-1260 multispectral scanner were integrated with vegetation and geologic information for subsequent statistical analyses, which included a chi-square test, an analysis of variance, stepwise discriminant analysis, and Duncan's multiple range test. Results indicate that ultramafic rock types are spectrally separable from nonultramafics based on vegetative cover through the use of statistical analyses.
Assessing size and strength of the clavicle for its usefulness for sex estimation in a British medieval sample.

PubMed

Atterton, Thomas; De Groote, Isabelle; Eliopoulos, Constantine

2016-10-01

The construction of the biological profile from human skeletal remains is the foundation of anthropological examination. However, remains may be fragmentary and the elements usually employed, such as the pelvis and skull, are not available. The clavicle has been successfully used for sex estimation in samples from Iran and Greece. In the present study, the aim was to test the suitability of the measurements used in those previous studies on a British Medieval population. In addition, the project tested whether discrimination between sexes was due to size or clavicular strength. The sample consisted of 23 females and 25 males of pre-determined sex from two medieval collections: Poulton and Gloucester. Six measurements were taken using an osteometric board, sliding calipers and graduated tape. In addition, putty rings and bi-planar radiographs were made and robusticity measures calculated. The resulting variables were used in stepwise discriminant analyses. The linear measurements allowed correct sex classification in 89.6% of all individuals. This demonstrates the applicability of the clavicle for sex estimation in British populations. The most powerful discriminant factor was maximum clavicular length and the best combination of factors was maximum clavicular length and circumference. This result is similar to that obtained by other studies. To further investigate the extent of sexual dimorphism of the clavicle, the biomechanical properties of the polar second moment of area J and the ratio of maximum to minimum bending rigidity are included in the analysis. These were found to have little influence when entered into the discriminant function analysis. Copyright © 2016 Elsevier GmbH. All rights reserved.
Feature extraction and selection from volatile compounds for analytical classification of Chinese red wines from different varieties.

PubMed

Zhang, Jian; Li, Li; Gao, Nianfa; Wang, Depei; Gao, Qiang; Jiang, Shengping

2010-03-10

This work was undertaken to evaluate whether it is possible to determine the variety of a Chinese wine on the basis of its volatile compounds, and to investigate if discrimination models could be developed with the experimental wines that could be used for the commercial ones. A headspace solid-phase microextraction gas chromatographic (HS-SPME-GC) procedure was used to determine the volatile compounds and a blind analysis based on Ac/Ais (peak area of volatile compound/peak area of internal standard) was carried out for statistical purposes. One way analysis of variance (ANOVA), principal component analysis (PCA) and stepwise linear discriminant analysis (SLDA) were used to process data and to develop discriminant models. Only 11 peaks enabled to differentiate and classify the experimental wines. SLDA allowed 100% recognition ability for three grape varieties, 100% prediction ability for Cabernet Sauvignon and Cabernet Gernischt wines, but only 92.31% for Merlot wines. A more valid and robust way was to use the PCA scores to do the discriminant analysis. When we performed SLDA this way, 100% recognition ability and 100% prediction ability were obtained. At last, 11 peaks which selected by SLDA from raw analysis set had been identified. When we demonstrated the models using commercial wines, the models showed 100% recognition ability for the wines collected directly from winery and without ageing, but only 65% for the others. Therefore, the varietal factor was currently discredited as a differentiating parameter for commercial wines in China. Nevertheless, this method could be applied as a screening tool and as a complement to other methods for grape base liquors which do not need ageing and blending procedures. 2010 Elsevier B.V. All rights reserved.
Multi-resolution analysis using integrated microscopic configuration with local patterns for benign-malignant mass classification

NASA Astrophysics Data System (ADS)

Rabidas, Rinku; Midya, Abhishek; Chakraborty, Jayasree; Sadhu, Anup; Arif, Wasim

2018-02-01

In this paper, Curvelet based local attributes, Curvelet-Local configuration pattern (C-LCP), is introduced for the characterization of mammographic masses as benign or malignant. Amid different anomalies such as micro- calcification, bilateral asymmetry, architectural distortion, and masses, the reason for targeting the mass lesions is due to their variation in shape, size, and margin which makes the diagnosis a challenging task. Being efficient in classification, multi-resolution property of the Curvelet transform is exploited and local information is extracted from the coefficients of each subband using Local configuration pattern (LCP). The microscopic measures in concatenation with the local textural information provide more discriminating capability than individual. The measures embody the magnitude information along with the pixel-wise relationships among the neighboring pixels. The performance analysis is conducted with 200 mammograms of the DDSM database containing 100 mass cases of each benign and malignant. The optimal set of features is acquired via stepwise logistic regression method and the classification is carried out with Fisher linear discriminant analysis. The best area under the receiver operating characteristic curve and accuracy of 0.95 and 87.55% are achieved with the proposed method, which is further compared with some of the state-of-the-art competing methods.
Discrimination of Active and Weakly Active Human BACE1 Inhibitors Using Self-Organizing Map and Support Vector Machine.

PubMed

Li, Hang; Wang, Maolin; Gong, Ya-Nan; Yan, Aixia

2016-01-01

β-secretase (BACE1) is an aspartyl protease, which is considered as a novel vital target in Alzheimer`s disease therapy. We collected a data set of 294 BACE1 inhibitors, and built six classification models to discriminate active and weakly active inhibitors using Kohonen's Self-Organizing Map (SOM) method and Support Vector Machine (SVM) method. Each molecular descriptor was calculated using the program ADRIANA.Code. We adopted two different methods: random method and Self-Organizing Map method, for training/test set split. The descriptors were selected by F-score and stepwise linear regression analysis. The best SVM model Model2C has a good prediction performance on test set with prediction accuracy, sensitivity (SE), specificity (SP) and Matthews correlation coefficient (MCC) of 89.02%, 90%, 88%, 0.78, respectively. Model 1A is the best SOM model, whose accuracy and MCC of the test set were 94.57% and 0.98, respectively. The lone pair electronegativity and polarizability related descriptors importantly contributed to bioactivity of BACE1 inhibitor. The Extended-Connectivity Finger-Prints_4 (ECFP_4) analysis found some vitally key substructural features, which could be helpful for further drug design research. The SOM and SVM models built in this study can be obtained from the authors by email or other contacts.
Comparing auditory filter bandwidths, spectral ripple modulation detection, spectral ripple discrimination, and speech recognition: Normal and impaired hearinga)

PubMed Central

Davies-Venn, Evelyn; Nelson, Peggy; Souza, Pamela

2015-01-01

Some listeners with hearing loss show poor speech recognition scores in spite of using amplification that optimizes audibility. Beyond audibility, studies have suggested that suprathreshold abilities such as spectral and temporal processing may explain differences in amplified speech recognition scores. A variety of different methods has been used to measure spectral processing. However, the relationship between spectral processing and speech recognition is still inconclusive. This study evaluated the relationship between spectral processing and speech recognition in listeners with normal hearing and with hearing loss. Narrowband spectral resolution was assessed using auditory filter bandwidths estimated from simultaneous notched-noise masking. Broadband spectral processing was measured using the spectral ripple discrimination (SRD) task and the spectral ripple depth detection (SMD) task. Three different measures were used to assess unamplified and amplified speech recognition in quiet and noise. Stepwise multiple linear regression revealed that SMD at 2.0 cycles per octave (cpo) significantly predicted speech scores for amplified and unamplified speech in quiet and noise. Commonality analyses revealed that SMD at 2.0 cpo combined with SRD and equivalent rectangular bandwidth measures to explain most of the variance captured by the regression model. Results suggest that SMD and SRD may be promising clinical tools for diagnostic evaluation and predicting amplification outcomes. PMID:26233047
Comparing auditory filter bandwidths, spectral ripple modulation detection, spectral ripple discrimination, and speech recognition: Normal and impaired hearing.

PubMed

Davies-Venn, Evelyn; Nelson, Peggy; Souza, Pamela

2015-07-01

Some listeners with hearing loss show poor speech recognition scores in spite of using amplification that optimizes audibility. Beyond audibility, studies have suggested that suprathreshold abilities such as spectral and temporal processing may explain differences in amplified speech recognition scores. A variety of different methods has been used to measure spectral processing. However, the relationship between spectral processing and speech recognition is still inconclusive. This study evaluated the relationship between spectral processing and speech recognition in listeners with normal hearing and with hearing loss. Narrowband spectral resolution was assessed using auditory filter bandwidths estimated from simultaneous notched-noise masking. Broadband spectral processing was measured using the spectral ripple discrimination (SRD) task and the spectral ripple depth detection (SMD) task. Three different measures were used to assess unamplified and amplified speech recognition in quiet and noise. Stepwise multiple linear regression revealed that SMD at 2.0 cycles per octave (cpo) significantly predicted speech scores for amplified and unamplified speech in quiet and noise. Commonality analyses revealed that SMD at 2.0 cpo combined with SRD and equivalent rectangular bandwidth measures to explain most of the variance captured by the regression model. Results suggest that SMD and SRD may be promising clinical tools for diagnostic evaluation and predicting amplification outcomes.

Non-targeted 1H NMR fingerprinting and multivariate statistical analyses for the characterisation of the geographical origin of Italian sweet cherries.

PubMed

Longobardi, F; Ventrella, A; Bianco, A; Catucci, L; Cafagna, I; Gallo, V; Mastrorilli, P; Agostiano, A

2013-12-01

In this study, non-targeted (1)H NMR fingerprinting was used in combination with multivariate statistical techniques for the classification of Italian sweet cherries based on their different geographical origins (Emilia Romagna and Puglia). As classification techniques, Soft Independent Modelling of Class Analogy (SIMCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Linear Discriminant Analysis (LDA) were carried out and the results were compared. For LDA, before performing a refined selection of the number/combination of variables, two different strategies for a preliminary reduction of the variable number were tested. The best average recognition and CV prediction abilities (both 100.0%) were obtained for all the LDA models, although PLS-DA also showed remarkable performances (94.6%). All the statistical models were validated by observing the prediction abilities with respect to an external set of cherry samples. The best result (94.9%) was obtained with LDA by performing a best subset selection procedure on a set of 30 principal components previously selected by a stepwise decorrelation. The metabolites that mostly contributed to the classification performances of such LDA model, were found to be malate, glucose, fructose, glutamine and succinate. Copyright © 2013 Elsevier Ltd. All rights reserved.
Methods for presentation and display of multivariate data

NASA Technical Reports Server (NTRS)

Myers, R. H.

1981-01-01

Methods for the presentation and display of multivariate data are discussed with emphasis placed on the multivariate analysis of variance problems and the Hotelling T(2) solution in the two-sample case. The methods utilize the concepts of stepwise discrimination analysis and the computation of partial correlation coefficients.
Sex determination from the mandibular ramus flexure of Koreans by discrimination function analysis using three-dimensional mandible models.

PubMed

Lin, Chenghe; Jiao, Benzheng; Liu, Shanshan; Guan, Feng; Chung, Nak-Eun; Han, Seung-Ho; Lee, U-Young

2014-03-01

It has been known that mandible ramus flexure is an important morphologic trait for sex determination. However, it will be unavailable when mandible is incomplete or fragmented. Therefore, the anthropometric analysis on incomplete or fragmented mandible becomes more important. The aim of this study is to investigate the sex-discriminant potential of mandible ramus flexure on the Korean three-dimensional (3D) mandible models with anthropometric analysis. The sample consists of 240 three dimensional mandibular models obtained from Korean population (M:F; 120:120, mean age 46.2 y), collected by The Catholic Institute for Applied Anatomy, The Catholic University of Korea. Anthropometric information about 11 metric was taken with Mimics, anthropometry libraries toolkit. These parameters were subjected to different discriminant function analyses using SPSS 17.0. Univariate analyses showed that the resubstitution accuracies for sex determination range from 50.4 to 77.1%. Mandibular flexure upper border (MFUB), maximum ramus vertical height (MRVH), and upper ramus vertical height (URVH) expressed the greatest dimorphism, 72.1 to 77.1%. Bivariate analyses indicated that the combination of MFUB and MRVH hold even higher resubstitution accuracy of 81.7%. Furthermore, the direct and stepwise discriminant analyses with the variables on the upper ramus above flexure could predict sex in 83.3 and 85.0%, respectively. When all variables of mandibular ramus flexure were input in stepwise discriminant analysis, the resubstitution accuracy arrived as high as 88.8%. Therefore, we concluded that the upper ramus above flexure hold the larger potentials than the mandibular ramus flexure itself to predict sexes, and that the equations in bivariate and multivariate analysis from our study will be helpful for sex determination on Korean population in forensic science and law. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Treatment of skeletal Class III malocclusions: orthognathic surgery or orthodontic camouflage? How to decide.

PubMed

Benyahia, Hicham; Azaroual, Mohamed Faouzi; Garcia, Claude; Hamou, Edith; Abouqal, Redouane; Zaoui, Fatima

2011-06-01

The choice of treatment in adult skeletal Class III occlusions often poses a particularly tricky problem for the orthodontist. Faced with the option of either orthodontic camouflage or orthognathic surgery, the clinician's clinical experience is of paramount importance, especially in borderline cases. The aim of our study was to uncover a guide model enabling the practitioner to distinguish between skeletal Class III cases which can be suitably treated with orthodontics and those requiring orthognathic surgery. The lateral headfilms of 47 adult patients exhibiting skeletal Class III occlusions were analyzed. The orthodontic group comprised 22 patients and the surgical group 25. Twenty-seven linear, proportional and angular measurements were scrutinized. Stepwise discriminant analysis was used to identify the dentoskeletal and esthetic variables which most distinguished the two groups. The Holdaway angle was chosen to differentiate between patients prior to treatment. This model enables us to classify 87.2% of patients correctly. Copyright © 2011 CEO. Published by Elsevier Masson SAS. All rights reserved.
The effect of combining two echo times in automatic brain tumor classification by MRS.

PubMed

García-Gómez, Juan M; Tortajada, Salvador; Vidal, César; Julià-Sapé, Margarida; Luts, Jan; Moreno-Torres, Angel; Van Huffel, Sabine; Arús, Carles; Robles, Montserrat

2008-11-01

(1)H MRS is becoming an accurate, non-invasive technique for initial examination of brain masses. We investigated if the combination of single-voxel (1)H MRS at 1.5 T at two different (TEs), short TE (PRESS or STEAM, 20-32 ms) and long TE (PRESS, 135-136 ms), improves the classification of brain tumors over using only one echo TE. A clinically validated dataset of 50 low-grade meningiomas, 105 aggressive tumors (glioblastoma and metastasis), and 30 low-grade glial tumors (astrocytomas grade II, oligodendrogliomas and oligoastrocytomas) was used to fit predictive models based on the combination of features from short-TEs and long-TE spectra. A new approach that combines the two consecutively was used to produce a single data vector from which relevant features of the two TE spectra could be extracted by means of three algorithms: stepwise, reliefF, and principal components analysis. Least squares support vector machines and linear discriminant analysis were applied to fit the pairwise and multiclass classifiers, respectively. Significant differences in performance were found when short-TE, long-TE or both spectra combined were used as input. In our dataset, to discriminate meningiomas, the combination of the two TE acquisitions produced optimal performance. To discriminate aggressive tumors from low-grade glial tumours, the use of short-TE acquisition alone was preferable. The classifier development strategy used here lends itself to automated learning and test performance processes, which may be of use for future web-based multicentric classifier development studies. Copyright (c) 2008 John Wiley & Sons, Ltd.
Accurate discrimination of Alzheimer's disease from other dementia and/or normal subjects using SPECT specific volume analysis

NASA Astrophysics Data System (ADS)

Iyatomi, Hitoshi; Hashimoto, Jun; Yoshii, Fumuhito; Kazama, Toshiki; Kawada, Shuichi; Imai, Yutaka

2014-03-01

Discrimination between Alzheimer's disease and other dementia is clinically significant, however it is often difficult. In this study, we developed classification models among Alzheimer's disease (AD), other dementia (OD) and/or normal subjects (NC) using patient factors and indices obtained by brain perfusion SPECT. SPECT is commonly used to assess cerebral blood flow (CBF) and allows the evaluation of the severity of hypoperfusion by introducing statistical parametric mapping (SPM). We investigated a total of 150 cases (50 cases each for AD, OD, and NC) from Tokai University Hospital, Japan. In each case, we obtained a total of 127 candidate parameters from: (A) 2 patient factors (age and sex), (B) 12 CBF parameters and 113 SPM parameters including (C) 3 from specific volume analysis (SVA), and (D) 110 from voxel-based analysis stereotactic extraction estimation (vbSEE). We built linear classifiers with a statistical stepwise feature selection and evaluated the performance with the leave-one-out cross validation strategy. Our classifiers achieved very high classification performances with reasonable number of selected parameters. In the most significant discrimination in clinical, namely those of AD from OD, our classifier achieved both sensitivity (SE) and specificity (SP) of 96%. In a similar way, our classifiers achieved a SE of 90% and a SP of 98% in AD from NC, as well as a SE of 88% and a SP of 86% in AD from OD and NC cases. Introducing SPM indices such as SVA and vbSEE, classification performances improved around 7-15%. We confirmed that these SPM factors are quite important for diagnosing Alzheimer's disease.
Development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team.

PubMed

Harrison, David A; Patel, Krishna; Nixon, Edel; Soar, Jasmeet; Smith, Gary B; Gwinnutt, Carl; Nolan, Jerry P; Rowan, Kathryn M

2014-08-01

The National Cardiac Arrest Audit (NCAA) is the UK national clinical audit for in-hospital cardiac arrest. To make fair comparisons among health care providers, clinical indicators require case mix adjustment using a validated risk model. The aim of this study was to develop and validate risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team in UK hospitals. Risk models for two outcomes-return of spontaneous circulation (ROSC) for greater than 20min and survival to hospital discharge-were developed and validated using data for in-hospital cardiac arrests between April 2011 and March 2013. For each outcome, a full model was fitted and then simplified by testing for non-linearity, combining categories and stepwise reduction. Finally, interactions between predictors were considered. Models were assessed for discrimination, calibration and accuracy. 22,479 in-hospital cardiac arrests in 143 hospitals were included (14,688 development, 7791 validation). The final risk model for ROSC>20min included: age (non-linear), sex, prior length of stay in hospital, reason for attendance, location of arrest, presenting rhythm, and interactions between presenting rhythm and location of arrest. The model for hospital survival included the same predictors, excluding sex. Both models had acceptable performance across the range of measures, although discrimination for hospital mortality exceeded that for ROSC>20min (c index 0.81 versus 0.72). Validated risk models for ROSC>20min and hospital survival following in-hospital cardiac arrest have been developed. These models will strengthen comparative reporting in NCAA and support local quality improvement. Copyright © 2014 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.
Development and validation of risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team☆

PubMed Central

Harrison, David A.; Patel, Krishna; Nixon, Edel; Soar, Jasmeet; Smith, Gary B.; Gwinnutt, Carl; Nolan, Jerry P.; Rowan, Kathryn M.

2014-01-01

Aim The National Cardiac Arrest Audit (NCAA) is the UK national clinical audit for in-hospital cardiac arrest. To make fair comparisons among health care providers, clinical indicators require case mix adjustment using a validated risk model. The aim of this study was to develop and validate risk models to predict outcomes following in-hospital cardiac arrest attended by a hospital-based resuscitation team in UK hospitals. Methods Risk models for two outcomes—return of spontaneous circulation (ROSC) for greater than 20 min and survival to hospital discharge—were developed and validated using data for in-hospital cardiac arrests between April 2011 and March 2013. For each outcome, a full model was fitted and then simplified by testing for non-linearity, combining categories and stepwise reduction. Finally, interactions between predictors were considered. Models were assessed for discrimination, calibration and accuracy. Results 22,479 in-hospital cardiac arrests in 143 hospitals were included (14,688 development, 7791 validation). The final risk model for ROSC > 20 min included: age (non-linear), sex, prior length of stay in hospital, reason for attendance, location of arrest, presenting rhythm, and interactions between presenting rhythm and location of arrest. The model for hospital survival included the same predictors, excluding sex. Both models had acceptable performance across the range of measures, although discrimination for hospital mortality exceeded that for ROSC > 20 min (c index 0.81 versus 0.72). Conclusions Validated risk models for ROSC > 20 min and hospital survival following in-hospital cardiac arrest have been developed. These models will strengthen comparative reporting in NCAA and support local quality improvement. PMID:24830872
Plant community variability on a small area in southeastern Montana

Treesearch

James G. MacCracken; Daniel W. Uresk; Richard M. Hansen

1984-01-01

Plant communities are inherently variable due to a number of environmental and biological forces. Canopy cover and aboveground biomass were determined for understory vegetation in plant communities of a prairie grassland-forest ecotone in southeastern Montana. Vegetation units were described using polar ordination and stepwise discriminant analysis. Nine of a total of...
Multivariate analysis of early and late nest sites of Abert's Towhees

Treesearch

Deborah M. Finch

1985-01-01

Seasonal variation in nest site selection by the Abert's towhee (Pipilo aberti) was studied in honey mesquite (Prosopis glandulosa) habitat along the lower Colorado River from March to July, 1981. Stepwise discriminant function analysis identified nest vegetation type, nest direction, and nest height as the three most important variables that characterized the...
[Correlation between physical characteristics of sticks and quality of traditional Chinese medicine pills prepared by plastic molded method].

PubMed

Wang, Ling; Xian, Jiechen; Hong, Yanlong; Lin, Xiao; Feng, Yi

2012-05-01

To quantify the physical characteristics of sticks of traditional Chinese medicine (TCM) honeyed pills prepared by the plastic molded method and the correlation of adhesiveness and plasticity-related parameters of sticks and quality of pills, in order to find major parameters and the appropriate range impacting pill quality. Sticks were detected by texture analyzer for their physical characteristic parameters such as hardness and compression action, and pills were observed by visual evaluation for their quality. The correlation of both data was determined by the stepwise discriminant analysis. Stick physical characteristic parameter l(CD) can exactly depict the adhesiveness, with the discriminant equation of Y0 - Y1 = 6.415 - 41.594l(CD). When Y0 < Y1, pills were scattered well; when Y0 > Y1, pills were adhesive with each other. Pills' physical characteristic parameters l(CD) and l(AC), Ar, Tr can exactly depict smoothness of pills, with the discriminant equation of Z0 - Z1 = -195.318 + 78.79l(AC) - 3 258. 982Ar + 3437.935Tr. When Z0 < Z1, pills were smooth on surface. When Z0 > Z1, pills were rough on surface. The stepwise discriminant analysis is made to show the obvious correlation between key physical characteristic parameters l(CD) and l(AC), Ar, Tr of sticks and appearance quality of pills, defining the molding process for preparing pills by the plastic molded and qualifying ranges of key physical characteristic parameters characterizing intermediate sticks, in order to provide theoretical basis for prescription screening and technical parameter adjustment for pills.
Detection of high-risk atherosclerotic lesions by time-resolved fluorescence spectroscopy based on the Laguerre deconvolution technique

NASA Astrophysics Data System (ADS)

Jo, J. A.; Fang, Q.; Papaioannou, T.; Qiao, J. H.; Fishbein, M. C.; Beseth, B.; Dorafshar, A. H.; Reil, T.; Baker, D.; Freischlag, J.; Marcu, L.

2006-02-01

This study introduces new methods of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) data analysis for tissue characterization. These analytical methods were applied for the detection of atherosclerotic vulnerable plaques. Upon pulsed nitrogen laser (337 nm, 1 ns) excitation, TR-LIFS measurements were obtained from carotid atherosclerotic plaque specimens (57 endarteroctomy patients) at 492 distinct areas. The emission was both spectrally- (360-600 nm range at 5 nm interval) and temporally- (0.3 ns resolution) resolved using a prototype clinically compatible fiber-optic catheter TR-LIFS apparatus. The TR-LIFS measurements were subsequently analyzed using a standard multiexponential deconvolution and a recently introduced Laguerre deconvolution technique. Based on their histopathology, the lesions were classified as early (thin intima), fibrotic (collagen-rich intima), and high-risk (thin cap over necrotic core and/or inflamed intima). Stepwise linear discriminant analysis (SLDA) was applied for lesion classification. Normalized spectral intensity values and Laguerre expansion coefficients (LEC) at discrete emission wavelengths (390, 450, 500 and 550 nm) were used as features for classification. The Laguerre based SLDA classifier provided discrimination of high-risk lesions with high sensitivity (SE>81%) and specificity (SP>95%). Based on these findings, we believe that TR-LIFS information derived from the Laguerre expansion coefficients can provide a valuable additional dimension for the diagnosis of high-risk vulnerable atherosclerotic plaques.
EEG-based mild depressive detection using feature selection methods and classifiers.

PubMed

Li, Xiaowei; Hu, Bin; Sun, Shuting; Cai, Hanshu

2016-11-01

Depression has become a major health burden worldwide, and effectively detection of such disorder is a great challenge which requires latest technological tool, such as Electroencephalography (EEG). This EEG-based research seeks to find prominent frequency band and brain regions that are most related to mild depression, as well as an optimal combination of classification algorithms and feature selection methods which can be used in future mild depression detection. An experiment based on facial expression viewing task (Emo_block and Neu_block) was conducted, and EEG data of 37 university students were collected using a 128 channel HydroCel Geodesic Sensor Net (HCGSN). For discriminating mild depressive patients and normal controls, BayesNet (BN), Support Vector Machine (SVM), Logistic Regression (LR), k-nearest neighbor (KNN) and RandomForest (RF) classifiers were used. And BestFirst (BF), GreedyStepwise (GSW), GeneticSearch (GS), LinearForwordSelection (LFS) and RankSearch (RS) based on Correlation Features Selection (CFS) were applied for linear and non-linear EEG features selection. Independent Samples T-test with Bonferroni correction was used to find the significantly discriminant electrodes and features. Data mining results indicate that optimal performance is achieved using a combination of feature selection method GSW based on CFS and classifier KNN for beta frequency band. Accuracies achieved 92.00% and 98.00%, and AUC achieved 0.957 and 0.997, for Emo_block and Neu_block beta band data respectively. T-test results validate the effectiveness of selected features by search method GSW. Simplified EEG system with only FP1, FP2, F3, O2, T3 electrodes was also explored with linear features, which yielded accuracies of 91.70% and 96.00%, AUC of 0.952 and 0.972, for Emo_block and Neu_block respectively. Classification results obtained by GSW + KNN are encouraging and better than previously published results. In the spatial distribution of features, we find that left parietotemporal lobe in beta EEG frequency band has greater effect on mild depression detection. And fewer EEG channels (FP1, FP2, F3, O2 and T3) combined with linear features may be good candidates for usage in portable systems for mild depression detection. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Oak decline risk rating for the southeastern United States

Treesearch

S. Oak; F. Tainter; J. Williams; D. Starkey

1996-01-01

Oak decline risk rating models were developed for upland hardwood forests in the southeastern United States using data gathered during regional oak decline surveys. Stepwise discriminant analyses were used to relate 12 stand and site variables with major oak decline incidence for each of three subregions plus one incorporating all subregions. The best model for the...
Application of stepwise multiple regression techniques to inversion of Nimbus 'IRIS' observations.

NASA Technical Reports Server (NTRS)

Ohring, G.

1972-01-01

Exploratory studies with Nimbus-3 infrared interferometer-spectrometer (IRIS) data indicate that, in addition to temperature, such meteorological parameters as geopotential heights of pressure surfaces, tropopause pressure, and tropopause temperature can be inferred from the observed spectra with the use of simple regression equations. The technique of screening the IRIS spectral data by means of stepwise regression to obtain the best radiation predictors of meteorological parameters is validated. The simplicity of application of the technique and the simplicity of the derived linear regression equations - which contain only a few terms - suggest usefulness for this approach. Based upon the results obtained, suggestions are made for further development and exploitation of the stepwise regression analysis technique.
Risk factors for eating disorders in Greek- and Anglo-Australian adolescent girls.

PubMed

Mildred, H; Paxton, S J; Wertheim, E H

1995-01-01

Past research indicates ethnicity may be related to eating disorder and related risk factors. The present study examines risk factors for eating disorders in 50 Anglo- and 50 Greek-Australian girls (mean age = 13.5 years). The variables assessed included bulimic tendencies, body dissatisfaction, use of extreme weight loss behaviors (EWLBs), self-esteem, depression and family cohesion and adaptability. Cultural eating patterns were also explored. A stepwise discriminant function analysis to examine whether the two groups could be discriminated on these variables was significant and correctly classified 73.9% of the sample, the chief discriminating variables being Pressure to Eat, EWLBs, and Family Adaptability. Univariate analyses indicated differences between the groups on Pressure to Eat, Family Adaptability, and Mother's Shape. Although the groups were discriminable, a number of variables generally associated with eating disorder did not contribute to the function. These data are discussed in terms of cultural assimilation.
The prediction of swimming performance in competition from behavioral information.

PubMed

Rushall, B S; Leet, D

1979-06-01

The swimming performances of the Canadian Team at the 1976 Olympic Games were categorized as being improved or worse than previous best times in the events contested. The two groups had been previously assessed on the Psychological Inventories for Competitive Swimmers. A stepwise multiple-discriminant analysis of the inventory responses revealed that 13 test questions produced a perfect discrimination of group membership. The resultant discriminant functions for predicting performance classification were applied to the test responses of 157 swimmers at the 1977 Canadian Winter National Swimming Championships. Using the same performance classification criteria the accuracy of prediction was not better than chance in three of four sex by performance classifications. This yielded a failure to locate a set of behavioral factors which determine swimming performance improvements in elite competitive circumstances. The possibility of sets of factors which do not discriminate between performances in similar environments or between similar groups of swimmers was raised.
Optimization of elution salt concentration in stepwise elution of protein chromatography using linear gradient elution data. Reducing residual protein A by cation-exchange chromatography in monoclonal antibody purification.

PubMed

Ishihara, Takashi; Kadoya, Toshihiko; Endo, Naomi; Yamamoto, Shuichi

2006-05-05

Our simple method for optimization of the elution salt concentration in stepwise elution was applied to the actual protein separation system, which involves several difficulties such as detection of the target. As a model separation system, reducing residual protein A by cation-exchange chromatography in human monoclonal antibody (hMab) purification was chosen. We carried out linear gradient elution experiments and obtained the data for the peak salt concentration of hMab and residual protein A, respectively. An enzyme-linked immunosorbent assay was applied to the measurement of the residual protein A. From these data, we calculated the distribution coefficient of the hMab and the residual protein A as a function of salt concentration. The optimal salt concentration of stepwise elution to reduce the residual protein A from the hMab was determined based on the relationship between the distribution coefficient and the salt concentration. Using the optimized condition, we successfully performed the separation, resulting in high recovery of hMab and the elimination of residual protein A.
A comparative analysis of predictive models of morbidity in intensive care unit after cardiac surgery - part II: an illustrative example.

PubMed

Cevenini, Gabriele; Barbini, Emanuela; Scolletta, Sabino; Biagioli, Bonizella; Giomarelli, Pierpaolo; Barbini, Paolo

2007-11-22

Popular predictive models for estimating morbidity probability after heart surgery are compared critically in a unitary framework. The study is divided into two parts. In the first part modelling techniques and intrinsic strengths and weaknesses of different approaches were discussed from a theoretical point of view. In this second part the performances of the same models are evaluated in an illustrative example. Eight models were developed: Bayes linear and quadratic models, k-nearest neighbour model, logistic regression model, Higgins and direct scoring systems and two feed-forward artificial neural networks with one and two layers. Cardiovascular, respiratory, neurological, renal, infectious and hemorrhagic complications were defined as morbidity. Training and testing sets each of 545 cases were used. The optimal set of predictors was chosen among a collection of 78 preoperative, intraoperative and postoperative variables by a stepwise procedure. Discrimination and calibration were evaluated by the area under the receiver operating characteristic curve and Hosmer-Lemeshow goodness-of-fit test, respectively. Scoring systems and the logistic regression model required the largest set of predictors, while Bayesian and k-nearest neighbour models were much more parsimonious. In testing data, all models showed acceptable discrimination capacities, however the Bayes quadratic model, using only three predictors, provided the best performance. All models showed satisfactory generalization ability: again the Bayes quadratic model exhibited the best generalization, while artificial neural networks and scoring systems gave the worst results. Finally, poor calibration was obtained when using scoring systems, k-nearest neighbour model and artificial neural networks, while Bayes (after recalibration) and logistic regression models gave adequate results. Although all the predictive models showed acceptable discrimination performance in the example considered, the Bayes and logistic regression models seemed better than the others, because they also had good generalization and calibration. The Bayes quadratic model seemed to be a convincing alternative to the much more usual Bayes linear and logistic regression models. It showed its capacity to identify a minimum core of predictors generally recognized as essential to pragmatically evaluate the risk of developing morbidity after heart surgery.
Lithium might be associated with better decision-making performance in euthymic bipolar patients.

PubMed

Adida, Marc; Jollant, Fabrice; Clark, Luke; Guillaume, Sebastien; Goodwin, Guy M; Azorin, Jean-Michel; Courtet, Philippe

2015-06-01

Bipolar disorder is associated with impaired decision-making. Little is known about how treatment, especially lithium, influences decision-making abilities in bipolar patients when euthymic. We aimed at testing for an association between lithium medication and decision-making performance in remitted bipolar patients. Decision-making was measured using the Iowa Gambling Task in 3 groups of subjects: 34 and 56 euthymic outpatients with bipolar disorder, treated with lithium (monotherapy and lithium combined with anticonvulsant or antipsychotic) and without lithium (anticonvulsant, antipsychotic and combination treatment), respectively, and 152 matched healthy controls. Performance was compared between the 3 groups. In the 90 euthymic patients, the relationship between different sociodemographic and clinical variables and decision-making was assessed by stepwise multivariate regression analysis. Euthymic patients with lithium (p=0.007) and healthy controls (p=0.001) selected significantly more cards from the safe decks than euthymic patients without lithium, with no significant difference between euthymic patients with lithium and healthy controls (p=0.9). In the 90 euthymic patients, the stepwise linear multivariate regression revealed that decision-making was significantly predicted (p<0.001) by lithium dose, level of education and no family history of bipolar disorder (all p≤0.01). Because medication was not randomized, it was not possible to discriminate the effect of different medications. Lithium medication might be associated with better decision-making in remitted bipolar patients. A randomized trial is required to test for the hypothesis that lithium, but not other mood stabilizers, may specifically improve decision-making abilities in bipolar disorder. Copyright © 2015 Elsevier B.V. and ECNP. All rights reserved.

[Discrimination of varieties of brake fluid using visual-near infrared spectra].

PubMed

Jiang, Lu-lu; Tan, Li-hong; Qiu, Zheng-jun; Lu, Jiang-feng; He, Yong

2008-06-01

A new method was developed to fast discriminate brands of brake fluid by means of visual-near infrared spectroscopy. Five different brands of brake fluid were analyzed using a handheld near infrared spectrograph, manufactured by ASD Company, and 60 samples were gotten from each brand of brake fluid. The samples data were pretreated using average smoothing and standard normal variable method, and then analyzed using principal component analysis (PCA). A 2-dimensional plot was drawn based on the first and the second principal components, and the plot indicated that the clustering characteristic of different brake fluid is distinct. The foregoing 6 principal components were taken as input variable, and the band of brake fluid as output variable to build the discriminate model by stepwise discriminant analysis method. Two hundred twenty five samples selected randomly were used to create the model, and the rest 75 samples to verify the model. The result showed that the distinguishing rate was 94.67%, indicating that the method proposed in this paper has good performance in classification and discrimination. It provides a new way to fast discriminate different brands of brake fluid.
Halobenzoquinone-mediated assembly of amino acid modified Mn-doped ZnS quantum dots for halobenzoquinones detection in drinking water.

PubMed

Jiao, Zhe; Zhang, Pengfei; Chen, Hongwei; Li, Jingwen; Zhong, Zhengquan; Fan, Hongbo; Cheng, Faliang

2018-10-05

Halobenzoquinones (HBQs) were reported as disinfection byproducts (DBPs) which had potential risk of bladder cancer. In this paper, a highly selective analytical method for HBQs was developed by HBQs-mediated assembly of amino acid modified Mn-doped ZnS/Quantum Dots (Mn: ZnS QDs). In the presence HBQs, a charge-transfer complex (CTC) was formed between aromatic rings of HBQs and the primary amino groups on the surface of the QDs. The formation of CTC led to the aggregation of QDs, as a result fluorescence decreasing occurred. The decrease was correlated with the concentration of HBQs. Then a fluorescence sensor array for discrimination of three kinds of HBQs including 2,6-Dichloro-1,4-benzoquinone (DCBQ), 2,6-Dibromo-1,4-benzoquinone (DBBQ) and 2,3,6-trichloro-1,4-benzoquinone (TCBQ) was developed. Four kinds of amino acids including cysteine, threonine, tyrosine and tryptophan were embellished on the Mn: ZnS QDs. The different extents of aggregation led to different fluorescence decreasing effect, thus distinct fluorescence patterns were created. It showed that three kinds of HBQs could be discriminated successfully by fluorescence sensor array at a range of concentrations through principal component analysis (PCA). The unknown samples were predicted by with a stepwise linear discriminant analysis (SLDA) using Mahalanobis distance as a selection criterion with accuracy of 100%. Remarkably, the practicability of the proposed sensor array was further validated by identification of three kinds of HBQs at different concentrations in real drinking water samples. Compared to LC/MS/MS, this fluorescent sensor array-based method was proved to be more convenient since the nanoparticles can be prepared flexibly according to the property of the target. Copyright © 2018 Elsevier B.V. All rights reserved.
Predictive Method for Correct Identification of Archaeological Charred Grape Seeds: Support for Advances in Knowledge of Grape Domestication Process

PubMed Central

Ucchesu, Mariano; Orrù, Martino; Grillo, Oscar; Venora, Gianfranco; Paglietti, Giacomo; Ardu, Andrea; Bacchetta, Gianluigi

2016-01-01

The identification of archaeological charred grape seeds is a difficult task due to the alteration of the morphological seeds shape. In archaeobotanical studies, for the correct discrimination between Vitis vinifera subsp. sylvestris and Vitis vinifera subsp. vinifera grape seeds it is very important to understand the history and origin of the domesticated grapevine. In this work, different carbonisation experiments were carried out using a hearth to reproduce the same burning conditions that occurred in archaeological contexts. In addition, several carbonisation trials on modern wild and cultivated grape seeds were performed using a muffle furnace. For comparison with archaeological materials, modern grape seed samples were obtained using seven different temperatures of carbonisation ranging between 180 and 340ºC for 120 min. Analysing the grape seed size and shape by computer vision techniques, and applying the stepwise linear discriminant analysis (LDA) method, discrimination of the wild from the cultivated charred grape seeds was possible. An overall correct classification of 93.3% was achieved. Applying the same statistical procedure to compare modern charred with archaeological grape seeds, found in Sardinia and dating back to the Early Bronze Age (2017–1751 2σ cal. BC), allowed 75.0% of the cases to be identified as wild grape. The proposed method proved to be a useful and effective procedure in identifying, with high accuracy, the charred grape seeds found in archaeological sites. Moreover, it may be considered valid support for advances in the knowledge and comprehension of viticulture adoption and the grape domestication process. The same methodology may also be successful when applied to other plant remains, and provide important information about the history of domesticated plants. PMID:26901361
Predictive Method for Correct Identification of Archaeological Charred Grape Seeds: Support for Advances in Knowledge of Grape Domestication Process.

PubMed

Ucchesu, Mariano; Orrù, Martino; Grillo, Oscar; Venora, Gianfranco; Paglietti, Giacomo; Ardu, Andrea; Bacchetta, Gianluigi

2016-01-01

The identification of archaeological charred grape seeds is a difficult task due to the alteration of the morphological seeds shape. In archaeobotanical studies, for the correct discrimination between Vitis vinifera subsp. sylvestris and Vitis vinifera subsp. vinifera grape seeds it is very important to understand the history and origin of the domesticated grapevine. In this work, different carbonisation experiments were carried out using a hearth to reproduce the same burning conditions that occurred in archaeological contexts. In addition, several carbonisation trials on modern wild and cultivated grape seeds were performed using a muffle furnace. For comparison with archaeological materials, modern grape seed samples were obtained using seven different temperatures of carbonisation ranging between 180 and 340ºC for 120 min. Analysing the grape seed size and shape by computer vision techniques, and applying the stepwise linear discriminant analysis (LDA) method, discrimination of the wild from the cultivated charred grape seeds was possible. An overall correct classification of 93.3% was achieved. Applying the same statistical procedure to compare modern charred with archaeological grape seeds, found in Sardinia and dating back to the Early Bronze Age (2017-1751 2σ cal. BC), allowed 75.0% of the cases to be identified as wild grape. The proposed method proved to be a useful and effective procedure in identifying, with high accuracy, the charred grape seeds found in archaeological sites. Moreover, it may be considered valid support for advances in the knowledge and comprehension of viticulture adoption and the grape domestication process. The same methodology may also be successful when applied to other plant remains, and provide important information about the history of domesticated plants.
Classification of typical and atypical antipsychotic drugs on the basis of dopamine D-1, D-2 and serotonin2 pKi values.

PubMed

Meltzer, H Y; Matsubara, S; Lee, J C

1989-10-01

The pKi values of 13 reference typical and 7 reference atypical antipsychotic drugs (APDs) for rat striatal dopamine D-1 and D-2 receptor binding sites and cortical serotonin (5-HT2) receptor binding sites were determined. The atypical antipsychotics had significantly lower pKi values for the D-2 but not 5-HT2 binding sites. There was a trend for a lower pKi value for the D-1 binding site for the atypical APD. The 5-HT2 and D-1 pKi values were correlated for the typical APD whereas the 5-HT2 and D-2 pKi values were correlated for the atypical APD. A stepwise discriminant function analysis to determine the independent contribution of each pKi value for a given binding site to the classification as a typical or atypical APD entered the D-2 pKi value first, followed by the 5-HT2 pKi value. The D-1 pKi value was not entered. A discriminant function analysis correctly classified 19 of 20 of these compounds plus 14 of 17 additional test compounds as typical or atypical APD for an overall correct classification rate of 89.2%. The major contributors to the discriminant function were the D-2 and 5-HT2 pKi values. A cluster analysis based only on the 5-HT2/D2 ratio grouped 15 of 17 atypical + one typical APD in one cluster and 19 of 20 typical + two atypical APDs in a second cluster, for an overall correct classification rate of 91.9%. When the stepwise discriminant function was repeated for all 37 compounds, only the D-2 and 5-HT2 pKi values were entered into the discriminant function.(ABSTRACT TRUNCATED AT 250 WORDS)
Controlled loading of cryoprotectants (CPAs) to oocyte with linear and complex CPA profiles on a microfluidic platform.

PubMed

Heo, Yun Seok; Lee, Ho-Joon; Hassell, Bryan A; Irimia, Daniel; Toth, Thomas L; Elmoazzen, Heidi; Toner, Mehmet

2011-10-21

Oocyte cryopreservation has become an essential tool in the treatment of infertility by preserving oocytes for women undergoing chemotherapy. However, despite recent advances, pregnancy rates from all cryopreserved oocytes remain low. The inevitable use of the cryoprotectants (CPAs) during preservation affects the viability of the preserved oocytes and pregnancy rates either through CPA toxicity or osmotic injury. Current protocols attempt to reduce CPA toxicity by minimizing CPA concentrations, or by minimizing the volume changes via the step-wise addition of CPAs to the cells. Although the step-wise addition decreases osmotic shock to oocytes, it unfortunately increases toxic injuries due to the long exposure times to CPAs. To address limitations of current protocols and to rationally design protocols that minimize the exposure to CPAs, we developed a microfluidic device for the quantitative measurements of oocyte volume during various CPA loading protocols. We spatially secured a single oocyte on the microfluidic device, created precisely controlled continuous CPA profiles (step-wise, linear and complex) for the addition of CPAs to the oocyte and measured the oocyte volumetric response to each profile. With both linear and complex profiles, we were able to load 1.5 M propanediol to oocytes in less than 15 min and with a volumetric change of less than 10%. Thus, we believe this single oocyte analysis technology will eventually help future advances in assisted reproductive technologies and fertility preservation.
Characterizing hydrochemical properties of springs in Taiwan based on their geological origins.

PubMed

Jang, Cheng-Shin; Chen, Jui-Sheng; Lin, Yun-Bin; Liu, Chen-Wuing

2012-01-01

This study was performed to characterize hydrochemical properties of springs based on their geological origins in Taiwan. Stepwise discriminant analysis (DA) was used to establish a linear classification model of springs using hydrochemical parameters. Two hydrochemical datasets-ion concentrations and relative proportions of equivalents per liter of major ions-were included to perform prediction of the geological origins of springs. Analyzed results reveal that DA using relative proportions of equivalents per liter of major ions yields a 95.6% right assignation, which is superior to DA using ion concentrations. This result indicates that relative proportions of equivalents of major hydrochemical parameters in spring water are more highly associated with the geological origins than ion concentrations do. Low percentages of Na(+) equivalents are common properties of springs emerging from acid-sulfate and neutral-sulfate igneous rock. Springs emerging from metamorphic rock show low percentages of Cl( - ) equivalents and high percentages of HCO[Formula: see text] equivalents, and springs emerging from sedimentary rock exhibit high Cl( - )/SO(2-)(4) ratios.
Diagnosis of vulnerable atherosclerotic plaques by time-resolved fluorescence spectroscopy and ultrasound imaging.

PubMed

Jo, J A; Fang, Q; Papaioannou, T; Qiao, J H; Fishbein, M C; Beseth, B; Dorafshar, A H; Reil, T; Baker, D; Freischlag, J; Shung, K K; Sun, L; Marcu, L

2006-01-01

In this study, time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) and ultrasonography were applied to detect vulnerable (high-risk) atherosclerotic plaque. A total of 813 TR-LIFS measurements were taken from carotid plaques of 65 patients, and subsequently analyzed using the Laguerre deconvolution technique. The investigated spots were classified by histopathology as thin, fibrotic, calcified, low-inflamed, inflamed and necrotic lesions. Spectral and time-resolved parameters (normalized intensity values and Laguerre expansion coefficients) were extracted from the TR-LIFS data. Feature selection for classification was performed by either analysis of variance (ANOVA) or principal component analysis (PCA). A stepwise linear discriminant analysis algorithm was developed for detecting inflamed and necrotic lesion, representing the most vulnerable plaques. These vulnerable plaques were detected with high sensitivity (>80%) and specificity (>90%). Ultrasound (US) imaging was obtained in 4 carotid plaques in addition to TR-LIFS examination. Preliminary results indicate that US provides important structural information of the plaques that could be combined with the compositional information obtained by TR-LIFS, to obtain a more accurate diagnosis of vulnerable atherosclerotic plaque.
Comparative Study of Phenolic Profile, Antioxidant Capacity, and Color-composition Relation of Roselle Cultivars with Contrasting Pigmentation.

PubMed

Camelo-Méndez, Gustavo A; Jara-Palacios, M José; Escudero-Gilete, M Luisa; Gordillo, Belén; Hernanz, Dolores; Paredes-López, Octavio; Vanegas-Espinoza, Pablo E; Del Villar-Martínez, Alma A; Heredia, Francisco J

2016-03-01

Roselle is a plant that accumulates anthocyanins significantly, hence its importance as food coloring and as a source of antioxidant compounds for human health. This study was aimed to determine phenolic composition and antioxidant capacity of methanolic extracts, and beverages obtained from native roselle cultivars in Mexico (Negra, Sudan, Rosa and Blanca) with different degrees of pigmentation, and to establish the color-composition relationship. Chromatographic methods were used to determine phenolic compounds: flavanols, flavonols, benzoic, hibiscus and phenolic acids as well as two main anthocyanins (cyanidin 3-sambubioside and delphinidin 3-sambubioside). The antioxidant capacity was evaluated by ABTS and FRAP assays. Tristimulus colorimetry showed to be a useful technique to determine the color-composition relationship, leading to equations that allowed to predict anthocyanin content of roselle (R > 0.84). Also, a stepwise linear discriminant analysis (SLDA) was developed in order to classify roselle cultivars. The obtained mathematical model could be an important tool to be used in colorimetric characterization of functional compounds used in food processing.
Using foreground/background analysis to determine leaf and canopy chemistry

NASA Technical Reports Server (NTRS)

Pinzon, J. E.; Ustin, S. L.; Hart, Q. J.; Jacquemoud, S.; Smith, M. O.

1995-01-01

Spectral Mixture Analysis (SMA) has become a well established procedure for analyzing imaging spectrometry data, however, the technique is relatively insensitive to minor sources of spectral variation (e.g., discriminating stressed from unstressed vegetation and variations in canopy chemistry). Other statistical approaches have been tried e.g., stepwise multiple linear regression analysis to predict canopy chemistry. Grossman et al. reported that SMLR is sensitive to measurement error and that the prediction of minor chemical components are not independent of patterns observed in more dominant spectral components like water. Further, they observed that the relationships were strongly dependent on the mode of expressing reflectance (R, -log R) and whether chemistry was expressed on a weight (g/g) or are basis (g/sq m). Thus, alternative multivariate techniques need to be examined. Smith et al. reported a revised SMA that they termed Foreground/Background Analysis (FBA) that permits directing the analysis along any axis of variance by identifying vectors through the n-dimensional spectral volume orthonormal to each other. Here, we report an application of the FBA technique for the detection of canopy chemistry using a modified form of the analysis.
Sparse Logistic Regression for Diagnosis of Liver Fibrosis in Rat by Using SCAD-Penalized Likelihood

PubMed Central

Yan, Fang-Rong; Lin, Jin-Guan; Liu, Yu

2011-01-01

The objective of the present study is to find out the quantitative relationship between progression of liver fibrosis and the levels of certain serum markers using mathematic model. We provide the sparse logistic regression by using smoothly clipped absolute deviation (SCAD) penalized function to diagnose the liver fibrosis in rats. Not only does it give a sparse solution with high accuracy, it also provides the users with the precise probabilities of classification with the class information. In the simulative case and the experiment case, the proposed method is comparable to the stepwise linear discriminant analysis (SLDA) and the sparse logistic regression with least absolute shrinkage and selection operator (LASSO) penalty, by using receiver operating characteristic (ROC) with bayesian bootstrap estimating area under the curve (AUC) diagnostic sensitivity for selected variable. Results show that the new approach provides a good correlation between the serum marker levels and the liver fibrosis induced by thioacetamide (TAA) in rats. Meanwhile, this approach might also be used in predicting the development of liver cirrhosis. PMID:21716672
Diversity of soil yeasts isolated from South Victoria Land, Antarctica

USGS Publications Warehouse

Connell, L.; Redman, R.; Craig, S.; Scorzetti, G.; Iszard, M.; Rodriguez, R.

2008-01-01

Unicellular fungi, commonly referred to as yeasts, were found to be components of the culturable soil fungal population in Taylor Valley, Mt. Discovery, Wright Valley, and two mountain peaks of South Victoria Land, Antarctica. Samples were taken from sites spanning a diversity of soil habitats that were not directly associated with vertebrate activity. A large proportion of yeasts isolated in this study were basidiomycetous species (89%), of which 43% may represent undescribed species, demonstrating that culturable yeasts remain incompletely described in these polar desert soils. Cryptococcus species represented the most often isolated genus (33%) followed by Leucosporidium (22%). Principle component analysis and multiple linear regression using stepwise selection was used to model the relation between abiotic variables (principle component 1 and principle component 2 scores) and yeast biodiversity (the number of species present at a given site). These analyses identified soil pH and electrical conductivity as significant predictors of yeast biodiversity. Species-specific PCR primers were designed to rapidly discriminate among the Dioszegia and Leucosporidium species collected in this study. ?? 2008 Springer Science+Business Media, LLC.
Orthogonal sparse linear discriminant analysis

NASA Astrophysics Data System (ADS)

Liu, Zhonghua; Liu, Gang; Pu, Jiexin; Wang, Xiaohong; Wang, Haijun

2018-03-01

Linear discriminant analysis (LDA) is a linear feature extraction approach, and it has received much attention. On the basis of LDA, researchers have done a lot of research work on it, and many variant versions of LDA were proposed. However, the inherent problem of LDA cannot be solved very well by the variant methods. The major disadvantages of the classical LDA are as follows. First, it is sensitive to outliers and noises. Second, only the global discriminant structure is preserved, while the local discriminant information is ignored. In this paper, we present a new orthogonal sparse linear discriminant analysis (OSLDA) algorithm. The k nearest neighbour graph is first constructed to preserve the locality discriminant information of sample points. Then, L2,1-norm constraint on the projection matrix is used to act as loss function, which can make the proposed method robust to outliers in data points. Extensive experiments have been performed on several standard public image databases, and the experiment results demonstrate the performance of the proposed OSLDA algorithm.
Simultaneous use of geological, geophysical, and LANDSAT digital data in uranium exploration. [Libya

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

Missallati, A.; Prelat, A.E.; Lyon, R.J.P.

1979-08-01

The simultaneous use of geological, geophysical and Landsat data in uranium exploration in southern Libya is reported. The values of 43 geological, geophysical and digital data variables, including age and type of rock, geological contacts, aeroradio-metric and aeromagnetic values and brightness ratios, were used as input into a geomathematical model. Stepwise discriminant analysis was used to select grid cells most favorable for detailed mineral exploration and to evaluate the significance of each variable in discriminating between the anomalous (radioactive) and nonanomalous (nonradioactive) areas. It is found that the geological contact relationships, Landsat Bands 6 and Band 7/4 ratio values weremore » most useful in the discrimination. The procedure was found to be statistically and geologically reliable, and applicable to similar regions using only the most important geological and Landsat data.« less
Assessing soil quality indicator under different land use and soil erosion using multivariate statistical techniques.

PubMed

Nosrati, Kazem

2013-04-01

Soil degradation associated with soil erosion and land use is a critical problem in Iran and there is little or insufficient scientific information in assessing soil quality indicator. In this study, factor analysis (FA) and discriminant analysis (DA) were used to identify the most sensitive indicators of soil quality for evaluating land use and soil erosion within the Hiv catchment in Iran and subsequently compare soil quality assessment using expert opinion based on soil surface factors (SSF) form of Bureau of Land Management (BLM) method. Therefore, 19 soil physical, chemical, and biochemical properties were measured from 56 different sampling sites covering three land use/soil erosion categories (rangeland/surface erosion, orchard/surface erosion, and rangeland/stream bank erosion). FA identified four factors that explained for 82 % of the variation in soil properties. Three factors showed significant differences among the three land use/soil erosion categories. The results indicated that based upon backward-mode DA, dehydrogenase, silt, and manganese allowed more than 80 % of the samples to be correctly assigned to their land use and erosional status. Canonical scores of discriminant functions were significantly correlated to the six soil surface indices derived of BLM method. Stepwise linear regression revealed that soil surface indices: soil movement, surface litter, pedestalling, and sum of SSF were also positively related to the dehydrogenase and silt. This suggests that dehydrogenase and silt are most sensitive to land use and soil erosion.
[Discriminant analysis to predict the clinical diagnosis of primary immunodeficiencies: a preliminary report].

PubMed

Murata, Chiharu; Ramírez, Ana Belén; Ramírez, Guadalupe; Cruz, Alonso; Morales, José Luis; Lugo-Reyes, Saul Oswaldo

2015-01-01

The features in a clinical history from a patient with suspected primary immunodeficiency (PID) direct the differential diagnosis through pattern recognition. PIDs are a heterogeneous group of more than 250 congenital diseases with increased susceptibility to infection, inflammation, autoimmunity, allergy and malignancy. Linear discriminant analysis (LDA) is a multivariate supervised classification method to sort objects of study into groups by finding linear combinations of a number of variables. To identify the features that best explain membership of pediatric PID patients to a group of defect or disease. An analytic cross-sectional study was done with a pre-existing database with clinical and laboratory records from 168 patients with PID, followed at the National Institute of Pediatrics during 1991-2012, it was used to build linear discriminant models that would explain membership of each patient to the different group defects and to the most prevalent PIDs in our registry. After a preliminary run only 30 features were included (4 demographic, 10 clinical, 10 laboratory, 6 germs), with which the training models were developed through a stepwise regression algorithm. We compared the automatic feature selection with a selection made by a human expert, and then assessed the diagnostic usefulness of the resulting models (sensitivity, specificity, prediction accuracy and kappa coefficient), with 95% confidence intervals. The models incorporated 6 to 14 features to explain membership of PID patients to the five most abundant defect groups (combined, antibody, well-defined, dysregulation and phagocytosis), and to the four most prevalent PID diseases (X-linked agammaglobulinemia, chronic granulomatous disease, common variable immunodeficiency and ataxiatelangiectasia). In practically all cases of feature selection the machine outperformed the human expert. Diagnosis prediction using the equations created had a global accuracy of 83 to 94%, with sensitivity of 60 to 100%, specificity of 83 to 95% and kappa coefficient of 0.37 to 0.76. In general, the selection of features has clinical plausibility, and the practical advantage of utilizing only clinical attributes, infecting germs and routine lab results (blood cell counts and serum immunoglobulins). The performance of the model as a diagnostic tool was acceptable. The study's main limitations are a limited sample size and a lack of cross validation. This is only the first step in the construction of a machine learning system, with a wider approach that includes a larger database and different methodologies, to assist the clinical diagnosis of primary immunodeficiencies.
A stepwise regression tree for nonlinear approximation: applications to estimating subpixel land cover

USGS Publications Warehouse

Huang, C.; Townshend, J.R.G.

2003-01-01

A stepwise regression tree (SRT) algorithm was developed for approximating complex nonlinear relationships. Based on the regression tree of Breiman et al . (BRT) and a stepwise linear regression (SLR) method, this algorithm represents an improvement over SLR in that it can approximate nonlinear relationships and over BRT in that it gives more realistic predictions. The applicability of this method to estimating subpixel forest was demonstrated using three test data sets, on all of which it gave more accurate predictions than SLR and BRT. SRT also generated more compact trees and performed better than or at least as well as BRT at all 10 equal forest proportion interval ranging from 0 to 100%. This method is appealing to estimating subpixel land cover over large areas.
A method for the selection of a functional form for a thermodynamic equation of state using weighted linear least squares stepwise regression

NASA Technical Reports Server (NTRS)

Jacobsen, R. T.; Stewart, R. B.; Crain, R. W., Jr.; Rose, G. L.; Myers, A. F.

1976-01-01

A method was developed for establishing a rational choice of the terms to be included in an equation of state with a large number of adjustable coefficients. The methods presented were developed for use in the determination of an equation of state for oxygen and nitrogen. However, a general application of the methods is possible in studies involving the determination of an optimum polynomial equation for fitting a large number of data points. The data considered in the least squares problem are experimental thermodynamic pressure-density-temperature data. Attention is given to a description of stepwise multiple regression and the use of stepwise regression in the determination of an equation of state for oxygen and nitrogen.
Otolith shape analysis for stock discrimination of two Collichthys genus croaker (Pieces: Sciaenidae,) from the northern Chinese coast

NASA Astrophysics Data System (ADS)

Zhao, Bo; Liu, Jinhu; Song, Junjie; Cao, Liang; Dou, Shuozeng

2017-08-01

The otolith morphology of two croaker species (Collichthys lucidus and Collichthys niveatus) from three areas (Liaodong Bay, LD; Huanghe (Yellow) River estuary, HRE; Jiaozhou Bay, JZ) along the northern Chinese coast were investigated for species identification and stock discrimination. The otolith contour shape described by elliptic Fourier coefficients (EFC) were analysed using principal components analysis (PCA) and stepwise canonical discriminant analysis (CDA) to identify species and stocks. The two species were well differentiated, with an overall classification success rate of 97.8%. And variations in the otolith shapes were significant enough to discriminate among the three geographical samples of C. lucidus (67.7%) or C. niveatus (65.2%). Relatively high mis-assignment occurred between the geographically adjacent LD and HRE samples, which implied that individual mixing may exist between the two samples. This study yielded information complementary to that derived from genetic studies and provided information for assessing the stock structure of C. lucidus and C. niveatus in the Bohai Sea and the Yellow Sea.
Order Selection for General Expression of Nonlinear Autoregressive Model Based on Multivariate Stepwise Regression

NASA Astrophysics Data System (ADS)

Shi, Jinfei; Zhu, Songqing; Chen, Ruwen

2017-12-01

An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.

A statistical model for Windstorm Variability over the British Isles based on Large-scale Atmospheric and Oceanic Mechanisms

NASA Astrophysics Data System (ADS)

Kirchner-Bossi, Nicolas; Befort, Daniel J.; Wild, Simon B.; Ulbrich, Uwe; Leckebusch, Gregor C.

2016-04-01

Time-clustered winter storms are responsible for a majority of the wind-induced losses in Europe. Over last years, different atmospheric and oceanic large-scale mechanisms as the North Atlantic Oscillation (NAO) or the Meridional Overturning Circulation (MOC) have been proven to drive some significant portion of the windstorm variability over Europe. In this work we systematically investigate the influence of different large-scale natural variability modes: more than 20 indices related to those mechanisms with proven or potential influence on the windstorm frequency variability over Europe - mostly SST- or pressure-based - are derived by means of ECMWF ERA-20C reanalysis during the last century (1902-2009), and compared to the windstorm variability for the European winter (DJF). Windstorms are defined and tracked as in Leckebusch et al. (2008). The derived indices are then employed to develop a statistical procedure including a stepwise Multiple Linear Regression (MLR) and an Artificial Neural Network (ANN), aiming to hindcast the inter-annual (DJF) regional windstorm frequency variability in a case study for the British Isles. This case study reveals 13 indices with a statistically significant coupling with seasonal windstorm counts. The Scandinavian Pattern (SCA) showed the strongest correlation (0.61), followed by the NAO (0.48) and the Polar/Eurasia Pattern (0.46). The obtained indices (standard-normalised) are selected as predictors for a windstorm variability hindcast model applied for the British Isles. First, a stepwise linear regression is performed, to identify which mechanisms can explain windstorm variability best. Finally, the indices retained by the stepwise regression are used to develop a multlayer perceptron-based ANN that hindcasted seasonal windstorm frequency and clustering. Eight indices (SCA, NAO, EA, PDO, W.NAtl.SST, AMO (unsmoothed), EA/WR and Trop.N.Atl SST) are retained by the stepwise regression. Among them, SCA showed the highest linear coefficient, followed by SST in western Atlantic, AMO and NAO. The explanatory regression model (considering all time steps) provided a Coefficient of Determination (R^2) of 0.75. A predictive version of the linear model applying a leave-one-out cross-validation (LOOCV) shows an R2 of 0.56 and a relative RMSE of 4.67 counts/season. An ANN-based nonlinear hindcast model for the seasonal windstorm frequency is developed with the aim to improve the stepwise hindcast ability and thus better predict a time-clustered season over the case study. A 7 node-hidden layer perceptron is set, and the LOOCV procedure reveals a R2 of 0.71. In comparison to the stepwise MLR the RMSE is reduced a 20%. This work shows that for the British Isles case study, most of the interannual variability can be explained by certain large-scale mechanisms, considering also nonlinear effects (ANN). This allows to discern a time-clustered season from a non-clustered one - a key issue for applications e.g., in the (re)insurance industry.
Factors associated with enrollment of African Americans into a clinical trial: results from the African American study of kidney disease and hypertension.

PubMed

Gadegbeku, Crystal A; Stillman, Phyllis Kreger; Huffman, Mark D; Jackson, James S; Kusek, John W; Jamerson, Kenneth A

2008-11-01

Recruitment of diverse populations into clinical trials remains challenging but is needed to fully understand disease processes and benefit the general public. Greater knowledge of key factors among ethnic and racial minority populations associated with the decision to participate in clinical research studies may facilitate recruitment and enhance the generalizibility of study results. Therefore, during the recruitment phase of the African American Study of Kidney Disease and Hypertension (AASK) trial, we conducted a telephone survey, using validated questions, to explore potential facilitators and barriers of research participation among eligible candidates residing in seven U.S. locations. Survey responses included a range of characteristics and perceptions among participants and non-participants and were compared using bivariate and step-wise logistic regression analyses. One-hundred forty-one respondents in the one-hundred forty (70 trial participants and 71 non-participants) completed the survey. Trial participants and non-participants were similar in multiple demographic characteristics and shared similar views on discrimination, physician mistrust, and research integrity. Key group differences were related to their perceptions of the impact of their research participation. Participants associated enrollment with personal and societal health benefits, while non-participants were influenced by the health risks. In a step-wise linear regression analysis, the most powerful significant positive predictors of participation were acknowledgement of health status as important in the enrollment decision (OR=4.54, p=0.006), employment (OR=3.12, p = 0.05) and healthcare satisfaction (OR=2.12, p<0.01). Racially-based mistrust did not emerge as a negative predictor and subjects' decisions were not influenced by the race of the research staff. In conclusion, these results suggest that health-related factors, and not psychosocial perceptions, have predominant influence on research participation among African Americans.
Delineating chalk sand distribution of Ekofisk formation using probabilistic neural network (PNN) and stepwise regression (SWR): Case study Danish North Sea field

NASA Astrophysics Data System (ADS)

Haris, A.; Nafian, M.; Riyanto, A.

2017-07-01

Danish North Sea Fields consist of several formations (Ekofisk, Tor, and Cromer Knoll) that was started from the age of Paleocene to Miocene. In this study, the integration of seismic and well log data set is carried out to determine the chalk sand distribution in the Danish North Sea field. The integration of seismic and well log data set is performed by using the seismic inversion analysis and seismic multi-attribute. The seismic inversion algorithm, which is used to derive acoustic impedance (AI), is model-based technique. The derived AI is then used as external attributes for the input of multi-attribute analysis. Moreover, the multi-attribute analysis is used to generate the linear and non-linear transformation of among well log properties. In the case of the linear model, selected transformation is conducted by weighting step-wise linear regression (SWR), while for the non-linear model is performed by using probabilistic neural networks (PNN). The estimated porosity, which is resulted by PNN shows better suited to the well log data compared with the results of SWR. This result can be understood since PNN perform non-linear regression so that the relationship between the attribute data and predicted log data can be optimized. The distribution of chalk sand has been successfully identified and characterized by porosity value ranging from 23% up to 30%.
Feature extraction with deep neural networks by a generalized discriminant analysis.

PubMed

Stuhlsatz, André; Lippel, Jens; Zielke, Thomas

2012-04-01

We present an approach to feature extraction that is a generalization of the classical linear discriminant analysis (LDA) on the basis of deep neural networks (DNNs). As for LDA, discriminative features generated from independent Gaussian class conditionals are assumed. This modeling has the advantages that the intrinsic dimensionality of the feature space is bounded by the number of classes and that the optimal discriminant function is linear. Unfortunately, linear transformations are insufficient to extract optimal discriminative features from arbitrarily distributed raw measurements. The generalized discriminant analysis (GerDA) proposed in this paper uses nonlinear transformations that are learnt by DNNs in a semisupervised fashion. We show that the feature extraction based on our approach displays excellent performance on real-world recognition and detection tasks, such as handwritten digit recognition and face detection. In a series of experiments, we evaluate GerDA features with respect to dimensionality reduction, visualization, classification, and detection. Moreover, we show that GerDA DNNs can preprocess truly high-dimensional input data to low-dimensional representations that facilitate accurate predictions even if simple linear predictors or measures of similarity are used.
Expanded image database of pistachio x-ray images and classification by conventional methods

NASA Astrophysics Data System (ADS)

Keagy, Pamela M.; Schatzki, Thomas F.; Le, Lan Chau; Casasent, David P.; Weber, David

1996-12-01

In order to develop sorting methods for insect damaged pistachio nuts, a large data set of pistachio x-ray images (6,759 nuts) was created. Both film and linescan sensor images were acquired, nuts dissected and internal conditions coded using the U.S. Grade standards and definitions for pistachios. A subset of 1199 good and 686 insect damaged nuts was used to calculate and test discriminant functions. Statistical parameters of image histograms were evaluated for inclusion by forward stepwise discrimination. Using three variables in the discriminant function, 89% of test set nuts were correctly identified. Comparable data for 6 human subjects ranged from 67 to 92%. If the loss of good nuts is held to 1% by requiring a high probability to discard a nut as insect damaged, approximately half of the insect damage present in clean pistachio nuts may be detected and removed by x-ray inspection.
Analysis of Optimal Sequential State Discrimination for Linearly Independent Pure Quantum States.

PubMed

Namkung, Min; Kwon, Younghun

2018-04-25

Recently, J. A. Bergou et al. proposed sequential state discrimination as a new quantum state discrimination scheme. In the scheme, by the successful sequential discrimination of a qubit state, receivers Bob and Charlie can share the information of the qubit prepared by a sender Alice. A merit of the scheme is that a quantum channel is established between Bob and Charlie, but a classical communication is not allowed. In this report, we present a method for extending the original sequential state discrimination of two qubit states to a scheme of N linearly independent pure quantum states. Specifically, we obtain the conditions for the sequential state discrimination of N = 3 pure quantum states. We can analytically provide conditions when there is a special symmetry among N = 3 linearly independent pure quantum states. Additionally, we show that the scenario proposed in this study can be applied to quantum key distribution. Furthermore, we show that the sequential state discrimination of three qutrit states performs better than the strategy of probabilistic quantum cloning.
Personality features, dissociation, self-stigma, hope, and the complex treatment of depressive disorder

PubMed Central

Prasko, Jan; Ociskova, Marie; Grambal, Ales; Sigmundova, Zuzana; Kasalova, Petra; Marackova, Marketa; Holubova, Michaela; Vrbova, Kristyna; Latalova, Klara; Slepecky, Milos

2016-01-01

Objective Identifying the predictors of response to psychiatric and psychotherapeutic treatments may be useful for increasing treatment efficacy in pharmacoresistant depressive patients. The goal of this study was to examine the influence of dissociation, hope, personality trait, and selected demographic factors in treatment response of this group of patients. Methods Pharmacoresistant depressive inpatients were enrolled in the study. All patients completed Clinical Global Impression – both objective and subjective form (CGI), Beck Depression Inventory (BDI), and Beck Anxiety Inventory (BAI) at baseline and after 6 weeks of combined pharmacotherapy and psychotherapy (group cognitive-behavioral or group psychodynamic) treatment as an outcome measures. The Internalized Stigma of Mental Illness Scale (ISMI), Dissociative Experience Scale (DES), Adult Dispositional Hope Scale (ADHS), and Temperament and Character Inventory (TCI-R) were completed at the start of the treatment with the intention to find the predictors of treatment efficacy. Results The study included 72 patients who were hospitalized for the pharmacoresistant major depression; 63 of them completed the study. The mean scores of BDI-II, BAI, subjCGI, and objCGI significantly decreased during the treatment. BDI-II relative change statistically significantly correlated with the total ISMI score, Discrimination Experience (ISMI subscale), and Harm Avoidance (TCI-R personality trait). According to stepwise regression, the strongest factors connected to BDI-II relative change were the duration of the disorder and Discrimination Experience (domain of ISMI). ObjCGI relative change significantly correlated with the level of dissociation (DES), the total ISMI score, hope in ADHS total score, and Self-Directedness (TCI-R). According to stepwise regression, the strongest factor connected to objCGI relative change was Discrimination Experience (domain of ISMI). The existence of comorbid personality disorder did not influence the treatment response. Conclusion According to the results of the present study, patients with pharmacoresistant depressive disorders, who have had more experience with discrimination because of their mental struggles, showed a poorer response to treatment. PMID:27785031
Word Problems: A "Meme" for Our Times.

ERIC Educational Resources Information Center

Leamnson, Robert N.

1996-01-01

Discusses a novel approach to word problems that involves linear relationships between variables. Argues that working stepwise through intermediates is the way our minds actually work and therefore this should be used in solving word problems. (JRH)
Estimating extent of mortality associated with the Douglas-fir beetle in the Central and Northern Rockies

Treesearch

Jose F. Negron; Willis C. Schaupp; Kenneth E. Gibson; John Anhold; Dawn Hansen; Ralph Thier; Phil Mocettini

1999-01-01

Data collected from Douglas-fir stands infected by the Douglas-fir beetle in Wyoming, Montana, Idaho, and Utah, were used to develop models to estimate amount of mortality in terms of basal area killed. Models were built using stepwise linear regression and regression tree approaches. Linear regression models using initial Douglas-fir basal area were built for all...
A face and palmprint recognition approach based on discriminant DCT feature extraction.

PubMed

Jing, Xiao-Yuan; Zhang, David

2004-12-01

In the field of image processing and recognition, discrete cosine transform (DCT) and linear discrimination are two widely used techniques. Based on them, we present a new face and palmprint recognition approach in this paper. It first uses a two-dimensional separability judgment to select the DCT frequency bands with favorable linear separability. Then from the selected bands, it extracts the linear discriminative features by an improved Fisherface method and performs the classification by the nearest neighbor classifier. We detailedly analyze theoretical advantages of our approach in feature extraction. The experiments on face databases and palmprint database demonstrate that compared to the state-of-the-art linear discrimination methods, our approach obtains better classification performance. It can significantly improve the recognition rates for face and palmprint data and effectively reduce the dimension of feature space.
Identification of International Classification of Functioning, Disability and Health categories for patients with peripheral arterial disease.

PubMed

Vyskocil, Erich; Gruther, Wolfgang; Steiner, Irene; Schuhfried, Othmar

2014-07-01

Disease-specific categories of the International Classification of Functioning, Disability and Health have not yet been described for patients with chronic peripheral arterial obstructive disease (PAD). The authors examined the relationship between the categories of the Brief Core Sets for ischemic heart diseases with the Peripheral Artery Questionnaire and the ankle-brachial index to determine which International Classification of Functioning, Disability and Health categories are most relevant for patients with PAD. This is a retrospective cohort study including 77 patients with verified PAD. Statistical analyses of the relationship between International Classification of Functioning, Disability and Health categories as independent variables and the endpoints Peripheral Artery Questionnaire or ankle-brachial index were carried out by simple and stepwise linear regression models adjusting for age, sex, and leg (left vs. right). The stepwise linear regression model with the ankle-brachial index as dependent variable revealed a significant effect of the variables blood vessel functions and muscle endurance functions. Calculating a stepwise linear regression model with the Peripheral Artery Questionnaire as dependent variable, a significant effect of age, emotional functions, energy and drive functions, carrying out daily routine, as well as walking could be observed. This study identifies International Classification of Functioning, Disability and Health categories in the Brief Core Sets for ischemic heart diseases that show a significant effect on the ankle-brachial index and the Peripheral Artery Questionnaire score in patients with PAD. These categories provide fundamental information on functioning of patients with PAD and patient-centered outcomes for rehabilitation interventions.
Angular velocity discrimination

NASA Technical Reports Server (NTRS)

Kaiser, Mary K.

1990-01-01

Three experiments designed to investigate the ability of naive observers to discriminate rotational velocities of two simultaneously viewed objects are described. Rotations are constrained to occur about the x and y axes, resulting in linear two-dimensional image trajectories. The results indicate that observers can discriminate angular velocities with a competence near that for linear velocities. However, perceived angular rate is influenced by structural aspects of the stimuli.
Field hyperspectral data analysis for discriminating spectral behavior of tea plantations under various management practices

NASA Astrophysics Data System (ADS)

Kumar, Amit; Manjunath, K. R.; Meenakshi; Bala, Renu; Sud, R. K.; Singh, R. D.; Panigrahy, Sushma

2013-08-01

The quality and yield of tea depends upon management of tea plantations, which takes into account the factors like type, age of plantation, growth stage, pruning status, light conditions, and disease incidence. Recognizing the importance of hyperspectral data in detecting minute spectral variations in vegetation, the present study was conducted to explore applicability of such data in evaluating these factors. Also stepwise discriminant analysis and principal component analysis were conducted to identify the appropriate bands for accessing the above mentioned factors. The Green region followed by NIR region was found as most appropriate best band for discriminating different types of tea plants, and the tea in sunlit and shade condition. For discriminating age of plantation, growth stage of tea, and diseased and healthy bush, Blue region was most appropriate. The Red and NIR regions were best bands to discriminate pruned and unpruned tea. The study concluded that field hyperspectral data can be efficiently used to know the plantation that need special care and may be an indicator of tea productivity. The spectral signature of these characteristics of tea plantations may also be used to classify the hyperspectral satellite data to derive these parameters at regional scale.
Discrimination of the rare medicinal plant Dendrobium officinale based on naringenin, bibenzyl, and polysaccharides.

PubMed

Chen, Xiaomei; Wang, Fangfei; Wang, Yunqiang; Li, Xuelan; Wang, Airong; Wang, Chunlan; Guo, Shunxing

2012-12-01

The aim of this study was to establish a method for discriminating Dendrobium officinale from four of its close relatives Dendrobium chrysanthum, Dendrobium crystallinum, Dendrobium aphyllum and Dendrobium devonianum based on chemical composition analysis. We analyzed 62 samples of 24 Dendrobium species. High performance liquid chromatography analysis confirmed that the four low molecular weight compounds 4',5,7-trihydroxyflavanone (naringenin), 3,4-dihydroxy-4',5-dime-thoxybibenzyl (DDB-2), 3',4-dihydroxy-3,5'-dimethoxybibenzyl (gigantol), and 4,4'-dihydroxy-3,3',5-trimethoxybibenzy (moscatilin), were common in the genus. The phenol-sulfuric acid method was used to quantify polysaccharides, and the monosaccharide composition of the polysaccharides was determined by gas chromatography. Stepwise discriminant analysis was used to differentiate among the five closely related species based on the chemical composition analysis. This proved to be a simple and accurate approach for discriminating among these species. The results also showed that the polysaccharide content, the amounts of the four low molecular weight compounds, and the mannose to glucose ratio, were important factors for species discriminant. Therefore, we propose that a chemical analysis based on quantification of naringenin, bibenzyl, and polysaccharides is effective for identifying D. officinale.
[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.
The Profile of Memory Function in Children With Autism

PubMed Central

Williams, Diane L.; Goldstein, Gerald; Minshew, Nancy J.

2007-01-01

A clinical memory test was administered to 38 high-functioning children with autism and 38 individually matched normal controls, 8–16 years of age. The resulting profile of memory abilities in the children with autism was characterized by relatively poor memory for complex visual and verbal information and spatial working memory with relatively intact associative learning ability, verbal working memory, and recognition memory. A stepwise discriminant function analysis of the subtests found that the Finger Windows subtest, a measure of spatial working memory, discriminated most accurately between the autism and normal control groups. A principal components analysis indicated that the factor structure of the subtests differed substantially between the children with autism and controls, suggesting differing organizations of memory ability. PMID:16460219
Utility of an Abbreviated Dizziness Questionnaire to Differentiate between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study

PubMed Central

Roland, Lauren T.; Kallogjeri, Dorina; Sinks, Belinda C.; Rauch, Steven D.; Shepard, Neil T.; White, Judith A.; Goebel, Joel A.

2015-01-01

Objective Test performance of a focused dizziness questionnaire’s ability to discriminate between peripheral and non-peripheral causes of vertigo. Study Design Prospective multi-center Setting Four academic centers with experienced balance specialists Patients New dizzy patients Interventions A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Main outcomes Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and non-peripheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. Results 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and non-peripheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central and other causes were considered good as measured by c-indices of 0.75, 0.7 and 0.78, respectively. Conclusions This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from non-peripheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed. PMID:26485598
Utility of an Abbreviated Dizziness Questionnaire to Differentiate Between Causes of Vertigo and Guide Appropriate Referral: A Multicenter Prospective Blinded Study.

PubMed

Roland, Lauren T; Kallogjeri, Dorina; Sinks, Belinda C; Rauch, Steven D; Shepard, Neil T; White, Judith A; Goebel, Joel A

2015-12-01

Test performance of a focused dizziness questionnaire's ability to discriminate between peripheral and nonperipheral causes of vertigo. Prospective multicenter. Four academic centers with experienced balance specialists. New dizzy patients. A 32-question survey was given to participants. Balance specialists were blinded and a diagnosis was established for all participating patients within 6 months. Multinomial logistic regression was used to evaluate questionnaire performance in predicting final diagnosis and differentiating between peripheral and nonperipheral vertigo. Univariate and multivariable stepwise logistic regression were used to identify questions as significant predictors of the ultimate diagnosis. C-index was used to evaluate performance and discriminative power of the multivariable models. In total, 437 patients participated in the study. Eight participants without confirmed diagnoses were excluded and 429 were included in the analysis. Multinomial regression revealed that the model had good overall predictive accuracy of 78.5% for the final diagnosis and 75.5% for differentiating between peripheral and nonperipheral vertigo. Univariate logistic regression identified significant predictors of three main categories of vertigo: peripheral, central, and other. Predictors were entered into forward stepwise multivariable logistic regression. The discriminative power of the final models for peripheral, central, and other causes was considered good as measured by c-indices of 0.75, 0.7, and 0.78, respectively. This multicenter study demonstrates a focused dizziness questionnaire can accurately predict diagnosis for patients with chronic/relapsing dizziness referred to outpatient clinics. Additionally, this survey has significant capability to differentiate peripheral from nonperipheral causes of vertigo and may, in the future, serve as a screening tool for specialty referral. Clinical utility of this questionnaire to guide specialty referral is discussed.
Intractable Ménière's disease. Modelling of the treatment by means of statistical analysis.

PubMed

Sanchez-Ferrandiz, Noelia; Fernandez-Gonzalez, Secundino; Guillen-Grima, Francisco; Perez-Fernandez, Nicolas

2010-08-01

To evaluate the value of different variables of the clinical history, auditory and vestibular tests and handicap measurements to define intractable or disabling Ménière's disease. This is a prospective study with 212 patients of which 155 were treated with intratympanic gentamicin and considered to be suffering a medically intractable Ménière's disease. Age and sex adjustments were performed with the 11 variables selected. Discriminant analysis was performed either using the aforementioned variables or following the stepwise method. Different variables needed to be sex and/or age adjusted and both data were included in the discriminant function. Two different mathematical formulas were obtained and four models were analyzed. With the model selected, diagnostic accuracy is 77.7%, sensitivity is 94.9% and specificity is 52.8%. After discriminant analysis we found that the most informative variables were the number of vertigo spells, the speech discrimination score, the time constant of the VOR and a measure of handicap, the "dizziness index". Copyright 2009 Elsevier Ireland Ltd. All rights reserved.
Novel candidate genes of the PARK7 interactome as mediators of apoptosis and acetylation in multiple sclerosis: An in silico analysis.

PubMed

Vavougios, George D; Zarogiannis, Sotirios G; Krogfelt, Karen Angeliki; Gourgoulianis, Konstantinos; Mitsikostas, Dimos Dimitrios; Hadjigeorgiou, Georgios

2018-01-01

currently only 4 studies have explored the potential role of PARK7's dysregulation in MS pathophysiology Currently, no study has evaluated the potential role of the PARK7 interactome in MS. The aim of our study was to assess the differential expression of PARK7 mRNA in peripheral blood mononuclears (PBMCs) donated from MS versus healthy patients using data mining techniques. The PARK7 interactome data from the GDS3920 profile were scrutinized for differentially expressed genes (DEGs); Gene Enrichment Analysis (GEA) was used to detect significantly enriched biological functions. 27 differentially expressed genes in the MS dataset were detected; 12 of these (NDUFA4, UBA2, TDP2, NPM1, NDUFS3, SUMO1, PIAS2, KIAA0101, RBBP4, NONO, RBBP7 AND HSPA4) are reported for the first time in MS. Stepwise Linear Discriminant Function Analysis constructed a predictive model (Wilk's λ = 0.176, χ 2 = 45.204, p = 1.5275e -10 ) with 2 variables (TIDP2, RBBP4) that achieved 96.6% accuracy when discriminating between patients and controls. Gene Enrichment Analysis revealed that induction and regulation of programmed / intrinsic cell death represented the most salient Gene Ontology annotations. Cross-validation on systemic lupus erythematosus and ischemic stroke datasets revealed that these functions are unique to the MS dataset. Based on our results, novel potential target genes are revealed; these differentially expressed genes regulate epigenetic and apoptotic pathways that may further elucidate underlying mechanisms of autorreactivity in MS. Copyright © 2017 Elsevier B.V. All rights reserved.

NCLscan: accurate identification of non-co-linear transcripts (fusion, trans-splicing and circular RNA) with a good balance between sensitivity and precision.

PubMed

Chuang, Trees-Juen; Wu, Chan-Shuo; Chen, Chia-Ying; Hung, Li-Yuan; Chiang, Tai-Wei; Yang, Min-Yu

2016-02-18

Analysis of RNA-seq data often detects numerous 'non-co-linear' (NCL) transcripts, which comprised sequence segments that are topologically inconsistent with their corresponding DNA sequences in the reference genome. However, detection of NCL transcripts involves two major challenges: removal of false positives arising from alignment artifacts and discrimination between different types of NCL transcripts (trans-spliced, circular or fusion transcripts). Here, we developed a new NCL-transcript-detecting method ('NCLscan'), which utilized a stepwise alignment strategy to almost completely eliminate false calls (>98% precision) without sacrificing true positives, enabling NCLscan outperform 18 other publicly-available tools (including fusion- and circular-RNA-detecting tools) in terms of sensitivity and precision, regardless of the generation strategy of simulated dataset, type of intragenic or intergenic NCL event, read depth of coverage, read length or expression level of NCL transcript. With the high accuracy, NCLscan was applied to distinguishing between trans-spliced, circular and fusion transcripts on the basis of poly(A)- and nonpoly(A)-selected RNA-seq data. We showed that circular RNAs were expressed more ubiquitously, more abundantly and less cell type-specifically than trans-spliced and fusion transcripts. Our study thus describes a robust pipeline for the discovery of NCL transcripts, and sheds light on the fundamental biology of these non-canonical RNA events in human transcriptome. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.
Extendable nickel complex tapes that reach NIR absorptions.

PubMed

Audi, Hassib; Chen, Zhongrui; Charaf-Eddin, Azzam; D'Aléo, Anthony; Canard, Gabriel; Jacquemin, Denis; Siri, Olivier

2014-12-14

Stepwise synthesis of linear nickel complex oligomer tapes with no need for solid-phase support has been achieved. The control of the length in flat arrays allows a fine-tuning of the absorption properties from the UV to the NIR region.
Clot formation is associated with fibrinogen and platelet forces in a cohort of severely-injured Emergency Department trauma patients

PubMed Central

White, Nathan J.; Newton, Jason C.; Martin, Erika J.; Mohammed, Bassem M.; Contaifer, Daniel; Bostic, Jessica L.; Brophy, Gretchen M.; Spiess, Bruce D.; Pusateri, Anthony E.; Ward, Kevin R.; Brophy, Donald F.

2015-01-01

Introduction Anticoagulation, fibrinogen consumption, fibrinolytic activation, and platelet dysfunction all interact to produce different clot formation responses after trauma. However, the relative contributions of these coagulation components to overall clot formation remains poorly defined. We examined for sources of heterogeneity in clot formation responses after trauma. Methods Blood was sampled in the Emergency Department from patients meeting trauma team activation criteria at an urban trauma center. Plasma prothrombin time (PT) ≥ 18 sec was used to define traumatic coagulopathy. Mean kaolin-activated thrombelastography (TEG) parameters were calculated and tested for heterogeneity using Analysis of Means (ANOM). Discriminant analysis and forward stepwise variable selection with linear regression were used to determine if PT, fibrinogen, platelet contractile force (PCF), and D-Dimer concentration, representing key mechanistic components of coagulopathy, each contribute to heterogeneous TEG responses after trauma. Results Of 95 subjects, 16% met criteria for coagulopathy. Coagulopathic subjects were more severely injured with greater shock, and received more blood products in the first 8 hours compared to non-coagulopathic subjects. Mean (SD) TEG maximal amplitude (MA) was significantly decreased in the coagulopathic group=57.5 (4.7) mm, vs. 62.7 (4.7), T test p<0.001. The MA also exceeded the ANOM predicted upper decision limit for the non-coagulopathic group and the lower decision limit for the coagulopathic group at alpha=0.05, suggesting significant heterogeneity from the overall cohort mean. Fibrinogen and PCF best discriminated TEG MA using discriminant analysis. Fibrinogen, PCF, and D-Dimer were primary covariates for TEG MA using regression analysis. Conclusion Heterogeneity in TEG-based clot formation in Emergency Department trauma patients was linked to changes in MA. Individual parameters representing fibrin polymerization, platelet contractile forces, and fibrinolysis were primarily associated with TEG MA after trauma and should be the focus of early hemostatic therapies. PMID:25643013
Noninvasive fluorescence excitation spectroscopy for the diagnosis of oral neoplasia in vivo

NASA Astrophysics Data System (ADS)

Ebenezar, Jeyasingh; Ganesan, Singaravelu; Aruna, Prakasarao; Muralinaidu, Radhakrishnan; Renganathan, Kannan; Saraswathy, Thillai Rajasekaran

2012-09-01

Fluorescence excitation spectroscopy (FES) is an emerging approach to cancer detection. The goal of this pilot study is to evaluate the diagnostic potential of FES technique for the detection and characterization of normal and cancerous oral lesions in vivo. Fluorescence excitation (FE) spectra from oral mucosa were recorded in the spectral range of 340 to 600 nm at 635 nm emission using a fiberoptic probe spectrofluorometer to obtain spectra from the buccal mucosa of 30 sites of 15 healthy volunteers and 15 sites of 10 cancerous patients. Significant FE spectral differences were observed between normal and well differentiated squamous cell carcinoma (WDSCC) oral lesions. The FE spectra of healthy volunteers consists of a broad emission band around 440 to 470 nm, whereas in WDSCC lesions, a new primary peak was seen at 410 nm with secondary peaks observed at 505, 540, and 580 nm due to the accumulation of porphyrins in oral lesions. The FE spectral bands of the WDSCC lesions resemble the typical absorption spectra of a porphyrin. Three potential ratios (I410/I505, I410/I540, and I410/I580) were calculated from the FE spectra and used as input variables for a stepwise linear discriminant analysis (SLDA) for normal and WDSCC groups. Leave-one-out (LOO) method of cross-validation was performed to check the reliability on spectral data for tissue characterization. The diagnostic sensitivity and specificity were determined for normal and WDSCC lesions from the scatter plot of the discriminant function scores. It was observed that diagnostic algorithm based on discriminant function scores obtained by SLDA-LOO method was able to distinguish WDSCC from normal lesions with a sensitivity of 100% and specificity of 100%. Results of the pilot study demonstrate that the FE spectral changes due to porphyrin have a good diagnostic potential; therefore, porphyrin can be used as a native tumor marker.
Neighborhood Structural Similarity Mapping for the Classification of Masses in Mammograms.

PubMed

Rabidas, Rinku; Midya, Abhishek; Chakraborty, Jayasree

2018-05-01

In this paper, two novel feature extraction methods, using neighborhood structural similarity (NSS), are proposed for the characterization of mammographic masses as benign or malignant. Since gray-level distribution of pixels is different in benign and malignant masses, more regular and homogeneous patterns are visible in benign masses compared to malignant masses; the proposed method exploits the similarity between neighboring regions of masses by designing two new features, namely, NSS-I and NSS-II, which capture global similarity at different scales. Complementary to these global features, uniform local binary patterns are computed to enhance the classification efficiency by combining with the proposed features. The performance of the features are evaluated using the images from the mini-mammographic image analysis society (mini-MIAS) and digital database for screening mammography (DDSM) databases, where a tenfold cross-validation technique is incorporated with Fisher linear discriminant analysis, after selecting the optimal set of features using stepwise logistic regression method. The best area under the receiver operating characteristic curve of 0.98 with an accuracy of is achieved with the mini-MIAS database, while the same for the DDSM database is 0.93 with accuracy .
A visual parallel-BCI speller based on the time-frequency coding strategy.

PubMed

Xu, Minpeng; Chen, Long; Zhang, Lixin; Qi, Hongzhi; Ma, Lan; Tang, Jiabei; Wan, Baikun; Ming, Dong

2014-04-01

Spelling is one of the most important issues in brain-computer interface (BCI) research. This paper is to develop a visual parallel-BCI speller system based on the time-frequency coding strategy in which the sub-speller switching among four simultaneously presented sub-spellers and the character selection are identified in a parallel mode. The parallel-BCI speller was constituted by four independent P300+SSVEP-B (P300 plus SSVEP blocking) spellers with different flicker frequencies, thereby all characters had a specific time-frequency code. To verify its effectiveness, 11 subjects were involved in the offline and online spellings. A classification strategy was designed to recognize the target character through jointly using the canonical correlation analysis and stepwise linear discriminant analysis. Online spellings showed that the proposed parallel-BCI speller had a high performance, reaching the highest information transfer rate of 67.4 bit min(-1), with an average of 54.0 bit min(-1) and 43.0 bit min(-1) in the three rounds and five rounds, respectively. The results indicated that the proposed parallel-BCI could be effectively controlled by users with attention shifting fluently among the sub-spellers, and highly improved the BCI spelling performance.
Characterization of French and Spanish dry-cured hams: influence of the volatiles from the muscles and the subcutaneous fat quantified by SPME-GC.

PubMed

Sánchez-Peña, Carolina M; Luna, Guadalupe; García-González, Diego L; Aparicio, Ramón

2005-04-01

The influence of the volatile compounds on the characterization of Spanish and French dry-cured hams was studied. Thirty volatiles were quantified in each one of four locations (biceps femoris, semimembranosus and semitendinosus muscles and subcutaneous fat) of 29 dry-cured hams by solid-phase microextraction gas-chromatography (SPME-GC). The Brown-Forsythe univariate test allowed determination of the volatiles that individually could characterize (p<0.05) the samples by their geographical origin (France, Spain) and breed type (Iberian, white). Stepwise linear discriminant procedure, under very strict conditions (F-to-Enter for a F-distribution>0.95), then selected the most remarkable volatile compounds. Four compounds from the subcutaneous fat (methyl benzene and octanol) and the semitendinosus muscle (2-butanone and 2-octanone) allowed 100% correct classifications by geographic origin. On the other hand, only two compounds from the subcutaneous fat (octanol) and the biceps femoris muscle (3-methyl 1-butanol) correctly classified all the samples by the breed type. The ability of these variables to classify the samples was checked by the unsupervised procedure of principal components.
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.
Discriminant forest classification method and system

DOEpatents

Chen, Barry Y.; Hanley, William G.; Lemmond, Tracy D.; Hiller, Lawrence J.; Knapp, David A.; Mugge, Marshall J.

2012-11-06

A hybrid machine learning methodology and system for classification that combines classical random forest (RF) methodology with discriminant analysis (DA) techniques to provide enhanced classification capability. A DA technique which uses feature measurements of an object to predict its class membership, such as linear discriminant analysis (LDA) or Andersen-Bahadur linear discriminant technique (AB), is used to split the data at each node in each of its classification trees to train and grow the trees and the forest. When training is finished, a set of n DA-based decision trees of a discriminant forest is produced for use in predicting the classification of new samples of unknown class.
Using tunable diode laser spectroscopy to measure carbon isotope discrimination and mesophyll conductance to CO₂ diffusion dynamically at different CO₂ concentrations.

PubMed

Tazoe, Youshi; VON Caemmerer, Susanne; Estavillo, Gonzalo M; Evans, John R

2011-04-01

In C₃ leaves, the mesophyll conductance to CO₂ diffusion, g(m) , determines the drawdown in CO₂ concentration from intercellular airspace to the chloroplast stroma. Both g(m) and stomatal conductance limit photosynthetic rate and vary in response to the environment. We investigated the response of g(m) to changes in CO₂ in two Arabidopsis genotypes (including a mutant with open stomata, ost1), tobacco and wheat. We combined measurements of gas exchange with carbon isotope discrimination using tunable diode laser absorption spectroscopy with a CO₂ calibration system specially designed for a range of CO₂ and O₂ concentrations. CO₂ was initially increased from 200 to 1000 ppm and then decreased stepwise to 200 ppm and increased stepwise back to 1000 ppm, or the sequence was reversed. In 2% O₂ a step increase from 200 to 1000 ppm significantly decreased g(m) by 26-40% in all three species, whereas following a step decrease from 1000 to 200 ppm, the 26-38% increase in g(m) was not statistically significant. The response of g(m) to CO₂ was less in 21% O₂. Comparing wild type against the ost1 revealed that mesophyll and stomatal conductance varied independently in response to CO₂. We discuss the effects of isotope fractionation factors on estimating g(m) . © 2011 Blackwell Publishing Ltd.
Linear Discriminant Analysis on a Spreadsheet.

ERIC Educational Resources Information Center

Busbey, Arthur Bresnahan III

1989-01-01

Described is a software package, "Trapeze," within which a routine called LinDis can be used. Discussed are teaching methods, the linear discriminant model and equations, the LinDis worksheet, and an example. The set up for this routine is included. (CW)
Statistical classification techniques for engineering and climatic data samples

NASA Technical Reports Server (NTRS)

Temple, E. C.; Shipman, J. R.

1981-01-01

Fisher's sample linear discriminant function is modified through an appropriate alteration of the common sample variance-covariance matrix. The alteration consists of adding nonnegative values to the eigenvalues of the sample variance covariance matrix. The desired results of this modification is to increase the number of correct classifications by the new linear discriminant function over Fisher's function. This study is limited to the two-group discriminant problem.
Classification of debtor credit status and determination amount of credit risk by using linier discriminant function

NASA Astrophysics Data System (ADS)

Aidi, Muhammad Nur; Sari, Resty Indah

2012-05-01

A decision of credit that given by bank or another creditur must have a risk and it called credit risk. Credit risk is an investor's risk of loss arising from a borrower who does not make payments as promised. The substantial of credit risk can lead to losses for the banks and the debtor. To minimize this problem need a further study to identify a potential new customer before the decision given. Identification of debtor can using various approaches analysis, one of them is by using discriminant analysis. Discriminant analysis in this study are used to classify whether belonging to the debtor's good credit or bad credit. The result of this study are two discriminant functions that can identify new debtor. Before step built the discriminant function, selection of explanatory variables should be done. Purpose of selection independent variable is to choose the variable that can discriminate the group maximally. Selection variables in this study using different test, for categoric variable selection of variable using proportion chi-square test, and stepwise discriminant for numeric variable. The result of this study are two discriminant functions that can identify new debtor. The selected variables that can discriminating two groups of debtor maximally are status of existing checking account, credit history, credit amount, installment rate in percentage of disposable income, sex, age in year, other installment plans, and number of people being liable to provide maintenance. This classification produce a classification accuracy rate is good enough, that is equal to 74,70%. Debtor classification using discriminant analysis has risk level that is small enough, and it ranged beetwen 14,992% and 17,608%. Based on that credit risk rate, using discriminant analysis on the classification of credit status can be used effectively.
Linear discriminant analysis with misallocation in training samples

NASA Technical Reports Server (NTRS)

Chhikara, R. (Principal Investigator); Mckeon, J.

1982-01-01

Linear discriminant analysis for a two-class case is studied in the presence of misallocation in training samples. A general appraoch to modeling of mislocation is formulated, and the mean vectors and covariance matrices of the mixture distributions are derived. The asymptotic distribution of the discriminant boundary is obtained and the asymptotic first two moments of the two types of error rate given. Certain numerical results for the error rates are presented by considering the random and two non-random misallocation models. It is shown that when the allocation procedure for training samples is objectively formulated, the effect of misallocation on the error rates of the Bayes linear discriminant rule can almost be eliminated. If, however, this is not possible, the use of Fisher rule may be preferred over the Bayes rule.
A model for predicting sulcus-to-sulcus diameter in posterior chamber phakic intraocular lens candidates: correlation between ocular biometric parameters.

PubMed

Ghoreishi, Mohammad; Abdi-Shahshahani, Mehdi; Peyman, Alireza; Pourazizi, Mohsen

2018-02-21

The aim of this study was to determine the correlation between ocular biometric parameters and sulcus-to-sulcus (STS) diameter. This was a cross-sectional study of preoperative ocular biometry data of patients who were candidates for phakic intraocular lens (IOL) surgery. Subjects underwent ocular biometry analysis, including refraction error evaluation using an autorefractor and Orbscan topography for white-to-white (WTW) corneal diameter and measurement. Pentacam was used to perform WTW corneal diameter and measurements of minimum and maximum keratometry (K). Measurements of STS and angle-to-angle (ATA) were obtained using a 50-MHz B-mode ultrasound device. Anterior optical coherence tomography was performed for anterior chamber depth measurement. Pearson's correlation test and stepwise linear regression analysis were used to find a model to predict STS. Fifty-eight eyes of 58 patients were enrolled. Mean age ± standard deviation of sample was 28.95 ± 6.04 years. The Pearson's correlation coefficient between STS with WTW, ATA, mean K was 0.383, 0.492, and - 0.353, respectively, which was statistically significant (all P < 0.001). Using stepwise linear regression analysis, there is a statistically significant association between STS with WTW (P = 0.011) and mean K (P = 0.025). The standardized coefficient was 0.323 and - 0.284 for WTW and mean K, respectively. The stepwise linear regression analysis equation was: (STS = 9.549 + 0.518 WTW - 0.083 mean K). Based on our result, given the correlation of STS with WTW and mean K and potential of direct and essay measurement of WTW and mean K, it seems that current IOL sizing protocols could be estimating with WTW and mean K.
Relationship between COMLEX-USA scores and performance on the American Osteopathic Board of Emergency Medicine Part I certifying examination.

PubMed

Li, Feiming; Gimpel, John R; Arenson, Ethan; Song, Hao; Bates, Bruce P; Ludwin, Fredric

2014-04-01

Few studies have investigated how well scores from the Comprehensive Osteopathic Medical Licensing Examination-USA (COMLEX-USA) series predict resident outcomes, such as performance on board certification examinations. To determine how well COMLEX-USA predicts performance on the American Osteopathic Board of Emergency Medicine (AOBEM) Part I certification examination. The target study population was first-time examinees who took AOBEM Part I in 2011 and 2012 with matched performances on COMLEX-USA Level 1, Level 2-Cognitive Evaluation (CE), and Level 3. Pearson correlations were computed between AOBEM Part I first-attempt scores and COMLEX-USA performances to measure the association between these examinations. Stepwise linear regression analysis was conducted to predict AOBEM Part I scores by the 3 COMLEX-USA scores. An independent t test was conducted to compare mean COMLEX-USA performances between candidates who passed and who failed AOBEM Part I, and a stepwise logistic regression analysis was used to predict the log-odds of passing AOBEM Part I on the basis of COMLEX-USA scores. Scores from AOBEM Part I had the highest correlation with COMLEX-USA Level 3 scores (.57) and slightly lower correlation with COMLEX-USA Level 2-CE scores (.53). The lowest correlation was between AOBEM Part I and COMLEX-USA Level 1 scores (.47). According to the stepwise regression model, COMLEX-USA Level 1 and Level 2-CE scores, which residency programs often use as selection criteria, together explained 30% of variance in AOBEM Part I scores. Adding Level 3 scores explained 37% of variance. The independent t test indicated that the 397 examinees passing AOBEM Part I performed significantly better than the 54 examinees failing AOBEM Part I in all 3 COMLEX-USA levels (P<.001 for all 3 levels). The logistic regression model showed that COMLEX-USA Level 1 and Level 3 scores predicted the log-odds of passing AOBEM Part I (P=.03 and P<.001, respectively). The present study empirically supported the predictive and discriminant validities of the COMLEX-USA series in relation to the AOBEM Part I certification examination. Although residency programs may use COMLEX-USA Level 1 and Level 2-CE scores as partial criteria in selecting residents, Level 3 scores, though typically not available at the time of application, are actually the most statistically related to performances on AOBEM Part I.
Fingerprint Ridge Density as a Potential Forensic Anthropological Tool for Sex Identification.

PubMed

Dhall, Jasmine Kaur; Kapoor, Anup Kumar

2016-03-01

In cases of partial or poor print recovery and lack of database/suspect print, fingerprint evidence is generally neglected. In light of such constraints, this study was designed to examine whether ridge density can aid in narrowing down the investigation for sex identification. The study was conducted on the right-hand index digit of 245 males and 246 females belonging to the Punjabis of Delhi region. Five ridge density count areas, namely upper radial, radial, ulnar, upper ulnar, and proximal, were selected and designated. Probability of sex origin was calculated, and stepwise discriminant function analysis was performed to determine the discriminating ability of the selected areas. Females were observed with a significantly higher ridge density than males in all the five areas. Discriminant function analysis and logistic regression exhibited 96.8% and 97.4% accuracy, respectively, in sex identification. Hence, fingerprint ridge density is a potential tool for sex identification, even from partial prints. © 2015 American Academy of Forensic Sciences.
A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction.

PubMed

Chen, C P; Wan, J Z

1999-01-01

A fast learning algorithm is proposed to find an optimal weights of the flat neural networks (especially, the functional-link network). Although the flat networks are used for nonlinear function approximation, they can be formulated as linear systems. Thus, the weights of the networks can be solved easily using a linear least-square method. This formulation makes it easier to update the weights instantly for both a new added pattern and a new added enhancement node. A dynamic stepwise updating algorithm is proposed to update the weights of the system on-the-fly. The model is tested on several time-series data including an infrared laser data set, a chaotic time-series, a monthly flour price data set, and a nonlinear system identification problem. The simulation results are compared to existing models in which more complex architectures and more costly training are needed. The results indicate that the proposed model is very attractive to real-time processes.
Causal correlation of foliar biochemical concentrations with AVIRIS spectra using forced entry linear regression

NASA Technical Reports Server (NTRS)

Dawson, Terence P.; Curran, Paul J.; Kupiec, John A.

1995-01-01

A major goal of airborne imaging spectrometry is to estimate the biochemical composition of vegetation canopies from reflectance spectra. Remotely-sensed estimates of foliar biochemical concentrations of forests would provide valuable indicators of ecosystem function at regional and eventually global scales. Empirical research has shown a relationship exists between the amount of radiation reflected from absorption features and the concentration of given biochemicals in leaves and canopies (Matson et al., 1994, Johnson et al., 1994). A technique commonly used to determine which wavelengths have the strongest correlation with the biochemical of interest is unguided (stepwise) multiple regression. Wavelengths are entered into a multivariate regression equation, in their order of importance, each contributing to the reduction of the variance in the measured biochemical concentration. A significant problem with the use of stepwise regression for determining the correlation between biochemical concentration and spectra is that of 'overfitting' as there are significantly more wavebands than biochemical measurements. This could result in the selection of wavebands which may be more accurately attributable to noise or canopy effects. In addition, there is a real problem of collinearity in that the individual biochemical concentrations may covary. A strong correlation between the reflectance at a given wavelength and the concentration of a biochemical of interest, therefore, may be due to the effect of another biochemical which is closely related. Furthermore, it is not always possible to account for potentially suitable waveband omissions in the stepwise selection procedure. This concern about the suitability of stepwise regression has been identified and acknowledged in a number of recent studies (Wessman et al., 1988, Curran, 1989, Curran et al., 1992, Peterson and Hubbard, 1992, Martine and Aber, 1994, Kupiec, 1994). These studies have pointed to the lack of a physical link between wavelengths chosen by stepwise regression and the biochemical of interest, and this in turn has cast doubts on the use of imaging spectrometry for the estimation of foliar biochemical concentrations at sites distant from the training sites. To investigate this problem, an analysis was conducted on the variation in canopy biochemical concentrations and reflectance spectra using forced entry linear regression.
Scintillation decay time and pulse shape discrimination in oxygenated and deoxygenated solutions of linear alkylbenzene for the SNO+ experiment

NASA Astrophysics Data System (ADS)

O'Keeffe, H. M.; O'Sullivan, E.; Chen, M. C.

2011-06-01

The SNO+ liquid scintillator experiment is under construction in the SNOLAB facility in Canada. The success of this experiment relies upon accurate characterization of the liquid scintillator, linear alkylbenzene (LAB). In this paper, scintillation decay times for alpha and electron excitations in LAB with 2 g/L PPO are presented for both oxygenated and deoxygenated solutions. While deoxygenation is expected to improve pulse shape discrimination in liquid scintillators, it is not commonly demonstrated in the literature. This paper shows that for linear alkylbenzene, deoxygenation improves discrimination between electron and alpha excitations in the scintillator.

Impact of stepwise ablation on the biatrial substrate in patients with persistent atrial fibrillation and heart failure.

PubMed

Jones, David G; Haldar, Shouvik K; Jarman, Julian W E; Johar, Sofian; Hussain, Wajid; Markides, Vias; Wong, Tom

2013-08-01

Ablation of persistent atrial fibrillation can be challenging, often involving not only pulmonary vein isolation (PVI) but also additional linear lesions and ablation of complex fractionated electrograms (CFE). We examined the impact of stepwise ablation on a human model of advanced atrial substrate of persistent atrial fibrillation in heart failure. In 30 patients with persistent atrial fibrillation and left ventricular ejection fraction ≤35%, high-density CFE maps were recorded biatrially at baseline, in the left atrium (LA) after PVI and linear lesions (roof and mitral isthmus), and biatrially after LA CFE ablation. Surface area of CFE (mean cycle length ≤120 ms) remote to PVI and linear lesions, defined as CFE area, was reduced after PVI (18.3±12.03 to 10.2±7.1 cm(2); P<0.001) and again after linear lesions (7.7±6.5 cm(2); P=0.006). Complete mitral isthmus block predicted greater CFE reduction (P=0.02). Right atrial CFE area was reduced by LA ablation, from 25.9±14.1 to 12.9±11.8 cm(2) (P<0.001). Estimated 1-year arrhythmia-free survival was 72% after a single procedure. Incomplete linear lesion block was an independent predictor of arrhythmia recurrence (hazard ratio, 4.69; 95% confidence interval, 1.05-21.06; P=0.04). Remote LA CFE area was progressively reduced following PVI and linear lesions, and LA ablation reduced right atrial CFE area. Reduction of CFE area at sites remote from ablation would suggest either regression of the advanced atrial substrate or that these CFE were functional phenomena. Nevertheless, in an advanced atrial fibrillation substrate, linear lesions after PVI diminished the target area for CFE ablation, and complete lesions resulted in a favorable clinical outcome.
Discriminating between Graduates and Failure in the USAF Medical Laboratory Specialist School: An Explorative Approach.

DTIC Science & Technology

1981-12-01

occurred on the Introversion Scale of the NMPI. 20 A review of the use of psychological tests on MT’s was accomplished by Driver and Feeley [1974...programs, Gondek [1981] has recommended that the best pro- cedure for variable inclusion when using a stepwise procedure is to use the threshold default...values supplied by the package, since no simple rules exist for determining entry or removal thresholds for partial F’s, tolerance statistics, or any of
Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures

NASA Astrophysics Data System (ADS)

Li, Quanbao; Wei, Fajie; Zhou, Shenghan

2017-05-01

The linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.
Development and Validation of a Disease Severity Scoring Model for Pediatric Sepsis.

PubMed

Hu, Li; Zhu, Yimin; Chen, Mengshi; Li, Xun; Lu, Xiulan; Liang, Ying; Tan, Hongzhuan

2016-07-01

Multiple severity scoring systems have been devised and evaluated in adult sepsis, but a simplified scoring model for pediatric sepsis has not yet been developed. This study aimed to develop and validate a new scoring model to stratify the severity of pediatric sepsis, thus assisting the treatment of sepsis in children. Data from 634 consecutive patients who presented with sepsis at Children's hospital of Hunan province in China in 2011-2013 were analyzed, with 476 patients placed in training group and 158 patients in validation group. Stepwise discriminant analysis was used to develop the accurate discriminate model. A simplified scoring model was generated using weightings defined by the discriminate coefficients. The discriminant ability of the model was tested by receiver operating characteristic curves (ROC). The discriminant analysis showed that prothrombin time, D-dimer, total bilirubin, serum total protein, uric acid, PaO2/FiO2 ratio, myoglobin were associated with severity of sepsis. These seven variables were assigned with values of 4, 3, 3, 4, 3, 3, 3 respectively based on the standardized discriminant coefficients. Patients with higher scores had higher risk of severe sepsis. The areas under ROC (AROC) were 0.836 for accurate discriminate model, and 0.825 for simplified scoring model in validation group. The proposed disease severity scoring model for pediatric sepsis showed adequate discriminatory capacity and sufficient accuracy, which has important clinical significance in evaluating the severity of pediatric sepsis and predicting its progress.
The mean-square error optimal linear discriminant function and its application to incomplete data vectors

NASA Technical Reports Server (NTRS)

Walker, H. F.

1979-01-01

In many pattern recognition problems, data vectors are classified although one or more of the data vector elements are missing. This problem occurs in remote sensing when the ground is obscured by clouds. Optimal linear discrimination procedures for classifying imcomplete data vectors are discussed.
Influence of genetic discrimination perceptions and knowledge on cancer genetics referral practice among clinicians.

PubMed

Lowstuter, Katrina J; Sand, Sharon; Blazer, Kathleen R; MacDonald, Deborah J; Banks, Kimberly C; Lee, Carol A; Schwerin, Barbara U; Juarez, Margaret; Uman, Gwen C; Weitzel, Jeffrey N

2008-09-01

To describe nongenetics clinicians' perceptions and knowledge of cancer genetics and laws prohibiting genetic discrimination, attitudes toward the use of cancer genetic testing, and referral practices. Invitations to participate were sent to a random stratified sample of California Medical Association members and to all members of California Association of Nurse Practitioners and California Latino Medical Association. Responders in active practice were eligible and completed a 47-item survey. There were 1181 qualified participants (62% physicians). Although 96% viewed genetic testing as beneficial for their patients, 75% believed fear of genetic discrimination would cause patients to decline testing. More than 60% were not aware of federal or California laws prohibiting health insurance discrimination--concern about genetic discrimination was selected as a reason for nonreferral by 11%. A positive attitude toward genetic testing was the strongest predictor of referral (odds ratio: 3.55 [95% confidence interval: 2.24-5.63], P < 0.001) in stepwise logistic regression analyses. The higher the belief in genetic discrimination, the less likely a participant was to refer (odds ratio: 0.72 [95% confidence interval: 0.518-0.991], P < 0.05), whereas more knowledge of genetic discrimination law was associated with comfort recommending (odds ratio: 1.18 [95% confidence interval: 1.11-1.25], P < 0.001) and actual referral (odds ratio: 3.55 [95% confidence interval: 2.24-5.63], P < 0.001). Concerns about genetic discrimination and knowledge deficits may be barriers to cancer genetics referrals. Clinician education may help promote access to cancer screening and prevention.
A discriminant function model as an alternative method to spirometry for COPD screening in primary care settings in China.

PubMed

Cui, Jiangyu; Zhou, Yumin; Tian, Jia; Wang, Xinwang; Zheng, Jingping; Zhong, Nanshan; Ran, Pixin

2012-12-01

COPD is often underdiagnosed in a primary care setting where the spirometry is unavailable. This study was aimed to develop a simple, economical and applicable model for COPD screening in those settings. First we established a discriminant function model based on Bayes' Rule by stepwise discriminant analysis, using the data from 243 COPD patients and 112 non-COPD subjects from our COPD survey in urban and rural communities and local primary care settings in Guangdong Province, China. We then used this model to discriminate COPD in additional 150 subjects (50 non-COPD and 100 COPD ones) who had been recruited by the same methods as used to have established the model. All participants completed pre- and post-bronchodilator spirometry and questionnaires. COPD was diagnosed according to the Global Initiative for Chronic Obstructive Lung Disease criteria. The sensitivity and specificity of the discriminant function model was assessed. THE ESTABLISHED DISCRIMINANT FUNCTION MODEL INCLUDED NINE VARIABLES: age, gender, smoking index, body mass index, occupational exposure, living environment, wheezing, cough and dyspnoea. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, accuracy and error rate of the function model to discriminate COPD were 89.00%, 82.00%, 4.94, 0.13, 86.66% and 13.34%, respectively. The accuracy and Kappa value of the function model to predict COPD stages were 70% and 0.61 (95% CI, 0.50 to 0.71). This discriminant function model may be used for COPD screening in primary care settings in China as an alternative option instead of spirometry.
Third-Degree Price Discrimination Revisited

ERIC Educational Resources Information Center

Kwon, Youngsun

2006-01-01

The author derives the probability that price discrimination improves social welfare, using a simple model of third-degree price discrimination assuming two independent linear demands. The probability that price discrimination raises social welfare increases as the preferences or incomes of consumer groups become more heterogeneous. He derives the…
Porphinogen Formation from the Co-Oligomerization of Formaldehyde and Pyrrole: Free Energy Pathways.

PubMed

Kua, Jeremy; Loli, Helen

2017-10-26

We have investigated the nonoxidative stepwise co-oligomerization of formaldehyde and pyrrole to form porphinogen using density functional theory calculations that include free energy corrections. While the addition of formaldehyde to the pyrrole nitrogen is kinetically favored, thermodynamics suggest that this reaction is reversible in aqueous solution. The more thermodynamically favorable addition of formaldehyde to the ortho-carbon of pyrrole begins a stepwise process, forming dipyrromethane via an azafulvene intermediate. Subsequent additions of formaldehyde and pyrrole lead to bilanes (linear tetrapyrroles), which favorably cyclize to form porphinogen. Porphinogen is a precursor to porphin, the simplest unsubstituted porphyrin that could have played a role in primitive metabolism at the origin of life.
[Development of the Spanish brief-version of the University of California Performance Skills Assessment (Sp-UPSA-Brief) in patients with schizophrenia and bipolar disorder].

PubMed

Garcia-Portilla, María Paz; Gomar, Jesús; Bobes-Bascaran, María Teresa; Menendez-Miranda, Isabel; Saiz, Pilar Alejandra; Muñiz, José; Arango, Celso; Patterson, Thomas; Harvey, Philip; Bobes, Julio; Goldberg, Terry

2014-01-01

In patients with severe mental disorders outcome measurement should include symptoms, cognition, functioning and quality of life at least. Shorter and efficient instruments have greater potential for pragmatic and valid clinical utility. Our aim was to develop the Spanish UPSA Brief scale (Sp-UPSA-Brief). Naturalistic, 6-month follow-up, multicentre study. 139 patients with schizophrenia, 57 with bipolar disorder and 31 controls were evaluated using the Sp-UPSA, CGI-S, GAF, and PSP. We conducted a multivariate linear regression model to identify candidate subscales for the Sp-UPSA-Brief. The stepwise regression model for patients with schizophrenia showed that communication and transportation Sp-UPSA subscales entered first and second at p<0.0001 (R(2)=0.88, model df=2, F=395.05). In patients with bipolar disorder transportation and communication Sp-UPSA subscales entered first and second at p<0.0001 (R(2)=0.87, model df=2, F=132.32). Cronbach's alpha was 0.78 in schizophrenia and 0.64 in bipolar patients. Test-retest was 0.66 and 0.64 (p<0.0001) respectively. Pearson correlation coefficients between Sp-UPSA and Sp-UPSA-Brief were 0.93 for schizophrenia and 0.92 for bipolar patients (p<0.0001).The Sp-UPSA-Brief discriminated between patients and controls. In schizophrenia patients it also discriminated among different levels of illness severity according to CGI-S scores. The Sp-UPSA-Brief is an alternate instrument to evaluate functional capacity that is valid and reliable. Having a shorter instrument makes it more feasible to assess functional capacity in patients with severe mental disorders, especially in everyday clinical practice. Copyright © 2013 SEP y SEPB. Published by Elsevier España. All rights reserved.
Comparing success levels of different neural network structures in extracting discriminative information from the response patterns of a temperature-modulated resistive gas sensor

NASA Astrophysics Data System (ADS)

Hosseini-Golgoo, S. M.; Bozorgi, H.; Saberkari, A.

2015-06-01

Performances of three neural networks, consisting of a multi-layer perceptron, a radial basis function, and a neuro-fuzzy network with local linear model tree training algorithm, in modeling and extracting discriminative features from the response patterns of a temperature-modulated resistive gas sensor are quantitatively compared. For response pattern recording, a voltage staircase containing five steps each with a 20 s plateau is applied to the micro-heater of the sensor, when 12 different target gases, each at 11 concentration levels, are present. In each test, the hidden layer neuron weights are taken as the discriminatory feature vector of the target gas. These vectors are then mapped to a 3D feature space using linear discriminant analysis. The discriminative information content of the feature vectors are determined by the calculation of the Fisher’s discriminant ratio, affording quantitative comparison among the success rates achieved by the different neural network structures. The results demonstrate a superior discrimination ratio for features extracted from local linear neuro-fuzzy and radial-basis-function networks with recognition rates of 96.27% and 90.74%, respectively.
Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition.

PubMed

Wang, Zhengjue; Wang, Yinghua; Liu, Hongwei; Zhang, Hao

2017-06-21

In this paper, we propose a new discriminative non-linear dictionary learning approach, called correlation constrained structured kernel KSVD, for object recognition. The objective function for dictionary learning contains a reconstructive term and a discriminative term. In the reconstructive term, signals are implicitly non-linearly mapped into a space, where a structured kernel dictionary, each sub-dictionary of which lies in the span of the mapped signals from the corresponding class, is established. In the discriminative term, by analyzing the classification mechanism, the correlation constraint is proposed in kernel form, constraining the correlations between different discriminative codes, and restricting the coefficient vectors to be transformed into a feature space, where the features are highly correlated inner-class and nearly independent between-classes. The objective function is optimized by the proposed structured kernel KSVD. During the classification stage, the specific form of the discriminative feature is needless to be known, while the inner product of the discriminative feature with kernel matrix embedded is available, and is suitable for a linear SVM classifier. Experimental results demonstrate that the proposed approach outperforms many state-of-the-art dictionary learning approaches for face, scene and synthetic aperture radar (SAR) vehicle target recognition.
Identification of wheat varieties with a parallel-plate capacitance sensor using fisher linear discriminant analysis

USDA-ARS?s Scientific Manuscript database

Fisher’s linear discriminant (FLD) models for wheat variety classification were developed and validated. The inputs to the FLD models were the capacitance (C), impedance (Z), and phase angle ('), measured at two frequencies. Classification of wheat varieties was obtained as output of the FLD mod...
A novel simple QSAR model for the prediction of anti-HIV activity using multiple linear regression analysis.

PubMed

Afantitis, Antreas; Melagraki, Georgia; Sarimveis, Haralambos; Koutentis, Panayiotis A; Markopoulos, John; Igglessi-Markopoulou, Olga

2006-08-01

A quantitative-structure activity relationship was obtained by applying Multiple Linear Regression Analysis to a series of 80 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine (HEPT) derivatives with significant anti-HIV activity. For the selection of the best among 37 different descriptors, the Elimination Selection Stepwise Regression Method (ES-SWR) was utilized. The resulting QSAR model (R (2) (CV) = 0.8160; S (PRESS) = 0.5680) proved to be very accurate both in training and predictive stages.
Robust linear discriminant analysis with distance based estimators

NASA Astrophysics Data System (ADS)

Lim, Yai-Fung; Yahaya, Sharipah Soaad Syed; Ali, Hazlina

2017-11-01

Linear discriminant analysis (LDA) is one of the supervised classification techniques concerning relationship between a categorical variable and a set of continuous variables. The main objective of LDA is to create a function to distinguish between populations and allocating future observations to previously defined populations. Under the assumptions of normality and homoscedasticity, the LDA yields optimal linear discriminant rule (LDR) between two or more groups. However, the optimality of LDA highly relies on the sample mean and pooled sample covariance matrix which are known to be sensitive to outliers. To alleviate these conflicts, a new robust LDA using distance based estimators known as minimum variance vector (MVV) has been proposed in this study. The MVV estimators were used to substitute the classical sample mean and classical sample covariance to form a robust linear discriminant rule (RLDR). Simulation and real data study were conducted to examine on the performance of the proposed RLDR measured in terms of misclassification error rates. The computational result showed that the proposed RLDR is better than the classical LDR and was comparable with the existing robust LDR.
Classifying northern forests using Thematic Mapper Simulator data

NASA Technical Reports Server (NTRS)

Nelson, R. F.; Latty, R. S.; Mott, G.

1984-01-01

Thematic Mapper Simulator data were collected over a 23,200 hectare forested area near Baxter State Park in north-central Maine. Photointerpreted ground reference information was used to drive a stratified random sampling procedure for waveband discriminant analyses and to generate training statistics and test pixel accuracies. Stepwise discriminant analyses indicated that the following bands best differentiated the thirteen level II - III cover types (in order of entry): near infrared (0.77 to 0.90 micron), blue (0.46 0.52 micron), first middle infrared (1.53 to 1.73 microns), second middle infrared (2.06 to 2.33 microsn), red (0.63 to 0.69 micron), thermal (10.32 to 12.33 microns). Classification accuracies peaked at 58 percent for thirteen level II-III land-cover classes and at 65 percent for ten level II classes.
Factors associated with cane use among community dwelling older adults.

PubMed

Aminzadeh, F; Edwards, N

2000-01-01

Guided by the Theory of Planned Behavior (TPB), this study examined factors associated with cane use among community dwelling older adults. Data were collected in a cross-sectional survey of a convenience sample of 106 community residing older adults in Ottawa, Canada. Using a stepwise discriminant analysis, subjective norms, attitudes, and age surfaced as the key variables associated with cane use in this sample. The discriminant function accounted for 67% of the variance in cane use and correctly classified 91% of cases (Wilks's lambda = 0.33, lambda2 = 110.12, df = 3, p < 0.0001). The findings provide evidence for the utility of the TPB in its application to understanding cane use behaviors of older persons and have important implications for the design of theory-based fall prevention interventions to enhance the acceptance and effective use of mobility aids.
Improved pulse shape discriminator for fast neutron-gamma ray detection system

NASA Technical Reports Server (NTRS)

Lockwood, J. A.; St. Onge, R.

1969-01-01

Discriminator in nuclear particle detection system distinguishes nuclear particle type and energy among many different nuclear particles. Discriminator incorporates passive, linear circuit elements so that it will operate over a wide dynamic range.
[Fast optimization of stepwise gradient conditions for ternary mobile phase in reversed-phase high performance liquid chromatography].

PubMed

Shan, Yi-chu; Zhang, Yu-kui; Zhao, Rui-huan

2002-07-01

In high performance liquid chromatography, it is necessary to apply multi-composition gradient elution for the separation of complex samples such as environmental and biological samples. Multivariate stepwise gradient elution is one of the most efficient elution modes, because it combines the high selectivity of multi-composition mobile phase and shorter analysis time of gradient elution. In practical separations, the separation selectivity of samples can be effectively adjusted by using ternary mobile phase. For the optimization of these parameters, the retention equation of samples must be obtained at first. Traditionally, several isocratic experiments are used to get the retention equation of solute. However, it is time consuming especially for the separation of complex samples with a wide range of polarity. A new method for the fast optimization of ternary stepwise gradient elution was proposed based on the migration rule of solute in column. First, the coefficients of retention equation of solute are obtained by running several linear gradient experiments, then the optimal separation conditions are searched according to the hierarchical chromatography response function which acts as the optimization criterion. For each kind of organic modifier, two initial linear gradient experiments are used to obtain the primary coefficients of retention equation of each solute. For ternary mobile phase, only four linear gradient runs are needed to get the coefficients of retention equation. Then the retention times of solutes under arbitrary mobile phase composition can be predicted. The initial optimal mobile phase composition is obtained by resolution mapping for all of the solutes. A hierarchical chromatography response function is used to evaluate the separation efficiencies and search the optimal elution conditions. In subsequent optimization, the migrating distance of solute in the column is considered to decide the mobile phase composition and sustaining time of the latter steps until all the solutes are eluted out. Thus the first stepwise gradient elution conditions are predicted. If the resolution of samples under the predicted optimal separation conditions is satisfactory, the optimization procedure is stopped; otherwise, the coefficients of retention equation are adjusted according to the experimental results under the previously predicted elution conditions. Then the new stepwise gradient elution conditions are predicted repeatedly until satisfactory resolution is obtained. Normally, the satisfactory separation conditions can be found only after six experiments by using the proposed method. In comparison with the traditional optimization method, the time needed to finish the optimization procedure can be greatly reduced. The method has been validated by its application to the separation of several samples such as amino acid derivatives, aromatic amines, in which satisfactory separations were obtained with predicted resolution.
Kernel PLS-SVC for Linear and Nonlinear Discrimination

NASA Technical Reports Server (NTRS)

Rosipal, Roman; Trejo, Leonard J.; Matthews, Bryan

2003-01-01

A new methodology for discrimination is proposed. This is based on kernel orthonormalized partial least squares (PLS) dimensionality reduction of the original data space followed by support vector machines for classification. Close connection of orthonormalized PLS and Fisher's approach to linear discrimination or equivalently with canonical correlation analysis is described. This gives preference to use orthonormalized PLS over principal component analysis. Good behavior of the proposed method is demonstrated on 13 different benchmark data sets and on the real world problem of the classification finger movement periods versus non-movement periods based on electroencephalogram.

Analysis of intraspecific seed diversity in Astragalus aquilanus (Fabaceae), an endemic species of Central Apennine.

PubMed

Di Cecco, V; Di Musciano, M; D'Archivio, A A; Frattaroli, A R; Di Martino, L

2018-05-20

This work aims to study seeds of the endemic species Astragalus aquilanus from four different populations of central Italy. We investigated seed morpho-colorimetric features (shape and size) and chemical differences (through infrared spectroscopy) among populations and between dark and light seeds. Seed morpho-colorimetric quantitative variables, describing shape, size and colour traits, were measured using image analysis techniques. Fourier transform infrared (FT-IR) spectroscopy was used to attempt seed chemical characterisation. The measured data were analysed by step-wise linear discriminant analysis (LDA). Moreover, we analysed the correlation between the four most important traits and six climatic variables extracted from WorldClim 2.0. The LDA on seeds traits shows clear differentiation of the four populations, which can be attributed to different chemical composition, as confirmed by Wilk's lambda test (P < 0.001). A strong correlation between morphometric traits and temperature (annual mean temperature, mean temperature of the warmest and coolest quarter), colorimetric traits and precipitation (annual precipitation, precipitation of wettest and driest quarter) was observed. The characterisation of A. aquilanus seeds shows large intraspecific plasticity both in morpho-colorimetric and chemical composition. These results confirm the strong relationship between the type of seed produced and the climatic variables. © 2018 German Society for Plant Sciences and The Royal Botanical Society of the Netherlands.
Manipulating attention via mindfulness induction improves P300-based brain-computer interface performance

NASA Astrophysics Data System (ADS)

Lakey, Chad E.; Berry, Daniel R.; Sellers, Eric W.

2011-04-01

In this study, we examined the effects of a short mindfulness meditation induction (MMI) on the performance of a P300-based brain-computer interface (BCI) task. We expected that MMI would harness present-moment attentional resources, resulting in two positive consequences for P300-based BCI use. Specifically, we believed that MMI would facilitate increases in task accuracy and promote the production of robust P300 amplitudes. Sixteen-channel electroencephalographic data were recorded from 18 subjects using a row/column speller task paradigm. Nine subjects participated in a 6 min MMI and an additional nine subjects served as a control group. Subjects were presented with a 6 × 6 matrix of alphanumeric characters on a computer monitor. Stimuli were flashed at a stimulus onset asynchrony (SOA) of 125 ms. Calibration data were collected on 21 items without providing feedback. These data were used to derive a stepwise linear discriminate analysis classifier that was applied to an additional 14 items to evaluate accuracy. Offline performance analyses revealed that MMI subjects were significantly more accurate than control subjects. Likewise, MMI subjects produced significantly larger P300 amplitudes than control subjects at Cz and PO7. The discussion focuses on the potential attentional benefits of MMI for P300-based BCI performance.
Multivariate class modeling techniques applied to multielement analysis for the verification of the geographical origin of chili pepper.

PubMed

Naccarato, Attilio; Furia, Emilia; Sindona, Giovanni; Tagarelli, Antonio

2016-09-01

Four class-modeling techniques (soft independent modeling of class analogy (SIMCA), unequal dispersed classes (UNEQ), potential functions (PF), and multivariate range modeling (MRM)) were applied to multielement distribution to build chemometric models able to authenticate chili pepper samples grown in Calabria respect to those grown outside of Calabria. The multivariate techniques were applied by considering both all the variables (32 elements, Al, As, Ba, Ca, Cd, Ce, Co, Cr, Cs, Cu, Dy, Fe, Ga, La, Li, Mg, Mn, Na, Nd, Ni, Pb, Pr, Rb, Sc, Se, Sr, Tl, Tm, V, Y, Yb, Zn) and variables selected by means of stepwise linear discriminant analysis (S-LDA). In the first case, satisfactory and comparable results in terms of CV efficiency are obtained with the use of SIMCA and MRM (82.3 and 83.2% respectively), whereas MRM performs better than SIMCA in terms of forced model efficiency (96.5%). The selection of variables by S-LDA permitted to build models characterized, in general, by a higher efficiency. MRM provided again the best results for CV efficiency (87.7% with an effective balance of sensitivity and specificity) as well as forced model efficiency (96.5%). Copyright © 2016 Elsevier Ltd. All rights reserved.
Relationship between self-reported upper limb disability and quantitative tests in hand-arm vibration syndrome.

PubMed

Poole, Kerry; Mason, Howard

2007-03-15

To establish the relationship between quantitative tests of hand function and upper limb disability, as measured by the Disability of the Arm, Shoulder and Hand (DASH) questionnaire, in hand-arm vibration syndrome (HAVS). A total of 228 individuals with HAVS were included in this study. Each had undergone a full HAVS assessment by an experienced physician, including quantitative tests of vibrotactile and thermal perception thresholds, maximal hand-grip strength (HG) and the Purdue pegboard (PP) test. Individuals were also asked to complete a DASH questionnaire. PP and HG of the quantitative tests gave the best and statistically significant individual correlations with the DASH disability score (r2 = 0.168 and 0.096). Stepwise linear regression analysis revealed that only PP and HG measurements were statistically significant predictors of upper limb disability (r2 = 0.178). Overall a combination of the PP and HG measurements, rather than each alone, gave slightly better discrimination, although not statistically significant, between normal and abnormal DASH scores with a sensitivity of 73.1% and specificity of 64.3%. Measurements of manual dexterity and hand-grip strength using PP and HG may be useful in helping to confirm lack of upper limb function and 'perceived' disability in HAVS.
A visual parallel-BCI speller based on the time-frequency coding strategy

NASA Astrophysics Data System (ADS)

Xu, Minpeng; Chen, Long; Zhang, Lixin; Qi, Hongzhi; Ma, Lan; Tang, Jiabei; Wan, Baikun; Ming, Dong

2014-04-01

Objective. Spelling is one of the most important issues in brain-computer interface (BCI) research. This paper is to develop a visual parallel-BCI speller system based on the time-frequency coding strategy in which the sub-speller switching among four simultaneously presented sub-spellers and the character selection are identified in a parallel mode. Approach. The parallel-BCI speller was constituted by four independent P300+SSVEP-B (P300 plus SSVEP blocking) spellers with different flicker frequencies, thereby all characters had a specific time-frequency code. To verify its effectiveness, 11 subjects were involved in the offline and online spellings. A classification strategy was designed to recognize the target character through jointly using the canonical correlation analysis and stepwise linear discriminant analysis. Main results. Online spellings showed that the proposed parallel-BCI speller had a high performance, reaching the highest information transfer rate of 67.4 bit min-1, with an average of 54.0 bit min-1 and 43.0 bit min-1 in the three rounds and five rounds, respectively. Significance. The results indicated that the proposed parallel-BCI could be effectively controlled by users with attention shifting fluently among the sub-spellers, and highly improved the BCI spelling performance.
Towards a truly mobile auditory brain-computer interface: exploring the P300 to take away.

PubMed

De Vos, Maarten; Gandras, Katharina; Debener, Stefan

2014-01-01

In a previous study we presented a low-cost, small, and wireless 14-channel EEG system suitable for field recordings (Debener et al., 2012, psychophysiology). In the present follow-up study we investigated whether a single-trial P300 response can be reliably measured with this system, while subjects freely walk outdoors. Twenty healthy participants performed a three-class auditory oddball task, which included rare target and non-target distractor stimuli presented with equal probabilities of 16%. Data were recorded in a seated (control condition) and in a walking condition, both of which were realized outdoors. A significantly larger P300 event-related potential amplitude was evident for targets compared to distractors (p<.001), but no significant interaction with recording condition emerged. P300 single-trial analysis was performed with regularized stepwise linear discriminant analysis and revealed above chance-level classification accuracies for most participants (19 out of 20 for the seated, 16 out of 20 for the walking condition), with mean classification accuracies of 71% (seated) and 64% (walking). Moreover, the resulting information transfer rates for the seated and walking conditions were comparable to a recently published laboratory auditory brain-computer interface (BCI) study. This leads us to conclude that a truly mobile auditory BCI system is feasible. © 2013.
A Hybrid Sensing Approach for Pure and Adulterated Honey Classification

PubMed Central

Subari, Norazian; Saleh, Junita Mohamad; Shakaff, Ali Yeon Md; Zakaria, Ammar

2012-01-01

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. PMID:23202033
Metallic content of wines from the Canary Islands (Spain). Application of artificial neural networks to the data analysis.

PubMed

Frías, Sergio; Conde, José E; Rodríguez, Miguel A; Dohnal, Vlasta; Pérez-Trujillo, Juan P

2002-10-01

Eleven elements, K, Na, Ca, Mg, Fe, Cu, Zn, Mn, Sr, Li and Rb, were determined in dry and sweet wines bearing the denominations of origin of El Hierro, La Palma and Lanzarote islands (Canary Islands, Spain). Analyses were performed by flame atomic absorption spectrophotometry, with the exceptions of Li and Rb for which flame atomic emission spectrophotometry was used. The content in copper and iron did not present risks of cases. All samples presented a copper and zinc content below the maximum amount recommended by the Office International de la Vigne et du Vin (OIV) for these elements. Significant differences in the metallic content were found among the different islands. Thus, Lanzarote presented the highest mean content in sodium and lithium and the lowest mean content in rubidium, and La Palma presented the highest mean content in strontium and rubidium. Sweet wines from La Palma, elaborated as naturally sweet with over-ripe grapes, presented mean contents significantly higher with regard to dry wines from the same island in the majority of the analysed elements. Cluster analysis and Kohonen self-organising maps showed differences in wines according to the island of origin and the ripening state of the grapes. Back-propagation artificial neural networks showed better prediction ability than stepwise linear discriminant analysis.
Label-free capacitive immunosensor based on quartz crystal Au electrode for rapid and sensitive detection of Escherichia coli O157:H7.

PubMed

Li, Dujuan; Feng, Yangyang; Zhou, Ling; Ye, Zunzhong; Wang, Jianping; Ying, Yibin; Ruan, Chuanmin; Wang, Ronghui; Li, Yanbin

2011-02-14

A label-free capacitive immunosensor based on quartz crystal Au electrode was developed for rapid and sensitive detection of Escherichia coli O157:H7. The immunosensor was fabricated by immobilizing affinity-purified anti-E. coli O157:H7 antibodies onto self-assembled monolayers (SAMs) of 3-mercaptopropionic acid (MPA) on the surface of a quartz crystal Au electrode. Bacteria suspended in solution became attached to the immobilized antibodies when the immunosensor was tested in liquid samples. The change in capacitance caused by the bacteria was directly measured by an electrochemical detector. An equivalent circuit was introduced to simulate the capacitive immunosensor. The immunosensor was evaluated for E. coli O157:H7 detection in pure culture and inoculated food samples. The experimental results indicated that the capacitance change was linearly correlated with the cell concentration of E. coli O157:H7. The immunosensor was able to discriminate between cellular concentrations of 10(2)-10(5) cfu mL(-1) and has applications in detecting pathogens in food samples. Cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) were also employed to characterize the stepwise assembly of the immunosensor. Copyright Â© 2010 Elsevier B.V. All rights reserved.
Endometrial cancer risk prediction including serum-based biomarkers: results from the EPIC cohort.

PubMed

Fortner, Renée T; Hüsing, Anika; Kühn, Tilman; Konar, Meric; Overvad, Kim; Tjønneland, Anne; Hansen, Louise; Boutron-Ruault, Marie-Christine; Severi, Gianluca; Fournier, Agnès; Boeing, Heiner; Trichopoulou, Antonia; Benetou, Vasiliki; Orfanos, Philippos; Masala, Giovanna; Agnoli, Claudia; Mattiello, Amalia; Tumino, Rosario; Sacerdote, Carlotta; Bueno-de-Mesquita, H B As; Peeters, Petra H M; Weiderpass, Elisabete; Gram, Inger T; Gavrilyuk, Oxana; Quirós, J Ramón; Maria Huerta, José; Ardanaz, Eva; Larrañaga, Nerea; Lujan-Barroso, Leila; Sánchez-Cantalejo, Emilio; Butt, Salma Tunå; Borgquist, Signe; Idahl, Annika; Lundin, Eva; Khaw, Kay-Tee; Allen, Naomi E; Rinaldi, Sabina; Dossus, Laure; Gunter, Marc; Merritt, Melissa A; Tzoulaki, Ioanna; Riboli, Elio; Kaaks, Rudolf

2017-03-15

Endometrial cancer risk prediction models including lifestyle, anthropometric and reproductive factors have limited discrimination. Adding biomarker data to these models may improve predictive capacity; to our knowledge, this has not been investigated for endometrial cancer. Using a nested case-control study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, we investigated the improvement in discrimination gained by adding serum biomarker concentrations to risk estimates derived from an existing risk prediction model based on epidemiologic factors. Serum concentrations of sex steroid hormones, metabolic markers, growth factors, adipokines and cytokines were evaluated in a step-wise backward selection process; biomarkers were retained at p < 0.157 indicating improvement in the Akaike information criterion (AIC). Improvement in discrimination was assessed using the C-statistic for all biomarkers alone, and change in C-statistic from addition of biomarkers to preexisting absolute risk estimates. We used internal validation with bootstrapping (1000-fold) to adjust for over-fitting. Adiponectin, estrone, interleukin-1 receptor antagonist, tumor necrosis factor-alpha and triglycerides were selected into the model. After accounting for over-fitting, discrimination was improved by 2.0 percentage points when all evaluated biomarkers were included and 1.7 percentage points in the model including the selected biomarkers. Models including etiologic markers on independent pathways and genetic markers may further improve discrimination. © 2016 UICC.
Variables Associated with Communicative Participation in People with Multiple Sclerosis: A Regression Analysis

ERIC Educational Resources Information Center

Baylor, Carolyn; Yorkston, Kathryn; Bamer, Alyssa; Britton, Deanna; Amtmann, Dagmar

2010-01-01

Purpose: To explore variables associated with self-reported communicative participation in a sample (n = 498) of community-dwelling adults with multiple sclerosis (MS). Method: A battery of questionnaires was administered online or on paper per participant preference. Data were analyzed using multiple linear backward stepwise regression. The…
Variation in Environmentalism among University Students: Majoring in Outdoor Recreation, Parks, and Tourism Predicts Environmental Concerns and Behaviors

ERIC Educational Resources Information Center

Arnocky, Steven; Stroink, Mirella L.

2011-01-01

In a survey of Canadian university students (N = 205), the relationship between majoring in an outdoor recreation university program and environmental concern, cooperation, and behavior were examined. Stepwise linear regression indicated that enrollment in outdoor recreation was predictive of environmental behavior and ecological cooperation; and…
A 4-gene panel as a marker at chromosome 8q in Asian gastric cancer patients.

PubMed

Cheng, Lei; Zhang, Qing; Yang, Sheng; Yang, Yanqing; Zhang, Wen; Gao, Hengjun; Deng, Xiaxing; Zhang, Qinghua

2013-10-01

A widely held viewpoint is that the use of multiple markers, combined in some type of algorithm, will be necessary to provide high enough discrimination between diseased cases and non-diseased. We applied stepwise logistic regression analysis to identify the best combination of the 32 biomarkers at chromosome 8q on an independent public microarray test set of 80 paired gastric samples. A combination of SULF1, INTS8, ATP6V1C1, and GPR172A was identified with a prediction accuracy of 98.0% for discriminating carcinomas from adjacent noncancerous tissues in our previous 25 paired samples. Interestingly, the overexpression of SULF1 was associated with tumor invasion and metastasis. Function prediction analysis revealed that the 4-marker panel was mainly associated with acidification of intracellular compartments. Taken together, we found a 4-gene panel that accurately discriminated gastric carcinomas from adjacent noncancerous tissues and these results had potential clinical significance in the early diagnosis and targeted treatment of gastric cancer. Copyright © 2013 Elsevier Inc. All rights reserved.
A multidisciplinary selection model for youth soccer: the Ghent Youth Soccer Project

PubMed Central

Vaeyens, R; Malina, R M; Janssens, M; Van Renterghem, B; Bourgois, J; Vrijens, J; Philippaerts, R M

2006-01-01

Objectives To determine the relationships between physical and performance characteristics and level of skill in youth soccer players aged 12–16 years. Methods Anthropometry, maturity status, functional and sport‐specific parameters were assessed in elite, sub‐elite, and non‐elite youth players in four age groups: U13 (n = 117), U14 (n = 136), U15 (n = 138) and U16 (n = 99). Results Multivariate analyses of covariance by age group with maturity status as the covariate showed that elite players scored better than the non‐elite players on strength, flexibility, speed, aerobic endurance, anaerobic capacity and several technical skills (p<0.05). Stepwise discriminant analyses showed that running speed and technical skills were the most important characteristics in U13 and U14 players, while cardiorespiratory endurance was more important in U15 and U16 players. The results suggest that discriminating characteristics change with competitive age levels. Conclusions Characteristics that discriminate youth soccer players vary by age group. Talent identification models should thus be dynamic and provide opportunities for changing parameters in a long‐term developmental context. PMID:16980535
A geobotanical investigation based on linear discriminant and profile analyses of airborne Thematic Mapper Simulator data

NASA Technical Reports Server (NTRS)

Schwaller, Mathew R.

1987-01-01

This paper discusses the application of linear discriminant and profile analyses to detailed investigation of an airborne Thematic Mapper Simulator (TMS) image collected over a geobotanical test site. The test site was located on the Keweenaw Peninsula of Michigan's Upper Peninsula, and remote sensing data collection coincided with the onset of leaf senescence in the regional deciduous flora. Linear discriminant analysis revealed that sites overlying soil geochemical anomalies were distinguishable from background sites by the reflectance and thermal emittance of the tree canopy imaged in the airborne TMS data. The correlation of individual bands with the linear discriminant function suggested that the TMS thermal Channel 7 (10.32-12.33 microns) contributed most, while TMS Bands 2 (0.53-0.60 microns), 3 (0.63-0.69 microns), and 5 (1.53-1.73 microns) contributed somewhat more modestly to the separation of anomalous and background sites imaged by the TMS. The observed changes in canopy reflectance and thermal emittance of the deciduous flora overlying geochemically anomalous areas are consistent with the biophysical changes which are known or presumed to occur as a result of injury induced in metal-stressed vegetation.
Joint Entropy for Space and Spatial Frequency Domains Estimated from Psychometric Functions of Achromatic Discrimination

PubMed Central

Silveira, Vladímir de Aquino; Souza, Givago da Silva; Gomes, Bruno Duarte; Rodrigues, Anderson Raiol; Silveira, Luiz Carlos de Lima

2014-01-01

We used psychometric functions to estimate the joint entropy for space discrimination and spatial frequency discrimination. Space discrimination was taken as discrimination of spatial extent. Seven subjects were tested. Gábor functions comprising unidimensionalsinusoidal gratings (0.4, 2, and 10 cpd) and bidimensionalGaussian envelopes (1°) were used as reference stimuli. The experiment comprised the comparison between reference and test stimulithat differed in grating's spatial frequency or envelope's standard deviation. We tested 21 different envelope's standard deviations around the reference standard deviation to study spatial extent discrimination and 19 different grating's spatial frequencies around the reference spatial frequency to study spatial frequency discrimination. Two series of psychometric functions were obtained for 2%, 5%, 10%, and 100% stimulus contrast. The psychometric function data points for spatial extent discrimination or spatial frequency discrimination were fitted with Gaussian functions using the least square method, and the spatial extent and spatial frequency entropies were estimated from the standard deviation of these Gaussian functions. Then, joint entropy was obtained by multiplying the square root of space extent entropy times the spatial frequency entropy. We compared our results to the theoretical minimum for unidimensional Gábor functions, 1/4π or 0.0796. At low and intermediate spatial frequencies and high contrasts, joint entropy reached levels below the theoretical minimum, suggesting non-linear interactions between two or more visual mechanisms. We concluded that non-linear interactions of visual pathways, such as the M and P pathways, could explain joint entropy values below the theoretical minimum at low and intermediate spatial frequencies and high contrasts. These non-linear interactions might be at work at intermediate and high contrasts at all spatial frequencies once there was a substantial decrease in joint entropy for these stimulus conditions when contrast was raised. PMID:24466158
Joint entropy for space and spatial frequency domains estimated from psychometric functions of achromatic discrimination.

PubMed

Silveira, Vladímir de Aquino; Souza, Givago da Silva; Gomes, Bruno Duarte; Rodrigues, Anderson Raiol; Silveira, Luiz Carlos de Lima

2014-01-01

We used psychometric functions to estimate the joint entropy for space discrimination and spatial frequency discrimination. Space discrimination was taken as discrimination of spatial extent. Seven subjects were tested. Gábor functions comprising unidimensionalsinusoidal gratings (0.4, 2, and 10 cpd) and bidimensionalGaussian envelopes (1°) were used as reference stimuli. The experiment comprised the comparison between reference and test stimulithat differed in grating's spatial frequency or envelope's standard deviation. We tested 21 different envelope's standard deviations around the reference standard deviation to study spatial extent discrimination and 19 different grating's spatial frequencies around the reference spatial frequency to study spatial frequency discrimination. Two series of psychometric functions were obtained for 2%, 5%, 10%, and 100% stimulus contrast. The psychometric function data points for spatial extent discrimination or spatial frequency discrimination were fitted with Gaussian functions using the least square method, and the spatial extent and spatial frequency entropies were estimated from the standard deviation of these Gaussian functions. Then, joint entropy was obtained by multiplying the square root of space extent entropy times the spatial frequency entropy. We compared our results to the theoretical minimum for unidimensional Gábor functions, 1/4π or 0.0796. At low and intermediate spatial frequencies and high contrasts, joint entropy reached levels below the theoretical minimum, suggesting non-linear interactions between two or more visual mechanisms. We concluded that non-linear interactions of visual pathways, such as the M and P pathways, could explain joint entropy values below the theoretical minimum at low and intermediate spatial frequencies and high contrasts. These non-linear interactions might be at work at intermediate and high contrasts at all spatial frequencies once there was a substantial decrease in joint entropy for these stimulus conditions when contrast was raised.
Robust L1-norm two-dimensional linear discriminant analysis.

PubMed

Li, Chun-Na; Shao, Yuan-Hai; Deng, Nai-Yang

2015-05-01

In this paper, we propose an L1-norm two-dimensional linear discriminant analysis (L1-2DLDA) with robust performance. Different from the conventional two-dimensional linear discriminant analysis with L2-norm (L2-2DLDA), where the optimization problem is transferred to a generalized eigenvalue problem, the optimization problem in our L1-2DLDA is solved by a simple justifiable iterative technique, and its convergence is guaranteed. Compared with L2-2DLDA, our L1-2DLDA is more robust to outliers and noises since the L1-norm is used. This is supported by our preliminary experiments on toy example and face datasets, which show the improvement of our L1-2DLDA over L2-2DLDA. Copyright © 2015 Elsevier Ltd. All rights reserved.
Computational technique for stepwise quantitative assessment of equation correctness

NASA Astrophysics Data System (ADS)

Othman, Nuru'l Izzah; Bakar, Zainab Abu

2017-04-01

Many of the computer-aided mathematics assessment systems that are available today possess the capability to implement stepwise correctness checking of a working scheme for solving equations. The computational technique for assessing the correctness of each response in the scheme mainly involves checking the mathematical equivalence and providing qualitative feedback. This paper presents a technique, known as the Stepwise Correctness Checking and Scoring (SCCS) technique that checks the correctness of each equation in terms of structural equivalence and provides quantitative feedback. The technique, which is based on the Multiset framework, adapts certain techniques from textual information retrieval involving tokenization, document modelling and similarity evaluation. The performance of the SCCS technique was tested using worked solutions on solving linear algebraic equations in one variable. 350 working schemes comprising of 1385 responses were collected using a marking engine prototype, which has been developed based on the technique. The results show that both the automated analytical scores and the automated overall scores generated by the marking engine exhibit high percent agreement, high correlation and high degree of agreement with manual scores with small average absolute and mixed errors.
Enhanced eumelanin emission by stepwise three-photon excitation

NASA Astrophysics Data System (ADS)

Kerimo, Josef; Rajadhyaksha, Milind; DiMarzio, Charles A.

2011-03-01

Eumelanin fluorescence from Sepia officinalis and black human hair was activated with near-infrared radiation and multiphoton excitation. A third order multiphoton absorption by a step-wise process appears to be the underlying mechanism. The activation was caused by a photochemical process since it could not be reproduced by simple heating. Both fluorescence and brightfield imaging indicate the near-infrared irradiation caused photodamage to the eumelanin and the activated emission originated from the photodamaged region. At least two different components with about thousand-fold enhanced fluorescence were activated and could be distinguished by their excitation properties. One component was excited with wavelengths in the visible region and exhibited linear absorption dependence. The second component could be excited with near-infrared wavelengths and had a third order dependence on the laser power. The third order dependence is explained by a step-wise excited state absorption (ESA) process since it could be observed equally with the CW and femtosecond lasers. The new method for photoactivating the eumelanin fluorescence was used to map the melanin content in human hair.

Sex determination from the radius and ulna in a modern South African sample.

PubMed

Barrier, I L O; L'Abbé, E N

2008-07-18

With a large number of unidentified skeletal remains found in South Africa, the development of population specific osteometric standards is imperative. Forensic anthropologists need to have access to a variety of techniques to establish accurate demographic profiles from complete, fragmentary and/or commingled remains. No research has been done on the forearm of African samples, even though these bones have been shown to exhibit sexual dimorphism. The purpose of this paper is to develop discriminant function formulae to determine sex from the radius and ulna in a South African population. The sample consisted of 200 male and 200 female skeletons from the Pretoria Bone (University of Pretoria) and Raymond A. Dart (Witwatersrand University) collections. Sixteen standard anthropometric measurements were taken from the radius (9) and ulna (7) and subjected to stepwise and direct discriminant function analysis. Distal breadth, minimum mid-shaft diameter and maximum head diameter were the best discriminators of sex for the radius, while minimum mid-shaft diameter and olecranon breadth were selected for the ulna. Classification accuracy for the forearm ranged from 76 to 86%. The radius and ulna can be considered moderate discriminators for determining sex in a South African group. However, it is advised that these formulae are used in conjunction with additional methods to determine sex.
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.
A chemiluminescence sensor array for discriminating natural sugars and artificial sweeteners.

PubMed

Niu, Weifen; Kong, Hao; Wang, He; Zhang, Yantu; Zhang, Sichun; Zhang, Xinrong

2012-01-01

In this paper, we report a chemiluminescence (CL) sensor array based on catalytic nanomaterials for the discrimination of ten sweeteners, including five natural sugars and five artificial sweeteners. The CL response patterns ("fingerprints") can be obtained for a given compound on the nanomaterial array and then identified through linear discriminant analysis (LDA). Moreover, each pure sweetener was quantified based on the emission intensities of selected sensor elements. The linear ranges for these sweeteners lie within 0.05-100 mM, but vary with the type of sweetener. The applicability of this array to real-life samples was demonstrated by applying it to various beverages, and the results showed that the sensor array possesses excellent discrimination power and reversibility.
Automated classification of maxillofacial cysts in cone beam CT images using contourlet transformation and Spherical Harmonics.

PubMed

Abdolali, Fatemeh; Zoroofi, Reza Aghaeizadeh; Otake, Yoshito; Sato, Yoshinobu

2017-02-01

Accurate detection of maxillofacial cysts is an essential step for diagnosis, monitoring and planning therapeutic intervention. Cysts can be of various sizes and shapes and existing detection methods lead to poor results. Customizing automatic detection systems to gain sufficient accuracy in clinical practice is highly challenging. For this purpose, integrating the engineering knowledge in efficient feature extraction is essential. This paper presents a novel framework for maxillofacial cysts detection. A hybrid methodology based on surface and texture information is introduced. The proposed approach consists of three main steps as follows: At first, each cystic lesion is segmented with high accuracy. Then, in the second and third steps, feature extraction and classification are performed. Contourlet and SPHARM coefficients are utilized as texture and shape features which are fed into the classifier. Two different classifiers are used in this study, i.e. support vector machine and sparse discriminant analysis. Generally SPHARM coefficients are estimated by the iterative residual fitting (IRF) algorithm which is based on stepwise regression method. In order to improve the accuracy of IRF estimation, a method based on extra orthogonalization is employed to reduce linear dependency. We have utilized a ground-truth dataset consisting of cone beam CT images of 96 patients, belonging to three maxillofacial cyst categories: radicular cyst, dentigerous cyst and keratocystic odontogenic tumor. Using orthogonalized SPHARM, residual sum of squares is decreased which leads to a more accurate estimation. Analysis of the results based on statistical measures such as specificity, sensitivity, positive predictive value and negative predictive value is reported. The classification rate of 96.48% is achieved using sparse discriminant analysis and orthogonalized SPHARM features. Classification accuracy at least improved by 8.94% with respect to conventional features. This study demonstrated that our proposed methodology can improve the computer assisted diagnosis (CAD) performance by incorporating more discriminative features. Using orthogonalized SPHARM is promising in computerized cyst detection and may have a significant impact in future CAD systems. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Gestational dating by metabolic profile at birth: a California cohort study.

PubMed

Jelliffe-Pawlowski, Laura L; Norton, Mary E; Baer, Rebecca J; Santos, Nicole; Rutherford, George W

2016-04-01

Accurate gestational dating is a critical component of obstetric and newborn care. In the absence of early ultrasound, many clinicians rely on less accurate measures, such as last menstrual period or symphysis-fundal height during pregnancy, or Dubowitz scoring or the Ballard (or New Ballard) method at birth. These measures often underestimate or overestimate gestational age and can lead to misclassification of babies as born preterm, which has both short- and long-term clinical care and public health implications. We sought to evaluate whether metabolic markers in newborns measured as part of routine screening for treatable inborn errors of metabolism can be used to develop a population-level metabolic gestational dating algorithm that is robust despite intrauterine growth restriction and can be used when fetal ultrasound dating is not available. We focused specifically on the ability of these markers to differentiate preterm births (PTBs) (<37 weeks) from term births and to assign a specific gestational age in the PTB group. We evaluated a cohort of 729,503 singleton newborns with a California birth in 2005 through 2011 who had routine newborn metabolic screening and fetal ultrasound dating at 11-20 weeks' gestation. Using training and testing subsets (divided in a ratio of 3:1) we evaluated the association among PTB, target newborn characteristics, acylcarnitines, amino acids, thyroid-stimulating hormone, 17-hydroxyprogesterone, and galactose-1-phosphate-uridyl-transferase. We used multivariate backward stepwise regression to test for associations and linear discriminate analyses to create a linear function for PTB and to assign a specific week of gestation. We used sensitivity, specificity, and positive predictive value to evaluate the performance of linear functions. Along with birthweight and infant age at test, we included 35 of the 51 metabolic markers measured in the final multivariate model comparing PTBs and term births. Using a linear discriminate analyses-derived linear function, we were able to sort PTBs and term births accurately with sensitivities and specificities of ≥95% in both the training and testing subsets. Assignment of a specific week of gestation in those identified as PTBs resulted in the correct assignment of week ±2 weeks in 89.8% of all newborns in the training and 91.7% of those in the testing subset. When PTB rates were modeled using the metabolic dating algorithm compared to fetal ultrasound, PTB rates were 7.15% vs 6.11% in the training subset and 7.31% vs 6.25% in the testing subset. When considered in combination with birthweight and hours of age at test, metabolic profile evaluated within 8 days of birth appears to be a useful measure of PTB and, among those born preterm, of specific week of gestation ±2 weeks. Dating by metabolic profile may be useful in instances where there is no fetal ultrasound due to lack of availability or late entry into care. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.
Gestational dating by metabolic profile at birth: a California cohort study

PubMed Central

Jelliffe-Pawlowski, Laura L.; Norton, Mary E.; Baer, Rebecca J.; Santos, Nicole; Rutherford, George W.

2016-01-01

Background Accurate gestational dating is a critical component of obstetric and newborn care. In the absence of early ultrasound, many clinicians rely on less accurate measures, such as last menstrual period or symphysis-fundal height during pregnancy, or Dubowitz scoring or the Ballard (or New Ballard) method at birth. These measures often underestimate or overestimate gestational age and can lead to misclassification of babies as born preterm, which has both short- and long-term clinical care and public health implications. Objective We sought to evaluate whether metabolic markers in newborns measured as part of routine screening for treatable inborn errors of metabolism can be used to develop a population-level metabolic gestational dating algorithm that is robust despite intrauterine growth restriction and can be used when fetal ultrasound dating is not available. We focused specifically on the ability of these markers to differentiate preterm births (PTBs) (<37 weeks) from term births and to assign a specific gestational age in the PTB group. Study Design We evaluated a cohort of 729,503 singleton newborns with a California birth in 2005 through 2011 who had routine newborn metabolic screening and fetal ultrasound dating at 11–20 weeks’ gestation. Using training and testing subsets (divided in a ratio of 3:1) we evaluated the association among PTB, target newborn characteristics, acylcarnitines, amino acids, thyroid-stimulating hormone, 17-hydroxyprogesterone, and galactose-1-phosphate-uridyl-transferase. We used multivariate backward stepwise regression to test for associations and linear discriminate analyses to create a linear function for PTB and to assign a specific week of gestation. We used sensitivity, specificity, and positive predictive value to evaluate the performance of linear functions. Results Along with birthweight and infant age at test, we included 35 of the 51 metabolic markers measured in the final multivariate model comparing PTBs and term births. Using a linear discriminate analyses-derived linear function, we were able to sort PTBs and term births accurately with sensitivities and specificities of ≥95% in both the training and testing subsets. Assignment of a specific week of gestation in those identified as PTBs resulted in the correct assignment of week ±2 weeks in 89.8% of all newborns in the training and 91.7% of those in the testing subset. When PTB rates were modeled using the metabolic dating algorithm compared to fetal ultrasound, PTB rates were 7.15% vs 6.11% in the training subset and 7.31% vs 6.25% in the testing subset. Conclusion When considered in combination with birthweight and hours of age at test, metabolic profile evaluated within 8 days of birth appears to be a useful measure of PTB and, among those born preterm, of specific week of gestation ±2 weeks. Dating by metabolic profile may be useful in instances where there is no fetal ultrasound due to lack of availability or late entry into care. PMID:26688490
Relative sensitivity of depth discrimination for ankle inversion and plantar flexion movements.

PubMed

Black, Georgia; Waddington, Gordon; Adams, Roger

2014-02-01

25 participants (20 women, 5 men) were tested for sensitivity in discrimination between sets of six movements centered on 8 degrees, 11 degrees, and 14 degrees, and separated by 0.3 degrees. Both inversion and plantar flexion movements were tested. Discrimination of the extent of inversion movement was observed to decline linearly with increasing depth; however, for plantar flexion, the discrimination function for movement extent was found to be non-linear. The relatively better discrimination of plantar flexion movements than inversion movements at around 11 degrees from horizontal is interpreted as an effect arising from differential amounts of practice through use, because this position is associated with the plantar flexion movement made in normal walking. The fact that plantar flexion movements are discriminated better than inversion at one region but not others argues against accounts of superior proprioceptive sensitivity for plantar flexion compared to inversion that are based on general properties of plantar flexion such as the number of muscle fibres on stretch.
Comparison of discriminant analysis methods: Application to occupational exposure to particulate matter

NASA Astrophysics Data System (ADS)

Ramos, M. Rosário; Carolino, E.; Viegas, Carla; Viegas, Sandra

2016-06-01

Health effects associated with occupational exposure to particulate matter have been studied by several authors. In this study were selected six industries of five different areas: Cork company 1, Cork company 2, poultry, slaughterhouse for cattle, riding arena and production of animal feed. The measurements tool was a portable device for direct reading. This tool provides information on the particle number concentration for six different diameters, namely 0.3 µm, 0.5 µm, 1 µm, 2.5 µm, 5 µm and 10 µm. The focus on these features is because they might be more closely related with adverse health effects. The aim is to identify the particles that better discriminate the industries, with the ultimate goal of classifying industries regarding potential negative effects on workers' health. Several methods of discriminant analysis were applied to data of occupational exposure to particulate matter and compared with respect to classification accuracy. The selected methods were linear discriminant analyses (LDA); linear quadratic discriminant analysis (QDA), robust linear discriminant analysis with selected estimators (MLE (Maximum Likelihood Estimators), MVE (Minimum Volume Elipsoid), "t", MCD (Minimum Covariance Determinant), MCD-A, MCD-B), multinomial logistic regression and artificial neural networks (ANN). The predictive accuracy of the methods was accessed through a simulation study. ANN yielded the highest rate of classification accuracy in the data set under study. Results indicate that the particle number concentration of diameter size 0.5 µm is the parameter that better discriminates industries.
Joint effect of unlinked genotypes: application to type 2 diabetes in the EPIC-Potsdam case-cohort study.

PubMed

Knüppel, Sven; Meidtner, Karina; Arregui, Maria; Holzhütter, Hermann-Georg; Boeing, Heiner

2015-07-01

Analyzing multiple single nucleotide polymorphisms (SNPs) is a promising approach to finding genetic effects beyond single-locus associations. We proposed the use of multilocus stepwise regression (MSR) to screen for allele combinations as a method to model joint effects, and compared the results with the often used genetic risk score (GRS), conventional stepwise selection, and the shrinkage method LASSO. In contrast to MSR, the GRS, conventional stepwise selection, and LASSO model each genotype by the risk allele doses. We reanalyzed 20 unlinked SNPs related to type 2 diabetes (T2D) in the EPIC-Potsdam case-cohort study (760 cases, 2193 noncases). No SNP-SNP interactions and no nonlinear effects were found. Two SNP combinations selected by MSR (Nagelkerke's R² = 0.050 and 0.048) included eight SNPs with mean allele combination frequency of 2%. GRS and stepwise selection selected nearly the same SNP combinations consisting of 12 and 13 SNPs (Nagelkerke's R² ranged from 0.020 to 0.029). LASSO showed similar results. The MSR method showed the best model fit measured by Nagelkerke's R² suggesting that further improvement may render this method a useful tool in genetic research. However, our comparison suggests that the GRS is a simple way to model genetic effects since it does not consider linkage, SNP-SNP interactions, and no non-linear effects. © 2015 John Wiley & Sons Ltd/University College London.
Discriminative analysis of non-linear brain connectivity for leukoaraiosis with resting-state fMRI

NASA Astrophysics Data System (ADS)

Lai, Youzhi; Xu, Lele; Yao, Li; Wu, Xia

2015-03-01

Leukoaraiosis (LA) describes diffuse white matter abnormalities on CT or MR brain scans, often seen in the normal elderly and in association with vascular risk factors such as hypertension, or in the context of cognitive impairment. The mechanism of cognitive dysfunction is still unclear. The recent clinical studies have revealed that the severity of LA was not corresponding to the cognitive level, and functional connectivity analysis is an appropriate method to detect the relation between LA and cognitive decline. However, existing functional connectivity analyses of LA have been mostly limited to linear associations. In this investigation, a novel measure utilizing the extended maximal information coefficient (eMIC) was applied to construct non-linear functional connectivity in 44 LA subjects (9 dementia, 25 mild cognitive impairment (MCI) and 10 cognitively normal (CN)). The strength of non-linear functional connections for the first 1% of discriminative power increased in MCI compared with CN and dementia, which was opposed to its linear counterpart. Further functional network analysis revealed that the changes of the non-linear and linear connectivity have similar but not completely the same spatial distribution in human brain. In the multivariate pattern analysis with multiple classifiers, the non-linear functional connectivity mostly identified dementia, MCI and CN from LA with a relatively higher accuracy rate than the linear measure. Our findings revealed the non-linear functional connectivity provided useful discriminative power in classification of LA, and the spatial distributed changes between the non-linear and linear measure may indicate the underlying mechanism of cognitive dysfunction in LA.
Beyond the Black-White Test Score Gap: Latinos' Early School Experiences and Literacy Outcomes

ERIC Educational Resources Information Center

Delgado, Enilda A.; Stoll, Laurie Cooper

2015-01-01

Data from the Early Childhood Longitudinal Survey-Birth Cohort are used to analyze the factors that lead to the reading readiness of children who participate in nonparental care the year prior to kindergarten (N = 4,550), with a specific focus on Latino children (N = 800). Stepwise multiple linear regression analysis demonstrates that reading…
Multiple linear regression analysis

NASA Technical Reports Server (NTRS)

Edwards, T. R.

1980-01-01

Program rapidly selects best-suited set of coefficients. User supplies only vectors of independent and dependent data and specifies confidence level required. Program uses stepwise statistical procedure for relating minimal set of variables to set of observations; final regression contains only most statistically significant coefficients. Program is written in FORTRAN IV for batch execution and has been implemented on NOVA 1200.
Of Heart & Kidneys: Hands-On Activities for Demonstrating Organ Function & Repair

ERIC Educational Resources Information Center

Kao, Robert M.

2014-01-01

A major challenge in teaching organ development and disease is deconstructing a complex choreography of molecular and cellular changes over time into a linear stepwise process for students. As an entry toward learning developmental concepts, I propose two inexpensive hands-on activities to help facilitate learning of (1) how to identify defects in…
Age, Sex, and Body Composition as Predictors of Children's Performance on Basic Motor Abilities and Health-Related Fitness Items.

ERIC Educational Resources Information Center

Pissanos, Becky W.; And Others

1983-01-01

Step-wise linear regressions were used to relate children's age, sex, and body composition to performance on basic motor abilities including balance, speed, agility, power, coordination, and reaction time, and to health-related fitness items including flexibility, muscle strength and endurance and cardiovascular functions. Eighty subjects were in…
Discrimination among spawning aggregations of lake herring from Lake Superior using whole-body morphometric characters

USGS Publications Warehouse

Hoff, Michael H.

2004-01-01

The lake herring (Coregonus artedi) was one of the most commercially and ecologically valuable Lake Superior fishes, but declined in the second half of the 20th century as the result of overharvest of putatively discrete stocks. No tools were previously available that described lake herring stock structure and accurately classified lake herring to their spawning stocks. The accuracy of discriminating among spawning aggregations was evaluated using whole-body morphometrics based on a truss network. Lake herring were collected from 11 spawning aggregations in Lake Superior and two inland Wisconsin lakes to evaluate morphometrics as a stock discrimination tool. Discriminant function analysis correctly classified 53% of all fish from all spawning aggregations, and fish from all but one aggregation were classified at greater rates than were possible by chance. Discriminant analysis also correctly classified 66% of fish to nearest neighbor groups, which were groups that accounted for the possibility of mixing among the aggregations. Stepwise discriminant analysis showed that posterior body length and depth measurements were among the best discriminators of spawning aggregations. These findings support other evidence that discrete stocks of lake herring exist in Lake Superior, and fishery managers should consider all but one of the spawning aggregations as discrete stocks. Abundance, annual harvest, total annual mortality rate, and exploitation data should be collected from each stock, and surplus production of each stock should be estimated. Prudent management of stock surplus production and exploitation rates will aid in restoration of stocks and will prevent a repeat of the stock collapses that occurred in the middle of the 20th century, when the species was nearly extirpated from the lake.
Predictive value of grade point average (GPA), Medical College Admission Test (MCAT), internal examinations (Block) and National Board of Medical Examiners (NBME) scores on Medical Council of Canada qualifying examination part I (MCCQE-1) scores.

PubMed

Roy, Banibrata; Ripstein, Ira; Perry, Kyle; Cohen, Barry

2016-01-01

To determine whether the pre-medical Grade Point Average (GPA), Medical College Admission Test (MCAT), Internal examinations (Block) and National Board of Medical Examiners (NBME) scores are correlated with and predict the Medical Council of Canada Qualifying Examination Part I (MCCQE-1) scores. Data from 392 admitted students in the graduating classes of 2010-2013 at University of Manitoba (UofM), College of Medicine was considered. Pearson's correlation to assess the strength of the relationship, multiple linear regression to estimate MCCQE-1 score and stepwise linear regression to investigate the amount of variance were employed. Complete data from 367 (94%) students were studied. The MCCQE-1 had a moderate-to-large positive correlation with NBME scores and Block scores but a low correlation with GPA and MCAT scores. The multiple linear regression model gives a good estimate of the MCCQE-1 (R2 =0.604). Stepwise regression analysis demonstrated that 59.2% of the variation in the MCCQE-1 was accounted for by the NBME, but only 1.9% by the Block exams, and negligible variation came from the GPA and the MCAT. Amongst all the examinations used at UofM, the NBME is most closely correlated with MCCQE-1.
Effects of measurement errors on psychometric measurements in ergonomics studies: Implications for correlations, ANOVA, linear regression, factor analysis, and linear discriminant analysis.

PubMed

Liu, Yan; Salvendy, Gavriel

2009-05-01

This paper aims to demonstrate the effects of measurement errors on psychometric measurements in ergonomics studies. A variety of sources can cause random measurement errors in ergonomics studies and these errors can distort virtually every statistic computed and lead investigators to erroneous conclusions. The effects of measurement errors on five most widely used statistical analysis tools have been discussed and illustrated: correlation; ANOVA; linear regression; factor analysis; linear discriminant analysis. It has been shown that measurement errors can greatly attenuate correlations between variables, reduce statistical power of ANOVA, distort (overestimate, underestimate or even change the sign of) regression coefficients, underrate the explanation contributions of the most important factors in factor analysis and depreciate the significance of discriminant function and discrimination abilities of individual variables in discrimination analysis. The discussions will be restricted to subjective scales and survey methods and their reliability estimates. Other methods applied in ergonomics research, such as physical and electrophysiological measurements and chemical and biomedical analysis methods, also have issues of measurement errors, but they are beyond the scope of this paper. As there has been increasing interest in the development and testing of theories in ergonomics research, it has become very important for ergonomics researchers to understand the effects of measurement errors on their experiment results, which the authors believe is very critical to research progress in theory development and cumulative knowledge in the ergonomics field.
Latent log-linear models for handwritten digit classification.

PubMed

Deselaers, Thomas; Gass, Tobias; Heigold, Georg; Ney, Hermann

2012-06-01

We present latent log-linear models, an extension of log-linear models incorporating latent variables, and we propose two applications thereof: log-linear mixture models and image deformation-aware log-linear models. The resulting models are fully discriminative, can be trained efficiently, and the model complexity can be controlled. Log-linear mixture models offer additional flexibility within the log-linear modeling framework. Unlike previous approaches, the image deformation-aware model directly considers image deformations and allows for a discriminative training of the deformation parameters. Both are trained using alternating optimization. For certain variants, convergence to a stationary point is guaranteed and, in practice, even variants without this guarantee converge and find models that perform well. We tune the methods on the USPS data set and evaluate on the MNIST data set, demonstrating the generalization capabilities of our proposed models. Our models, although using significantly fewer parameters, are able to obtain competitive results with models proposed in the literature.
Above-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform - A case study in Wild Duck Lake Wetland, Beijing, China

NASA Astrophysics Data System (ADS)

Jing, Ran; Gong, Zhaoning; Zhao, Wenji; Pu, Ruiliang; Deng, Lei

2017-12-01

Above-bottom biomass (ABB) is considered as an important parameter for measuring the growth status of aquatic plants, and is of great significance for assessing health status of wetland ecosystems. In this study, Structure from Motion (SfM) technique was used to rebuild the study area with high overlapped images acquired by an unmanned aerial vehicle (UAV). We generated orthoimages and SfM dense point cloud data, from which vegetation indices (VIs) and SfM point cloud variables including average height (HAVG), standard deviation of height (HSD) and coefficient of variation of height (HCV) were extracted. These VIs and SfM point cloud variables could effectively characterize the growth status of aquatic plants, and thus they could be used to develop a simple linear regression model (SLR) and a stepwise linear regression model (SWL) with field measured ABB samples of aquatic plants. We also utilized a decision tree method to discriminate different types of aquatic plants. The experimental results indicated that (1) the SfM technique could effectively process high overlapped UAV images and thus be suitable for the reconstruction of fine texture feature of aquatic plant canopy structure; and (2) an SWL model based on point cloud variables: HAVG, HSD, HCV and two VIs: NGRDI, ExGR as independent variables has produced the best predictive result of ABB of aquatic plants in the study area, with a coefficient of determination of 0.84 and a relative root mean square error of 7.13%. In this analysis, a novel method for the quantitative inversion of a growth parameter (i.e., ABB) of aquatic plants in wetlands was demonstrated.
Assessment of computer techniques for processing digital LANDSAT MSS data for lithological discrimination of Serra do Ramalho, State of Bahia

NASA Technical Reports Server (NTRS)

Paradella, W. R. (Principal Investigator); Vitorello, I.; Monteiro, M. D.

1984-01-01

Enhancement techniques and thematic classifications were applied to the metasediments of Bambui Super Group (Upper Proterozoic) in the Region of Serra do Ramalho, SW of the state of Bahia. Linear contrast stretch, band-ratios with contrast stretch, and color-composites allow lithological discriminations. The effects of human activities and of vegetation cover mask and limit, in several ways, the lithological discrimination with digital MSS data. Principal component images and color composite of linear contrast stretch of these products, show lithological discrimination through tonal gradations. This set of products allows the delineations of several metasedimentary sequences to a level superior to reconnaissance mapping. Supervised (maximum likelihood classifier) and nonsupervised (K-Means classifier) classification of the limestone sequence, host to fluorite mineralization show satisfactory results.

Experimental optimal maximum-confidence discrimination and optimal unambiguous discrimination of two mixed single-photon states

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

Steudle, Gesine A.; Knauer, Sebastian; Herzog, Ulrike

2011-05-15

We present an experimental implementation of optimum measurements for quantum state discrimination. Optimum maximum-confidence discrimination and optimum unambiguous discrimination of two mixed single-photon polarization states were performed. For the latter the states of rank 2 in a four-dimensional Hilbert space are prepared using both path and polarization encoding. Linear optics and single photons from a true single-photon source based on a semiconductor quantum dot are utilized.
Detection of non-milk fat in milk fat by gas chromatography and linear discriminant analysis.

PubMed

Gutiérrez, R; Vega, S; Díaz, G; Sánchez, J; Coronado, M; Ramírez, A; Pérez, J; González, M; Schettino, B

2009-05-01

Gas chromatography was utilized to determine triacylglycerol profiles in milk and non-milk fat. The values of triacylglycerol were subjected to linear discriminant analysis to detect and quantify non-milk fat in milk fat. Two groups of milk fat were analyzed: A) raw milk fat from the central region of Mexico (n = 216) and B) ultrapasteurized milk fat from 3 industries (n = 36), as well as pork lard (n = 2), bovine tallow (n = 2), fish oil (n = 2), peanut (n = 2), corn (n = 2), olive (n = 2), and soy (n = 2). The samples of raw milk fat were adulterated with non-milk fats in proportions of 0, 5, 10, 15, and 20% to form 5 groups. The first function obtained from the linear discriminant analysis allowed the correct classification of 94.4% of the samples with levels <10% of adulteration. The triacylglycerol values of the ultrapasteurized milk fats were evaluated with the discriminant function, demonstrating that one industry added non-milk fat to its product in 80% of the samples analyzed.
Origin of fine dust in urban environmental zones--Evidence from element patterns received by dichotomous collection and INAA.

PubMed

Weckwerth, G

2010-10-01

In order to fulfil the EU-limitations of fine dust and traffic-produced gases Cologne installed 2008 one of the first German environmental zones, from which stepwise vehicles with too high emissions will be locked out. Verification of effectiveness and the research on further strategies to reduce fine dust are studied as promising applications of a method on discrimination of aerosol components from different origins (Weckwerth, 2001). New measurements in Cologne gave several implications on supports, especially in connection with traffic abrasion from brakes, tires and rails. Copyright 2010. Published by Elsevier Ltd.
Application of remote sensing for fishery resources assessment and monitoring. [Gulf of Mexico

NASA Technical Reports Server (NTRS)

Savastano, K. J. (Principal Investigator)

1975-01-01

The author has identified the following significant results. The distribution and abundance of white marlin correlated with the chlorophyll, water temperature, and Secchi depth sea truth measurements. Results of correlation analyses for dolphin were inconclusive. Predicition models for white marlin were developed using stepwise multiple regression and discriminant function analysis techniques which demonstrated a potential for increasing the probability of game fishing success. The S190A and B imagery was density sliced/color enhanced with white marlin location superimposed on the image, but no density/white marlin relationship could be established.
Children's attitudes toward violence on television.

PubMed

Hough, K J; Erwin, P G

1997-07-01

Children's attitudes toward television violence were studied. A 47-item questionnaire collecting attitudinal and personal information was administered to 316 children aged 11 to 16 years. Cluster analysis was used to split the participants into two groups based on their attitudes toward television violence. A stepwise discriminant function analysis was performed to determine which personal characteristics would predict group membership. The only significant predictor of attitudes toward violence on television was the amount of television watched on school days (p < .05), but we also found that the impact of other predictor variables may have been mediated by this factor.
Apolipoprotein E4 serum concentration for increased sensitivity and specificity of diagnosis of drug treated Alzheimer's disease patients vs. drug treated parkinson's disease patients vs. age-matched normal controls.

PubMed

Goldknopf, Ira L; Park, Helen R; Sabbagh, Marwan

2012-12-01

Inasmuch as Alzheimer's disease (AD) is difficult to diagnose, patients with suspected dementias are often given FDA approved medications, including donepezil, rivastigmine, memantine HCl, or a combination, prior to diagnosis, and some respond with improved cognition. The present study demonstrates how concentrations of a select group of serum protein biomarkers can provide the basis for sensitive and specific differential diagnosis of AD in drug treated patients. Optimization is addressed by taking into account whether the patients and controls have or do not have increased risk of AD die to the presence or absence of Apolipoprotein E4. For differential diagnosis of AD, prospectively collected newly drawn blood serum samples were obtained from drug treated Alzheimer's disease and Parkinson's disease patients from a first (39 drug treated DTAD, and 31 age matched normal controls) and second medical center (56 drug treated DTPD, 47 age-matched normal controls). Analytically validated quantitative 2D gel electrophoresis (%CV ≤ 20%; LOD ≥ 0.5 ng/spot, 300 μg/ml of blood serum) was employed with patient and control sera for differential diagnosis of AD. Protein quantitation was subjected to statistical analysis by single variable Dot, Box and Whiskers and Receiver Operator Characteristics (ROC) plots for individual biomarker performance, and multivariate linear discriminant analysis for joint performance of groups of biomarkers. Protein spots were identified and characterized by LC MS/MS of in-gel trypsin digests, amino acid sequence spans of the identified peptides, and the protein spot molecular weights and isoelectric points. The single variable statistical profiles of 58 individual protein biomarker concentrations of the DTAD patient group differed from those of the normal and/or the disease control groups. Multivariate linear discriminant analysis of blood serum concentrations of the 58 proteins distinguished drug treated Alzheimer's disease (DTAD) patients from drug treated Parkinson's disease (DTPD) patients and age matched normal controls (collectively not-DTAD, DTAD Sensitivity 87.2%, Not-DTAD Specificity 87.2). Moreover, when the patients and controls were stratified into carriers or non-carriers of Alzheimer's high risk Apolipoprotein E 4 allele and/or the Apolipoprotein E4 protein, the DTAD, DTPD and control Apo E4 (+) profiles were more divergent from one another than the corresponding Apo E4 (-) profiles. Multivariate stepwise linear discriminant analysis selected 17 of the 58 biomarkers as optimal and complimentary for distinguishing Apo E4 (+) DTAD patients from Apo E4 (+) DTPD and Apo E4 (+) controls (collectively Apo E4 (+) not-DTAD, DTAD Sensitivity 100%, not-DTAD Specificity 100%) and 22 of the 58 biomarkers for distinguishing Apo E4 (-) DTAD patients from Apo E4 (-) DTPD and Apo E4 (-) controls (collectively Apo E4 (-) not-DTAD, DTAD Sensitivity 94.4%, not- DTAD Specificity 94.4%). Only 6 of the selected proteins were common to both the Apo E4 (+) and the Apo E4 (-) discriminant functions. Recombining of the results of Apo E4 (+) and Apo E4 (-) discriminations provided overall sensitivity for total DTAD of 97.4% and specificity for total not-DTAD of 95.7%. These results can form the basis of a blood test for differential diagnosis of Alzheimer's disease patients already under treatment (DTAD) by anti dementia drugs, including donepezil, rivastigmine, memantine HCl, or a combination thereof. Also, the profile differences and the rise in specificity and sensitivity obtained by handling the Apo E4 (+) and Apo E4 (-) groups separately supports the concept that they are different patient and control populations in terms of the "normal" physiology, the pathophysiology of disease, and the response to drug treatment. Taking that into account enables increased sensitivity and specificity of differential diagnosis of Alzheimer's disease.
Validation of SplitVectors Encoding for Quantitative Visualization of Large-Magnitude-Range Vector Fields

PubMed Central

Zhao, Henan; Bryant, Garnett W.; Griffin, Wesley; Terrill, Judith E.; Chen, Jian

2017-01-01

We designed and evaluated SplitVectors, a new vector field display approach to help scientists perform new discrimination tasks on large-magnitude-range scientific data shown in three-dimensional (3D) visualization environments. SplitVectors uses scientific notation to display vector magnitude, thus improving legibility. We present an empirical study comparing the SplitVectors approach with three other approaches - direct linear representation, logarithmic, and text display commonly used in scientific visualizations. Twenty participants performed three domain analysis tasks: reading numerical values (a discrimination task), finding the ratio between values (a discrimination task), and finding the larger of two vectors (a pattern detection task). Participants used both mono and stereo conditions. Our results suggest the following: (1) SplitVectors improve accuracy by about 10 times compared to linear mapping and by four times to logarithmic in discrimination tasks; (2) SplitVectors have no significant differences from the textual display approach, but reduce cluttering in the scene; (3) SplitVectors and textual display are less sensitive to data scale than linear and logarithmic approaches; (4) using logarithmic can be problematic as participants' confidence was as high as directly reading from the textual display, but their accuracy was poor; and (5) Stereoscopy improved performance, especially in more challenging discrimination tasks. PMID:28113469
Validation of SplitVectors Encoding for Quantitative Visualization of Large-Magnitude-Range Vector Fields.

PubMed

Henan Zhao; Bryant, Garnett W; Griffin, Wesley; Terrill, Judith E; Jian Chen

2017-06-01

We designed and evaluated SplitVectors, a new vector field display approach to help scientists perform new discrimination tasks on large-magnitude-range scientific data shown in three-dimensional (3D) visualization environments. SplitVectors uses scientific notation to display vector magnitude, thus improving legibility. We present an empirical study comparing the SplitVectors approach with three other approaches - direct linear representation, logarithmic, and text display commonly used in scientific visualizations. Twenty participants performed three domain analysis tasks: reading numerical values (a discrimination task), finding the ratio between values (a discrimination task), and finding the larger of two vectors (a pattern detection task). Participants used both mono and stereo conditions. Our results suggest the following: (1) SplitVectors improve accuracy by about 10 times compared to linear mapping and by four times to logarithmic in discrimination tasks; (2) SplitVectors have no significant differences from the textual display approach, but reduce cluttering in the scene; (3) SplitVectors and textual display are less sensitive to data scale than linear and logarithmic approaches; (4) using logarithmic can be problematic as participants' confidence was as high as directly reading from the textual display, but their accuracy was poor; and (5) Stereoscopy improved performance, especially in more challenging discrimination tasks.
Assessment of weather-associated causes of red spruce winter injury and consequences to aboveground carbon sequestration

Treesearch

Paul G. Schaberg; Brynne E. Lazarus; Gary J. Hawley; Joshua M. Halman; Catherine H. Borer; Christopher F. Hansen

2011-01-01

Despite considerable study, it remains uncertain what environmental factors contribute to red spruce (Picea rubens Sarg.) foliar winter injury and how much this injury influences tree C stores. We used a long-term record of winter injury in a plantation in New Hampshire and conducted stepwise linear regression analyses with local weather and regional...
Assessment of plant species diversity based on hyperspectral indices at a fine scale.

PubMed

Peng, Yu; Fan, Min; Song, Jingyi; Cui, Tiantian; Li, Rui

2018-03-19

Fast and nondestructive approaches of measuring plant species diversity have been a subject of excessive scientific curiosity and disquiet to environmentalists and field ecologists worldwide. In this study, we measured the hyperspectral reflectances and plant species diversity indices at a fine scale (0.8 meter) in central Hunshandak Sandland of Inner Mongolia, China. The first-order derivative value (FD) at each waveband and 37 hyperspectral indices were used to assess plant species diversity. Results demonstrated that the stepwise linear regression of FD can accurately estimate the Simpson (R 2 = 0.83), Pielou (R 2 = 0.87) and Shannon-Wiener index (R 2 = 0.88). Stepwise linear regression of FD (R 2 = 0.81, R 2 = 0.82) and spectral vegetation indices (R 2 = 0.51, R 2 = 0.58) significantly predicted the Margalef and Gleason index. It was proposed that the Simpson, Pielou and Shannon-Wiener indices, which are widely used as plant species diversity indicators, can be precisely estimated through hyperspectral indices at a fine scale. This research promotes the development of methods for assessment of plant diversity using hyperspectral data.
Handheld Tissue Hardness Meters for Assessing the Mechanical Properties of Skeletal Muscle: A Feasibility Study.

PubMed

Chino, Kentaro; Takahashi, Hideyuki

2016-09-01

The purpose of this study was to examine the feasibility of using handheld tissue hardness meters to assess the mechanical properties of skeletal muscle. This observational study included 33 healthy men (age, 22.4 ± 4.4 years) and 33 healthy women (age, 23.7 ± 4.2 years). Participants were placed in a supine position, and tissue hardness overlying the rectus femoris and the shear modulus of the muscle were measured on the right side of the body at 50% thigh length. In the same position, subcutaneous adipose tissue thickness and muscle thickness were measured using B-mode ultrasonography. To examine the associations of subcutaneous adipose tissue thickness, muscle thickness, and muscle shear modulus with tissue hardness, linear regression using a stepwise bidirectional elimination approach was performed. Stepwise linear regression revealed that subcutaneous adipose tissue thickness (r = -0.38, P = .002) and muscle shear modulus (r = 0.27, P = .03) were significantly associated with tissue hardness. Significant associations among adipose tissue thickness, muscle shear modulus, and tissue hardness show the limitations and feasibility of handheld tissue hardness meters for assessing the mechanical properties of skeletal muscles. Copyright © 2016. Published by Elsevier Inc.
Super-resolution fluorescence microscopy by stepwise optical saturation

PubMed Central

Zhang, Yide; Nallathamby, Prakash D.; Vigil, Genevieve D.; Khan, Aamir A.; Mason, Devon E.; Boerckel, Joel D.; Roeder, Ryan K.; Howard, Scott S.

2018-01-01

Super-resolution fluorescence microscopy is an important tool in biomedical research for its ability to discern features smaller than the diffraction limit. However, due to its difficult implementation and high cost, the super-resolution microscopy is not feasible in many applications. In this paper, we propose and demonstrate a saturation-based super-resolution fluorescence microscopy technique that can be easily implemented and requires neither additional hardware nor complex post-processing. The method is based on the principle of stepwise optical saturation (SOS), where M steps of raw fluorescence images are linearly combined to generate an image with a M-fold increase in resolution compared with conventional diffraction-limited images. For example, linearly combining (scaling and subtracting) two images obtained at regular powers extends the resolution by a factor of 1.4 beyond the diffraction limit. The resolution improvement in SOS microscopy is theoretically infinite but practically is limited by the signal-to-noise ratio. We perform simulations and experimentally demonstrate super-resolution microscopy with both one-photon (confocal) and multiphoton excitation fluorescence. We show that with the multiphoton modality, the SOS microscopy can provide super-resolution imaging deep in scattering samples. PMID:29675306
Proton radius from electron scattering data

NASA Astrophysics Data System (ADS)

Higinbotham, Douglas W.; Kabir, Al Amin; Lin, Vincent; Meekins, David; Norum, Blaine; Sawatzky, Brad

2016-05-01

Background: The proton charge radius extracted from recent muonic hydrogen Lamb shift measurements is significantly smaller than that extracted from atomic hydrogen and electron scattering measurements. The discrepancy has become known as the proton radius puzzle. Purpose: In an attempt to understand the discrepancy, we review high-precision electron scattering results from Mainz, Jefferson Lab, Saskatoon, and Stanford. Methods: We make use of stepwise regression techniques using the F test as well as the Akaike information criterion to systematically determine the predictive variables to use for a given set and range of electron scattering data as well as to provide multivariate error estimates. Results: Starting with the precision, low four-momentum transfer (Q2) data from Mainz (1980) and Saskatoon (1974), we find that a stepwise regression of the Maclaurin series using the F test as well as the Akaike information criterion justify using a linear extrapolation which yields a value for the proton radius that is consistent with the result obtained from muonic hydrogen measurements. Applying the same Maclaurin series and statistical criteria to the 2014 Rosenbluth results on GE from Mainz, we again find that the stepwise regression tends to favor a radius consistent with the muonic hydrogen radius but produces results that are extremely sensitive to the range of data included in the fit. Making use of the high-Q2 data on GE to select functions which extrapolate to high Q2, we find that a Padé (N =M =1 ) statistical model works remarkably well, as does a dipole function with a 0.84 fm radius, GE(Q2) =(1+Q2/0.66 GeV2) -2 . Conclusions: Rigorous applications of stepwise regression techniques and multivariate error estimates result in the extraction of a proton charge radius that is consistent with the muonic hydrogen result of 0.84 fm; either from linear extrapolation of the extremely-low-Q2 data or by use of the Padé approximant for extrapolation using a larger range of data. Thus, based on a purely statistical analysis of electron scattering data, we conclude that the electron scattering results and the muonic hydrogen results are consistent. It is the atomic hydrogen results that are the outliers.
Demographic and clinical features related to perceived discrimination in schizophrenia.

PubMed

Fresán, Ana; Robles-García, Rebeca; Madrigal, Eduardo; Tovilla-Zarate, Carlos-Alfonso; Martínez-López, Nicolás; Arango de Montis, Iván

2018-04-01

Perceived discrimination contributes to the development of internalized stigma among those with schizophrenia. Evidence on demographic and clinical factors related to the perception of discrimination among this population is both contradictory and scarce in low- and middle-income countries. Accordingly, the main purpose of this study is to determine the demographic and clinical factors predicting the perception of discrimination among Mexican patients with schizophrenia. Two hundred and seventeen adults with paranoid schizophrenia completed an interview on their demographic status and clinical characteristics. Symptom severity was assessed using the Positive and Negative Syndrome Scale; and perceived discrimination using 13 items from the King's Internalized Stigma Scale. Bivariate linear associations were determined to identify the variables of interest to be included in a linear regression analysis. Years of education, age of illness onset and length of hospitalization were associated with discrimination. However, only age of illness onset and length of hospitalization emerged as predictors of perceived discrimination in the final regression analysis, with longer length of hospitalization being the independent variable with the greatest contribution. Fortunately, this is a modifiable factor regarding the perception of discrimination and self-stigma. Strategies for achieving this as part of community-based mental health care are also discussed. Copyright © 2017 Elsevier B.V. All rights reserved.
Ultrahigh-Dimensional Multiclass Linear Discriminant Analysis by Pairwise Sure Independence Screening

PubMed Central

Pan, Rui; Wang, Hansheng; Li, Runze

2016-01-01

This paper is concerned with the problem of feature screening for multi-class linear discriminant analysis under ultrahigh dimensional setting. We allow the number of classes to be relatively large. As a result, the total number of relevant features is larger than usual. This makes the related classification problem much more challenging than the conventional one, where the number of classes is small (very often two). To solve the problem, we propose a novel pairwise sure independence screening method for linear discriminant analysis with an ultrahigh dimensional predictor. The proposed procedure is directly applicable to the situation with many classes. We further prove that the proposed method is screening consistent. Simulation studies are conducted to assess the finite sample performance of the new procedure. We also demonstrate the proposed methodology via an empirical analysis of a real life example on handwritten Chinese character recognition. PMID:28127109
Talent identification and selection in elite youth football: An Australian context.

PubMed

O'Connor, Donna; Larkin, Paul; Mark Williams, A

2016-10-01

We identified the perceptual-cognitive skills and player history variables that differentiate players selected or not selected into an elite youth football (i.e. soccer) programme in Australia. A sample of elite youth male football players (n = 127) completed an adapted participation history questionnaire and video-based assessments of perceptual-cognitive skills. Following data collection, 22 of these players were offered a full-time scholarship for enrolment at an elite player residential programme. Participants selected for the scholarship programme recorded superior performance on the combined perceptual-cognitive skills tests compared to the non-selected group. There were no significant between group differences on the player history variables. Stepwise discriminant function analysis identified four predictor variables that resulted in the best categorization of selected and non-selected players (i.e. recent match-play performance, region, number of other sports participated, combined perceptual-cognitive performance). The effectiveness of the discriminant function is reflected by 93.7% of players being correctly classified, with the four variables accounting for 57.6% of the variance. Our discriminating model for selection may provide a greater understanding of the factors that influence elite youth talent selection and identification.
Discrimination and chemical characterization of different Paeonia lactifloras (Radix Paeoniae Alba and Radix Paeoniae Rubra) by infrared macro-fingerprint analysis-through-separation

NASA Astrophysics Data System (ADS)

Wang, Yang; Wang, Ping; Xu, Changhua; Sun, Suqin; Zhou, Qun; Shi, Zhe; Li, Jin; Chen, Tao; Li, Zheng; Cui, Weili

2015-11-01

Paeonia lactiflora, a commonly used herbal medicine (HM) in Traditional Chinese Medicine (TCM), mainly has two species, Radix Paeoniae Alba (RPA) and Radix Paeoniae Rubra (RPR), for different clinical applications in TCM. For expounding the chemical profile of RPA and RPR and ensuring the clinical efficacy and safety, an infrared macro-fingerprint analysis-through-separation method integrated with statistical pattern recognition was developed to analyze and discriminate the two Paeonia lactifloras. In IR spectra, the major difference between the two was in the range of 1200-900 cm-1: the strongest peak of RPA was at 1024 cm-1, while that of RPR was 1049 cm-1. The difference was magnified in second derivative spectra. The findings were further verified by investigating the separation process of total glucosides, stepwisely monitored by both of IR and UPLC-MS/MS. Simultaneously, the aqueous extracts of RPA and RPR had been separated continuously to acquire the comprehensively hierarchical chemical characteristics for undoubtedly identification and subsequently discrimination of the two herbs. Moreover, 60 batches of the two HMs (30 for each) were objectively classified by principal component regression (PCR) model based on IR macro-fingerprints.
Cognitive Strategies and Physical Activity in Older Adults: A Discriminant Analysis

PubMed Central

Ferrand, Claude; Audiffren, Michel

2018-01-01

Background Although a number of studies have examined sociodemographic, psychosocial, and environmental determinants of the level of physical activity (PA) for older people, little attention has been paid to the predictive power of cognitive strategies for independently living older adults. However, cognitive strategies have recently been considered to be critical in the management of day-to-day living. Methods Data were collected from 243 men and women aged 55 years and older living in France using face-to-face interviews between 2011 and 2013. Results A stepwise discriminant analysis selected five predictor variables (age, perceived health status, barriers' self-efficacy, internal memory, and attentional control strategies) of the level of PA. The function showed that the rate of correct prediction was 73% for the level of PA. The calculated discriminant function based on the five predictor variables is useful for detecting individuals at high risk of lapses once engaged in regular PA. Conclusions This study highlighted the need to consider cognitive functions as a determinant of the level of PA and, more specifically, those cognitive functions related to executive functions (internal memory and attentional control), to facilitate the maintenance of regular PA. These results are discussed in relation to successful aging. PMID:29850247
Cognitive Strategies and Physical Activity in Older Adults: A Discriminant Analysis.

PubMed

André, Nathalie; Ferrand, Claude; Albinet, Cédric; Audiffren, Michel

2018-01-01

Although a number of studies have examined sociodemographic, psychosocial, and environmental determinants of the level of physical activity (PA) for older people, little attention has been paid to the predictive power of cognitive strategies for independently living older adults. However, cognitive strategies have recently been considered to be critical in the management of day-to-day living. Data were collected from 243 men and women aged 55 years and older living in France using face-to-face interviews between 2011 and 2013. A stepwise discriminant analysis selected five predictor variables (age, perceived health status, barriers' self-efficacy, internal memory, and attentional control strategies) of the level of PA. The function showed that the rate of correct prediction was 73% for the level of PA. The calculated discriminant function based on the five predictor variables is useful for detecting individuals at high risk of lapses once engaged in regular PA. This study highlighted the need to consider cognitive functions as a determinant of the level of PA and, more specifically, those cognitive functions related to executive functions (internal memory and attentional control), to facilitate the maintenance of regular PA. These results are discussed in relation to successful aging.
Profiling of barrier capacitance and spreading resistance using a transient linearly increasing voltage technique.

PubMed

Gaubas, E; Ceponis, T; Kusakovskij, J

2011-08-01

A technique for the combined measurement of barrier capacitance and spreading resistance profiles using a linearly increasing voltage pulse is presented. The technique is based on the measurement and analysis of current transients, due to the barrier and diffusion capacitance, and the spreading resistance, between a needle probe and sample. To control the impact of deep traps in the barrier capacitance, a steady state bias illumination with infrared light was employed. Measurements of the spreading resistance and barrier capacitance profiles using a stepwise positioned probe on cross sectioned silicon pin diodes and pnp structures are presented.

Improving prediction of metal uptake by Chinese cabbage (Brassica pekinensis L.) based on a soil-plant stepwise analysis.

PubMed

Zhang, Sha; Song, Jing; Gao, Hui; Zhang, Qiang; Lv, Ming-Chao; Wang, Shuang; Liu, Gan; Pan, Yun-Yu; Christie, Peter; Sun, Wenjie

2016-11-01

It is crucial to develop predictive soil-plant transfer (SPT) models to derive the threshold values of toxic metals in contaminated arable soils. The present study was designed to examine the heavy metal uptake pattern and to improve the prediction of metal uptake by Chinese cabbage grown in agricultural soils with multiple contamination by Cd, Cu, Ni, Pb, and Zn. Pot experiments were performed with 25 historically contaminated soils to determine metal accumulation in different parts of Chinese cabbage. Different soil bioavailable metal fractions were determined using different extractants (0.43M HNO3, 0.01M CaCl2, 0.005M DTPA, and 0.01M LWMOAs), soil moisture samplers, and diffusive gradients in thin films (DGT), and the fractions were compared with shoot metal uptake using both direct and stepwise multiple regression analysis. The stepwise approach significantly improved the prediction of metal uptake by cabbage over the direct approach. Strongly pH dependent or nonlinear relationships were found for the adsorption of root surfaces and in root-shoot uptake processes. Metals were linearly translocated from the root surface to the root. Therefore, the nonlinearity of uptake pattern is an important explanation for the inadequacy of the direct approach in some cases. The stepwise approach offers an alternative and robust method to study the pattern of metal uptake by Chinese cabbage (Brassica pekinensis L.). Copyright © 2016. Published by Elsevier B.V.
Sex estimation from the patella in an African American population.

PubMed

Peckmann, Tanya R; Fisher, Brooke

2018-02-01

The skull and pelvis have been used for the estimation of sex for unknown human remains. However, in forensic cases where skeletal remains often exhibit postmortem damage and taphonomic changes the patella may be used for the estimation of sex as it is a preservationally favoured bone. The goal of the present research was to derive discriminant function equations from the patella for estimation of sex from an historic African American population. Six parameters were measured on 200 individuals (100 males and 100 females), ranging in age from 20 to 80 years old, from the Robert J. Terry Anatomical Skeleton Collection. The statistical analyses showed that all variables were sexually dimorphic. Discriminant function score equations were generated for use in sex estimation. The overall accuracy of sex classification ranged from 80.0% to 85.0% for the direct method and 80.0%-84.5% for the stepwise method. Overall, when the Spanish and Black South African discriminant functions were applied to the African American population they showed low accuracy rates for sexing the African American sample. However, when the White South African discriminant functions were applied to the African American sample they displayed high accuracy rates for sexing the African American population. The patella was shown to be accurate for sex estimation in the historic African American population. Copyright © 2017 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.
Vapor permeation-stepwise injection simultaneous determination of methanol and ethanol in biodiesel with voltammetric detection.

PubMed

Shishov, Andrey; Penkova, Anastasia; Zabrodin, Andrey; Nikolaev, Konstantin; Dmitrenko, Maria; Ermakov, Sergey; Bulatov, Andrey

2016-02-01

A novel vapor permeation-stepwise injection (VP-SWI) method for the determination of methanol and ethanol in biodiesel samples is discussed. In the current study, stepwise injection analysis was successfully combined with voltammetric detection and vapor permeation. This method is based on the separation of methanol and ethanol from a sample using a vapor permeation module (VPM) with a selective polymer membrane based on poly(phenylene isophtalamide) (PA) containing high amounts of a residual solvent. After the evaporation into the headspace of the VPM, methanol and ethanol were transported, by gas bubbling, through a PA membrane to a mixing chamber equipped with a voltammetric detector. Ethanol was selectively detected at +0.19 V, and both compounds were detected at +1.20 V. Current subtractions (using a correction factor) were used for the selective determination of methanol. A linear range between 0.05 and 0.5% (m/m) was established for each analyte. The limits of detection were estimated at 0.02% (m/m) for ethanol and methanol. The sample throughput was 5 samples h(-1). The method was successfully applied to the analysis of biodiesel samples. Copyright © 2015 Elsevier B.V. All rights reserved.
Regularization strategies for hyperplane classifiers: application to cancer classification with gene expression data.

PubMed

Andries, Erik; Hagstrom, Thomas; Atlas, Susan R; Willman, Cheryl

2007-02-01

Linear discrimination, from the point of view of numerical linear algebra, can be treated as solving an ill-posed system of linear equations. In order to generate a solution that is robust in the presence of noise, these problems require regularization. Here, we examine the ill-posedness involved in the linear discrimination of cancer gene expression data with respect to outcome and tumor subclasses. We show that a filter factor representation, based upon Singular Value Decomposition, yields insight into the numerical ill-posedness of the hyperplane-based separation when applied to gene expression data. We also show that this representation yields useful diagnostic tools for guiding the selection of classifier parameters, thus leading to improved performance.
Hyperspectral Features of Oil-Polluted Sea Ice and the Response to the Contamination Area Fraction

PubMed Central

Li, Ying; Liu, Chengyu; Xie, Feng

2018-01-01

Researchers have studied oil spills in open waters using remote sensors, but few have focused on extracting reflectance features of oil pollution on sea ice. An experiment was conducted on natural sea ice in Bohai Bay, China, to obtain the spectral reflectance of oil-contaminated sea ice. The spectral absorption index (SAI), spectral peak height (SPH), and wavelet detail coefficient (DWT d5) were calculated using stepwise multiple linear regression. The reflectances of some false targets were measured and analysed. The simulated false targets were sediment, iron ore fines, coal dust, and the melt pool. The measured reflectances were resampled using five common sensors (GF-2, Landsat8-OLI, Sentinel3-OLCI, MODIS, and AVIRIS). Some significant spectral features could discriminate between oil-polluted and clean sea ice. The indices correlated well with the oil area fractions. All of the adjusted R2 values exceeded 0.9. The SPH model1, based on spectral features at 507–670 and 1627–1746 nm, displayed the best fitting. The resampled data indicated that these multi-spectral and hyper-spectral sensors could be used to detect crude oil on the sea ice if the effect of noise and spatial resolution are neglected. The spectral features and their identified changes may provide reference on sensor design and band selection. PMID:29342945
In-Vivo Fluorescence Spectroscopy Of Normal And Atherosclerotic Arteries

NASA Astrophysics Data System (ADS)

Deckelbaum, Lawrence I.; Sarembock, Ian J.; Stetz, Mark L.; O'Brien, Kenneth M.; Cutruzzola, Francis W.; Gmitro, Arthur F.; Ezekowitz, Michael D.

1988-06-01

Laser-induced fluorescence spectroscopy can discriminate atherosclerotic from normal arteries in-vitro and may thus potentially guide laser angioplasty. To evaluate the feasibility of laser-induced fluorescence spectroscopy in a living blood-filled arterial system we performed fiberoptic laser-induced fluorescence spectroscopy in a rabbit model of focal femoral atherosclerosis. A laser-induced fluorescence spectroscopy score was derived from stepwise linear regression analysis of in-vitro spectra to distinguish normal aorta (score>0) from atherosclerotic femoral artery (score<0). A 400 u silica fiber, coupled to a helium cadmium laser and optical multichannel analyzer, was inserted through a 5F catheter to induce and record in-vivo fluorescence from femoral and aortoiliac arteries. Arterial spectra could be recorded in all animals (n=10: 5 occlusions, 5 stenoses). Blood spectra were of low intensity and were easily distinguished from arterial spectra. The scores (mean ± SEM) for the in-vivo spectra were -0.69 +/- 0.29 for artherosclerotic femoral, and +0.54 ±. 0.15 for normal aorta (p<.01 p=NS compared to in-vitro spectra). In-vitro, a fiber tip to tissue distance <50 u was necessary for adequate arterial LIFS in blood. At larger distances low intensity blood spectra were recorded (1/20 the intensity of tissue spectra). Thus, fiberoptic laser-induced fluorescence spectroscopy can be sucessfully performed in a blood filled artery provided the fiber tip is approximated to the tissue.
Determinants of the half-turn with the ball in sub-elite youth soccer players.

PubMed

Zago, Matteo; Codari, Marina; Grilli, Massimo; Bellistri, Giuseppe; Lovecchio, Nicola; Sforza, Chiarella

2016-06-01

We explored the biomechanics of the 180° change-of-direction with the ball (half-turn) in soccer. We aimed at identifying movement strategies which enhance the players' half-turning performance, by characterising technique kinematics and understanding the structure of biomechanical and anthropometrics variables. Ten Under-13 sub-elite male players were recorded with an optoelectronic motion analyser while performing a 5-m straight dribbling followed by a half-turn with the sole. Joints kinematics differences between faster and slower trials were found in support-side hip rotation, driving-side hip adduction, trunk flexion and rotation, and arms abduction. To unveil the data-set structure, a principal component (PC) analysis and a stepwise linear discriminant analysis were performed using 30 biomechanical parameters and four anthropometric variables for each trial. Seven retained PCs explained 79% of the overall variability, featuring combinations of original variables that help in understanding the factors facilitating fast half-turns: keeping short steps, minimising lateral and forward body movements, and centre-of-mass lowering, even with ample lower limbs ranges of motion (RoM); abducting the upper limbs while limiting trunk flexion and pelvic inclination RoM. Balance and task-constrained exercises may be proposed to improve this technique. Moreover, a quantitative knowledge of the movement structure could give coaches objective insights to better instruct young players.
A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns

PubMed Central

Townsend, G.; LaPallo, B.K.; Boulay, C.B.; Krusienski, D.J.; Frye, G.E.; Hauser, C.K.; Schwartz, N.E.; Vaughan, T.M.; Wolpaw, J.R.; Sellers, E.W.

2010-01-01

Objective An electroencephalographic brain-computer interface (BCI) can provide a non-muscular means of communication for people with amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. We present a novel P300-based BCI stimulus presentation – the checkerboard paradigm (CBP). CBP performance is compared to that of the standard row/column paradigm (RCP) introduced by Farwell and Donchin (1988). Methods Using an 8×9 matrix of alphanumeric characters and keyboard commands, 18 participants used the CBP and RCP in counter-balanced fashion. With approximately 9 – 12 minutes of calibration data, we used a stepwise linear discriminant analysis for online classification of subsequent data. Results Mean online accuracy was significantly higher for the CBP, 92%, than for the RCP, 77%. Correcting for extra selections due to errors, mean bit rate was also significantly higher for the CBP, 23 bits/min, than for the RCP, 17 bits/min. Moreover, the two paradigms produced significantly different waveforms. Initial tests with three advanced ALS participants produced similar results. Furthermore, these individuals preferred the CBP to the RCP. Conclusions These results suggest that the CBP is markedly superior to the RCP in performance and user acceptability. Significance The CBP has the potential to provide a substantially more effective BCI than the RCP. This is especially important for people with severe neuromuscular disabilities. PMID:20347387
Evaluative priming reveals dissociable effects of cognitive versus physiological anxiety on action monitoring.

PubMed

De Saedeleer, Lien; Pourtois, Gilles

2016-06-01

Performance monitoring enables the rapid detection of mismatches between goals or intentions and actions, as well as subsequent behavioral adjustment by means of enhanced attention control. These processes are not encapsulated, but they are readily influenced by affective or motivational variables, including negative affect. Here we tested the prediction that worry, the cognitive component of anxiety, and arousal, its physiological counterpart, can each influence specific processes during performance monitoring. In 2 experiments, participants were asked to discriminate the valence of emotional words that were preceded by either correct (good) or incorrect (bad) actions, serving as primes in a standard evaluative priming procedure. In Experiment 1 (n = 36) we examined the influence of trait worry and arousal. Additionally, we included a face priming task to examine the specificity of this effect. Stepwise linear regression analyses showed that increased worry, but not arousal, weakened the evaluative priming effect and therefore the rapid and automatic processing of actions as good or bad. By contrast, arousal, but not worry, increased posterror slowing. In Experiment 2 (n = 30) state worry was induced using an anagram task. Effects of worry on action monitoring were trait but not state dependent, and only evidenced when actions were directly used as primes. These results suggest a double dissociation between worry and arousal during performance monitoring. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Brain-computer interface (BCI) evaluation in people with amyotrophic lateral sclerosis.

PubMed

McCane, Lynn M; Sellers, Eric W; McFarland, Dennis J; Mak, Joseph N; Carmack, C Steve; Zeitlin, Debra; Wolpaw, Jonathan R; Vaughan, Theresa M

2014-06-01

Brain-computer interfaces (BCIs) might restore communication to people severely disabled by amyotrophic lateral sclerosis (ALS) or other disorders. We sought to: 1) define a protocol for determining whether a person with ALS can use a visual P300-based BCI; 2) determine what proportion of this population can use the BCI; and 3) identify factors affecting BCI performance. Twenty-five individuals with ALS completed an evaluation protocol using a standard 6 × 6 matrix and parameters selected by stepwise linear discrimination. With an 8-channel EEG montage, the subjects fell into two groups in BCI accuracy (chance accuracy 3%). Seventeen averaged 92 (± 3)% (range 71-100%), which is adequate for communication (G70 group). Eight averaged 12 (± 6)% (range 0-36%), inadequate for communication (L40 subject group). Performance did not correlate with disability: 11/17 (65%) of G70 subjects were severely disabled (i.e. ALSFRS-R < 5). All L40 subjects had visual impairments (e.g. nystagmus, diplopia, ptosis). P300 was larger and more anterior in G70 subjects. A 16-channel montage did not significantly improve accuracy. In conclusion, most people severely disabled by ALS could use a visual P300-based BCI for communication. In those who could not, visual impairment was the principal obstacle. For these individuals, auditory P300-based BCIs might be effective.
Stationary-phase optimized selectivity liquid chromatography: development of a linear gradient prediction algorithm.

PubMed

De Beer, Maarten; Lynen, Fréderic; Chen, Kai; Ferguson, Paul; Hanna-Brown, Melissa; Sandra, Pat

2010-03-01

Stationary-phase optimized selectivity liquid chromatography (SOS-LC) is a tool in reversed-phase LC (RP-LC) to optimize the selectivity for a given separation by combining stationary phases in a multisegment column. The presently (commercially) available SOS-LC optimization procedure and algorithm are only applicable to isocratic analyses. Step gradient SOS-LC has been developed, but this is still not very elegant for the analysis of complex mixtures composed of components covering a broad hydrophobicity range. A linear gradient prediction algorithm has been developed allowing one to apply SOS-LC as a generic RP-LC optimization method. The algorithm allows operation in isocratic, stepwise, and linear gradient run modes. The features of SOS-LC in the linear gradient mode are demonstrated by means of a mixture of 13 steroids, whereby baseline separation is predicted and experimentally demonstrated.
Stable orthogonal local discriminant embedding for linear dimensionality reduction.

PubMed

Gao, Quanxue; Ma, Jingjie; Zhang, Hailin; Gao, Xinbo; Liu, Yamin

2013-07-01

Manifold learning is widely used in machine learning and pattern recognition. However, manifold learning only considers the similarity of samples belonging to the same class and ignores the within-class variation of data, which will impair the generalization and stableness of the algorithms. For this purpose, we construct an adjacency graph to model the intraclass variation that characterizes the most important properties, such as diversity of patterns, and then incorporate the diversity into the discriminant objective function for linear dimensionality reduction. Finally, we introduce the orthogonal constraint for the basis vectors and propose an orthogonal algorithm called stable orthogonal local discriminate embedding. Experimental results on several standard image databases demonstrate the effectiveness of the proposed dimensionality reduction approach.
Transthyretin familial amyloid polyneuropathy (TTR-FAP): Parameters for early diagnosis.

PubMed

Escolano-Lozano, Fabiola; Barreiros, Ana Paula; Birklein, Frank; Geber, Christian

2018-01-01

Familial transthyretin amyloidosis is a life-threatening disease presenting with sensorimotor and autonomic polyneuropathy. Delayed diagnosis has a detrimental effect on treatment and prognosis. To facilitate diagnosis, we analyzed data patterns of patients with transthyretin familial amyloid polyneuropathy (TTR-FAP) and compared them to polyneuropathies of different etiology for clinical and electrophysiological discriminators. Twenty-four patients with TTR-FAP and 48 patients with diabetic polyneuropathy (dPNP) were investigated (neurological impairment score NIS; neurological disability score NDS) in a cross-sectional design. Both groups were matched for gender and presence of pain. Quantitative sensory testing (QST), sympathetic skin response (SSR), heart rate variability (HRV), and nerve conduction studies (NCV) were performed. Both groups were compared using univariate analysis. In a stepwise discriminant analysis, discriminators between both neuropathies were identified. These discriminators were validated comparing TTR-FAP patients with a cohort of patients with chemotherapy-induced polyneuropathy (CIN) and chronic inflammatory demyelinating neuropathy (CIDP). TTR-FAP patients scored higher in NDS and NIS and had impaired cold detection (CDT, p = .024), cold-warm discrimination (TSL, p = .019) and mechanical hyperalgesia (MPT, p = .029) at the hands, SSR (upper limb, p = .022) HRV and ulnar and sural NCS (all p < .05) were more affected in TTR-FAP. Ulnar nerve sensory NCV, CDT, and the MPT but not the other parameters discriminated TTR-FAP from dPNP (82% of cases), from CIN (86.7%) and from CIDP (68%; only ulnar sNCV). Low ulnar SNCV, impaired cold perception, and mechanical hyperalgesia at the hands seem to characterize TTR-FAP and might help to differentiate from other polyneuropathies.
Deformation-Aware Log-Linear Models

NASA Astrophysics Data System (ADS)

Gass, Tobias; Deselaers, Thomas; Ney, Hermann

In this paper, we present a novel deformation-aware discriminative model for handwritten digit recognition. Unlike previous approaches our model directly considers image deformations and allows discriminative training of all parameters, including those accounting for non-linear transformations of the image. This is achieved by extending a log-linear framework to incorporate a latent deformation variable. The resulting model has an order of magnitude less parameters than competing approaches to handling image deformations. We tune and evaluate our approach on the USPS task and show its generalization capabilities by applying the tuned model to the MNIST task. We gain interesting insights and achieve highly competitive results on both tasks.
Investigating the sources of sediment in a Canadian agricultural watershed using a colour-based fingerprinting technique

NASA Astrophysics Data System (ADS)

Barthod, Louise; Lobb, David; Owens, Philip; Martinez-Carreras, Nuria; Koiter, Alexander; Petticrew, Ellen; McCullough, Gregory

2014-05-01

The development of beneficial management practises to minimize adverse impacts of agriculture on soil and water quality requires information on the sources of sediment at the watershed scale. Sediment fingerprinting allows for the determination of sediment sources and apportionment of their contribution within a watershed, using unique physical, radiochemical or biogeochemical properties, or fingerprints, of the potential sediment sources. The use of sediment colour as a fingerprint is an emerging technique that can provide a rapid and inexpensive means of investigating sediment sources. This technique is currently being utilized to determine sediment sources within the South Tobacco Creek Watershed, an agricultural watershed located in the Canadian prairies (south-central Manitoba). Suspended sediment and potential source (topsoil, channel bank and shale bedrock material) samples were collected between 2009 and 2011 at six locations along the main stem of the creek. Sample colour was quantified from diffuse reflectance spectrometry measurements over the visible wavelength range using a spectroradiometer (ASD Field Spec Pro, 400-2500 nm). Sixteen colour coefficients were derived from several colour space models (CIE XYZ, CIE xyY, CIE Lab, CIE Luv, CIE Lch, Landsat RGB, Redness Index). The individual discrimination power of the colour coefficients, after passing several prerequisite tests (e.g., linearly additive behaviour), was assessed using discriminant function analysis. A stepwise discriminant analysis, based on the Wilk's lambda criterion, was then performed in order to determine the best-suited colour coefficient fingerprints which maximized the discrimination between the potential sources. The selected fingerprints classified the source samples in the correct category 86% of the time. The misclassification is due to intra-source variability and source overlap which can lead to higher uncertainty in sediment source apportionment. The selected fingerprints were then included in a Bayesian mixing model using Monte-Carlo simulation (Stable Isotope Analysis in R, SIAR) in order to apportion the contribution of the different sources to the sediment collected at each location. A switch in the dominant sediment source between the headwaters and the outlet of the watershed is observed. Sediment is enriched with shale bedrock and depleted of topsoil sources as the stream crosses and down-cuts through the Manitoba Escarpment. The switch in sources highlights the importance of the sampling location in relation to the scale and geomorphic connectivity of the watershed. Although the results include considerable uncertainty, they are consistent with the findings from a classical fingerprinting approach undertaken in the same watershed (i.e., geochemical and radionuclide fingerprints), and confirm the potential of sediment colour parameters as suitable fingerprints.
Estimation of standard liver volume in Chinese adult living donors.

PubMed

Fu-Gui, L; Lu-Nan, Y; Bo, L; Yong, Z; Tian-Fu, W; Ming-Qing, X; Wen-Tao, W; Zhe-Yu, C

2009-12-01

To determine a formula predicting the standard liver volume based on body surface area (BSA) or body weight in Chinese adults. A total of 115 consecutive right-lobe living donors not including the middle hepatic vein underwent right hemi-hepatectomy. No organs were used from prisoners, and no subjects were prisoners. Donor anthropometric data including age, gender, body weight, and body height were recorded prospectively. The weights and volumes of the right lobe liver grafts were measured at the back table. Liver weights and volumes were calculated from the right lobe graft weight and volume obtained at the back table, divided by the proportion of the right lobe on computed tomography. By simple linear regression analysis and stepwise multiple linear regression analysis, we correlated calculated liver volume and body height, body weight, or body surface area. The subjects had a mean age of 35.97 +/- 9.6 years, and a female-to-male ratio of 60:55. The mean volume of the right lobe was 727.47 +/- 136.17 mL, occupying 55.59% +/- 6.70% of the whole liver by computed tomography. The volume of the right lobe was 581.73 +/- 96.137 mL, and the estimated liver volume was 1053.08 +/- 167.56 mL. Females of the same body weight showed a slightly lower liver weight. By simple linear regression analysis and stepwise multiple linear regression analysis, a formula was derived based on body weight. All formulae except the Hong Kong formula overestimated liver volume compared to this formula. The formula of standard liver volume, SLV (mL) = 11.508 x body weight (kg) + 334.024, may be applied to estimate liver volumes in Chinese adults.
Short Personality and Life Event scale for detection of suicide attempters.

PubMed

Artieda-Urrutia, Paula; Delgado-Gómez, David; Ruiz-Hernández, Diego; García-Vega, Juan Manuel; Berenguer, Nuria; Oquendo, Maria A; Blasco-Fontecilla, Hilario

2015-01-01

To develop a brief and reliable psychometric scale to identify individuals at risk for suicidal behaviour. Case-control study. 182 individuals (61 suicide attempters, 57 psychiatric controls, and 64 psychiatrically healthy controls) aged 18 or older, admitted to the Emergency Department at Puerta de Hierro University Hospital in Madrid, Spain. All participants completed a form including their socio-demographic and clinical characteristics, and the Personality and Life Events scale (27 items). To assess Axis I diagnoses, all psychiatric patients (including suicide attempters) were administered the Mini International Neuropsychiatric Interview. Descriptive statistics were computed for the socio-demographic factors. Additionally, χ(2) independence tests were applied to evaluate differences in socio-demographic and clinical variables, and the Personality and Life Events scale between groups. A stepwise linear regression with backward variable selection was conducted to build the Short Personality Life Event (S-PLE) scale. In order to evaluate the accuracy, a ROC analysis was conducted. The internal reliability was assessed using Cronbach's α, and the external reliability was evaluated using a test-retest procedure. The S-PLE scale, composed of just 6 items, showed good performance in discriminating between medical controls, psychiatric controls and suicide attempters in an independent sample. For instance, the S-PLE scale discriminated between past suicide and past non-suicide attempters with sensitivity of 80% and specificity of 75%. The area under the ROC curve was 88%. A factor analysis extracted only one factor, revealing a single dimension of the S-PLE scale. Furthermore, the S-PLE scale provides values of internal and external reliability between poor (test-retest: 0.55) and acceptable (Cronbach's α: 0.65) ranges. Administration time is about one minute. The S-PLE scale is a useful and accurate instrument for estimating the risk of suicidal behaviour in settings where the time is scarce. Copyright © 2015 SEP y SEPB. Published by Elsevier España. All rights reserved.
Genetic predisposition scores associate with muscular strength, size, and trainability.

PubMed

Thomaes, Tom; Thomis, Martine; Onkelinx, Steven; Goetschalckx, Kaatje; Fagard, Robert; Lambrechts, Diether; Vanhees, Luc

2013-08-01

The number of studies trying to identify genetic sequence variation related to muscular phenotypes has increased enormously. The aim of this study was to identify the role of a genetic predisposition score (GPS) based on earlier identified gene variants for different muscular endophenotypes to explain the individual differences in muscular fitness characteristics and the response to training in patients with coronary artery disease. Two hundred and sixty coronary artery disease patients followed a standard ambulatory, 3-month supervised training program for cardiac patients. Maximal knee extension strength (KES) and rectus femoris diameter were measured at baseline and after rehabilitation. Sixty-five single nucleotide polymorphisms (SNP) in 30 genes were selected based on genotype-phenotype association literature. Backward regression analysis revealed subsets of SNP associated with the different phenotypes. GPS were constructed for all sets of SNP by adding up the strength-increasing alleles. General linear models and multiple stepwise regression analysis were used to test the explained variance of the GPS in baseline and strength responses. Receiver operating characteristic curve analyses were performed to discriminate between high- and low-responder status. GPS were significantly associated with the rectus femoris diameter (P < 0.01) and its response (P < 0.0001), the isometric KES (P < 0.05) and its response (P < 0.01), the isokinetic KES at 60° · s (P < 0.05) and 180° · s (P < 0.001) and their responses to training (P < 0.0001), and the isokinetic KES endurance (P < 0.001) and its change after training (P < 0.0001). The GPS was shown as an independent determinant in baseline and response phenotypes with partial explained variance up to 23%. Receiver operating characteristic analysis showed a significant discriminating accuracy of the models, including the GPS for responses to training, with areas under the curve ranging from 0.62 to 0.85. GPS for muscular phenotypes showed to be associated with baseline KES, muscle diameter, and the response to training in cardiac rehabilitation patients.
Information theoretic partitioning and confidence based weight assignment for multi-classifier decision level fusion in hyperspectral target recognition applications

NASA Astrophysics Data System (ADS)

Prasad, S.; Bruce, L. M.

2007-04-01

There is a growing interest in using multiple sources for automatic target recognition (ATR) applications. One approach is to take multiple, independent observations of a phenomenon and perform a feature level or a decision level fusion for ATR. This paper proposes a method to utilize these types of multi-source fusion techniques to exploit hyperspectral data when only a small number of training pixels are available. Conventional hyperspectral image based ATR techniques project the high dimensional reflectance signature onto a lower dimensional subspace using techniques such as Principal Components Analysis (PCA), Fisher's linear discriminant analysis (LDA), subspace LDA and stepwise LDA. While some of these techniques attempt to solve the curse of dimensionality, or small sample size problem, these are not necessarily optimal projections. In this paper, we present a divide and conquer approach to address the small sample size problem. The hyperspectral space is partitioned into contiguous subspaces such that the discriminative information within each subspace is maximized, and the statistical dependence between subspaces is minimized. We then treat each subspace as a separate source in a multi-source multi-classifier setup and test various decision fusion schemes to determine their efficacy. Unlike previous approaches which use correlation between variables for band grouping, we study the efficacy of higher order statistical information (using average mutual information) for a bottom up band grouping. We also propose a confidence measure based decision fusion technique, where the weights associated with various classifiers are based on their confidence in recognizing the training data. To this end, training accuracies of all classifiers are used for weight assignment in the fusion process of test pixels. The proposed methods are tested using hyperspectral data with known ground truth, such that the efficacy can be quantitatively measured in terms of target recognition accuracies.
Score-moment combined linear discrimination analysis (SMC-LDA) as an improved discrimination method.

PubMed

Han, Jintae; Chung, Hoeil; Han, Sung-Hwan; Yoon, Moon-Young

2007-01-01

A new discrimination method called the score-moment combined linear discrimination analysis (SMC-LDA) has been developed and its performance has been evaluated using three practical spectroscopic datasets. The key concept of SMC-LDA was to use not only the score from principal component analysis (PCA), but also the moment of the spectrum, as inputs for LDA to improve discrimination. Along with conventional score, moment is used in spectroscopic fields as an effective alternative for spectral feature representation. Three different approaches were considered. Initially, the score generated from PCA was projected onto a two-dimensional feature space by maximizing Fisher's criterion function (conventional PCA-LDA). Next, the same procedure was performed using only moment. Finally, both score and moment were utilized simultaneously for LDA. To evaluate discrimination performances, three different spectroscopic datasets were employed: (1) infrared (IR) spectra of normal and malignant stomach tissue, (2) near-infrared (NIR) spectra of diesel and light gas oil (LGO) and (3) Raman spectra of Chinese and Korean ginseng. For each case, the best discrimination results were achieved when both score and moment were used for LDA (SMC-LDA). Since the spectral representation character of moment was different from that of score, inclusion of both score and moment for LDA provided more diversified and descriptive information.

Does perceived racial discrimination predict changes in psychological distress and substance use over time? An examination among Black emerging adults.

PubMed

Hurd, Noelle M; Varner, Fatima A; Caldwell, Cleopatra H; Zimmerman, Marc A

2014-07-01

We assessed whether perceived discrimination predicted changes in psychological distress and substance use over time and whether psychological distress and substance use predicted change in perceived discrimination over time. We also assessed whether associations between these constructs varied by gender. Our sample included 607 Black emerging adults (53% female) followed for 4 years. Participants reported the frequency with which they had experienced racial hassles during the past year, symptoms of anxiety and depression during the past week, and cigarette and alcohol use during the past 30 days. We estimated a series of latent growth models to test our study hypotheses. We found that the intercept of perceived discrimination predicted the linear slopes of anxiety symptoms, depressive symptoms, and alcohol use. We did not find any associations between the intercept factors of our mental health or substance use variables and the perceived discrimination linear slope factor. We found limited differences across paths by gender. Our findings suggest a temporal ordering in the associations among perceived racial discrimination, psychological distress, and alcohol use over time among emerging adults. Further, our findings suggest that perceived racial discrimination may be similarly harmful among men and women. (PsycINFO Database Record (c) 2014 APA, all rights reserved).
Research of Face Recognition with Fisher Linear Discriminant

NASA Astrophysics Data System (ADS)

Rahim, R.; Afriliansyah, T.; Winata, H.; Nofriansyah, D.; Ratnadewi; Aryza, S.

2018-01-01

Face identification systems are developing rapidly, and these developments drive the advancement of biometric-based identification systems that have high accuracy. However, to develop a good face recognition system and to have high accuracy is something that’s hard to find. Human faces have diverse expressions and attribute changes such as eyeglasses, mustache, beard and others. Fisher Linear Discriminant (FLD) is a class-specific method that distinguishes facial image images into classes and also creates distance between classes and intra classes so as to produce better classification.
L1-norm kernel discriminant analysis via Bayes error bound optimization for robust feature extraction.

PubMed

Zheng, Wenming; Lin, Zhouchen; Wang, Haixian

2014-04-01

A novel discriminant analysis criterion is derived in this paper under the theoretical framework of Bayes optimality. In contrast to the conventional Fisher's discriminant criterion, the major novelty of the proposed one is the use of L1 norm rather than L2 norm, which makes it less sensitive to the outliers. With the L1-norm discriminant criterion, we propose a new linear discriminant analysis (L1-LDA) method for linear feature extraction problem. To solve the L1-LDA optimization problem, we propose an efficient iterative algorithm, in which a novel surrogate convex function is introduced such that the optimization problem in each iteration is to simply solve a convex programming problem and a close-form solution is guaranteed to this problem. Moreover, we also generalize the L1-LDA method to deal with the nonlinear robust feature extraction problems via the use of kernel trick, and hereafter proposed the L1-norm kernel discriminant analysis (L1-KDA) method. Extensive experiments on simulated and real data sets are conducted to evaluate the effectiveness of the proposed method in comparing with the state-of-the-art methods.
Polar cloud and surface classification using AVHRR imagery - An intercomparison of methods

NASA Technical Reports Server (NTRS)

Welch, R. M.; Sengupta, S. K.; Goroch, A. K.; Rabindra, P.; Rangaraj, N.; Navar, M. S.

1992-01-01

Six Advanced Very High-Resolution Radiometer local area coverage (AVHRR LAC) arctic scenes are classified into ten classes. Three different classifiers are examined: (1) the traditional stepwise discriminant analysis (SDA) method; (2) the feed-forward back-propagation (FFBP) neural network; and (3) the probabilistic neural network (PNN). More than 200 spectral and textural measures are computed. These are reduced to 20 features using sequential forward selection. Theoretical accuracy of the classifiers is determined using the bootstrap approach. Overall accuracy is 85.6 percent, 87.6 percent, and 87.0 percent for the SDA, FFBP, and PNN classifiers, respectively, with standard deviations of approximately 1 percent.
Synthesis and analysis of discriminators under influence of broadband non-Gaussian noise

NASA Astrophysics Data System (ADS)

Artyushenko, V. M.; Volovach, V. I.

2018-01-01

We considered the problems of the synthesis and analysis of discriminators, when the useful signal is exposed to non-Gaussian additive broadband noise. It is shown that in this case, the discriminator of the tracking meter should contain the nonlinear transformation unit, the characteristics of which are determined by the Fisher information relative to the probability density function of the mixture of non-Gaussian broadband noise and mismatch errors. The parameters of the discriminatory and phase characteristics of the discriminators working under the above conditions are obtained. It is shown that the efficiency of non-linear processing depends on the ratio of power of FM noise to the power of Gaussian noise. The analysis of the information loss of signal transformation caused by the linear section of discriminatory characteristics of the unit of nonlinear transformations of the discriminator is carried out. It is shown that the average slope of the nonlinear transformation characteristic is determined by the Fisher information relative to the probability density function of the mixture of non-Gaussian noise and mismatch errors.
Relationship between acculturation, discrimination, and suicidal ideation and attempts among US Hispanics in the National Epidemiologic Survey of Alcohol and Related Conditions.

PubMed

Perez-Rodriguez, M Mercedes; Baca-Garcia, Enrique; Oquendo, Maria A; Wang, Shuai; Wall, Melanie M; Liu, Shang-Min; Blanco, Carlos

2014-04-01

Acculturation is the process by which immigrants acquire the culture of the dominant society. Little is known about the relationship between acculturation and suicidal ideation and attempts among US Hispanics. Our aim was to examine the impact of 5 acculturation measures (age at migration, time in the United States, social network composition, language, race/ethnic orientation) on suicidal ideation and attempts in the largest available nationally representative sample of US Hispanics. Study participants were US Hispanics (N = 6,359) from Wave 2 of the 2004-2005 National Epidemiologic Survey of Alcohol and Related Conditions (N = 34,653). We used linear χ(2) tests and logistic regression models to analyze the association between acculturation and risk of suicidal ideation and attempts. Factors associated with a linear increase in lifetime risk for suicidal ideation and attempts were (1) younger age at migration (linear χ(2)(1) = 57.15; P < .0001), (2) longer time in the United States (linear χ(2)(1)= 36.09; P < .0001), (3) higher degree of English-language orientation (linear χ(2)(1) = 74.08; P <.0001), (4) lower Hispanic composition of social network (linear χ(2)(1) = 36.34; P < .0001), and (5) lower Hispanic racial/ethnic identification (linear χ(2)(1) = 47.77; P <.0001). Higher levels of perceived discrimination were associated with higher lifetime risk for suicidal ideation (β = 0.051; P < .001) and attempts (β = 0.020; P = .003). There was a linear association between multiple dimensions of acculturation and lifetime suicidal ideation and attempts. Discrimination was also associated with lifetime risk for suicidal ideation and attempts. Our results highlight protective aspects of the traditional Hispanic culture, such as high social support, coping strategies, and moral objections to suicide, which are modifiable factors and potential targets for public health interventions aimed at decreasing suicide risk. Culturally sensitive mental health resources need to be made more available to decrease discrimination and stigma. © Copyright 2014 Physicians Postgraduate Press, Inc.
Serum chemerin levels are independently associated with quality of life in colorectal cancer survivors: A pilot study

PubMed Central

Lee, Jee-Yon; Lee, Mi-Kyung; Kim, Nam-Kyu; Chu, Sang-Hui; Lee, Duk-Chul; Lee, Hye-Sun

2017-01-01

Background Colorectal cancer (CRC) survivors are known to experience various symptoms that significantly affect their quality of life (QOL); therefore, it is important to identify clinical markers related with CRC survivor QOL. Here we investigated the relationship between serum chemerin levels, a newly identified proinflammatory adipokine, and QOL in CRC survivors. Methods A data of total of 110 CRC survivors were analysed in the study. Serum chemerin levels were measured with an enzyme immunoassay analyser. Functional Assessment of Cancer Therapy (FACT) scores were used as an indicator of QOL in CRC survivors. Results Weak but not negligible relationships were observed between serum chemerin levels and FACT-General (G) (r = -0.22, p<0.02), FACT-Colorectal cancer (C) (r = -0.23, p<0.02) and FACT-Fatigue (F) scores (r = -0.27, p<0.01) after adjusting for confounding factors. Both stepwise and enter method multiple linear regression analyses confirmed that serum chemerin levels were independently associated with FACT-G (stepwise: β = -0.15, p<0.01; enter: β = -0.12, p = 0.02), FACT-C (stepwise: β = -0.19, p<0.01; enter; β = -0.14, p = 0.02) and FACT-F scores (stepwise: β = -0.23, p<0.01; enter: β = -0.20, p<0.01). Conclusions Our results demonstrate a weak inverse relationship between serum chemerin and CRC survivor QOL. Although it is impossible to determine causality, our findings suggest that serum chemerin levels may have a significant association with CRC survivor QOL. Further prospective studies are required to confirm the clinical significance of our pilot study. PMID:28475614
K/Ar dating of lunar soils. IV - Orange glass from 74220 and agglutinates from 14259 and 14163

NASA Technical Reports Server (NTRS)

Alexander, E. C., Jr.; Coscio, M. R., Jr.; Dragon, J. C.; Saito, K.

1980-01-01

Total fusion Ar-40 - A-39 analyses of orange glass from lunar soil 74220 combined with the sums of earlier stepwise heating data by other workers have yielded a precise K/Ar isochron with a slope corresponding to an age of 3.66 + or - 0.03 G.y. for the orange glass. The result is in marginal agreement with Huneke's (1978) age of 3.60 + or - 0.04 G.y. for 74220 glass. The Ar systematics in the agglutinates from 14259 and 14163 are dominated by volume correlated argon. Step-wise heating analyses yield data which define experimentally reproducible linear arrays in Ar-40/Ar-36 vs. K-40/Ar-36 diagrams. The slopes of these arrays correspond formally to very old ages, but it is not clear, however, that such ages have any physical significance.
Motor Nerve Conduction Velocity In Postmenopausal Women with Peripheral Neuropathy.

PubMed

Singh, Akanksha; Asif, Naiyer; Singh, Paras Nath; Hossain, Mohd Mobarak

2016-12-01

The post-menopausal phase is characterized by a decline in the serum oestrogen and progesterone levels. This phase is also associated with higher incidence of peripheral neuropathy. To explore the relationship between the peripheral motor nerve status and serum oestrogen and progesterone levels through assessment of Motor Nerve Conduction Velocity (MNCV) in post-menopausal women with peripheral neuropathy. This cross-sectional study was conducted at Jawaharlal Nehru Medical College during 2011-2013. The study included 30 post-menopausal women with peripheral neuropathy (age: 51.4±7.9) and 30 post-menopausal women without peripheral neuropathy (control) (age: 52.5±4.9). They were compared for MNCV in median, ulnar and common peroneal nerves and serum levels of oestrogen and progesterone estimated through enzyme immunoassays. To study the relationship between hormone levels and MNCV, a stepwise linear regression analysis was done. The post-menopausal women with peripheral neuropathy had significantly lower MNCV and serum oestrogen and progesterone levels as compared to control subjects. Stepwise linear regression analysis showed oestrogen with main effect on MNCV. The findings of the present study suggest that while the post-menopausal age group is at a greater risk of peripheral neuropathy, it is the decline in the serum estrogen levels which is critical in the development of peripheral neuropathy.
Predicting Retention Times of Naturally Occurring Phenolic Compounds in Reversed-Phase Liquid Chromatography: A Quantitative Structure-Retention Relationship (QSRR) Approach

PubMed Central

Akbar, Jamshed; Iqbal, Shahid; Batool, Fozia; Karim, Abdul; Chan, Kim Wei

2012-01-01

Quantitative structure-retention relationships (QSRRs) have successfully been developed for naturally occurring phenolic compounds in a reversed-phase liquid chromatographic (RPLC) system. A total of 1519 descriptors were calculated from the optimized structures of the molecules using MOPAC2009 and DRAGON softwares. The data set of 39 molecules was divided into training and external validation sets. For feature selection and mapping we used step-wise multiple linear regression (SMLR), unsupervised forward selection followed by step-wise multiple linear regression (UFS-SMLR) and artificial neural networks (ANN). Stable and robust models with significant predictive abilities in terms of validation statistics were obtained with negation of any chance correlation. ANN models were found better than remaining two approaches. HNar, IDM, Mp, GATS2v, DISP and 3D-MoRSE (signals 22, 28 and 32) descriptors based on van der Waals volume, electronegativity, mass and polarizability, at atomic level, were found to have significant effects on the retention times. The possible implications of these descriptors in RPLC have been discussed. All the models are proven to be quite able to predict the retention times of phenolic compounds and have shown remarkable validation, robustness, stability and predictive performance. PMID:23203132
Supervised linear dimensionality reduction with robust margins for object recognition

NASA Astrophysics Data System (ADS)

Dornaika, F.; Assoum, A.

2013-01-01

Linear Dimensionality Reduction (LDR) techniques have been increasingly important in computer vision and pattern recognition since they permit a relatively simple mapping of data onto a lower dimensional subspace, leading to simple and computationally efficient classification strategies. Recently, many linear discriminant methods have been developed in order to reduce the dimensionality of visual data and to enhance the discrimination between different groups or classes. Many existing linear embedding techniques relied on the use of local margins in order to get a good discrimination performance. However, dealing with outliers and within-class diversity has not been addressed by margin-based embedding method. In this paper, we explored the use of different margin-based linear embedding methods. More precisely, we propose to use the concepts of Median miss and Median hit for building robust margin-based criteria. Based on such margins, we seek the projection directions (linear embedding) such that the sum of local margins is maximized. Our proposed approach has been applied to the problem of appearance-based face recognition. Experiments performed on four public face databases show that the proposed approach can give better generalization performance than the classic Average Neighborhood Margin Maximization (ANMM). Moreover, thanks to the use of robust margins, the proposed method down-grades gracefully when label outliers contaminate the training data set. In particular, we show that the concept of Median hit was crucial in order to get robust performance in the presence of outliers.
Rapid differentiation of Ghana cocoa beans by FT-NIR spectroscopy coupled with multivariate classification

NASA Astrophysics Data System (ADS)

Teye, Ernest; Huang, Xingyi; Dai, Huang; Chen, Quansheng

2013-10-01

Quick, accurate and reliable technique for discrimination of cocoa beans according to geographical origin is essential for quality control and traceability management. This current study presents the application of Near Infrared Spectroscopy technique and multivariate classification for the differentiation of Ghana cocoa beans. A total of 194 cocoa bean samples from seven cocoa growing regions were used. Principal component analysis (PCA) was used to extract relevant information from the spectral data and this gave visible cluster trends. The performance of four multivariate classification methods: Linear discriminant analysis (LDA), K-nearest neighbors (KNN), Back propagation artificial neural network (BPANN) and Support vector machine (SVM) were compared. The performances of the models were optimized by cross validation. The results revealed that; SVM model was superior to all the mathematical methods with a discrimination rate of 100% in both the training and prediction set after preprocessing with Mean centering (MC). BPANN had a discrimination rate of 99.23% for the training set and 96.88% for prediction set. While LDA model had 96.15% and 90.63% for the training and prediction sets respectively. KNN model had 75.01% for the training set and 72.31% for prediction set. The non-linear classification methods used were superior to the linear ones. Generally, the results revealed that NIR Spectroscopy coupled with SVM model could be used successfully to discriminate cocoa beans according to their geographical origins for effective quality assurance.
Computer-assisted sperm morphometry fluorescence-based analysis has potential to determine progeny sex.

PubMed

Santolaria, Pilar; Pauciullo, Alfredo; Silvestre, Miguel A; Vicente-Fiel, Sandra; Villanova, Leyre; Pinton, Alain; Viruel, Juan; Sales, Ester; Yániz, Jesús L

2016-01-01

This study was designed to determine the ability of computer-assisted sperm morphometry analysis (CASA-Morph) with fluorescence to discriminate between spermatozoa carrying different sex chromosomes from the nuclear morphometrics generated and different statistical procedures in the bovine species. The study was divided into two experiments. The first was to study the morphometric differences between X- and Y-chromosome-bearing spermatozoa (SX and SY, respectively). Spermatozoa from eight bulls were processed to assess simultaneously the sex chromosome by FISH and sperm morphometry by fluorescence-based CASA-Morph. SX cells were larger than SY cells on average (P < 0.001) although with important differences between bulls. A simultaneous evaluation of all the measured features by discriminant analysis revealed that nuclear area and average fluorescence intensity were the variables selected by stepwise discriminant function analysis as the best discriminators between SX and SY. In the second experiment, the sperm nuclear morphometric results from CASA-Morph in nonsexed (mixed SX and SY) and sexed (SX) semen samples from four bulls were compared. FISH allowed a successful classification of spermatozoa according to their sex chromosome content. X-sexed spermatozoa displayed a larger size and fluorescence intensity than nonsexed spermatozoa (P < 0.05). We conclude that the CASA-Morph fluorescence-based method has the potential to find differences between X- and Y-chromosome-bearing spermatozoa in bovine species although more studies are needed to increase the precision of sex determination by this technique.
Determination of sex from the patella in a contemporary Spanish population.

PubMed

Peckmann, Tanya R; Meek, Susan; Dilkie, Natasha; Rozendaal, Andrew

2016-11-01

The skull and pelvis have been used for the determination of sex for unknown human remains. However, in forensic cases where skeletal remains often exhibit postmortem damage and taphonomic changes the patella may be used for the determination of sex as it is a preservationally favoured bone. The goal of the present research was to derive discriminant function equations from the patella for estimation of sex from a contemporary Spanish population. Six parameters were measured on 106 individuals (55 males and 51 females), ranging in age from 22 to 85 years old, from the Granada Osteological Collection. The statistical analyses showed that all variables were sexually dimorphic. Discriminant function score equations were generated for use in sex determination. The overall accuracy of sex classification ranged from 75.2% to 84.8% for the direct method and 75.5%-83.8% for the stepwise method. When the South African White discriminant functions were applied to the Spanish sample they showed high accuracy rates for sexing female patellae (90%-95.9%) and low accuracy rates for sexing male patellae (52.7%-58.2%). When the South African Black discriminant functions were applied to the Spanish sample they showed high accuracy rates for sexing male patellae (90.9%) and low accuracy rates for sexing female patellae (70%-75.5%). The patella was shown to be useful for sex determination in the contemporary Spanish population. Copyright © 2016 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.
Measurements of the talus in the assessment of population affinity.

PubMed

Bidmos, Mubarak A; Dayal, Manisha R; Adegboye, Oyelola A

2018-06-01

As part of their routine work, forensic anthropologists are expected to report population affinity as part of the biological profile of an individual. The skull is the most widely used bone for the estimation of population affinity but it is not always present in a forensic case. Thus, other bones that preserve well have been shown to give a good indication of either the sex or population affinity of an individual. In this study, the potential of measurements of the talus was investigated for the purpose of estimating population affinity in South Africans. Nine measurements from two hundred and twenty tali of South African Africans (SAA) and South African Whites (SAW) from the Raymond A. Dart Collection of Human Skeletons were used. Direct and step-wise discriminant function and logistic regression analyses were carried out using SPSS and SAS. Talar length was the best single variable for discriminating between these two groups for males while in females the head height was the best single predictor. Average accuracies for correct population affinity classification using logistic regression analysis were higher than those obtained from discriminant function analysis. This study was the first of its type to employ discriminant function analyses and logistic regression analyses to estimate the population affinity of an individual from the talus. Thus these equations can now be used by South African anthropologists when estimating the population affinity of dismembered or damaged or incomplete skeletal remains of SAA and SAW. Copyright © 2018 Elsevier B.V. All rights reserved.
Use of handheld X-ray fluorescence as a non-invasive method to distinguish between Asian and African elephant tusks

PubMed Central

Buddhachat, Kittisak; Thitaram, Chatchote; Brown, Janine L.; Klinhom, Sarisa; Bansiddhi, Pakkanut; Penchart, Kitichaya; Ouitavon, Kanita; Sriaksorn, Khanittha; Pa-in, Chalermpol; Kanchanasaka, Budsabong; Somgird, Chaleamchat; Nganvongpanit, Korakot

2016-01-01

We describe the use of handheld X-ray fluorescence, for elephant tusk species identification. Asian (n = 72) and African (n = 85) elephant tusks were scanned and we utilized the species differences in elemental composition to develop a functional model differentiating between species with high precision. Spatially, the majority of measured elements (n = 26) exhibited a homogeneous distribution in cross-section, but a more heterologous pattern in the longitudinal direction. Twenty-one of twenty four elements differed between Asian and African samples. Data were subjected to hierarchical cluster analysis followed by a stepwise discriminant analysis, which identified elements for the functional equation. The best equation consisted of ratios of Si, S, Cl, Ti, Mn, Ag, Sb and W, with Zr as the denominator. Next, Bayesian binary regression model analysis was conducted to predict the probability that a tusk would be of African origin. A cut-off value was established to improve discrimination. This Bayesian hybrid classification model was then validated by scanning an additional 30 Asian and 41 African tusks, which showed high accuracy (94%) and precision (95%) rates. We conclude that handheld XRF is an accurate, non-invasive method to discriminate origin of elephant tusks provides rapid results applicable to use in the field. PMID:27097717
Sex determination from the talus in a contemporary Greek population using discriminant function analysis.

PubMed

Peckmann, Tanya R; Orr, Kayla; Meek, Susan; Manolis, Sotiris K

2015-07-01

The determination of sex is an important part of building the biological profile for unknown human remains. Many of the bones traditionally used for the determination of sex are often found fragmented or incomplete in forensic and archaeological cases. The goal of the present research was to derive discriminant function equations from the talus, a preservationally favoured bone, for sexing skeletons from a contemporary Greek population. Nine parameters were measured on 182 individuals (96 males and 86 females) from the University of Athens Human Skeletal Reference Collection. The individuals ranged in age from 20 to 99 years old. The statistical analyses showed that all measured parameters were sexually dimorphic. Discriminant function score equations were generated for use in sex determination. The average accuracy of sex classification ranged from 65.2% to 93.4% for the univariate analysis, 90%-96.5% for the direct method and 86.7% for the stepwise method. Comparisons to other populations were made. Overall, the cross-validated accuracies ranged from 65.5% to 83.2% and males were most often correctly identified. The talus was shown to be useful for sex determination in the modern Greek population. Copyright © 2015 Elsevier Ltd and Faculty of Forensic and Legal Medicine. All rights reserved.
Use of handheld X-ray fluorescence as a non-invasive method to distinguish between Asian and African elephant tusks

NASA Astrophysics Data System (ADS)

Buddhachat, Kittisak; Thitaram, Chatchote; Brown, Janine L.; Klinhom, Sarisa; Bansiddhi, Pakkanut; Penchart, Kitichaya; Ouitavon, Kanita; Sriaksorn, Khanittha; Pa-in, Chalermpol; Kanchanasaka, Budsabong; Somgird, Chaleamchat; Nganvongpanit, Korakot

2016-04-01

We describe the use of handheld X-ray fluorescence, for elephant tusk species identification. Asian (n = 72) and African (n = 85) elephant tusks were scanned and we utilized the species differences in elemental composition to develop a functional model differentiating between species with high precision. Spatially, the majority of measured elements (n = 26) exhibited a homogeneous distribution in cross-section, but a more heterologous pattern in the longitudinal direction. Twenty-one of twenty four elements differed between Asian and African samples. Data were subjected to hierarchical cluster analysis followed by a stepwise discriminant analysis, which identified elements for the functional equation. The best equation consisted of ratios of Si, S, Cl, Ti, Mn, Ag, Sb and W, with Zr as the denominator. Next, Bayesian binary regression model analysis was conducted to predict the probability that a tusk would be of African origin. A cut-off value was established to improve discrimination. This Bayesian hybrid classification model was then validated by scanning an additional 30 Asian and 41 African tusks, which showed high accuracy (94%) and precision (95%) rates. We conclude that handheld XRF is an accurate, non-invasive method to discriminate origin of elephant tusks provides rapid results applicable to use in the field.
Applying Neural Networks to Hyperspectral and Multispectral Field Data for Discrimination of Cruciferous Weeds in Winter Crops

PubMed Central

de Castro, Ana-Isabel; Jurado-Expósito, Montserrat; Gómez-Casero, María-Teresa; López-Granados, Francisca

2012-01-01

In the context of detection of weeds in crops for site-specific weed control, on-ground spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops. Field studies were conducted for four years at different locations in Spain. We aimed to distinguish cruciferous weeds in wheat and broad bean crops, using hyperspectral and multispectral readings in the visible and near-infrared spectrum. To identify differences in reflectance between cruciferous weeds, we applied three classification methods: stepwise discriminant (STEPDISC) analysis and two neural networks, specifically, multilayer perceptron (MLP) and radial basis function (RBF). Hyperspectral and multispectral signatures of cruciferous weeds, and wheat and broad bean crops can be classified using STEPDISC analysis, and MLP and RBF neural networks with different success, being the MLP model the most accurate with 100%, or higher than 98.1%, of classification performance for all the years. Classification accuracy from hyperspectral signatures was similar to that from multispectral and spectral indices, suggesting that little advantage would be obtained by using more expensive airborne hyperspectral imagery. Therefore, for next investigations, we recommend using multispectral remote imagery to explore whether they can potentially discriminate these weeds and crops. PMID:22629171
Applying neural networks to hyperspectral and multispectral field data for discrimination of cruciferous weeds in winter crops.

PubMed

de Castro, Ana-Isabel; Jurado-Expósito, Montserrat; Gómez-Casero, María-Teresa; López-Granados, Francisca

2012-01-01

In the context of detection of weeds in crops for site-specific weed control, on-ground spectral reflectance measurements are the first step to determine the potential of remote spectral data to classify weeds and crops. Field studies were conducted for four years at different locations in Spain. We aimed to distinguish cruciferous weeds in wheat and broad bean crops, using hyperspectral and multispectral readings in the visible and near-infrared spectrum. To identify differences in reflectance between cruciferous weeds, we applied three classification methods: stepwise discriminant (STEPDISC) analysis and two neural networks, specifically, multilayer perceptron (MLP) and radial basis function (RBF). Hyperspectral and multispectral signatures of cruciferous weeds, and wheat and broad bean crops can be classified using STEPDISC analysis, and MLP and RBF neural networks with different success, being the MLP model the most accurate with 100%, or higher than 98.1%, of classification performance for all the years. Classification accuracy from hyperspectral signatures was similar to that from multispectral and spectral indices, suggesting that little advantage would be obtained by using more expensive airborne hyperspectral imagery. Therefore, for next investigations, we recommend using multispectral remote imagery to explore whether they can potentially discriminate these weeds and crops.

Optimal design of stimulus experiments for robust discrimination of biochemical reaction networks.

PubMed

Flassig, R J; Sundmacher, K

2012-12-01

Biochemical reaction networks in the form of coupled ordinary differential equations (ODEs) provide a powerful modeling tool for understanding the dynamics of biochemical processes. During the early phase of modeling, scientists have to deal with a large pool of competing nonlinear models. At this point, discrimination experiments can be designed and conducted to obtain optimal data for selecting the most plausible model. Since biological ODE models have widely distributed parameters due to, e.g. biologic variability or experimental variations, model responses become distributed. Therefore, a robust optimal experimental design (OED) for model discrimination can be used to discriminate models based on their response probability distribution functions (PDFs). In this work, we present an optimal control-based methodology for designing optimal stimulus experiments aimed at robust model discrimination. For estimating the time-varying model response PDF, which results from the nonlinear propagation of the parameter PDF under the ODE dynamics, we suggest using the sigma-point approach. Using the model overlap (expected likelihood) as a robust discrimination criterion to measure dissimilarities between expected model response PDFs, we benchmark the proposed nonlinear design approach against linearization with respect to prediction accuracy and design quality for two nonlinear biological reaction networks. As shown, the sigma-point outperforms the linearization approach in the case of widely distributed parameter sets and/or existing multiple steady states. Since the sigma-point approach scales linearly with the number of model parameter, it can be applied to large systems for robust experimental planning. An implementation of the method in MATLAB/AMPL is available at http://www.uni-magdeburg.de/ivt/svt/person/rf/roed.html. flassig@mpi-magdeburg.mpg.de Supplementary data are are available at Bioinformatics online.
Detection and recognition of simple spatial forms

NASA Technical Reports Server (NTRS)

Watson, A. B.

1983-01-01

A model of human visual sensitivity to spatial patterns is constructed. The model predicts the visibility and discriminability of arbitrary two-dimensional monochrome images. The image is analyzed by a large array of linear feature sensors, which differ in spatial frequency, phase, orientation, and position in the visual field. All sensors have one octave frequency bandwidths, and increase in size linearly with eccentricity. Sensor responses are processed by an ideal Bayesian classifier, subject to uncertainty. The performance of the model is compared to that of the human observer in detecting and discriminating some simple images.
On non-autonomous dynamical systems

NASA Astrophysics Data System (ADS)

Anzaldo-Meneses, A.

2015-04-01

In usual realistic classical dynamical systems, the Hamiltonian depends explicitly on time. In this work, a class of classical systems with time dependent nonlinear Hamiltonians is analyzed. This type of problems allows to find invariants by a family of Veronese maps. The motivation to develop this method results from the observation that the Poisson-Lie algebra of monomials in the coordinates and momenta is clearly defined in terms of its brackets and leads naturally to an infinite linear set of differential equations, under certain circumstances. To perform explicit analytic and numerical calculations, two examples are presented to estimate the trajectories, the first given by a nonlinear problem and the second by a quadratic Hamiltonian with three time dependent parameters. In the nonlinear problem, the Veronese approach using jets is shown to be equivalent to a direct procedure using elliptic functions identities, and linear invariants are constructed. For the second example, linear and quadratic invariants as well as stability conditions are given. Explicit solutions are also obtained for stepwise constant forces. For the quadratic Hamiltonian, an appropriated set of coordinates relates the geometric setting to that of the three dimensional manifold of central conic sections. It is shown further that the quantum mechanical problem of scattering in a superlattice leads to mathematically equivalent equations for the wave function, if the classical time is replaced by the space coordinate along a superlattice. The mathematical method used to compute the trajectories for stepwise constant parameters can be applied to both problems. It is the standard method in quantum scattering calculations, as known for locally periodic systems including a space dependent effective mass.
Local repair of stoma prolapse: Case report of an in vivo application of linear stapler devices.

PubMed

Monette, Margaret M; Harney, Rodney T; Morris, Melanie S; Chu, Daniel I

2016-11-01

One of the most common late complications following stoma construction is prolapse. Although the majority of prolapse can be managed conservatively, surgical revision is required with incarceration/strangulation and in certain cases laparotomy and/or stoma reversal are not appropriate. This report will inform surgeons on safe and effective approaches to revising prolapsed stomas using local techniques. A 58 year old female with an obstructing rectal cancer previously received a diverting transverse loop colostomy. On completion of neoadjuvant treatment, re-staging found new lung metastases. She was scheduled for further chemotherapy but incarcerated a prolapsed segment of her loop colostomy. As there was no plan to resect her primary rectal tumor at the time, a local revision was preferred. Linear staplers were applied to the prolapsed stoma in step-wise fashion to locally revise the incarcerated prolapse. Post-operative recovery was satisfactory with no complications or recurrence of prolapse. We detail in step-wise fashion a technique using linear stapler devices that can be used to locally revise prolapsed stoma segments and therefore avoid a laparotomy. The procedure is technically easy to perform with satisfactory post-operative outcomes. We additionally review all previous reports of local repairs and show the evolution of local prolapse repair to the currently reported technique. This report offers surgeons an alternative, efficient and effective option for addressing the complications of stoma prolapse. While future studies are needed to assess long-term outcomes, in the short-term, our report confirms the safety and effectiveness of this local technique.
Proton radius from electron scattering data

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

Higinbotham, Douglas W.; Kabir, Al Amin; Lin, Vincent

Background: The proton charge radius extracted from recent muonic hydrogen Lamb shift measurements is significantly smaller than that extracted from atomic hydrogen and electron scattering measurements. The discrepancy has become known as the proton radius puzzle. Purpose: In an attempt to understand the discrepancy, we review high-precision electron scattering results from Mainz, Jefferson Lab, Saskatoon and Stanford. Methods: We make use of stepwise regression techniques using the F-test as well as the Akaike information criterion to systematically determine the predictive variables to use for a given set and range of electron scattering data as well as to provide multivariate errormore » estimates. Results: Starting with the precision, low four-momentum transfer (Q 2) data from Mainz (1980) and Saskatoon (1974), we find that a stepwise regression of the Maclaurin series using the F-test as well as the Akaike information criterion justify using a linear extrapolation which yields a value for the proton radius that is consistent with the result obtained from muonic hydrogen measurements. Applying the same Maclaurin series and statistical criteria to the 2014 Rosenbluth results on GE from Mainz, we again find that the stepwise regression tends to favor a radius consistent with the muonic hydrogen radius but produces results that are extremely sensitive to the range of data included in the fit. Making use of the high-Q 2 data on G E to select functions which extrapolate to high Q 2, we find that a Pad´e (N = M = 1) statistical model works remarkably well, as does a dipole function with a 0.84 fm radius, G E(Q 2) = (1 + Q 2/0.66 GeV 2) -2. Conclusions: Rigorous applications of stepwise regression techniques and multivariate error estimates result in the extraction of a proton charge radius that is consistent with the muonic hydrogen result of 0.84 fm; either from linear extrapolation of the extreme low-Q 2 data or by use of the Pad´e approximant for extrapolation using a larger range of data. Thus, based on a purely statistical analysis of electron scattering data, we conclude that the electron scattering result and the muonic hydrogen result are consistent. Lastly, it is the atomic hydrogen results that are the outliers.« less
Proton radius from electron scattering data

DOE PAGES

Higinbotham, Douglas W.; Kabir, Al Amin; Lin, Vincent; ...

2016-05-31

Background: The proton charge radius extracted from recent muonic hydrogen Lamb shift measurements is significantly smaller than that extracted from atomic hydrogen and electron scattering measurements. The discrepancy has become known as the proton radius puzzle. Purpose: In an attempt to understand the discrepancy, we review high-precision electron scattering results from Mainz, Jefferson Lab, Saskatoon and Stanford. Methods: We make use of stepwise regression techniques using the F-test as well as the Akaike information criterion to systematically determine the predictive variables to use for a given set and range of electron scattering data as well as to provide multivariate errormore » estimates. Results: Starting with the precision, low four-momentum transfer (Q 2) data from Mainz (1980) and Saskatoon (1974), we find that a stepwise regression of the Maclaurin series using the F-test as well as the Akaike information criterion justify using a linear extrapolation which yields a value for the proton radius that is consistent with the result obtained from muonic hydrogen measurements. Applying the same Maclaurin series and statistical criteria to the 2014 Rosenbluth results on GE from Mainz, we again find that the stepwise regression tends to favor a radius consistent with the muonic hydrogen radius but produces results that are extremely sensitive to the range of data included in the fit. Making use of the high-Q 2 data on G E to select functions which extrapolate to high Q 2, we find that a Pad´e (N = M = 1) statistical model works remarkably well, as does a dipole function with a 0.84 fm radius, G E(Q 2) = (1 + Q 2/0.66 GeV 2) -2. Conclusions: Rigorous applications of stepwise regression techniques and multivariate error estimates result in the extraction of a proton charge radius that is consistent with the muonic hydrogen result of 0.84 fm; either from linear extrapolation of the extreme low-Q 2 data or by use of the Pad´e approximant for extrapolation using a larger range of data. Thus, based on a purely statistical analysis of electron scattering data, we conclude that the electron scattering result and the muonic hydrogen result are consistent. Lastly, it is the atomic hydrogen results that are the outliers.« less
Plausibility assessment of a 2-state self-paced mental task-based BCI using the no-control performance analysis.

PubMed

Faradji, Farhad; Ward, Rabab K; Birch, Gary E

2009-06-15

The feasibility of having a self-paced brain-computer interface (BCI) based on mental tasks is investigated. The EEG signals of four subjects performing five mental tasks each are used in the design of a 2-state self-paced BCI. The output of the BCI should only be activated when the subject performs a specific mental task and should remain inactive otherwise. For each subject and each task, the feature coefficient and the classifier that yield the best performance are selected, using the autoregressive coefficients as the features. The classifier with a zero false positive rate and the highest true positive rate is selected as the best classifier. The classifiers tested include: linear discriminant analysis, quadratic discriminant analysis, Mahalanobis discriminant analysis, support vector machine, and radial basis function neural network. The results show that: (1) some classifiers obtained the desired zero false positive rate; (2) the linear discriminant analysis classifier does not yield acceptable performance; (3) the quadratic discriminant analysis classifier outperforms the Mahalanobis discriminant analysis classifier and performs almost as well as the radial basis function neural network; and (4) the support vector machine classifier has the highest true positive rates but unfortunately has nonzero false positive rates in most cases.
Discrimination and Acculturative Stress among First-Generation Dominicans

ERIC Educational Resources Information Center

Dawson, Beverly Araujo; Panchanadeswaran, Subadra

2010-01-01

The present study examined the relationship between discriminatory experiences and acculturative stress levels among a sample of 283 Dominican immigrants. Findings from a linear regression analysis revealed that experiences of daily racial discrimination and major racist events were significant predictors of acculturative stress after controlling…
Toward a Model-Based Predictive Controller Design in Brain–Computer Interfaces

PubMed Central

Kamrunnahar, M.; Dias, N. S.; Schiff, S. J.

2013-01-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain–computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8–23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications. PMID:21267657
Toward a model-based predictive controller design in brain-computer interfaces.

PubMed

Kamrunnahar, M; Dias, N S; Schiff, S J

2011-05-01

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.
Associations Between Discrimination and Cardiovascular Health Among Asian Indians in the United States

PubMed Central

Dulin-Keita, A.; Salas, C.; Kanaya, A. M.; Kandula, Namratha R.

2016-01-01

Asian Indians (AI) have a high risk of atherosclerotic cardiovascular disease. The study investigated associations between discrimination and (1) cardiovascular risk and (2) self-rated health among AI. Higher discrimination scores were hypothesized to relate to a higher cardiovascular risk score (CRS) and poorer self-rated health. Asian Indians (n = 757) recruited between 2010 and 2013 answered discrimination and self-reported health questions. The CRS (0–8 points) included body-mass index, systolic blood pressure, total cholesterol, and fasting blood glucose levels of AI. Multiple linear regression analyses were conducted to evaluate relationships between discrimination and the CRS and discrimination and self-rated health, adjusting for psychosocial and clinical factors. There were no significant relationships between discrimination and the CRS (p ≥ .05). Discrimination was related to poorer self-reported health, B = −.41 (SE = .17), p = .02. Findings suggest perhaps there are important levels at which discrimination may harm health. PMID:27039100
Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification.

PubMed

Haoliang Yuan; Yuan Yan Tang

2017-04-01

Classification of the pixels in hyperspectral image (HSI) is an important task and has been popularly applied in many practical applications. Its major challenge is the high-dimensional small-sized problem. To deal with this problem, lots of subspace learning (SL) methods are developed to reduce the dimension of the pixels while preserving the important discriminant information. Motivated by ridge linear regression (RLR) framework for SL, we propose a spectral-spatial shared linear regression method (SSSLR) for extracting the feature representation. Comparing with RLR, our proposed SSSLR has the following two advantages. First, we utilize a convex set to explore the spatial structure for computing the linear projection matrix. Second, we utilize a shared structure learning model, which is formed by original data space and a hidden feature space, to learn a more discriminant linear projection matrix for classification. To optimize our proposed method, an efficient iterative algorithm is proposed. Experimental results on two popular HSI data sets, i.e., Indian Pines and Salinas demonstrate that our proposed methods outperform many SL methods.
Local classification: Locally weighted-partial least squares-discriminant analysis (LW-PLS-DA).

PubMed

Bevilacqua, Marta; Marini, Federico

2014-08-01

The possibility of devising a simple, flexible and accurate non-linear classification method, by extending the locally weighted partial least squares (LW-PLS) approach to the cases where the algorithm is used in a discriminant way (partial least squares discriminant analysis, PLS-DA), is presented. In particular, to assess which category an unknown sample belongs to, the proposed algorithm operates by identifying which training objects are most similar to the one to be predicted and building a PLS-DA model using these calibration samples only. Moreover, the influence of the selected training samples on the local model can be further modulated by adopting a not uniform distance-based weighting scheme which allows the farthest calibration objects to have less impact than the closest ones. The performances of the proposed locally weighted-partial least squares-discriminant analysis (LW-PLS-DA) algorithm have been tested on three simulated data sets characterized by a varying degree of non-linearity: in all cases, a classification accuracy higher than 99% on external validation samples was achieved. Moreover, when also applied to a real data set (classification of rice varieties), characterized by a high extent of non-linearity, the proposed method provided an average correct classification rate of about 93% on the test set. By the preliminary results, showed in this paper, the performances of the proposed LW-PLS-DA approach have proved to be comparable and in some cases better than those obtained by other non-linear methods (k nearest neighbors, kernel-PLS-DA and, in the case of rice, counterpropagation neural networks). Copyright © 2014 Elsevier B.V. All rights reserved.
Linear combination methods to improve diagnostic/prognostic accuracy on future observations

PubMed Central

Kang, Le; Liu, Aiyi; Tian, Lili

2014-01-01

Multiple diagnostic tests or biomarkers can be combined to improve diagnostic accuracy. The problem of finding the optimal linear combinations of biomarkers to maximise the area under the receiver operating characteristic curve has been extensively addressed in the literature. The purpose of this article is threefold: (1) to provide an extensive review of the existing methods for biomarker combination; (2) to propose a new combination method, namely, the nonparametric stepwise approach; (3) to use leave-one-pair-out cross-validation method, instead of re-substitution method, which is overoptimistic and hence might lead to wrong conclusion, to empirically evaluate and compare the performance of different linear combination methods in yielding the largest area under receiver operating characteristic curve. A data set of Duchenne muscular dystrophy was analysed to illustrate the applications of the discussed combination methods. PMID:23592714
A multiple linear regression analysis of hot corrosion attack on a series of nickel base turbine alloys

NASA Technical Reports Server (NTRS)

Barrett, C. A.

1985-01-01

Multiple linear regression analysis was used to determine an equation for estimating hot corrosion attack for a series of Ni base cast turbine alloys. The U transform (i.e., 1/sin (% A/100) to the 1/2) was shown to give the best estimate of the dependent variable, y. A complete second degree equation is described for the centered" weight chemistries for the elements Cr, Al, Ti, Mo, W, Cb, Ta, and Co. In addition linear terms for the minor elements C, B, and Zr were added for a basic 47 term equation. The best reduced equation was determined by the stepwise selection method with essentially 13 terms. The Cr term was found to be the most important accounting for 60 percent of the explained variability hot corrosion attack.
Metazoan parasite infection in the swordfish, Xiphias gladius, from the Mediterranean Sea and comparison with Atlantic populations: implications for its stock characterization

PubMed Central

Mattiucci, Simonetta; Garcia, Alexandra; Cipriani, Paolo; Santos, Miguel Neves; Nascetti, Giuseppe; Cimmaruta, Roberta

2014-01-01

Thirteen parasite taxa were identified in the Mediterranean swordfish by morphological and genetic/molecular methods. The comparison of the identified parasite taxa and parasitic infection values observed in the Mediterranean swordfish showed statistically significant differences with respect to those reported for its Atlantic populations. A stepwise Linear Discriminant Analysis of the individual fish examined showed a separation among three groups: one including fish from the Mediterranean Sea (CTS, STS, and IOS); one consisting of fish from the Central South (CS), Eastern Tropical (ET), and Equatorial (TEQ) Atlantic; and a third comprising the fish sampled from the North-West Atlantic (NW); the CN Atlantic sample was more similar to the first group rather than to the other Atlantic ones. The nematodes Hysterothylacium petteri and Anisakis pegreffii were the species that contributed most to the characterization of the Mediterranean swordfish samples with respect to these Atlantic ones. Anisakis brevispiculata, A. physeteris, A. paggiae, Anisakis sp. 2, Hysterothylacium incurvum, Hepatoxylon trichiuri, Sphyriocephalus viridis, and their high infection levels were associated with the swordfish from the Central and the Southern Atlantic areas. Finally, H. corrugatum, A. simplex (s.s.), Rhadinorhynchus pristis, and Bolbosoma vasculosum were related to the fish from the North-West (NW) Atlantic area. These results indicate that some parasites, particularly Anisakis spp. larvae identified by genetic markers, could be used as “biological tags” and support the existence of a Mediterranean swordfish stock. PMID:25057787
Solid-phase extraction versus matrix solid-phase dispersion: Application to white grapes.

PubMed

Dopico-García, M S; Valentão, P; Jagodziñska, A; Klepczyñska, J; Guerra, L; Andrade, P B; Seabra, R M

2007-11-15

The use of matrix solid-phase dispersion (MSPD) was tested to, separately, extract phenolic compounds and organic acids from white grapes. This method was compared with a more conventional analytical method previously developed that combines solid liquid extraction (SL) to simultaneously extract phenolic compounds and organic acids followed by a solid-phase extraction (SPE) to separate the two types of compounds. Although the results were qualitatively similar for both techniques, the levels of extracted compounds were in general quite lower on using MSPD, especially for organic acids. Therefore, SL-SPE method was preferred to analyse white "Vinho Verde" grapes. Twenty samples of 10 different varieties (Alvarinho, Avesso, Asal-Branco, Batoca, Douradinha, Esganoso de Castelo Paiva, Loureiro, Pedernã, Rabigato and Trajadura) from four different locations in Minho (Portugal) were analysed in order to study the effects of variety and origin on the profile of the above mentioned compounds. Principal component analysis (PCA) was applied separately to establish the main sources of variability present in the data sets for phenolic compounds, organic acids and for the global data. PCA of phenolic compounds accounted for the highest variability (77.9%) with two PCs, enabling characterization of the varieties of samples according to their higher content in flavonol derivatives or epicatechin. Additionally, a strong effect of sample origin was observed. Stepwise linear discriminant analysis (SLDA) was used for differentiation of grapes according to the origin and variety, resulting in a correct classification of 100 and 70%, respectively.
Neural network classification of sweet potato embryos

NASA Astrophysics Data System (ADS)

Molto, Enrique; Harrell, Roy C.

1993-05-01

Somatic embryogenesis is a process that allows for the in vitro propagation of thousands of plants in sub-liter size vessels and has been successfully applied to many significant species. The heterogeneity of maturity and quality of embryos produced with this technique requires sorting to obtain a uniform product. An automated harvester is being developed at the University of Florida to sort embryos in vitro at different stages of maturation in a suspension culture. The system utilizes machine vision to characterize embryo morphology and a fluidic based separation device to isolate embryos associated with a pre-defined, targeted morphology. Two different backpropagation neural networks (BNN) were used to classify embryos based on information extracted from the vision system. One network utilized geometric features such as embryo area, length, and symmetry as inputs. The alternative network utilized polar coordinates of an embryo's perimeter with respect to its centroid as inputs. The performances of both techniques were compared with each other and with an embryo classification method based on linear discriminant analysis (LDA). Similar results were obtained with all three techniques. Classification efficiency was improved by reducing the dimension of the feature vector trough a forward stepwise analysis by LDA. In order to enhance the purity of the sample selected as harvestable, a reject to classify option was introduced in the model and analyzed. The best classifier performances (76% overall correct classifications, 75% harvestable objects properly classified, homogeneity improvement ratio 1.5) were obtained using 8 features in a BNN.
Quantitative structure-activity relationship of the curcumin-related compounds using various regression methods

NASA Astrophysics Data System (ADS)

Khazaei, Ardeshir; Sarmasti, Negin; Seyf, Jaber Yousefi

2016-03-01

Quantitative structure activity relationship were used to study a series of curcumin-related compounds with inhibitory effect on prostate cancer PC-3 cells, pancreas cancer Panc-1 cells, and colon cancer HT-29 cells. Sphere exclusion method was used to split data set in two categories of train and test set. Multiple linear regression, principal component regression and partial least squares were used as the regression methods. In other hand, to investigate the effect of feature selection methods, stepwise, Genetic algorithm, and simulated annealing were used. In two cases (PC-3 cells and Panc-1 cells), the best models were generated by a combination of multiple linear regression and stepwise (PC-3 cells: r2 = 0.86, q2 = 0.82, pred_r2 = 0.93, and r2m (test) = 0.43, Panc-1 cells: r2 = 0.85, q2 = 0.80, pred_r2 = 0.71, and r2m (test) = 0.68). For the HT-29 cells, principal component regression with stepwise (r2 = 0.69, q2 = 0.62, pred_r2 = 0.54, and r2m (test) = 0.41) is the best method. The QSAR study reveals descriptors which have crucial role in the inhibitory property of curcumin-like compounds. 6ChainCount, T_C_C_1, and T_O_O_7 are the most important descriptors that have the greatest effect. With a specific end goal to design and optimization of novel efficient curcumin-related compounds it is useful to introduce heteroatoms such as nitrogen, oxygen, and sulfur atoms in the chemical structure (reduce the contribution of T_C_C_1 descriptor) and increase the contribution of 6ChainCount and T_O_O_7 descriptors. Models can be useful in the better design of some novel curcumin-related compounds that can be used in the treatment of prostate, pancreas, and colon cancers.
Complexity-reduced implementations of complete and null-space-based linear discriminant analysis.

PubMed

Lu, Gui-Fu; Zheng, Wenming

2013-10-01

Dimensionality reduction has become an important data preprocessing step in a lot of applications. Linear discriminant analysis (LDA) is one of the most well-known dimensionality reduction methods. However, the classical LDA cannot be used directly in the small sample size (SSS) problem where the within-class scatter matrix is singular. In the past, many generalized LDA methods has been reported to address the SSS problem. Among these methods, complete linear discriminant analysis (CLDA) and null-space-based LDA (NLDA) provide good performances. The existing implementations of CLDA are computationally expensive. In this paper, we propose a new and fast implementation of CLDA. Our proposed implementation of CLDA, which is the most efficient one, is equivalent to the existing implementations of CLDA in theory. Since CLDA is an extension of null-space-based LDA (NLDA), our implementation of CLDA also provides a fast implementation of NLDA. Experiments on some real-world data sets demonstrate the effectiveness of our proposed new CLDA and NLDA algorithms. Copyright © 2013 Elsevier Ltd. All rights reserved.

Evidence for the Stepwise Stress Model: Gambusia holbrooki and Daphnia magna under acid mine drainage and acidified reference water stress.

PubMed

Gerhardt, Almut; Janssens de Bisthoven, Luc; Soares, Amadeu M V

2005-06-01

The Stepwise Stress Model (SSM) states that a cascade of regulative behavioral responses with different intrinsic sensitivities and threshold values offers increased behavioral plasticity and thus a wider range of tolerance for environmental changes or pollutant exposures. We tested the SSM with a widely introduced fish Gambusia holbrooki (Girard) (Pisces, Poeciliidae) and the standard laboratory test species Daphnia magna Straus (Crustacea, Daphniidae). The stress was simulated by short-term exposure to acid mine drainage (AMD) and to acidified reference water (ACID). Recording of behavioral responses with the multispecies freshwater biomonitor (MFB) generated continuous time-dependent dose-response data that were modeled in three-dimensional (3D) surface plots. Both the pH-dependent mortalities and the strong linear correlations between pH and aqueous metals confirmed the toxicity of the AMD and ACID gradients, respectively, for fish and Daphnia, the latter being more sensitive. AMD stress at pH < or = 5.5 amplified circadian rhythmicity in both species, while ACID stress did so only in G. holbrooki. A behavioral stepwise stress response was found in both species: D. magna decreased locomotion and ventilation (first step) (AMD, ACID), followed by increased ventilation (second step) (AMD). G. holbrooki decreased locomotion (first step) (AMD, ACID) and increased ventilation at intermediate pH levels (second step) (AMD). Both species, although from different taxonomic groups and feeding habits, followed the SSM, which might be expanded to a general concept for describing the behavioral responses of aquatic organims to pollution. Stepwise stress responses might be applied in online biomonitors to provide more sensitive and graduated alarm settings, hence optimizing the "early warning" detection of pollution waves.
Discrimination and mental health problems among homeless minority young people.

PubMed

Milburn, Norweeta G; Batterham, Philip; Ayala, George; Rice, Eric; Solorio, Rosa; Desmond, Kate; Lord, Lynwood; Iribarren, Javier; Rotheram-Borus, Mary Jane

2010-01-01

We examined the associations among perceived discrimination, racial/ethnic identification, and emotional distress in newly homeless adolescents. We assessed a sample of newly homeless adolescents (n=254) in Los Angeles, California, with measures of perceived discrimination and racial/ethnic identification. We assessed emotional distress using the Brief Symptom Inventory and used multivariate linear regression modeling to gauge the impact of discrimination and racial identity on emotional distress. Controlling for race and immigration status, gender, and age, young people with a greater sense of ethnic identification experienced less emotional distress. Young people with a history of racial/ethnic discrimination experienced more emotional distress. Intervention programs that contextualize discrimination and enhance racial/ethnic identification and pride among homeless young people are needed.
Comparative decision models for anticipating shortage of food grain production in India

NASA Astrophysics Data System (ADS)

Chattopadhyay, Manojit; Mitra, Subrata Kumar

2018-01-01

This paper attempts to predict food shortages in advance from the analysis of rainfall during the monsoon months along with other inputs used for crop production, such as land used for cereal production, percentage of area covered under irrigation and fertiliser use. We used six binary classification data mining models viz., logistic regression, Multilayer Perceptron, kernel lab-Support Vector Machines, linear discriminant analysis, quadratic discriminant analysis and k-Nearest Neighbors Network, and found that linear discriminant analysis and kernel lab-Support Vector Machines are equally suitable for predicting per capita food shortage with 89.69 % accuracy in overall prediction and 92.06 % accuracy in predicting food shortage ( true negative rate). Advance information of food shortage can help policy makers to take remedial measures in order to prevent devastating consequences arising out of food non-availability.
Multiple degree of freedom object recognition using optical relational graph decision nets

NASA Technical Reports Server (NTRS)

Casasent, David P.; Lee, Andrew J.

1988-01-01

Multiple-degree-of-freedom object recognition concerns objects with no stable rest position with all scale, rotation, and aspect distortions possible. It is assumed that the objects are in a fairly benign background, so that feature extractors are usable. In-plane distortion invariance is provided by use of a polar-log coordinate transform feature space, and out-of-plane distortion invariance is provided by linear discriminant function design. Relational graph decision nets are considered for multiple-degree-of-freedom pattern recognition. The design of Fisher (1936) linear discriminant functions and synthetic discriminant function for use at the nodes of binary and multidecision nets is discussed. Case studies are detailed for two-class and multiclass problems. Simulation results demonstrate the robustness of the processors to quantization of the filter coefficients and to noise.
The Association Between Racial and Gender Discrimination and Body Mass Index Among Residents Living in Lower-income Housing

PubMed Central

Shelton, Rachel C.; Puleo, Elaine; Bennett, Gary G.; McNeill, Lorna H.; Sorensen, Glorian; Emmons, Karen M.

2010-01-01

Background Research on the association between self-reported racial or gender discrimination and body mass index (BMI) has been limited and inconclusive to date, particularly among lower-income populations. Objectives The aim of the current study was to examine the association between self-reported racial and gender discrimination and BMI among a sample of adult residents living in 12 urban lower-income housing sites in Boston, Masschusetts (USA). Methods Baseline survey data were collected among 1,307 (weighted N=1907) study participants. For analyses, linear regression models with a cluster design were conducted using SUDAAN and SAS statistical software. Results Our sample was predominately Black (weighted n=956) and Hispanic (weighted n=857), and female (weighted n=1420), with a mean age of 49.3 (SE: .40) and mean BMI of 30.2 kg m−2 (SE: .19). Nearly 47% of participants reported ever experiencing racial discrimination, and 24.8% reported ever experiencing gender discrimination. In bivariate and multivariable linear regression models, no main effect association was found between either racial or gender discrimination and BMI. Conclusions While our findings suggest that self-reported discrimination is not a key determinant of BMI among lower-income housing residents, these results should be considered in light of study limitations. Future researchers may want to investigate this association among other relevant samples, and other social contextual and cultural factors should be explored to understand how they contribute to disparities. PMID:19769005
The association between racial and gender discrimination and body mass index among residents living in lower-income housing.

PubMed

Shelton, Rachel C; Puleo, Elaine; Bennett, Gary G; McNeill, Lorna H; Sorensen, Glorian; Emmons, Karen M

2009-01-01

Research on the association between self-reported racial or gender discrimination and body mass index (BMI) has been limited and inconclusive to date, particularly among lower-income populations. The aim of the current study was to examine the association between self-reported racial and gender discrimination and BMI among a sample of adult residents living in 12 urban lower-income housing sites in Boston, Masschusetts (USA). Baseline survey data were collected among 1,307 (weighted N = 1907) study participants. For analyses, linear regression models with a cluster design were conducted using SUDAAN and SAS statistical software. Our sample was predominately Black (weighted n = 956) and Hispanic (weighted n = 857), and female (weighted n = 1420), with a mean age of 49.3 (SE: .40) and mean BMI of 30.2 kg m(-2) (SE: .19). Nearly 47% of participants reported ever experiencing racial discrimination, and 24.8% reported ever experiencing gender discrimination. In bivariate and multivariable linear regression models, no main effect association was found between either racial or gender discrimination and BMI. While our findings suggest that self-reported discrimination is not a key determinant of BMI among lower-income housing residents, these results should be considered in light of study limitations. Future researchers may want to investigate this association among other relevant samples, and other social contextual and cultural factors should be explored to understand how they contribute to disparities.
Intraoperative optical biopsy for brain tumors using spectro-lifetime properties of intrinsic fluorophores

NASA Astrophysics Data System (ADS)

Vasefi, Fartash; Kittle, David S.; Nie, Zhaojun; Falcone, Christina; Patil, Chirag G.; Chu, Ray M.; Mamelak, Adam N.; Black, Keith L.; Butte, Pramod V.

2016-04-01

We have developed and tested a system for real-time intra-operative optical identification and classification of brain tissues using time-resolved fluorescence spectroscopy (TRFS). A supervised learning algorithm using linear discriminant analysis (LDA) employing selected intrinsic fluorescence decay temporal points in 6 spectral bands was employed to maximize statistical significance difference between training groups. The linear discriminant analysis on in vivo human tissues obtained by TRFS measurements (N = 35) were validated by histopathologic analysis and neuronavigation correlation to pre-operative MRI images. These results demonstrate that TRFS can differentiate between normal cortex, white matter and glioma.
Characterization of Armillaria spp. from peach orchards in the southeastern United States using fatty acid methyl ester profiling.

PubMed

Cox, K D; Scherm, H; Riley, M B

2006-04-01

Limited information is available regarding the composition of cellular fatty acids in Armillaria and the extent to which fatty acid profiles can be used to characterize species in this genus. Fatty acid methyl ester (FAME) profiles generated from cultures of A. tabescens, A. mellea, and A. gallica consisted of 16-18 fatty acids ranging from 12-24 carbons in length, although some of these were present only in trace amounts. Across the three species, 9-cis,12-cis-octadecadienoic acid (9,12-C18:2), hexadecanoic acid (16:0), heneicosanoic acid (21:0), 9-cis-octadecenoic acid (9-C18:1), and 2-hydroxy-docosanoic acid (OH-22:0) were the most abundant fatty acids. FAME profiles from different thallus morphologies (mycelium, sclerotial crust, or rhizomorphs) displayed by cultures of A. gallica showed that thallus type had no significant effect on cellular fatty acid composition (P > 0.05), suggesting that FAME profiling is sufficiently robust for species differentiation despite potential differences in thallus morphology within and among species. The three Armillaria species included in this study could be distinguished from other lignicolous basidiomycete species commonly occurring on peach (Schizophyllum commune, Ganoderma lucidum, Stereum hirsutum, and Trametes versicolor) on the basis of FAME profiles using stepwise discriminant analysis (average squared canonical correlation = 0.953), whereby 9-C18:1, 9,12-C18:2, and 10-cis-hexadecenoic acid (10-C16:1) were the three strongest contributors. In a separate stepwise discriminant analysis, A. tabescens, A. mellea, and A. gallica were separated from one another based on their fatty acid profiles (average squared canonical correlation = 0.924), with 11-cis-octadecenoic acid (11-C18:1), 9-C18:1, and 2-hydroxy-hexadecanoic acid (OH-16:0) being most important for species separation. When fatty acids were extracted directly from mycelium dissected from naturally infected host tissue, the FAME-based discriminant functions developed in the preceding experiments classified all samples (n = 16) as A. tabescens; when applied to cultures derived from the same naturally infected samples, all unknowns were similarly classified as A. tabescens. Thus, FAME species classification of Armillaria unknowns directly from infected tissues may be feasible. Species designation of unknown Armillaria cultures by FAME analysis was identical to that indicated by IGS-RFLP classification with AluI.
Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

NASA Astrophysics Data System (ADS)

Phinyomark, A.; Hu, H.; Phukpattaranont, P.; Limsakul, C.

2012-01-01

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier.
Using color histograms and SPA-LDA to classify bacteria.

PubMed

de Almeida, Valber Elias; da Costa, Gean Bezerra; de Sousa Fernandes, David Douglas; Gonçalves Dias Diniz, Paulo Henrique; Brandão, Deysiane; de Medeiros, Ana Claudia Dantas; Véras, Germano

2014-09-01

In this work, a new approach is proposed to verify the differentiating characteristics of five bacteria (Escherichia coli, Enterococcus faecalis, Streptococcus salivarius, Streptococcus oralis, and Staphylococcus aureus) by using digital images obtained with a simple webcam and variable selection by the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA). In this sense, color histograms in the red-green-blue (RGB), hue-saturation-value (HSV), and grayscale channels and their combinations were used as input data, and statistically evaluated by using different multivariate classifiers (Soft Independent Modeling by Class Analogy (SIMCA), Principal Component Analysis-Linear Discriminant Analysis (PCA-LDA), Partial Least Squares Discriminant Analysis (PLS-DA) and Successive Projections Algorithm-Linear Discriminant Analysis (SPA-LDA)). The bacteria strains were cultivated in a nutritive blood agar base layer for 24 h by following the Brazilian Pharmacopoeia, maintaining the status of cell growth and the nature of nutrient solutions under the same conditions. The best result in classification was obtained by using RGB and SPA-LDA, which reached 94 and 100 % of classification accuracy in the training and test sets, respectively. This result is extremely positive from the viewpoint of routine clinical analyses, because it avoids bacterial identification based on phenotypic identification of the causative organism using Gram staining, culture, and biochemical proofs. Therefore, the proposed method presents inherent advantages, promoting a simpler, faster, and low-cost alternative for bacterial identification.
Associations of multiple domains of self-esteem with four dimensions of stigma in schizophrenia

PubMed Central

Lysaker, Paul H.; Tsai, Jack; Yanos, Philip; Roe, David

2011-01-01

Research suggests global self-esteem among persons with schizophrenia may be negatively affected by stigma or stereotyped beliefs about persons with severe mental illness. Less clear however, is whether particular dimensions of self-esteem are linked to particular domains of stigma. To examine this we surveyed a range of self-esteem dimensions including lovability, personal power, competence and moral self-approval and four domains of stigma: Stereotype endorsement, Discrimination experience, Social withdrawal and Stigma rejection. Participants were 133 adults with diagnoses of schizophrenia or schizoaffective disorder. Stepwise multiple regressions controlling for a possible defensive response bias suggested that aspects of self-esteem related to lovability by others were more closely linked with lesser feelings of being alienated from others due to mental illness. Aspects of self-esteem related to the ability to manage one’s own affairs were more closely associated with the rejection of stereotypes of mental illness. A sense of being able to influence others was linked to both the absence of discrimination experiences and the ability to ward off stigma. Implications for treatment are discussed. PMID:18029145
Sex discrimination potential of buccolingual and mesiodistal tooth dimensions.

PubMed

Acharya, Ashith B; Mainali, Sneedha

2008-07-01

Tooth crown dimensions are reasonably accurate predictors of sex and are useful adjuncts in sex assessment. This study explores the utility of buccolingual (BL) and mesiodistal (MD) measurements in sex differentiation when used independently. BL and MD measurements of 28 teeth (third molars excluded) were obtained from a group of 53 Nepalese subjects (22 women and 31 men) aged 19-28 years. Stepwise discriminant analyses were undertaken separately for both types of tooth crown variables and their accuracy in sex classification compared with one another. MD dimensions had recognizably greater accuracy (77.4-83%) in sex identification than BL measurements (62.3-64.2%)--results that are consistent with previous reports. However, the accuracy of MD variables is not high enough to warrant their exclusive use in odontometric sex assessment--higher accuracy levels have been obtained when both types of dimensions were used concurrently, implying that BL variables contribute to sex assessment to some extent. Hence, it is inferred that optimal results in dental sex assessment are obtained when both MD and BL variables are used together.
Commercial sheep flocks--fatty acid and fat-soluble antioxidant composition of milk and cheese related to changes in feeding management throughout lactation.

PubMed

Valdivielso, Izaskun; Bustamante, María Ángeles; Buccioni, Arianna; Franci, Oreste; Ruiz de Gordoa, Juan Carlos; de Renobales, Mertxe; Barron, Luis Javier R

2015-08-01

Fatty acids (FAs), tocopherols and retinoids were analysed in raw milk and cheese from six commercial sheep flocks monitored from early lactation in winter to late lactation in summer. In winter, animals received concentrate and forage indoors; in early spring, animals grazed part-time on cultivated or natural valley grasslands; and from mid spring on, animals were kept outdoors constantly on mountain natural pastures. Mountain grazing in late lactation significantly increased the amount of healthy desirable unsaturated FAs such as C18:1t11 (VA), C18:2c9t11 (RA), C18:2t11c13, C18:3c9c12c15 (ALA) and C20:5c5c8c11c14c17 (EPA), and those of α-tocopherol and α-tocotrienol of milk and cheese. Stepwise discriminant analysis was applied to classify cheese samples according to seasonal feeding management. The multivariate approach was able to discriminate beyond doubt mountain cheeses from those of indoor feeding and part-time valley grazing.
Discriminating between the vocalizations of Indo-Pacific humpback and Australian snubfin dolphins in Queensland, Australia.

PubMed

Berg Soto, Alvaro; Marsh, Helene; Everingham, Yvette; Smith, Joshua N; Parra, Guido J; Noad, Michael

2014-08-01

Australian snubfin and Indo-Pacific humpback dolphins co-occur throughout most of their range in coastal waters of tropical Australia. Little is known of their ecology or acoustic repertoires. Vocalizations from humpback and snubfin dolphins were recorded in two locations along the Queensland coast during 2008 and 2010 to describe their vocalizations and evaluate the acoustic differences between these two species. Broad vocalization types were categorized qualitatively. Both species produced click trains burst pulses and whistles. Principal component analysis of the nine acoustic variables extracted from the whistles produced nine principal components that were input into discriminant function analyses to classify 96% of humpback dolphin whistles and about 78% of snubfin dolphin calls correctly. Results indicate clear acoustic differences between the vocal whistle repertoires of these two species. A stepwise routine identified two principal components as significantly distinguishable between whistles of each species: frequency parameters and frequency trend ratio. The capacity to identify these species using acoustic monitoring techniques has the potential to provide information on presence/absence, habitat use and relative abundance for each species.
Destination bedside: using research findings to visualize optimal unit layouts and health information technology in support of bedside care.

PubMed

Watkins, Nicholas; Kennedy, Mary; Lee, Nelson; O'Neill, Michael; Peavey, Erin; Ducharme, Maria; Padula, Cynthia

2012-05-01

This study explored the impact of unit design and healthcare information technology (HIT) on nursing workflow and patient-centered care (PCC). Healthcare information technology and unit layout-related predictors of nursing workflow and PCC were measured during a 3-phase study involving questionnaires and work sampling methods. Stepwise multiple linear regressions demonstrated several HIT and unit layout-related factors that impact nursing workflow and PCC.
Comparison of three portable instruments to measure compression pressure.

PubMed

Partsch, H; Mosti, G

2010-10-01

Measurement of interface pressure between the skin and a compression device has gained practical importance not only for characterizing the efficacy of different compression products in physiological and clinical studies but also for the training of medical staff. A newly developed portable pneumatic pressure transducer (Picopress®) was compared with two established systems (Kikuhime® and SIGaT tester®) measuring linearity, variability and accuracy on a cylindrical model using a stepwise inflated sphygmomanometer as the reference. In addition the variation coefficients were measured by applying the transducers repeatedly under a blood pressure cuff on the distal lower leg of a healthy human subject with stepwise inflation. In the pressure range between 10 and 80 mmHg all three devices showed a linear association compared with the sphygmomanometer values (Pearson r>0.99). The best reproducibility (variation coefficients between 1.05-7.4%) and the highest degree of accuracy demonstrated by Bland-Altman plots was achieved with the Picopress® transducer. Repeated measurements of pressure in a human leg revealed average variation coefficients for the three devices of 4.17% (Kikuhime®), 8.52% (SIGaT®) and 2.79% (Picopress®). The results suggest that the Picopress® transducer, which also allows dynamic pressure tracing in connection with a software program and which may be left under a bandage for several days, is a reliable instrument for measuring the pressure under a compression device.
The influence of attention deficits on functional recovery post stroke during the first 12 months after discharge from hospital.

PubMed

Hyndman, D; Pickering, R M; Ashburn, A

2008-06-01

Attention deficits have been linked to poor recovery after stroke and may predict outcome. We explored the influence of attention on functional recovery post stroke in the first 12 months after discharge from hospital. People with stroke completed measures of attention, balance, mobility and activities of daily living (ADL) ability at the point of discharge from hospital, and 6 and 12 months later. We used correlational analysis and stepwise linear regression to explore potential predictors of outcome. We recruited 122 men and women, mean age 70 years. At discharge, 56 (51%) had deficits of divided attention, 45 (37%) of sustained attention, 43 (36%) of auditory selective attention and 41 (37%) had visual selective attention deficits. Attention at discharge correlated with mobility, balance and ADL outcomes 12 months later. After controlling for the level of the outcome at discharge, correlations remained significant in only five of the 12 relationships. Stepwise linear regression revealed that the outcome measured at discharge, days until discharge and number of medications were better predictors of outcome: in no case was an attention variable at discharge selected as a predictor of outcome at 12 months. Although attention and function correlated significantly, this correlation was reduced after controlling for functional ability at discharge. Furthermore, side of lesion and the attention variables were not demonstrated as important predictors of outcome 12 months later.
Periodontitis in coronary heart disease patients: strong association between bleeding on probing and systemic biomarkers.

PubMed

Bokhari, Syed Akhtar H; Khan, Ayyaz A; Butt, Arshad K; Hanif, Mohammad; Izhar, Mateen; Tatakis, Dimitris N; Ashfaq, Mohammad

2014-11-01

Few studies have examined the relationship of individual periodontal parameters with individual systemic biomarkers. This study assessed the possible association between specific clinical parameters of periodontitis and systemic biomarkers of coronary heart disease risk in coronary heart disease patients with periodontitis. Angiographically proven coronary heart disease patients with periodontitis (n = 317), aged >30 years and without other systemic illness were examined. Periodontal clinical parameters of bleeding on probing (BOP), probing depth (PD), and clinical attachment level (CAL) and systemic levels of high-sensitivity C-reactive protein (CRP), fibrinogen (FIB) and white blood cells (WBC) were noted and analyzed to identify associations through linear and stepwise multiple regression analyses. Unadjusted linear regression showed significant associations between periodontal and systemic parameters; the strongest association (r = 0.629; p < 0.001) was found between BOP and CRP levels, the periodontal and systemic inflammation marker, respectively. Stepwise regression analysis models revealed that BOP was a predictor of systemic CRP levels (p < 0.0001). BOP was the only periodontal parameter significantly associated with each systemic parameter (CRP, FIB, and WBC). In coronary heart disease patients with periodontitis, BOP is strongly associated with systemic CRP levels; this association possibly reflects the potential significance of the local periodontal inflammatory burden for systemic inflammation. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Explanation of the variance in quality of life and activity capacity of patients with heart failure by laboratory data.

PubMed

Athanasopoulos, Leonidas V; Dritsas, Athanasios; Doll, Helen A; Cokkinos, Dennis V

2010-08-01

This study was conducted to explain the variance in quality of life (QoL) and activity capacity of patients with congestive heart failure from pathophysiological changes as estimated by laboratory data. Peak oxygen consumption (peak VO2) and ventilation (VE)/carbon dioxide output (VCO2) slope derived from cardiopulmonary exercise testing, plasma N-terminal prohormone of B-type natriuretic peptide (NT-proBNP), and echocardiographic markers [left atrium (LA), left ventricular ejection fraction (LVEF)] were measured in 62 patients with congestive heart failure, who also completed the Minnesota Living with Heart Failure Questionnaire and the Specific Activity Questionnaire. All regression models were adjusted for age and sex. On linear regression analysis, peak VO2 with P value less than 0.001, VE/VCO2 slope with P value less than 0.01, LVEF with P value less than 0.001, LA with P=0.001, and logNT-proBNP with P value less than 0.01 were found to be associated with QoL. On stepwise multiple linear regression, peak VO2 and LVEF continued to be predictive, accounting for 40% of the variability in Minnesota Living with Heart Failure Questionnaire score. On linear regression analysis, peak VO2 with P value less than 0.001, VE/VCO2 slope with P value less than 0.001, LVEF with P value less than 0.05, LA with P value less than 0.001, and logNT-proBNP with P value less than 0.001 were found to be associated with activity capacity. On stepwise multiple linear regression, peak VO2 and LA continued to be predictive, accounting for 53% of the variability in Specific Activity Questionnaire score. Peak VO2 is independently associated both with QoL and activity capacity. In addition to peak VO2, LVEF is independently associated with QoL, and LA with activity capacity.
MIDAS: Regionally linear multivariate discriminative statistical mapping.

PubMed

Varol, Erdem; Sotiras, Aristeidis; Davatzikos, Christos

2018-07-01

Statistical parametric maps formed via voxel-wise mass-univariate tests, such as the general linear model, are commonly used to test hypotheses about regionally specific effects in neuroimaging cross-sectional studies where each subject is represented by a single image. Despite being informative, these techniques remain limited as they ignore multivariate relationships in the data. Most importantly, the commonly employed local Gaussian smoothing, which is important for accounting for registration errors and making the data follow Gaussian distributions, is usually chosen in an ad hoc fashion. Thus, it is often suboptimal for the task of detecting group differences and correlations with non-imaging variables. Information mapping techniques, such as searchlight, which use pattern classifiers to exploit multivariate information and obtain more powerful statistical maps, have become increasingly popular in recent years. However, existing methods may lead to important interpretation errors in practice (i.e., misidentifying a cluster as informative, or failing to detect truly informative voxels), while often being computationally expensive. To address these issues, we introduce a novel efficient multivariate statistical framework for cross-sectional studies, termed MIDAS, seeking highly sensitive and specific voxel-wise brain maps, while leveraging the power of regional discriminant analysis. In MIDAS, locally linear discriminative learning is applied to estimate the pattern that best discriminates between two groups, or predicts a variable of interest. This pattern is equivalent to local filtering by an optimal kernel whose coefficients are the weights of the linear discriminant. By composing information from all neighborhoods that contain a given voxel, MIDAS produces a statistic that collectively reflects the contribution of the voxel to the regional classifiers as well as the discriminative power of the classifiers. Critically, MIDAS efficiently assesses the statistical significance of the derived statistic by analytically approximating its null distribution without the need for computationally expensive permutation tests. The proposed framework was extensively validated using simulated atrophy in structural magnetic resonance imaging (MRI) and further tested using data from a task-based functional MRI study as well as a structural MRI study of cognitive performance. The performance of the proposed framework was evaluated against standard voxel-wise general linear models and other information mapping methods. The experimental results showed that MIDAS achieves relatively higher sensitivity and specificity in detecting group differences. Together, our results demonstrate the potential of the proposed approach to efficiently map effects of interest in both structural and functional data. Copyright © 2018. Published by Elsevier Inc.

Feasibility of detecting aflatoxin B1 on inoculated maize kernels surface using Vis/NIR hyperspectral imaging.

PubMed

Wang, Wei; Heitschmidt, Gerald W; Windham, William R; Feldner, Peggy; Ni, Xinzhi; Chu, Xuan

2015-01-01

The feasibility of using a visible/near-infrared hyperspectral imaging system with a wavelength range between 400 and 1000 nm to detect and differentiate different levels of aflatoxin B1 (AFB1 ) artificially titrated on maize kernel surface was examined. To reduce the color effects of maize kernels, image analysis was limited to a subset of original spectra (600 to 1000 nm). Residual staining from the AFB1 on the kernels surface was selected as regions of interest for analysis. Principal components analysis (PCA) was applied to reduce the dimensionality of hyperspectral image data, and then a stepwise factorial discriminant analysis (FDA) was performed on latent PCA variables. The results indicated that discriminant factors F2 can be used to separate control samples from all of the other groups of kernels with AFB1 inoculated, whereas the discriminant factors F1 can be used to identify maize kernels with levels of AFB1 as low as 10 ppb. An overall classification accuracy of 98% was achieved. Finally, the peaks of β coefficients of the discrimination factors F1 and F2 were analyzed and several key wavelengths identified for differentiating maize kernels with and without AFB1 , as well as those with differing levels of AFB1 inoculation. Results indicated that Vis/NIR hyperspectral imaging technology combined with the PCA-FDA was a practical method to detect and differentiate different levels of AFB1 artificially inoculated on the maize kernels surface. However, indicated the potential to detect and differentiate naturally occurring toxins in maize kernel. © 2014 Institute of Food Technologists®
Uncovering of melanin fluorescence in human skin tissue

NASA Astrophysics Data System (ADS)

Scholz, Matthias; Stankovic, Goran; Seewald, Gunter; Leupold, Dieter

2007-07-01

Due to its extremely low fluorescence quantum yield, in the conventionally (one-photon) excited autofluorescence of skin tissue, melanin fluorescence is masked by several other endogenous and possibly also exogenous fluorophores (e.g. NADH, FAD, Porphyrins). A first step to enhance the melanin contribution had been realized by two-photon fs-pulse excitation in the red/near IR, based on the fact that melanin can be excited by stepwise two-photon absorption, whereas all other fluorophores in this spectral region allow only simultaneous two-photon excitation. Now, the next and decisive step has been realized: Using an extremely sensitive detection system, for the first time twophoton fluorescence of skin tissue excited with pulses in the ns-range could be measured. The motivation for this step was based on the fact that the population density of the fluorescent level resulting from a stepwise excitation has a different dependence of the pulse duration than that from a simultaneous excitation (Δt2 vs. Δt). Due to this strong discrimination between the fluorophores, practically pure melanin fluorescence can be obtained. Examples for in-vivo, ex-vivo as well as paraffin embedded skin tissue will be shown. The content of information with respect to early diagnosis of skin deseases will be discussed.
A Java-based tool for the design of classification microarrays.

PubMed

Meng, Da; Broschat, Shira L; Call, Douglas R

2008-08-04

Classification microarrays are used for purposes such as identifying strains of bacteria and determining genetic relationships to understand the epidemiology of an infectious disease. For these cases, mixed microarrays, which are composed of DNA from more than one organism, are more effective than conventional microarrays composed of DNA from a single organism. Selection of probes is a key factor in designing successful mixed microarrays because redundant sequences are inefficient and limited representation of diversity can restrict application of the microarray. We have developed a Java-based software tool, called PLASMID, for use in selecting the minimum set of probe sequences needed to classify different groups of plasmids or bacteria. The software program was successfully applied to several different sets of data. The utility of PLASMID was illustrated using existing mixed-plasmid microarray data as well as data from a virtual mixed-genome microarray constructed from different strains of Streptococcus. Moreover, use of data from expression microarray experiments demonstrated the generality of PLASMID. In this paper we describe a new software tool for selecting a set of probes for a classification microarray. While the tool was developed for the design of mixed microarrays-and mixed-plasmid microarrays in particular-it can also be used to design expression arrays. The user can choose from several clustering methods (including hierarchical, non-hierarchical, and a model-based genetic algorithm), several probe ranking methods, and several different display methods. A novel approach is used for probe redundancy reduction, and probe selection is accomplished via stepwise discriminant analysis. Data can be entered in different formats (including Excel and comma-delimited text), and dendrogram, heat map, and scatter plot images can be saved in several different formats (including jpeg and tiff). Weights generated using stepwise discriminant analysis can be stored for analysis of subsequent experimental data. Additionally, PLASMID can be used to construct virtual microarrays with genomes from public databases, which can then be used to identify an optimal set of probes.
The relationship between hemoglobin level and the type 1 diabetic nephropathy in Anhui Han's patients.

PubMed

Jiang, Jun; Lei, Lan; Zhou, Xiaowan; Li, Peng; Wei, Ren

2018-02-20

Recent studies have shown that low hemoglobin (Hb) level promote the progression of chronic kidney disease. This study assessed the relationship between Hb level and type 1 diabetic nephropathy (DN) in Anhui Han's patients. There were a total of 236 patients diagnosed with type 1 diabetes mellitus and (T1DM) seen between January 2014 and December 2016 in our centre. Hemoglobin levels in patients with DN were compared with those without DN. The relationship between Hb level and the urinary albumin-creatinine ratio (ACR) was examined by Spearman's correlational analysis and multiple stepwise regression analysis. The binary logistic multivariate regression analysis was performed to analyze the correlated factors for type 1 DN, calculate the Odds Ratio (OR) and 95%confidence interval (CI). The predicting value of Hb level for DN was evaluated by area under receiver operation characteristic curve (AUROC) for discrimination and Hosmer-Lemeshow goodness-of-fit test for calibration. The average Hb levels in the DN group (116.1 ± 20.8 g/L) were significantly lower than the non-DN group (131.9 ± 14.4 g/L) , P < 0.001. Hb levels were independently correlated with the urinary ACR in multiple stepwise regression analysis. The logistic multivariate regression analysis showed that the Hb level (OR: 0.936, 95% CI: 0.910 to 0.963, P < 0.001) was inversely correlated with DN in patients with T1DM. In sub-analysis, low Hb level (Hb < 120g/L in female, Hb < 130g/L in male) was still negatively associated with DN in patients with T1DM. The AUROC was 0.721 (95% CI: 0.655 to 0.787) in assessing the discrimination of the Hb level for DN. The value of P was 0.593 in Hosmer-Lemeshow goodness-of-fit test. In Anhui Han's patients with T1DM, the Hb level is inversely correlated with urinary ACR and DN. This article is protected by copyright. All rights reserved.
Socio-economic factors associated with infant mortality in Italy: an ecological study.

PubMed

Dallolio, Laura; Di Gregori, Valentina; Lenzi, Jacopo; Franchino, Giuseppe; Calugi, Simona; Domenighetti, Gianfranco; Fantini, Maria Pia

2012-08-16

One issue that continues to attract the attention of public health researchers is the possible relationship in high-income countries between income, income inequality and infant mortality (IM). The aim of this study was to assess the associations between IM and major socio-economic determinants in Italy. Associations between infant mortality rates in the 20 Italian regions (2006-2008) and the Gini index of income inequality, mean household income, percentage of women with at least 8 years of education, and percentage of unemployed aged 15-64 years were assessed using Pearson correlation coefficients. Univariate linear regression and multiple stepwise linear regression analyses were performed to determine the magnitude and direction of the effect of the four socio-economic variables on IM. The Gini index and the total unemployment rate showed a positive strong correlation with IM (r = 0.70; p < 0.001 and r = 0.84; p < 0.001 respectively), mean household income showed a strong negative correlation (r = -0.78; p < 0.001), while female educational attainment presented a weak negative correlation (r = -0.45; p < 0.05). Using a multiple stepwise linear regression model, only unemployment rate was independently associated with IM (b = 0.15, p < 0.001). In Italy, a high-income country where health care is universally available, variations in IM were strongly associated with relative and absolute income and unemployment rate. These results suggest that in Italy IM is not only related to income distribution, as demonstrated for other developed countries, but also to economic factors such as absolute income and unemployment. In order to reduce IM and the existing inequalities, the challenge for Italian decision makers is to promote economic growth and enhance employment levels.
Evaluation of job satisfaction and working atmosphere of dental nurses in Germany.

PubMed

Goetz, Katja; Hasse, Philipp; Campbell, Stephen M; Berger, Sarah; Dörfer, Christof E; Hahn, Karolin; Szecsenyi, Joachim

2016-02-01

The purpose of the study was to assess the level of job satisfaction of dental nurses in ambulatory care and to explore the impact of aspects of working atmosphere on and their association with job satisfaction. This cross-sectional study was based on a job satisfaction survey. Data were collected from 612 dental nurses working in 106 dental care practices. Job satisfaction was measured with the 10-item Warr-Cook-Wall job satisfaction scale. Working atmosphere was measured with five items. Linear regression analyses were performed in which each item of the job satisfaction scale was handled as dependent variables. A stepwise linear regression analysis was performed with overall job satisfaction and the five items of working atmosphere, job satisfaction, and individual characteristics. The response rate was 88.3%. Dental nurses were satisfied with 'colleagues' and least satisfied with 'income.' Different aspects of job satisfaction were mostly associated with the following working atmosphere issues: 'responsibilities within the practice team are clear,' 'suggestions for improvement are taken seriously,' 'working atmosphere in the practice team is good,' and 'made easier to admit own mistakes.' Within the stepwise linear regression analysis, the aspect 'physical working condition' (β = 0.304) showed the highest association with overall job satisfaction. The total explained variance of the 14 associated variables was 0.722 with overall job satisfaction. Working atmosphere within this discrete sample of dental care practice seemed to be an important influence on reported working condition and job satisfaction for dental nurses. Because of the high association of job satisfaction with physical working condition, the importance of paying more attention to an ergonomic working position for dental nurses to ensure optimal quality of care is highlighted. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
Improving the Accuracy of Mapping Urban Vegetation Carbon Density by Combining Shadow Remove, Spectral Unmixing Analysis and Spatial Modeling

NASA Astrophysics Data System (ADS)

Qie, G.; Wang, G.; Wang, M.

2016-12-01

Mixed pixels and shadows due to buildings in urban areas impede accurate estimation and mapping of city vegetation carbon density. In most of previous studies, these factors are often ignored, which thus result in underestimation of city vegetation carbon density. In this study we presented an integrated methodology to improve the accuracy of mapping city vegetation carbon density. Firstly, we applied a linear shadow remove analysis (LSRA) on remotely sensed Landsat 8 images to reduce the shadow effects on carbon estimation. Secondly, we integrated a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) and k-Nearest Neighbors (kNN), and utilized and compared the integrated models on shadow-removed images to map vegetation carbon density. This methodology was examined in Shenzhen City of Southeast China. A data set from a total of 175 sample plots measured in 2013 and 2014 was used to train the models. The independent variables statistically significantly contributing to improving the fit of the models to the data and reducing the sum of squared errors were selected from a total of 608 variables derived from different image band combinations and transformations. The vegetation fraction from LSUA was then added into the models as an important independent variable. The estimates obtained were evaluated using a cross-validation method. Our results showed that higher accuracies were obtained from the integrated models compared with the ones using traditional methods which ignore the effects of mixed pixels and shadows. This study indicates that the integrated method has great potential on improving the accuracy of urban vegetation carbon density estimation. Key words: Urban vegetation carbon, shadow, spectral unmixing, spatial modeling, Landsat 8 images
Generating linear regression model to predict motor functions by use of laser range finder during TUG.

PubMed

Adachi, Daiki; Nishiguchi, Shu; Fukutani, Naoto; Hotta, Takayuki; Tashiro, Yuto; Morino, Saori; Shirooka, Hidehiko; Nozaki, Yuma; Hirata, Hinako; Yamaguchi, Moe; Yorozu, Ayanori; Takahashi, Masaki; Aoyama, Tomoki

2017-05-01

The purpose of this study was to investigate which spatial and temporal parameters of the Timed Up and Go (TUG) test are associated with motor function in elderly individuals. This study included 99 community-dwelling women aged 72.9 ± 6.3 years. Step length, step width, single support time, variability of the aforementioned parameters, gait velocity, cadence, reaction time from starting signal to first step, and minimum distance between the foot and a marker placed to 3 in front of the chair were measured using our analysis system. The 10-m walk test, five times sit-to-stand (FTSTS) test, and one-leg standing (OLS) test were used to assess motor function. Stepwise multivariate linear regression analysis was used to determine which TUG test parameters were associated with each motor function test. Finally, we calculated a predictive model for each motor function test using each regression coefficient. In stepwise linear regression analysis, step length and cadence were significantly associated with the 10-m walk test, FTSTS and OLS test. Reaction time was associated with the FTSTS test, and step width was associated with the OLS test. Each predictive model showed a strong correlation with the 10-m walk test and OLS test (P < 0.01), which was not significant higher correlation than TUG test time. We showed which TUG test parameters were associated with each motor function test. Moreover, the TUG test time regarded as the lower extremity function and mobility has strong predictive ability in each motor function test. Copyright © 2017 The Japanese Orthopaedic Association. Published by Elsevier B.V. All rights reserved.
Automated fine structure image analysis method for discrimination of diabetic retinopathy stage using conjunctival microvasculature images

PubMed Central

Khansari, Maziyar M; O’Neill, William; Penn, Richard; Chau, Felix; Blair, Norman P; Shahidi, Mahnaz

2016-01-01

The conjunctiva is a densely vascularized mucus membrane covering the sclera of the eye with a unique advantage of accessibility for direct visualization and non-invasive imaging. The purpose of this study is to apply an automated quantitative method for discrimination of different stages of diabetic retinopathy (DR) using conjunctival microvasculature images. Fine structural analysis of conjunctival microvasculature images was performed by ordinary least square regression and Fisher linear discriminant analysis. Conjunctival images between groups of non-diabetic and diabetic subjects at different stages of DR were discriminated. The automated method’s discriminate rates were higher than those determined by human observers. The method allowed sensitive and rapid discrimination by assessment of conjunctival microvasculature images and can be potentially useful for DR screening and monitoring. PMID:27446692
Signal Detection Methods and Discriminant Analysis Applied to Categorization of Newspaper and Government Documents: A Preliminary Study.

ERIC Educational Resources Information Center

Ng, Kwong Bor; Rieh, Soo Young; Kantor, Paul

2000-01-01

Discussion of natural language processing focuses on experiments using linear discriminant analysis to distinguish "Wall Street Journal" texts from "Federal Register" tests using information about the frequency of occurrence of word boundaries, sentence boundaries, and punctuation marks. Displays and interprets results in terms…
Energy-discrimination x-ray computed tomography system utilizing a scanning cadmium-telluride detector

NASA Astrophysics Data System (ADS)

Sato, Eiichi; Abduraxit, Ablajan; Enomoto, Toshiyuki; Watanabe, Manabu; Hitomi, Keitaro; Takahashi, Kiyomi; Sato, Shigehiro; Ogawa, Akira; Onagawa, Jun

2010-04-01

An energy-discrimination K-edge x-ray computed tomography (CT) system is useful for controlling the image contrast of a target region by selecting both the photon energy and the energy width. The CT system has an oscillation-type linear cadmium telluride (CdTe) detectror. CT is performed by repeated linear scans and rotations of an object. Penetrating x-ray photons from the object are detected by a CdTe detector, and event signals of x-ray photons are produced using charge-sensitive and shaping amplifiers. Both photon energy and energy width are selected out using a multichannel analyzer, and the number of photons is counted by a counter card. In energy-discrimination CT, the tube voltage and tube current were 80 kV and 20 μA, respectively, and the x-ray intensity was 1.92 μGy/s at a distance of 1.0 m from the source and a tube voltage of 80 kV. The energy-discrimination CT was carried out by selecting x-ray photon energies.
Associations Between Discrimination and Cardiovascular Health Among Asian Indians in the United States.

PubMed

Nadimpalli, S B; Dulin-Keita, A; Salas, C; Kanaya, A M; Kandula, Namratha R

2016-12-01

Asian Indians (AI) have a high risk of atherosclerotic cardiovascular disease. The study investigated associations between discrimination and (1) cardiovascular risk and (2) self-rated health among AI. Higher discrimination scores were hypothesized to relate to a higher cardiovascular risk score (CRS) and poorer self-rated health. Asian Indians (n = 757) recruited between 2010 and 2013 answered discrimination and self-reported health questions. The CRS (0-8 points) included body-mass index, systolic blood pressure, total cholesterol, and fasting blood glucose levels of AI. Multiple linear regression analyses were conducted to evaluate relationships between discrimination and the CRS and discrimination and self-rated health, adjusting for psychosocial and clinical factors. There were no significant relationships between discrimination and the CRS (p ≥ .05). Discrimination was related to poorer self-reported health, B = -.41 (SE = .17), p = .02. Findings suggest perhaps there are important levels at which discrimination may harm health.
Sex determination from the calcaneus in a 20th century Greek population using discriminant function analysis.

PubMed

Peckmann, Tanya R; Orr, Kayla; Meek, Susan; Manolis, Sotiris K

2015-12-01

The skull and post-cranium have been used for the determination of sex for unknown human remains. However, in forensic cases where skeletal remains often exhibit postmortem damage and taphonomic changes the calcaneus may be used for the determination of sex as it is a preservationally favored bone. The goal of the present research was to derive discriminant function equations from the calcaneus for estimation of sex from a contemporary Greek population. Nine parameters were measured on 198 individuals (103 males and 95 females), ranging in age from 20 to 99 years old, from the University of Athens Human Skeletal Reference Collection. The statistical analyses showed that all variables were sexually dimorphic. Discriminant function score equations were generated for use in sex determination. The average accuracy of sex classification ranged from 70% to 90% for the univariate analysis, 82.9% to 87.5% for the direct method, and 86.2% for the stepwise method. Comparisons to other populations were made. Overall, the cross-validated accuracies ranged from 48.6% to 56.1% with males most often identified correctly and females most often misidentified. The calcaneus was shown to be useful for sex determination in the twentieth century Greek population. Copyright © 2015 The Chartered Society of Forensic Sciences. Published by Elsevier Ireland Ltd. All rights reserved.
Diagnostic value of fibronectin discriminant score for predicting liver fibrosis stages in chronic hepatitis C virus patients.

PubMed

Attallah, Abdelfattah M; Abdallah, Sanaa O; Attallah, Ahmed A; Omran, Mohamed M; Farid, Khaled; Nasif, Wesam A; Shiha, Gamal E; Abdel-Aziz, Abdel-Aziz F; Rasafy, Nancy; Shaker, Yehia M

2013-01-01

Several noninvasive predictive models were developed to substitute liver biopsy for fibrosis assessment. To evaluate the diagnostic value of fibronectin which reflect extracellular matrix metabolism and standard liver functions tests which reflect alterations in hepatic functions. Chronic hepatitis C (CHC) patients (n = 145) were evaluated using ROC curves and stepwise multivariate discriminant analysis (MDA) and was validated in 180 additional patients. Liver biochemical profile including transaminases, bilirubin, alkaline phosphatase, albumin, complete blood count were estimated. Fibronectin concentration was determined using monoclonal antibody and ELISA. A novel index named fibronectin discriminant score (FDS) based on fibronectin, APRI and albumin was developed. FDS produced areas under ROC curves (AUC) of 0.91 for significant fibrosis and 0.81 for advanced fibrosis. The FDS correctly classified 79% of the significant liver fibrosis patients (F2-F4) with 87% sensitivity and 75% specificity. The relative risk [odds ratio (OR)] of having significant liver fibrosis using the cut-off values determined by ROC curve analyses were 6.1 for fibronectin, 4.9 for APRI, and 4.2 for albumin. FDS predicted liver fibrosis with an OR of 16.8 for significant fibrosis and 8.6 for advanced fibrosis. The FDS had similar AUC and OR in the validation group to the estimation group without statistically significant difference. FDS predicted liver fibrosis with high degree of accuracy, potentially decreasing the number of liver biopsy required.
Discriminative analysis of early Alzheimer's disease based on two intrinsically anti-correlated networks with resting-state fMRI.

PubMed

Wang, Kun; Jiang, Tianzi; Liang, Meng; Wang, Liang; Tian, Lixia; Zhang, Xinqing; Li, Kuncheng; Liu, Zhening

2006-01-01

In this work, we proposed a discriminative model of Alzheimer's disease (AD) on the basis of multivariate pattern classification and functional magnetic resonance imaging (fMRI). This model used the correlation/anti-correlation coefficients of two intrinsically anti-correlated networks in resting brains, which have been suggested by two recent studies, as the feature of classification. Pseudo-Fisher Linear Discriminative Analysis (pFLDA) was then performed on the feature space and a linear classifier was generated. Using leave-one-out (LOO) cross validation, our results showed a correct classification rate of 83%. We also compared the proposed model with another one based on the whole brain functional connectivity. Our proposed model outperformed the other one significantly, and this implied that the two intrinsically anti-correlated networks may be a more susceptible part of the whole brain network in the early stage of AD.
Tracing the Geographical Origin of Onions by Strontium Isotope Ratio and Strontium Content.

PubMed

Hiraoka, Hisaaki; Morita, Sakie; Izawa, Atsunobu; Aoyama, Keisuke; Shin, Ki-Cheol; Nakano, Takanori

2016-01-01

The strontium (Sr) isotope ratio ((87)Sr/(86)Sr) and Sr content were used to trace the geographical origin of onions from Japan and other countries, including China, the United States of America, New Zealand, Australia, and Thailand. The mean (87)Sr/(86)Sr ratio and Sr content (dry weight basis) for onions from Japan were 0.70751 and 4.6 mg kg(-1), respectively, and the values for onions from the other countries were 0.71199 and 12.4 mg kg(-1), respectively. Linear discriminant analysis was performed to classify onions produced in Japan from those produced in the other countries based on the Sr data. The discriminant equation derived from linear discriminant analysis was evaluated by 10-fold cross validation. As a result, the origins of 92% of onions were correctly classified between Japan and the other countries.
Direction-dependent secondary bonds and their stepwise melting in a uracil-based molecular crystal studied by infrared spectroscopy and theoretical modeling

NASA Astrophysics Data System (ADS)

Szekrényes, Zsolt; Nagy, Péter R.; Tarczay, György; Maggini, Laura; Bonifazi, Davide; Kamarás, Katalin

2018-01-01

Three types of supramolecular interactions are identified in the three crystallographic directions in crystals of 1,4-bis[(1-hexylurac-6-yl) ethynyl]benzene, a uracil-based molecule with a linear backbone. These three interactions, characterized by their strongest component, are: intermolecular double H-bonds along the molecular axis, London dispersion interaction of hexyl chains connecting these linear assemblies, and π - π stacking of the aromatic rings perpendicular to the molecular planes. On heating, two transitions happen, disordering of hexyl chains at 473 K, followed by H-bond melting at 534 K. The nature of the bonds and transitions was established by matrix-isolation and temperature-dependent infrared spectroscopy and supported by theoretical computations.
Computer Mapping of Water Quality in Saginaw Bay with LANDSAT Digital Data

NASA Technical Reports Server (NTRS)

Rogers, R. H. (Principal Investigator); Shah, N. J.; Smith, V. E.; Mckeon, J. B.

1976-01-01

The author has identified the following significant results. LANDSAT digital data and ground truth measurements for Saginaw Bay (Lake Huron), Michigan, for 31 July 1975 were correlated by stepwise linear regression and the resulting equations used to estimate invisible water quality parameters in nonsampled areas. Chloride, conductivity, total Kjeldahl nitrogen, total phosphorus, and chlorophyll a were best correlated with the ratio of LANDSAT Band 4 to Band 5. Temperature and Secchi depth correlate best with Band 5.
Transverse Motion of a Particle with an Oscillating Charge and Variable Mass in a Magnetic Field

NASA Astrophysics Data System (ADS)

Alisultanov, Z. Z.; Ragimkhanov, G. B.

2018-03-01

The problem of motion of a particle with an oscillating electric charge and variable mass in an uniform magnetic field has been solved. Three laws of mass variation have been considered: linear growth, oscillations, and stepwise growth. Analytical expressions for the particle velocity at different time dependences of the particle mass are obtained. It is established that simultaneous consideration of changes in the mass and charge leads to a significant change in the particle trajectory.
Time dependent shear stress and temperature distribution over an insulated flat plate moving at hypersonic speed.

NASA Technical Reports Server (NTRS)

Rodkiewicz, C. M.; Gupta, R. N.

1971-01-01

The laminar two-dimensional flow over a stepwise accelerated flat plate moving with hypersonic speed at zero angle of attack is analysed. The governing equations in the self-similar form are linearized and solved numerically for small times. The solutions obtained are the deviations of the velocity and the temperature profiles from those of steady state. The presented results may be used to find the first order boundary layer induced pressure on the plate.

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.
Fast Depiction Invariant Visual Similarity for Content Based Image Retrieval Based on Data-driven Visual Similarity using Linear Discriminant Analysis

NASA Astrophysics Data System (ADS)

Wihardi, Y.; Setiawan, W.; Nugraha, E.

2018-01-01

On this research we try to build CBIRS based on Learning Distance/Similarity Function using Linear Discriminant Analysis (LDA) and Histogram of Oriented Gradient (HoG) feature. Our method is invariant to depiction of image, such as similarity of image to image, sketch to image, and painting to image. LDA can decrease execution time compared to state of the art method, but it still needs an improvement in term of accuracy. Inaccuracy in our experiment happen because we did not perform sliding windows search and because of low number of negative samples as natural-world images.
Comparing Linear Discriminant Function with Logistic Regression for the Two-Group Classification Problem.

ERIC Educational Resources Information Center

Fan, Xitao; Wang, Lin

The Monte Carlo study compared the performance of predictive discriminant analysis (PDA) and that of logistic regression (LR) for the two-group classification problem. Prior probabilities were used for classification, but the cost of misclassification was assumed to be equal. The study used a fully crossed three-factor experimental design (with…
Fast characterization of cheeses by dynamic headspace-mass spectrometry.

PubMed

Pérès, Christophe; Denoyer, Christian; Tournayre, Pascal; Berdagué, Jean-Louis

2002-03-15

This study describes a rapid method to characterize cheeses by analysis of their volatile fraction using dynamic headspace-mass spectrometry. Major factors governing the extraction and concentration of the volatile components were first studied. These components were extracted from the headspace of the cheeses in a stream of helium and concentrated on a Tenax TA trap. They were then desorbed by heating and injected directly into the source of a mass spectrometer via a short deactivated silica transfer line. The mass spectra of the mixture of volatile components were considered as fingerprints of the analyzed substances. Forward stepwise factorial discriminant analysis afforded a limited number of characteristic mass fragments that allowed a good classification of the batches of cheeses studied.
Joint recognition and discrimination in nonlinear feature space

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1997-09-01

A new general method for linear and nonlinear feature extraction is presented. It is novel since it provides both representation and discrimination while most other methods are concerned with only one of these issues. We call this approach the maximum representation and discrimination feature (MRDF) method and show that the Bayes classifier and the Karhunen- Loeve transform are special cases of it. We refer to our nonlinear feature extraction technique as nonlinear eigen- feature extraction. It is new since it has a closed-form solution and produces nonlinear decision surfaces with higher rank than do iterative methods. Results on synthetic databases are shown and compared with results from standard Fukunaga- Koontz transform and Fisher discriminant function methods. The method is also applied to an automated product inspection problem (discrimination) and to the classification and pose estimation of two similar objects (representation and discrimination).
Everyday Discrimination, Diabetes-Related Distress, and Depressive Symptoms Among African Americans and Latinos with Diabetes

PubMed Central

Valerio, Melissa A.; Kieffer, Edith; Sinco, Brandy; Rosland, Ann-Marie; Hawkins, Jaclynn; Espitia, Nicolaus; Palmisano, Gloria; Spencer, Michael

2013-01-01

It is not known how discrimination might affect diabetes-related distress (DRD), an important correlate of diabetes outcomes. We examined correlates of discrimination and the influence of discrimination on DRD and depressive symptoms (DS) for African Americans and Latinos with type 2 diabetes. We analyzed survey data (n = 157) collected at enrollment into a diabetes management intervention. Using multiple linear regression, we examined correlates of discrimination and the association between discrimination and DRD and DS. Discrimination was significantly associated with higher DRD for Latinos (b 1.58, 95 % CI 1.08, 2.31, p < 0.05), but not significant for African Americans (b 0.96, 95 % CI 0.59, 1.57). Discrimination was marginally significantly associated with more DS for Latinos (b 1.43, 95 % CI 0.97, 2.12, p < 0.10), but not significant for African Americans (b 1.21, 95 % CI 0.87, 1.70). These findings suggest the need to address stressors unique to racial/ethnic minorities to improve diabetes-related outcomes. PMID:23689972
Pattern recognition and genetic algorithms for discrimination of orange juices and reduction of significant components from headspace solid-phase microextraction.

PubMed

Rinaldi, Maurizio; Gindro, Roberto; Barbeni, Massimo; Allegrone, Gianna

2009-01-01

Orange (Citrus sinensis L.) juice comprises a complex mixture of volatile components that are difficult to identify and quantify. Classification and discrimination of the varieties on the basis of the volatile composition could help to guarantee the quality of a juice and to detect possible adulteration of the product. To provide information on the amounts of volatile constituents in fresh-squeezed juices from four orange cultivars and to establish suitable discrimination rules to differentiate orange juices using new chemometric approaches. Fresh juices of four orange cultivars were analysed by headspace solid-phase microextraction (HS-SPME) coupled with GC-MS. Principal component analysis, linear discriminant analysis and heuristic methods, such as neural networks, allowed clustering of the data from HS-SPME analysis while genetic algorithms addressed the problem of data reduction. To check the quality of the results the chemometric techniques were also evaluated on a sample. Thirty volatile compounds were identified by HS-SPME and GC-MS analyses and their relative amounts calculated. Differences in composition of orange juice volatile components were observed. The chosen orange cultivars could be discriminated using neural networks, genetic relocation algorithms and linear discriminant analysis. Genetic algorithms applied to the data were also able to detect the most significant compounds. SPME is a useful technique to investigate orange juice volatile composition and a flexible chemometric approach is able to correctly separate the juices.
Discrimination and Depressive Symptoms Among Latina/o Adolescents of Immigrant Parents.

PubMed

Lopez, William D; LeBrón, Alana M W; Graham, Louis F; Grogan-Kaylor, Andrew

2016-01-01

Discrimination is associated with negative mental health outcomes for Latina/o adolescents. While Latino/a adolescents experience discrimination from a number of sources and across contexts, little research considers how the source of discrimination and the context in which it occurs affect mental health outcomes among Latina/o children of immigrants. We examined the association between source-specific discrimination, racial or ethnic background of the source, and school ethnic context with depressive symptoms for Latina/o adolescents of immigrant parents. Using multilevel linear regression with time-varying covariates, we regressed depressive symptoms on source-specific discrimination, racial or ethnic background of the source of discrimination, and school percent Latina/o. Discrimination from teachers (β = 0.06, p < .05), students (β = 0.05, p < .05), Cubans (β = 0.19, p < .001), and Latinas/os (β = 0.19, p < .001) were positively associated with depressive symptoms. These associations were not moderated by school percent Latina/o. The findings indicate a need to reduce discrimination to improve Latina/o adolescents' mental health. © The Author(s) 2016.
Relationship between Spiritual Health and Quality of Life in Patients with Cancer.

PubMed

Mohebbifar, Rafat; Pakpour, Amir H; Nahvijou, Azin; Sadeghi, Atefeh

2015-01-01

As the essence of health in humans, spiritual health is a fundamental concept for discussing chronic diseases such as cancer and a major approach for improving quality of life in patients is through creating meaningfulness and purpose. The present descriptive analytical study was conducted to assess the relationship between spiritual health and quality of life in 210 patients with cancer admitted to the Cancer Institute of Iran, selected through convenience sampling in 2014. Data were collected using Spiritual Health Questionnaire and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC-QLQ). Patients' performance was assessed through the Karnofsky Performance Status Indicator and their cognitive status through the Mini-Mental State Examination (MMSE). Data were analyzed in SPSS-16 using descriptive statistics and stepwise linear regression. The results obtained reported the mean and standard deviation of the patients' spiritual health scoreas 78.4±16.1and the mean and standard deviation of their quality of life score as 58.1±18.7. The stepwise linear regression analysis confirmed a positive and significant relationship between spiritual health and quality of life in patients with cancer (β=0.688 and r=0.00). The results of the study show that spiritual health should be more emphasized and reinforced as a factor involved in improving quality of life in patients with cancer. Designing care therapies and spiritual interventions is a priority in the treatment of these patients.
Estimating erosion risk on forest lands using improved methods of discriminant analysis

Treesearch

J. Lewis; R. M. Rice

1990-01-01

A population of 638 timber harvest areas in northwestern California was sampled for data related to the occurrence of critical amounts of erosion (>153 m3 within 0.81 ha). Separate analyses were done for forest roads and logged areas. Linear discriminant functions were computed in each analysis to contrast site conditions at critical plots with randomly selected...
Evaluation of volatile aldehydes as discriminating parameters in quality vinegars with protected European geographical indication.

PubMed

Durán-Guerrero, Enrique; Chinnici, Fabio; Natali, Nadia; Riponi, Claudio

2015-09-01

Thirty-six high-quality vinegars with geographical indication belonging to Sherry and Modena areas (vinegars of Jerez, balsamic vinegars of Modena and traditional balsamic vinegars of Modena) with all possible aging periods were analyzed to determine the content of volatile aldehydes. A solid-phase extraction method with in-cartridge derivatization using O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine followed by gas chromatography-mass spectrometry was employed. Twenty-two volatile aldehydes were identified and determined in the samples. Analysis of variance provided significant differences among the samples as a function of the type of vinegar, aging time and raw material. Principal component analysis and linear discriminant analysis demonstrated the possibility of discriminating the samples in terms of aging time and raw material. Linear aldehydes and compounds such as furfural, methional, nonenal, hexenal, 2-methylbutanal and i-butyraldehyde were the most significant variables able to discriminate the samples. Aldehyde content of premium quality vinegars is a function of both ageing time and raw material. Their evaluation could be a useful tool with a view to ascertaining vinegar origin and genuineness. © 2014 Society of Chemical Industry.
Suppressing flashes of items surrounding targets during calibration of a P300-based brain-computer interface improves performance

NASA Astrophysics Data System (ADS)

Frye, G. E.; Hauser, C. K.; Townsend, G.; Sellers, E. W.

2011-04-01

Since the introduction of the P300 brain-computer interface (BCI) speller by Farwell and Donchin in 1988, the speed and accuracy of the system has been significantly improved. Larger electrode montages and various signal processing techniques are responsible for most of the improvement in performance. New presentation paradigms have also led to improvements in bit rate and accuracy (e.g. Townsend et al (2010 Clin. Neurophysiol. 121 1109-20)). In particular, the checkerboard paradigm for online P300 BCI-based spelling performs well, has started to document what makes for a successful paradigm, and is a good platform for further experimentation. The current paper further examines the checkerboard paradigm by suppressing items which surround the target from flashing during calibration (i.e. the suppression condition). In the online feedback mode the standard checkerboard paradigm is used with a stepwise linear discriminant classifier derived from the suppression condition and one classifier derived from the standard checkerboard condition, counter-balanced. The results of this research demonstrate that using suppression during calibration produces significantly more character selections/min ((6.46) time between selections included) than the standard checkerboard condition (5.55), and significantly fewer target flashes are needed per selection in the SUP condition (5.28) as compared to the RCP condition (6.17). Moreover, accuracy in the SUP and RCP conditions remained equivalent (~90%). Mean theoretical bit rate was 53.62 bits/min in the suppression condition and 46.36 bits/min in the standard checkerboard condition (ns). Waveform morphology also showed significant differences in amplitude and latency.
A discriminant analysis prediction model of non-syndromic cleft lip with or without cleft palate based on risk factors.

PubMed

Li, Huixia; Luo, Miyang; Luo, Jiayou; Zheng, Jianfei; Zeng, Rong; Du, Qiyun; Fang, Junqun; Ouyang, Na

2016-11-23

A risk prediction model of non-syndromic cleft lip with or without cleft palate (NSCL/P) was established by a discriminant analysis to predict the individual risk of NSCL/P in pregnant women. A hospital-based case-control study was conducted with 113 cases of NSCL/P and 226 controls without NSCL/P. The cases and the controls were obtained from 52 birth defects' surveillance hospitals in Hunan Province, China. A questionnaire was administered in person to collect the variables relevant to NSCL/P by face to face interviews. Logistic regression models were used to analyze the influencing factors of NSCL/P, and a stepwise Fisher discriminant analysis was subsequently used to construct the prediction model. In the univariate analysis, 13 influencing factors were related to NSCL/P, of which the following 8 influencing factors as predictors determined the discriminant prediction model: family income, maternal occupational hazards exposure, premarital medical examination, housing renovation, milk/soymilk intake in the first trimester of pregnancy, paternal occupational hazards exposure, paternal strong tea drinking, and family history of NSCL/P. The model had statistical significance (lambda = 0.772, chi-square = 86.044, df = 8, P < 0.001). Self-verification showed that 83.8 % of the participants were correctly predicted to be NSCL/P cases or controls with a sensitivity of 74.3 % and a specificity of 88.5 %. The area under the receiver operating characteristic curve (AUC) was 0.846. The prediction model that was established using the risk factors of NSCL/P can be useful for predicting the risk of NSCL/P. Further research is needed to improve the model, and confirm the validity and reliability of the model.
Suicidal ideation in outpatients with chronic musculoskeletal pain: an exploratory study of the role of sleep onset insomnia and pain intensity.

PubMed

Smith, Michael T; Perlis, Michael L; Haythornthwaite, Jennifer A

2004-01-01

Sleep disturbance, depression, and heightened risk of suicide are among the most clinically significant sequelae of chronic pain. While sleep disturbance is associated with suicidality in patients with major depression and is a significant independent predictor of completed suicide in psychiatric patients, it is not known whether sleep disturbance is associated with suicidal behavior in chronic pain. This exploratory study evaluates the importance of insomnia in discriminating suicidal ideation in chronic pain relative to depression severity and other pain-related factors. Fifty-one outpatients with non-cancer chronic pain were recruited. Subjects completed a pain and sleep survey, the Pittsburgh Sleep Quality Index, the Beck Depression Inventory, and the Multidimensional Pain Inventory. Subjects were classified as "suicidal ideators" or "non-ideators" based on their responses to BDI-Item 9 (Suicide). Bivariate analyses and multivariate discriminant function analyses were conducted. Twenty-four percent reported suicidal ideation (without intent). Suicidal ideators endorsed higher levels of: sleep onset insomnia, pain intensity, medication usage, pain-related interference, affective distress, and depressive symptoms (P < 0.03). These 6 variables were entered into stepwise discriminant function analyses. Two variables predicted group membership: Sleep Onset Insomnia Severity and Pain Intensity, respectively. The discriminant function correctly classified 84.3% of the cases (P < 0.0001). Chronic pain patients who self-reported severe and frequent initial insomnia with concomitant daytime dysfunction and high pain intensity were more likely to report passive suicidal ideation, independent from the effects of depression severity. Future research aimed at determining whether sleep disturbance is a modifiable risk factor for suicidal ideation in chronic pain is warranted.
Community-based comprehensive intervention for people with schizophrenia in Guangzhou, China: Effects on clinical symptoms, social functioning, internalized stigma and discrimination.

PubMed

Li, Jie; Huang, Yuan-Guang; Ran, Mao-Sheng; Fan, Yu; Chen, Wen; Evans-Lacko, Sara; Thornicroft, Graham

2018-04-01

Comprehensive interventions including components of stigma and discrimination reduction in schizophrenia in low- and middle-income countries (LMICs) are lacking. We developed a community-based comprehensive intervention to evaluate its effects on clinical symptoms, social functioning, internalized stigma and discrimination among patients with schizophrenia. A randomized controlled trial including an intervention group (n = 169) and a control group (n = 158) was performed. The intervention group received comprehensive intervention (strategies against stigma and discrimination, psycho-education, social skills training and cognitive behavioral therapy) and the control group received face to face interview. Both lasted for nine months. Participants were measured at baseline, 6 months and 9 months using the Internalized Stigma of Mental Illness scale (ISMI), Discrimination and Stigma Scale (DISC-12), Global Assessment of Functioning (GAF), Schizophrenia Quality of Life Scale (SQLS), Self-Esteem Scale (SES), Brief Psychiatric Rating Scale (BPRS) and PANSS negative scale (PANSS-N). Insight and medication compliance were evaluated by senior psychiatrists. Data were analyzed by descriptive statistics, t-test, chi-square test or Fisher's exact test. Linear Mixed Models were used to show intervention effectiveness on scales. General Linear Mixed Models with multinomial logistic link function were used to assess the effectiveness on medication compliance and insight. We found a significant reduction on anticipated discrimination, BPRS and PANSS-N total scores, and an elevation on overcoming stigma and GAF in the intervention group after 9 months. These suggested the intervention may be effective in reducing anticipated discrimination, increasing skills overcoming stigma as well as improving clinical symptoms and social functioning in Chinese patients with schizophrenia. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.
[Discrimination of Red Tide algae by fluorescence spectra and principle component analysis].

PubMed

Su, Rong-guo; Hu, Xu-peng; Zhang, Chuan-song; Wang, Xiu-lin

2007-07-01

Fluorescence discrimination technology for 11 species of the Red Tide algae at genus level was constructed by principle component analysis and non-negative least squares. Rayleigh and Raman scattering peaks of 3D fluorescence spectra were eliminated by Delaunay triangulation method. According to the results of Fisher linear discrimination, the first principle component score and the second component score of 3D fluorescence spectra were chosen as discriminant feature and the feature base was established. The 11 algae species were tested, and more than 85% samples were accurately determinated, especially for Prorocentrum donghaiense, Skeletonema costatum, Gymnodinium sp., which have frequently brought Red tide in the East China Sea. More than 95% samples were right discriminated. The results showed that the genus discriminant feature of 3D fluorescence spectra of Red Tide algae given by principle component analysis could work well.
Discrimination, acculturation and other predictors of depression among pregnant Hispanic women.

PubMed

Walker, Janiece L; Ruiz, R Jeanne; Chinn, Juanita J; Marti, Nathan; Ricks, Tiffany N

2012-01-01

The purpose of our study was to examine the effects of socioeconomic status, acculturative stress, discrimination, and marginalization as predictors of depression in pregnant Hispanic women. A prospective observational design was used. Central and Gulf coast areas of Texas in obstetrical offices. A convenience sample of 515 pregnant, low income, low medical risk, and self-identified Hispanic women who were between 22-24 weeks gestation was used to collect data. The predictor variables were socioeconomic status, discrimination, acculturative stress, and marginalization. The outcome variable was depression. Education, frequency of discrimination, age, and Anglo marginality were significant predictors of depressive symptoms in a linear regression model, F (6, 458) = 8.36, P<.0001. Greater frequency of discrimination was the strongest positive predictor of increased depressive symptoms. It is important that health care providers further understand the impact that age and experiences of discrimination throughout the life course have on depressive symptoms during pregnancy.
Boronlectin/Polyelectrolyte Ensembles as Artificial Tongue: Design, Construction, and Application for Discriminative Sensing of Complex Glycoconjugates from Panax ginseng.

PubMed

Zhang, Xiao-Tai; Wang, Shu; Xing, Guo-Wen

2017-02-01

Ginsenoside is a large family of triterpenoid saponins from Panax ginseng, which possesses various important biological functions. Due to the very similar structures of these complex glycoconjugates, it is crucial to develop a powerful analytic method to identify ginsenosides qualitatively or quantitatively. We herein report an eight-channel fluorescent sensor array as artificial tongue to achieve the discriminative sensing of ginsenosides. The fluorescent cross-responsive array was constructed by four boronlectins bearing flexible boronic acid moieties (FBAs) with multiple reactive sites and two linear poly(phenylene-ethynylene) (PPEs). An "on-off-on" response pattern was afforded on the basis of superquenching of fluorescent indicator PPEs and an analyte-induced allosteric indicator displacement (AID) process. Most importantly, it was found that the canonical distribution of ginsenoside data points analyzed by linear discriminant analysis (LDA) was highly correlated with the inherent molecular structures of the analytes, and the absence of overlaps among the five point groups reflected the effectiveness of the sensor array in the discrimination process. Almost all of the unknown ginsenoside samples at different concentrations were correctly identified on the basis of the established mathematical model. Our current work provided a general and constructive method to improve the quality assessment and control of ginseng and its extracts, which are useful and helpful for further discriminating other complex glycoconjugate families.
1H nuclear magnetic resonance spectroscopy-based metabonomic study in patients with cirrhosis and hepatic encephalopathy

PubMed Central

Dabos, Konstantinos John; Parkinson, John Andrew; Sadler, Ian Howard; Plevris, John Nicholas; Hayes, Peter Clive

2015-01-01

AIM: To identify plasma metabolites used as biomarkers in order to distinguish cirrhotics from controls and encephalopathics. METHODS: A clinical study involving stable cirrhotic patients with and without overt hepatic encephalopathy was designed. A control group of healthy volunteers was used. Plasma from those patients was analysed using 1H - nuclear magnetic resonance spectroscopy. We used the Carr Purcell Meiboom Gill sequence to process the sample spectra at ambient probe temperature. We used a gated secondary irradiation field for water signal suppression. Samples were calibrated and referenced using the sodium trimethyl silyl propionate peak at 0.00 ppm. For each sample 128 transients (FID’s) were acquired into 32 K complex data points over a spectral width of 6 KHz. 30 degree pulses were applied with an acquisition time of 4.0 s in order to achieve better resolution, followed by a recovery delay of 12 s, to allow for complete relaxation and recovery of the magnetisation. A metabolic profile was created for stable cirrhotic patients without signs of overt hepatic encephalopathy and encephalopathic patients as well as healthy controls. Stepwise discriminant analysis was then used and discriminant factors were created to differentiate between the three groups. RESULTS: Eighteen stabled cirrhotic patients, eighteen patients with overt hepatic encephalopathy and seventeen healthy volunteers were recruited. Patients with cirrhosis had significantly impaired ketone body metabolism, urea synthesis and gluconeogenesis. This was demonstrated by higher concentrations of acetoacetate (0.23 ± 0.02 vs 0.05 ± 0.00, P < 0.01), and b-hydroxybutarate (0.58 ± 0.14 vs 0.08 ± 0.00, P < 0.01), lower concentrations of glutamine (0.44 ± 0.08 vs 0.63 ± 0.03, P < 0.05), histidine (0.16 ± 0.01 vs 0.36 ± 0.04, P < 0.01) and arginine (0.08 ± 0.01 vs 0.14 ± 0.02, P < 0.03) and higher concentrations of glutamate (1.36 ± 0.25 vs 0.58 ± 0.04, P < 0.01), lactate (1.53 ± 0.11 vs 0.42 ± 0.05, P < 0.01), pyruvate (0.11 ± 0.02 vs 0.03 ± 0.00, P < 0.01) threonine (0.39 ± 0.02 vs 0.08 ± 0.01, P < 0.01) and aspartate (0.37 ± 0.03 vs 0.03 ± 0.01). A five metabolite signature by stepwise discriminant analysis could separate between controls and cirrhotic patients with an accuracy of 98%. In patients with encephalopathy we observed further derangement of ketone body metabolism, impaired production of glycerol and myoinositol, reversal of Fischer’s ratio and impaired glutamine production as demonstrated by lower b-hydroxybutyrate (0.58 ± 0.14 vs 0.16 ± 0.02, P < 0.0002), higher acetoacetate (0.23 ± 0.02 vs 0.41 ± 0.16, P < 0.05), leucine (0.33 ± 0.02 vs 0.49 ± 0.05, P < 0.005) and isoleucine (0.12 ± 0.02 vs 0.27 ± 0.02, P < 0.0004) and lower glutamine (0.44 ± 0.08 vs 0.36 ± 0.04, P < 0.013), glycerol (0.53 ± 0.03 vs 0.19 ± 0.02, P < 0.000) and myoinositol (0.36 ± 0.04 vs 0.18 ± 0.02, P < 0.010) concentrations. A four metabolite signature by stepwise discriminant analysis could separate between encephalopathic and cirrhotic patients with an accuracy of 87%. CONCLUSION: Patients with cirrhosis and patients with hepatic encephalopathy exhibit distinct metabolic abnormalities and the use of metabonomics can select biomarkers for these diseases. PMID:26140090
[Study on the genuineness and producing area of Panax notoginseng based on infrared spectroscopy combined with discriminant analysis].

PubMed

Liu, Fei; Wang, Yuan-zhong; Yang, Chun-yan; Jin, Hang

2015-01-01

The genuineness and producing area of Panax notoginseng were studied based on infrared spectroscopy combined with discriminant analysis. The infrared spectra of 136 taproots of P. notoginseng from 13 planting point in 11 counties were collected and the second derivate spectra were calculated by Omnic 8. 0 software. The infrared spectra and their second derivate spectra in the range 1 800 - 700 cm-1 were used to build model by stepwise discriminant analysis, which was in order to distinguish study on the genuineness of P. notoginseng. The model built based on the second derivate spectra showed the better recognition effect for the genuineness of P. notoginseng. The correct rate of returned classification reached to 100%, and the prediction accuracy was 93. 4%. The stability of model was tested by cross validation and the method was performed extrapolation validation. The second derivate spectra combined with the same discriminant analysis method were used to distinguish the producing area of P. notoginseng. The recognition effect of models built based on different range of spectrum and different numbers of samples were compared and found that when the model was built by collecting 8 samples from each planting point as training sample and the spectrum in the range 1 500 - 1 200 cm-1 , the recognition effect was better, with the correct rate of returned classification reached to 99. 0%, and the prediction accuracy was 76. 5%. The results indicated that infrared spectroscopy combined with discriminant analysis showed good recognition effect for the genuineness of P. notoginseng. The method might be a hopeful new method for identification of genuineness of P. notoginseng in practice. The method could recognize the producing area of P. notoginseng to some extent and could be a new thought for identification of the producing area of P. natoginseng.

Three-dimensionally derived interlandmark distances for sex estimation in intact and fragmentary crania.

PubMed

Small, Candice; Schepartz, Lynne; Hemingway, Jason; Brits, Desiré

2018-06-01

The skull is the element most frequently presented to forensic anthropologists for analysis yet weathering, corpse maiming, and scavenger activity often result in damage and fragmentation. This fragmentation results in a reduction in the number of traditional calliper derived measurements that can be obtained and subjected to discriminant based analyses for sex estimation. In this investigation, we employed three-dimensional geometric morphometric methods to derive novel interlandmark distance measures across six regions of the cranium including the basicranium, basipalate, zygoma, orbits and the cranium globally to create functions to discriminate sex with high efficacy, even in the event of fragmentation. Forty-five homologous landmarks were digitised across each of 227 (114 males and 113 females) South African crania of European descent (white) sampled from the Raymond A Dart Collection of Human Skeletons, housed in the School of Anatomical Sciences, University of the Witwatersrand, South Africa. A total of 990 interlandmark distances (ILDs) were mathematically derived using Pythagorean geometry. These ILDs were then filtered by region and subjected to both direct and stepwise discriminant function analyses. Discriminant equations where derived for each region and achieved the following average cross-validated sex estimation accuracies: basicranium-74%; basipalate-80.2%; zygomatic-82.4; orbits-71.8%; nasomaxilla-83.7%; global cranium-88.2%. A large number of the ILDs used to derive the discriminant functions are novel, demonstrating the efficacy of geometric morphometric methods and illustrating the need to reassess old methods of data collection using modern methods to determine whether they best capture biological differences. The results of this study provide an invaluable contribution to forensic anthropology in South Africa as it provides an accurate, practical means of assessing sex using fragmentary material that may otherwise have been disregarded. These will undeniable aid in accurate sex estimation and ultimately, victim identification. Copyright © 2018 Elsevier B.V. All rights reserved.
Characterizing entanglement with global and marginal entropic measures

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

Adesso, Gerardo; Illuminati, Fabrizio; De Siena, Silvio

2003-12-01

We qualify the entanglement of arbitrary mixed states of bipartite quantum systems by comparing global and marginal mixednesses quantified by different entropic measures. For systems of two qubits we discriminate the class of maximally entangled states with fixed marginal mixednesses, and determine an analytical upper bound relating the entanglement of formation to the marginal linear entropies. This result partially generalizes to mixed states the quantification of entanglement with marginal mixednesses holding for pure states. We identify a class of entangled states that, for fixed marginals, are globally more mixed than product states when measured by the linear entropy. Such statesmore » cannot be discriminated by the majorization criterion.« less
Life satisfaction and trauma in clinical and non-clinical children living in a war-torn environment: A discriminant analysis.

PubMed

Veronese, Guido; Pepe, Alessandro

2017-07-01

The aim of this work was to discriminate between healthy children and children at risk of developing mental impairments by evaluating the impact on contextual and individual factors of a context characterized by war. We tested the hypothesis that a linear discriminant function composed of trauma, life satisfaction, and affect balance has the power to classify the children as community or clinical referred. Membership of the clinical-referred group was associated with poorer life satisfaction and higher levels of trauma. Community-referred profiles were associated with lesser trauma. Perceived life satisfaction regarding family and school was the main contributor to the discriminant function.
Exploring nonlinear feature space dimension reduction and data representation in breast Cadx with Laplacian eigenmaps and t-SNE.

PubMed

Jamieson, Andrew R; Giger, Maryellen L; Drukker, Karen; Li, Hui; Yuan, Yading; Bhooshan, Neha

2010-01-01

In this preliminary study, recently developed unsupervised nonlinear dimension reduction (DR) and data representation techniques were applied to computer-extracted breast lesion feature spaces across three separate imaging modalities: Ultrasound (U.S.) with 1126 cases, dynamic contrast enhanced magnetic resonance imaging with 356 cases, and full-field digital mammography with 245 cases. Two methods for nonlinear DR were explored: Laplacian eigenmaps [M. Belkin and P. Niyogi, "Laplacian eigenmaps for dimensionality reduction and data representation," Neural Comput. 15, 1373-1396 (2003)] and t-distributed stochastic neighbor embedding (t-SNE) [L. van der Maaten and G. Hinton, "Visualizing data using t-SNE," J. Mach. Learn. Res. 9, 2579-2605 (2008)]. These methods attempt to map originally high dimensional feature spaces to more human interpretable lower dimensional spaces while preserving both local and global information. The properties of these methods as applied to breast computer-aided diagnosis (CADx) were evaluated in the context of malignancy classification performance as well as in the visual inspection of the sparseness within the two-dimensional and three-dimensional mappings. Classification performance was estimated by using the reduced dimension mapped feature output as input into both linear and nonlinear classifiers: Markov chain Monte Carlo based Bayesian artificial neural network (MCMC-BANN) and linear discriminant analysis. The new techniques were compared to previously developed breast CADx methodologies, including automatic relevance determination and linear stepwise (LSW) feature selection, as well as a linear DR method based on principal component analysis. Using ROC analysis and 0.632+bootstrap validation, 95% empirical confidence intervals were computed for the each classifier's AUC performance. In the large U.S. data set, sample high performance results include, AUC0.632+ = 0.88 with 95% empirical bootstrap interval [0.787;0.895] for 13 ARD selected features and AUC0.632+ = 0.87 with interval [0.817;0.906] for four LSW selected features compared to 4D t-SNE mapping (from the original 81D feature space) giving AUC0.632+ = 0.90 with interval [0.847;0.919], all using the MCMC-BANN. Preliminary results appear to indicate capability for the new methods to match or exceed classification performance of current advanced breast lesion CADx algorithms. While not appropriate as a complete replacement of feature selection in CADx problems, DR techniques offer a complementary approach, which can aid elucidation of additional properties associated with the data. Specifically, the new techniques were shown to possess the added benefit of delivering sparse lower dimensional representations for visual interpretation, revealing intricate data structure of the feature space.
An interative solution of an integral equation for radiative transfer by using variational technique

NASA Technical Reports Server (NTRS)

Yoshikawa, K. K.

1973-01-01

An effective iterative technique is introduced to solve a nonlinear integral equation frequently associated with radiative transfer problems. The problem is formulated in such a way that each step of an iterative sequence requires the solution of a linear integral equation. The advantage of a previously introduced variational technique which utilizes a stepwise constant trial function is exploited to cope with the nonlinear problem. The method is simple and straightforward. Rapid convergence is obtained by employing a linear interpolation of the iterative solutions. Using absorption coefficients of the Milne-Eddington type, which are applicable to some planetary atmospheric radiation problems. Solutions are found in terms of temperature and radiative flux. These solutions are presented numerically and show excellent agreement with other numerical solutions.
An automated image processing method for classification of diabetic retinopathy stages from conjunctival microvasculature images

NASA Astrophysics Data System (ADS)

Khansari, Maziyar M.; O'Neill, William; Penn, Richard; Blair, Norman P.; Chau, Felix; Shahidi, Mahnaz

2017-03-01

The conjunctiva is a densely vascularized tissue of the eye that provides an opportunity for imaging of human microcirculation. In the current study, automated fine structure analysis of conjunctival microvasculature images was performed to discriminate stages of diabetic retinopathy (DR). The study population consisted of one group of nondiabetic control subjects (NC) and 3 groups of diabetic subjects, with no clinical DR (NDR), non-proliferative DR (NPDR), or proliferative DR (PDR). Ordinary least square regression and Fisher linear discriminant analyses were performed to automatically discriminate images between group pairs of subjects. Human observers who were masked to the grouping of subjects performed image discrimination between group pairs. Over 80% and 70% of images of subjects with clinical and non-clinical DR were correctly discriminated by the automated method, respectively. The discrimination rates of the automated method were higher than human observers. The fine structure analysis of conjunctival microvasculature images provided discrimination of DR stages and can be potentially useful for DR screening and monitoring.
Medical mistrust as a key mediator in the association between perceived discrimination and adherence to antiretroviral therapy among HIV-positive Latino men.

PubMed

Galvan, Frank H; Bogart, Laura M; Klein, David J; Wagner, Glenn J; Chen, Ying-Tung

2017-10-01

Discrimination has been found to have deleterious effects on physical health. The goal of the present study was to examine the association between perceived discrimination and adherence to antiretroviral therapy (ART) among HIV-positive Latino men and the extent to which medical mistrust serves as a mediator of that association. A series of linear and logistic regression models was used to test for mediation for three types of perceived discrimination (related to being Latino, being perceived as gay and being HIV-positive). Medical mistrust was found to be significantly associated with perceived discrimination based on Latino ethnicity and HIV serostatus. Medical mistrust was found to mediate the associations between two types of perceived discrimination (related to being Latino and being HIV-positive) and ART adherence. Given these findings, interventions should be developed that increase the skills of HIV-positive Latino men to address both perceived discrimination and medical mistrust.
Perceived Interpersonal Discrimination and Older Women’s Mental Health: Accumulation Across Domains, Attributions, and Time

PubMed Central

Bécares, Laia; Zhang, Nan

2018-01-01

Abstract Experiencing discrimination is associated with poor mental health, but how cumulative experiences of perceived interpersonal discrimination across attributes, domains, and time are associated with mental disorders is still unknown. Using data from the Study of Women’s Health Across the Nation (1996–2008), we applied latent class analysis and generalized linear models to estimate the association between cumulative exposure to perceived interpersonal discrimination and older women’s mental health. We found 4 classes of perceived interpersonal discrimination, ranging from cumulative exposure to discrimination over attributes, domains, and time to none or minimal reports of discrimination. Women who experienced cumulative perceived interpersonal discrimination over time and across attributes and domains had the highest risk of depression (Center for Epidemiologic Studies Depression Scale score ≥16) compared with women in all other classes. This was true for all women regardless of race/ethnicity, although the type and severity of perceived discrimination differed across racial/ethnic groups. Cumulative exposure to perceived interpersonal discrimination across attributes, domains, and time has an incremental negative long-term association with mental health. Studies that examine exposure to perceived discrimination due to a single attribute in 1 domain or at 1 point in time underestimate the magnitude and complexity of discrimination and its association with health. PMID:29036550
A high surface area Zr(IV)-based metal–organic framework showing stepwise gas adsorption and selective dye uptake

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

Lv, Xiu-Liang; Tong, Minman; Huang, Hongliang

2015-03-15

Exploitation of new metal–organic framework (MOF) materials with high surface areas has been attracting great attention in related research communities due to their broad potential applications. In this work, a new Zr(IV)-based MOF, [Zr{sub 6}O{sub 4}(OH){sub 4}(eddb){sub 6}] (BUT-30, H{sub 2}eddb=4,4′-(ethyne-1,2-diyl)dibenzoic acid) has been solvothermally synthesized, characterized, and explored for gases and dyes adsorptions. Single-crystal X-ray diffraction analysis demonstrates a three-dimensional cubic framework structure of this MOF, in which each Zr{sub 6}O{sub 4}(OH){sub 4} building unit is linked by 12 linear eddb ligands. BUT-30 has been found stable up to 400 °C and has a Brunauer–Emmett–Teller (BET) surface area asmore » high as 3940.6 m{sup 2} g{sup −1} (based on the N{sub 2} adsorption at 77 K) and total pore volume of 1.55 cm{sup 3} g{sup −1}. It is more interesting that this MOF exhibits stepwise adsorption behaviors for Ar, N{sub 2}, and CO{sub 2} at low temperatures, and selective uptakes towards different ionic dyes. - Graphical abstract: A new Zr(IV)-based MOF with high surface area has been synthesized and structurally characterized, which shows stepwise gas adsorption at low temperature and selective dye uptake from solution. - Highlights: • A new Zr-based MOF was synthesized and structurally characterized. • This MOF shows a higher surface area compared with its analogous UiO-67 and 68. • This MOF shows a rare stepwise adsorption towards light gases at low temperature. • This MOF performs selective uptakes towards cationic dyes over anionic ones. • Using triple-bond spacer is confirmed feasible in enhancing MOF surface areas.« less
Factors that influence the efficacy of acarbose and metformin as initial therapy in Chinese patients with newly diagnosed type 2 diabetes: a subanalysis of the MARCH trial.

PubMed

Zhang, Jinping; Wang, Na; Xing, Xiaoyan; Yang, Zhaojun; Wang, Xin; Yang, Wenying

2016-01-01

To conduct a subanalysis of the randomized MARCH (Metformin and AcaRbose in Chinese as the initial Hypoglycemic treatment) trial to investigate whether specific characteristics are associated with the efficacy of either acarbose or metformin as initial therapy. A total of 657 type 2 diabetes patients who were randomly assigned to 48 weeks of therapy with either acarbose or metformin in the MARCH trial were divided into two groups based upon their hemoglobin A1c (HbA1c) levels at the end of follow-up: HbA1c <7% (<53 mmol/mol) and ≥7% (≥53 mmol/mol). Univariate, multivariate, and stepwise linear regression analyses were applied to identify the factors associated with treatment efficacy. Because this was a subanalysis, no measurement was performed. Univariate analysis showed that the efficacy of acarbose and metformin was influenced by HbA1c, fasting blood glucose (FBG), and 2 hour postprandial venous blood glucose (2hPPG) levels, as well as by changes in body mass index (BMI) (p ≤ 0.006). Multivariate analysis and stepwise linear regression analyses indicated that lower baseline 2hPPG values and greater changes in BMI were factors that positively influenced efficacy in both treatment groups (p ≤ 0.05). Stepwise regression model analysis also revealed that a lower baseline homeostasis model assessment-estimated insulin resistance (HOMA-IR) and higher serum insulin area under the curve (AUC) were factors positively influencing HbA1c normalization in all patients (p ≤ 0.032). Newly diagnosed type 2 diabetes patients with lower baseline 2hPPG and HOMA-IR values are more likely to achieve glucose control with acarbose or metformin treatment. Furthermore, the change in BMI after acarbose or metformin treatment is also a factor influencing HbA1c normalization. A prospective study with a larger sample size is necessary to confirm our results as well as measure β cell function and examine the influence of the patients' dietary habits.
Decision-Related Activity in Macaque V2 for Fine Disparity Discrimination Is Not Compatible with Optimal Linear Readout

PubMed Central

Clery, Stephane; Cumming, Bruce G.

2017-01-01

Fine judgments of stereoscopic depth rely mainly on relative judgments of depth (relative binocular disparity) between objects, rather than judgments of the distance to where the eyes are fixating (absolute disparity). In macaques, visual area V2 is the earliest site in the visual processing hierarchy for which neurons selective for relative disparity have been observed (Thomas et al., 2002). Here, we found that, in macaques trained to perform a fine disparity discrimination task, disparity-selective neurons in V2 were highly selective for the task, and their activity correlated with the animals' perceptual decisions (unexplained by the stimulus). This may partially explain similar correlations reported in downstream areas. Although compatible with a perceptual role of these neurons for the task, the interpretation of such decision-related activity is complicated by the effects of interneuronal “noise” correlations between sensory neurons. Recent work has developed simple predictions to differentiate decoding schemes (Pitkow et al., 2015) without needing measures of noise correlations, and found that data from early sensory areas were compatible with optimal linear readout of populations with information-limiting correlations. In contrast, our data here deviated significantly from these predictions. We additionally tested this prediction for previously reported results of decision-related activity in V2 for a related task, coarse disparity discrimination (Nienborg and Cumming, 2006), thought to rely on absolute disparity. Although these data followed the predicted pattern, they violated the prediction quantitatively. This suggests that optimal linear decoding of sensory signals is not generally a good predictor of behavior in simple perceptual tasks. SIGNIFICANCE STATEMENT Activity in sensory neurons that correlates with an animal's decision is widely believed to provide insights into how the brain uses information from sensory neurons. Recent theoretical work developed simple predictions to differentiate decoding schemes, and found support for optimal linear readout of early sensory populations with information-limiting correlations. Here, we observed decision-related activity for neurons in visual area V2 of macaques performing fine disparity discrimination, as yet the earliest site for this task. These findings, and previously reported results from V2 in a different task, deviated from the predictions for optimal linear readout of a population with information-limiting correlations. Our results suggest that optimal linear decoding of early sensory information is not a general decoding strategy used by the brain. PMID:28100751
Pattern classification of fMRI data: applications for analysis of spatially distributed cortical networks.

PubMed

Yourganov, Grigori; Schmah, Tanya; Churchill, Nathan W; Berman, Marc G; Grady, Cheryl L; Strother, Stephen C

2014-08-01

The field of fMRI data analysis is rapidly growing in sophistication, particularly in the domain of multivariate pattern classification. However, the interaction between the properties of the analytical model and the parameters of the BOLD signal (e.g. signal magnitude, temporal variance and functional connectivity) is still an open problem. We addressed this problem by evaluating a set of pattern classification algorithms on simulated and experimental block-design fMRI data. The set of classifiers consisted of linear and quadratic discriminants, linear support vector machine, and linear and nonlinear Gaussian naive Bayes classifiers. For linear discriminant, we used two methods of regularization: principal component analysis, and ridge regularization. The classifiers were used (1) to classify the volumes according to the behavioral task that was performed by the subject, and (2) to construct spatial maps that indicated the relative contribution of each voxel to classification. Our evaluation metrics were: (1) accuracy of out-of-sample classification and (2) reproducibility of spatial maps. In simulated data sets, we performed an additional evaluation of spatial maps with ROC analysis. We varied the magnitude, temporal variance and connectivity of simulated fMRI signal and identified the optimal classifier for each simulated environment. Overall, the best performers were linear and quadratic discriminants (operating on principal components of the data matrix) and, in some rare situations, a nonlinear Gaussian naïve Bayes classifier. The results from the simulated data were supported by within-subject analysis of experimental fMRI data, collected in a study of aging. This is the first study that systematically characterizes interactions between analysis model and signal parameters (such as magnitude, variance and correlation) on the performance of pattern classifiers for fMRI. Copyright © 2014 Elsevier Inc. All rights reserved.
Delineation of sympatric morphotypes of lake trout in Lake Superior

USGS Publications Warehouse

Moore, Seth A.; Bronte, Charles R.

2001-01-01

Three morphotypes of lake trout Salvelinus namaycush are recognized in Lake Superior: lean, siscowet, and humper. Absolute morphotype assignment can be difficult. We used a size-free, whole-body morphometric analysis (truss protocol) to determine whether differences in body shape existed among lake trout morphotypes. Our results showed discrimination where traditional morphometric characters and meristic measurements failed to detect differences. Principal components analysis revealed some separation of all three morphotypes based on head and caudal peduncle shape, but it also indicated considerable overlap in score values. Humper lake trout have smaller caudal peduncle widths to head length and depth characters than do lean or siscowet lake trout. Lean lake trout had larger head measures to caudal widths, whereas siscowet had higher caudal peduncle to head measures. Backward stepwise discriminant function analysis retained two head measures, three midbody measures, and four caudal peduncle measures; correct classification rates when using these variables were 83% for leans, 80% for siscowets, and 83% for humpers, which suggests the measures we used for initial classification were consistent. Although clear ecological reasons for these differences are not readily apparent, patterns in misclassification rates may be consistent with evolutionary hypotheses for lake trout within the Laurentian Great Lakes.
Linear operating region in the ozone dial photon counting system

NASA Technical Reports Server (NTRS)

Andrawis, Madeleine

1995-01-01

Ozone is a relatively unstable molecule found in Earth's atmosphere. An ozone molecule is made up of three atoms of oxygen. Depending on where ozone resides, it can protect or harm life on Earth. High in the atmosphere, about 15 miles up, ozone acts as a shield to protect Earth's surface from the sun's harmful ultraviolet radiation. Without this shield, we would be more susceptible to skin cancer, cataracts, and impaired immune systems. Closer to Earth, in the air we breathe, ozone is a harmful pollutant that causes damage to lung tissue and plants. Since the early 1980's, airborne lidar systems have been used for making measurements of ozone. The differential absorption lidar (DIAL) technique is used in the remote measurement of O3. This system allows the O3 to be measured as function of the range in the atmosphere. Two frequency-doubled Nd:YAG lasers are used to pump tunable dye lasers. The lasers are operating at 289 nm for the DIAL on-line wavelength of O3, and the other one is operated at 300 nm for the off-line wavelength. The DIAL wavelengths are produced in sequential laser pulses with a time separation of 300 micro s. The backscattered laser energy is collected by telescopes and measured using photon counting systems. The photon counting system measures the light signal by making use of the photon nature of light. The output pulse from the Photo-Multiplier Tube (PE), caused by a photon striking the PMT photo-cathode, is amplified and passed to a pulse height discriminator. The peak value of the pulse is compared to a reference voltage (discrimination level). If the pulse amplitude exceeds the discrimination level, the discriminator generates a standard pulse which is counted by the digital counter. Non-linearity in the system is caused by the overlapping of pulses and the finite response time of the electronics. At low count rates one expects the system to register one event for each output pulse from the PMT corresponding to a photon incident upon the photocathode, however, at higher rates the limitations of the discrimination/counting system will cause the observed count rate to be non-linear with respect to the true count rate. Depending on the pulse height distribution and the discriminator level, the overlapping of pulses (pulse pile-up) can cause count loss or even an additional apparent count gain as the signal levels increase. Characterization of the system, including the pulse height distribution, the signal to noise ratio, and the effect of the discriminator threshold level, is critical in maximizing the linear operating region of the system, thus greatly increasing the useful dynamic range of the system.
Documenting Differences between Early Stone Age Flake Production Systems: An Experimental Model and Archaeological Verification.

PubMed

Presnyakova, Darya; Archer, Will; Braun, David R; Flear, Wesley

2015-01-01

This study investigates morphological differences between flakes produced via "core and flake" technologies and those resulting from bifacial shaping strategies. We investigate systematic variation between two technological groups of flakes using experimentally produced assemblages, and then apply the experimental model to the Cutting 10 Mid -Pleistocene archaeological collection from Elandsfontein, South Africa. We argue that a specific set of independent variables--and their interactions--including external platform angle, platform depth, measures of thickness variance and flake curvature should distinguish between these two technological groups. The role of these variables in technological group separation was further investigated using the Generalized Linear Model as well as Linear Discriminant Analysis. The Discriminant model was used to classify archaeological flakes from the Cutting 10 locality in terms of their probability of association, within either experimentally developed technological group. The results indicate that the selected independent variables play a central role in separating core and flake from bifacial technologies. Thickness evenness and curvature had the greatest effect sizes in both the Generalized Linear and Discriminant models. Interestingly the interaction between thickness evenness and platform depth was significant and played an important role in influencing technological group membership. The identified interaction emphasizes the complexity in attempting to distinguish flake production strategies based on flake morphological attributes. The results of the discriminant function analysis demonstrate that the majority of flakes at the Cutting 10 locality were not associated with the production of the numerous Large Cutting Tools found at the site, which corresponds with previous suggestions regarding technological behaviors reflected in this assemblage.
Documenting Differences between Early Stone Age Flake Production Systems: An Experimental Model and Archaeological Verification

PubMed Central

Presnyakova, Darya; Archer, Will; Braun, David R.; Flear, Wesley

2015-01-01

This study investigates morphological differences between flakes produced via “core and flake” technologies and those resulting from bifacial shaping strategies. We investigate systematic variation between two technological groups of flakes using experimentally produced assemblages, and then apply the experimental model to the Cutting 10 Mid -Pleistocene archaeological collection from Elandsfontein, South Africa. We argue that a specific set of independent variables—and their interactions—including external platform angle, platform depth, measures of thickness variance and flake curvature should distinguish between these two technological groups. The role of these variables in technological group separation was further investigated using the Generalized Linear Model as well as Linear Discriminant Analysis. The Discriminant model was used to classify archaeological flakes from the Cutting 10 locality in terms of their probability of association, within either experimentally developed technological group. The results indicate that the selected independent variables play a central role in separating core and flake from bifacial technologies. Thickness evenness and curvature had the greatest effect sizes in both the Generalized Linear and Discriminant models. Interestingly the interaction between thickness evenness and platform depth was significant and played an important role in influencing technological group membership. The identified interaction emphasizes the complexity in attempting to distinguish flake production strategies based on flake morphological attributes. The results of the discriminant function analysis demonstrate that the majority of flakes at the Cutting 10 locality were not associated with the production of the numerous Large Cutting Tools found at the site, which corresponds with previous suggestions regarding technological behaviors reflected in this assemblage. PMID:26111251
[Application of ICP-MS to Identify the Botanic Source of Characteristic Honey in South Yunnan].

PubMed

Wei, Yue; Chen, Fang; Wang, Yong; Chen, Lan-zhen; Zhang, Xue-wen; Wang, Yan-hui; Wu, Li-ming; Zhou, Qun

2016-01-01

By adopting inductively coupled plasma mass spectrometry (ICP-MS) combined with chemometric analysis technology, 23 kinds of minerals in four kinds of characteristic honey derived from Yunnan province were analyzed. The result showed that 21 kinds of mineral elements, namely Na, Mg, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Sr, Mo, Cd, Sb, Ba, Tl and Pb, have significant differences among different varieties of honey. The results of principal component analysis (PCA) showed that the cumulative variance contribution rate of the first four main components reached 77.74%, seven kinds of elements (Mg, Ca, Mn, Co, Sr, Cd, Ba) from the first main component contained most of the honey information. Through the stepwise discriminant analysis, seven kinds of elements (Mg, K, Ca, Cr, Mn, Sr, Pb) were filtered. out and used to establish the discriminant function model, and the correct classification rates of the proposed model reached 90% and 86.7%, respectively, which showed elements contents could be effectively indicators to discriminate the four kinds characteristic honey in southern Yunnan Province. In view of all the honey samples were harvested from apiaries located at south Yunnan Province where have similar climate, soil and other environment conditions, the differences of the mineral elements contents for the honey samples mainly due to their corresponding nectariferous plant. Therefore, it is feasible to identify honey botanical source through the differences of mineral elements.
Quantifying prognosis with risk predictions.

PubMed

Pace, Nathan L; Eberhart, Leopold H J; Kranke, Peter R

2012-01-01

Prognosis is a forecast, based on present observations in a patient, of their probable outcome from disease, surgery and so on. Research methods for the development of risk probabilities may not be familiar to some anaesthesiologists. We briefly describe methods for identifying risk factors and risk scores. A probability prediction rule assigns a risk probability to a patient for the occurrence of a specific event. Probability reflects the continuum between absolute certainty (Pi = 1) and certified impossibility (Pi = 0). Biomarkers and clinical covariates that modify risk are known as risk factors. The Pi as modified by risk factors can be estimated by identifying the risk factors and their weighting; these are usually obtained by stepwise logistic regression. The accuracy of probabilistic predictors can be separated into the concepts of 'overall performance', 'discrimination' and 'calibration'. Overall performance is the mathematical distance between predictions and outcomes. Discrimination is the ability of the predictor to rank order observations with different outcomes. Calibration is the correctness of prediction probabilities on an absolute scale. Statistical methods include the Brier score, coefficient of determination (Nagelkerke R2), C-statistic and regression calibration. External validation is the comparison of the actual outcomes to the predicted outcomes in a new and independent patient sample. External validation uses the statistical methods of overall performance, discrimination and calibration and is uniformly recommended before acceptance of the prediction model. Evidence from randomised controlled clinical trials should be obtained to show the effectiveness of risk scores for altering patient management and patient outcomes.
Behavioral phenomenology in Alzheimer's disease, frontotemporal dementia, and late-life depression: a retrospective analysis.

PubMed

Swartz, J R; Miller, B L; Lesser, I M; Booth, R; Darby, A; Wohl, M; Benson, D F

1997-04-01

Often patients in the early stages of Alzheimer's disease (AD), frontotemporal dementia (FTD), and late-life depression can be difficult to differentiate clinically. Although subtle cognitive distinctions exist between these disorders, noncognitive behavioral phenomenology may provide additional discriminating power. In 19 subjects with AD, 19 with FTD, 16 with late-life psychotic depression (LLPD), and 19 with late-life nonpsychotic depression (LLNPD), noncognitive behavioral symptoms were quantified retrospectively using the Schedules for Clinical Assessment in Neuropsychiatry (SCAN) and compared using both a one-way ANOVA and a multivariate stepwise discriminant analysis, which utilized a jackknife procedure. The FTD group showed the highest mean total SCAN score, while the AD group showed the lowest. ANOVA showed significant differences in the mean total SCAN scores between the four diagnostic groups (P < .0001). With the discriminant analysis, the four disorders demonstrated different clusters of behavioral abnormalities and were differentiated by these symptoms (P < .0001). A subset of 14 SCAN item group symptoms was identified that collectively classified the following percentages of subjects in each diagnostic category: AD 94.7%, FTD 100%, LLPD 87.5%, and LLNPD 100%. These results indicate that AD, FTD, LLPD, and LLNPD were distinguished retrospectively by the SCAN without using cognitive data. Better definition of the longitudinal course of noncognitive behavioral symptoms in different dementias and psychiatric disorders will be valuable both for diagnosis and to help define behavioral syndromes that are associated with selective neuroanatomic and neurochemical brain pathology.
Discriminative and predictive validity of the scoliosis research society-22 questionnaire in management and curve-severity subgroups of adolescents with idiopathic scoliosis.

PubMed

Parent, Eric C; Hill, Doug; Mahood, Jim; Moreau, Marc; Raso, Jim; Lou, Edmond

2009-10-15

Prospective cross-sectional measurement study. To determine the ability of the Scoliosis Research Society (SRS)-22 questionnaire to discriminate among management and scoliosis severity subgroups and to correlate with internal and external measures of curve severity. In earlier studies of the SRS-22 discriminative ability, age was not a controlled factor. The ability of the SRS-22 to predict curve severity has not been thoroughly examined. The SRS-22 was completed by 227 females with adolescent idiopathic scoliosis. Using Analysis of covariance analyses controlling for age, the SRS-22 scores were compared among management subgroups (observation, brace, presurgery, and postsurgery) and curve-severity subgroups (in nonoperated subjects: Cobb angles of <30 degrees, 30 degrees -50 degrees, and >50 degrees). A stepwise discriminant analysis was used to identify the SRS-22 domains most discriminative for curve-severity categories. Correlation between SRS-22 scores and radiographic or surface topography measurements was used to determine the predictive ability of the questionnaire. Pain was better for subjects treated with braces than for those planning surgery. Self-image was better for subjects under observation or postsurgery than for those planning surgery. Satisfaction was better for the brace and postsurgery subgroups than for the observation or presurgery subgroups. Statistically significant mean differences between subgroups were all larger than 0.5, which is within the range of minimal clinically important differences recommended for each of the 5-point SRS-22 domain scoring scales. Pain and mental health were worse for those with Cobb angles of >50 degrees than with Cobb angles of 30 degrees to 50 degrees. Self-image and total scores were worse for those with Cobb angles of >50 degrees than both other subgroups. Using discriminant analysis, self-image was the only SRS-22 domain score selected to classify subjects within curve severity subgroups. The percentage of patients accurately classified was 54% when trying to classify within 3 curve severity subgroups. The percentage of patients accurately classified was 73% when classifying simply as those with curves larger or smaller than 50 degrees . Pain, self-image, and satisfaction scores could discriminate among management subgroups, but function, mental health and total scores could not. The total score and all domain scores except satisfaction discriminated among curve-severity subgroups. Using discriminant analysis, self-image was the only domain retained in a model predicting curve-severity categories.

Is Perceived Discrimination in Pregnancy Prospectively Linked to Postpartum Depression? Exploring the Role of Education.

PubMed

Stepanikova, Irena; Kukla, Lubomir

2017-08-01

Objectives The role of perceived discrimination in postpartum depression is largely unknown. We investigate whether perceived discrimination reported in pregnancy contributes to postpartum depression, and whether its impact varies by education level. Methods Prospective data are a part of European Longitudinal Study of Pregnancy and Childhood, the Czech Republic. Surveys were collected in mid-pregnancy and at 6 months after delivery. Depression was measured using Edinburgh Postnatal Depression Scale. Generalized linear models were estimated to test the effects of perceived discrimination on postpartum depression. Results Multivariate models revealed that among women with low education, discrimination in pregnancy was prospectively associated with 2.43 times higher odds of postpartum depression (p < .01), after adjusting for antenatal depression, history of earlier depression, and socio-demographic background. In contrast, perceived discrimination was not linked to postpartum depression among women with high education. Conclusions Perceived discrimination is a risk factor for postpartum depression among women with low education. Screening for discrimination and socio-economic disadvantage during pregnancy could benefit women who are at risk for mental health problems.
A risk analysis approach for using discriminant functions to manage logging-related landslides on granitic terrain

Treesearch

Raymond M. Rice; Norman H. Pillsbury; Kurt W. Schmidt

1985-01-01

Abstract - A linear discriminant function, developed to predict debris avalanches after clearcut logging on a granitic batholith in northwestern California, was tested on data from two batholiths. The equation was inaccurate in predicting slope stability on one of them. A new equation based on slope, crown cover, and distance from a stream (retained from the original...
Identification of Variables Associated with Group Separation in Descriptive Discriminant Analysis: Comparison of Methods for Interpreting Structure Coefficients

ERIC Educational Resources Information Center

Finch, Holmes

2010-01-01

Discriminant Analysis (DA) is a tool commonly used for differentiating among 2 or more groups based on 2 or more predictor variables. DA works by finding 1 or more linear combinations of the predictors that yield maximal difference among the groups. One common goal of researchers using DA is to characterize the nature of group difference by…
Distinguishing fibromyalgia from rheumatoid arthritis and systemic lupus in clinical questionnaires: an analysis of the revised Fibromyalgia Impact Questionnaire (FIQR) and its variant, the Symptom Impact Questionnaire (SIQR), along with pain locations

PubMed Central

2011-01-01

Introduction The purpose of this study was to explore a data set of patients with fibromyalgia (FM), rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) who completed the Revised Fibromyalgia Impact Questionnaire (FIQR) and its variant, the Symptom Impact Questionnaire (SIQR), for discriminating features that could be used to differentiate FM from RA and SLE in clinical surveys. Methods The frequency and means of comparing FM, RA and SLE patients on all pain sites and SIQR variables were calculated. Multiple regression analysis was then conducted to identify the significant pain sites and SIQR predictors of group membership. Thereafter stepwise multiple regression analysis was performed to identify the order of variables in predicting their maximal statistical contribution to group membership. Partial correlations assessed their unique contribution, and, last, two-group discriminant analysis provided a classification table. Results The data set contained information on the SIQR and also pain locations in 202 FM, 31 RA and 20 SLE patients. As the SIQR and pain locations did not differ much between the RA and SLE patients, they were grouped together (RA/SLE) to provide a more robust analysis. The combination of eight SIQR items and seven pain sites correctly classified 99% of FM and 90% of RA/SLE patients in a two-group discriminant analysis. The largest reported SIQR differences (FM minus RA/SLE) were seen for the parameters "tenderness to touch," "difficulty cleaning floors" and "discomfort on sitting for 45 minutes." Combining the SIQR and pain locations in a stepwise multiple regression analysis revealed that the seven most important predictors of group membership were mid-lower back pain (29%; 79% vs. 16%), tenderness to touch (11.5%; 6.86 vs. 3.02), neck pain (6.8%; 91% vs. 39%), hand pain (5%; 64% vs. 77%), arm pain (3%; 69% vs. 18%), outer lower back pain (1.7%; 80% vs. 22%) and sitting for 45 minutes (1.4%; 5.56 vs. 1.49). Conclusions A combination of two SIQR questions ("tenderness to touch" and "difficulty sitting for 45 minutes") plus pain in the lower back, neck, hands and arms may be useful in the construction of clinical questionnaires designed for patients with musculoskeletal pain. This combination provided the correct diagnosis in 97% of patients, with only 7 of 253 patients misclassified. PMID:21477308
Development of a universal water signature for the LANDSAT-3 Multispectral Scanner, part 1

NASA Technical Reports Server (NTRS)

Schlosser, E. H.

1980-01-01

A generalized four channel hyperplane to discriminate water from nonwater was developed using LANDSAT-3 multispectral scaner (MSS) scenes and matching same/next day color infrared aerial photography. The MSS scenes varied in sun elevation angle from 40 to 58 deg. The 28 matching air photo frames contained over 1400 water bodies larger than one surface acre. A preliminary water discriminant, was used to screen the data and eliminate from further consideration all pixels distant from water in MSS spectral space. A linear discriminant was iteratively fitted to the labelled pixels. This discriminant correctly classified 98.7% of the water pixels and 98.6% of the nonwater pixels. The discriminant detected 91.3% of the 414 water bodies over 10 acres in surface area, and misclassified as water 36 groups of contiguous nonwater pixels.
A continuous damage model based on stepwise-stress creep rupture tests

NASA Technical Reports Server (NTRS)

Robinson, D. N.

1985-01-01

A creep damage accumulation model is presented that makes use of the Kachanov damage rate concept with a provision accounting for damage that results from a variable stress history. This is accomplished through the introduction of an additional term in the Kachanov rate equation that is linear in the stress rate. Specification of the material functions and parameters in the model requires two types of constituting a data base: (1) standard constant-stress creep rupture tests, and (2) a sequence of two-step creep rupture tests.
Characterization of bovine cartilage by fiber Bragg grating-based stress relaxation measurements

NASA Astrophysics Data System (ADS)

Baier, V.; Marchi, G.; Foehr, P.; Burgkart, R.; Roths, J.

2017-04-01

A fiber-based device for testing mechanical properties of cartilage is presented within this study. The measurement principle is based on stepwise indentation into the tissue and observing of corresponding relaxation of the stress. The indenter tip is constituted of a cleaved optical fiber that includes a fiber Bragg grating which is used as the force sensor. Stress relaxation measurements at 25 different positions on a healthy bovine cartilage sample were performed to assess the behavior of healthy cartilage. For each indentation step a good agreement was found with a viscoelastic model that included two time constants. The model parameters showed low variability and a clear dependence with indentation depth. The parameters can be used as reference values for discriminating healthy and degenerated cartilage.
Active microwave responses - An aid in improved crop classification

NASA Technical Reports Server (NTRS)

Rosenthal, W. D.; Blanchard, B. J.

1984-01-01

A study determined the feasibility of using visible, infrared, and active microwave data to classify agricultural crops such as corn, sorghum, alfalfa, wheat stubble, millet, shortgrass pasture and bare soil. Visible through microwave data were collected by instruments on board the NASA C-130 aircraft over 40 agricultural fields near Guymon, OK in 1978 and Dalhart, TX in 1980. Results from stepwise and discriminant analysis techniques indicated 4.75 GHz, 1.6 GHz, and 0.4 GHz cross-polarized microwave frequencies were the microwave frequencies most sensitive to crop type differences. Inclusion of microwave data in visible and infrared classification models improved classification accuracy from 73 percent to 92 percent. Despite the results, further studies are needed during different growth stages to validate the visible, infrared, and active microwave responses to vegetation.
Land use/land cover mapping (1:25000) of Taiwan, Republic of China by automated multispectral interpretation of LANDSAT imagery

NASA Technical Reports Server (NTRS)

Sung, Q. C.; Miller, L. D.

1977-01-01

Three methods were tested for collection of the training sets needed to establish the spectral signatures of the land uses/land covers sought due to the difficulties of retrospective collection of representative ground control data. Computer preprocessing techniques applied to the digital images to improve the final classification results were geometric corrections, spectral band or image ratioing and statistical cleaning of the representative training sets. A minimal level of statistical verification was made based upon the comparisons between the airphoto estimates and the classification results. The verifications provided a further support to the selection of MSS band 5 and 7. It also indicated that the maximum likelihood ratioing technique can achieve more agreeable classification results with the airphoto estimates than the stepwise discriminant analysis.
Modelling subject-specific childhood growth using linear mixed-effect models with cubic regression splines.

PubMed

Grajeda, Laura M; Ivanescu, Andrada; Saito, Mayuko; Crainiceanu, Ciprian; Jaganath, Devan; Gilman, Robert H; Crabtree, Jean E; Kelleher, Dermott; Cabrera, Lilia; Cama, Vitaliano; Checkley, William

2016-01-01

Childhood growth is a cornerstone of pediatric research. Statistical models need to consider individual trajectories to adequately describe growth outcomes. Specifically, well-defined longitudinal models are essential to characterize both population and subject-specific growth. Linear mixed-effect models with cubic regression splines can account for the nonlinearity of growth curves and provide reasonable estimators of population and subject-specific growth, velocity and acceleration. We provide a stepwise approach that builds from simple to complex models, and account for the intrinsic complexity of the data. We start with standard cubic splines regression models and build up to a model that includes subject-specific random intercepts and slopes and residual autocorrelation. We then compared cubic regression splines vis-à-vis linear piecewise splines, and with varying number of knots and positions. Statistical code is provided to ensure reproducibility and improve dissemination of methods. Models are applied to longitudinal height measurements in a cohort of 215 Peruvian children followed from birth until their fourth year of life. Unexplained variability, as measured by the variance of the regression model, was reduced from 7.34 when using ordinary least squares to 0.81 (p < 0.001) when using a linear mixed-effect models with random slopes and a first order continuous autoregressive error term. There was substantial heterogeneity in both the intercept (p < 0.001) and slopes (p < 0.001) of the individual growth trajectories. We also identified important serial correlation within the structure of the data (ρ = 0.66; 95 % CI 0.64 to 0.68; p < 0.001), which we modeled with a first order continuous autoregressive error term as evidenced by the variogram of the residuals and by a lack of association among residuals. The final model provides a parametric linear regression equation for both estimation and prediction of population- and individual-level growth in height. We show that cubic regression splines are superior to linear regression splines for the case of a small number of knots in both estimation and prediction with the full linear mixed effect model (AIC 19,352 vs. 19,598, respectively). While the regression parameters are more complex to interpret in the former, we argue that inference for any problem depends more on the estimated curve or differences in curves rather than the coefficients. Moreover, use of cubic regression splines provides biological meaningful growth velocity and acceleration curves despite increased complexity in coefficient interpretation. Through this stepwise approach, we provide a set of tools to model longitudinal childhood data for non-statisticians using linear mixed-effect models.
A generalized fuzzy linear programming approach for environmental management problem under uncertainty.

PubMed

Fan, Yurui; Huang, Guohe; Veawab, Amornvadee

2012-01-01

In this study, a generalized fuzzy linear programming (GFLP) method was developed to deal with uncertainties expressed as fuzzy sets that exist in the constraints and objective function. A stepwise interactive algorithm (SIA) was advanced to solve GFLP model and generate solutions expressed as fuzzy sets. To demonstrate its application, the developed GFLP method was applied to a regional sulfur dioxide (SO2) control planning model to identify effective SO2 mitigation polices with a minimized system performance cost under uncertainty. The results were obtained to represent the amount of SO2 allocated to different control measures from different sources. Compared with the conventional interval-parameter linear programming (ILP) approach, the solutions obtained through GFLP were expressed as fuzzy sets, which can provide intervals for the decision variables and objective function, as well as related possibilities. Therefore, the decision makers can make a tradeoff between model stability and the plausibility based on solutions obtained through GFLP and then identify desired policies for SO2-emission control under uncertainty.
Factors Associated with Medical Doctors' Intentions to Discriminate Against Transgender Patients in Kuala Lumpur, Malaysia.

PubMed

Vijay, Aishwarya; Earnshaw, Valerie A; Tee, Ying Chew; Pillai, Veena; White Hughto, Jaclyn M; Clark, Kirsty; Kamarulzaman, Adeeba; Altice, Frederick L; Wickersham, Jeffrey A

2018-01-01

Transgender people are frequent targets of discrimination. Discrimination against transgender people in the context of healthcare can lead to poor health outcomes and facilitate the growth of health disparities. This study explores factors associated with medical doctors' intentions to discriminate against transgender people in Malaysia. A total of 436 physicians at two major university medical centers in Kuala Lumpur, Malaysia, completed an online survey. Sociodemographic characteristics, stigma-related constructs, and intentions to discriminate against transgender people were measured. Bivariate and multivariate linear regression were used to evaluate independent covariates of discrimination intent. Medical doctors who felt more fearful of transgender people and more personal shame associated with transgender people expressed greater intention to discriminate against transgender people, whereas doctors who endorsed the belief that transgender people deserve good care reported lower discrimination intent. Stigma-related constructs accounted for 42% of the variance and 8% was accounted for by sociodemographic characteristics. Constructs associated with transgender stigma play an important role in medical doctors' intentions to discriminate against transgender patients. Development of interventions to improve medical doctors' knowledge about and attitudes toward transgender people are necessary to reduce discriminatory intent in healthcare settings.
Changes in experiences with discrimination across pregnancy and postpartum: age differences and consequences for mental health.

PubMed

Rosenthal, Lisa; Earnshaw, Valerie A; Lewis, Tené T; Reid, Allecia E; Lewis, Jessica B; Stasko, Emily C; Tobin, Jonathan N; Ickovics, Jeannette R

2015-04-01

We aimed to contribute to growing research and theory suggesting the importance of examining patterns of change over time and critical life periods to fully understand the effects of discrimination on health, with a focus on the period of pregnancy and postpartum and mental health outcomes. We used hierarchical linear modeling to examine changes across pregnancy and postpartum in everyday discrimination and the resulting consequences for mental health among predominantly Black and Latina, socioeconomically disadvantaged young women who were receiving prenatal care in New York City. Patterns of change in experiences with discrimination varied according to age. Among the youngest participants, discrimination increased from the second to third trimesters and then decreased to lower than the baseline level by 1 year postpartum; among the oldest participants, discrimination decreased from the second trimester to 6 months postpartum and then returned to the baseline level by 1 year postpartum. Within-subjects changes in discrimination over time predicted changes in depressive and anxiety symptoms at subsequent points. Discrimination more strongly predicted anxiety symptoms among participants reporting food insecurity. Our results support a life course approach to understanding the impact of experiences with discrimination on health and when to intervene.
Factors Associated with Medical Doctors' Intentions to Discriminate Against Transgender Patients in Kuala Lumpur, Malaysia

PubMed Central

Vijay, Aishwarya; Earnshaw, Valerie A.; Tee, Ying Chew; Pillai, Veena; White Hughto, Jaclyn M.; Clark, Kirsty; Kamarulzaman, Adeeba; Altice, Frederick L.

2018-01-01

Abstract Purpose: Transgender people are frequent targets of discrimination. Discrimination against transgender people in the context of healthcare can lead to poor health outcomes and facilitate the growth of health disparities. This study explores factors associated with medical doctors' intentions to discriminate against transgender people in Malaysia. Methods: A total of 436 physicians at two major university medical centers in Kuala Lumpur, Malaysia, completed an online survey. Sociodemographic characteristics, stigma-related constructs, and intentions to discriminate against transgender people were measured. Bivariate and multivariate linear regression were used to evaluate independent covariates of discrimination intent. Results: Medical doctors who felt more fearful of transgender people and more personal shame associated with transgender people expressed greater intention to discriminate against transgender people, whereas doctors who endorsed the belief that transgender people deserve good care reported lower discrimination intent. Stigma-related constructs accounted for 42% of the variance and 8% was accounted for by sociodemographic characteristics. Conclusions: Constructs associated with transgender stigma play an important role in medical doctors' intentions to discriminate against transgender patients. Development of interventions to improve medical doctors' knowledge about and attitudes toward transgender people are necessary to reduce discriminatory intent in healthcare settings. PMID:29227183
Detection of Genetically Modified Sugarcane by Using Terahertz Spectroscopy and Chemometrics

NASA Astrophysics Data System (ADS)

Liu, J.; Xie, H.; Zha, B.; Ding, W.; Luo, J.; Hu, C.

2018-03-01

A methodology is proposed to identify genetically modified sugarcane from non-genetically modified sugarcane by using terahertz spectroscopy and chemometrics techniques, including linear discriminant analysis (LDA), support vector machine-discriminant analysis (SVM-DA), and partial least squares-discriminant analysis (PLS-DA). The classification rate of the above mentioned methods is compared, and different types of preprocessing are considered. According to the experimental results, the best option is PLS-DA, with an identification rate of 98%. The results indicated that THz spectroscopy and chemometrics techniques are a powerful tool to identify genetically modified and non-genetically modified sugarcane.
Personality and affect characteristics of outpatients with depression.

PubMed

Petrocelli, J V; Glaser, B A; Calhoun, G B; Campbell, L F

2001-08-01

This investigation was designed to examine the relationship between depression severity and personality disorders measured by the Millon Clinical Multiaxial Inventory-II (Millon, 1987) and affectivity measured by the Positive Affectivity/Negative Affectivity Schedule (Watson, Clark, & Tellegen, 1988). Discriminant analyses were employed to identify the personality and affective dimensions that maximally discriminate between 4 different levels of depressive severity. Differences between the 4 levels of depressive severity are suggestive of unique patterns of personality characteristics. Discriminant analysis showed that 74.8% of the cases were correctly classified by a single linear discriminant function, and that 61% of the variance in depression severity was accounted for by selected personality and affect variables. Results extend current conceptualizations of comorbidity and are discussed with respect to depression severity.
Forest discrimination with multipolarization imaging radar

NASA Technical Reports Server (NTRS)

Ford, J. P.; Wickland, D. E.

1985-01-01

The use of radar polarization diversity for discriminating forest canopy variables on airborne synthetic-aperture radar (SAR) images is evaluated. SAR images were acquired at L-Band (24.6 cm) simultaneously in four linear polarization states (HH, HV, VH, and VV) in South Carolina on March 1, 1984. In order to relate the polarization signatures to biophysical properties, false-color composite images were compared to maps of forest stands in the timber compartment. In decreasing order, the most useful correlative forest data are stand basal area, forest age, site condition index, and forest management type. It is found that multipolarization images discriminate variation in tree density and difference in the amount of understory, but do not discriminate between evergreen and deciduous forest types.
Perceptual asymmetry in texture perception.

PubMed

Williams, D; Julesz, B

1992-07-15

A fundamental property of human visual perception is our ability to distinguish between textures. A concerted effort has been made to account for texture segregation in terms of linear spatial filter models and their nonlinear extensions. However, for certain texture pairs the ease of discrimination changes when the role of figure and ground are reversed. This asymmetry poses a problem for both linear and nonlinear models. We have isolated a property of texture perception that can account for this asymmetry in discrimination: subjective closure. This property, which is also responsible for visual illusions, appears to be explainable by early visual processes alone. Our results force a reexamination of the process of human texture segregation and of some recent models that were introduced to explain it.
Socioeconomic status discrimination and C-reactive protein in African-American and White adults.

PubMed

Van Dyke, Miriam E; Vaccarino, Viola; Dunbar, Sandra B; Pemu, Priscilla; Gibbons, Gary H; Quyyumi, Arshed A; Lewis, Tené T

2017-08-01

We examined the association between socioeconomic status (SES) discrimination and C-reactive protein (CRP) in a biracial cohort of middle-aged adults using an intersectionality framework. Participants were 401 African-American and White adults from a population-based cohort in the Southeastern United States. SES discrimination was self-reported with a modified Experiences of Discrimination Scale, and CRP levels were assayed from blood samples. Linear regression analyses were used to examine the associations among SES discrimination, race, education, and CRP after controlling for age, gender, racial and gender discrimination, financial and general stress, body mass index, smoking, sleep quality, and depressive symptoms. Intersectional effects were tested using race×SES discrimination, education×SES discrimination and race×education×SES discrimination interactions. Adjusting for sociodemographics, racial discrimination, gender discrimination, and all relevant two-way interaction terms, we observed a significant race×education×SES discrimination interaction (p=0.019). In adjusted models stratified by race and education, SES discrimination was associated with elevated CRP among higher educated African-Americans (β=0.29, p=0.018), but not lower educated African-Americans (β=-0.13, p=0.32); or lower educated (β=-0.02, p=0.92) or higher educated (β=-0.01, p=0.90) Whites. Findings support the relevance of SES discrimination as an important discriminatory stressor for CRP specifically among higher educated African-Americans. Copyright © 2017 Elsevier Ltd. All rights reserved.
Quantum teleportation via quantum channels with non-maximal Schmidt rank

NASA Astrophysics Data System (ADS)

Solís-Prosser, M. A.; Jiménez, O.; Neves, L.; Delgado, A.

2013-03-01

We study the problem of teleporting unknown pure states of a single qudit via a pure quantum channel with non-maximal Schmidt rank. We relate this process to the discrimination of linearly dependent symmetric states with the help of the maximum-confidence discrimination strategy. We show that with a certain probability, it is possible to teleport with a fidelity larger than the fidelity optimal deterministic teleportation.

ASTM clustering for improving coal analysis by near-infrared spectroscopy.

PubMed

Andrés, J M; Bona, M T

2006-11-15

Multivariate analysis techniques have been applied to near-infrared (NIR) spectra coals to investigate the relationship between nine coal properties (moisture (%), ash (%), volatile matter (%), fixed carbon (%), heating value (kcal/kg), carbon (%), hydrogen (%), nitrogen (%) and sulphur (%)) and the corresponding predictor variables. In this work, a whole set of coal samples was grouped into six more homogeneous clusters following the ASTM reference method for classification prior to the application of calibration methods to each coal set. The results obtained showed a considerable improvement of the error determination compared with the calibration for the whole sample set. For some groups, the established calibrations approached the quality required by the ASTM/ISO norms for laboratory analysis. To predict property values for a new coal sample it is necessary the assignation of that sample to its respective group. Thus, the discrimination and classification ability of coal samples by Diffuse Reflectance Infrared Fourier Transform Spectroscopy (DRIFTS) in the NIR range was also studied by applying Soft Independent Modelling of Class Analogy (SIMCA) and Linear Discriminant Analysis (LDA) techniques. Modelling of the groups by SIMCA led to overlapping models that cannot discriminate for unique classification. On the other hand, the application of Linear Discriminant Analysis improved the classification of the samples but not enough to be satisfactory for every group considered.
Plasma amino acid profile associated with fatty liver disease and co-occurrence of metabolic risk factors.

PubMed

Yamakado, Minoru; Tanaka, Takayuki; Nagao, Kenji; Imaizumi, Akira; Komatsu, Michiharu; Daimon, Takashi; Miyano, Hiroshi; Tani, Mizuki; Toda, Akiko; Yamamoto, Hiroshi; Horimoto, Katsuhisa; Ishizaka, Yuko

2017-11-03

Fatty liver disease (FLD) increases the risk of diabetes, cardiovascular disease, and steatohepatitis, which leads to fibrosis, cirrhosis, and hepatocellular carcinoma. Thus, the early detection of FLD is necessary. We aimed to find a quantitative and feasible model for discriminating the FLD, based on plasma free amino acid (PFAA) profiles. We constructed models of the relationship between PFAA levels in 2,000 generally healthy Japanese subjects and the diagnosis of FLD by abdominal ultrasound scan by multiple logistic regression analysis with variable selection. The performance of these models for FLD discrimination was validated using an independent data set of 2,160 subjects. The generated PFAA-based model was able to identify FLD patients. The area under the receiver operating characteristic curve for the model was 0.83, which was higher than those of other existing liver function-associated markers ranging from 0.53 to 0.80. The value of the linear discriminant in the model yielded the adjusted odds ratio (with 95% confidence intervals) for a 1 standard deviation increase of 2.63 (2.14-3.25) in the multiple logistic regression analysis with known liver function-associated covariates. Interestingly, the linear discriminant values were significantly associated with the progression of FLD, and patients with nonalcoholic steatohepatitis also exhibited higher values.
Comparison Of Eigenvector-Based Statistical Pattern Recognition Algorithms For Hybrid Processing

NASA Astrophysics Data System (ADS)

Tian, Q.; Fainman, Y.; Lee, Sing H.

1989-02-01

The pattern recognition algorithms based on eigenvector analysis (group 2) are theoretically and experimentally compared in this part of the paper. Group 2 consists of Foley-Sammon (F-S) transform, Hotelling trace criterion (HTC), Fukunaga-Koontz (F-K) transform, linear discriminant function (LDF) and generalized matched filter (GMF). It is shown that all eigenvector-based algorithms can be represented in a generalized eigenvector form. However, the calculations of the discriminant vectors are different for different algorithms. Summaries on how to calculate the discriminant functions for the F-S, HTC and F-K transforms are provided. Especially for the more practical, underdetermined case, where the number of training images is less than the number of pixels in each image, the calculations usually require the inversion of a large, singular, pixel correlation (or covariance) matrix. We suggest solving this problem by finding its pseudo-inverse, which requires inverting only the smaller, non-singular image correlation (or covariance) matrix plus multiplying several non-singular matrices. We also compare theoretically the effectiveness for classification with the discriminant functions from F-S, HTC and F-K with LDF and GMF, and between the linear-mapping-based algorithms and the eigenvector-based algorithms. Experimentally, we compare the eigenvector-based algorithms using a set of image data bases each image consisting of 64 x 64 pixels.
Smart smoke alarm

DOEpatents

Warmack, Robert J. Bruce; Wolf, Dennis A; Frank, Steven Shane

2015-04-28

Methods and apparatus for smoke detection are disclosed. In one embodiment, a smoke detector uses linear discriminant analysis (LDA) to determine whether observed conditions indicate that an alarm is warranted.
Self-Reported Experiences of Discrimination and Depression in Native Hawaiians.

PubMed

Antonio, Mapuana Ck; Ahn, Hyeong Jun; Ing, Claire Townsend; Dillard, Adrienne; Cassel, Kevin; Kekauoha, B Puni; Kaholokula, Joseph Keawe'aimoku

2016-09-01

Discrimination is an acute and chronic stressor that negatively impacts the health of many ethnic groups in the United States. Individuals who perceive increased levels of discrimination are at risk of experiencing psychological distress and symptoms of depression. No study to date has examined the relationship between perceived discrimination and mental health in Native Hawaiians. The purpose of this study is to explore the relationship between perceived discrimination and depression based on the Homestead Health Survey mailed to Native Hawaiian residents of Hawaiian Home Lands. This study also explores the role of cultural identity and how it may impact experiences of discrimination and symptoms of depression. Based on cross-sectional data obtained from 104 Native Hawaiian residents, a significant positive correlation was found between perceived discrimination and symptoms of depression (r= 0.32, P<.001). Cultural identity did not significantly correlate with discrimination or depression. Multiple linear regression analyses indicate that the relationship between depression and discrimination remained statistically significant (coefficient estimate of 0.18; P<.01), after accounting for differences in socio-demographics and degree of identification with the Native Hawaiian and American cultures. These findings are consistent with other studies that have focused on the effects of discrimination on psychological wellbeing for other ethnic minority populations.
Self-Reported Experiences of Discrimination and Depression in Native Hawaiians

PubMed Central

Ahn, Hyeong Jun; Ing, Claire Townsend; Dillard, Adrienne; Cassel, Kevin; Kekauoha, B Puni; Kaholokula, Joseph Keawe‘aimoku

2016-01-01

Discrimination is an acute and chronic stressor that negatively impacts the health of many ethnic groups in the United States. Individuals who perceive increased levels of discrimination are at risk of experiencing psychological distress and symptoms of depression. No study to date has examined the relationship between perceived discrimination and mental health in Native Hawaiians. The purpose of this study is to explore the relationship between perceived discrimination and depression based on the Homestead Health Survey mailed to Native Hawaiian residents of Hawaiian Home Lands. This study also explores the role of cultural identity and how it may impact experiences of discrimination and symptoms of depression. Based on cross-sectional data obtained from 104 Native Hawaiian residents, a significant positive correlation was found between perceived discrimination and symptoms of depression (r= 0.32, P<.001). Cultural identity did not significantly correlate with discrimination or depression. Multiple linear regression analyses indicate that the relationship between depression and discrimination remained statistically significant (coefficient estimate of 0.18; P<.01), after accounting for differences in socio-demographics and degree of identification with the Native Hawaiian and American cultures. These findings are consistent with other studies that have focused on the effects of discrimination on psychological wellbeing for other ethnic minority populations. PMID:27688952
Racial Discrimination and Alcohol Use: The Moderating Role of Religious Orientation.

PubMed

Parenteau, Stacy C; Waters, Kristen; Cox, Brittany; Patterson, Tarsha; Carr, Richard

2017-01-02

An outgrowth of research has established a relationship between racial discrimination and alcohol use, as well as factors that moderate this association. The main objective of this study was to determine if religious orientation moderates the relationship between perceived racial discrimination and alcohol use. This study utilized a cross-sectional data collection strategy to examine the relationship among discrimination, religious orientation, and alcohol use among undergraduate students (N = 349) at a midsize southeastern university. Data was collected in 2014. Participants completed a demographic questionnaire, the General Ethnic Discrimination Scale, the Extrinsic/Intrinsic Religious Orientation Scale-Revised and the Drinking and Drug Habits Questionnaire. Analyses using hierarchical linear regression indicate a significant interaction effect (lifetime discrimination × extrinsic religious orientation) on problem drinking. Additional moderation analyses reveal a significant interaction effect between lifetime discrimination and the extrinsic-personal religious orientation on problem drinking. Results suggest that an extrinsic religious orientation, and particularly, an extrinsic-personal religious orientation, moderates the relationship between lifetime discrimination and problem drinking, suggesting that turning to religion for comfort and protection, rather than for the superficial purpose of seeing/making friends at church, may buffer against the deleterious effects of discrimination-specifically, engaging in problem drinking to cope with the stress of discrimination. Limitations, directions for future research, and clinical implications are discussed.
Optimizing working space in laparoscopy: CT measurement of the effect of pre-stretching of the abdominal wall in a porcine model.

PubMed

Vlot, John; Wijnen, René; Stolker, Robert Jan; Bax, Klaas N

2014-03-01

Determinants of working space in minimal access surgery have not been well studied. Using computed tomography (CT) to measure volumes and linear dimensions, we are studying the effect of a number of determinants of CO2 working space in a porcine laparoscopy model. Here we report the effects of pre-stretching of the abdominal wall. Earlier we had noted an increase in CO2 pneumoperitoneum volume at repeat insufflation with an intra-abdominal pressure (IAP) of 5 mmHg after previous stepwise insufflation up to an IAP of 15 mmHg. We reviewed the data of this serendipity group; data of 16 pigs were available. In a new group of eight pigs, we also explored this effect at repeat IAPs of 10 and 15 mmHg. Volumes and linear dimensions of the CO2 pneumoperitoneum were measured on reconstructed CT images and compared between the initial and repeat insufflation runs. Previous stepwise insufflation of the abdomen with CO2 up to 15 mmHg significantly (p < 0.01) increased subsequent working-space volume at a repeat IAP of 5 mmHg by 21 %, 7 % at a repeat IAP of 10 mmHg and 3 % at a repeat IAP of 15 mmHg. The external anteroposterior diameter significantly (p < 0.01) increased by 0.5 cm (14 %) at repeat 5 mmHg. Other linear dimensions showed a much smaller change. There was no statistically significant correlation between the duration of the insufflation run and the volume increase after pre-stretching at all IAP levels. Pre-stretching of the abdominal wall allows for the same surgical-field exposure at lower IAPs, reducing the negative effects of prolonged high-pressure CO2 pneumoperitoneum on the cardiorespiratory system and microcirculation. Pre-stretching has important scientific consequences in studies addressing ways of increasing working space in that its effect may confound the possible effects of other interventions aimed at increasing working space.
Recent trends of groundwater temperatures in Austria

NASA Astrophysics Data System (ADS)

Benz, Susanne A.; Bayer, Peter; Winkler, Gerfried; Blum, Philipp

2018-06-01

Climate change is one of if not the most pressing challenge modern society faces. Increasing temperatures are observed all over the planet and the impact of climate change on the hydrogeological cycle has long been shown. However, so far we have insufficient knowledge on the influence of atmospheric warming on shallow groundwater temperatures. While some studies analyse the implication climate change has for selected wells, large-scale studies are so far lacking. Here we focus on the combined impact of climate change in the atmosphere and local hydrogeological conditions on groundwater temperatures in 227 wells in Austria, which have in part been observed since 1964. A linear analysis finds a temperature change of +0.7 ± 0.8 K in the years from 1994 to 2013. In the same timeframe surface air temperatures in Austria increased by 0.5 ± 0.3 K, displaying a much smaller variety. However, most of the extreme changes in groundwater temperatures can be linked to local hydrogeological conditions. Correlation between groundwater temperatures and nearby surface air temperatures was additionally analysed. They vary greatly, with correlation coefficients of -0.3 in central Linz to 0.8 outside of Graz. In contrast, the correlation of nationwide groundwater temperatures and surface air temperatures is high, with a correlation coefficient of 0.83. All of these findings indicate that while atmospheric climate change can be observed in nationwide groundwater temperatures, individual wells are often primarily dominated by local hydrogeological conditions. In addition to the linear temperature trend, a step-wise model was also applied that identifies climate regime shifts, which were observed globally in the late 70s, 80s, and 90s. Hinting again at the influence of local conditions, at most 22 % of all wells show these climate regime shifts. However, we were able to identify an additional shift in 2007, which was observed by 37 % of all wells. Overall, the step-wise representation provides a slightly more accurate picture of observed temperatures than the linear trend.
Socio-economic factors associated with infant mortality in Italy: an ecological study

PubMed Central

2012-01-01

Introduction One issue that continues to attract the attention of public health researchers is the possible relationship in high-income countries between income, income inequality and infant mortality (IM). The aim of this study was to assess the associations between IM and major socio-economic determinants in Italy. Methods Associations between infant mortality rates in the 20 Italian regions (2006–2008) and the Gini index of income inequality, mean household income, percentage of women with at least 8 years of education, and percentage of unemployed aged 15–64 years were assessed using Pearson correlation coefficients. Univariate linear regression and multiple stepwise linear regression analyses were performed to determine the magnitude and direction of the effect of the four socio-economic variables on IM. Results The Gini index and the total unemployment rate showed a positive strong correlation with IM (r = 0.70; p < 0.001 and r = 0.84; p < 0.001 respectively), mean household income showed a strong negative correlation (r = −0.78; p < 0.001), while female educational attainment presented a weak negative correlation (r = −0.45; p < 0.05). Using a multiple stepwise linear regression model, only unemployment rate was independently associated with IM (b = 0.15, p < 0.001). Conclusions In Italy, a high-income country where health care is universally available, variations in IM were strongly associated with relative and absolute income and unemployment rate. These results suggest that in Italy IM is not only related to income distribution, as demonstrated for other developed countries, but also to economic factors such as absolute income and unemployment. In order to reduce IM and the existing inequalities, the challenge for Italian decision makers is to promote economic growth and enhance employment levels. PMID:22898293
Emission and distribution of phosphine in paddy fields and its relationship with greenhouse gases.

PubMed

Chen, Weiyi; Niu, Xiaojun; An, Shaorong; Sheng, Hong; Tang, Zhenghua; Yang, Zhiquan; Gu, Xiaohong

2017-12-01

Phosphine (PH 3 ), as a gaseous phosphide, plays an important role in the phosphorus cycle in ecosystems. In this study, the emission and distribution of phosphine, carbon dioxide (CO 2 ) and methane (CH 4 ) in paddy fields were investigated to speculate the future potential impacts of enhanced greenhouse effect on phosphorus cycle involved in phosphine by the method of Pearson correlation analysis and multiple linear regression analysis. During the whole period of rice growth, there was a significant positive correlation between CO 2 emission flux and PH 3 emission flux (r=0.592, p=0.026, n=14). Similarly, a significant positive correlation of emission flux was also observed between CH 4 and PH 3 (r=0.563, p=0.036, n=14). The linear regression relationship was determined as [PH 3 ] flux =0.007[CO 2 ] flux +0.063[CH 4 ] flux -4.638. No significant differences were observed for all values of matrix-bound phosphine (MBP), soil carbon dioxide (SCO 2 ), and soil methane (SCH 4 ) in paddy soils. However, there was a significant positive correlation between MBP and SCO 2 at heading, flowering and ripening stage. The correlation coefficients were 0.909, 0.890 and 0.827, respectively. In vertical distribution, MBP had the analogical variation trend with SCO 2 and SCH 4 . Through Pearson correlation analysis and multiple stepwise linear regression analysis, pH, redox potential (Eh), total phosphorus (TP) and acid phosphatase (ACP) were identified as the principal factors affecting MBP levels, with correlative rankings of Eh>pH>TP>ACP. The multiple stepwise regression model ([MBP]=0.456∗[ACP]+0.235∗[TP]-1.458∗[Eh]-36.547∗[pH]+352.298) was obtained. The findings in this study hold great reference values to the global biogeochemical cycling of phosphorus in the future. Copyright © 2017 Elsevier B.V. All rights reserved.
Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests

PubMed Central

2011-01-01

Background Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Results Press' Q test showed that all classifiers performed better than chance alone (p < 0.05). Support Vector Machines showed the larger overall classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most sensitivity was around or even lower than a median value of 0.5. Conclusions When taking into account sensitivity, specificity and overall classification accuracy Random Forests and Linear Discriminant analysis rank first among all the classifiers tested in prediction of dementia using several neuropsychological tests. These methods may be used to improve accuracy, sensitivity and specificity of Dementia predictions from neuropsychological testing. PMID:21849043
Contrast effects on speed perception for linear and radial motion.

PubMed

Champion, Rebecca A; Warren, Paul A

2017-11-01

Speed perception is vital for safe activity in the environment. However, considerable evidence suggests that perceived speed changes as a function of stimulus contrast, with some investigators suggesting that this might have meaningful real-world consequences (e.g. driving in fog). In the present study we investigate whether the neural effects of contrast on speed perception occur at the level of local or global motion processing. To do this we examine both speed discrimination thresholds and contrast-dependent speed perception for two global motion configurations that have matched local spatio-temporal structure. Specifically we compare linear and radial configurations, the latter of which arises very commonly due to self-movement. In experiment 1 the stimuli comprised circular grating patches. In experiment 2, to match stimuli even more closely, motion was presented in multiple local Gabor patches equidistant from central fixation. Each patch contained identical linear motion but the global configuration was either consistent with linear or radial motion. In both experiments 1 and 2, discrimination thresholds and contrast-induced speed biases were similar in linear and radial conditions. These results suggest that contrast-based speed effects occur only at the level of local motion processing, irrespective of global structure. This result is interpreted in the context of previous models of speed perception and evidence suggesting differences in perceived speed of locally matched linear and radial stimuli. Copyright © 2017 Elsevier Ltd. All rights reserved.
Discrimination, Acculturation and Other Predictors of Depression among Pregnant Hispanic Women

PubMed Central

Walker, Janiece L.; Ruiz, R. Jeanne; Chinn, Juanita J.; Marti, Nathan; Ricks, Tiffany N.

2012-01-01

Objective The purpose of our study was to examine the effects of socioeconomic status, acculturative stress, discrimination, and marginalization as predictors of depression in pregnant Hispanic women. Design A prospective observational design was used. Setting Central and Gulf coast areas of Texas in obstetrical offices. Participants A convenience sample of 515 pregnant, low income, low medical risk, and self-identified Hispanic women who were between 22–24 weeks gestation was used to collect data. Measures The predictor variables were socioeconomic status, discrimination, acculturative stress, and marginalization. The outcome variable was depression. Results Education, frequency of discrimination, age, and Anglo marginality were significant predictors of depressive symptoms in a linear regression model, F (6, 458) = 8.36, P<.0001. Greater frequency of discrimination was the strongest positive predictor of increased depressive symptoms. Conclusions It is important that health care providers further understand the impact that age and experiences of discrimination throughout the life course have on depressive symptoms during pregnancy. PMID:23140083
Sources of variability in satellite-derived estimates of phytoplankton production in the eastern tropical Pacific

NASA Technical Reports Server (NTRS)

Banse, Karl; Yong, Marina

1990-01-01

As a proxy for satellite CZCS observations and concurrent measurements of primary production rates, data from 138 stations occupied seasonally during 1967-1968 in the offshore eastern tropical Pacific were analyzed in terms of six temporal groups and our current regimes. Multiple linear regressions on column production Pt show that simulated satellite pigment is generally weakly correlated, but sometimes not correlated with Pt, and that incident irradiance, sea surface temperature, nitrate, transparency, and depths of mixed layer or nitracline assume little or no importance. After a proxy for the light-saturated chlorophyll-specific photosynthetic rate P(max) is added, the coefficient of determination ranges from 0.55 to 0.91 (median of 0.85) for the 10 cases. In stepwise multiple linear regressions the P(max) proxy is the best predictor for Pt.
Discriminating Among Probability Weighting Functions Using Adaptive Design Optimization

PubMed Central

Cavagnaro, Daniel R.; Pitt, Mark A.; Gonzalez, Richard; Myung, Jay I.

2014-01-01

Probability weighting functions relate objective probabilities and their subjective weights, and play a central role in modeling choices under risk within cumulative prospect theory. While several different parametric forms have been proposed, their qualitative similarities make it challenging to discriminate among them empirically. In this paper, we use both simulation and choice experiments to investigate the extent to which different parametric forms of the probability weighting function can be discriminated using adaptive design optimization, a computer-based methodology that identifies and exploits model differences for the purpose of model discrimination. The simulation experiments show that the correct (data-generating) form can be conclusively discriminated from its competitors. The results of an empirical experiment reveal heterogeneity between participants in terms of the functional form, with two models (Prelec-2, Linear in Log Odds) emerging as the most common best-fitting models. The findings shed light on assumptions underlying these models. PMID:24453406
Temperature Gradient Effect on Gas Discrimination Power of a Metal-Oxide Thin-Film Sensor Microarray

PubMed Central

Sysoev, Victor V.; Kiselev, Ilya; Frietsch, Markus; Goschnick, Joachim

2004-01-01

The paper presents results concerning the effect of spatial inhomogeneous operating temperature on the gas discrimination power of a gas-sensor microarray, with the latter based on a thin SnO2 film employed in the KAMINA electronic nose. Three different temperature distributions over the substrate are discussed: a nearly homogeneous one and two temperature gradients, equal to approx. 3.3 °C/mm and 6.7 °C/mm, applied across the sensor elements (segments) of the array. The gas discrimination power of the microarray is judged by using the Mahalanobis distance in the LDA (Linear Discrimination Analysis) coordinate system between the data clusters obtained by the response of the microarray to four target vapors: ethanol, acetone, propanol and ammonia. It is shown that the application of a temperature gradient increases the gas discrimination power of the microarray by up to 35 %.
Statistical classification approach to discrimination between weak earthquakes and quarry blasts recorded by the Israel Seismic Network

NASA Astrophysics Data System (ADS)

Kushnir, A. F.; Troitsky, E. V.; Haikin, L. M.; Dainty, A.

1999-06-01

A semi-automatic procedure has been developed to achieve statistically optimum discrimination between earthquakes and explosions at local or regional distances based on a learning set specific to a given region. The method is used for step-by-step testing of candidate discrimination features to find the optimum (combination) subset of features, with the decision taken on a rigorous statistical basis. Linear (LDF) and Quadratic (QDF) Discriminant Functions based on Gaussian distributions of the discrimination features are implemented and statistically grounded; the features may be transformed by the Box-Cox transformation z=(1/ α)( yα-1) to make them more Gaussian. Tests of the method were successfully conducted on seismograms from the Israel Seismic Network using features consisting of spectral ratios between and within phases. Results showed that the QDF was more effective than the LDF and required five features out of 18 candidates for the optimum set. It was found that discrimination improved with increasing distance within the local range, and that eliminating transformation of the features and failing to correct for noise led to degradation of discrimination.
Direct discriminant locality preserving projection with Hammerstein polynomial expansion.

PubMed

Chen, Xi; Zhang, Jiashu; Li, Defang

2012-12-01

Discriminant locality preserving projection (DLPP) is a linear approach that encodes discriminant information into the objective of locality preserving projection and improves its classification ability. To enhance the nonlinear description ability of DLPP, we can optimize the objective function of DLPP in reproducing kernel Hilbert space to form a kernel-based discriminant locality preserving projection (KDLPP). However, KDLPP suffers the following problems: 1) larger computational burden; 2) no explicit mapping functions in KDLPP, which results in more computational burden when projecting a new sample into the low-dimensional subspace; and 3) KDLPP cannot obtain optimal discriminant vectors, which exceedingly optimize the objective of DLPP. To overcome the weaknesses of KDLPP, in this paper, a direct discriminant locality preserving projection with Hammerstein polynomial expansion (HPDDLPP) is proposed. The proposed HPDDLPP directly implements the objective of DLPP in high-dimensional second-order Hammerstein polynomial space without matrix inverse, which extracts the optimal discriminant vectors for DLPP without larger computational burden. Compared with some other related classical methods, experimental results for face and palmprint recognition problems indicate the effectiveness of the proposed HPDDLPP.
Discrimination of lymphoma using laser-induced breakdown spectroscopy conducted on whole blood samples

PubMed Central

Chen, Xue; Li, Xiaohui; Yang, Sibo; Yu, Xin; Liu, Aichun

2018-01-01

Lymphoma is a significant cancer that affects the human lymphatic and hematopoietic systems. In this work, discrimination of lymphoma using laser-induced breakdown spectroscopy (LIBS) conducted on whole blood samples is presented. The whole blood samples collected from lymphoma patients and healthy controls are deposited onto standard quantitative filter papers and ablated with a 1064 nm Q-switched Nd:YAG laser. 16 atomic and ionic emission lines of calcium (Ca), iron (Fe), magnesium (Mg), potassium (K) and sodium (Na) are selected to discriminate the cancer disease. Chemometric methods, including principal component analysis (PCA), linear discriminant analysis (LDA) classification, and k nearest neighbor (kNN) classification are used to build the discrimination models. Both LDA and kNN models have achieved very good discrimination performances for lymphoma, with an accuracy of over 99.7%, a sensitivity of over 0.996, and a specificity of over 0.997. These results demonstrate that the whole-blood-based LIBS technique in combination with chemometric methods can serve as a fast, less invasive, and accurate method for detection and discrimination of human malignancies. PMID:29541503

Demonstration of iodine K-edge imaging by use of an energy-discrimination X-ray computed tomography system with a cadmium telluride detector.

PubMed

Abudurexiti, Abulajiang; Kameda, Masashi; Sato, Eiichi; Abderyim, Purkhet; Enomoto, Toshiyuki; Watanabe, Manabu; Hitomi, Keitaro; Tanaka, Etsuro; Mori, Hidezo; Kawai, Toshiaki; Takahashi, Kiyomi; Sato, Shigehiro; Ogawa, Akira; Onagawa, Jun

2010-07-01

An energy-discrimination K-edge X-ray computed tomography (CT) system is useful for increasing the contrast resolution of a target region by utilizing contrast media. The CT system has a cadmium telluride (CdTe) detector, and a projection curve is obtained by linear scanning with use of the CdTe detector in conjunction with an X-stage. An object is rotated by a rotation step angle with use of a turntable between the linear scans. Thus, CT is carried out by repetition of the linear scanning and the rotation of an object. Penetrating X-ray photons from the object are detected by the CdTe detector, and event signals of X-ray photons are produced with use of charge-sensitive and shaping amplifiers. Both the photon energy and the energy width are selected by use of a multi-channel analyzer, and the number of photons is counted by a counter card. For performing energy discrimination, a low-dose-rate X-ray generator for photon counting was developed; the maximum tube voltage and the minimum tube current were 110 kV and 1.0 microA, respectively. In energy-discrimination CT, the tube voltage and the current were 60 kV and 20.0 microA, respectively, and the X-ray intensity was 0.735 microGy/s at 1.0 m from the source and with a tube voltage of 60 kV. Demonstration of enhanced iodine K-edge X-ray CT was carried out by selection of photons with energies just beyond the iodine K-edge energy of 33.2 keV.
Exploring the association between parental rearing styles and medical students' critical thinking disposition in China.

PubMed

Huang, Lei; Wang, Zhaoxin; Yao, Yuhong; Shan, Chang; Wang, Haojie; Zhu, Mengyi; Lu, Yuan; Sun, Pengfei; Zhao, Xudong

2015-05-14

Critical thinking is an essential ability for medical students. However, the relationship between parental rearing styles and medical students' critical thinking disposition has rarely been considered. The aim of this study was to investigate whether parental rearing styles were significant predictors of critical thinking disposition among Chinese medical students. 1,075 medical students from the first year to the fifth year attending one of three medical schools in China were recruited via multistage stratified cluster sampling. The Chinese Critical Thinking Disposition Inventory(CTDI-CV) and The Egna Minnen av Barndoms Uppfostran (EMBU) questionnaire were applied to collect data and to conduct descriptive analysis. Stepwise multiple linear regression was used to analyze the data. The critical thinking disposition average mean score was 287.44 with 632 participants (58.79%) demonstrating positive critical thinking disposition. Stepwise multiple linear regression analysis revealed that the rearing styles of fathers, including "overprotection", "emotional warmth and understanding", "rejection" and "over-interference" were significant predictors of medical students' critical thinking disposition that explained 79.0% of the variance in critical thinking ability. Rearing styles of mothers including "emotional warmth and understanding", "punishing" and "rejection" were also found to be significant predictors, and explained 77.0% of the variance. Meaningful association has been evidenced between parental rearing styles and Chinese medical students' critical thinking disposition. Parental rearing styles should be considered as one of the many potential determinant factors that contribute to the cultivation of medical students' critical thinking capability. Positive parental rearing styles should be encouraged in the cultivation of children's critical thinking skills.
Relationship between masticatory performance using a gummy jelly and masticatory movement.

PubMed

Uesugi, Hanako; Shiga, Hiroshi

2017-10-01

The purpose of this study was to clarify the relationship between masticatory performance using a gummy jelly and masticatory movement. Thirty healthy males were asked to chew a gummy jelly on their habitual chewing side for 20s, and the parameters of masticatory performance and masticatory movement were calculated as follows. For evaluating the masticatory performance, the amount of glucose extraction during chewing of a gummy jelly was measured. For evaluating the masticatory movement, the movement of the mandibular incisal point was recorded using the MKG K6-I, and ten parameters of the movement path (opening distance and masticatory width), movement rhythm (opening time, closing time, occluding time, and cycle time), stability of movement (stability of path and stability of rhythm), and movement velocity (opening maximum velocity and closing maximum velocity) were calculated from 10 cycles of chewing beginning with the fifth cycle. The relationship between the amount of glucose extraction and parameters representing masticatory movement was investigated and then stepwise multiple linear regression analysis was performed. The amount of glucose extraction was associated with 7 parameters representing the masticatory movement. Stepwise multiple linear regression analysis showed that the opening distance, closing time, stability of rhythm, and closing maximum velocity were the most important factors affecting the glucose extraction. From these results it was suggested that there was a close relation between masticatory performance and masticatory movement, and that the masticatory performance could be increased by rhythmic, rapid and stable mastication with a large opening distance. Copyright © 2017 Japan Prosthodontic Society. Published by Elsevier Ltd. All rights reserved.
The use of machine learning for the identification of peripheral artery disease and future mortality risk.

PubMed

Ross, Elsie Gyang; Shah, Nigam H; Dalman, Ronald L; Nead, Kevin T; Cooke, John P; Leeper, Nicholas J

2016-11-01

A key aspect of the precision medicine effort is the development of informatics tools that can analyze and interpret "big data" sets in an automated and adaptive fashion while providing accurate and actionable clinical information. The aims of this study were to develop machine learning algorithms for the identification of disease and the prognostication of mortality risk and to determine whether such models perform better than classical statistical analyses. Focusing on peripheral artery disease (PAD), patient data were derived from a prospective, observational study of 1755 patients who presented for elective coronary angiography. We employed multiple supervised machine learning algorithms and used diverse clinical, demographic, imaging, and genomic information in a hypothesis-free manner to build models that could identify patients with PAD and predict future mortality. Comparison was made to standard stepwise linear regression models. Our machine-learned models outperformed stepwise logistic regression models both for the identification of patients with PAD (area under the curve, 0.87 vs 0.76, respectively; P = .03) and for the prediction of future mortality (area under the curve, 0.76 vs 0.65, respectively; P = .10). Both machine-learned models were markedly better calibrated than the stepwise logistic regression models, thus providing more accurate disease and mortality risk estimates. Machine learning approaches can produce more accurate disease classification and prediction models. These tools may prove clinically useful for the automated identification of patients with highly morbid diseases for which aggressive risk factor management can improve outcomes. Copyright © 2016 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.
Evaluation of interpolation techniques for the creation of gridded daily precipitation (1 × 1 km2); Cyprus, 1980-2010

NASA Astrophysics Data System (ADS)

Camera, Corrado; Bruggeman, Adriana; Hadjinicolaou, Panos; Pashiardis, Stelios; Lange, Manfred A.

2014-01-01

High-resolution gridded daily data sets are essential for natural resource management and the analyses of climate changes and their effects. This study aims to evaluate the performance of 15 simple or complex interpolation techniques in reproducing daily precipitation at a resolution of 1 km2 over topographically complex areas. Methods are tested considering two different sets of observation densities and different rainfall amounts. We used rainfall data that were recorded at 74 and 145 observational stations, respectively, spread over the 5760 km2 of the Republic of Cyprus, in the Eastern Mediterranean. Regression analyses utilizing geographical copredictors and neighboring interpolation techniques were evaluated both in isolation and combined. Linear multiple regression (LMR) and geographically weighted regression methods (GWR) were tested. These included a step-wise selection of covariables, as well as inverse distance weighting (IDW), kriging, and 3D-thin plate splines (TPS). The relative rank of the different techniques changes with different station density and rainfall amounts. Our results indicate that TPS performs well for low station density and large-scale events and also when coupled with regression models. It performs poorly for high station density. The opposite is observed when using IDW. Simple IDW performs best for local events, while a combination of step-wise GWR and IDW proves to be the best method for large-scale events and high station density. This study indicates that the use of step-wise regression with a variable set of geographic parameters can improve the interpolation of large-scale events because it facilitates the representation of local climate dynamics.
Clinical utility of the AlphaFIM® instrument in stroke rehabilitation.

PubMed

Lo, Alexander; Tahair, Nicola; Sharp, Shelley; Bayley, Mark T

2012-02-01

The AlphaFIM instrument is an assessment tool designed to facilitate discharge planning of stroke patients from acute care, by extrapolating overall functional status from performance in six key Functional Independence Measure (FIM) instrument items. To determine whether acute care AlphaFIM rating is correlated to stroke rehabilitation outcomes. In this prospective observational study, data were analyzed from 891 patients referred for inpatient stroke rehabilitation through an Internet-based referral system. Simple linear and stepwise regression models determined correlations between rehabilitation-ready AlphaFIM rating and rehabilitation outcomes (admission and discharge FIM ratings, FIM gain, FIM efficiency, and length of stay). Covariates including demographic data, stroke characteristics, medical history, cognitive deficits, and activity tolerance were included in the stepwise regressions. The AlphaFIM instrument was significant in predicting admission and discharge FIM ratings at rehabilitation (adjusted R² 0.40 and 0.28, respectively; P < 0.0001) and was weakly correlated with FIM gain and length of stay (adjusted R² 0.04 and 0.09, respectively; P < 0.0001), but not FIM efficiency. AlphaFIM rating was inversely related to FIM gain. Age, bowel incontinence, left hemiparesis, and previous infarcts were negative predictors of discharge FIM rating on stepwise regression. Intact executive function and physical activity tolerance of 30 to 60 mins were predictors of FIM gain. The AlphaFIM instrument is a valuable tool for triaging stroke patients from acute care to rehabilitation and predicts functional status at discharge from rehabilitation. Patients with low AlphaFIM ratings have the potential to make significant functional gains and should not be denied admission to inpatient rehabilitation programs.
Analysis of blood flow in the long posterior ciliary artery of the cat.

PubMed

Koss, M C

1999-03-01

Experiments were undertaken to use a new technique for direct on-line measurement of blood flow in the long posterior ciliary artery (LPCA) in cats and to evaluate possible physiological mechanisms controlling blood flow in the vascular beds perfused by this artery. Blood flow in the temporal LPCA was measured on a continuous basis using ultrasonic flowmetry in anesthetized cats. Effects of acute sectioning of the sympathetic nerve and changes in LPCA and cerebral blood flows in response to altered levels of inspired CO2 and O2 were tested in some animals. In others, the presence of vascular autoregulatory mechanisms in response to stepwise elevations of intraocular pressure was studied. Blood flow in the temporal LPCA averaged 0.58+/-0.03 ml/min in 45 cats anesthetized with pentobarbital. Basal LPCA blood flow was not altered by acute sectioning of the sympathetic nerve or by changes in low levels of inspired CO2 and O2, although 10% CO2 caused a modest increase. Stepwise elevations of intraocular pressure resulted in comparable stepwise decreases of LPCA blood flow, with perfusion pressure declining in a linear manner throughout the perfusion-pressure range. Ultrasonic flowmetry seems to be a useful tool for continuous on-line measurement of LPCA blood flow in the cat eye. Blood flow to vascular beds perfused by this artery does not seem to be under sympathetic neural control and is refractory to modest alterations of blood gas levels of CO2 and O2. Blood vessels perfused by the LPCA show no clear autoregulatory mechanisms.
Gender classification of running subjects using full-body kinematics

NASA Astrophysics Data System (ADS)

Williams, Christina M.; Flora, Jeffrey B.; Iftekharuddin, Khan M.

2016-05-01

This paper proposes novel automated gender classification of subjects while engaged in running activity. The machine learning techniques include preprocessing steps using principal component analysis followed by classification with linear discriminant analysis, and nonlinear support vector machines, and decision-stump with AdaBoost. The dataset consists of 49 subjects (25 males, 24 females, 2 trials each) all equipped with approximately 80 retroreflective markers. The trials are reflective of the subject's entire body moving unrestrained through a capture volume at a self-selected running speed, thus producing highly realistic data. The classification accuracy using leave-one-out cross validation for the 49 subjects is improved from 66.33% using linear discriminant analysis to 86.74% using the nonlinear support vector machine. Results are further improved to 87.76% by means of implementing a nonlinear decision stump with AdaBoost classifier. The experimental findings suggest that the linear classification approaches are inadequate in classifying gender for a large dataset with subjects running in a moderately uninhibited environment.
Discriminatory experiences associated with posttraumatic stress disorder symptoms among transgender adults

PubMed Central

Reisner, Sari L.; White Hughto, Jaclyn M.; Gamarel, Kristi E.; Keuroghlian, Alex S.; Mizock, Lauren; Pachankis, John

2016-01-01

Discrimination has been shown to disproportionately burden transgender people; however, there has been a lack of clinical attention to the mental health sequelae of discrimination, including posttraumatic stress disorder (PTSD) symptoms. Additionally, few studies contextualize discrimination alongside other traumatic stressors in predicting PTSD symptomatology. The current study sought to fill these gaps. A community-based sample of 412 transgender adults (mean age 33, SD=13; 63% female-to-male spectrum; 19% people of color; 88% sampled online) completed a cross-sectional self-report survey of everyday discrimination experiences and PTSD symptoms. Multivariable linear regression models examined the association between self-reported everyday discrimination experiences, number of attributed domains of discrimination, and PTSD symptoms, adjusting for prior trauma, sociodemographics, and psychosocial co-morbidity. The mean number of discrimination attributions endorsed was 4.8 (SD=2.4) and the five most frequently reported reasons for discrimination were: gender identity and/or expression (83%), masculine and feminine appearance (79%), sexual orientation (68%), sex (57%), and age (44%). Higher everyday discrimination scores (β=0.25; 95% CL=0.21–0.30) and greater number of attributed reasons for discrimination experiences (β=0.05; 95% CL=0.01–0.10) were independently associated with PTSD symptoms, even after adjusting for prior trauma experiences. Everyday discrimination experiences from multiple sources necessitate clinical consideration in treatment for PTSD symptoms in transgender people. PMID:26866637
Discriminatory experiences associated with posttraumatic stress disorder symptoms among transgender adults.

PubMed

Reisner, Sari L; White Hughto, Jaclyn M; Gamarel, Kristi E; Keuroghlian, Alex S; Mizock, Lauren; Pachankis, John E

2016-10-01

Discrimination has been shown to disproportionately burden transgender people; however, there has been a lack of clinical attention to the mental health sequelae of discrimination, including posttraumatic stress disorder (PTSD) symptoms. Additionally, few studies contextualize discrimination alongside other traumatic stressors in predicting PTSD symptomatology. The current study sought to fill these gaps. A community-based sample of 412 transgender adults (mean age 33, SD = 13; 63% female-to-male spectrum; 19% people of color; 88% sampled online) completed a cross-sectional self-report survey of everyday discrimination experiences and PTSD symptoms. Multivariable linear regression models examined the association between self-reported everyday discrimination experiences, number of attributed domains of discrimination, and PTSD symptoms, adjusting for prior trauma, sociodemographics, and psychosocial comorbidity. The mean number of discrimination attributions endorsed was 4.8 (SD = 2.4) and the 5 most frequently reported reasons for discrimination were: gender identity and/or expression (83%), masculine and feminine appearance (79%), sexual orientation (68%), sex (57%), and age (44%). Higher everyday discrimination scores (β = 0.25; 95% CL [0.21, 0.30]) and greater number of attributed reasons for discrimination experiences (β = 0.05; 95% CL [0.01, 0.10]) were independently associated with PTSD symptoms, even after adjusting for prior trauma experiences. Everyday discrimination experiences from multiple sources necessitate clinical consideration in treatment for PTSD symptoms in transgender people. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Near-infrared Raman spectroscopy for estimating biochemical changes associated with different pathological conditions of cervix

NASA Astrophysics Data System (ADS)

Daniel, Amuthachelvi; Prakasarao, Aruna; Ganesan, Singaravelu

2018-02-01

The molecular level changes associated with oncogenesis precede the morphological changes in cells and tissues. Hence molecular level diagnosis would promote early diagnosis of the disease. Raman spectroscopy is capable of providing specific spectral signature of various biomolecules present in the cells and tissues under various pathological conditions. The aim of this work is to develop a non-linear multi-class statistical methodology for discrimination of normal, neoplastic and malignant cells/tissues. The tissues were classified as normal, pre-malignant and malignant by employing Principal Component Analysis followed by Artificial Neural Network (PC-ANN). The overall accuracy achieved was 99%. Further, to get an insight into the quantitative biochemical composition of the normal, neoplastic and malignant tissues, a linear combination of the major biochemicals by non-negative least squares technique was fit to the measured Raman spectra of the tissues. This technique confirms the changes in the major biomolecules such as lipids, nucleic acids, actin, glycogen and collagen associated with the different pathological conditions. To study the efficacy of this technique in comparison with histopathology, we have utilized Principal Component followed by Linear Discriminant Analysis (PC-LDA) to discriminate the well differentiated, moderately differentiated and poorly differentiated squamous cell carcinoma with an accuracy of 94.0%. And the results demonstrated that Raman spectroscopy has the potential to complement the good old technique of histopathology.
Evaluation of linear discriminant analysis for automated Raman histological mapping of esophageal high-grade dysplasia

NASA Astrophysics Data System (ADS)

Hutchings, Joanne; Kendall, Catherine; Shepherd, Neil; Barr, Hugh; Stone, Nicholas

2010-11-01

Rapid Raman mapping has the potential to be used for automated histopathology diagnosis, providing an adjunct technique to histology diagnosis. The aim of this work is to evaluate the feasibility of automated and objective pathology classification of Raman maps using linear discriminant analysis. Raman maps of esophageal tissue sections are acquired. Principal component (PC)-fed linear discriminant analysis (LDA) is carried out using subsets of the Raman map data (6483 spectra). An overall (validated) training classification model performance of 97.7% (sensitivity 95.0 to 100% and specificity 98.6 to 100%) is obtained. The remainder of the map spectra (131,672 spectra) are projected onto the classification model resulting in Raman images, demonstrating good correlation with contiguous hematoxylin and eosin (HE) sections. Initial results suggest that LDA has the potential to automate pathology diagnosis of esophageal Raman images, but since the classification of test spectra is forced into existing training groups, further work is required to optimize the training model. A small pixel size is advantageous for developing the training datasets using mapping data, despite lengthy mapping times, due to additional morphological information gained, and could facilitate differentiation of further tissue groups, such as the basal cells/lamina propria, in the future, but larger pixels sizes (and faster mapping) may be more feasible for clinical application.
Application of recurrence quantification analysis to automatically estimate infant sleep states using a single channel of respiratory data.

PubMed

Terrill, Philip I; Wilson, Stephen J; Suresh, Sadasivam; Cooper, David M; Dakin, Carolyn

2012-08-01

Previous work has identified that non-linear variables calculated from respiratory data vary between sleep states, and that variables derived from the non-linear analytical tool recurrence quantification analysis (RQA) are accurate infant sleep state discriminators. This study aims to apply these discriminators to automatically classify 30 s epochs of infant sleep as REM, non-REM and wake. Polysomnograms were obtained from 25 healthy infants at 2 weeks, 3, 6 and 12 months of age, and manually sleep staged as wake, REM and non-REM. Inter-breath interval data were extracted from the respiratory inductive plethysmograph, and RQA applied to calculate radius, determinism and laminarity. Time-series statistic and spectral analysis variables were also calculated. A nested cross-validation method was used to identify the optimal feature subset, and to train and evaluate a linear discriminant analysis-based classifier. The RQA features radius and laminarity and were reliably selected. Mean agreement was 79.7, 84.9, 84.0 and 79.2 % at 2 weeks, 3, 6 and 12 months, and the classifier performed better than a comparison classifier not including RQA variables. The performance of this sleep-staging tool compares favourably with inter-human agreement rates, and improves upon previous systems using only respiratory data. Applications include diagnostic screening and population-based sleep research.
Prediction of aquatic toxicity mode of action using linear discriminant and random forest models.

PubMed

Martin, Todd M; Grulke, Christopher M; Young, Douglas M; Russom, Christine L; Wang, Nina Y; Jackson, Crystal R; Barron, Mace G

2013-09-23

The ability to determine the mode of action (MOA) for a diverse group of chemicals is a critical part of ecological risk assessment and chemical regulation. However, existing MOA assignment approaches in ecotoxicology have been limited to a relatively few MOAs, have high uncertainty, or rely on professional judgment. In this study, machine based learning algorithms (linear discriminant analysis and random forest) were used to develop models for assigning aquatic toxicity MOA. These methods were selected since they have been shown to be able to correlate diverse data sets and provide an indication of the most important descriptors. A data set of MOA assignments for 924 chemicals was developed using a combination of high confidence assignments, international consensus classifications, ASTER (ASessment Tools for the Evaluation of Risk) predictions, and weight of evidence professional judgment based an assessment of structure and literature information. The overall data set was randomly divided into a training set (75%) and a validation set (25%) and then used to develop linear discriminant analysis (LDA) and random forest (RF) MOA assignment models. The LDA and RF models had high internal concordance and specificity and were able to produce overall prediction accuracies ranging from 84.5 to 87.7% for the validation set. These results demonstrate that computational chemistry approaches can be used to determine the acute toxicity MOAs across a large range of structures and mechanisms.
Discrimination of curvature from motion during smooth pursuit eye movements and fixation.

PubMed

Ross, Nicholas M; Goettker, Alexander; Schütz, Alexander C; Braun, Doris I; Gegenfurtner, Karl R

2017-09-01

Smooth pursuit and motion perception have mainly been investigated with stimuli moving along linear trajectories. Here we studied the quality of pursuit movements to curved motion trajectories in human observers and examined whether the pursuit responses would be sensitive enough to discriminate various degrees of curvature. In a two-interval forced-choice task subjects pursued a Gaussian blob moving along a curved trajectory and then indicated in which interval the curve was flatter. We also measured discrimination thresholds for the same curvatures during fixation. Motion curvature had some specific effects on smooth pursuit properties: trajectories with larger amounts of curvature elicited lower open-loop acceleration, lower pursuit gain, and larger catch-up saccades compared with less curved trajectories. Initially, target motion curvatures were underestimated; however, ∼300 ms after pursuit onset pursuit responses closely matched the actual curved trajectory. We calculated perceptual thresholds for curvature discrimination, which were on the order of 1.5 degrees of visual angle (°) for a 7.9° curvature standard. Oculometric sensitivity to curvature discrimination based on the whole pursuit trajectory was quite similar to perceptual performance. Oculometric thresholds based on smaller time windows were higher. Thus smooth pursuit can quite accurately follow moving targets with curved trajectories, but temporal integration over longer periods is necessary to reach perceptual thresholds for curvature discrimination. NEW & NOTEWORTHY Even though motion trajectories in the real world are frequently curved, most studies of smooth pursuit and motion perception have investigated linear motion. We show that pursuit initially underestimates the curvature of target motion and is able to reproduce the target curvature ∼300 ms after pursuit onset. Temporal integration of target motion over longer periods is necessary for pursuit to reach the level of precision found in perceptual discrimination of curvature. Copyright © 2017 the American Physiological Society.
Improved neutron-gamma discrimination for a 3He neutron detector using subspace learning methods

DOE PAGES

Wang, C. L.; Funk, L. L.; Riedel, R. A.; ...

2017-02-10

3He gas based neutron linear-position-sensitive detectors (LPSDs) have been applied for many neutron scattering instruments. Traditional Pulse-Height Analysis (PHA) for Neutron-Gamma Discrimination (NGD) resulted in the neutron-gamma efficiency ratio on the orders of 10 5-10 6. The NGD ratios of 3He detectors need to be improved for even better scientific results from neutron scattering. Digital Signal Processing (DSP) analyses of waveforms were proposed for obtaining better NGD ratios, based on features extracted from rise-time, pulse amplitude, charge integration, a simplified Wiener filter, and the cross-correlation between individual and template waveforms of neutron and gamma events. Fisher linear discriminant analysis (FLDA)more » and three multivariate analyses (MVAs) of the features were performed. The NGD ratios are improved by about 10 2-10 3 times compared with the traditional PHA method. Finally, our results indicate the NGD capabilities of 3He tube detectors can be significantly improved with subspace-learning based methods, which may result in a reduced data-collection time and better data quality for further data reduction.« less
Combustion monitoring of a water tube boiler using a discriminant radial basis network.

PubMed

Sujatha, K; Pappa, N

2011-01-01

This research work includes a combination of Fisher's linear discriminant (FLD) analysis and a radial basis network (RBN) for monitoring the combustion conditions for a coal fired boiler so as to allow control of the air/fuel ratio. For this, two-dimensional flame images are required, which were captured with a CCD camera; the features of the images-average intensity, area, brightness and orientation etc of the flame-are extracted after preprocessing the images. The FLD is applied to reduce the n-dimensional feature size to a two-dimensional feature size for faster learning of the RBN. Also, three classes of images corresponding to different burning conditions of the flames have been extracted from continuous video processing. In this, the corresponding temperatures, and the carbon monoxide (CO) emissions and those of other flue gases have been obtained through measurement. Further, the training and testing of Fisher's linear discriminant radial basis network (FLDRBN), with the data collected, have been carried out and the performance of the algorithms is presented. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.
Fast neutron-gamma discrimination on neutron emission profile measurement on JT-60U.

PubMed

Ishii, K; Shinohara, K; Ishikawa, M; Baba, M; Isobe, M; Okamoto, A; Kitajima, S; Sasao, M

2010-10-01

A digital signal processing (DSP) system is applied to stilbene scintillation detectors of the multichannel neutron emission profile monitor in JT-60U. Automatic analysis of the neutron-γ pulse shape discrimination is a key issue to diminish the processing time in the DSP system, and it has been applied using the two-dimensional (2D) map. Linear discriminant function is used to determine the dividing line between neutron events and γ-ray events on a 2D map. In order to verify the validity of the dividing line determination, the pulse shape discrimination quality is evaluated. As a result, the γ-ray contamination in most of the beam heating phase was negligible compared with the statistical error with 10 ms time resolution.
Assay based on electrical impedance spectroscopy to discriminate between normal and cancerous mammalian cells

NASA Astrophysics Data System (ADS)

Giana, Fabián Eduardo; Bonetto, Fabián José; Bellotti, Mariela Inés

2018-03-01

In this work we present an assay to discriminate between normal and cancerous cells. The method is based on the measurement of electrical impedance spectra of in vitro cell cultures. We developed a protocol consisting on four consecutive measurement phases, each of them designed to obtain different information about the cell cultures. Through the analysis of the measured data, 26 characteristic features were obtained for both cell types. From the complete set of features, we selected the most relevant in terms of their discriminant capacity by means of conventional statistical tests. A linear discriminant analysis was then carried out on the selected features, allowing the classification of the samples in normal or cancerous with 4.5% of false positives and no false negatives.
Assay based on electrical impedance spectroscopy to discriminate between normal and cancerous mammalian cells.

PubMed

Giana, Fabián Eduardo; Bonetto, Fabián José; Bellotti, Mariela Inés

2018-03-01

In this work we present an assay to discriminate between normal and cancerous cells. The method is based on the measurement of electrical impedance spectra of in vitro cell cultures. We developed a protocol consisting on four consecutive measurement phases, each of them designed to obtain different information about the cell cultures. Through the analysis of the measured data, 26 characteristic features were obtained for both cell types. From the complete set of features, we selected the most relevant in terms of their discriminant capacity by means of conventional statistical tests. A linear discriminant analysis was then carried out on the selected features, allowing the classification of the samples in normal or cancerous with 4.5% of false positives and no false negatives.

Fast neutron-gamma discrimination on neutron emission profile measurement on JT-60U

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

Ishii, K.; Okamoto, A.; Kitajima, S.

2010-10-15

A digital signal processing (DSP) system is applied to stilbene scintillation detectors of the multichannel neutron emission profile monitor in JT-60U. Automatic analysis of the neutron-{gamma} pulse shape discrimination is a key issue to diminish the processing time in the DSP system, and it has been applied using the two-dimensional (2D) map. Linear discriminant function is used to determine the dividing line between neutron events and {gamma}-ray events on a 2D map. In order to verify the validity of the dividing line determination, the pulse shape discrimination quality is evaluated. As a result, the {gamma}-ray contamination in most of themore » beam heating phase was negligible compared with the statistical error with 10 ms time resolution.« less
Decision-Related Activity in Macaque V2 for Fine Disparity Discrimination Is Not Compatible with Optimal Linear Readout.

PubMed

Clery, Stephane; Cumming, Bruce G; Nienborg, Hendrikje

2017-01-18

Fine judgments of stereoscopic depth rely mainly on relative judgments of depth (relative binocular disparity) between objects, rather than judgments of the distance to where the eyes are fixating (absolute disparity). In macaques, visual area V2 is the earliest site in the visual processing hierarchy for which neurons selective for relative disparity have been observed (Thomas et al., 2002). Here, we found that, in macaques trained to perform a fine disparity discrimination task, disparity-selective neurons in V2 were highly selective for the task, and their activity correlated with the animals' perceptual decisions (unexplained by the stimulus). This may partially explain similar correlations reported in downstream areas. Although compatible with a perceptual role of these neurons for the task, the interpretation of such decision-related activity is complicated by the effects of interneuronal "noise" correlations between sensory neurons. Recent work has developed simple predictions to differentiate decoding schemes (Pitkow et al., 2015) without needing measures of noise correlations, and found that data from early sensory areas were compatible with optimal linear readout of populations with information-limiting correlations. In contrast, our data here deviated significantly from these predictions. We additionally tested this prediction for previously reported results of decision-related activity in V2 for a related task, coarse disparity discrimination (Nienborg and Cumming, 2006), thought to rely on absolute disparity. Although these data followed the predicted pattern, they violated the prediction quantitatively. This suggests that optimal linear decoding of sensory signals is not generally a good predictor of behavior in simple perceptual tasks. Activity in sensory neurons that correlates with an animal's decision is widely believed to provide insights into how the brain uses information from sensory neurons. Recent theoretical work developed simple predictions to differentiate decoding schemes, and found support for optimal linear readout of early sensory populations with information-limiting correlations. Here, we observed decision-related activity for neurons in visual area V2 of macaques performing fine disparity discrimination, as yet the earliest site for this task. These findings, and previously reported results from V2 in a different task, deviated from the predictions for optimal linear readout of a population with information-limiting correlations. Our results suggest that optimal linear decoding of early sensory information is not a general decoding strategy used by the brain. Copyright © 2017 the authors 0270-6474/17/370715-11$15.00/0.
A photonic chip based frequency discriminator for a high performance microwave photonic link.

PubMed

Marpaung, David; Roeloffzen, Chris; Leinse, Arne; Hoekman, Marcel

2010-12-20

We report a high performance phase modulation direct detection microwave photonic link employing a photonic chip as a frequency discriminator. The photonic chip consists of five optical ring resonators (ORRs) which are fully programmable using thermo-optical tuning. In this discriminator a drop-port response of an ORR is cascaded with a through response of another ORR to yield a linear phase modulation (PM) to intensity modulation (IM) conversion. The balanced photonic link employing the PM to IM conversion exhibits high second-order and third-order input intercept points of + 46 dBm and + 36 dBm, respectively, which are simultaneously achieved at one bias point.
General methodology for simultaneous representation and discrimination of multiple object classes

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-03-01

We address a new general method for linear and nonlinear feature extraction for simultaneous representation and classification. We call this approach the maximum representation and discrimination feature (MRDF) method. We develop a novel nonlinear eigenfeature extraction technique to represent data with closed-form solutions and use it to derive a nonlinear MRDF algorithm. Results of the MRDF method on synthetic databases are shown and compared with results from standard Fukunaga-Koontz transform and Fisher discriminant function methods. The method is also applied to an automated product inspection problem and for classification and pose estimation of two similar objects under 3D aspect angle variations.
Linear photonic frequency discriminator on As₂S₃-ring-on-Ti:LiNbO₃ hybrid platform.

PubMed

Kim, Jaehyun; Sung, Won Ju; Eknoyan, Ohannes; Madsen, Christi K

2013-10-21

We report a photonic frequency discriminator built on the vertically integrated As₂S₃-ring-on-Ti:LiNbO₃ hybrid platform. The discriminator consists of a Mach Zehnder interferometer (MZI) formed by the optical path length difference (OPD) between polarization modes of Ti-diffused waveguide on LiNbO₃ substrate and a vertically integrated As₂S₃ race-track ring resonator on top of the substrate. The figures of merit of the device, enhancement of the signal-to-3rd order intermodulation distortion (IMD3) power ratio and corresponding 3rd order intercept point (IP3) over a traditional MZI, are demonstrated through device characterization.
Graphical methods for the sensitivity analysis in discriminant analysis

DOE PAGES

Kim, Youngil; Anderson-Cook, Christine M.; Dae-Heung, Jang

2015-09-30

Similar to regression, many measures to detect influential data points in discriminant analysis have been developed. Many follow similar principles as the diagnostic measures used in linear regression in the context of discriminant analysis. Here we focus on the impact on the predicted classification posterior probability when a data point is omitted. The new method is intuitive and easily interpretative compared to existing methods. We also propose a graphical display to show the individual movement of the posterior probability of other data points when a specific data point is omitted. This enables the summaries to capture the overall pattern ofmore » the change.« less
Application of an oscillation-type linear cadmium telluride detector to enhanced gadolinium K-edge computed tomography

NASA Astrophysics Data System (ADS)

Matsukiyo, Hiroshi; Sato, Eiichi; Hagiwara, Osahiko; Abudurexiti, Abulajiang; Osawa, Akihiro; Enomoto, Toshiyuki; Watanabe, Manabu; Nagao, Jiro; Sato, Shigehiro; Ogawa, Akira; Onagawa, Jun

2011-03-01

A linear cadmium telluride (CdTe) detector is useful for carrying out energy-discrimination X-ray imaging, including computed tomography (CT). To perform enhanced gadolinium K-edge CT, we used an oscillation-type linear CdTe detector with an energy resolution of 1.2 keV. CT is performed by repeating the linear scan and the rotation of an object. Penetrating X-ray photons from the object are detected by the CdTe detector, and event signals of X-ray photons are produced using charge-sensitive and shaping amplifiers. Both the photon energy and the energy width are selected using a multichannel analyzer, and the number of photons is counted by a counter card. In energy-discrimination CT, tube voltage and current were 80 kV and 20 μA, respectively, and X-ray intensity was 1.55 μGy/s at 1.0 m from the source at a tube voltage of 80 kV. Demonstration of enhanced gadolinium K-edge X-ray CT was carried out by selecting photons with energies just beyond gadolinium K-edge energy of 50.3 keV.
Changes in Experiences With Discrimination Across Pregnancy and Postpartum: Age Differences and Consequences for Mental Health

PubMed Central

Earnshaw, Valerie A.; Lewis, Tené T.; Reid, Allecia E.; Lewis, Jessica B.; Stasko, Emily C.; Tobin, Jonathan N.; Ickovics, Jeannette R.

2015-01-01

Objectives. We aimed to contribute to growing research and theory suggesting the importance of examining patterns of change over time and critical life periods to fully understand the effects of discrimination on health, with a focus on the period of pregnancy and postpartum and mental health outcomes. Methods. We used hierarchical linear modeling to examine changes across pregnancy and postpartum in everyday discrimination and the resulting consequences for mental health among predominantly Black and Latina, socioeconomically disadvantaged young women who were receiving prenatal care in New York City. Results. Patterns of change in experiences with discrimination varied according to age. Among the youngest participants, discrimination increased from the second to third trimesters and then decreased to lower than the baseline level by 1 year postpartum; among the oldest participants, discrimination decreased from the second trimester to 6 months postpartum and then returned to the baseline level by 1 year postpartum. Within-subjects changes in discrimination over time predicted changes in depressive and anxiety symptoms at subsequent points. Discrimination more strongly predicted anxiety symptoms among participants reporting food insecurity. Conclusions. Our results support a life course approach to understanding the impact of experiences with discrimination on health and when to intervene. PMID:24922166
Linear discriminant analysis based on L1-norm maximization.

PubMed

Zhong, Fujin; Zhang, Jiashu

2013-08-01

Linear discriminant analysis (LDA) is a well-known dimensionality reduction technique, which is widely used for many purposes. However, conventional LDA is sensitive to outliers because its objective function is based on the distance criterion using L2-norm. This paper proposes a simple but effective robust LDA version based on L1-norm maximization, which learns a set of local optimal projection vectors by maximizing the ratio of the L1-norm-based between-class dispersion and the L1-norm-based within-class dispersion. The proposed method is theoretically proved to be feasible and robust to outliers while overcoming the singular problem of the within-class scatter matrix for conventional LDA. Experiments on artificial datasets, standard classification datasets and three popular image databases demonstrate the efficacy of the proposed method.
Spectral discrimination of lithologic facies in the granite of the Pedra Branca Goias using LANDSAT 1 digital imagery

NASA Technical Reports Server (NTRS)

Parada, N. D. J.; Almeido, R., Jr.

1982-01-01

The applicability of LANDSAT MSS imagery for discriminating geobotanical associations observed in zones of cassiterite-rich metasomatic alterations in the granitic body of Serra da Pedra Branca was investigated. Computer compatible tapes of dry and rainy season imagery were analyzed. Image enlargement, corrections, linear contrast stretch, and ratioing of noncorrelated spectral bands were performed using the Image 100 with a grey scale of 256 levels between zero and 255. Only bands 5 and 7 were considered. Band ratioing of noncorrelated channels (5 and 7) of rainy season imagery permits distinction of areas with different vegetation coverage percentage, which corresponds to geobotanial associations in the area studied. The linear contrast stretch of channel 5, especially of the dry season image is very unsatisfactory in this area.
Comparative study on fast classification of brick samples by combination of principal component analysis and linear discriminant analysis using stand-off and table-top laser-induced breakdown spectroscopy

NASA Astrophysics Data System (ADS)

Vítková, Gabriela; Prokeš, Lubomír; Novotný, Karel; Pořízka, Pavel; Novotný, Jan; Všianský, Dalibor; Čelko, Ladislav; Kaiser, Jozef

2014-11-01

Focusing on historical aspect, during archeological excavation or restoration works of buildings or different structures built from bricks it is important to determine, preferably in-situ and in real-time, the locality of bricks origin. Fast classification of bricks on the base of Laser-Induced Breakdown Spectroscopy (LIBS) spectra is possible using multivariate statistical methods. Combination of principal component analysis (PCA) and linear discriminant analysis (LDA) was applied in this case. LIBS was used to classify altogether the 29 brick samples from 7 different localities. Realizing comparative study using two different LIBS setups - stand-off and table-top it is shown that stand-off LIBS has a big potential for archeological in-field measurements.
The association between income, education, and experiences of discrimination in older African American and European American patients.

PubMed

Halanych, Jewell H; Safford, Monika M; Shikany, James M; Cuffee, Yendelela; Person, Sharina D; Scarinci, Isabel C; Kiefe, Catarina I; Allison, Jeroan J

2011-01-01

Racial/ethnic discrimination has adverse effects on health outcomes, as does low income and education, but the relationship between discrimination, income, and education is not well characterized. In this study, we describe the associations of discrimination with income and education in elderly African Americans (AA) and European Americans (EA). Cross-sectional observational study involving computer-assisted telephone survey. Southeastern United States. AA and EA Medicare managed care enrollees. Discrimination was measured with the Experience of Discrimination (EOD) scale (range 0-35). We used zero-inflated negative binomial models to determine the association between self-reported income and education and 1) presence of any discrimination and 2) intensity of discrimination. Among 1,800 participants (45% AA, 56% female, and mean age 73 years), EA reported less discrimination than AA (4% vs. 47%; P < .001). AA men reported more discrimination and more intense discrimination than AA women (EOD scores 4.35 vs. 2.50; P < .001). Both income and education were directly and linearly associated with both presence of discrimination and intensity of discrimination in AA, so that people with higher incomes and education experienced more discrimination. In adjusted models, predicted EOD scores among AA decreased with increasing age categories (3.42, 3.21, 2.99, 2.53; P < .01) and increased with increasing income (2.36, 3.44, 4.17; P < .001) and education categories (2.31, 3.09, 5.12; P < .001). This study suggests future research should focus less on differences between racial/ethnic groups and more on factors within minority populations that may contribute to healthcare disparities.
The Effect of Aging and Severity of Sleep Apnea on Heart Rate Variability Indices in Obstructive Sleep Apnea Syndrome

PubMed Central

Song, Man-Kyu; Ha, Jee Hyun; Ryu, Seung-Ho; Yu, Jaehak

2012-01-01

Objective This study aims to analyze how much heart rate variability (HRV) indices discriminatively respond to age and severity of sleep apnea in the obstructive sleep apnea syndrome (OSAS). Methods 176 male OSAS patients were classified into four groups according to their age and apnea-hypopnea index (AHI). The HRV indices were compared via analysis of covariance (ANCOVA). In particular, the partial correlation method was performed to identify the most statistically significant HRV indices in the time and frequency domains. Stepwise multiple linear regressions were further executed to examine the effects of age, AHI, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), and sleep parameters on the significant HRV indices. Results The partial correlation analysis yielded the NN50 count (defined as the number of adjacent R-wave to R-wave intervals differing by more than 50 ms) and low frequency/high frequency (LF/HF) ratio to be two most statistically significant HRV indices in both time and frequency domains. The two indices showed significant differences between the groups. The NN50 count was affected by age (p<0.001) and DBP (p=0.039), while the LF/HF ratio was affected by AHI (p<0.001), the amount of Stage 2 sleep (p=0.005), and age (p=0.021) in the order named in the regression analysis. Conclusion The NN50 count more sensitively responded to age than to AHI, suggesting that the index is mainly associated with an age-related parasympathetic system. On the contrary, the LF/HF ratio responded to AHI more sensitively than to age, suggesting that it is mainly associated with a sympathetic tone likely reflecting the severity of sleep apnea. PMID:22396687
Imaging pathologic pulmonary air and fluid accumulation by functional and absolute EIT.

PubMed

Hahn, G; Just, A; Dudykevych, T; Frerichs, I; Hinz, J; Quintel, M; Hellige, G

2006-05-01

The increasing use of EIT in clinical research on severely ill lung patients requires a clarification of the influence of pathologic impedance distributions on the validity of the resulting tomograms. Significant accumulation of low-conducting air (e.g. pneumothorax or emphysema) or well-conducting liquid (e.g. haematothorax or atelectases) may conflict with treating the imaging problem as purely linear. First, we investigated the influence of stepwise inflation and deflation by up to 300 ml of air and 300 ml of Ringer solution into the pleural space of five pigs on the resulting tomograms during ventilation at constant tidal volume. Series of EIT images representing relative impedance changes were generated on the basis of a modified Sheffield back projection algorithm and ventilation distribution was displayed as functional (f-EIT) tomograms. In addition, a modified simultaneous iterative reconstruction technique (SIRT) was applied to quantify the resistivity distribution on an absolute level scaled in Omega m (a-EIT). Second, we applied these two EIT techniques on four intensive care patients with inhomogeneous air and fluid distribution and compared the EIT results to computed tomography (CT) and to a reference set of intrathoracic resistivity data of 20 healthy volunteers calculated by SIRT. The results of the animal model show that f-EIT based on back projection is not disturbed by the artificial pneumo- or haematothorax. Application of SIRT allows reliable discrimination and detection of the location and amplitude of pneumo- or haematothorax. These results were supported by the good agreement between the electrical impedance tomograms and CT scans on patients and by the significant differences of regional resistivity data between patients and healthy volunteers.
Functional Brain Connectivity as a New Feature for P300 Speller.

PubMed

Kabbara, Aya; Khalil, Mohamad; El-Falou, Wassim; Eid, Hassan; Hassan, Mahmoud

2016-01-01

The brain is a large-scale complex network often referred to as the "connectome". Cognitive functions and information processing are mainly based on the interactions between distant brain regions. However, most of the 'feature extraction' methods used in the context of Brain Computer Interface (BCI) ignored the possible functional relationships between different signals recorded from distinct brain areas. In this paper, the functional connectivity quantified by the phase locking value (PLV) was introduced to characterize the evoked responses (ERPs) obtained in the case of target and non-targets visual stimuli. We also tested the possibility of using the functional connectivity in the context of 'P300 speller'. The proposed approach was compared to the well-known methods proposed in the state of the art of "P300 Speller", mainly the peak picking, the area, time/frequency based features, the xDAWN spatial filtering and the stepwise linear discriminant analysis (SWLDA). The electroencephalographic (EEG) signals recorded from ten subjects were analyzed offline. The results indicated that phase synchrony offers relevant information for the classification in a P300 speller. High synchronization between the brain regions was clearly observed during target trials, although no significant synchronization was detected for a non-target trial. The results showed also that phase synchrony provides higher performance than some existing methods for letter classification in a P300 speller principally when large number of trials is available. Finally, we tested the possible combination of both approaches (classical features and phase synchrony). Our findings showed an overall improvement of the performance of the P300-speller when using Peak picking, the area and frequency based features. Similar performances were obtained compared to xDAWN and SWLDA when using large number of trials.
Puberty timing and fluid intelligence: a study of correlations between testosterone and intelligence in 8- to 12-year-old Chinese boys.

PubMed

Shangguan, Fangfang; Shi, Jiannong

2009-08-01

Sex hormone such as testosterone was recently recognized as an important contributor of spatial cognition and intelligence during development, but the relationship between puberty timing and intelligence especially in children is largely unknown. Here in this study, we investigated the potential relationship between the level of sex hormones in saliva and fluid intelligence in 8- to 12-year-old Chinese boys. Fluid intelligence was measured by the Cattell's Culture Fair Intelligence Test. 1600 children aged 8-12 years were included in the Cattell's Culture Fair Intelligence Test and saliva samples were collected thereafter from 166 boys with normal intelligence distribution, composed of 49, 54 and 63 boys in 8-, 10- and 12-year-old group respectively. The level of salivary testosterone and estradiol was measured with enzyme-immunoassay technique. Data of BMI and age were collected. The relationship between the level of salivary sex hormones and fluid intelligence was analysed by correlation test. There was no significant correlation between salivary testosterone level and fluid intelligence in 8-year-old boys, whereas there was a significant positive correlation in 10-year-old boys and a significant negative correlation in 12-year-old boys between those two variable. To verify the correlation, we performed stepwise multivariate linear regression and discriminant analysis, with both the age and BMI of the boys and their parents, and salivary estradiol level considered. The results showed that the level of testosterone and intelligence was correlated, and the correlation was much stronger when the level of salivary testosterone was higher than 14 pg/ml. In summary, the study suggests that the relationship of testosterone and intelligence varies from late childhood to early adolescence, and the puberty timing is closely related with fluid intelligence.
Computer-aided detection of bladder mass within non-contrast-enhanced region of CT Urography (CTU)

NASA Astrophysics Data System (ADS)

Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.; Weizer, Alon; Zhou, Chuan

2016-03-01

We are developing a computer-aided detection system for bladder cancer in CT urography (CTU). We have previously developed methods for detection of bladder masses within the contrast-enhanced region of the bladder. In this study, we investigated methods for detection of bladder masses within the non-contrast enhanced region. The bladder was first segmented using a newly developed deep-learning convolutional neural network in combination with level sets. The non-contrast-enhanced region was separated from the contrast-enhanced region with a maximum-intensityprojection- based method. The non-contrast region was smoothed and a gray level threshold was employed to segment the bladder wall and potential masses. The bladder wall was transformed into a straightened thickness profile, which was analyzed to identify lesion candidates as a prescreening step. The lesion candidates were segmented using our autoinitialized cascaded level set (AI-CALS) segmentation method, and 27 morphological features were extracted for each candidate. Stepwise feature selection with simplex optimization and leave-one-case-out resampling were used for training and validation of a false positive (FP) classifier. In each leave-one-case-out cycle, features were selected from the training cases and a linear discriminant analysis (LDA) classifier was designed to merge the selected features into a single score for classification of the left-out test case. A data set of 33 cases with 42 biopsy-proven lesions in the noncontrast enhanced region was collected. During prescreening, the system obtained 83.3% sensitivity at an average of 2.4 FPs/case. After feature extraction and FP reduction by LDA, the system achieved 81.0% sensitivity at 2.0 FPs/case, and 73.8% sensitivity at 1.5 FPs/case.
Computer-aided classification of breast microcalcification clusters: merging of features from image processing and radiologists

NASA Astrophysics Data System (ADS)

Lo, Joseph Y.; Gavrielides, Marios A.; Markey, Mia K.; Jesneck, Jonathan L.

2003-05-01

We developed an ensemble classifier for the task of computer-aided diagnosis of breast microcalcification clusters,which are very challenging to characterize for radiologists and computer models alike. The purpose of this study is to help radiologists identify whether suspicious calcification clusters are benign vs. malignant, such that they may potentially recommend fewer unnecessary biopsies for actually benign lesions. The data consists of mammographic features extracted by automated image processing algorithms as well as manually interpreted by radiologists according to a standardized lexicon. We used 292 cases from a publicly available mammography database. From each cases, we extracted 22 image processing features pertaining to lesion morphology, 5 radiologist features also pertaining to morphology, and the patient age. Linear discriminant analysis (LDA) models were designed using each of the three data types. Each local model performed poorly; the best was one based upon image processing features which yielded ROC area index AZ of 0.59 +/- 0.03 and partial AZ above 90% sensitivity of 0.08 +/- 0.03. We then developed ensemble models using different combinations of those data types, and these models all improved performance compared to the local models. The final ensemble model was based upon 5 features selected by stepwise LDA from all 28 available features. This ensemble performed with AZ of 0.69 +/- 0.03 and partial AZ of 0.21 +/- 0.04, which was statistically significantly better than the model based on the image processing features alone (p<0.001 and p=0.01 for full and partial AZ respectively). This demonstrated the value of the radiologist-extracted features as a source of information for this task. It also suggested there is potential for improved performance using this ensemble classifier approach to combine different sources of currently available data.
Multivariate analysis of sexual size dimorphism in local turkeys (Meleagris gallopavo) in Nigeria.

PubMed

Ajayi, Oyeyemi O; Yakubu, Abdulmojeed; Jayeola, Oluwaseun O; Imumorin, Ikhide G; Takeet, Michael I; Ozoje, Michael O; Ikeobi, Christian O N; Peters, Sunday O

2012-06-01

Sexual size dimorphism is a key evolutionary feature that can lead to important biological insights. To improve methods of sexing live birds in the field, we assessed sexual size dimorphism in Nigerian local turkeys (Meleagris gallopavo) using multivariate techniques. Measurements were taken on 125 twenty-week-old birds reared under the intensive management system. The body parameters measured were body weight, body length, breast girth, thigh length, shank length, keel length, wing length and wing span. Univariate analysis revealed that toms (males) had significantly (P < 0.05) higher mean values than hens (females) in all the measured traits. Positive phenotypic correlations between body weight and body measurements ranged from 0.445 to 0.821 in toms and 0.053-0.660 in hens, respectively. Three principal components (PC1, PC2 and PC3) were extracted in toms, each accounting for 63.70%, 19.42% and 5.72% of the total variance, respectively. However, four principal components (PC1, PC2, PC3 and PC4) were extracted in hens, which explained 54.03%, 15.29%, 11.68% and 6.95%, respectively of the generalised variance. A stepwise discriminant function analysis of the eight morphological traits indicated that body weight, body length, tail length and wing span were the most discriminating variables in separating the sexes. The single discriminant function obtained was able to correctly classify 100% of the birds into their source population. The results obtained from the present study could aid future management decisions, ecological studies and conservation of local turkeys in a developing economy.
Predicting death from kala-azar: construction, development, and validation of a score set and accompanying software.

PubMed

Costa, Dorcas Lamounier; Rocha, Regina Lunardi; Chaves, Eldo de Brito Ferreira; Batista, Vivianny Gonçalves de Vasconcelos; Costa, Henrique Lamounier; Costa, Carlos Henrique Nery

2016-01-01

Early identification of patients at higher risk of progressing to severe disease and death is crucial for implementing therapeutic and preventive measures; this could reduce the morbidity and mortality from kala-azar. We describe a score set composed of four scales in addition to software for quick assessment of the probability of death from kala-azar at the point of care. Data from 883 patients diagnosed between September 2005 and August 2008 were used to derive the score set, and data from 1,031 patients diagnosed between September 2008 and November 2013 were used to validate the models. Stepwise logistic regression analyses were used to derive the optimal multivariate prediction models. Model performance was assessed by its discriminatory accuracy. A computational specialist system (Kala-Cal(r)) was developed to speed up the calculation of the probability of death based on clinical scores. The clinical prediction score showed high discrimination (area under the curve [AUC] 0.90) for distinguishing death from survival for children ≤2 years old. Performance improved after adding laboratory variables (AUC 0.93). The clinical score showed equivalent discrimination (AUC 0.89) for older children and adults, which also improved after including laboratory data (AUC 0.92). The score set also showed a high, although lower, discrimination when applied to the validation cohort. This score set and Kala-Cal(r) software may help identify individuals with the greatest probability of death. The associated software may speed up the calculation of the probability of death based on clinical scores and assist physicians in decision-making.

LCFIPlus: A framework for jet analysis in linear collider studies

NASA Astrophysics Data System (ADS)

Suehara, Taikan; Tanabe, Tomohiko

2016-02-01

We report on the progress in flavor identification tools developed for a future e+e- linear collider such as the International Linear Collider (ILC) and Compact Linear Collider (CLIC). Building on the work carried out by the LCFIVertex collaboration, we employ new strategies in vertex finding and jet finding, and introduce new discriminating variables for jet flavor identification. We present the performance of the new algorithms in the conditions simulated using a detector concept designed for the ILC. The algorithms have been successfully used in ILC physics simulation studies, such as those presented in the ILC Technical Design Report.
Physical disability, life stress, and psychosocial adjustment in multiple sclerosis.

PubMed

Zeldow, P B; Pavlou, M

1984-02-01

Eighty-one outpatients with diagnosed multiple sclerosis were studied in an effort to examine the relative contributions of physical health status, life stress, duration of illness, age, sex, marital status, and social class on various aspects of personal and interpersonal functioning. Stepwise multiple regression analyses were performed to identify the most significant discriminators of the seven psychosocial measures. Physical health status exerted the broadest influence, affecting personal efficiency and well-being, capacity for independent thought and action, self-confidence, self-reliance, and number of meaningful social contacts. Life stress was associated with lowered personal efficiency and sense of well-being. Duration of illness and the demographic variables had few or no effects on psychosocial adjustment. Discussion contrasts the present findings with others in the rehabilitation literature and specifies certain limitations of the study's design.
Consumption value theory and the marketing of public health: an effective formative research tool.

PubMed

Nelson, Douglas G; Byus, Kent

2002-01-01

Contemporary public health requires the support and participation of its constituency. This study assesses the capacity of consumption value theory to identify the basis of this support. A telephone survey design used simple random sampling of adult residents of Cherokee County, Oklahoma. Factor analysis and stepwise discriminant analysis was used to identify and classify personal and societal level support variables. Most residents base societal level support on epistemic values. Direct services clientele base their support on positive emotional values derived from personal contact and attractive programs. Residents are curious about public health and want to know more about the health department. Where marketing the effectiveness of public health programs would yield relatively little support, marketing health promotion activities may attract public opposition. This formative research tool suggests a marketing strategy for public health practitioners.
[An examination of the determinants of social withdrawal and affinity for social withdrawal].

PubMed

Watanabe, Asami; Matsui, Yutaka; Takatsuka, Yusuke

2010-12-01

This study examined the determinants of social withdrawal using data from a survey by the Tokyo Metropolitan Government Office for Youth Affairs and Public Safety (2008). In addition, this study identified young people who showed an affinity for social withdrawal although they were not in a state of withdrawal, and examined the determinants of an affinity for social withdrawal. The results of stepwise discriminant analysis showed that factors such as social phobia, depression, violence, and emotional bonds with family differentiated between the general youth group and the social withdrawal group and the "affinity group". Social phobia, violence, and refusal to be interfered in self-decision making differentiated between the social withdrawal group and the "affinity group". This study shows that an "affinity group" should be cared as well as an actual withdrawal group.
Stepwise pumping approach to improve free phase light hydrocarbon recovery from unconfined aquifers

NASA Astrophysics Data System (ADS)

Cooper, Grant S.; Peralta, Richard C.; Kaluarachchi, Jagath J.

1995-04-01

A stepwise, time-varying pumping approach is developed to improve free phase oil recovery of light non-aqueous phase liquids (LNAPL) from a homogeneous, unconfined aquifer. Stepwise pumping is used to contain the floating oil plume and obtain efficient free oil recovery. The graphical plots. The approach uses ARMOS ©, an areal two-dimensional multiphase flow, finite-element simulation model. Systematic simulations of free oil area changes to pumping rates are analyzed. Pumping rates are determined that achieve LNAPL plume containment at different times (i.e. 90, 180 and 360 days) for a planning period of 360 days. These pumping rates are used in reverse order as a stepwise (monotonically increasing) pumping strategy. This stepwise pumping strategy is analyzed further by performing additional simulations at different pumping rates for the last pumping period. The final stepwise pumping strategy is varied by factors of -25% and +30% to evaluate sensitivity in the free oil recovery process. Stepwise pumping is compared to steady pumping rates to determine the best free oil recovery strategy. Stepwise pumping is shown to improve oil recovery by increasing recoveredoil volume (11%) and decreasing residual oil (15%) when compared with traditional steady pumping strategies. The best stepwise pumping strategy recovers more free oil by reducing the amount of residual oil left in the system due to pumping drawdown. This stepwise pumping pproach can be used to enhance free oil recovery and provide for cost-effective design and management of LNAPL cleanup.
Adaptive velocity-based six degree of freedom load control for real-time unconstrained biomechanical testing.

PubMed

Lawless, I M; Ding, B; Cazzolato, B S; Costi, J J

2014-09-22

Robotic biomechanics is a powerful tool for further developing our understanding of biological joints, tissues and their repair. Both velocity-based and hybrid force control methods have been applied to biomechanics but the complex and non-linear properties of joints have limited these to slow or stepwise loading, which may not capture the real-time behaviour of joints. This paper presents a novel force control scheme combining stiffness and velocity based methods aimed at achieving six degree of freedom unconstrained force control at physiological loading rates. Copyright © 2014 Elsevier Ltd. All rights reserved.
Racial discrimination associated with higher diastolic blood pressure in a sample of American Indian adults

PubMed Central

Thayer, Zaneta M.; Blair, Irene V.; Buchwald, Dedra S.; Manson, Spero M.

2017-01-01

Objectives Hypertension prevalence is high among American Indians (AIs). AIs experience a substantial burden of interpersonal racial discrimination, which in other populations has been associated with higher blood pressure. The purpose of this study is to understand whether racial discrimination experiences are associated with higher blood pressure in AIs. Materials and Methods We used the Everyday Discrimination Scale to evaluate the relationship between discrimination and measured blood pressure among 77 AIs from two reservation communities in the Northern Plains. We used multivariate linear regression to evaluate the association of racial discrimination with systolic and diastolic blood pressure, respectively. Racial discrimination, systolic blood pressure, and diastolic blood pressure were analyzed as continuous variables. All analyses adjusted for sex, waist circumference, age, posttraumatic stress disorder status, and education. Results We found that 61% of participants experienced discrimination that they attributed to their race or ancestry. Racial discrimination was associated with significantly higher diastolic blood pressure (β = 0.22, SE = 0.09, P = 0.02), and with a similar non-significant trend toward higher systolic blood pressure (β = 0.25, SE = 0.15, P = 0.09). Conclusion The results of this analysis suggest that racial discrimination may contribute to higher diastolic blood pressure within Native communities. These findings highlight one pathway through which the social environment can shape patterns of biology and health in AI and other socially and politically marginalized groups. PMID:28198537
Multifactorial discrimination as a fundamental cause of mental health inequities.

PubMed

Khan, Mariam; Ilcisin, Misja; Saxton, Katherine

2017-03-04

The theory of fundamental causes explains why health disparities persist over time, even as risk factors, mechanisms, and diseases change. Using an intersectional framework, we evaluated multifactorial discrimination as a fundamental cause of mental health disparities. Using baseline data from the Project STRIDE: Stress, Identity, and Mental Health study, we examined the health effects of discrimination among individuals who self-identified as lesbian, gay, or bisexual. We used logistic and linear regression to assess whether multifactorial discrimination met the four criteria designating a fundamental cause, namely that the cause: 1) influences multiple health outcomes, 2) affects multiple risk factors, 3) involves access to resources that can be leveraged to reduce consequences of disease, and 4) reproduces itself in varied contexts through changing mechanisms. Multifactorial discrimination predicted high depression scores, psychological well-being, and substance use disorder diagnosis. Discrimination was positively associated with risk factors for high depression scores: chronic strain and total number of stressful life events. Discrimination was associated with significantly lower levels of mastery and self-esteem, protective factors for depressive symptomatology. Even after controlling for risk factors, discrimination remained a significant predictor for high depression scores. Among subjects with low depression scores, multifactorial discrimination also predicted anxiety and aggregate mental health scores. Multifactorial discrimination should be considered a fundamental cause of mental health inequities and may be an important cause of broad health disparities among populations with intersecting social identities.
An analysis of the relationship of seven selected variables to State Board Test Pool Examination performance of the University of Tennessee, Knoxville, College of Nursing.

PubMed

Sharp, T G

1984-02-01

The study was designed to determine whether any one of seven selected variables or a combination of the variables is predictive of performance on the State Board Test Pool Examination. The selected variables studied were: high school grade point average (HSGPA), The University of Tennessee, Knoxville, College of Nursing grade point average (GPA), and American College Test Assessment (ACT) standard scores (English, ENG; mathematics, MA; social studies, SS; natural sciences, NSC; composite, COMP). Data utilized were from graduates of the baccalaureate program of The University of Tennessee, Knoxville, College of Nursing from 1974 through 1979. The sample of 322 was selected from a total population of 572. The Statistical Analysis System (SAS) was designed to accomplish analysis of the predictive relationship of each of the seven selected variables to State Board Test Pool Examination performance (result of pass or fail), a stepwise discriminant analysis was designed for determining the predictive relationship of the strongest combination of the independent variables to overall State Board Test Pool Examination performance (result of pass or fail), and stepwise multiple regression analysis was designed to determine the strongest predictive combination of selected variables for each of the five subexams of the State Board Test Pool Examination. The selected variables were each found to be predictive of SBTPE performance (result of pass or fail). The strongest combination for predicting SBTPE performance (result of pass or fail) was found to be GPA, MA, and NSC.
Biometric parameters in different stages of primary angle closure using low-coherence interferometry.

PubMed

Yazdani, Shahin; Akbarian, Shadi; Pakravan, Mohammad; Doozandeh, Azadeh; Afrouzifar, Mohsen

2015-03-01

To compare ocular biometric parameters using low-coherence interferometry among siblings affected with different degrees of primary angle closure (PAC). In this cross-sectional comparative study, a total of 170 eyes of 86 siblings from 47 families underwent low-coherence interferometry (LenStar 900; Haag-Streit, Koeniz, Switzerland) to determine central corneal thickness, anterior chamber depth (ACD), aqueous depth (AD), lens thickness (LT), vitreous depth, and axial length (AL). Regression coefficients were applied to show the trend of the measured variables in different stages of angle closure. To evaluate the discriminative power of the parameters, receiver operating characteristic curves were used. Best cutoff points were selected based on the Youden index. Sensitivity, specificity, positive and negative predicative values, positive and negative likelihood ratios, and diagnostic accuracy were determined for each variable. All biometric parameters changed significantly from normal eyes to PAC suspects, PAC, and PAC glaucoma; there was a significant stepwise decrease in central corneal thickness, ACD, AD, vitreous depth, and AL, and an increase in LT and LT/AL. Anterior chamber depth and AD had the best diagnostic power for detecting angle closure; best levels of sensitivity and specificity were obtained with cutoff values of 3.11 mm for ACD and 2.57 mm for AD. Biometric parameters measured by low-coherence interferometry demonstrated a significant and stepwise change among eyes affected with various degrees of angle closure. Although the current classification scheme for angle closure is based on anatomical features, it has excellent correlation with biometric parameters.
Potential use of ionic species for identifying source land-uses of stormwater runoff.

PubMed

Lee, Dong Hoon; Kim, Jin Hwi; Mendoza, Joseph A; Lee, Chang-Hee; Kang, Joo-Hyon

2017-02-01

Identifying critical land-uses or source areas is important to prioritize resources for cost-effective stormwater management. This study investigated the use of information on ionic composition as a fingerprint to identify the source land-use of stormwater runoff. We used 12 ionic species in stormwater runoff monitored for a total of 20 storm events at five sites with different land-use compositions during the 2012-2014 wet seasons. A stepwise forward discriminant function analysis (DFA) with the jack-knifed cross validation approach was used to select ionic species that better discriminate the land-use of its source. Of the 12 ionic species, 9 species (K + , Mg 2+ , Na + , NH 4 + , Br - , Cl - , F - , NO 2 - , and SO 4 2- ) were selected for better performance of the DFA. The DFA successfully differentiated stormwater samples from urban, rural, and construction sites using concentrations of the ionic species (70%, 95%, and 91% of correct classification, respectively). Over 80% of the new data cases were correctly classified by the trained DFA model. When applied to data cases from a mixed land-use catchment and downstream, the DFA model showed the greater impact of urban areas and rural areas respectively in the earlier and later parts of a storm event.
Young swimmers' classification based on kinematics, hydrodynamics, and anthropometrics.

PubMed

Barbosa, Tiago M; Morais, Jorge E; Costa, Mário J; Goncalves, José; Marinho, Daniel A; Silva, António J

2014-04-01

The aim of this article has been to classify swimmers based on kinematics, hydrodynamics, and anthropometrics. Sixty-seven young swimmers made a maximal 25 m front-crawl to measure with a speedometer the swimming velocity (v), speed-fluctuation (dv) and dv normalized to v (dv/v). Another two 25 m bouts with and without carrying a perturbation device were made to estimate active drag coefficient (CDa). Trunk transverse surface area (S) was measured with photogrammetric technique on land and in the hydrodynamic position. Cluster 1 was related to swimmers with a high speed fluctuation (ie, dv and dv/v), cluster 2 with anthropometrics (ie, S) and cluster 3 with a high hydrodynamic profile (ie, CDa). The variable that seems to discriminate better the clusters was the dv/v (F=53.680; P<.001), followed by the dv (F=28.506; P<.001), CDa (F=21.025; P<.001), S (F=6.297; P<.01) and v (F=5.375; P=.01). Stepwise discriminant analysis extracted 2 functions: Function 1 was mainly defined by dv/v and S (74.3% of variance), whereas function 2 was mainly defined by CDa (25.7% of variance). It can be concluded that kinematics, hydrodynamics and anthropometrics are determinant domains in which to classify and characterize young swimmers' profiles.
Diagnostic features of Alzheimer's disease extracted from PET sinograms

NASA Astrophysics Data System (ADS)

Sayeed, A.; Petrou, M.; Spyrou, N.; Kadyrov, A.; Spinks, T.

2002-01-01

Texture analysis of positron emission tomography (PET) images of the brain is a very difficult task, due to the poor signal to noise ratio. As a consequence, very few techniques can be implemented successfully. We use a new global analysis technique known as the Trace transform triple features. This technique can be applied directly to the raw sinograms to distinguish patients with Alzheimer's disease (AD) from normal volunteers. FDG-PET images of 18 AD and 10 normal controls obtained from the same CTI ECAT-953 scanner were used in this study. The Trace transform triple feature technique was used to extract features that were invariant to scaling, translation and rotation, referred to as invariant features, as well as features that were sensitive to rotation but invariant to scaling and translation, referred to as sensitive features in this study. The features were used to classify the groups using discriminant function analysis. Cross-validation tests using stepwise discriminant function analysis showed that combining both sensitive and invariant features produced the best results, when compared with the clinical diagnosis. Selecting the five best features produces an overall accuracy of 93% with sensitivity of 94% and specificity of 90%. This is comparable with the classification accuracy achieved by Kippenhan et al (1992), using regional metabolic activity.
Multivariate Profiles of Selected versus Non-Selected Elite Youth Brazilian Soccer Players

PubMed Central

Alves, Isabella S.; Padilha, Maickel B.; Casanova, Filipe; Puggina, Enrico F.; Maia, José

2017-01-01

Abstract This study determined whether a multivariate profile more effectively discriminated selected than non-selected elite youth Brazilian soccer players. This examination was carried out on 66 youth soccer players (selected, n = 28, mean age 16.3 ± 0.1; non-selected, n = 38, mean age 16.7 ± 0.4) using objective instruments. Multivariate profiles were assessed through anthropometric characteristics, biological maturation, tactical-technical skills, and motor performance. The Student’s t-test identified that selected players exhibited significantly higher values for height (t = 2.331, p = 0.02), lean body mass (t = 2.441, p = 0.01), and maturity offset (t = 4.559, p < 0.001), as well as performed better in declarative tactical knowledge (t = 10.484, p < 0.001), shooting (t = 2.188, p = 0.03), dribbling (t = 5.914, p < 0.001), speed – 30 m (t = 8.304, p < 0.001), countermovement jump (t = 2.718, p = 0.008), and peak power tests (t = 2.454, p = 0.01). Forward stepwise discriminant function analysis showed that declarative tactical knowledge, running speed –30 m, maturity offset, dribbling, height, and peak power correctly classified 97% of the selected players. These findings may have implications for a highly efficient selection process with objective measures of youth players in soccer clubs. PMID:29339991
Understanding college students' fruit consumption. Integrating habit strength in the theory of planned behaviour.

PubMed

de Bruijn, Gert-Jan

2010-02-01

The additive and interactive effect of habit strength in the explanation of young adults' fruit consumption was studied within the context of the theory of planned behaviour (TPB). Additionally, behavioural and control beliefs were modelled as predictors of profile membership based on current fruit consumption, motivation and habit strength towards fruit consumption. Cross-sectional data were available from undergraduate students (n=538; mean age=21.19; S.D.=2.57) who completed measures of fruit consumption, habit strength, TPB-concepts, and behavioural and control beliefs. Data were analyzed using stepwise regression analysis, simple slope analysis, and discriminant function analysis. Results showed that, based on a significant intention x habit interaction (beta=.13), the intention-fruit consumption relationship was more than twice as strong at low levels of habit strength (beta=.39) than at high levels of habit strength (beta=.16). Furthermore, beliefs regarding health and weight management were relatively unable to distinguish profiles created from motivation, habit strength and current fruit consumption. Rather, beliefs about controllability of fruit consumption were amongst the most consistent discriminating beliefs. Findings suggest that stronger fruit consumption habits make fruit consumption less intentional and that interventions aiming to increase fruit consumption may need to develop persuasive messages focusing on situational beliefs, rather than emphasizing health outcomes. 2009 Elsevier Ltd. All rights reserved.
Classification and Progression Based on CFS-GA and C5.0 Boost Decision Tree of TCM Zheng in Chronic Hepatitis B.

PubMed

Chen, Xiao Yu; Ma, Li Zhuang; Chu, Na; Zhou, Min; Hu, Yiyang

2013-01-01

Chronic hepatitis B (CHB) is a serious public health problem, and Traditional Chinese Medicine (TCM) plays an important role in the control and treatment for CHB. In the treatment of TCM, zheng discrimination is the most important step. In this paper, an approach based on CFS-GA (Correlation based Feature Selection and Genetic Algorithm) and C5.0 boost decision tree is used for zheng classification and progression in the TCM treatment of CHB. The CFS-GA performs better than the typical method of CFS. By CFS-GA, the acquired attribute subset is classified by C5.0 boost decision tree for TCM zheng classification of CHB, and C5.0 decision tree outperforms two typical decision trees of NBTree and REPTree on CFS-GA, CFS, and nonselection in comparison. Based on the critical indicators from C5.0 decision tree, important lab indicators in zheng progression are obtained by the method of stepwise discriminant analysis for expressing TCM zhengs in CHB, and alterations of the important indicators are also analyzed in zheng progression. In conclusion, all the three decision trees perform better on CFS-GA than on CFS and nonselection, and C5.0 decision tree outperforms the two typical decision trees both on attribute selection and nonselection.
Rainfall induced landslide susceptibility mapping using weight-of-evidence, linear and quadratic discriminant and logistic model tree method

NASA Astrophysics Data System (ADS)

Hong, H.; Zhu, A. X.

2017-12-01

Climate change is a common phenomenon and it is very serious all over the world. The intensification of rainfall extremes with climate change is of key importance to society and then it may induce a large impact through landslides. This paper presents GIS-based new ensemble data mining techniques that weight-of-evidence, logistic model tree, linear and quadratic discriminant for landslide spatial modelling. This research was applied in Anfu County, which is a landslide-prone area in Jiangxi Province, China. According to a literature review and research the study area, we select the landslide influencing factor and their maps were digitized in a GIS environment. These landslide influencing factors are the altitude, plan curvature, profile curvature, slope degree, slope aspect, topographic wetness index (TWI), Stream Power Index (SPI), Topographic Wetness Index (SPI), distance to faults, distance to rivers, distance to roads, soil, lithology, normalized difference vegetation index and land use. According to historical information of individual landslide events, interpretation of the aerial photographs, and field surveys supported by the government of Jiangxi Meteorological Bureau of China, 367 landslides were identified in the study area. The landslide locations were divided into two subsets, namely, training and validating (70/30), based on a random selection scheme. In this research, Pearson's correlation was used for the evaluation of the relationship between the landslides and influencing factors. In the next step, three data mining techniques combined with the weight-of-evidence, logistic model tree, linear and quadratic discriminant, were used for the landslide spatial modelling and its zonation. Finally, the landslide susceptibility maps produced by the mentioned models were evaluated by the ROC curve. The results showed that the area under the curve (AUC) of all of the models was > 0.80. At the same time, the highest AUC value was for the linear and quadratic discriminant model (0.864), followed by logistic model tree (0.832), and weight-of-evidence (0.819). In general, the landslide maps can be applied for land use planning and management in the Anfu area.
Feature selection from hyperspectral imaging for guava fruit defects detection

NASA Astrophysics Data System (ADS)

Mat Jafri, Mohd. Zubir; Tan, Sou Ching

2017-06-01

Development of technology makes hyperspectral imaging commonly used for defect detection. In this research, a hyperspectral imaging system was setup in lab to target for guava fruits defect detection. Guava fruit was selected as the object as to our knowledge, there is fewer attempts were made for guava defect detection based on hyperspectral imaging. The common fluorescent light source was used to represent the uncontrolled lighting condition in lab and analysis was carried out in a specific wavelength range due to inefficiency of this particular light source. Based on the data, the reflectance intensity of this specific setup could be categorized in two groups. Sequential feature selection with linear discriminant (LD) and quadratic discriminant (QD) function were used to select features that could potentially be used in defects detection. Besides the ordinary training method, training dataset in discriminant was separated in two to cater for the uncontrolled lighting condition. These two parts were separated based on the brighter and dimmer area. Four evaluation matrixes were evaluated which are LD with common training method, QD with common training method, LD with two part training method and QD with two part training method. These evaluation matrixes were evaluated using F1-score with total 48 defected areas. Experiment shown that F1-score of linear discriminant with the compensated method hitting 0.8 score, which is the highest score among all.
Psychometric functions for pure-tone frequency discrimination.

PubMed

Dai, Huanping; Micheyl, Christophe

2011-07-01

The form of the psychometric function (PF) for auditory frequency discrimination is of theoretical interest and practical importance. In this study, PFs for pure-tone frequency discrimination were measured for several standard frequencies (200-8000 Hz) and levels [35-85 dB sound pressure level (SPL)] in normal-hearing listeners. The proportion-correct data were fitted using a cumulative-Gaussian function of the sensitivity index, d', computed as a power transformation of the frequency difference, Δf. The exponent of the power function corresponded to the slope of the PF on log(d')-log(Δf) coordinates. The influence of attentional lapses on PF-slope estimates was investigated. When attentional lapses were not taken into account, the estimated PF slopes on log(d')-log(Δf) coordinates were found to be significantly lower than 1, suggesting a nonlinear relationship between d' and Δf. However, when lapse rate was included as a free parameter in the fits, PF slopes were found not to differ significantly from 1, consistent with a linear relationship between d' and Δf. This was the case across the wide ranges of frequencies and levels tested in this study. Therefore, spectral and temporal models of frequency discrimination must account for a linear relationship between d' and Δf across a wide range of frequencies and levels. © 2011 Acoustical Society of America
Quantifying and visualizing variations in sets of images using continuous linear optimal transport

NASA Astrophysics Data System (ADS)

Kolouri, Soheil; Rohde, Gustavo K.

2014-03-01

Modern advancements in imaging devices have enabled us to explore the subcellular structure of living organisms and extract vast amounts of information. However, interpreting the biological information mined in the captured images is not a trivial task. Utilizing predetermined numerical features is usually the only hope for quantifying this information. Nonetheless, direct visual or biological interpretation of results obtained from these selected features is non-intuitive and difficult. In this paper, we describe an automatic method for modeling visual variations in a set of images, which allows for direct visual interpretation of the most significant differences, without the need for predefined features. The method is based on a linearized version of the continuous optimal transport (OT) metric, which provides a natural linear embedding for the image data set, in which linear combination of images leads to a visually meaningful image. This enables us to apply linear geometric data analysis techniques such as principal component analysis and linear discriminant analysis in the linearly embedded space and visualize the most prominent modes, as well as the most discriminant modes of variations, in the dataset. Using the continuous OT framework, we are able to analyze variations in shape and texture in a set of images utilizing each image at full resolution, that otherwise cannot be done by existing methods. The proposed method is applied to a set of nuclei images segmented from Feulgen stained liver tissues in order to investigate the major visual differences in chromatin distribution of Fetal-Type Hepatoblastoma (FHB) cells compared to the normal cells.

The association of cumulative discrimination on quality of care, patient-centered care, and dissatisfaction with care in adults with type 2 diabetes.

PubMed

Cykert, David M; Williams, Joni S; Walker, Rebekah J; Davis, Kimberly S; Egede, Leonard E

2017-01-01

Discrimination is linked to negative health outcomes, but little research has investigated how the cumulative effect of discrimination impacts perceptions of care. This study investigated the influence of cumulative perceived discrimination on quality of care, patient-centeredness, and dissatisfaction with care in adults with type 2 diabetes. Six hundred two patients from two primary care clinics in Charleston, SC. Linear regression models assessed associations between perceived discrimination and quality of care, patient-centered care, and dissatisfaction with care. The models control for race, site, age, gender, marital status, duration of diabetes, education, hours worked weekly, income, and health status. The mean age was 61.5years, with 66.3% non-Hispanic blacks, and 41.9% earning less than $20,000 annually. In final adjusted analyses, lower patient-centered care was associated with a higher discrimination score (β=-0.28; p=0.006), reporting at least 1 category of discrimination (β=-1.47; p=0.002), and reporting at least 2 categories of discrimination (β=-1.34; p=0.004). Dissatisfaction with care was associated with at least 2 categories of discrimination (β=0.45; p=0.002). No significant associations were seen with quality of care indicators. Increased cumulative discrimination was associated with decreased feeling of patient-centeredness and increased dissatisfaction with care. However, these perceptions of discrimination were not significantly associated with quality indicators. Copyright © 2017 Elsevier Inc. All rights reserved.
Ethnic identity, racial discrimination and attenuated psychotic symptoms in an urban population of emerging adults.

PubMed

Anglin, Deidre M; Lui, Florence; Espinosa, Adriana; Tikhonov, Aleksandr; Ellman, Lauren

2018-06-01

Studies suggest strong ethnic identity generally protects against negative mental health outcomes associated with racial discrimination. In light of evidence suggesting racial discrimination may enhance psychosis risk in racial and ethnic minority (REM) populations, the present study explored the relationship between ethnic identity and attenuated positive psychotic symptoms (APPS) and whether ethnic identity moderates the association between racial discrimination and these symptoms. A sample of 644 non-help-seeking REM emerging adults was administered self-report inventories for psychosis risk, experiences of discrimination and ethnic identity. Latent class analysis was applied to determine the nature and number of ethnic identity types in this population. The direct association between ethnic identity and APPS and the interaction between ethnic identity and racial discrimination on APPS were determined in linear regression analyses. Results indicated three ethnic identity classes (very low, moderate to high and very high). Ethnic identity was not directly related to APPS; however, it was related to APPS under racially discriminating conditions. Specifically, participants who experienced discrimination in the moderate to high or very high ethnic identity classes reported fewer symptoms than participants who experienced discrimination in the very low ethnic identity class. Strong ethnic group affiliation and connection may serve a protective function for psychosis risk in racially discriminating environments and contexts among REM young adults. The possible social benefits of strong ethnic identification among REM youth who face racial discrimination should be explored further in clinical high-risk studies. © 2016 John Wiley & Sons Australia, Ltd.
The hydrodeoxygenation of bioderived furans into alkanes.

PubMed

Sutton, Andrew D; Waldie, Fraser D; Wu, Ruilian; Schlaf, Marcel; Silks, Louis A Pete; Gordon, John C

2013-05-01

The conversion of biomass into fuels and chemical feedstocks is one part of a drive to reduce the world's dependence on crude oil. For transportation fuels in particular, wholesale replacement of a fuel is logistically problematic, not least because of the infrastructure that is already in place. Here, we describe the catalytic defunctionalization of a series of biomass-derived molecules to provide linear alkanes suitable for use as transportation fuels. These biomass-derived molecules contain a variety of functional groups, including olefins, furan rings and carbonyl groups. We describe the removal of these in either a stepwise process or a one-pot process using common reagents and catalysts under mild reaction conditions to provide n-alkanes in good yields and with high selectivities. Our general synthetic approach is applicable to a range of precursors with different carbon content (chain length). This allows the selective generation of linear alkanes with carbon chain lengths between eight and sixteen carbons.
The hydrodeoxygenation of bioderived furans into alkanes

NASA Astrophysics Data System (ADS)

Sutton, Andrew D.; Waldie, Fraser D.; Wu, Ruilian; Schlaf, Marcel; ‘Pete' Silks, Louis A.; Gordon, John C.

2013-05-01

The conversion of biomass into fuels and chemical feedstocks is one part of a drive to reduce the world's dependence on crude oil. For transportation fuels in particular, wholesale replacement of a fuel is logistically problematic, not least because of the infrastructure that is already in place. Here, we describe the catalytic defunctionalization of a series of biomass-derived molecules to provide linear alkanes suitable for use as transportation fuels. These biomass-derived molecules contain a variety of functional groups, including olefins, furan rings and carbonyl groups. We describe the removal of these in either a stepwise process or a one-pot process using common reagents and catalysts under mild reaction conditions to provide n-alkanes in good yields and with high selectivities. Our general synthetic approach is applicable to a range of precursors with different carbon content (chain length). This allows the selective generation of linear alkanes with carbon chain lengths between eight and sixteen carbons.
Attitude control/momentum management of the Space Station Freedom for large angle torque-equilibrium-attitude configurations

NASA Technical Reports Server (NTRS)

Parlos, Alexander G.; Sunkel, John W.

1990-01-01

An attitude-control and momentum-management (ACMM) system for the Space Station in a large-angle torque-equilibrium-attitude (TEA) configuration is developed analytically and demonstrated by means of numerical simulations. The equations of motion for a rigid-body Space Station model are outlined; linearized equations for an arbitrary TEA (resulting from misalignment of control and body axes) are derived; the general requirements for an ACMM are summarized; and a pole-placement linear-quadratic regulator solution based on scheduled gains is proposed. Results are presented in graphs for (1) simulations based on configuration MB3 (showing the importance of accounting for the cross-inertia terms in the TEA estimate) and (2) simulations of a stepwise change from configuration MB3 to the 'assembly complete' stage over 130 orbits (indicating that the present ACCM scheme maintains sufficient control over slowly varying Space Station dynamics).
Facial Affect Recognition Using Regularized Discriminant Analysis-Based Algorithms

NASA Astrophysics Data System (ADS)

Lee, Chien-Cheng; Huang, Shin-Sheng; Shih, Cheng-Yuan

2010-12-01

This paper presents a novel and effective method for facial expression recognition including happiness, disgust, fear, anger, sadness, surprise, and neutral state. The proposed method utilizes a regularized discriminant analysis-based boosting algorithm (RDAB) with effective Gabor features to recognize the facial expressions. Entropy criterion is applied to select the effective Gabor feature which is a subset of informative and nonredundant Gabor features. The proposed RDAB algorithm uses RDA as a learner in the boosting algorithm. The RDA combines strengths of linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). It solves the small sample size and ill-posed problems suffered from QDA and LDA through a regularization technique. Additionally, this study uses the particle swarm optimization (PSO) algorithm to estimate optimal parameters in RDA. Experiment results demonstrate that our approach can accurately and robustly recognize facial expressions.
Workplace discrimination and alcohol consumption: findings from the San Francisco Muni Health and Safety Study.

PubMed

Yen, I H; Ragland, D R; Greiner, B A; Fisher, J M

1999-01-01

There is evidence of an association between occupational stress and alcohol consumption. This study investigates the association between workplace racial discrimination and alcohol consumption in a sample of urban transit operators. During 1993-1995, after undergoing a medical exam, 1,542 transit operators completed an interview. Depending on the outcome, we used logistic or linear regression models to examine the cross-sectional relationship between discrimination experience and alcohol consumption. Operators who reported discrimination in at least one situation, out of a possible four, were more likely to have had negative life consequences as a result of drinking (adjusted OR = 1.97; 95% CI, 1.20-3.83) and were more likely to be classified as having an alcohol disorder (OR = 1.56 [0.96-2.54]), compared to those who reported no instances of workplace discrimination. Results adjusted simultaneously for age, sex, race/ethnicity, education, income, marital status, and seniority. There was no association between workplace discrimination and heavy drinking or drinks per month. Cross-sectional data from a sample of urban transit operators indicates an association between workplace racial discrimination and some measures of alcohol consumption.
Fluorescent polymer sensor array for detection and discrimination of explosives in water.

PubMed

Woodka, Marc D; Schnee, Vincent P; Polcha, Michael P

2010-12-01

A fluorescent polymer sensor array (FPSA) was made from commercially available fluorescent polymers coated onto glass beads and was tested to assess the ability of the array to discriminate between different analytes in aqueous solution. The array was challenged with exposures to 17 different analytes, including the explosives trinitrotoluene (TNT), tetryl, and RDX, various explosive-related compounds (ERCs), and nonexplosive electron-withdrawing compounds (EWCs). The array exhibited a natural selectivity toward EWCs, while the non-electron-withdrawing explosive 1,3,5-trinitroperhydro-1,3,5-triazine (RDX) produced no response. Response signatures were visualized by principal component analysis (PCA), and classified by linear discriminant analysis (LDA). RDX produced the same response signature as the sampled blanks and was classified accordingly. The array exhibited excellent discrimination toward all other compounds, with the exception of the isomers of nitrotoluene and aminodinitrotoluene. Of particular note was the ability of the array to discriminate between the three isomers of dinitrobenzene. The natural selectivity of the FPSA toward EWCs, plus the ability of the FPSA to discriminate between different EWCs, could be used to design a sensor with a low false alarm rate and an excellent ability to discriminate between explosives and explosive-related compounds.
Racial Discrimination and HIV-related Risk Behaviors in Southeast Louisiana

PubMed Central

Kaplan, Kathryn C.; Hormes, Julia M.; Wallace, Maeve; Rountree, Michele; Theall, Katherine P.

2016-01-01

Objectives We examined the relationship between cumulative experiences of racial discrimination and HIV-related risk taking, and whether these relationships are mediated through alcohol use among African Americans in semi-rural southeast Louisiana. Methods Participants (N = 214) reported on experiences of discrimination, HIV sexual risk-taking, history of sexually transmitted infection (STI), and health behaviors including alcohol use in the previous 90 days. Experiences of discrimination (scaled both by frequency of occurrence and situational counts) as a predictor of a sexual risk composite score as well as a history of STI was assessed using multivariate linear and logistic regression, respectively, including tests for mediation by alcohol use. Results Discrimination was common in this cohort, with respondents confirming their experience on average 7 of the 9 potential situations and on more than 34 separate occasions. After adjustment, discrimination was significantly associated with increasing sexual risk-taking and lifetime history of STI when measured either by frequency of occurrence or number of situations, although there was no evidence that these relationships were mediated through alcohol use. Conclusions Cumulative experiences of discrimination may play a significant role in sexual risk behavior and consequently increase vulnerability to HIV and other STIs. PMID:26685822
A multiple maximum scatter difference discriminant criterion for facial feature extraction.

PubMed

Song, Fengxi; Zhang, David; Mei, Dayong; Guo, Zhongwei

2007-12-01

Maximum scatter difference (MSD) discriminant criterion was a recently presented binary discriminant criterion for pattern classification that utilizes the generalized scatter difference rather than the generalized Rayleigh quotient as a class separability measure, thereby avoiding the singularity problem when addressing small-sample-size problems. MSD classifiers based on this criterion have been quite effective on face-recognition tasks, but as they are binary classifiers, they are not as efficient on large-scale classification tasks. To address the problem, this paper generalizes the classification-oriented binary criterion to its multiple counterpart--multiple MSD (MMSD) discriminant criterion for facial feature extraction. The MMSD feature-extraction method, which is based on this novel discriminant criterion, is a new subspace-based feature-extraction method. Unlike most other subspace-based feature-extraction methods, the MMSD computes its discriminant vectors from both the range of the between-class scatter matrix and the null space of the within-class scatter matrix. The MMSD is theoretically elegant and easy to calculate. Extensive experimental studies conducted on the benchmark database, FERET, show that the MMSD out-performs state-of-the-art facial feature-extraction methods such as null space method, direct linear discriminant analysis (LDA), eigenface, Fisherface, and complete LDA.
Discrimination Enhancement with Transient Feature Analysis of a Graphene Chemical Sensor.

PubMed

Nallon, Eric C; Schnee, Vincent P; Bright, Collin J; Polcha, Michael P; Li, Qiliang

2016-01-19

A graphene chemical sensor is subjected to a set of structurally and chemically similar hydrocarbon compounds consisting of toluene, o-xylene, p-xylene, and mesitylene. The fractional change in resistance of the sensor upon exposure to these compounds exhibits a similar response magnitude among compounds, whereas large variation is observed within repetitions for each compound, causing a response overlap. Therefore, traditional features depending on maximum response change will cause confusion during further discrimination and classification analysis. More robust features that are less sensitive to concentration, sampling, and drift variability would provide higher quality information. In this work, we have explored the advantage of using transient-based exponential fitting coefficients to enhance the discrimination of similar compounds. The advantages of such feature analysis to discriminate each compound is evaluated using principle component analysis (PCA). In addition, machine learning-based classification algorithms were used to compare the prediction accuracies when using fitting coefficients as features. The additional features greatly enhanced the discrimination between compounds while performing PCA and also improved the prediction accuracy by 34% when using linear discrimination analysis.
Geometric mean for subspace selection.

PubMed

Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

2009-02-01

Subspace selection approaches are powerful tools in pattern classification and data visualization. One of the most important subspace approaches is the linear dimensionality reduction step in the Fisher's linear discriminant analysis (FLDA), which has been successfully employed in many fields such as biometrics, bioinformatics, and multimedia information management. However, the linear dimensionality reduction step in FLDA has a critical drawback: for a classification task with c classes, if the dimension of the projected subspace is strictly lower than c - 1, the projection to a subspace tends to merge those classes, which are close together in the original feature space. If separate classes are sampled from Gaussian distributions, all with identical covariance matrices, then the linear dimensionality reduction step in FLDA maximizes the mean value of the Kullback-Leibler (KL) divergences between different classes. Based on this viewpoint, the geometric mean for subspace selection is studied in this paper. Three criteria are analyzed: 1) maximization of the geometric mean of the KL divergences, 2) maximization of the geometric mean of the normalized KL divergences, and 3) the combination of 1 and 2. Preliminary experimental results based on synthetic data, UCI Machine Learning Repository, and handwriting digits show that the third criterion is a potential discriminative subspace selection method, which significantly reduces the class separation problem in comparing with the linear dimensionality reduction step in FLDA and its several representative extensions.
QSRR modeling for diverse drugs using different feature selection methods coupled with linear and nonlinear regressions.

PubMed

Goodarzi, Mohammad; Jensen, Richard; Vander Heyden, Yvan

2012-12-01

A Quantitative Structure-Retention Relationship (QSRR) is proposed to estimate the chromatographic retention of 83 diverse drugs on a Unisphere poly butadiene (PBD) column, using isocratic elutions at pH 11.7. Previous work has generated QSRR models for them using Classification And Regression Trees (CART). In this work, Ant Colony Optimization is used as a feature selection method to find the best molecular descriptors from a large pool. In addition, several other selection methods have been applied, such as Genetic Algorithms, Stepwise Regression and the Relief method, not only to evaluate Ant Colony Optimization as a feature selection method but also to investigate its ability to find the important descriptors in QSRR. Multiple Linear Regression (MLR) and Support Vector Machines (SVMs) were applied as linear and nonlinear regression methods, respectively, giving excellent correlation between the experimental, i.e. extrapolated to a mobile phase consisting of pure water, and predicted logarithms of the retention factors of the drugs (logk(w)). The overall best model was the SVM one built using descriptors selected by ACO. Copyright © 2012 Elsevier B.V. All rights reserved.
Exploring nonlinear feature space dimension reduction and data representation in breast CADx with Laplacian eigenmaps and t-SNE

PubMed Central

Jamieson, Andrew R.; Giger, Maryellen L.; Drukker, Karen; Li, Hui; Yuan, Yading; Bhooshan, Neha

2010-01-01

Purpose: In this preliminary study, recently developed unsupervised nonlinear dimension reduction (DR) and data representation techniques were applied to computer-extracted breast lesion feature spaces across three separate imaging modalities: Ultrasound (U.S.) with 1126 cases, dynamic contrast enhanced magnetic resonance imaging with 356 cases, and full-field digital mammography with 245 cases. Two methods for nonlinear DR were explored: Laplacian eigenmaps [M. Belkin and P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation,” Neural Comput. 15, 1373–1396 (2003)] and t-distributed stochastic neighbor embedding (t-SNE) [L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” J. Mach. Learn. Res. 9, 2579–2605 (2008)]. Methods: These methods attempt to map originally high dimensional feature spaces to more human interpretable lower dimensional spaces while preserving both local and global information. The properties of these methods as applied to breast computer-aided diagnosis (CADx) were evaluated in the context of malignancy classification performance as well as in the visual inspection of the sparseness within the two-dimensional and three-dimensional mappings. Classification performance was estimated by using the reduced dimension mapped feature output as input into both linear and nonlinear classifiers: Markov chain Monte Carlo based Bayesian artificial neural network (MCMC-BANN) and linear discriminant analysis. The new techniques were compared to previously developed breast CADx methodologies, including automatic relevance determination and linear stepwise (LSW) feature selection, as well as a linear DR method based on principal component analysis. Using ROC analysis and 0.632+bootstrap validation, 95% empirical confidence intervals were computed for the each classifier’s AUC performance. Results: In the large U.S. data set, sample high performance results include, AUC0.632+=0.88 with 95% empirical bootstrap interval [0.787;0.895] for 13 ARD selected features and AUC0.632+=0.87 with interval [0.817;0.906] for four LSW selected features compared to 4D t-SNE mapping (from the original 81D feature space) giving AUC0.632+=0.90 with interval [0.847;0.919], all using the MCMC-BANN. Conclusions: Preliminary results appear to indicate capability for the new methods to match or exceed classification performance of current advanced breast lesion CADx algorithms. While not appropriate as a complete replacement of feature selection in CADx problems, DR techniques offer a complementary approach, which can aid elucidation of additional properties associated with the data. Specifically, the new techniques were shown to possess the added benefit of delivering sparse lower dimensional representations for visual interpretation, revealing intricate data structure of the feature space. PMID:20175497
An effective biometric discretization approach to extract highly discriminative, informative, and privacy-protective binary representation

NASA Astrophysics Data System (ADS)

Lim, Meng-Hui; Teoh, Andrew Beng Jin

2011-12-01

Biometric discretization derives a binary string for each user based on an ordered set of biometric features. This representative string ought to be discriminative, informative, and privacy protective when it is employed as a cryptographic key in various security applications upon error correction. However, it is commonly believed that satisfying the first and the second criteria simultaneously is not feasible, and a tradeoff between them is always definite. In this article, we propose an effective fixed bit allocation-based discretization approach which involves discriminative feature extraction, discriminative feature selection, unsupervised quantization (quantization that does not utilize class information), and linearly separable subcode (LSSC)-based encoding to fulfill all the ideal properties of a binary representation extracted for cryptographic applications. In addition, we examine a number of discriminative feature-selection measures for discretization and identify the proper way of setting an important feature-selection parameter. Encouraging experimental results vindicate the feasibility of our approach.
Development of a universal water signature for the LANDSAT-3 Multispectral Scanner, part 2 of 2

NASA Technical Reports Server (NTRS)

Schlosser, E. H.

1980-01-01

A generalized four-channel hyperplane to discriminate water from non-water was developed using LANDSAT-3 multispectral scanner (MSS) scences and matching same/next-day color infrared aerial photography. The MSS scenes over upstate New York, eastern Washington, Montana and Louisiana taken between May and October 1978 varied in Sun elevation angle from 40 to 58 degrees. The 28 matching air photo frames selected for analysis contained over 1400 water bodies larger than one surface acre. A preliminary water discriminant was used to screen the data and eliminate from further consideration all pixels distant from water in MSS spectral space. Approximately 1300 pixels, half of them non-edge water pixels and half non-water pixels spectrally close to water, were labelled. A linear discriminant was iteratively fitted to the labelled pixels, giving more weight to those pixels that were difficult to discriminate. This discriminant correctly classified 98.7 percent of the water pixels and 98.6 percent of the non-water pixels.
Discrimination of almonds (Prunus dulcis) geographical origin by minerals and fatty acids profiling.

PubMed

Amorello, Diana; Orecchio, Santino; Pace, Andrea; Barreca, Salvatore

2016-09-01

Twenty-one almond samples from three different geographical origins (Sicily, Spain and California) were investigated by determining minerals and fatty acids compositions. Data were used to discriminate by chemometry almond origin by linear discriminant analysis. With respect to previous PCA profiling studies, this work provides a simpler analytical protocol for the identification of almonds geographical origin. Classification by using mineral contents data only was correct in 77% of the samples, while, by using fatty acid profiles, the percentages of samples correctly classified reached 82%. The coupling of mineral contents and fatty acid profiles lead to an increased efficiency of the classification with 87% of samples correctly classified.
Probing thyroglobulin in undiluted human serum based on pattern recognition and competitive adsorption of proteins

NASA Astrophysics Data System (ADS)

Wang, Ran; Huang, Shuai; Li, Jing; Chae, Junseok

2014-10-01

Thyroglobulin (Tg) is a sensitive indicator of persistent or recurrent differentiated thyroid cancer of follicular cell origin. Detection of Tg in human serum is challenging as bio-receptors, such as anti-Tg, used in immunoassay have relatively weak binding affinity. We engineer sensing surfaces using the competitive adsorption of proteins, termed the Vroman Effect. Coupled with Surface Plasmon Resonance, the "cross-responsive" interactions of Tg on the engineered surfaces produce uniquely distinguishable multiple signature patterns, which are discriminated using Linear Discriminant Analysis. Tg-spiked samples, down to 2 ng/ml Tg in undiluted human serum, are sensitively and selectively discriminated from the control (undiluted human serum).
Advanced microwave soil moisture studies. [Big Sioux River Basin, Iowa

NASA Technical Reports Server (NTRS)

Dalsted, K. J.; Harlan, J. C.

1983-01-01

Comparisons of low level L-band brightness temperature (TB) and thermal infrared (TIR) data as well as the following data sets: soil map and land cover data; direct soil moisture measurement; and a computer generated contour map were statistically evaluated using regression analysis and linear discriminant analysis. Regression analysis of footprint data shows that statistical groupings of ground variables (soil features and land cover) hold promise for qualitative assessment of soil moisture and for reducing variance within the sampling space. Dry conditions appear to be more conductive to producing meaningful statistics than wet conditions. Regression analysis using field averaged TB and TIR data did not approach the higher sq R values obtained using within-field variations. The linear discriminant analysis indicates some capacity to distinguish categories with the results being somewhat better on a field basis than a footprint basis.
Aerobic Fitness Does Not Contribute to Prediction of Orthostatic Intolerance

NASA Technical Reports Server (NTRS)

Convertino, Victor A.; Sather, Tom M.; Goldwater, Danielle J.; Alford, William R.

1986-01-01

Several investigations have suggested that orthostatic tolerance may be inversely related to aerobic fitness (VO (sub 2max)). To test this hypothesis, 18 males (age 29 to 51 yr) underwent both treadmill VO(sub 2max) determination and graded lower body negative pressures (LBNP) exposure to tolerance. VO(2max) was measured during the last minute of a Bruce treadmill protocol. LBNP was terminated based on pre-syncopal symptoms and LBNP tolerance (peak LBNP) was expressed as the cumulative product of LBNP and time (torr-min). Changes in heart rate, stroke volume cardiac output, blood pressure and impedance rheographic indices of mid-thigh-leg initial accumulation were measured at rest and during the final minute of LBNP. For all 18 subjects, mean (plus or minus SE) fluid accumulation index and leg venous compliance index at peak LBNP were 139 plus or minus 3.9 plus or minus 0.4 ml-torr-min(exp -2) x 10(exp 3), respectively. Pearson product-moment correlations and step-wise linear regression were used to investigate relationships with peak LBNP. Variables associated with endurance training, such as VO(sub 2max) and percent body fat were not found to correlate significantly (P is less than 0.05) with peak LBNP and did not add sufficiently to the prediction of peak LBNP to be included in the step-wise regression model. The step-wise regression model included only fluid accumulation index leg venous compliance index, and blood volume and resulted in a squared multiple correlation coefficient of 0.978. These data do not support the hypothesis that orthostatic tolerance as measured by LBNP is lower in individuals with high aerobic fitness.

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