On Algorithms for Generating Computationally Simple Piecewise Linear Classifiers

DTIC Science & Technology

1989-05-01

suffers. - Waveform classification, e.g. speech recognition, seismic analysis (i.e. discrimination between earthquakes and nuclear explosions), target...assuming Gaussian distributions (B-G) d) Bayes classifier with probability densities estimated with the k-N-N method (B- kNN ) e) The -arest neighbour...range of classifiers are chosen including a fast, easy computable and often used classifier (B-G), reliable and complex classifiers (B- kNN and NNR

Two Methods for Teaching Simple Visual Discriminations to Learners with Severe Disabilities

ERIC Educational Resources Information Center

Graff, Richard B.; Green, Gina

2004-01-01

Simple discriminations are involved in many functional skills; additionally, they are components of conditional discriminations (identity and arbitrary matching-to-sample), which are involved in a wide array of other important performances. Many individuals with severe disabilities have difficulty acquiring simple discriminations with standard…

Design a New Strategy Based on Nanoparticle-Enhanced Chemiluminescence Sensor Array for Biothiols Discrimination

NASA Astrophysics Data System (ADS)

Shahrajabian, Maryam; Hormozi-Nezhad, M. Reza

2016-08-01

Array-based sensor is an interesting approach that suggests an alternative to expensive analytical methods. In this work, we introduce a novel, simple, and sensitive nanoparticle-based chemiluminescence (CL) sensor array for discrimination of biothiols (e.g., cysteine, glutathione and glutathione disulfide). The proposed CL sensor array is based on the CL efficiencies of four types of enhanced nanoparticle-based CL systems. The intensity of CL was altered to varying degrees upon interaction with biothiols, producing unique CL response patterns. These distinct CL response patterns were collected as “fingerprints” and were then identified through chemometric methods, including linear discriminant analysis (LDA) and hierarchical cluster analysis (HCA). The developed array was able to successfully differentiate between cysteine, glutathione and glutathione disulfide in a wide concentration range. Moreover, it was applied to distinguish among the above analytes in human plasma.

Comprehensive Chemical Fingerprinting of High-Quality Cocoa at Early Stages of Processing: Effectiveness of Combined Untargeted and Targeted Approaches for Classification and Discrimination.

PubMed

Magagna, Federico; Guglielmetti, Alessandro; Liberto, Erica; Reichenbach, Stephen E; Allegrucci, Elena; Gobino, Guido; Bicchi, Carlo; Cordero, Chiara

2017-08-02

This study investigates chemical information of volatile fractions of high-quality cocoa (Theobroma cacao L. Malvaceae) from different origins (Mexico, Ecuador, Venezuela, Columbia, Java, Trinidad, and Sao Tomè) produced for fine chocolate. This study explores the evolution of the entire pattern of volatiles in relation to cocoa processing (raw, roasted, steamed, and ground beans). Advanced chemical fingerprinting (e.g., combined untargeted and targeted fingerprinting) with comprehensive two-dimensional gas chromatography coupled with mass spectrometry allows advanced pattern recognition for classification, discrimination, and sensory-quality characterization. The entire data set is analyzed for 595 reliable two-dimensional peak regions, including 130 known analytes and 13 potent odorants. Multivariate analysis with unsupervised exploration (principal component analysis) and simple supervised discrimination methods (Fisher ratios and linear regression trees) reveal informative patterns of similarities and differences and identify characteristic compounds related to sample origin and manufacturing step.

Technical notes and correspondence: Stochastic robustness of linear time-invariant control systems

NASA Technical Reports Server (NTRS)

Stengel, Robert F.; Ray, Laura R.

1991-01-01

A simple numerical procedure for estimating the stochastic robustness of a linear time-invariant system is described. Monte Carlo evaluations of the system's eigenvalues allows the probability of instability and the related stochastic root locus to be estimated. This analysis approach treats not only Gaussian parameter uncertainties but non-Gaussian cases, including uncertain-but-bounded variation. Confidence intervals for the scalar probability of instability address computational issues inherent in Monte Carlo simulation. Trivial extensions of the procedure admit consideration of alternate discriminants; thus, the probabilities that stipulated degrees of instability will be exceeded or that closed-loop roots will leave desirable regions can also be estimated. Results are particularly amenable to graphical presentation.

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

Shumway, R.H.; McQuarrie, A.D.

Robust statistical approaches to the problem of discriminating between regional earthquakes and explosions are developed. We compare linear discriminant analysis using descriptive features like amplitude and spectral ratios with signal discrimination techniques using the original signal waveforms and spectral approximations to the log likelihood function. Robust information theoretic techniques are proposed and all methods are applied to 8 earthquakes and 8 mining explosions in Scandinavia and to an event from Novaya Zemlya of unknown origin. It is noted that signal discrimination approaches based on discrimination information and Renyi entropy perform better in the test sample than conventional methods based onmore » spectral ratios involving the P and S phases. Two techniques for identifying the ripple-firing pattern for typical mining explosions are proposed and shown to work well on simulated data and on several Scandinavian earthquakes and explosions. We use both cepstral analysis in the frequency domain and a time domain method based on the autocorrelation and partial autocorrelation functions. The proposed approach strips off underlying smooth spectral and seasonal spectral components corresponding to the echo pattern induced by two simple ripple-fired models. For two mining explosions, a pattern is identified whereas for two earthquakes, no pattern is evident.« less

Linear high-boost fusion of Stokes vector imagery for effective discrimination and recognition of real targets in the presence of multiple identical decoys

NASA Astrophysics Data System (ADS)

El-Saba, Aed; Sakla, Wesam A.

2010-04-01

Recently, the use of imaging polarimetry has received considerable attention for use in automatic target recognition (ATR) applications. In military remote sensing applications, there is a great demand for sensors that are capable of discriminating between real targets and decoys. Accurate discrimination of decoys from real targets is a challenging task and often requires the fusion of various sensor modalities that operate simultaneously. In this paper, we use a simple linear fusion technique known as the high-boost fusion method for effective discrimination of real targets in the presence of multiple decoys. The HBF assigns more weight to the polarization-based imagery in forming the final fused image that is used for detection. We have captured both intensity and polarization-based imagery from an experimental laboratory arrangement containing a mixture of sand/dirt, rocks, vegetation, and other objects for the purpose of simulating scenery that would be acquired in a remote sensing military application. A target object and three decoys that are identical in physical appearance (shape, surface structure and color) and different in material composition have also been placed in the scene. We use the wavelet-filter joint transform correlation (WFJTC) technique to perform detection between input scenery and the target object. Our results show that use of the HBF method increases the correlation performance metrics associated with the WFJTC-based detection process when compared to using either the traditional intensity or polarization-based images.

Improving EMG based classification of basic hand movements using EMD.

PubMed

Sapsanis, Christos; Georgoulas, George; Tzes, Anthony; Lymberopoulos, Dimitrios

2013-01-01

This paper presents a pattern recognition approach for the identification of basic hand movements using surface electromyographic (EMG) data. The EMG signal is decomposed using Empirical Mode Decomposition (EMD) into Intrinsic Mode Functions (IMFs) and subsequently a feature extraction stage takes place. Various combinations of feature subsets are tested using a simple linear classifier for the detection task. Our results suggest that the use of EMD can increase the discrimination ability of the conventional feature sets extracted from the raw EMG signal.

Predictor of increase in caregiver burden for disabled elderly at home.

PubMed

Okamoto, Kazushi; Harasawa, Yuko

2009-01-01

In order to classify the caregivers at high risk of increase in their burden early, linear discriminant analysis was performed to obtain an effective discriminant model for differentiation of the presence or absence of increase in caregiver burden. The data obtained by self-administered questionnaire from 193 caregivers of frail elderly from January to February of 2005 were used. The discriminant analysis yielded a statistically significant function explaining 35.0% (Rc=0.59; d.f.=6; p=0.0001). The configuration indicated that the psychological predictors of change in caregiver burden with much perceived stress (1.47), high caregiver burden at baseline (1.28), emotional control (0.75), effort to achieve (-0.28), symptomatic depression (0.20) and "ikigai" (purpose in life) (0.18) made statistically significant contributions to the differentiation between no increase and increase in caregiver burden. The discriminant function showed a sensitivity of 86% and specificity of 81%, and successfully classified 83% of the caregivers. The function at baseline is a simple and useful method for screening of an increase in caregiver burden among caregivers for the frail elderly at home.

Object recognition with hierarchical discriminant saliency networks.

PubMed

Han, Sunhyoung; Vasconcelos, Nuno

2014-01-01

The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and computer vision literatures. This demonstrates benefits for all the functional enhancements of the HDSN, the class tuning inherent to discriminant saliency, and saliency layers based on templates of increasing target selectivity and invariance. Altogether, these experiments suggest that there are non-trivial benefits in integrating attention and recognition.

How pigeons discriminate the relative frequency of events.

PubMed

Keen, R; Machado, A

1999-09-01

This study examined how pigeons discriminate the relative frequencies of events when the events occur serially. In a discrete-trials procedure, 6 pigeons were shown one light nf times and then another nl times. Next, they received food for choosing the light that had occurred the least number of times during the sample. At issue were (a) how the discrimination was related to two variables, the difference between the frequencies of the two lights, D = nf - nl, and the total number of lights in the sample, T = nf + nl; and (b) whether a simple mathematical model of the discrimination process could account for the data. In contrast with models that assume that pigeons count the stimulus lights, engage in mental arithmetic on numerons, or remember the number of stimuli, the present model assumed only that the influence of a sample stimulus on choice increases linearly when the stimulus is presented, but decays exponentially when the stimulus is absent. The results showed that, overall, the pigeons discriminated the relative frequencies well. Their accuracy always increased with the absolute value of the difference D and, for D > 0, it decreased with T. Performance also showed clear recency, primacy, and contextual effects. The model accounted well for the major trends in the data.

Near-complete teleportation of a superposed coherent state

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

Cheong, Yong Wook; Kim, Hyunjae; Lee, Hai-Woong

2004-09-01

The four Bell-type entangled coherent states, {alpha}>-{alpha}>{+-}-{alpha}>{alpha}> and {alpha}>{alpha}>{+-}-{alpha}>-{alpha}>, can be discriminated with a high probability using only linear optical means, as long as {alpha} is not too small. Based on this observation, we propose a simple scheme to almost completely teleport a superposed coherent state. The nonunitary transformation that is required to complete the teleportation can be achieved by embedding the receiver's field state in a larger Hilbert space consisting of the field and a single atom and performing a unitary transformation on this Hilbert space00.

Classification of speech dysfluencies using LPC based parameterization techniques.

PubMed

Hariharan, M; Chee, Lim Sin; Ai, Ooi Chia; Yaacob, Sazali

2012-06-01

The goal of this paper is to discuss and compare three feature extraction methods: Linear Predictive Coefficients (LPC), Linear Prediction Cepstral Coefficients (LPCC) and Weighted Linear Prediction Cepstral Coefficients (WLPCC) for recognizing the stuttered events. Speech samples from the University College London Archive of Stuttered Speech (UCLASS) were used for our analysis. The stuttered events were identified through manual segmentation and were used for feature extraction. Two simple classifiers namely, k-nearest neighbour (kNN) and Linear Discriminant Analysis (LDA) were employed for speech dysfluencies classification. Conventional validation method was used for testing the reliability of the classifier results. The study on the effect of different frame length, percentage of overlapping, value of ã in a first order pre-emphasizer and different order p were discussed. The speech dysfluencies classification accuracy was found to be improved by applying statistical normalization before feature extraction. The experimental investigation elucidated LPC, LPCC and WLPCC features can be used for identifying the stuttered events and WLPCC features slightly outperforms LPCC features and LPC features.

Coupled-cavity surface-emitting lasers: spectral and polarization threshold characteristics and electrooptic switching.

PubMed

Panajotov, Krassimir P; Zujewski, Mateusz; Thienpont, Hugo

2010-12-20

We study spectral and polarization threshold characteristics of coupled-cavity Vertical-Surface-Emitting Lasers (CC-VCSEL) on the base of a simple matrix approach. We show that strong wavelength discrimination can be achieved in CC-VCSELs by slightly detuning the cavities. However, polarization discrimination is not provided by the coupled-cavity design. We also consider the case of reverse-biasing one of the cavities, i.e. using it as a modulator via linear and/or quadratic electrooptic effect. Such a CC-VCSEL can act as a voltage-controlled polarization or wavelength switching device that is decoupled from the laser design and can be optimized for high modulation speed. We also show that using QD stack instead of quantum wells in the top cavity would lead to significant reduction of the driving electrical field.

Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy

NASA Astrophysics Data System (ADS)

Zhu, Ying; Tan, Tuck Lee

2016-04-01

An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects.

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.

TACD: a transportable ant colony discrimination model for corporate bankruptcy prediction

NASA Astrophysics Data System (ADS)

Lalbakhsh, Pooia; Chen, Yi-Ping Phoebe

2017-05-01

This paper presents a transportable ant colony discrimination strategy (TACD) to predict corporate bankruptcy, a topic of vital importance that is attracting increasing interest in the field of economics. The proposed algorithm uses financial ratios to build a binary prediction model for companies with the two statuses of bankrupt and non-bankrupt. The algorithm takes advantage of an improved version of continuous ant colony optimisation (CACO) at the core, which is used to create an accurate, simple and understandable linear model for discrimination. This also enables the algorithm to work with continuous values, leading to more efficient learning and adaption by avoiding data discretisation. We conduct a comprehensive performance evaluation on three real-world data sets under a stratified cross-validation strategy. In three different scenarios, TACD is compared with 11 other bankruptcy prediction strategies. We also discuss the efficiency of the attribute selection methods used in the experiments. In addition to its simplicity and understandability, statistical significance tests prove the efficiency of TACD against the other prediction algorithms in both measures of AUC and accuracy.

Molecular discriminators using single wall carbon nanotubes

NASA Astrophysics Data System (ADS)

Bhattacharyya, Tamoghna; Dasgupta, Anjan Kr; Ranjan Ray, Nihar; Sarkar, Sabyasachi

2012-09-01

The interaction between single wall carbon nanotubes (SWNTs) and amphiphilic molecules has been studied in a solid phase. SWNTs are allowed to interact with different amphiphilic probes (e.g. lipids) in a narrow capillary interface. Contact between strong hydrophobic and amphiphilic interfaces leads to a molecular restructuring of the lipids at the interface. The geometry of the diffusion front and the rate and the extent of diffusion of the interface are dependent on the structure of the lipid at the interface. Lecithin having a linear tail showed greater mobility of the interface as compared to a branched tail lipid like dipalmitoyl phosphatidylcholine, indicating the hydrophobic interaction between single wall carbon nanotube core and the hydrophobic tail of the lipid. Solid phase interactions between SWNT and lipids can thus become a very simple but efficient means of discriminating amphiphilic molecules in general and lipids in particular.

Penalized discriminant analysis for the detection of wild-grown and cultivated Ganoderma lucidum using Fourier transform infrared spectroscopy.

PubMed

Zhu, Ying; Tan, Tuck Lee

2016-04-15

An effective and simple analytical method using Fourier transform infrared (FTIR) spectroscopy to distinguish wild-grown high-quality Ganoderma lucidum (G. lucidum) from cultivated one is of essential importance for its quality assurance and medicinal value estimation. Commonly used chemical and analytical methods using full spectrum are not so effective for the detection and interpretation due to the complex system of the herbal medicine. In this study, two penalized discriminant analysis models, penalized linear discriminant analysis (PLDA) and elastic net (Elnet),using FTIR spectroscopy have been explored for the purpose of discrimination and interpretation. The classification performances of the two penalized models have been compared with two widely used multivariate methods, principal component discriminant analysis (PCDA) and partial least squares discriminant analysis (PLSDA). The Elnet model involving a combination of L1 and L2 norm penalties enabled an automatic selection of a small number of informative spectral absorption bands and gave an excellent classification accuracy of 99% for discrimination between spectra of wild-grown and cultivated G. lucidum. Its classification performance was superior to that of the PLDA model in a pure L1 setting and outperformed the PCDA and PLSDA models using full wavelength. The well-performed selection of informative spectral features leads to substantial reduction in model complexity and improvement of classification accuracy, and it is particularly helpful for the quantitative interpretations of the major chemical constituents of G. lucidum regarding its anti-cancer effects. Copyright © 2016 Elsevier B.V. All rights reserved.

Analysis of lithology: Vegetation mixes in multispectral images

NASA Technical Reports Server (NTRS)

Adams, J. B.; Smith, M.; Adams, J. D.

1982-01-01

Discrimination and identification of lithologies from multispectral images is discussed. Rock/soil identification can be facilitated by removing the component of the signal in the images that is contributed by the vegetation. Mixing models were developed to predict the spectra of combinations of pure end members, and those models were refined using laboratory measurements of real mixtures. Models in use include a simple linear (checkerboard) mix, granular mixing, semi-transparent coatings, and combinations of the above. The use of interactive computer techniques that allow quick comparison of the spectrum of a pixel stack (in a multiband set) with laboratory spectra is discussed.

Robust Visual Tracking via Online Discriminative and Low-Rank Dictionary Learning.

PubMed

Zhou, Tao; Liu, Fanghui; Bhaskar, Harish; Yang, Jie

2017-09-12

In this paper, we propose a novel and robust tracking framework based on online discriminative and low-rank dictionary learning. The primary aim of this paper is to obtain compact and low-rank dictionaries that can provide good discriminative representations of both target and background. We accomplish this by exploiting the recovery ability of low-rank matrices. That is if we assume that the data from the same class are linearly correlated, then the corresponding basis vectors learned from the training set of each class shall render the dictionary to become approximately low-rank. The proposed dictionary learning technique incorporates a reconstruction error that improves the reliability of classification. Also, a multiconstraint objective function is designed to enable active learning of a discriminative and robust dictionary. Further, an optimal solution is obtained by iteratively computing the dictionary, coefficients, and by simultaneously learning the classifier parameters. Finally, a simple yet effective likelihood function is implemented to estimate the optimal state of the target during tracking. Moreover, to make the dictionary adaptive to the variations of the target and background during tracking, an online update criterion is employed while learning the new dictionary. Experimental results on a publicly available benchmark dataset have demonstrated that the proposed tracking algorithm performs better than other state-of-the-art trackers.

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.

Factorization-based texture segmentation

DOE PAGES

Yuan, Jiangye; Wang, Deliang; Cheriyadat, Anil M.

2015-06-17

This study introduces a factorization-based approach that efficiently segments textured images. We use local spectral histograms as features, and construct an M × N feature matrix using M-dimensional feature vectors in an N-pixel image. Based on the observation that each feature can be approximated by a linear combination of several representative features, we factor the feature matrix into two matrices-one consisting of the representative features and the other containing the weights of representative features at each pixel used for linear combination. The factorization method is based on singular value decomposition and nonnegative matrix factorization. The method uses local spectral histogramsmore » to discriminate region appearances in a computationally efficient way and at the same time accurately localizes region boundaries. Finally, the experiments conducted on public segmentation data sets show the promise of this simple yet powerful approach.« less

Systemic oxidative stress associated with the neurological diseases of aging.

PubMed

Serra, Jorge A; Domínguez, Raúl O; Marschoff, Enrique R; Guareschi, Eduardo M; Famulari, Arturo L; Boveris, Alberto

2009-12-01

Markers of oxidative stress were measured in blood samples of 338 subjects (965 observations): Alzheimer's, vascular dementia, diabetes (type II) superimposed to dementias, Parkinson's disease and controls. Patients showed increased thiobarbituric acid reactive substances (+21%; P < 0.05), copper-zinc superoxide dismutase (+64%; P < 0.001) and decreased antioxidant capacity (-28%; P < 0.001); pairs of variables resulted linearly related across groups (P < 0.001). Catalase and glutathione peroxidase, involved in discrimination between diseases, resulted non-significant. When diabetes is superimposed with dementias, changes resulted less marked but significant. Also, superoxide dismutase resulted not linearly correlated with any other variable or age-related (pure Alzheimer's peaks at 70 years, P < 0.001). Systemic oxidative stress was significantly associated (P < 0.001) with all diseases indicating a disbalance in peripheral/adaptive responses to oxidative disorders through different free radical metabolic pathways. While other changes - methionine cycle, insulin correlation - are also associated with dementias, the responses presented here show a simple linear relation between prooxidants and antioxidant defenses.

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.

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.

Liquid density analysis of sucrose and alcoholic beverages using polyimide guided Love-mode acoustic wave sensors

NASA Astrophysics Data System (ADS)

Turton, Andrew; Bhattacharyya, Debabrata; Wood, David

2006-02-01

A liquid density sensor using Love-mode acoustic waves has been developed which is suitable for use in the food and drinks industries. The sensor has an open flat surface allowing immersion into a sample and simple cleaning. A polyimide waveguide layer allows cheap and simple fabrication combined with a robust chemically resistant surface. The low shear modulus of polyimide allows thin guiding layers giving a high sensitivity. A dual structure with a smooth reference device exhibiting viscous coupling with the wave, and a patterned sense area to trap the liquid causing mass loading, allows discrimination of the liquid density from the square root of the density-viscosity product (ρη)0.5. Frequency shift and insertion loss change were proportional to (ρη)0.5 with a non-linear response due to the non-Newtonian nature of viscous liquids at high frequencies. Measurements were made with sucrose solutions up to 50% and different alcoholic drinks. A maximum sensitivity of 0.13 µg cm-3 Hz-1 was achieved, with a linear frequency response to density. This is the highest liquid density sensitivity obtained for acoustic mode sensors to the best of our knowledge.

Single-trial classification of motor imagery differing in task complexity: a functional near-infrared spectroscopy study

PubMed Central

2011-01-01

Background For brain computer interfaces (BCIs), which may be valuable in neurorehabilitation, brain signals derived from mental activation can be monitored by non-invasive methods, such as functional near-infrared spectroscopy (fNIRS). Single-trial classification is important for this purpose and this was the aim of the presented study. In particular, we aimed to investigate a combined approach: 1) offline single-trial classification of brain signals derived from a novel wireless fNIRS instrument; 2) to use motor imagery (MI) as mental task thereby discriminating between MI signals in response to different tasks complexities, i.e. simple and complex MI tasks. Methods 12 subjects were asked to imagine either a simple finger-tapping task using their right thumb or a complex sequential finger-tapping task using all fingers of their right hand. fNIRS was recorded over secondary motor areas of the contralateral hemisphere. Using Fisher's linear discriminant analysis (FLDA) and cross validation, we selected for each subject a best-performing feature combination consisting of 1) one out of three channel, 2) an analysis time interval ranging from 5-15 s after stimulation onset and 3) up to four Δ[O2Hb] signal features (Δ[O2Hb] mean signal amplitudes, variance, skewness and kurtosis). Results The results of our single-trial classification showed that using the simple combination set of channels, time intervals and up to four Δ[O2Hb] signal features comprising Δ[O2Hb] mean signal amplitudes, variance, skewness and kurtosis, it was possible to discriminate single-trials of MI tasks differing in complexity, i.e. simple versus complex tasks (inter-task paired t-test p ≤ 0.001), over secondary motor areas with an average classification accuracy of 81%. Conclusions Although the classification accuracies look promising they are nevertheless subject of considerable subject-to-subject variability. In the discussion we address each of these aspects, their limitations for future approaches in single-trial classification and their relevance for neurorehabilitation. PMID:21682906

Evaluation of a Decision Support System for Obstructive Sleep Apnea with Nonlinear Analysis of Respiratory Signals.

PubMed

Kaimakamis, Evangelos; Tsara, Venetia; Bratsas, Charalambos; Sichletidis, Lazaros; Karvounis, Charalambos; Maglaveras, Nikolaos

2016-01-01

Obstructive Sleep Apnea (OSA) is a common sleep disorder requiring the time/money consuming polysomnography for diagnosis. Alternative methods for initial evaluation are sought. Our aim was the prediction of Apnea-Hypopnea Index (AHI) in patients potentially suffering from OSA based on nonlinear analysis of respiratory biosignals during sleep, a method that is related to the pathophysiology of the disorder. Patients referred to a Sleep Unit (135) underwent full polysomnography. Three nonlinear indices (Largest Lyapunov Exponent, Detrended Fluctuation Analysis and Approximate Entropy) extracted from two biosignals (airflow from a nasal cannula, thoracic movement) and one linear derived from Oxygen saturation provided input to a data mining application with contemporary classification algorithms for the creation of predictive models for AHI. A linear regression model presented a correlation coefficient of 0.77 in predicting AHI. With a cutoff value of AHI = 8, the sensitivity and specificity were 93% and 71.4% in discrimination between patients and normal subjects. The decision tree for the discrimination between patients and normal had sensitivity and specificity of 91% and 60%, respectively. Certain obtained nonlinear values correlated significantly with commonly accepted physiological parameters of people suffering from OSA. We developed a predictive model for the presence/severity of OSA using a simple linear equation and additional decision trees with nonlinear features extracted from 3 respiratory recordings. The accuracy of the methodology is high and the findings provide insight to the underlying pathophysiology of the syndrome. Reliable predictions of OSA are possible using linear and nonlinear indices from only 3 respiratory signals during sleep. The proposed models could lead to a better study of the pathophysiology of OSA and facilitate initial evaluation/follow up of suspected patients OSA utilizing a practical low cost methodology. ClinicalTrials.gov NCT01161381.

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.

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.

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.

Resting electrocardiogram and stress myocardial perfusion imaging in the determination of left ventricular systolic function: an assessment enhancing the performance of gated SPET.

PubMed

Moralidis, Efstratios; Spyridonidis, Tryfon; Arsos, Georgios; Skeberis, Vassilios; Anagnostopoulos, Constantinos; Gavrielidis, Stavros

2010-01-01

This study aimed to determine systolic dysfunction and estimate resting left ventricular ejection fraction (LVEF) from information collected during routine evaluation of patients with suspected or known coronary heart disease. This approach was then compared to gated single photon emission tomography (SPET). Patients having undergone stress (201)Tl myocardial perfusion imaging followed by equilibrium radionuclide angiography (ERNA) were separated into derivation (n=954) and validation (n=309) groups. Logistic regression analysis was used to develop scoring systems, containing clinical, electrocardiographic (ECG) and scintigraphic data, for the discrimination of an ERNA-LVEF<0.50. Linear regression analysis provided equations predicting ERNA-LVEF from those scores. In 373 patients LVEF was also assessed with (201)Tl gated SPET. Our results showed that an ECG-Scintigraphic scoring system was the best simple predictor of an ERNA-LVEF<0.50 in comparison to other models including ECG, clinical and scintigraphic variables in both the derivation and validation subpopulations. A simple linear equation was derived also for the assessment of resting LVEF from the ECG-Scintigraphic model. Equilibrium radionuclide angiography-LVEF had a good correlation with the ECG-Scintigraphic model LVEF (r=0.716, P=0.000), (201)Tl gated SPET LVEF (r=0.711, P=0.000) and the average LVEF from those assessments (r=0.796, P=0.000). The Bland-Altman statistic (mean+/-2SD) provided values of 0.001+/-0.176, 0.071+/-0.196 and 0.040+/-0.152, respectively. The average LVEF was a better discriminator of systolic dysfunction than gated SPET-LVEF in receiver operating characteristic (ROC) analysis and identified more patients (89%) with a

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.

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.

Sensitivity and Bias in Decision-Making under Risk: Evaluating the Perception of Reward, Its Probability and Value

PubMed Central

Sharp, Madeleine E.; Viswanathan, Jayalakshmi; Lanyon, Linda J.; Barton, Jason J. S.

2012-01-01

Background There are few clinical tools that assess decision-making under risk. Tests that characterize sensitivity and bias in decisions between prospects varying in magnitude and probability of gain may provide insights in conditions with anomalous reward-related behaviour. Objective We designed a simple test of how subjects integrate information about the magnitude and the probability of reward, which can determine discriminative thresholds and choice bias in decisions under risk. Design/Methods Twenty subjects were required to choose between two explicitly described prospects, one with higher probability but lower magnitude of reward than the other, with the difference in expected value between the two prospects varying from 3 to 23%. Results Subjects showed a mean threshold sensitivity of 43% difference in expected value. Regarding choice bias, there was a ‘risk premium’ of 38%, indicating a tendency to choose higher probability over higher reward. An analysis using prospect theory showed that this risk premium is the predicted outcome of hypothesized non-linearities in the subjective perception of reward value and probability. Conclusions This simple test provides a robust measure of discriminative value thresholds and biases in decisions under risk. Prospect theory can also make predictions about decisions when subjective perception of reward or probability is anomalous, as may occur in populations with dopaminergic or striatal dysfunction, such as Parkinson's disease and schizophrenia. PMID:22493669

Sensitivity and bias in decision-making under risk: evaluating the perception of reward, its probability and value.

PubMed

Sharp, Madeleine E; Viswanathan, Jayalakshmi; Lanyon, Linda J; Barton, Jason J S

2012-01-01

There are few clinical tools that assess decision-making under risk. Tests that characterize sensitivity and bias in decisions between prospects varying in magnitude and probability of gain may provide insights in conditions with anomalous reward-related behaviour. We designed a simple test of how subjects integrate information about the magnitude and the probability of reward, which can determine discriminative thresholds and choice bias in decisions under risk. Twenty subjects were required to choose between two explicitly described prospects, one with higher probability but lower magnitude of reward than the other, with the difference in expected value between the two prospects varying from 3 to 23%. Subjects showed a mean threshold sensitivity of 43% difference in expected value. Regarding choice bias, there was a 'risk premium' of 38%, indicating a tendency to choose higher probability over higher reward. An analysis using prospect theory showed that this risk premium is the predicted outcome of hypothesized non-linearities in the subjective perception of reward value and probability. This simple test provides a robust measure of discriminative value thresholds and biases in decisions under risk. Prospect theory can also make predictions about decisions when subjective perception of reward or probability is anomalous, as may occur in populations with dopaminergic or striatal dysfunction, such as Parkinson's disease and schizophrenia.

Enhancement of equivalence class formation by pretraining discriminative functions.

PubMed

Nartey, Richard K; Arntzen, Erik; Fields, Lanny

2015-03-01

The present experiment showed that a simple discriminative function acquired by an abstract stimulus through simultaneous and/or successive discrimination training enhanced the formation of an equivalence class of which that stimulus was a member. College students attempted to form three equivalence classes composed of three nodes and five members (A→B→C→D→E), using the simultaneous protocol. In the PIC group, the C stimuli were pictures and the A, B, D, and E stimuli were abstract shapes. In the ABS group, all of the stimuli were abstract shapes. In the SIM + SUCC (simultaneous and successive) group, simple discriminations were formed with the C stimuli through both simultaneous and successive discrimination training before class formation. Finally, in the SIM-only and SUCC-only groups, prior to class formation, simple discriminations were established for the C stimuli with a simultaneous procedure and a successive procedure, respectively. Equivalence classes were formed by 80% and 70% of the participants in the PIC and SIM + SUCC groups respectively, by 30% in the SUCC-only group, and by 10% apiece in the ABS and SIM-only groups. Thus, pretraining of combined simultaneous and successive discriminations enhanced class formation, as did the inclusion of a meaningful stimulus in a class. The isolated effect of forming successive discriminations was more influential than that of forming simultaneous discriminations. The establishment of both discriminations together produced an enhancement greater than the sum of the two procedures alone. Finally, a sorting test documented the maintenance of the classes formed during the simultaneous protocol. These results also provide a stimulus control-function account of the class-enhancing effects of meaningful stimuli.

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.

Detection of indoor biological hazards using the man-portable laser induced breakdown spectrometer

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

Munson, Chase A.; Gottfried, Jennifer L.; Snyder, Emily Gibb

2008-11-01

The performance of a man-portable laser induced breakdown spectrometer was evaluated for the detection of biological powders on indoor office surfaces and wipe materials. Identification of pure unknown powders was performed by comparing against a library of spectra containing biological agent surrogates and confusant materials, such as dusts, diesel soot, natural and artificial sweeteners, and drink powders, using linear correlation analysis. Simple models constructed using a second technique, partial least squares discriminant analysis, successfully identified Bacillus subtilis (BG) spores on wipe materials and office surfaces. Furthermore, these models were able to identify BG on materials not used in the trainingmore » of the model.« less

[Feature extraction for breast cancer data based on geometric algebra theory and feature selection using differential evolution].

PubMed

Li, Jing; Hong, Wenxue

2014-12-01

The feature extraction and feature selection are the important issues in pattern recognition. Based on the geometric algebra representation of vector, a new feature extraction method using blade coefficient of geometric algebra was proposed in this study. At the same time, an improved differential evolution (DE) feature selection method was proposed to solve the elevated high dimension issue. The simple linear discriminant analysis was used as the classifier. The result of the 10-fold cross-validation (10 CV) classification of public breast cancer biomedical dataset was more than 96% and proved superior to that of the original features and traditional feature extraction method.

Learning and recall of form discriminations during reversible cooling deactivation of ventral-posterior suprasylvian cortex in the cat.

PubMed Central

Lomber, S G; Payne, B R; Cornwell, P

1996-01-01

Extrastriate visual cortex of the ventral-posterior suprasylvian gyrus (vPS cortex) of freely behaving cats was reversibly deactivated with cooling to determine its role in performance on a battery of simple or masked two-dimensional pattern discriminations, and three-dimensional object discriminations. Deactivation of vPS cortex by cooling profoundly impaired the ability of the cats to recall the difference between all previously learned pattern and object discriminations. However, the cats' ability to learn or relearn pattern and object discriminations while vPS was deactivated depended upon the nature of the pattern or object and the cats' prior level of exposure to them. During cooling of vPS cortex, the cats could neither learn the novel object discriminations nor relearn a highly familiar masked or partially occluded pattern discrimination, although they could relearn both the highly familiar object and simple pattern discriminations. These cooling-induced deficits resemble those induced by cooling of the topologically equivalent inferotemporal cortex of monkeys and provides evidence that the equivalent regions contribute to visual processing in similar ways. Images Fig. 1 Fig. 3 PMID:8643686

Computing local edge probability in natural scenes from a population of oriented simple cells

PubMed Central

Ramachandra, Chaithanya A.; Mel, Bartlett W.

2013-01-01

A key computation in visual cortex is the extraction of object contours, where the first stage of processing is commonly attributed to V1 simple cells. The standard model of a simple cell—an oriented linear filter followed by a divisive normalization—fits a wide variety of physiological data, but is a poor performing local edge detector when applied to natural images. The brain's ability to finely discriminate edges from nonedges therefore likely depends on information encoded by local simple cell populations. To gain insight into the corresponding decoding problem, we used Bayes's rule to calculate edge probability at a given location/orientation in an image based on a surrounding filter population. Beginning with a set of ∼ 100 filters, we culled out a subset that were maximally informative about edges, and minimally correlated to allow factorization of the joint on- and off-edge likelihood functions. Key features of our approach include a new, efficient method for ground-truth edge labeling, an emphasis on achieving filter independence, including a focus on filters in the region orthogonal rather than tangential to an edge, and the use of a customized parametric model to represent the individual filter likelihood functions. The resulting population-based edge detector has zero parameters, calculates edge probability based on a sum of surrounding filter influences, is much more sharply tuned than the underlying linear filters, and effectively captures fine-scale edge structure in natural scenes. Our findings predict nonmonotonic interactions between cells in visual cortex, wherein a cell may for certain stimuli excite and for other stimuli inhibit the same neighboring cell, depending on the two cells' relative offsets in position and orientation, and their relative activation levels. PMID:24381295

Rapid Discrimination for Traditional Complex Herbal Medicines from Different Parts, Collection Time, and Origins Using High-Performance Liquid Chromatography and Near-Infrared Spectral Fingerprints with Aid of Pattern Recognition Methods

PubMed Central

Fu, Haiyan; Fan, Yao; Zhang, Xu; Lan, Hanyue; Yang, Tianming; Shao, Mei; Li, Sihan

2015-01-01

As an effective method, the fingerprint technique, which emphasized the whole compositions of samples, has already been used in various fields, especially in identifying and assessing the quality of herbal medicines. High-performance liquid chromatography (HPLC) and near-infrared (NIR), with their unique characteristics of reliability, versatility, precision, and simple measurement, played an important role among all the fingerprint techniques. In this paper, a supervised pattern recognition method based on PLSDA algorithm by HPLC and NIR has been established to identify the information of Hibiscus mutabilis L. and Berberidis radix, two common kinds of herbal medicines. By comparing component analysis (PCA), linear discriminant analysis (LDA), and particularly partial least squares discriminant analysis (PLSDA) with different fingerprint preprocessing of NIR spectra variables, PLSDA model showed perfect functions on the analysis of samples as well as chromatograms. Most important, this pattern recognition method by HPLC and NIR can be used to identify different collection parts, collection time, and different origins or various species belonging to the same genera of herbal medicines which proved to be a promising approach for the identification of complex information of herbal medicines. PMID:26345990

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.

Simplest chronoscope. III. Further comparisons between reaction times obtained by meterstick versus machine.

PubMed

Montare, Alberto

2013-06-01

The three classical Donders' reaction time (RT) tasks (simple, choice, and discriminative RTs) were employed to compare reaction time scores from college students obtained by use of Montare's simplest chronoscope (meterstick) methodology to scores obtained by use of a digital-readout multi-choice reaction timer (machine). Five hypotheses were tested. Simple RT, choice RT, and discriminative RT were faster when obtained by meterstick than by machine. The meterstick method showed higher reliability than the machine method and was less variable. The meterstick method of the simplest chronoscope may help to alleviate the longstanding problems of low reliability and high variability of reaction time performances; while at the same time producing faster performance on Donders' simple, choice and discriminative RT tasks than the machine method.

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...

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.

Discrimination of Aurantii Fructus Immaturus and Fructus Poniciri Trifoliatae Immaturus by Flow Injection UV Spectroscopy (FIUV) and 1H NMR using Partial Least-squares Discriminant Analysis (PLS-DA)

USDA-ARS?s Scientific Manuscript database

Two simple fingerprinting methods, flow-injection UV spectroscopy (FIUV) and 1H nuclear magnetic resonance (NMR), for discrimination of Aurantii FructusImmaturus and Fructus Poniciri TrifoliataeImmaturususing were described. Both methods were combined with partial least-squares discriminant analysis...

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.

A comparison of methods for teaching receptive labeling to children with autism spectrum disorders.

PubMed

Grow, Laura L; Carr, James E; Kodak, Tiffany M; Jostad, Candice M; Kisamore, April N

2011-01-01

Many early intervention curricular manuals recommend teaching auditory-visual conditional discriminations (i.e., receptive labeling) using the simple-conditional method in which component simple discriminations are taught in isolation and in the presence of a distracter stimulus before the learner is required to respond conditionally. Some have argued that this procedure might be susceptible to faulty stimulus control such as stimulus overselectivity (Green, 2001). Consequently, there has been a call for the use of alternative teaching procedures such as the conditional-only method, which involves conditional discrimination training from the onset of intervention. The purpose of the present study was to compare the simple-conditional and conditional-only methods for teaching receptive labeling to 3 young children diagnosed with autism spectrum disorders. The data indicated that the conditional-only method was a more reliable and efficient teaching procedure. In addition, several error patterns emerged during training using the simple-conditional method. The implications of the results with respect to current teaching practices in early intervention programs are discussed.

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.

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.

Ambiguity domain-based identification of altered gait pattern in ALS disorder

NASA Astrophysics Data System (ADS)

Sugavaneswaran, L.; Umapathy, K.; Krishnan, S.

2012-08-01

The onset of a neurological disorder, such as amyotrophic lateral sclerosis (ALS), is so subtle that the symptoms are often overlooked, thereby ruling out the option of early detection of the abnormality. In the case of ALS, over 75% of the affected individuals often experience awkwardness when using their limbs, which alters their gait, i.e. stride and swing intervals. The aim of this work is to suitably represent the non-stationary characteristics of gait (fluctuations in stride and swing intervals) in order to facilitate discrimination between normal and ALS subjects. We define a simple-yet-representative feature vector space by exploiting the ambiguity domain (AD) to achieve efficient classification between healthy and pathological gait stride interval. The stride-to-stride fluctuations and the swing intervals of 16 healthy control and 13 ALS-affected subjects were analyzed. Three features that are representative of the gait signal characteristics were extracted from the AD-space and are fed to linear discriminant analysis and neural network classifiers, respectively. Overall, maximum accuracies of 89.2% (LDA) and 100% (NN) were obtained in classifying the ALS gait.

Simple method to distinguish between primary and secondary C3 deficiencies.

PubMed

Pereira de Carvalho Florido, Marlene; Ferreira de Paula, Patrícia; Isaac, Lourdes

2003-03-01

Due to the increasing numbers of reported clinical cases of complement deficiency in medical centers, clinicians are now more aware of the role of the complement system in the protection against infections caused by microorganisms. Therefore, clinical laboratories are now prepared to perform a number of diagnostic tests of the complement system other than the standard 50% hemolytic component assay. Deficiencies of alternative complement pathway proteins are related to severe and recurrent infections; and the application of easy, reliable, and low-cost methods for their detection and distinction are always welcome, notably in developing countries. When activation of the alternative complement pathway is evaluated in hemolytic agarose plates, some but not all human sera cross-react to form a late linear lysis. Since the formation of this linear lysis is dependent on C3 and factor B, it is possible to use late linear lysis to routinely screen for the presence of deficiencies of alternative human complement pathway proteins such as factor B. Furthermore, since linear lysis is observed between normal human serum and primary C3-deficient serum but not between normal human serum and secondary C3-deficient serum caused by the lack of factor H or factor I, this assay may also be used to discriminate between primary and secondary C3 deficiencies.

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.

A Derived Transfer of Simple Discrimination and Self-Reported Arousal Functions in Spider Fearful and Non-Spider-Fearful Participants

ERIC Educational Resources Information Center

Smyth, Sinead; Barnes-Holmes, Dermot; Forsyth, John P.

2006-01-01

Two experiments investigated the derived transfer of functions through equivalence relations established using a stimulus pairing observation procedure. In Experiment 1, participants were trained on a simple discrimination (A1+/A2-) and then a stimulus pairing observation procedure was used to establish 4 stimulus pairings (A1-B1, A2-B2, B1-C1,…

Analysis of longitudinal diffusion-weighted images in healthy and pathological aging: An ADNI study.

PubMed

Kruggel, Frithjof; Masaki, Fumitaro; Solodkin, Ana

2017-02-15

The widely used framework of voxel-based morphometry for analyzing neuroimages is extended here to model longitudinal imaging data by exchanging the linear model with a linear mixed-effects model. The new approach is employed for analyzing a large longitudinal sample of 756 diffusion-weighted images acquired in 177 subjects of the Alzheimer's Disease Neuroimaging initiative (ADNI). While sample- and group-level results from both approaches are equivalent, the mixed-effect model yields information at the single subject level. Interestingly, the neurobiological relevance of the relevant parameter at the individual level describes specific differences associated with aging. In addition, our approach highlights white matter areas that reliably discriminate between patients with Alzheimer's disease and healthy controls with a predictive power of 0.99 and include the hippocampal alveus, the para-hippocampal white matter, the white matter of the posterior cingulate, and optic tracts. In this context, notably the classifier includes a sub-population of patients with minimal cognitive impairment into the pathological domain. Our classifier offers promising features for an accessible biomarker that predicts the risk of conversion to Alzheimer's disease. Data used in preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how to apply/ADNI Acknowledgement List.pdf. Significance statement This study assesses neuro-degenerative processes in the brain's white matter as revealed by diffusion-weighted imaging, in order to discriminate healthy from pathological aging in a large sample of elderly subjects. The analysis of time-series examinations in a linear mixed effects model allowed the discrimination of population-based aging processes from individual determinants. We demonstrate that a simple classifier based on white matter imaging data is able to predict the conversion to Alzheimer's disease with a high predictive power. Copyright © 2017 Elsevier B.V. All rights reserved.

The performance of ravens on simple discrimination tasks: a preliminary study

PubMed Central

Range, Friederike; Bugnyar, Thomas; Kotrschal, Kurt

2015-01-01

Recent studies suggest the existence of primate-like cognitive abilities in corvids. Although the learning abilities of corvids in comparison to other species have been investigated before, little is known on how corvids perform on simple discrimination tasks if tested in experimental settings comparable to those that have been used for studying complex cognitive abilities. In this study, we tested a captive group of 12 ravens (Corvus corax) on four discrimination problems and their reversals. In contrast to other studies investigating learning abilities, our ravens were not food deprived and participation in experiments was voluntary. This preliminary study showed that all ravens successfully solved feature and position discriminations and several of the ravens could solve new tasks in a few trials, making very few mistakes. PMID:25948877

Tissue differentiation by diffuse reflectance spectroscopy for automated oral and maxillofacial laser surgery: ex vivo pilot study

NASA Astrophysics Data System (ADS)

Zam, Azhar; Stelzle, Florian; Tangermann-Gerk, Katja; Adler, Werner; Nkenke, Emeka; Schmidt, Michael; Douplik, Alexandre

2010-02-01

Remote laser surgery lacks of haptic feedback during the laser ablation of tissue. Hence, there is a risk of iatrogenic damage or destruction of anatomical structures like nerves or salivary glands. Diffuse reflectance spectroscopy provides a straightforward and simple approach for optical tissue differentiation. We measured diffuse reflectance from seven various tissue types ex vivo. We applied Linear Discriminant Analysis (LDA) to differentiate the seven tissue types and computed the area under the ROC curve (AUC). Special emphasis was taken on the identification of nerves and salivary glands as the most crucial tissue for maxillofacial surgery. The results show a promise for differentiating tissues as guidance for oral and maxillofacial laser surgery by means of diffuse reflectance.

Testing of a simplified LED based vis/NIR system for rapid ripeness evaluation of white grape (Vitis vinifera L.) for Franciacorta wine.

PubMed

Giovenzana, Valentina; Civelli, Raffaele; Beghi, Roberto; Oberti, Roberto; Guidetti, Riccardo

2015-11-01

The aim of this work was to test a simplified optical prototype for a rapid estimation of the ripening parameters of white grape for Franciacorta wine directly in field. Spectral acquisition based on reflectance at four wavelengths (630, 690, 750 and 850 nm) was proposed. The integration of a simple processing algorithm in the microcontroller software would allow to visualize real time values of spectral reflectance. Non-destructive analyses were carried out on 95 grape bunches for a total of 475 berries. Samplings were performed weekly during the last ripening stages. Optical measurements were carried out both using the simplified system and a portable commercial vis/NIR spectrophotometer, as reference instrument for performance comparison. Chemometric analyses were performed in order to extract the maximum useful information from optical data. Principal component analysis (PCA) was performed for a preliminary evaluation of the data. Correlations between the optical data matrix and ripening parameters (total soluble solids content, SSC; titratable acidity, TA) were carried out using partial least square (PLS) regression for spectra and using multiple linear regression (MLR) for data from the simplified device. Classification analysis were also performed with the aim of discriminate ripe and unripe samples. PCA, MLR and classification analyses show the effectiveness of the simplified system in separating samples among different sampling dates and in discriminating ripe from unripe samples. Finally, simple equations for SSC and TA prediction were calculated. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Relating Stellar Cycle Periods to Dynamo Calculations

NASA Technical Reports Server (NTRS)

Tobias, S. M.

1998-01-01

Stellar magnetic activity in slowly rotating stars is often cyclic, with the period of the magnetic cycle depending critically on the rotation rate and the convective turnover time of the star. Here we show that the interpretation of this law from dynamo models is not a simple task. It is demonstrated that the period is (unsurprisingly) sensitive to the precise type of non-linearity employed. Moreover the calculation of the wave-speed of plane-wave solutions does not (as was previously supposed) give an indication of the magnetic period in a more realistic dynamo model, as the changes in length-scale of solutions are not easily captured by this approach. Progress can be made, however, by considering a realistic two-dimensional model, in which the radial length-scale of waves is included. We show that it is possible in this case to derive a more robust relation between cycle period and dynamo number. For all the non-linearities considered in the most realistic model, the magnetic cycle period is a decreasing function of IDI (the amplitude of the dynamo number). However, discriminating between different non-linearities is difficult in this case and care must therefore be taken before advancing explanations for the magnetic periods of stars.

Simple and conditional visual discrimination with wheel running as reinforcement in rats.

PubMed

Iversen, I H

1998-09-01

Three experiments explored whether access to wheel running is sufficient as reinforcement to establish and maintain simple and conditional visual discriminations in nondeprived rats. In Experiment 1, 2 rats learned to press a lit key to produce access to running; responding was virtually absent when the key was dark, but latencies to respond were longer than for customary food and water reinforcers. Increases in the intertrial interval did not improve the discrimination performance. In Experiment 2, 3 rats acquired a go-left/go-right discrimination with a trial-initiating response and reached an accuracy that exceeded 80%; when two keys showed a steady light, pressing the left key produced access to running whereas pressing the right key produced access to running when both keys showed blinking light. Latencies to respond to the lights shortened when the trial-initiation response was introduced and became much shorter than in Experiment 1. In Experiment 3, 1 rat acquired a conditional discrimination task (matching to sample) with steady versus blinking lights at an accuracy exceeding 80%. A trial-initiation response allowed self-paced trials as in Experiment 2. When the rat was exposed to the task for 19 successive 24-hr periods with access to food and water, the discrimination performance settled in a typical circadian pattern and peak accuracy exceeded 90%. When the trial-initiation response was under extinction, without access to running, the circadian activity pattern determined the time of spontaneous recovery. The experiments demonstrate that wheel-running reinforcement can be used to establish and maintain simple and conditional visual discriminations in nondeprived rats.

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.

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.

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.

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.

A COMPARISON OF METHODS FOR TEACHING RECEPTIVE LABELING TO CHILDREN WITH AUTISM SPECTRUM DISORDERS

PubMed Central

Grow, Laura L; Carr, James E; Kodak, Tiffany M; Jostad, Candice M; Kisamore, April N

2011-01-01

Many early intervention curricular manuals recommend teaching auditory-visual conditional discriminations (i.e., receptive labeling) using the simple-conditional method in which component simple discriminations are taught in isolation and in the presence of a distracter stimulus before the learner is required to respond conditionally. Some have argued that this procedure might be susceptible to faulty stimulus control such as stimulus overselectivity (Green, 2001). Consequently, there has been a call for the use of alternative teaching procedures such as the conditional-only method, which involves conditional discrimination training from the onset of intervention. The purpose of the present study was to compare the simple-conditional and conditional-only methods for teaching receptive labeling to 3 young children diagnosed with autism spectrum disorders. The data indicated that the conditional-only method was a more reliable and efficient teaching procedure. In addition, several error patterns emerged during training using the simple-conditional method. The implications of the results with respect to current teaching practices in early intervention programs are discussed. PMID:21941380

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.

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.

Use of the Oxford Handicap Scale at hospital discharge to predict Glasgow Outcome Scale at 6 months in patients with traumatic brain injury.

PubMed

Perel, Pablo; Edwards, Phil; Shakur, Haleema; Roberts, Ian

2008-11-06

Traumatic brain injury (TBI) is an important cause of acquired disability. In evaluating the effectiveness of clinical interventions for TBI it is important to measure disability accurately. The Glasgow Outcome Scale (GOS) is the most widely used outcome measure in randomised controlled trials (RCTs) in TBI patients. However GOS measurement is generally collected at 6 months after discharge when loss to follow up could have occurred. The objectives of this study were to evaluate the association and predictive validity between a simple disability scale at hospital discharge, the Oxford Handicap Scale (OHS), and the GOS at 6 months among TBI patients. The study was a secondary analysis of a randomised clinical trial among TBI patients (MRC CRASH Trial). A Spearman correlation was estimated to evaluate the association between the OHS and GOS. The validity of different dichotomies of the OHS for predicting GOS at 6 months was assessed by calculating sensitivity, specificity and the C statistic. Uni and multivariate logistic regression models were fitted including OHS as explanatory variable. For each model we analysed its discrimination and calibration. We found that the OHS is highly correlated with GOS at 6 months (spearman correlation 0.75) with evidence of a linear relationship between the two scales. The OHS dichotomy that separates patients with severe dependency or death showed the greatest discrimination (C statistic: 84.3). Among survivors at hospital discharge the OHS showed a very good discrimination (C statistic 0.78) and excellent calibration when used to predict GOS outcome at 6 months. We have shown that the OHS, a simple disability scale available at hospital discharge can predict disability accurately, according to the GOS, at 6 months. OHS could be used to improve the design and analysis of clinical trials in TBI patients and may also provide a valuable clinical tool for physicians to improve communication with patients and relatives when assessing a patient's prognosis at hospital discharge.

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.

Lack of power enhances visual perceptual discrimination.

PubMed

Weick, Mario; Guinote, Ana; Wilkinson, David

2011-09-01

Powerless individuals face much challenge and uncertainty. As a consequence, they are highly vigilant and closely scrutinize their social environments. The aim of the present research was to determine whether these qualities enhance performance in more basic cognitive tasks involving simple visual feature discrimination. To test this hypothesis, participants performed a series of perceptual matching and search tasks involving colour, texture, and size discrimination. As predicted, those primed with powerlessness generated shorter reaction times and made fewer eye movements than either powerful or control participants. The results indicate that the heightened vigilance shown by powerless individuals is associated with an advantage in performing simple types of psychophysical discrimination. These findings highlight, for the first time, an underlying competency in perceptual cognition that sets powerless individuals above their powerful counterparts, an advantage that may reflect functional adaptation to the environmental challenge and uncertainty that they face. © 2011 Canadian Psychological Association

Stimuli that signal the absence of reinforcement are paid more attention than are irrelevant stimuli.

PubMed

Dopson, Jemma C; Williams, Natalie A; Esber, Guillem R; Pearce, John M

2010-11-01

According to established theories of attention (e.g., Mackintosh, 1975; Sutherland & Mackintosh, 1971), simple discriminations of the form AX+ BX- result in an increase in attention to stimuli A and B, which are relevant to the outcome that follows them, at the expense of X, which is irrelevant. Experiments that have apparently shown such changes in attention have failed to determine whether attention is enhanced to both A and B, which signal reinforcement and nonreinforcement, respectively, or just to A. In Experiments 1 and 2, pigeons were trained with a number of discriminations of the kind AX+ BX-, before compounds that had been consistently nonreinforced were involved in a subsequent discrimination. Both experiments provided support for theories that propose that more attention is paid to stimuli that consistently signal nonreinforcement than to irrelevant stimuli in simple discriminations.

Advanced statistics: linear regression, part I: simple linear regression.

PubMed

Marill, Keith A

2004-01-01

Simple linear regression is a mathematical technique used to model the relationship between a single independent predictor variable and a single dependent outcome variable. In this, the first of a two-part series exploring concepts in linear regression analysis, the four fundamental assumptions and the mechanics of simple linear regression are reviewed. The most common technique used to derive the regression line, the method of least squares, is described. The reader will be acquainted with other important concepts in simple linear regression, including: variable transformations, dummy variables, relationship to inference testing, and leverage. Simplified clinical examples with small datasets and graphic models are used to illustrate the points. This will provide a foundation for the second article in this series: a discussion of multiple linear regression, in which there are multiple predictor variables.

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.

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.

Classification image analysis: estimation and statistical inference for two-alternative forced-choice experiments

NASA Technical Reports Server (NTRS)

Abbey, Craig K.; Eckstein, Miguel P.

2002-01-01

We consider estimation and statistical hypothesis testing on classification images obtained from the two-alternative forced-choice experimental paradigm. We begin with a probabilistic model of task performance for simple forced-choice detection and discrimination tasks. Particular attention is paid to general linear filter models because these models lead to a direct interpretation of the classification image as an estimate of the filter weights. We then describe an estimation procedure for obtaining classification images from observer data. A number of statistical tests are presented for testing various hypotheses from classification images based on some more compact set of features derived from them. As an example of how the methods we describe can be used, we present a case study investigating detection of a Gaussian bump profile.

DIFFERENTIATION OF AURANTII FRUCTUS IMMATURUS AND FRUCTUS PONICIRI TRIFOLIATAE IMMATURUS BY FLOW-INJECTION WITH ULTRAVIOLET SPECTROSCOPIC DETECTION AND PROTON NUCLEAR MAGNETIC RESONANCE USING PARTIAL LEAST-SQUARES DISCRIMINANT ANALYSIS.

PubMed

Zhang, Mengliang; Zhao, Yang; Harrington, Peter de B; Chen, Pei

2016-03-01

Two simple fingerprinting methods, flow-injection coupled to ultraviolet spectroscopy and proton nuclear magnetic resonance, were used for discriminating between Aurantii fructus immaturus and Fructus poniciri trifoliatae immaturus . Both methods were combined with partial least-squares discriminant analysis. In the flow-injection method, four data representations were evaluated: total ultraviolet absorbance chromatograms, averaged ultraviolet spectra, absorbance at 193, 205, 225, and 283 nm, and absorbance at 225 and 283 nm. Prediction rates of 100% were achieved for all data representations by partial least-squares discriminant analysis using leave-one-sample-out cross-validation. The prediction rate for the proton nuclear magnetic resonance data by partial least-squares discriminant analysis with leave-one-sample-out cross-validation was also 100%. A new validation set of data was collected by flow-injection with ultraviolet spectroscopic detection two weeks later and predicted by partial least-squares discriminant analysis models constructed by the initial data representations with no parameter changes. The classification rates were 95% with the total ultraviolet absorbance chromatograms datasets and 100% with the other three datasets. Flow-injection with ultraviolet detection and proton nuclear magnetic resonance are simple, high throughput, and low-cost methods for discrimination studies.

Aptamer-aided target capturing with biocatalytic metal deposition: an electrochemical platform for sensitive detection of cancer cells.

PubMed

Yi, Zi; Li, Xiao-Yan; Gao, Qing; Tang, Li-Juan; Chu, Xia

2013-04-07

A novel aptamer biosensor for cancer cell assay has been reported on the basis of ultrasensitive electrochemical detection. Cancer cell capturing is first accomplished via aptamer-aided recognition, and the cell-aptamer binding events then mediate an alkaline phosphatase-catalyzed silver deposition reaction which can be probed by electrochemical detection. Following biocatalytic silver deposition, an efficient amplification approach for sensitive electrochemical measurements is demonstrated, for cell detection with high sensitivity. Ramos cell are used as a model case, a typical biomarker of the acute blood cell cancer, Burkitt's lymphoma. The results reveal that the developed technique displays desirable selectivity in Ramos cell discrimination, and linear response range from 10 to 10(6) cells with a detection limit as low as 10 cells. Due to the simple procedures, label-free and electrochemistry based detection format, this technique is simple and cost-effective, and exhibits excellent compatibility with miniaturization technologies. The electrochemical cell detection strategy may create an intrinsically specific and sensitive platform for cancer cell assay and associated studies.

Using UV-Vis spectroscopy for simultaneous geographical and varietal classification of tea infusions simulating a home-made tea cup.

PubMed

Diniz, Paulo Henrique Gonçalves Dias; Barbosa, Mayara Ferreira; de Melo Milanez, Karla Danielle Tavares; Pistonesi, Marcelo Fabián; de Araújo, Mário César Ugulino

2016-02-01

In this work we proposed a method to verify the differentiating characteristics of simple tea infusions prepared in boiling water alone (simulating a home-made tea cup), which represents the final product as ingested by the consumers. For this purpose we used UV-Vis spectroscopy and variable selection through the Successive Projections Algorithm associated with Linear Discriminant Analysis (SPA-LDA) for simultaneous classification of the teas according to their variety and geographic origin. For comparison, KNN, CART, SIMCA, PLS-DA and PCA-LDA were also used. SPA-LDA and PCA-LDA provided significantly better results for tea classification of the five studied classes (Argentinean green tea; Brazilian green tea; Argentinean black tea; Brazilian black tea; and Sri Lankan black tea). The proposed methodology provides a simpler, faster and more affordable classification of simple tea infusions, and can be used as an alternative approach to traditional tea quality evaluation as made by skilful tasters, which is evidently partial and cannot assess geographic origins. Copyright © 2015 Elsevier Ltd. All rights reserved.

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

A paper-based cantilever array sensor: Monitoring volatile organic compounds with naked eye.

PubMed

Fraiwan, Arwa; Lee, Hankeun; Choi, Seokheun

2016-09-01

Volatile organic compound (VOC) detection is critical for controlling industrial and commercial emissions, environmental monitoring, and public health. Simple, portable, rapid and low-cost VOC sensing platforms offer the benefits of on-site and real-time monitoring anytime and anywhere. The best and most practically useful approaches to monitoring would include equipment-free and power-free detection by the naked eye. In this work, we created a novel, paper-based cantilever sensor array that allows simple and rapid naked-eye VOC detection without the need for power, electronics or readout interface/equipment. This simple VOC detection method was achieved using (i) low-cost paper materials as a substrate and (ii) swellable thin polymers adhered to the paper. Upon exposure to VOCs, the polymer swelling adhered to the paper-based cantilever, inducing mechanical deflection that generated a distinctive composite pattern of the deflection angles for a specific VOC. The angle is directly measured by the naked eye on a 3-D protractor printed on a paper facing the cantilevers. The generated angle patterns are subjected to statistical algorithms (linear discriminant analysis (LDA)) to classify each VOC sample and selectively detect a VOC. We classified four VOC samples with 100% accuracy using LDA. Copyright © 2016 Elsevier B.V. All rights reserved.

A simple and fast representation space for classifying complex time series

NASA Astrophysics Data System (ADS)

Zunino, Luciano; Olivares, Felipe; Bariviera, Aurelio F.; Rosso, Osvaldo A.

2017-03-01

In the context of time series analysis considerable effort has been directed towards the implementation of efficient discriminating statistical quantifiers. Very recently, a simple and fast representation space has been introduced, namely the number of turning points versus the Abbe value. It is able to separate time series from stationary and non-stationary processes with long-range dependences. In this work we show that this bidimensional approach is useful for distinguishing complex time series: different sets of financial and physiological data are efficiently discriminated. Additionally, a multiscale generalization that takes into account the multiple time scales often involved in complex systems has been also proposed. This multiscale analysis is essential to reach a higher discriminative power between physiological time series in health and disease.

THz Discrimination of Materials: Development of an Apparatus Based on Room Temperature Detection and Metasurfaces Selective Filters

NASA Astrophysics Data System (ADS)

Carelli, P.; Chiarello, F.; Torrioli, G.; Castellano, M. G.

2017-03-01

We present an apparatus for terahertz discrimination of materials designed to be fast, simple, compact, and economical in order to be suitable for preliminary on-field analysis. The system working principles, bio-inspired by the human vision of colors, are based on the use of an incoherent source, a room temperature detector, a series of microfabricated metamaterials selective filters, a very compact optics based on metallic ellipsoidal mirrors in air, and a treatment of the mirrors' surfaces that select the frequency band of interest. We experimentally demonstrate the operation of the apparatus in discriminating simple substances such as salt, staple foods, and grease. We present the system and the obtained results and discuss issues and possible developments.

Volatile-Compound Fingerprinting by Headspace-Gas-Chromatography Ion-Mobility Spectrometry (HS-GC-IMS) as a Benchtop Alternative to 1H NMR Profiling for Assessment of the Authenticity of Honey.

PubMed

Gerhardt, Natalie; Birkenmeier, Markus; Schwolow, Sebastian; Rohn, Sascha; Weller, Philipp

2018-02-06

This work describes a simple approach for the untargeted profiling of volatile compounds for the authentication of the botanical origins of honey based on resolution-optimized HS-GC-IMS combined with optimized chemometric techniques, namely PCA, LDA, and kNN. A direct comparison of the PCA-LDA models between the HS-GC-IMS and 1 H NMR data demonstrated that HS-GC-IMS profiling could be used as a complementary tool to NMR-based profiling of honey samples. Whereas NMR profiling still requires comparatively precise sample preparation, pH adjustment in particular, HS-GC-IMS fingerprinting may be considered an alternative approach for a truly fully automatable, cost-efficient, and in particular highly sensitive method. It was demonstrated that all tested honey samples could be distinguished on the basis of their botanical origins. Loading plots revealed the volatile compounds responsible for the differences among the monofloral honeys. The HS-GC-IMS-based PCA-LDA model was composed of two linear functions of discrimination and 10 selected PCs that discriminated canola, acacia, and honeydew honeys with a predictive accuracy of 98.6%. Application of the LDA model to an external test set of 10 authentic honeys clearly proved the high predictive ability of the model by correctly classifying them into three variety groups with 100% correct classifications. The constructed model presents a simple and efficient method of analysis and may serve as a basis for the authentication of other food types.

Processing of unattended, simple negative pictures resists perceptual load.

PubMed

Sand, Anders; Wiens, Stefan

2011-05-11

As researchers debate whether emotional pictures can be processed irrespective of spatial attention and perceptual load, negative and neutral pictures of simple figure-ground composition were shown at fixation and were surrounded by one, two, or three letters. When participants performed a picture discrimination task, there was evidence for motivated attention; that is, an early posterior negativity (EPN) and late positive potential (LPP) to negative versus neutral pictures. When participants performed a letter discrimination task, the EPN was unaffected whereas the LPP was reduced. Although performance decreased substantially with the number of letters (one to three), the LPP did not decrease further. Therefore, attention to simple, negative pictures at fixation seems to resist manipulations of perceptual load.

Differentiation of tea varieties using UV-Vis spectra and pattern recognition techniques

NASA Astrophysics Data System (ADS)

Palacios-Morillo, Ana; Alcázar, Ángela.; de Pablos, Fernando; Jurado, José Marcos

2013-02-01

Tea, one of the most consumed beverages all over the world, is of great importance in the economies of a number of countries. Several methods have been developed to classify tea varieties or origins based in pattern recognition techniques applied to chemical data, such as metal profile, amino acids, catechins and volatile compounds. Some of these analytical methods become tedious and expensive to be applied in routine works. The use of UV-Vis spectral data as discriminant variables, highly influenced by the chemical composition, can be an alternative to these methods. UV-Vis spectra of methanol-water extracts of tea have been obtained in the interval 250-800 nm. Absorbances have been used as input variables. Principal component analysis was used to reduce the number of variables and several pattern recognition methods, such as linear discriminant analysis, support vector machines and artificial neural networks, have been applied in order to differentiate the most common tea varieties. A successful classification model was built by combining principal component analysis and multilayer perceptron artificial neural networks, allowing the differentiation between tea varieties. This rapid and simple methodology can be applied to solve classification problems in food industry saving economic resources.

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.

Does linear separability really matter? Complex visual search is explained by simple search

PubMed Central

Vighneshvel, T.; Arun, S. P.

2013-01-01

Visual search in real life involves complex displays with a target among multiple types of distracters, but in the laboratory, it is often tested using simple displays with identical distracters. Can complex search be understood in terms of simple searches? This link may not be straightforward if complex search has emergent properties. One such property is linear separability, whereby search is hard when a target cannot be separated from its distracters using a single linear boundary. However, evidence in favor of linear separability is based on testing stimulus configurations in an external parametric space that need not be related to their true perceptual representation. We therefore set out to assess whether linear separability influences complex search at all. Our null hypothesis was that complex search performance depends only on classical factors such as target-distracter similarity and distracter homogeneity, which we measured using simple searches. Across three experiments involving a variety of artificial and natural objects, differences between linearly separable and nonseparable searches were explained using target-distracter similarity and distracter heterogeneity. Further, simple searches accurately predicted complex search regardless of linear separability (r = 0.91). Our results show that complex search is explained by simple search, refuting the widely held belief that linear separability influences visual search. PMID:24029822

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.

Objective evaluation of surgical competency for minimally invasive surgery with a collection of simple tests

PubMed Central

Gonzalez-Neira, Eliana Maria; Jimenez-Mendoza, Claudia Patricia; Rugeles-Quintero, Saul

2016-01-01

Objective: This study aims at determining if a collection of 16 motor tests on a physical simulator can objectively discriminate and evaluate practitioners' competency level, i.e. novice, resident, and expert. Methods: An experimental design with three study groups (novice, resident, and expert) was developed to test the evaluation power of each of the 16 simple tests. An ANOVA and a Student Newman-Keuls (SNK) test were used to analyze results of each test to determine which of them can discriminate participants' competency level. Results: Four of the 16 tests used discriminated all of the three competency levels and 15 discriminated at least two of the three groups (α= 0.05). Moreover, other two tests differentiate beginners' level from intermediate, and other seven tests differentiate intermediate level from expert. Conclusion: The competency level of a practitioner of minimally invasive surgery can be evaluated by a specific collection of basic tests in a physical surgical simulator. Reduction of the number of tests needed to discriminate the competency level of surgeons can be the aim of future research. PMID:27226664

Dynamic functional brain networks involved in simple visual discrimination learning.

PubMed

Fidalgo, Camino; Conejo, Nélida María; González-Pardo, Héctor; Arias, Jorge Luis

2014-10-01

Visual discrimination tasks have been widely used to evaluate many types of learning and memory processes. However, little is known about the brain regions involved at different stages of visual discrimination learning. We used cytochrome c oxidase histochemistry to evaluate changes in regional brain oxidative metabolism during visual discrimination learning in a water-T maze at different time points during training. As compared with control groups, the results of the present study reveal the gradual activation of cortical (prefrontal and temporal cortices) and subcortical brain regions (including the striatum and the hippocampus) associated to the mastery of a simple visual discrimination task. On the other hand, the brain regions involved and their functional interactions changed progressively over days of training. Regions associated with novelty, emotion, visuo-spatial orientation and motor aspects of the behavioral task seem to be relevant during the earlier phase of training, whereas a brain network comprising the prefrontal cortex was found along the whole learning process. This study highlights the relevance of functional interactions among brain regions to investigate learning and memory processes. Copyright © 2014 Elsevier Inc. All rights reserved.

Objective evaluation of surgical competency for minimally invasive surgery with a collection of simple tests.

PubMed

Gonzalez-Neira, Eliana Maria; Jimenez-Mendoza, Claudia Patricia; Suarez, Daniel R; Rugeles-Quintero, Saul

2016-03-30

This study aims at determining if a collection of 16 motor tests on a physical simulator can objectively discriminate and evaluate practitioners' competency level, i.e. novice, resident, and expert. An experimental design with three study groups (novice, resident, and expert) was developed to test the evaluation power of each of the 16 simple tests. An ANOVA and a Student Newman-Keuls (SNK) test were used to analyze results of each test to determine which of them can discriminate participants' competency level. Four of the 16 tests used discriminated all of the three competency levels and 15 discriminated at least two of the three groups (α= 0.05). Moreover, other two tests differentiate beginners' level from intermediate, and other seven tests differentiate intermediate level from expert. The competency level of a practitioner of minimally invasive surgery can be evaluated by a specific collection of basic tests in a physical surgical simulator. Reduction of the number of tests needed to discriminate the competency level of surgeons can be the aim of future research.

Corona discharge radical emission spectroscopy: a multi-channel detector with nose-type function for discrimination analysis.

PubMed

Tian, Yunfei; Wu, Peng; Wu, Xi; Jiang, Xiaoming; Xu, Kailai; Hou, Xiandeng

2013-04-21

A simple and economical multi-channel optical sensor using corona discharge radical emission spectroscopy is developed and explored as an optical nose for discrimination analysis of volatile organic compounds, wines, and even isomers.

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.

Compact characterization of liquid absorption and emission spectra using linear variable filters integrated with a CMOS imaging camera.

PubMed

Wan, Yuhang; Carlson, John A; Kesler, Benjamin A; Peng, Wang; Su, Patrick; Al-Mulla, Saoud A; Lim, Sung Jun; Smith, Andrew M; Dallesasse, John M; Cunningham, Brian T

2016-07-08

A compact analysis platform for detecting liquid absorption and emission spectra using a set of optical linear variable filters atop a CMOS image sensor is presented. The working spectral range of the analysis platform can be extended without a reduction in spectral resolution by utilizing multiple linear variable filters with different wavelength ranges on the same CMOS sensor. With optical setup reconfiguration, its capability to measure both absorption and fluorescence emission is demonstrated. Quantitative detection of fluorescence emission down to 0.28 nM for quantum dot dispersions and 32 ng/mL for near-infrared dyes has been demonstrated on a single platform over a wide spectral range, as well as an absorption-based water quality test, showing the versatility of the system across liquid solutions for different emission and absorption bands. Comparison with a commercially available portable spectrometer and an optical spectrum analyzer shows our system has an improved signal-to-noise ratio and acceptable spectral resolution for discrimination of emission spectra, and characterization of colored liquid's absorption characteristics generated by common biomolecular assays. This simple, compact, and versatile analysis platform demonstrates a path towards an integrated optical device that can be utilized for a wide variety of applications in point-of-use testing and point-of-care diagnostics.

Detecting natural occlusion boundaries using local cues

PubMed Central

DiMattina, Christopher; Fox, Sean A.; Lewicki, Michael S.

2012-01-01

Occlusion boundaries and junctions provide important cues for inferring three-dimensional scene organization from two-dimensional images. Although several investigators in machine vision have developed algorithms for detecting occlusions and other edges in natural images, relatively few psychophysics or neurophysiology studies have investigated what features are used by the visual system to detect natural occlusions. In this study, we addressed this question using a psychophysical experiment where subjects discriminated image patches containing occlusions from patches containing surfaces. Image patches were drawn from a novel occlusion database containing labeled occlusion boundaries and textured surfaces in a variety of natural scenes. Consistent with related previous work, we found that relatively large image patches were needed to attain reliable performance, suggesting that human subjects integrate complex information over a large spatial region to detect natural occlusions. By defining machine observers using a set of previously studied features measured from natural occlusions and surfaces, we demonstrate that simple features defined at the spatial scale of the image patch are insufficient to account for human performance in the task. To define machine observers using a more biologically plausible multiscale feature set, we trained standard linear and neural network classifiers on the rectified outputs of a Gabor filter bank applied to the image patches. We found that simple linear classifiers could not match human performance, while a neural network classifier combining filter information across location and spatial scale compared well. These results demonstrate the importance of combining a variety of cues defined at multiple spatial scales for detecting natural occlusions. PMID:23255731

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.

PCANet: A Simple Deep Learning Baseline for Image Classification?

PubMed

Chan, Tsung-Han; Jia, Kui; Gao, Shenghua; Lu, Jiwen; Zeng, Zinan; Ma, Yi

2015-12-01

In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal component analysis (PCA); 2) binary hashing; and 3) blockwise histograms. In the proposed architecture, the PCA is employed to learn multistage filter banks. This is followed by simple binary hashing and block histograms for indexing and pooling. This architecture is thus called the PCA network (PCANet) and can be extremely easily and efficiently designed and learned. For comparison and to provide a better understanding, we also introduce and study two simple variations of PCANet: 1) RandNet and 2) LDANet. They share the same topology as PCANet, but their cascaded filters are either randomly selected or learned from linear discriminant analysis. We have extensively tested these basic networks on many benchmark visual data sets for different tasks, including Labeled Faces in the Wild (LFW) for face verification; the MultiPIE, Extended Yale B, AR, Facial Recognition Technology (FERET) data sets for face recognition; and MNIST for hand-written digit recognition. Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with the state-of-the-art features either prefixed, highly hand-crafted, or carefully learned [by deep neural networks (DNNs)]. Even more surprisingly, the model sets new records for many classification tasks on the Extended Yale B, AR, and FERET data sets and on MNIST variations. Additional experiments on other public data sets also demonstrate the potential of PCANet to serve as a simple but highly competitive baseline for texture classification and object recognition.

Enhanced pure-tone pitch discrimination among persons with autism but not Asperger syndrome.

PubMed

Bonnel, Anna; McAdams, Stephen; Smith, Bennett; Berthiaume, Claude; Bertone, Armando; Ciocca, Valter; Burack, Jacob A; Mottron, Laurent

2010-07-01

Persons with Autism spectrum disorders (ASD) display atypical perceptual processing in visual and auditory tasks. In vision, Bertone, Mottron, Jelenic, and Faubert (2005) found that enhanced and diminished visual processing is linked to the level of neural complexity required to process stimuli, as proposed in the neural complexity hypothesis. Based on these findings, Samson, Mottron, Jemel, Belin, and Ciocca (2006) proposed to extend the neural complexity hypothesis to the auditory modality. They hypothesized that persons with ASD should display enhanced performance for simple tones that are processed in primary auditory cortical regions, but diminished performance for complex tones that require additional processing in associative auditory regions, in comparison to typically developing individuals. To assess this hypothesis, we designed four auditory discrimination experiments targeting pitch, non-vocal and vocal timbre, and loudness. Stimuli consisted of spectro-temporally simple and complex tones. The participants were adolescents and young adults with autism, Asperger syndrome, and typical developmental histories, all with IQs in the normal range. Consistent with the neural complexity hypothesis and enhanced perceptual functioning model of ASD (Mottron, Dawson, Soulières, Hubert, & Burack, 2006), the participants with autism, but not with Asperger syndrome, displayed enhanced pitch discrimination for simple tones. However, no discrimination-thresholds differences were found between the participants with ASD and the typically developing persons across spectrally and temporally complex conditions. These findings indicate that enhanced pure-tone pitch discrimination may be a cognitive correlate of speech-delay among persons with ASD. However, auditory discrimination among this group does not appear to be directly contingent on the spectro-temporal complexity of the stimuli. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Family support, self-esteem, and perceived racial discrimination among Asian American male college students.

PubMed

Wei, Meifen; Yeh, Christine Jean; Chao, Ruth Chu-Lien; Carrera, Stephanie; Su, Jenny C

2013-07-01

This study was conducted to examine under what situation (i.e., when individuals used more or less family support) and for whom (i.e., those with high or low self-esteem) perceived racial discrimination would or would not have a significant positive association with psychological distress. A total of 95 Asian American male college students completed an online survey. A hierarchical regression analysis indicated a significant 3-way interaction of family support, self-esteem, and perceived racial discrimination in predicting psychological distress after controlling for perceived general stress. A simple effect analysis was used to explore the nature of the interaction. When Asian American male college students used more family support to cope with racial discrimination, the association between perceived racial discrimination and psychological distress was not significant for those with high or low self-esteem. The result from the simple interaction indicated that, when more family support was used, the 2 slopes for high and low self-esteem were not significantly different from each other. Conversely, when they used less family support, the association between perceived racial discrimination and psychological distress was not significant for those with high self-esteem, but was significantly positive for those with low self-esteem. The result from the simple interaction indicated that, when less family support was used, the slopes for high and low self-esteem were significantly different. The result suggested that low use of family support may put these male students with low self-esteem at risk for psychological distress. Limitations, future research directions, and clinical implications were discussed. PsycINFO Database Record (c) 2013 APA, all rights reserved.

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).

A comparison of methods for teaching receptive labeling to children with autism spectrum disorders: a systematic replication.

PubMed

Grow, Laura L; Kodak, Tiffany; Carr, James E

2014-01-01

Previous research has demonstrated that the conditional-only method (starting with a multiple-stimulus array) is more efficient than the simple-conditional method (progressive incorporation of more stimuli into the array) for teaching receptive labeling to children with autism spectrum disorders (Grow, Carr, Kodak, Jostad, & Kisamore,). The current study systematically replicated the earlier study by comparing the 2 approaches using progressive prompting with 2 boys with autism. The results showed that the conditional-only method was a more efficient and reliable teaching procedure than the simple-conditional method. The results further call into question the practice of teaching simple discriminations to facilitate acquisition of conditional discriminations. © Society for the Experimental Analysis of Behavior.

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.

Soft tissue differentiation by diffuse reflectance spectroscopy

NASA Astrophysics Data System (ADS)

Zam, Azhar; Stelzle, Florian; Nkenke, Emeka; Tangermann-Gerk, Katja; Schmidt, Michael; Adler, Werner; Douplik, Alexandre

2009-07-01

Laser surgery gives the possibility to work remotely which leads to high precision, little trauma and high level sterility. However these advantages are coming with the lack of haptic feedback during the laser ablation of tissue. Therefore additional means are required to control tissue-specific ablation during laser surgery supporting the surgeon regardless of experience and skills. Diffuse Reflectance Spectroscopy provides a straightforward and simple approach for optical tissue differentiation. We measured diffuse reflectance from four various tissue types ex vivo. We applied Linear Discriminant Analysis (LDA) to differentiate the four tissue types and computed the area under the ROC curve (AUC). Special emphasis was taken on the identification of nerve as the most crucial tissue for maxillofacial surgery. The results show a promise for differentiating soft tissues as guidance for tissue-specific laser surgery by means of the diffuse reflectance.

Beyond δ: Tailoring marked statistics to reveal modified gravity

NASA Astrophysics Data System (ADS)

Valogiannis, Georgios; Bean, Rachel

2018-01-01

Models which attempt to explain the accelerated expansion of the universe through large-scale modifications to General Relativity (GR), must satisfy the stringent experimental constraints of GR in the solar system. Viable candidates invoke a “screening” mechanism, that dynamically suppresses deviations in high density environments, making their overall detection challenging even for ambitious future large-scale structure surveys. We present methods to efficiently simulate the non-linear properties of such theories, and consider how a series of statistics that reweight the density field to accentuate deviations from GR can be applied to enhance the overall signal-to-noise ratio in differentiating the models from GR. Our results demonstrate that the cosmic density field can yield additional, invaluable cosmological information, beyond the simple density power spectrum, that will enable surveys to more confidently discriminate between modified gravity models and ΛCDM.

Telling apart Felidae and Ursidae from the distribution of nucleotides in mitochondrial DNA

NASA Astrophysics Data System (ADS)

Rovenchak, Andrij

2018-02-01

Rank-frequency distributions of nucleotide sequences in mitochondrial DNA are defined in a way analogous to the linguistic approach, with the highest-frequent nucleobase serving as a whitespace. For such sequences, entropy and mean length are calculated. These parameters are shown to discriminate the species of the Felidae (cats) and Ursidae (bears) families. From purely numerical values we are able to see in particular that giant pandas are bears while koalas are not. The observed linear relation between the parameters is explained using a simple probabilistic model. The approach based on the non-additive generalization of the Bose distribution is used to analyze the frequency spectra of the nucleotide sequences. In this case, the separation of families is not very sharp. Nevertheless, the distributions for Felidae have on average longer tails comparing to Ursidae.

Application of mass spectrometry based electronic nose and chemometrics for fingerprinting radiation treatment

NASA Astrophysics Data System (ADS)

Gupta, Sumit; Variyar, Prasad S.; Sharma, Arun

2015-01-01

Volatile compounds were isolated from apples and grapes employing solid phase micro extraction (SPME) and subsequently analyzed by GC/MS equipped with a transfer line without stationary phase. Single peak obtained was integrated to obtain total mass spectrum of the volatile fraction of samples. A data matrix having relative abundance of all mass-to-charge ratios was subjected to principal component analysis (PCA) and linear discriminant analysis (LDA) to identify radiation treatment. PCA results suggested that there is sufficient variability between control and irradiated samples to build classification models based on supervised techniques. LDA successfully aided in segregating control from irradiated samples at all doses (0.1, 0.25, 0.5, 1.0, 1.5, 2.0 kGy). SPME-MS with chemometrics was successfully demonstrated as simple screening method for radiation treatment.

Information-Processing Correlates of Computer-Assisted Word Learning by Mentally Retarded Students.

ERIC Educational Resources Information Center

Conners, Frances A.; Detterman, Douglas K.

1987-01-01

Nineteen moderately/severely retarded students (ages 9-22) completed ten 15-minute computer-assisted instruction sessions and seven basic cognitive tasks measuring simple learning, choice reaction time, relearning, probed recall, stimulus discrimination, tachictoscopic threshold, and recognition memory. Stimulus discrimination, probed recall, and…

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.

Advanced Signal Processing Analysis of Laser-Induced Breakdown Spectroscopy Data for the Discrimination of Obsidian Sources

DTIC Science & Technology

2012-02-09

different sources [12,13], but the analytical techniques needed for such analysis (XRD, INAA , & ICP-MS) are time consuming and require expensive...partial least-squares discriminant analysis (PLSDA) that used the SIMPLS solving method [33]. In the experi- ment design, a leave-one-sample-out (LOSO) para...REPORT Advanced signal processing analysis of laser-induced breakdown spectroscopy data for the discrimination of obsidian sources 14. ABSTRACT 16

Heisenberg scaling with weak measurement: a quantum state discrimination point of view

DTIC Science & Technology

2015-03-18

a quantum state discrimination point of view. The Heisenberg scaling of the photon number for the precision of the interaction parameter between...coherent light and a spin one-half particle (or pseudo-spin) has a simple interpretation in terms of the interaction rotating the quantum state to an...release; distribution is unlimited. Heisenberg scaling with weak measurement: a quantum state discrimination point of view The views, opinions and/or

Synthesis of Survey Questions That Accurately Discriminate the Elements of the TPACK Framework

ERIC Educational Resources Information Center

Jaikaran-Doe, Seeta; Doe, Peter Edward

2015-01-01

A number of validated survey instruments for assessing technological pedagogical content knowledge (TPACK) do not accurately discriminate between the seven elements of the TPACK framework particularly technological content knowledge (TCK) and technological pedagogical knowledge (TPK). By posing simple questions that assess technological,…

Experience-Based Discrimination: Classroom Games

ERIC Educational Resources Information Center

Fryer, Roland G., Jr.; Goeree, Jacob K.; Holt, Charles A.

2005-01-01

The authors present a simple classroom game in which students are randomly designated as employers, purple workers, or green workers. This environment may generate "statistical" discrimination if workers of one color tend not to invest because they anticipate lower opportunities in the labor market, and these beliefs are self-confirming as…

Teaching Third-Degree Price Discrimination

ERIC Educational Resources Information Center

Round, David K.; McIver, Ron P.

2006-01-01

Third-degree price discrimination is taught in almost every intermediate microeconomics class. The theory, geometry, and the algebra behind the concept are simple, and the phenomenon is commonly associated with the sale of many of the goods and services used frequently by students. Classroom discussion is usually vibrant as students can relate…

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.

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.

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.

Intracultural Differences in Local Botanical Knowledge and Knowledge Loss among the Mexican Isthmus Zapotecs.

PubMed

Saynes-Vásquez, Alfredo; Vibrans, Heike; Vergara-Silva, Francisco; Caballero, Javier

2016-01-01

This study reports on the socio-demographic and locality factors that influence ethnobiological knowledge in three communities of Zapotec indigenous people of the Isthmus of Tehuantepec, Mexico. It uses local botanical nomenclature as a proxy for general ethnobiological knowledge. In each of these communities (one urban and two rural), 100 adult men were interviewed aided with a field herbarium. Fifty had a background in farming, and 50 worked in the secondary or tertiary sector as their main economic activity, totaling 300 interviews. Using a field herbarium with samples of 30 common and rare wild regional species, we documented visual recognition, knowledge of the local life form, generic and specific names and uses (five knowledge levels measuring knowledge depth). The relationship between sociodemographic variables and knowledge was analyzed with simple correlations. Differences between the three communities and the five knowledge levels were then evaluated with a discriminant analysis. A general linear analysis identified factors and covariables that influenced the observed differences. Differences between the groups with different economic activities were estimated with a t-test for independent samples. Most of the relationships found between sociodemographic variables and plant knowledge were expected: age and rurality were positively related with knowledge and years of formal schooling was negatively related. However, the somewhat less rural site had more traditional knowledge due to local circumstances. The general linear model explained 70-77% of the variation, a high value. It showed that economic activity was by far the most important factor influencing knowledge, by a factor of five. The interaction of locality and economic activity followed. The discriminant analysis assigned interviewees correctly to their localities in 94% of the cases, strengthening the evidence for intracultural variation. Both sociodemographic and historic intracultural differences heavily influence local knowledge.

Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study

PubMed Central

Burgansky-Eliash, Zvia; Wollstein, Gadi; Chu, Tianjiao; Ramsey, Joseph D.; Glymour, Clark; Noecker, Robert J.; Ishikawa, Hiroshi; Schuman, Joel S.

2007-01-01

Purpose Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma detection. Methods Forty-seven patients with glaucoma (47 eyes) and 42 healthy subjects (42 eyes) were included in this cross-sectional study. Of the glaucoma patients, 27 had early disease (visual field mean deviation [MD] ≥ −6 dB) and 20 had advanced glaucoma (MD < −6 dB). Machine-learning classifiers were trained to discriminate between glaucomatous and healthy eyes using parameters derived from OCT output. The classifiers were trained with all 38 parameters as well as with only 8 parameters that correlated best with the visual field MD. Five classifiers were tested: linear discriminant analysis, support vector machine, recursive partitioning and regression tree, generalized linear model, and generalized additive model. For the last two classifiers, a backward feature selection was used to find the minimal number of parameters that resulted in the best and most simple prediction. The cross-validated receiver operating characteristic (ROC) curve and accuracies were calculated. Results The largest area under the ROC curve (AROC) for glaucoma detection was achieved with the support vector machine using eight parameters (0.981). The sensitivity at 80% and 95% specificity was 97.9% and 92.5%, respectively. This classifier also performed best when judged by cross-validated accuracy (0.966). The best classification between early glaucoma and advanced glaucoma was obtained with the generalized additive model using only three parameters (AROC = 0.854). Conclusions Automated machine classifiers of OCT data might be useful for enhancing the utility of this technology for detecting glaucomatous abnormality. PMID:16249492

[Prognostic estimation in critical patients. Validation of a new and very simple system of prognostic estimation of survival in an intensive care unit].

PubMed

Abizanda, R; Padron, A; Vidal, B; Mas, S; Belenguer, A; Madero, J; Heras, A

2006-04-01

To make the validation of a new system of prognostic estimation of survival in critical patients (EPEC) seen in a multidisciplinar Intensive care unit (ICU). Prospective analysis of a patient cohort seen in the ICU of a multidisciplinar Intensive Medicine Service of a reference teaching hospital with 19 beds. Four hundred eighty four patients admitted consecutively over 6 months in 2003. Data collection of a basic minimum data set that includes patient identification data (gender, age), reason for admission and their origin, prognostic estimation of survival by EPEC, MPM II 0 and SAPS II (the latter two considered as gold standard). Mortality was evaluated on hospital discharge. EPEC validation was done with analysis of its discriminating capacity (ROC curve), calibration of its prognostic capacity (Hosmer Lemeshow C test), resolution of the 2 x 2 Contingency tables around different probability values (20, 50, 70 and mean value of prognostic estimation). The standardized mortality rate (SMR) for each one of the methods was calculated. Linear regression of the EPEC regarding the MPM II 0 and SAPS II was established and concordance analyses were done (Bland-Altman test) of the prediction of mortality by the three systems. In spite of an apparently good linear correlation, similar accuracy of prediction and discrimination capacity, EPEC is not well-calibrated (no likelihood of death greater than 50%) and the concordance analyses show that more than 10% of the pairs were outside the 95% confidence interval. In spite of its ease of application and calculation and of incorporating delay of admission in ICU as a variable, EPEC does not offer any predictive advantage on MPM II 0 or SAPS II, and its predictions adapt to reality worse.

Intracultural Differences in Local Botanical Knowledge and Knowledge Loss among the Mexican Isthmus Zapotecs

PubMed Central

Saynes-Vásquez, Alfredo; Vibrans, Heike; Vergara-Silva, Francisco; Caballero, Javier

2016-01-01

This study reports on the socio-demographic and locality factors that influence ethnobiological knowledge in three communities of Zapotec indigenous people of the Isthmus of Tehuantepec, Mexico. It uses local botanical nomenclature as a proxy for general ethnobiological knowledge. In each of these communities (one urban and two rural), 100 adult men were interviewed aided with a field herbarium. Fifty had a background in farming, and 50 worked in the secondary or tertiary sector as their main economic activity, totaling 300 interviews. Using a field herbarium with samples of 30 common and rare wild regional species, we documented visual recognition, knowledge of the local life form, generic and specific names and uses (five knowledge levels measuring knowledge depth). The relationship between sociodemographic variables and knowledge was analyzed with simple correlations. Differences between the three communities and the five knowledge levels were then evaluated with a discriminant analysis. A general linear analysis identified factors and covariables that influenced the observed differences. Differences between the groups with different economic activities were estimated with a t-test for independent samples. Most of the relationships found between sociodemographic variables and plant knowledge were expected: age and rurality were positively related with knowledge and years of formal schooling was negatively related. However, the somewhat less rural site had more traditional knowledge due to local circumstances. The general linear model explained 70–77% of the variation, a high value. It showed that economic activity was by far the most important factor influencing knowledge, by a factor of five. The interaction of locality and economic activity followed. The discriminant analysis assigned interviewees correctly to their localities in 94% of the cases, strengthening the evidence for intracultural variation. Both sociodemographic and historic intracultural differences heavily influence local knowledge. PMID:26986077

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…

Natural concepts in a juvenile gorilla (gorilla gorilla gorilla) at three levels of abstraction.

PubMed Central

Vonk, Jennifer; MacDonald, Suzanne E

2002-01-01

The extent to which nonhumans are able to form conceptual versus perceptual discriminations remains a matter of debate. Among the great apes, only chimpanzees have been tested for conceptual understanding, defined as the ability to form discriminations not based solely on simple perceptual features of stimuli, and to transfer this learning to novel stimuli. In the present investigation, a young captive female gorilla was trained at three levels of abstraction (concrete, intermediate, and abstract) involving sets of photographs representing natural categories (e.g., orangutans vs. humans, primates vs. nonprimate animals, animals vs. foods). Within each level of abstraction, when the gorilla had learned to discriminate positive from negative exemplars in one set of photographs, a novel set was introduced. Transfer was defined in terms of high accuracy during the first two sessions with the new stimuli. The gorilla acquired discriminations at all three levels of abstraction but showed unambiguous transfer only with the concrete and abstract stimulus sets. Detailed analyses of response patterns revealed little evidence of control by simple stimulus features. Acquisition and transfer involving abstract stimulus sets suggest a conceptual basis for gorilla categorization. The gorilla's relatively poor performance with intermediate-level discriminations parallels findings with pigeons, and suggests a need to reconsider the role of perceptual information in discriminations thought to indicate conceptual behavior in nonhumans. PMID:12507006

Bootstrap Methods: A Very Leisurely Look.

ERIC Educational Resources Information Center

Hinkle, Dennis E.; Winstead, Wayland H.

The Bootstrap method, a computer-intensive statistical method of estimation, is illustrated using a simple and efficient Statistical Analysis System (SAS) routine. The utility of the method for generating unknown parameters, including standard errors for simple statistics, regression coefficients, discriminant function coefficients, and factor…

A simple test of choice stepping reaction time for assessing fall risk in people with multiple sclerosis.

PubMed

Tijsma, Mylou; Vister, Eva; Hoang, Phu; Lord, Stephen R

2017-03-01

Purpose To determine (a) the discriminant validity for established fall risk factors and (b) the predictive validity for falls of a simple test of choice stepping reaction time (CSRT) in people with multiple sclerosis (MS). Method People with MS (n = 210, 21-74y) performed the CSRT, sensorimotor, balance and neuropsychological tests in a single session. They were then followed up for falls using monthly fall diaries for 6 months. Results The CSRT test had excellent discriminant validity with respect to established fall risk factors. Frequent fallers (≥3 falls) performed significantly worse in the CSRT test than non-frequent fallers (0-2 falls). With the odds of suffering frequent falls increasing 69% with each SD increase in CSRT (OR = 1.69, 95% CI: 1.27-2.26, p = <0.001). In regression analysis, CSRT was best explained by sway, time to complete the 9-Hole Peg test, knee extension strength of the weaker leg, proprioception and the time to complete the Trails B test (multiple R 2   =   0.449, p < 0.001). Conclusions A simple low tech CSRT test has excellent discriminative and predictive validity in relation to falls in people with MS. This test may prove useful in documenting longitudinal changes in fall risk in relation to MS disease progression and effects of interventions. Implications for rehabilitation Good choice stepping reaction time (CSRT) is required for maintaining balance. A simple low-tech CSRT test has excellent discriminative and predictive validity in relation to falls in people with MS. This test may prove useful documenting longitudinal changes in fall risk in relation to MS disease progression and effects of interventions.

Nearest clusters based partial least squares discriminant analysis for the classification of spectral data.

PubMed

Song, Weiran; Wang, Hui; Maguire, Paul; Nibouche, Omar

2018-06-07

Partial Least Squares Discriminant Analysis (PLS-DA) is one of the most effective multivariate analysis methods for spectral data analysis, which extracts latent variables and uses them to predict responses. In particular, it is an effective method for handling high-dimensional and collinear spectral data. However, PLS-DA does not explicitly address data multimodality, i.e., within-class multimodal distribution of data. In this paper, we present a novel method termed nearest clusters based PLS-DA (NCPLS-DA) for addressing the multimodality and nonlinearity issues explicitly and improving the performance of PLS-DA on spectral data classification. The new method applies hierarchical clustering to divide samples into clusters and calculates the corresponding centre of every cluster. For a given query point, only clusters whose centres are nearest to such a query point are used for PLS-DA. Such a method can provide a simple and effective tool for separating multimodal and nonlinear classes into clusters which are locally linear and unimodal. Experimental results on 17 datasets, including 12 UCI and 5 spectral datasets, show that NCPLS-DA can outperform 4 baseline methods, namely, PLS-DA, kernel PLS-DA, local PLS-DA and k-NN, achieving the highest classification accuracy most of the time. Copyright © 2018 Elsevier B.V. All rights reserved.

Variations in recollection: the effects of complexity on source recognition.

PubMed

Parks, Colleen M; Murray, Linda J; Elfman, Kane; Yonelinas, Andrew P

2011-07-01

Whether recollection is a threshold or signal detection process is highly controversial, and the controversy has centered in part on the shape of receiver operating characteristics (ROCs) and z-transformed ROCs (zROCs). U-shaped zROCs observed in tests thought to rely heavily on recollection, such as source memory tests, have provided evidence in favor of the threshold assumption, but zROCs are not always as U-shaped as threshold theory predicts. Source zROCs have been shown to become more linear when the contribution of familiarity to source discriminations is increased, and this may account for the existing results. However, another way in which source zROCs may become more linear is if the recollection threshold begins to break down and recollection becomes more graded and Gaussian. We tested the "graded recollection" account in the current study. We found that increasing stimulus complexity (i.e., changing from single words to sentences) or increasing source complexity (i.e., changing the sources from audio to videos of speakers) resulted in flatter source zROCs. In addition, conditions expected to reduce recollection (i.e., divided attention and amnesia) had comparable effects on source memory in simple and complex conditions, suggesting that differences between simple and complex conditions were due to differences in the nature of recollection, rather than differences in the utility of familiarity. The results suggest that under conditions of high complexity, recollection can appear more graded, and it can produce curved ROCs. The results have implications for measurement models and for current theories of recognition memory.

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

No Child Left Behind? Sociology Ignored!

ERIC Educational Resources Information Center

Karen, David

2005-01-01

Too many American children are segregated into schools without standards, shuffled from grade-to-grade because of their age, regard less of their knowledge. This is discrimination, pure and simple--the soft bigotry of low expectations. And our nation should treat it like other forms of discrimination. We should end it. One size does not fit all…

A simple and versatile data acquisition system for software coincidence and pulse-height discrimination in 4πβ-γ coincidence experiments.

PubMed

Kawada, Y; Yamada, T; Unno, Y; Yunoki, A; Sato, Y; Hino, Y

2012-09-01

A simple but versatile data acquisition system for software coincidence experiments is described, in which any time stamping and live time controller are not provided. Signals from β- and γ-channels are fed to separately two fast ADCs (16 bits, 25 MHz clock maximum) via variable delay circuits and pulse-height stretchers, and also to pulse-height discriminators. The discriminating level was set to just above the electronic noise. Two ADCs were controlled with a common clock signal, and triggered simultaneously by the logic OR pulses from both discriminators. Paired digital signals for each sampling were sent to buffer memories connected to main PC with a FIFO (First-In, First-Out) pipe via USB. After data acquisition in list mode, various processing including pulse-height analyses was performed using MS-Excel (version 2007 and later). The usefulness of this system was demonstrated for 4πβ(PS)-4πγ coincidence measurements of (60)Co, (134)Cs and (152)Eu. Possibilities of other extended applications will be touched upon. Copyright © 2012 Elsevier Ltd. All rights reserved.

Compact characterization of liquid absorption and emission spectra using linear variable filters integrated with a CMOS imaging camera

PubMed Central

Wan, Yuhang; Carlson, John A.; Kesler, Benjamin A.; Peng, Wang; Su, Patrick; Al-Mulla, Saoud A.; Lim, Sung Jun; Smith, Andrew M.; Dallesasse, John M.; Cunningham, Brian T.

2016-01-01

A compact analysis platform for detecting liquid absorption and emission spectra using a set of optical linear variable filters atop a CMOS image sensor is presented. The working spectral range of the analysis platform can be extended without a reduction in spectral resolution by utilizing multiple linear variable filters with different wavelength ranges on the same CMOS sensor. With optical setup reconfiguration, its capability to measure both absorption and fluorescence emission is demonstrated. Quantitative detection of fluorescence emission down to 0.28 nM for quantum dot dispersions and 32 ng/mL for near-infrared dyes has been demonstrated on a single platform over a wide spectral range, as well as an absorption-based water quality test, showing the versatility of the system across liquid solutions for different emission and absorption bands. Comparison with a commercially available portable spectrometer and an optical spectrum analyzer shows our system has an improved signal-to-noise ratio and acceptable spectral resolution for discrimination of emission spectra, and characterization of colored liquid’s absorption characteristics generated by common biomolecular assays. This simple, compact, and versatile analysis platform demonstrates a path towards an integrated optical device that can be utilized for a wide variety of applications in point-of-use testing and point-of-care diagnostics. PMID:27389070

Compact characterization of liquid absorption and emission spectra using linear variable filters integrated with a CMOS imaging camera

NASA Astrophysics Data System (ADS)

Wan, Yuhang; Carlson, John A.; Kesler, Benjamin A.; Peng, Wang; Su, Patrick; Al-Mulla, Saoud A.; Lim, Sung Jun; Smith, Andrew M.; Dallesasse, John M.; Cunningham, Brian T.

2016-07-01

A compact analysis platform for detecting liquid absorption and emission spectra using a set of optical linear variable filters atop a CMOS image sensor is presented. The working spectral range of the analysis platform can be extended without a reduction in spectral resolution by utilizing multiple linear variable filters with different wavelength ranges on the same CMOS sensor. With optical setup reconfiguration, its capability to measure both absorption and fluorescence emission is demonstrated. Quantitative detection of fluorescence emission down to 0.28 nM for quantum dot dispersions and 32 ng/mL for near-infrared dyes has been demonstrated on a single platform over a wide spectral range, as well as an absorption-based water quality test, showing the versatility of the system across liquid solutions for different emission and absorption bands. Comparison with a commercially available portable spectrometer and an optical spectrum analyzer shows our system has an improved signal-to-noise ratio and acceptable spectral resolution for discrimination of emission spectra, and characterization of colored liquid’s absorption characteristics generated by common biomolecular assays. This simple, compact, and versatile analysis platform demonstrates a path towards an integrated optical device that can be utilized for a wide variety of applications in point-of-use testing and point-of-care diagnostics.

Geometry-based ensembles: toward a structural characterization of the classification boundary.

PubMed

Pujol, Oriol; Masip, David

2009-06-01

This paper introduces a novel binary discriminative learning technique based on the approximation of the nonlinear decision boundary by a piecewise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points-points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and nonlinear behavior is obtained. The simplicity of the method allows its extension to cope with some of today's machine learning challenges, such as online learning, large-scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database, comparing with several state-of-the-art classification techniques. Finally, we apply our technique in online and large-scale scenarios and in six real-life computer vision and pattern recognition problems: gender recognition based on face images, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease myocardial damage severity detection, old musical scores clef classification, and action recognition using 3D accelerometer data from a wearable device. The results are promising and this paper opens a line of research that deserves further attention.

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.

On learning navigation behaviors for small mobile robots with reservoir computing architectures.

PubMed

Antonelo, Eric Aislan; Schrauwen, Benjamin

2015-04-01

This paper proposes a general reservoir computing (RC) learning framework that can be used to learn navigation behaviors for mobile robots in simple and complex unknown partially observable environments. RC provides an efficient way to train recurrent neural networks by letting the recurrent part of the network (called reservoir) be fixed while only a linear readout output layer is trained. The proposed RC framework builds upon the notion of navigation attractor or behavior that can be embedded in the high-dimensional space of the reservoir after learning. The learning of multiple behaviors is possible because the dynamic robot behavior, consisting of a sensory-motor sequence, can be linearly discriminated in the high-dimensional nonlinear space of the dynamic reservoir. Three learning approaches for navigation behaviors are shown in this paper. The first approach learns multiple behaviors based on the examples of navigation behaviors generated by a supervisor, while the second approach learns goal-directed navigation behaviors based only on rewards. The third approach learns complex goal-directed behaviors, in a supervised way, using a hierarchical architecture whose internal predictions of contextual switches guide the sequence of basic navigation behaviors toward the goal.

A simple bias correction in linear regression for quantitative trait association under two-tail extreme selection.

PubMed

Kwan, Johnny S H; Kung, Annie W C; Sham, Pak C

2011-09-01

Selective genotyping can increase power in quantitative trait association. One example of selective genotyping is two-tail extreme selection, but simple linear regression analysis gives a biased genetic effect estimate. Here, we present a simple correction for the bias.

Crowding with detection and coarse discrimination of simple visual features.

PubMed

Põder, Endel

2008-04-24

Some recent studies have suggested that there are actually no crowding effects with detection and coarse discrimination of simple visual features. The present study tests the generality of this idea. A target Gabor patch, surrounded by either 2 or 6 flanker Gabors, was presented briefly at 4 deg eccentricity of the visual field. Each Gabor patch was oriented either vertically or horizontally (selected randomly). Observers' task was either to detect the presence of the target (presented with probability 0.5) or to identify the orientation of the target. The target-flanker distance was varied. Results were similar for the two tasks but different for 2 and 6 flankers. The idea that feature detection and coarse discrimination are immune to crowding may be valid for the two-flanker condition only. With six flankers, a normal crowding effect was observed. It is suggested that the complexity of the full pattern (target plus flankers) could explain the difference.

Practical Session: Simple Linear Regression

NASA Astrophysics Data System (ADS)

Clausel, M.; Grégoire, G.

2014-12-01

Two exercises are proposed to illustrate the simple linear regression. The first one is based on the famous Galton's data set on heredity. We use the lm R command and get coefficients estimates, standard error of the error, R2, residuals …In the second example, devoted to data related to the vapor tension of mercury, we fit a simple linear regression, predict values, and anticipate on multiple linear regression. This pratical session is an excerpt from practical exercises proposed by A. Dalalyan at EPNC (see Exercises 1 and 2 of http://certis.enpc.fr/~dalalyan/Download/TP_ENPC_4.pdf).

Effects of temperature variation on suicide in five U.S. counties, 1991-2001

NASA Astrophysics Data System (ADS)

Dixon, P. G.; McDonald, A. N.; Scheitlin, K. N.; Stapleton, J. E.; Allen, J. S.; Carter, W. M.; Holley, M. R.; Inman, D. D.; Roberts, J. B.

2007-05-01

Effects of weather variables on suicide are well-documented, but there is still little consistency among the results of most studies. Nevertheless, most studies show a peak in suicides during the spring season, and this is often attributed to increased temperatures. The purpose of this study is to test the relationship between monthly temperature and monthly suicide, independent of months or seasons, for five counties located across the United States. Harmonic analysis shows that four of the five counties display some seasonal components in the suicide data. However, simple linear regression shows no correlation between suicide and temperature, and discriminant analysis shows that monthly departure from mean annual suicide rates is not a useful tool for identifying months with temperatures that are colder or warmer than the annual average. Therefore, it appears that the seasonality of suicides is due to factors other than temperature.

Optimization and validation of Folin-Ciocalteu method for the determination of total polyphenol content of Pu-erh tea.

PubMed

Musci, Marilena; Yao, Shicong

2017-12-01

Pu-erh tea is a post-fermented tea that has recently gained popularity worldwide, due to potential health benefits related to the antioxidant activity resulting from its high polyphenolic content. The Folin-Ciocalteu method is a simple, rapid, and inexpensive assay widely applied for the determination of total polyphenol content. Over the past years, it has been subjected to many modifications, often without any systematic optimization or validation. In our study, we sought to optimize the Folin-Ciocalteu method, evaluate quality parameters including linearity, precision and stability, and then apply the optimized model to determine the total polyphenol content of 57 Chinese teas, including green tea, aged and ripened Pu-erh tea. Our optimized Folin-Ciocalteu method reduced analysis time, allowed for the analysis of a large number of samples, to discriminate among the different teas, and to assess the effect of the post-fermentation process on polyphenol content.

Ratio maps of iron ore deposits Atlantic City district, Wyoming

NASA Technical Reports Server (NTRS)

Vincent, R. K.

1973-01-01

Preliminary results of a spectral rationing technique are shown for a region at the southern end of the Wind River Range, Wyoming. Digital ratio graymaps and analog ratio images have been produced for the test site, but ground truth is not yet available for thorough interpretation of these products. ERTS analog ratio images were found generally better than either ERTS single-channel images or high altitude aerial photos for the discrimination of vegetation from non-vegetation in the test site region. Some linear geological features smaller than the ERTS spatial resolution are seen as well in ERTS ratio and single-channel images as in high altitude aerial photography. Geochemical information appears to be extractable from ERTS data. Good preliminary quantitative agreement between ERTS-derived ratios and laboratory-derived reflectance ratios of rocks and minerals encourage plans to use lab data as training sets for a simple ratio gating logic approach to automatic recognition maps.

Dual-angle technique for simultaneous measurement of refractive index and temperature based on a surface plasmon resonance sensor.

PubMed

Luo, Wei; Chen, Sheng; Chen, Lei; Li, Hualong; Miao, Pengcheng; Gao, Huiyi; Hu, Zelin; Li, Miao

2017-05-29

We describe a theoretical model to analyze temperature effects on the Kretschmann surface plasmon resonance (SPR) sensor, and describe a new double-incident angle technique to simultaneously measure changes in refractive index (RI) and temperature. The method uses the observation that output signals obtained from two different incident angles each have a linear dependence on RI and temperature, and are independent. A proof-of-concept experiment using different NaCl concentration solutions as analytes demonstrates the ability of the technique. The optical design is as simple and robust as conventional SPR detection, but provides a way to discriminate between RI-induced and temperature-induced SPR changes. This technique facilitates a way for traditional SPR sensors to detect RI in different temperature environments, and may lead to better design and fabrication of SPR sensors against temperature variation.

Relationships Among Peripheral and Central Electrophysiological Measures of Spatial and Spectral Selectivity and Speech Perception in Cochlear Implant Users.

PubMed

Scheperle, Rachel A; Abbas, Paul J

2015-01-01

The ability to perceive speech is related to the listener's ability to differentiate among frequencies (i.e., spectral resolution). Cochlear implant (CI) users exhibit variable speech-perception and spectral-resolution abilities, which can be attributed in part to the extent of electrode interactions at the periphery (i.e., spatial selectivity). However, electrophysiological measures of peripheral spatial selectivity have not been found to correlate with speech perception. The purpose of this study was to evaluate auditory processing at the periphery and cortex using both simple and spectrally complex stimuli to better understand the stages of neural processing underlying speech perception. The hypotheses were that (1) by more completely characterizing peripheral excitation patterns than in previous studies, significant correlations with measures of spectral selectivity and speech perception would be observed, (2) adding information about processing at a level central to the auditory nerve would account for additional variability in speech perception, and (3) responses elicited with spectrally complex stimuli would be more strongly correlated with speech perception than responses elicited with spectrally simple stimuli. Eleven adult CI users participated. Three experimental processor programs (MAPs) were created to vary the likelihood of electrode interactions within each participant. For each MAP, a subset of 7 of 22 intracochlear electrodes was activated: adjacent (MAP 1), every other (MAP 2), or every third (MAP 3). Peripheral spatial selectivity was assessed using the electrically evoked compound action potential (ECAP) to obtain channel-interaction functions for all activated electrodes (13 functions total). Central processing was assessed by eliciting the auditory change complex with both spatial (electrode pairs) and spectral (rippled noise) stimulus changes. Speech-perception measures included vowel discrimination and the Bamford-Kowal-Bench Speech-in-Noise test. Spatial and spectral selectivity and speech perception were expected to be poorest with MAP 1 (closest electrode spacing) and best with MAP 3 (widest electrode spacing). Relationships among the electrophysiological and speech-perception measures were evaluated using mixed-model and simple linear regression analyses. All electrophysiological measures were significantly correlated with each other and with speech scores for the mixed-model analysis, which takes into account multiple measures per person (i.e., experimental MAPs). The ECAP measures were the best predictor. In the simple linear regression analysis on MAP 3 data, only the cortical measures were significantly correlated with speech scores; spectral auditory change complex amplitude was the strongest predictor. The results suggest that both peripheral and central electrophysiological measures of spatial and spectral selectivity provide valuable information about speech perception. Clinically, it is often desirable to optimize performance for individual CI users. These results suggest that ECAP measures may be most useful for within-subject applications when multiple measures are performed to make decisions about processor options. They also suggest that if the goal is to compare performance across individuals based on a single measure, then processing central to the auditory nerve (specifically, cortical measures of discriminability) should be considered.

Optimal single-shot strategies for discrimination of quantum measurements

NASA Astrophysics Data System (ADS)

Sedlák, Michal; Ziman, Mário

2014-11-01

We study discrimination of m quantum measurements in the scenario when the unknown measurement with n outcomes can be used only once. We show that ancilla-assisted discrimination procedures provide a nontrivial advantage over simple (ancilla-free) schemes for perfect distinguishability and we prove that inevitably m ≤n . We derive necessary and sufficient conditions of perfect distinguishability of general binary measurements. We show that the optimization of the discrimination of projective qubit measurements and their mixtures with white noise is equivalent to the discrimination of specific quantum states. In particular, the optimal protocol for discrimination of projective qubit measurements with fixed failure rate (exploiting maximally entangled test state) is described. While minimum-error discrimination of two projective qubit measurements can be realized without any need of entanglement, we show that discrimination of three projective qubit measurements requires a bipartite probe state. Moreover, when the measurements are not projective, the non-maximally entangled test states can outperform the maximally entangled ones. Finally, we rephrase the unambiguous discrimination of measurements as quantum key distribution protocol.

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.

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.

Band selective small flip angle COSY: a simple experiment for the analyses of 1H NMR spectra of small chiral molecules.

PubMed

Prabhu, Uday Ramesh; Suryaprakash, N

2008-12-01

The NMR spectroscopic discrimination of enantiomers in the chiral liquid crystalline solvent is more often carried out using (2)H detection in its natural abundance. The employment of (1)H detection for such a purpose is severely hampered due to significant loss of resolution in addition to indistinguishable overlap of the spectra from the two enantiomers. This study demonstrates that the band selected small flip angle homonuclear correlation experiment is a simple and robust technique that provides unambiguous discrimination, very high spectral resolution, reduced multiplicity of transitions, relative signs of the couplings and enormous saving of instrument time.

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.

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.

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.

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.

Simple taper: Taper equations for the field forester

Treesearch

David R. Larsen

2017-01-01

"Simple taper" is set of linear equations that are based on stem taper rates; the intent is to provide taper equation functionality to field foresters. The equation parameters are two taper rates based on differences in diameter outside bark at two points on a tree. The simple taper equations are statistically equivalent to more complex equations. The linear...

Using Simple Linear Regression to Assess the Success of the Montreal Protocol in Reducing Atmospheric Chlorofluorocarbons

ERIC Educational Resources Information Center

Nelson, Dean

2009-01-01

Following the Guidelines for Assessment and Instruction in Statistics Education (GAISE) recommendation to use real data, an example is presented in which simple linear regression is used to evaluate the effect of the Montreal Protocol on atmospheric concentration of chlorofluorocarbons. This simple set of data, obtained from a public archive, can…

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.

Image Discrimination Models for Object Detection in Natural Backgrounds

NASA Technical Reports Server (NTRS)

Ahumada, A. J., Jr.

2000-01-01

This paper reviews work accomplished and in progress at NASA Ames relating to visual target detection. The focus is on image discrimination models, starting with Watson's pioneering development of a simple spatial model and progressing through this model's descendents and extensions. The application of image discrimination models to target detection will be described and results reviewed for Rohaly's vehicle target data and the Search 2 data. The paper concludes with a description of work we have done to model the process by which observers learn target templates and methods for elucidating those templates.

Analytical Studies on the Synchronization of a Network of Linearly-Coupled Simple Chaotic Systems

NASA Astrophysics Data System (ADS)

Sivaganesh, G.; Arulgnanam, A.; Seethalakshmi, A. N.; Selvaraj, S.

2018-05-01

We present explicit generalized analytical solutions for a network of linearly-coupled simple chaotic systems. Analytical solutions are obtained for the normalized state equations of a network of linearly-coupled systems driven by a common chaotic drive system. Two parameter bifurcation diagrams revealing the various hidden synchronization regions, such as complete, phase and phase-lag synchronization are identified using the analytical results. The synchronization dynamics and their stability are studied using phase portraits and the master stability function, respectively. Further, experimental results for linearly-coupled simple chaotic systems are presented to confirm the analytical results. The synchronization dynamics of a network of chaotic systems studied analytically is reported for the first time.

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.

76 FR 21755 - Guidance to Federal Financial Assistance Recipients Regarding Title VI Prohibition Against...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-04-18

... identified language needs of the LEP populations they serve. Having such a policy, however simple, can serve... program in question. This plan need not be intricate. It may be as simple as being prepared to use a... Assistance Recipients Regarding Title VI Prohibition Against National Origin Discrimination Affecting Limited...

Fast and Simple Discriminative Analysis of Anthocyanins-Containing Berries Using LC/MS Spectral Data.

PubMed

Yang, Heejung; Kim, Hyun Woo; Kwon, Yong Soo; Kim, Ho Kyong; Sung, Sang Hyun

2017-09-01

Anthocyanins are potent antioxidant agents that protect against many degenerative diseases; however, they are unstable because they are vulnerable to external stimuli including temperature, pH and light. This vulnerability hinders the quality control of anthocyanin-containing berries using classical high-performance liquid chromatography (HPLC) analytical methodologies based on UV or MS chromatograms. To develop an alternative approach for the quality assessment and discrimination of anthocyanin-containing berries, we used MS spectral data acquired in a short analytical time rather than UV or MS chromatograms. Mixtures of anthocyanins were separated from other components in a short gradient time (5 min) due to their higher polarity, and the representative MS spectrum was acquired from the MS chromatogram corresponding to the mixture of anthocyanins. The chemometric data from the representative MS spectra contained reliable information for the identification and relative quantification of anthocyanins in berries with good precision and accuracy. This fast and simple methodology, which consists of a simple sample preparation method and short gradient analysis, could be applied to reliably discriminate the species and geographical origins of different anthocyanin-containing berries. These features make the technique useful for the food industry. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

"Work ethic" in pigeons: reward value is directly related to the effort or time required to obtain the reward.

PubMed

Clement, T S; Feltus, J R; Kaiser, D H; Zentall, T R

2000-03-01

Stimuli associated with less effort or with shorter delays to reinforcement are generally preferred over those associated with greater effort or longer delays to reinforcement. However, the opposite appears to be true of stimuli that follow greater effort or longer delays. In training, a simple simultaneous discrimination followed a single peck to an initial stimulus (S+FR1 S-FR1) and a different simple simultaneous discrimination followed 20 pecks to the initial stimulus (S+FR20 S-FR20). On test trials, pigeons preferred S+FR20 over S+FR1 and S-FR20 over S-FR1. These data support the view that the state of the animal immediately prior to presentation of the discrimination affects the value of the reinforcement that follows it. This contrast effect is analogous to effects that when they occur in humans have been attributed to more complex cognitive and social factors.

Time-over-threshold for pulse shape discrimination in a time-of-flight phoswich PET detector.

PubMed

Chang, Chen-Ming; Cates, Joshua W; Levin, Craig S

2017-01-07

It is well known that a PET detector capable of measuring both photon time-of-flight (TOF) and depth-of-interaction (DOI) improves the image quality and accuracy. Phoswich designs have been realized in PET detectors to measure DOI for more than a decade. However, PET detectors based on phoswich designs put great demand on the readout circuits, which have to differentiate the pulse shape produced by different crystal layers. A simple pulse shape discrimination approach is required to realize the phoswich designs in a clinical PET scanner, which consists of thousands of scintillation crystal elements. In this work, we studied time-over-threshold (ToT) as a pulse shape parameter for DOI. The energy, timing and DOI performance were evaluated for a phoswich detector design comprising [Formula: see text] mm LYSO:Ce crystal optically coupled to [Formula: see text] mm calcium co-doped LSO:Ce,Ca(0.4%) crystal read out by a silicon photomultiplier (SiPM). A DOI accuracy of 97.2% has been achieved for photopeak events using the proposed time-over-threshold (ToT) processing. The energy resolution without correction for SiPM non-linearity was [Formula: see text]% and [Formula: see text]% FWHM at 511 keV for LYSO and LSO crystal layers, respectively. The coincidence time resolution for photopeak events ranges from 164.6 ps to 183.1 ps FWHM, depending on the layer combinations. The coincidence time resolution for inter-crystal scatter events ranges from 214.6 ps to 418.3 ps FWHM, depending on the energy windows applied. These results show great promises of using ToT for pulse shape discrimination in a TOF phoswich detector since a ToT measurement can be easily implemented in readout electronics.

Correlation and simple linear regression.

PubMed

Eberly, Lynn E

2007-01-01

This chapter highlights important steps in using correlation and simple linear regression to address scientific questions about the association of two continuous variables with each other. These steps include estimation and inference, assessing model fit, the connection between regression and ANOVA, and study design. Examples in microbiology are used throughout. This chapter provides a framework that is helpful in understanding more complex statistical techniques, such as multiple linear regression, linear mixed effects models, logistic regression, and proportional hazards regression.

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…

Estimating linear temporal trends from aggregated environmental monitoring data

USGS Publications Warehouse

Erickson, Richard A.; Gray, Brian R.; Eager, Eric A.

2017-01-01

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.

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.

An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data

NASA Astrophysics Data System (ADS)

Sheykhizadeh, Saheleh; Naseri, Abdolhossein

2018-04-01

Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively.

Reaction time, processing speed and sustained attention in schizophrenia: impact on social functioning.

PubMed

Lahera, Guillermo; Ruiz, Alicia; Brañas, Antía; Vicens, María; Orozco, Arantxa

Previous studies have linked processing speed with social cognition and functioning of patients with schizophrenia. A discriminant analysis is needed to determine the different components of this neuropsychological construct. This paper analyzes the impact of processing speed, reaction time and sustained attention on social functioning. 98 outpatients between 18 and 65 with DSM-5 diagnosis of schizophrenia, with a period of 3 months of clinical stability, were recruited. Sociodemographic and clinical data were collected, and the following variables were measured: processing speed (Trail Making Test [TMT], symbol coding [BACS], verbal fluency), simple and elective reaction time, sustained attention, recognition of facial emotions and global functioning. Processing speed (measured only through the BACS), sustained attention (CPT) and elective reaction time (but not simple) were associated with functioning. Recognizing facial emotions (FEIT) correlated significantly with scores on measures of processing speed (BACS, Animals, TMT), sustained attention (CPT) and reaction time. The linear regression model showed a significant relationship between functioning, emotion recognition (P=.015) and processing speed (P=.029). A deficit in processing speed and facial emotion recognition are associated with worse global functioning in patients with schizophrenia. Copyright © 2017 SEP y SEPB. Publicado por Elsevier España, S.L.U. All rights reserved.

Interfacial gas nanobubbles or oil nanodroplets?

PubMed

Wang, Xingya; Zhao, Binyu; Hu, Jun; Wang, Shuo; Tai, Renzhong; Gao, Xingyu; Zhang, Lijuan

2017-01-04

The existence of nanobubbles at a solid-liquid interface with high stability has been confirmed by myriad experimental studies, and their gaseous nature has also been extensively verified. However, nanodroplets of polydimethylsiloxane (PDMS) recently observed in the atomic force microscopy (AFM) measurement of nanobubbles plague the nanobubble community. It may easily lead to wrong interpretations of the AFM results and thus hinders further application of the already widely used AFM in nanobubble studies. Therefore, finding a direct experimental solution to distinguish nanobubbles from nanodroplets in AFM measurements is a matter of great urgency. Herein, we first developed an effective and reproducible method to produce PDMS nanodroplets at the highly ordered pyrolytic graphite (HOPG)/water interface. From their size, contact angle, and stiffness, the formed PDMS nanodroplets are not distinguishable from nanobubbles. However, the force curves on these two objects are strikingly different from each other, i.e., a peculiar plateau in both the approach and retraction curves was found on nanobubbles whereas they changed linearly between the jump-in and jump-off point on PDMS nanodroplets. Thus, the present study not only provided a simple and effective procedure to generate PDMS nanodroplets but also paved a simple practical and in situ way to discriminate nanobubbles from the PDMS nanodroplets by direct AFM force measurements.

An efficient swarm intelligence approach to feature selection based on invasive weed optimization: Application to multivariate calibration and classification using spectroscopic data.

PubMed

Sheykhizadeh, Saheleh; Naseri, Abdolhossein

2018-04-05

Variable selection plays a key role in classification and multivariate calibration. Variable selection methods are aimed at choosing a set of variables, from a large pool of available predictors, relevant to the analyte concentrations estimation, or to achieve better classification results. Many variable selection techniques have now been introduced among which, those which are based on the methodologies of swarm intelligence optimization have been more respected during a few last decades since they are mainly inspired by nature. In this work, a simple and new variable selection algorithm is proposed according to the invasive weed optimization (IWO) concept. IWO is considered a bio-inspired metaheuristic mimicking the weeds ecological behavior in colonizing as well as finding an appropriate place for growth and reproduction; it has been shown to be very adaptive and powerful to environmental changes. In this paper, the first application of IWO, as a very simple and powerful method, to variable selection is reported using different experimental datasets including FTIR and NIR data, so as to undertake classification and multivariate calibration tasks. Accordingly, invasive weed optimization - linear discrimination analysis (IWO-LDA) and invasive weed optimization- partial least squares (IWO-PLS) are introduced for multivariate classification and calibration, respectively. Copyright © 2018 Elsevier B.V. All rights reserved.

Influence of age, sex, and education on the Visual Object and Space Perception Battery (VOSP) in a healthy normal elderly population.

PubMed

Herrera-Guzmán, I; Peña-Casanova, J; Lara, J P; Gudayol-Ferré, E; Böhm, P

2004-08-01

The assessment of visual perception and cognition forms an important part of any general cognitive evaluation. We have studied the possible influence of age, sex, and education on a normal elderly Spanish population (90 healthy subjects) in performance in visual perception tasks. To evaluate visual perception and cognition, we have used the subjects performance with The Visual Object and Space Perception Battery (VOSP). The test consists of 8 subtests: 4 measure visual object perception (Incomplete Letters, Silhouettes, Object Decision, and Progressive Silhouettes) while the other 4 measure visual space perception (Dot Counting, Position Discrimination, Number Location, and Cube Analysis). The statistical procedures employed were either simple or multiple linear regression analyses (subtests with normal distribution) and Mann-Whitney tests, followed by ANOVA with Scheffe correction (subtests without normal distribution). Age and sex were found to be significant modifying factors in the Silhouettes, Object Decision, Progressive Silhouettes, Position Discrimination, and Cube Analysis subtests. Educational level was found to be a significant predictor of function for the Silhouettes and Object Decision subtests. The results of the sample were adjusted in line with the differences observed. Our study also offers preliminary normative data for the administration of the VOSP to an elderly Spanish population. The results are discussed and compared with similar studies performed in different cultural backgrounds.

Linking local circuit inhibition to olfactory behavior: a critical role for granule cells in olfactory discrimination.

PubMed

Strowbridge, Ben W

2010-02-11

In this issue of Neuron, Abraham et al. report a direct connection between inhibitory function and olfactory behavior. Using molecular methods to alter glutamate receptor subunit composition in olfactory bulb granule cells, the authors found a selective modulation in the time required for difficult, but not simple, olfactory discrimination tasks. Copyright 2010 Elsevier Inc. All rights reserved.

Measuring the effect of ethnic and non-ethnic discrimination on Europeans' self-rated health.

PubMed

Alvarez-Galvez, Javier

2016-04-01

The study of perceived discrimination based on race and ethnic traits belongs to a long-held tradition in this field, but recent studies have found that non-ethnic discrimination based on factors such as gender, disability or age is also a crucial predictor of health outcomes. Using data from the European Social Survey (2010), and applying Boolean Factor Analysis and Ordered Logistic Regression models, this study is aimed to compare how ethnic and non-ethnic types of discrimination might affect self-rated health in the European context. We found that non-ethnic types of discrimination produce stronger differences on health outcomes. This result indicates that the probabilities of presenting a poor state of health are significantly higher when individuals feel they are being discriminated against for social or demographic conditions (gender, age, sexuality or disability) rather than for ethnic reasons (nationality, race, ethnicity, language or religiosity). This study offers a clear comparison of health inequalities based on ethnic and non-ethnic types of discrimination in the European context, overcoming analytical based on binary indicators and simple measures of discrimination.

Multivariate and repeated measures (MRM): A new toolbox for dependent and multimodal group-level neuroimaging data

PubMed Central

McFarquhar, Martyn; McKie, Shane; Emsley, Richard; Suckling, John; Elliott, Rebecca; Williams, Stephen

2016-01-01

Repeated measurements and multimodal data are common in neuroimaging research. Despite this, conventional approaches to group level analysis ignore these repeated measurements in favour of multiple between-subject models using contrasts of interest. This approach has a number of drawbacks as certain designs and comparisons of interest are either not possible or complex to implement. Unfortunately, even when attempting to analyse group level data within a repeated-measures framework, the methods implemented in popular software packages make potentially unrealistic assumptions about the covariance structure across the brain. In this paper, we describe how this issue can be addressed in a simple and efficient manner using the multivariate form of the familiar general linear model (GLM), as implemented in a new MATLAB toolbox. This multivariate framework is discussed, paying particular attention to methods of inference by permutation. Comparisons with existing approaches and software packages for dependent group-level neuroimaging data are made. We also demonstrate how this method is easily adapted for dependency at the group level when multiple modalities of imaging are collected from the same individuals. Follow-up of these multimodal models using linear discriminant functions (LDA) is also discussed, with applications to future studies wishing to integrate multiple scanning techniques into investigating populations of interest. PMID:26921716

Simple Test Functions in Meshless Local Petrov-Galerkin Methods

NASA Technical Reports Server (NTRS)

Raju, Ivatury S.

2016-01-01

Two meshless local Petrov-Galerkin (MLPG) methods based on two different trial functions but that use a simple linear test function were developed for beam and column problems. These methods used generalized moving least squares (GMLS) and radial basis (RB) interpolation functions as trial functions. These two methods were tested on various patch test problems. Both methods passed the patch tests successfully. Then the methods were applied to various beam vibration problems and problems involving Euler and Beck's columns. Both methods yielded accurate solutions for all problems studied. The simple linear test function offers considerable savings in computing efforts as the domain integrals involved in the weak form are avoided. The two methods based on this simple linear test function method produced accurate results for frequencies and buckling loads. Of the two methods studied, the method with radial basis trial functions is very attractive as the method is simple, accurate, and robust.

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.

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...

Relationships Among Peripheral and Central Electrophysiological Measures of Spatial and Spectral Selectivity and Speech Perception in Cochlear Implant Users

PubMed Central

Scheperle, Rachel A.; Abbas, Paul J.

2014-01-01

Objectives The ability to perceive speech is related to the listener’s ability to differentiate among frequencies (i.e., spectral resolution). Cochlear implant (CI) users exhibit variable speech-perception and spectral-resolution abilities, which can be attributed in part to the extent of electrode interactions at the periphery (i.e., spatial selectivity). However, electrophysiological measures of peripheral spatial selectivity have not been found to correlate with speech perception. The purpose of this study was to evaluate auditory processing at the periphery and cortex using both simple and spectrally complex stimuli to better understand the stages of neural processing underlying speech perception. The hypotheses were that (1) by more completely characterizing peripheral excitation patterns than in previous studies, significant correlations with measures of spectral selectivity and speech perception would be observed, (2) adding information about processing at a level central to the auditory nerve would account for additional variability in speech perception, and (3) responses elicited with spectrally complex stimuli would be more strongly correlated with speech perception than responses elicited with spectrally simple stimuli. Design Eleven adult CI users participated. Three experimental processor programs (MAPs) were created to vary the likelihood of electrode interactions within each participant. For each MAP, a subset of 7 of 22 intracochlear electrodes was activated: adjacent (MAP 1), every-other (MAP 2), or every third (MAP 3). Peripheral spatial selectivity was assessed using the electrically evoked compound action potential (ECAP) to obtain channel-interaction functions for all activated electrodes (13 functions total). Central processing was assessed by eliciting the auditory change complex (ACC) with both spatial (electrode pairs) and spectral (rippled noise) stimulus changes. Speech-perception measures included vowel-discrimination and the Bamford-Kowal-Bench Sentence-in-Noise (BKB-SIN) test. Spatial and spectral selectivity and speech perception were expected to be poorest with MAP 1 (closest electrode spacing) and best with MAP 3 (widest electrode spacing). Relationships among the electrophysiological and speech-perception measures were evaluated using mixed-model and simple linear regression analyses. Results All electrophysiological measures were significantly correlated with each other and with speech perception for the mixed-model analysis, which takes into account multiple measures per person (i.e. experimental MAPs). The ECAP measures were the best predictor of speech perception. In the simple linear regression analysis on MAP 3 data, only the cortical measures were significantly correlated with speech; spectral ACC amplitude was the strongest predictor. Conclusions The results suggest that both peripheral and central electrophysiological measures of spatial and spectral selectivity provide valuable information about speech perception. Clinically, it is often desirable to optimize performance for individual CI users. These results suggest that ECAP measures may be the most useful for within-subject applications, when multiple measures are performed to make decisions about processor options. They also suggest that if the goal is to compare performance across individuals based on single measure, then processing central to the auditory nerve (specifically, cortical measures of discriminability) should be considered. PMID:25658746

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.

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.

Species discrimination of Radix Bupleuri through the simultaneous determination of ten saikosaponins by high performance liquid chromatography with evaporative light scattering detection and electrospray ionization mass spectrometry.

PubMed

Lee, Jaehyun; Yang, Dong-Hyug; Suh, Joon Hyuk; Kim, Unyong; Eom, Han Young; Kim, Junghyun; Lee, Mi Young; Kim, Jinwoong; Han, Sang Beom

2011-12-15

A simple, rapid and robust high performance liquid chromatography-evaporative light scattering detection (HPLC-ELSD) method was established for the species discrimination and quality evaluation of Radix Bupleuri through the simultaneous determination of ten saikosaponins, namely saikosaponin-a, -b(1), -b(2), -b(3), -b(4), -c, -d, -g, -h, and -i. These compounds were chromatographed on an Ascentis(®) Express C18 column with a gradient elution of acetonitrile and water containing 0.1% acetic acid at a flow rate of 1.0 mL/min. Saikosaponins were monitored by ELSD, which was operated at a 50°C drift tube temperature and 3.0 bar nebulizer gas (N(2)) pressure. The developed method was validated with respect to linearity, intra- and inter-day accuracy and precision, limit of quantification (LOQ), recovery, robustness and stability, thereby showing good precision and accuracy, with intra- and inter-assay coefficients of variation less than 15% at all concentrations. Furthermore, a high performance liquid chromatography-electrospray ionization mass spectrometry (HPLC-ESI-MS) method was developed to certify the existence of ten saikosaponins, as well as to confirm the reliability of ELSD. The extraction conditions of saikosaponins from Radix Bupleuri were also optimized by investigating the effect of extraction methods (sonication, reflux and maceration) and various solvents on the extraction efficiencies for saikosaponins. Sonication with 70% methanol for 40 min was found to be simple and effective for extraction of major saikosaponins. This analytical method was applied to determine saikosaponin profiles in 20 real samples consisting of four Bupleurum species, namely B. falcatum, B. chinense, B. sibiricum and the poisonous B. longiradiatum. It was found that three major saikosaponin-a, -c and -d were the major constituents in B. falcatum, B. chinense, and B. longiradiatum, while one major saikosaponin (saikosaponin-c) was not identified from B. sibiricum. In addition, no saikosaponin-b(3) was detected in B. longiradiatum samples, indicating that the toxic B. longiradiatum may be tentatively distinguished from officially listed Bupleurum species (B. falcatum and B. chinense) based on their saikosaponin profiles. Overall the simultaneous determination of ten saikosaponins in Radix Bupleuri was shown to be a promising tool to adopt for the discrimination and quality control of closely related Bupleurum species. Copyright © 2011 Elsevier B.V. All rights reserved.

Asymptotic state discrimination and a strict hierarchy in distinguishability norms

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

Chitambar, Eric; Hsieh, Min-Hsiu

2014-11-15

In this paper, we consider the problem of discriminating quantum states by local operations and classical communication (LOCC) when an arbitrarily small amount of error is permitted. This paradigm is known as asymptotic state discrimination, and we derive necessary conditions for when two multipartite states of any size can be discriminated perfectly by asymptotic LOCC. We use this new criterion to prove a gap in the LOCC and separable distinguishability norms. We then turn to the operational advantage of using two-way classical communication over one-way communication in LOCC processing. With a simple two-qubit product state ensemble, we demonstrate a strictmore » majorization of the two-way LOCC norm over the one-way norm.« less

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.

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

The relationship of rain-induced cross-polarization discrimination to attenuation for 10 to 30 GHz earth-space radio links

NASA Technical Reports Server (NTRS)

Stutzman, W. L.; Runyon, D. L.

1984-01-01

Rain depolarization is quantified through the cross-polarization discrimination (XPD) versus attenuation relationship. Such a relationship is derived by curve fitting to a rigorous theoretical model (the multiple scattering model) to determine the variation of the parameters involved. This simple isolation model (SIM) is compared to data from several earth-space link experiments and to three other models.

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.

Automatic processing of tones and speech stimuli in children with specific language impairment.

PubMed

Uwer, Ruth; Albrecht, Ronald; von Suchodoletz, W

2002-08-01

It is well known from behavioural experiments that children with specific language impairment (SLI) have difficulties discriminating consonant-vowel (CV) syllables such as /ba/, /da/, and /ga/. Mismatch negativity (MMN) is an auditory event-related potential component that represents the outcome of an automatic comparison process. It could, therefore, be a promising tool for assessing central auditory processing deficits for speech and non-speech stimuli in children with SLI. MMN is typically evoked by occasionally occurring 'deviant' stimuli in a sequence of identical 'standard' sounds. In this study MMN was elicited using simple tone stimuli, which differed in frequency (1000 versus 1200 Hz) and duration (175 versus 100 ms) and to digitized CV syllables which differed in place of articulation (/ba/, /da/, and /ga/) in children with expressive and receptive SLI and healthy control children (n=21 in each group, 46 males and 17 females; age range 5 to 10 years). Mean MMN amplitudes between groups were compared. Additionally, the behavioural discrimination performance was assessed. Children with SLI had attenuated MMN amplitudes to speech stimuli, but there was no significant difference between the two diagnostic subgroups. MMN to tone stimuli did not differ between the groups. Children with SLI made more errors in the discrimination task, but discrimination scores did not correlate with MMN amplitudes. The present data suggest that children with SLI show a specific deficit in automatic discrimination of CV syllables differing in place of articulation, whereas the processing of simple tone differences seems to be unimpaired.

Morse Code, Scrabble, and the Alphabet

ERIC Educational Resources Information Center

Richardson, Mary; Gabrosek, John; Reischman, Diann; Curtiss, Phyliss

2004-01-01

In this paper we describe an interactive activity that illustrates simple linear regression. Students collect data and analyze it using simple linear regression techniques taught in an introductory applied statistics course. The activity is extended to illustrate checks for regression assumptions and regression diagnostics taught in an…

Using a chemiresistor-based alkane sensor to distinguish exhaled breaths of lung cancer patients from subjects with no lung cancer.

PubMed

Tan, Jiunn-Liang; Yong, Zheng-Xin; Liam, Chong-Kin

2016-10-01

Breath alkanes are reported to be able to discriminate lung cancer patients from healthy people. A simple chemiresistor-based sensor was designed to respond to alkanes by a change in resistance measured by a digital multimeter connected to the sensor. In preclinical experiments, the sensor response was found to have a strong positive linear relationship with alkane compounds and not responsive to water. This study aimed to determine the ability of the alkane sensor to distinguish the exhaled breaths of lung cancer patients from that of chronic obstructive pulmonary disease (COPD) patients and control subjects without lung cancer. In this cross-sectional study, 12 treatment-naive patients with lung cancer, 12 ex- or current smokers with COPD and 13 never-smokers without lung disease were asked to exhale through a drinking straw into a prototype breath-in apparatus made from an empty 125 mL Vitagen ® bottle with the chemiresistor sensor attached at its inside bottom to measure the sensor peak output (percentage change of baseline resistance measured before exhalation to peak resistance) and the time taken for the baseline resistance to reach peak resistance. Analysis of multivariate variance and post-hoc Tukey test revealed that the peak output and the time to peak values for the lung cancer patients were statistically different from that for both the COPD patients and the controls without lung disease, Pillai's Trace =0.393, F=3.909, df = (4, 64), P=0.007. A 2.20% sensor peak output and a 90-s time to peak gave 83.3% sensitivity and 88% specificity in diagnosing lung cancer. Tobacco smoking did not affect the diagnostic accuracy of the sensor. The alkane sensor could discriminate patients with lung cancer from COPD patients and people without lung disease. Its potential utility as a simple, cheap and non-invasive test for early lung cancer detection needs further studies.

Using a chemiresistor-based alkane sensor to distinguish exhaled breaths of lung cancer patients from subjects with no lung cancer

PubMed Central

Tan, Jiunn-Liang; Yong, Zheng-Xin

2016-01-01

Background Breath alkanes are reported to be able to discriminate lung cancer patients from healthy people. A simple chemiresistor-based sensor was designed to respond to alkanes by a change in resistance measured by a digital multimeter connected to the sensor. In preclinical experiments, the sensor response was found to have a strong positive linear relationship with alkane compounds and not responsive to water. This study aimed to determine the ability of the alkane sensor to distinguish the exhaled breaths of lung cancer patients from that of chronic obstructive pulmonary disease (COPD) patients and control subjects without lung cancer. Methods In this cross-sectional study, 12 treatment-naive patients with lung cancer, 12 ex- or current smokers with COPD and 13 never-smokers without lung disease were asked to exhale through a drinking straw into a prototype breath-in apparatus made from an empty 125 mL Vitagen® bottle with the chemiresistor sensor attached at its inside bottom to measure the sensor peak output (percentage change of baseline resistance measured before exhalation to peak resistance) and the time taken for the baseline resistance to reach peak resistance. Results Analysis of multivariate variance and post-hoc Tukey test revealed that the peak output and the time to peak values for the lung cancer patients were statistically different from that for both the COPD patients and the controls without lung disease, Pillai’s Trace =0.393, F=3.909, df = (4, 64), P=0.007. A 2.20% sensor peak output and a 90-s time to peak gave 83.3% sensitivity and 88% specificity in diagnosing lung cancer. Tobacco smoking did not affect the diagnostic accuracy of the sensor. Conclusions The alkane sensor could discriminate patients with lung cancer from COPD patients and people without lung disease. Its potential utility as a simple, cheap and non-invasive test for early lung cancer detection needs further studies. PMID:27867553

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

Factors Influencing Outcomes after Ulnar Nerve Stability-Based Surgery for Cubital Tunnel Syndrome: A Prospective Cohort Study

PubMed Central

Kang, Ho Jung; Oh, Won Taek; Koh, Il Hyun; Kim, Sungmin

2016-01-01

Purpose Simple decompression of the ulnar nerve has outcomes similar to anterior transposition for cubital tunnel syndrome; however, there is no consensus on the proper technique for patients with an unstable ulnar nerve. We hypothesized that 1) simple decompression or anterior ulnar nerve transposition, depending on nerve stability, would be effective for cubital tunnel syndrome and that 2) there would be determining factors of the clinical outcome at two years. Materials and Methods Forty-one patients with cubital tunnel syndrome underwent simple decompression (n=30) or anterior transposition (n=11) according to an assessment of intra-operative ulnar nerve stability. Clinical outcome was assessed using grip and pinch strength, two-point discrimination, the mean of the disabilities of arm, shoulder, and hand (DASH) survey, and the modified Bishop Scale. Results Preoperatively, two patients were rated as mild, another 20 as moderate, and the remaining 19 as severe according to the Dellon Scale. At 2 years after operation, mean grip/pinch strength increased significantly from 19.4/3.2 kg to 31.1/4.1 kg, respectively. Two-point discrimination improved from 6.0 mm to 3.2 mm. The DASH score improved from 31.0 to 14.5. All but one patient scored good or excellent according to the modified Bishop Scale. Correlations were found between the DASH score at two years and age, pre-operative grip strength, and two-point discrimination. Conclusion An ulnar nerve stability-based approach to surgery selection for cubital tunnel syndrome was effective based on 2-year follow-up data. Older age, worse preoperative grip strength, and worse two-point discrimination were associated with worse outcomes at 2 years. PMID:26847300

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.

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

Chip-based nanoflow high performance liquid chromatography coupled to mass spectrometry for profiling of soybean flavonoids.

PubMed

Chang, Yuwei; Zhao, Chunxia; Wu, Zeming; Zhou, Jia; Zhao, Sumin; Lu, Xin; Xu, Guowang

2012-08-01

In this work a chip-based nano HPLC coupled MS (HPLC-chip/MS) method with a simple sample preparation procedure was developed for the flavonoid profiling of soybean. The analytical properties of the method including the linearity (R(2) , 0.992-0.995), reproducibility (RSD, 1.50-7.66%), intraday precision (RSD, 1.41-5.14%) and interday precision (RSD, 2.76-16.90%) were satisfactory. Compared with the conventional HPLC/MS method, a fast extraction and analysis procedure was applied and more flavonoids were detected in a single run. Additionally, 13 flavonoids in soybean seed were identified for the first time. The method was then applied to the profiling of six varieties of soybean sowed at the same place. A clear discrimination was observed among different cultivars, three isoflavones, accounting for nearly 80% of total flavonoid contents, were found increased in the spring soybeans compared with the summer cultivars. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

New perspectives in face correlation: discrimination enhancement in face recognition based on iterative algorithm

NASA Astrophysics Data System (ADS)

Wang, Q.; Alfalou, A.; Brosseau, C.

2016-04-01

Here, we report a brief review on the recent developments of correlation algorithms. Several implementation schemes and specific applications proposed in recent years are also given to illustrate powerful applications of these methods. Following a discussion and comparison of the implementation of these schemes, we believe that all-numerical implementation is the most practical choice for application of the correlation method because the advantages of optical processing cannot compensate the technical and/or financial cost needed for an optical implementation platform. We also present a simple iterative algorithm to optimize the training images of composite correlation filters. By making use of three or four iterations, the peak-to-correlation energy (PCE) value of correlation plane can be significantly enhanced. A simulation test using the Pointing Head Pose Image Database (PHPID) illustrates the effectiveness of this statement. Our method can be applied in many composite filters based on linear composition of training images as an optimization means.

Neutron time-of-flight spectroscopy measurement using a waveform digitizer

NASA Astrophysics Data System (ADS)

Liu, Long-Xiang; Wang, Hong-Wei; Ma, Yu-Gang; Cao, Xi-Guang; Cai, Xiang-Zhou; Chen, Jin-Gen; Zhang, Gui-Lin; Han, Jian-Long; Zhang, Guo-Qiang; Hu, Ji-Feng; Wang, Xiao-He

2016-05-01

The photoneutron source (PNS, phase 1), an electron linear accelerator (linac)-based pulsed neutron facility that uses the time-of-flight (TOF) technique, was constructed for the acquisition of nuclear data from the Thorium Molten Salt Reactor (TMSR) at the Shanghai Institute of Applied Physics (SINAP). The neutron detector signal used for TOF calculation, with information on the pulse arrival time, pulse shape, and pulse height, was recorded by using a waveform digitizer (WFD). By using the pulse height and pulse-shape discrimination (PSD) analysis to identify neutrons and γ-rays, the neutron TOF spectrum was obtained by employing a simple electronic design, and a new WFD-based DAQ system was developed and tested in this commissioning experiment. The DAQ system developed is characterized by a very high efficiency with respect to millisecond neutron TOF spectroscopy. Supported by Strategic Priority Research Program of the Chinese Academy of Science(TMSR) (XDA02010100), National Natural Science Foundation of China(NSFC)(11475245,No.11305239), Shanghai Key Laboratory of Particle Physics and Cosmology (11DZ2260700)

Effects of MDMA on olfactory memory and reversal learning in rats

PubMed Central

Hawkey, Andrew; April, L. Brooke; Galizio, Mark

2014-01-01

The effects of acute and sub-chronic MDMA were assessed using a procedure designed to test rodent working memory capacity: the odor span task (OST). Rats were trained to select an odor that they had not previously encountered within the current session, and the number of odors to remember was incremented up to 24 during the course of each session. In order to separate drug effects on the OST from more general performance impairment, a simple olfactory discrimination was also assessed in each session. In Experiment 1, acute doses of MDMA were administered prior to select sessions. MDMA impaired memory span in a dose-dependent fashion, but impairment was seen only at doses (1.8 and 3.0 mg/kg) that also increased response omissions on both the simple discrimination and the OST. In Experiment 2, a sub-chronic regimen of MDMA (10.0 mg/kg, twice daily over four days) was administered after OST training. There was no evidence of reduced memory span following sub-chronic MDMA, but a temporary increase in omission errors on the OST was observed. In addition, rats exposed to sub-chronic MDMA showed delayed learning when the simple discrimination was reversed. Overall, the disruptive effects of both acute and sub-chronic MDMA appeared to be due to non-mnemonic processes, rather than effects on specific memory functions. PMID:25017644

Vibrational Markovian modelling of footprints after the interaction of antibiotics with the packaging region of HIV type 1.

PubMed

Díaz, Humberto González; de Armas, Ronal Ramos; Molina, Reinaldo

2003-11-01

The design of novel anti-HIV compounds has now become a crucial area for scientists working in numerous interrelated fields of science such as molecular biology, medicinal chemistry, mathematical biology, molecular modelling and bioinformatics. In this context, the development of simple but physically meaningful mathematical models to represent the interaction between anti-HIV drugs and their biological targets is of major interest. One such area currently under investigation involves the targets in the HIV-RNA-packaging region. In the work described here, we applied Markov chain theory in an attempt to describe the interaction between the antibiotic paromomycin and the packaging region of the RNA in Type-1 HIV. In this model, a nucleic acid squeezed graph is used. The vertices of the graph represent the nucleotides while the edges are the phosphodiester bonds. A stochastic (Markovian) matrix was subsequently defined on this graph, an operation that codifies the probabilities of interaction between specific nucleotides of HIV-RNA and the antibiotic. The strength of these local interactions can be calculated through an inelastic vibrational model. The successive power of this matrix codifies the probabilities with which the vibrations after drug-RNA interactions vanish along the polynucleotide main chain. The sums of self-return probabilities in the k-vicinity of each nucleotide represent physically meaningful descriptors. A linear discriminant function was developed and gave rise to excellent discrimination in 80.8% of interacting and footprinted nucleotides. The Jackknife method was employed to assess the stability and predictability of the model. On the other hand, a linear regression model predicted the local binding affinity constants between a specific nucleotide and the antibiotic (R(2)=0.91, Q(2)=0.86). These kinds of models could play an important role either in the discovery of new anti-HIV compounds or the study of their mode of action.

Validation of parental reports of asthma trajectory, burden, and risk by using the pediatric asthma control and communication instrument.

PubMed

Okelo, Sande O; Eakin, Michelle N; Riekert, Kristin A; Teodoro, Alvin P; Bilderback, Andrew L; Thompson, Darcy A; Loiaza-Martinez, Antonio; Rand, Cynthia S; Thyne, Shannon; Diette, Gregory B; Patino, Cecilia M

2014-01-01

Despite a growing interest, few pediatric asthma questionnaires assess multiple dimensions of asthma morbidity, as recommended by national asthma guidelines, or use patient-reported outcomes. To evaluate a questionnaire that measures multiple dimensions of parent-reported asthma morbidity (Direction, Bother, and Risk). We administered the Pediatric Asthma Control and Communication Instrument (PACCI) and assessed asthma control (PACCI Control), quality of life, and lung function among children who presented for routine asthma care. The PACCI was evaluated for discriminative validity. A total of 317 children participated (mean age, 8.2 years; 58% boys; 44% African American). As parent-reported PACCI Direction changed from "better" to "worse," we observed poorer asthma control (P < .001), mean Pediatric Asthma Caregiver Quality of Life Questionnaire (PACQLQ) scores (P < .001), and FEV1% (P = .025). Linear regression showed that, for each change in PACCI Direction, the mean PACQLQ score decreased by -0.6 (95% CI, -0.8 to -0.4). As parent-reported PACCI Bother changed from "not bothered" to "very bothered," we observed poorer asthma control (P < .001) and lower mean PACQLQ scores (P < .001). Linear regression showed that, for each change in PACCI Bother category, the mean PACQLQ score decreased by -1.1 (95% CI, -1.3 to -0.9). Any reported PACCI Risk event (emergency department visit, hospitalization, or use of an oral corticosteroid) was associated with poorer asthma control (P < .05) and PACQLQ scores (P < .01). PACCI Direction, Bother, and Risk are valid measures of parent-reported outcomes and show good discriminative validity. The PACCI is a simple clinical tool to assess multiple dimensions of parent-reported asthma morbidity, in addition to risk and control. Copyright © 2014 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Item validity vs. item discrimination index: a redundancy?

NASA Astrophysics Data System (ADS)

Panjaitan, R. L.; Irawati, R.; Sujana, A.; Hanifah, N.; Djuanda, D.

2018-03-01

In several literatures about evaluation and test analysis, it is common to find that there are calculations of item validity as well as item discrimination index (D) with different formula for each. Meanwhile, other resources said that item discrimination index could be obtained by calculating the correlation between the testee’s score in a particular item and the testee’s score on the overall test, which is actually the same concept as item validity. Some research reports, especially undergraduate theses tend to include both item validity and item discrimination index in the instrument analysis. It seems that these concepts might overlap for both reflect the test quality on measuring the examinees’ ability. In this paper, examples of some results of data processing on item validity and item discrimination index were compared. It would be discussed whether item validity and item discrimination index can be represented by one of them only or it should be better to present both calculations for simple test analysis, especially in undergraduate theses where test analyses were included.

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…

THE ROLE OF THE HIPPOCAMPUS IN OBJECT DISCRIMINATION BASED ON VISUAL FEATURES.

PubMed

Levcik, David; Nekovarova, Tereza; Antosova, Eliska; Stuchlik, Ales; Klement, Daniel

2018-06-07

The role of rodent hippocampus has been intensively studied in different cognitive tasks. However, its role in discrimination of objects remains controversial due to conflicting findings. We tested whether the number and type of features available for the identification of objects might affect the strategy (hippocampal-independent vs. hippocampal-dependent) that rats adopt to solve object discrimination tasks. We trained rats to discriminate 2D visual objects presented on a computer screen. The objects were defined either by their shape only or by multiple-features (a combination of filling pattern and brightness in addition to the shape). Our data showed that objects displayed as simple geometric shapes are not discriminated by trained rats after their hippocampi had been bilaterally inactivated by the GABA A -agonist muscimol. On the other hand, objects containing a specific combination of non-geometric features in addition to the shape are discriminated even without the hippocampus. Our results suggest that the involvement of the hippocampus in visual object discrimination depends on the abundance of object's features. Copyright © 2018. Published by Elsevier Inc.

Pseudorandom Noise Code-Based Technique for Thin Cloud Discrimination with CO2 and O2 Absorption Measurements

NASA Technical Reports Server (NTRS)

Campbell, Joel F.; Prasad, Narasimha S.; Flood, Michael A.

2011-01-01

NASA Langley Research Center is working on a continuous wave (CW) laser based remote sensing scheme for the detection of CO2 and O2 from space based platforms suitable for ACTIVE SENSING OF CO2 EMISSIONS OVER NIGHTS, DAYS, AND SEASONS (ASCENDS) mission. ASCENDS is a future space-based mission to determine the global distribution of sources and sinks of atmospheric carbon dioxide (CO2). A unique, multi-frequency, intensity modulated CW (IMCW) laser absorption spectrometer (LAS) operating at 1.57 micron for CO2 sensing has been developed. Effective aerosol and cloud discrimination techniques are being investigated in order to determine concentration values with accuracies less than 0.3%. In this paper, we discuss the demonstration of a pseudo noise (PN) code based technique for cloud and aerosol discrimination applications. The possibility of using maximum length (ML)-sequences for range and absorption measurements is investigated. A simple model for accomplishing this objective is formulated, Proof-of-concept experiments carried out using SONAR based LIDAR simulator that was built using simple audio hardware provided promising results for extension into optical wavelengths.

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.

Nontargeted metabolomics approach for the differentiation of cultivation ages of mountain cultivated ginseng leaves using UHPLC/QTOF-MS.

PubMed

Chang, Xiangwei; Zhang, Juanjuan; Li, Dekun; Zhou, Dazheng; Zhang, Yuling; Wang, Jincheng; Hu, Bing; Ju, Aichun; Ye, Zhengliang

2017-07-15

The adulteration or falsification of the cultivation age of mountain cultivated ginseng (MCG) has been a serious problem in the commercial MCG market. To develop an efficient discrimination tool for the cultivation age and to explore potential age-dependent markers, an optimized ultra high-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UHPLC/QTOF-MS)-based metabolomics approach was applied in the global metabolite profiling of 156 MCG leaf (MGL) samples aged from 6 to 18 years. Multivariate statistical methods such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to compare the derived patterns between MGL samples of different cultivation ages. The present study demonstrated that 6-18-year-old MGL samples can be successfully discriminated using two simple successive steps, together with four PLS-DA discrimination models. Furthermore, 39 robust age-dependent markers enabling differentiation among the 6-18-year-old MGL samples were discovered. The results were validated by a permutation test and an external test set to verify the predictability and reliability of the established discrimination models. More importantly, without destroying the MCG roots, the proposed approach could also be applied to discriminate MCG root ages indirectly, using a minimum amount of homophyletic MGL samples combined with the established four PLS-DA models and identified markers. Additionally, to the best of our knowledge, this is the first study in which 6-18-year-old MCG root ages have been nondestructively differentiated by analyzing homophyletic MGL samples using UHPLC/QTOF-MS analysis and two simple successive steps together with four PLS-DA models. The method developed in this study can be used as a standard protocol for discriminating and predicting MGL ages directly and homophyletic MCG root ages indirectly. Copyright © 2017 Elsevier B.V. All rights reserved.

Speech-discrimination scores modeled as a binomial variable.

PubMed

Thornton, A R; Raffin, M J

1978-09-01

Many studies have reported variability data for tests of speech discrimination, and the disparate results of these studies have not been given a simple explanation. Arguments over the relative merits of 25- vs 50-word tests have ignored the basic mathematical properties inherent in the use of percentage scores. The present study models performance on clinical tests of speech discrimination as a binomial variable. A binomial model was developed, and some of its characteristics were tested against data from 4120 scores obtained on the CID Auditory Test W-22. A table for determining significant deviations between scores was generated and compared to observed differences in half-list scores for the W-22 tests. Good agreement was found between predicted and observed values. Implications of the binomial characteristics of speech-discrimination scores are discussed.

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.

A simple smoothness indicator for the WENO scheme with adaptive order

NASA Astrophysics Data System (ADS)

Huang, Cong; Chen, Li Li

2018-01-01

The fifth order WENO scheme with adaptive order is competent for solving hyperbolic conservation laws, its reconstruction is a convex combination of a fifth order linear reconstruction and three third order linear reconstructions. Note that, on uniform mesh, the computational cost of smoothness indicator for fifth order linear reconstruction is comparable with the sum of ones for three third order linear reconstructions, thus it is too heavy; on non-uniform mesh, the explicit form of smoothness indicator for fifth order linear reconstruction is difficult to be obtained, and its computational cost is much heavier than the one on uniform mesh. In order to overcome these problems, a simple smoothness indicator for fifth order linear reconstruction is proposed in this paper.

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.

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.

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.

Blessing of dimensionality: mathematical foundations of the statistical physics of data.

PubMed

Gorban, A N; Tyukin, I Y

2018-04-28

The concentrations of measure phenomena were discovered as the mathematical background to statistical mechanics at the end of the nineteenth/beginning of the twentieth century and have been explored in mathematics ever since. At the beginning of the twenty-first century, it became clear that the proper utilization of these phenomena in machine learning might transform the curse of dimensionality into the blessing of dimensionality This paper summarizes recently discovered phenomena of measure concentration which drastically simplify some machine learning problems in high dimension, and allow us to correct legacy artificial intelligence systems. The classical concentration of measure theorems state that i.i.d. random points are concentrated in a thin layer near a surface (a sphere or equators of a sphere, an average or median-level set of energy or another Lipschitz function, etc.). The new stochastic separation theorems describe the thin structure of these thin layers: the random points are not only concentrated in a thin layer but are all linearly separable from the rest of the set, even for exponentially large random sets. The linear functionals for separation of points can be selected in the form of the linear Fisher's discriminant. All artificial intelligence systems make errors. Non-destructive correction requires separation of the situations (samples) with errors from the samples corresponding to correct behaviour by a simple and robust classifier. The stochastic separation theorems provide us with such classifiers and determine a non-iterative (one-shot) procedure for their construction.This article is part of the theme issue 'Hilbert's sixth problem'. © 2018 The Author(s).

Blessing of dimensionality: mathematical foundations of the statistical physics of data

NASA Astrophysics Data System (ADS)

Gorban, A. N.; Tyukin, I. Y.

2018-04-01

The concentrations of measure phenomena were discovered as the mathematical background to statistical mechanics at the end of the nineteenth/beginning of the twentieth century and have been explored in mathematics ever since. At the beginning of the twenty-first century, it became clear that the proper utilization of these phenomena in machine learning might transform the curse of dimensionality into the blessing of dimensionality. This paper summarizes recently discovered phenomena of measure concentration which drastically simplify some machine learning problems in high dimension, and allow us to correct legacy artificial intelligence systems. The classical concentration of measure theorems state that i.i.d. random points are concentrated in a thin layer near a surface (a sphere or equators of a sphere, an average or median-level set of energy or another Lipschitz function, etc.). The new stochastic separation theorems describe the thin structure of these thin layers: the random points are not only concentrated in a thin layer but are all linearly separable from the rest of the set, even for exponentially large random sets. The linear functionals for separation of points can be selected in the form of the linear Fisher's discriminant. All artificial intelligence systems make errors. Non-destructive correction requires separation of the situations (samples) with errors from the samples corresponding to correct behaviour by a simple and robust classifier. The stochastic separation theorems provide us with such classifiers and determine a non-iterative (one-shot) procedure for their construction. This article is part of the theme issue `Hilbert's sixth problem'.

Correlation and simple linear regression.

PubMed

Zou, Kelly H; Tuncali, Kemal; Silverman, Stuart G

2003-06-01

In this tutorial article, the concepts of correlation and regression are reviewed and demonstrated. The authors review and compare two correlation coefficients, the Pearson correlation coefficient and the Spearman rho, for measuring linear and nonlinear relationships between two continuous variables. In the case of measuring the linear relationship between a predictor and an outcome variable, simple linear regression analysis is conducted. These statistical concepts are illustrated by using a data set from published literature to assess a computed tomography-guided interventional technique. These statistical methods are important for exploring the relationships between variables and can be applied to many radiologic studies.

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.

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.

Teaching the Concept of Breakdown Point in Simple Linear Regression.

ERIC Educational Resources Information Center

Chan, Wai-Sum

2001-01-01

Most introductory textbooks on simple linear regression analysis mention the fact that extreme data points have a great influence on ordinary least-squares regression estimation; however, not many textbooks provide a rigorous mathematical explanation of this phenomenon. Suggests a way to fill this gap by teaching students the concept of breakdown…

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.

Linear analysis of auto-organization in Hebbian neural networks.

PubMed

Carlos Letelier, J; Mpodozis, J

1995-01-01

The self-organization of neurotopies where neural connections follow Hebbian dynamics is framed in terms of linear operator theory. A general and exact equation describing the time evolution of the overall synaptic strength connecting two neural laminae is derived. This linear matricial equation, which is similar to the equations used to describe oscillating systems in physics, is modified by the introduction of non-linear terms, in order to capture self-organizing (or auto-organizing) processes. The behavior of a simple and small system, that contains a non-linearity that mimics a metabolic constraint, is analyzed by computer simulations. The emergence of a simple "order" (or degree of organization) in this low-dimensionality model system is discussed.

Prediction of Nonalcoholic Fatty Liver Disease Via a Novel Panel of Serum Adipokines

PubMed Central

Jamali, Raika; Arj, Abbas; Razavizade, Mohsen; Aarabi, Mohammad Hossein

2016-01-01

Abstract Considering limitations of liver biopsy for diagnosis of nonalcoholic liver disease (NAFLD), biomarkers’ panels were proposed. The aims of this study were to establish models based on serum adipokines for discriminating NAFLD from healthy individuals and nonalcoholic steatohepatitis (NASH) from simple steatosis. This case-control study was conducted in patients with persistent elevated serum aminotransferase levels and fatty liver on ultrasound. Individuals with evidence of alcohol consumption, hepatotoxic medication, viral hepatitis, and known liver disease were excluded. Liver biopsy was performed in the remaining patients to distinguish NAFLD/NASH. Histologic findings were interpreted using “nonalcoholic fatty liver activity score.” Control group consisted of healthy volunteers with normal physical examination, liver function tests, and liver ultrasound. Binary logistic regression analysis was applied to ascertain the effects of independent variables on the likelihood that participants have NAFLD/NASH. Decreased serum adiponectin and elevated serum visfatin, IL-6, TNF-a were associated with an increased likelihood of exhibiting NAFLD. NAFLD discriminant score was developed as the following: [(−0.298 × adiponectin) + (0.022 × TNF-a) + (1.021 × Log visfatin) + (0.709 × Log IL-6) + 1.154]. In NAFLD discriminant score, 86.4% of original grouped cases were correctly classified. Discriminant score threshold value of (−0.29) yielded a sensitivity and specificity of 91% and 83% respectively, for discriminating NAFLD from healthy controls. Decreased serum adiponectin and elevated serum visfatin, IL-8, TNF-a were correlated with an increased probability of NASH. NASH discriminant score was proposed as the following: [(−0.091 × adiponectin) + (0.044 × TNF-a) + (1.017 × Log visfatin) + (0.028 × Log IL-8) − 1.787] In NASH model, 84% of original cases were correctly classified. Discriminant score threshold value of (−0.22) yielded a sensitivity and specificity of 90% and 66% respectively, for separating NASH from simple steatosis. New discriminant scores were introduced for differentiating NAFLD/NASH patients with a high accuracy. If verified by future studies, application of suggested models for screening of NAFLD/NASH seems reasonable. PMID:26844476

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.

Evaluation of Two Statistical Methods Provides Insights into the Complex Patterns of Alternative Polyadenylation Site Switching

PubMed Central

Li, Jie; Li, Rui; You, Leiming; Xu, Anlong; Fu, Yonggui; Huang, Shengfeng

2015-01-01

Switching between different alternative polyadenylation (APA) sites plays an important role in the fine tuning of gene expression. New technologies for the execution of 3’-end enriched RNA-seq allow genome-wide detection of the genes that exhibit significant APA site switching between different samples. Here, we show that the independence test gives better results than the linear trend test in detecting APA site-switching events. Further examination suggests that the discrepancy between these two statistical methods arises from complex APA site-switching events that cannot be represented by a simple change of average 3’-UTR length. In theory, the linear trend test is only effective in detecting these simple changes. We classify the switching events into four switching patterns: two simple patterns (3’-UTR shortening and lengthening) and two complex patterns. By comparing the results of the two statistical methods, we show that complex patterns account for 1/4 of all observed switching events that happen between normal and cancerous human breast cell lines. Because simple and complex switching patterns may convey different biological meanings, they merit separate study. We therefore propose to combine both the independence test and the linear trend test in practice. First, the independence test should be used to detect APA site switching; second, the linear trend test should be invoked to identify simple switching events; and third, those complex switching events that pass independence testing but fail linear trend testing can be identified. PMID:25875641

Valid statistical approaches for analyzing sholl data: Mixed effects versus simple linear models.

PubMed

Wilson, Machelle D; Sethi, Sunjay; Lein, Pamela J; Keil, Kimberly P

2017-03-01

The Sholl technique is widely used to quantify dendritic morphology. Data from such studies, which typically sample multiple neurons per animal, are often analyzed using simple linear models. However, simple linear models fail to account for intra-class correlation that occurs with clustered data, which can lead to faulty inferences. Mixed effects models account for intra-class correlation that occurs with clustered data; thus, these models more accurately estimate the standard deviation of the parameter estimate, which produces more accurate p-values. While mixed models are not new, their use in neuroscience has lagged behind their use in other disciplines. A review of the published literature illustrates common mistakes in analyses of Sholl data. Analysis of Sholl data collected from Golgi-stained pyramidal neurons in the hippocampus of male and female mice using both simple linear and mixed effects models demonstrates that the p-values and standard deviations obtained using the simple linear models are biased downwards and lead to erroneous rejection of the null hypothesis in some analyses. The mixed effects approach more accurately models the true variability in the data set, which leads to correct inference. Mixed effects models avoid faulty inference in Sholl analysis of data sampled from multiple neurons per animal by accounting for intra-class correlation. Given the widespread practice in neuroscience of obtaining multiple measurements per subject, there is a critical need to apply mixed effects models more widely. Copyright © 2017 Elsevier B.V. All rights reserved.

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.

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

Study of phenotype evolution during childhood in Marfan syndrome to improve clinical recognition.

PubMed

Stheneur, Chantal; Tubach, Florence; Jouneaux, Marlène; Roy, Carine; Benoist, Gregoire; Chevallier, Bertrand; Boileau, Catherine; Jondeau, Guillaume

2014-03-01

Because diagnosis of Marfan syndrome is difficult during infancy, we used a large cohort of children to describe the evolution of the Marfan syndrome phenotype with age. Two hundred and fifty-nine children carrying an FBN1 gene mutation and fulfilling Ghent criteria were compared with 474 non-Marfan syndrome children. Prevalence of skeletal features changed with aging: prevalence of pectus deformity increased from 43% at 0-6 years to 62% at 15-17 years, wrist signs increased from 28 to 67%, and scoliosis increased from 16 to 59%. Hypermobility decreased from 67 to 47% and pes planus decreased from 73 to 65%. Striae increased from 2 to 84%. Prevalence of ectopia lentis remained stable, varying from 66 to 72%, similar to aortic root dilatation (varying from 75 to 80%). Aortic root dilatation remained stable during follow-up in this population receiving β-blocker therapy. When comparing Marfan syndrome children with non-Marfan syndrome children, height appeared to be a simple and discriminant criterion when it was >3.3 SD above the mean. Ectopia lentis and aortic dilatation were both similarly discriminating. Ectopia lentis and aortic dilatation are the best-discriminating features, but height remains a simple discriminating variable for general practitioners when >3.3 SD above the mean. Mean aortic dilatation remains stable in infancy when children receive a β-blocker.

A Pilot Study Combining a GC-Sensor Device with a Statistical Model for the Identification of Bladder Cancer from Urine Headspace

PubMed Central

Khalid, Tanzeela; White, Paul; De Lacy Costello, Ben; Persad, Raj; Ewen, Richard; Johnson, Emmanuel; Probert, Chris S.; Ratcliffe, Norman

2013-01-01

There is a need to reduce the number of cystoscopies on patients with haematuria. Presently there are no reliable biomarkers to screen for bladder cancer. In this paper, we evaluate a new simple in–house fabricated, GC-sensor device in the diagnosis of bladder cancer based on volatiles. Sensor outputs from 98 urine samples were used to build and test diagnostic models. Samples were taken from 24 patients with transitional (urothelial) cell carcinoma (age 27-91 years, median 71 years) and 74 controls presenting with urological symptoms, but without a urological malignancy (age 29-86 years, median 64 years); results were analysed using two statistical approaches to assess the robustness of the methodology. A two-group linear discriminant analysis method using a total of 9 time points (which equates to 9 biomarkers) correctly assigned 24/24 (100%) of cancer cases and 70/74 (94.6%) controls. Under leave-one-out cross-validation 23/24 (95.8%) of cancer cases were correctly predicted with 69/74 (93.2%) of controls. For partial least squares discriminant analysis, the correct leave-one-out cross-validation prediction values were 95.8% (cancer cases) and 94.6% (controls). These data are an improvement on those reported by other groups studying headspace gases and also superior to current clinical techniques. This new device shows potential for the diagnosis of bladder cancer, but the data must be reproduced in a larger study. PMID:23861976

Surgery for left ventricular aneurysm: early and late survival after simple linear repair and endoventricular patch plasty.

PubMed

Lundblad, Runar; Abdelnoor, Michel; Svennevig, Jan Ludvig

2004-09-01

Simple linear resection and endoventricular patch plasty are alternative techniques to repair postinfarction left ventricular aneurysm. The aim of the study was to compare these 2 methods with regard to early mortality and long-term survival. We retrospectively reviewed 159 patients undergoing operations between 1989 and 2003. The epidemiologic design was of an exposed (simple linear repair, n = 74) versus nonexposed (endoventricular patch plasty, n = 85) cohort with 2 endpoints: early mortality and long-term survival. The crude effect of aneurysm repair technique versus endpoint was estimated by odds ratio, rate ratio, or relative risk and their 95% confidence intervals. Stratification analysis by using the Mantel-Haenszel method was done to quantify confounders and pinpoint effect modifiers. Adjustment for multiconfounders was performed by using logistic regression and Cox regression analysis. Survival curves were analyzed with the Breslow test and the log-rank test. Early mortality was 8.2% for all patients, 13.5% after linear repair and 3.5% after endoventricular patch plasty. When adjusted for multiconfounders, the risk of early mortality was significantly higher after simple linear repair than after endoventricular patch plasty (odds ratio, 4.4; 95% confidence interval, 1.1-17.8). Mean follow-up was 5.8 +/- 3.8 years (range, 0-14.0 years). Overall 5-year cumulative survival was 78%, 70.1% after linear repair and 91.4% after endoventricular patch plasty. The risk of total mortality was significantly higher after linear repair than after endoventricular patch plasty when controlled for multiconfounders (relative risk, 4.5; 95% confidence interval, 2.0-9.7). Linear repair dominated early in the series and patch plasty dominated later, giving a possible learning-curve bias in favor of patch plasty that could not be adjusted for in the regression analysis. Postinfarction left ventricular aneurysm can be repaired with satisfactory early and late results. Surgical risk was lower and long-term survival was higher after endoventricular patch plasty than simple linear repair. Differences in outcome should be interpreted with care because of the retrospective study design and the chronology of the 2 repair methods.

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.

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 %.

Pre-operative prediction of surgical morbidity in children: comparison of five statistical models.

PubMed

Cooper, Jennifer N; Wei, Lai; Fernandez, Soledad A; Minneci, Peter C; Deans, Katherine J

2015-02-01

The accurate prediction of surgical risk is important to patients and physicians. Logistic regression (LR) models are typically used to estimate these risks. However, in the fields of data mining and machine-learning, many alternative classification and prediction algorithms have been developed. This study aimed to compare the performance of LR to several data mining algorithms for predicting 30-day surgical morbidity in children. We used the 2012 National Surgical Quality Improvement Program-Pediatric dataset to compare the performance of (1) a LR model that assumed linearity and additivity (simple LR model) (2) a LR model incorporating restricted cubic splines and interactions (flexible LR model) (3) a support vector machine, (4) a random forest and (5) boosted classification trees for predicting surgical morbidity. The ensemble-based methods showed significantly higher accuracy, sensitivity, specificity, PPV, and NPV than the simple LR model. However, none of the models performed better than the flexible LR model in terms of the aforementioned measures or in model calibration or discrimination. Support vector machines, random forests, and boosted classification trees do not show better performance than LR for predicting pediatric surgical morbidity. After further validation, the flexible LR model derived in this study could be used to assist with clinical decision-making based on patient-specific surgical risks. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

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.

Ranking Forestry Investments With Parametric Linear Programming

Treesearch

Paul A. Murphy

1976-01-01

Parametric linear programming is introduced as a technique for ranking forestry investments under multiple constraints; it combines the advantages of simple tanking and linear programming as capital budgeting tools.

A Simple Lightning Flash Polarity Discriminating Counter.

ERIC Educational Resources Information Center

Devan, K. R. S.; Jayaratne, E. R.

1990-01-01

Described are the apparatus and procedures needed for a demonstration of a determination of the polarity of charges carried by individual ground flashes of lightning. Discussed are materials, apparatus construction, and experimental results. (CW)

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.

Near optimal discrimination of binary coherent signals via atom–light interaction

NASA Astrophysics Data System (ADS)

Han, Rui; Bergou, János A.; Leuchs, Gerd

2018-04-01

We study the discrimination of weak coherent states of light with significant overlaps by nondestructive measurements on the light states through measuring atomic states that are entangled to the coherent states via dipole coupling. In this way, the problem of measuring and discriminating coherent light states is shifted to finding the appropriate atom–light interaction and atomic measurements. We show that this scheme allows us to attain a probability of error extremely close to the Helstrom bound, the ultimate quantum limit for discriminating binary quantum states, through the simple Jaynes–Cummings interaction between the field and ancilla with optimized light–atom coupling and projective measurements on the atomic states. Moreover, since the measurement is nondestructive on the light state, information that is not detected by one measurement can be extracted from the post-measurement light states through subsequent measurements.

Decoding intentions from movement kinematics

PubMed Central

Cavallo, Andrea; Koul, Atesh; Ansuini, Caterina; Capozzi, Francesca; Becchio, Cristina

2016-01-01

How do we understand the intentions of other people? There has been a longstanding controversy over whether it is possible to understand others’ intentions by simply observing their movements. Here, we show that indeed movement kinematics can form the basis for intention detection. By combining kinematics and psychophysical methods with classification and regression tree (CART) modeling, we found that observers utilized a subset of discriminant kinematic features over the total kinematic pattern in order to detect intention from observation of simple motor acts. Intention discriminability covaried with movement kinematics on a trial-by-trial basis, and was directly related to the expression of discriminative features in the observed movements. These findings demonstrate a definable and measurable relationship between the specific features of observed movements and the ability to discriminate intention, providing quantitative evidence of the significance of movement kinematics for anticipating others’ intentional actions. PMID:27845434

[Approach to the Development of Mind and Persona].

PubMed

Sawaguchi, Toshiko

2018-01-01

To access medical specialists by health specialists working in the regional health field, the possibility of utilizing the voice approach for dissociative identity disorder (DID) patients as a health assessment for medical access (HAMA) was investigated. The first step is to investigate whether the plural personae in a single DID patient can be discriminated by voice analysis. Voices of DID patients including these with different personae were extracted from YouTube and were analysed using the software PRAAT with basic frequency, oral factors, chin factors and tongue factors. In addition, RAKUGO story teller voices made artificially and dramatically were analysed in the same manner. Quantitive and qualitative analysis method were carried out and nested logistic regression and a nested generalized linear model was developed. The voice from different personae in one DID patient could be visually and easily distinquished using basic frequency curve, cluster analysis and factor analysis. In the canonical analysis, only Roy's maximum root was <0.01. In the nested generalized linear model, the model using a standard deviation (SD) indicator fit best and some other possibilities are shown here. In DID patients, the short transition time among plural personae could guide to the risky situation such as suicide. So if the voice approach can show the time threshold of changes between the different personae, it would be useful as an Access Assessment in the form of a simple HAMA.

Multivariate and repeated measures (MRM): A new toolbox for dependent and multimodal group-level neuroimaging data.

PubMed

McFarquhar, Martyn; McKie, Shane; Emsley, Richard; Suckling, John; Elliott, Rebecca; Williams, Stephen

2016-05-15

Repeated measurements and multimodal data are common in neuroimaging research. Despite this, conventional approaches to group level analysis ignore these repeated measurements in favour of multiple between-subject models using contrasts of interest. This approach has a number of drawbacks as certain designs and comparisons of interest are either not possible or complex to implement. Unfortunately, even when attempting to analyse group level data within a repeated-measures framework, the methods implemented in popular software packages make potentially unrealistic assumptions about the covariance structure across the brain. In this paper, we describe how this issue can be addressed in a simple and efficient manner using the multivariate form of the familiar general linear model (GLM), as implemented in a new MATLAB toolbox. This multivariate framework is discussed, paying particular attention to methods of inference by permutation. Comparisons with existing approaches and software packages for dependent group-level neuroimaging data are made. We also demonstrate how this method is easily adapted for dependency at the group level when multiple modalities of imaging are collected from the same individuals. Follow-up of these multimodal models using linear discriminant functions (LDA) is also discussed, with applications to future studies wishing to integrate multiple scanning techniques into investigating populations of interest. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

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.

Aging and curvature discrimination from static and dynamic touch.

PubMed

Norman, J Farley; Kappers, Astrid M L; Cheeseman, Jacob R; Ronning, Cecilia; Thomason, Kelsey E; Baxter, Michael W; Calloway, Autum B; Lamirande, Davora N

2013-01-01

Two experiments evaluated the ability of 30 older and younger adults to discriminate the curvature of simple object surfaces from static and dynamic touch. The ages of the older adults ranged from 66 to 85 years, while those of the younger adults ranged from 20 to 29 years. For each participant in both experiments, the minimum curvature magnitude needed to reliably discriminate between convex and concave surfaces was determined. In Experiment 1, participants used static touch to make their judgments of curvature, while dynamic touch was used in Experiment 2. When static touch was used to discriminate curvature, a large effect of age occurred (the thresholds were 0.67 & 1.11/m for the younger and older participants, respectively). However, when participants used dynamic touch, there was no significant difference between the ability of younger and older participants to discriminate curvature (the thresholds were 0.58 & 0.59/m for the younger and older participants, respectively). The results of the current study demonstrate that while older adults can accurately discriminate surface curvature from dynamic touch, they possess significant impairments for static touch.

Aging and Curvature Discrimination from Static and Dynamic Touch

PubMed Central

Norman, J. Farley; Kappers, Astrid M. L.; Cheeseman, Jacob R.; Ronning, Cecilia; Thomason, Kelsey E.; Baxter, Michael W.; Calloway, Autum B.; Lamirande, Davora N.

2013-01-01

Two experiments evaluated the ability of 30 older and younger adults to discriminate the curvature of simple object surfaces from static and dynamic touch. The ages of the older adults ranged from 66 to 85 years, while those of the younger adults ranged from 20 to 29 years. For each participant in both experiments, the minimum curvature magnitude needed to reliably discriminate between convex and concave surfaces was determined. In Experiment 1, participants used static touch to make their judgments of curvature, while dynamic touch was used in Experiment 2. When static touch was used to discriminate curvature, a large effect of age occurred (the thresholds were 0.67 & 1.11/m for the younger and older participants, respectively). However, when participants used dynamic touch, there was no significant difference between the ability of younger and older participants to discriminate curvature (the thresholds were 0.58 & 0.59/m for the younger and older participants, respectively). The results of the current study demonstrate that while older adults can accurately discriminate surface curvature from dynamic touch, they possess significant impairments for static touch. PMID:23844224

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.

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.

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.

Honeybees can discriminate between Monet and Picasso paintings.

PubMed

Wu, Wen; Moreno, Antonio M; Tangen, Jason M; Reinhard, Judith

2013-01-01

Honeybees (Apis mellifera) have remarkable visual learning and discrimination abilities that extend beyond learning simple colours, shapes or patterns. They can discriminate landscape scenes, types of flowers, and even human faces. This suggests that in spite of their small brain, honeybees have a highly developed capacity for processing complex visual information, comparable in many respects to vertebrates. Here, we investigated whether this capacity extends to complex images that humans distinguish on the basis of artistic style: Impressionist paintings by Monet and Cubist paintings by Picasso. We show that honeybees learned to simultaneously discriminate between five different Monet and Picasso paintings, and that they do not rely on luminance, colour, or spatial frequency information for discrimination. When presented with novel paintings of the same style, the bees even demonstrated some ability to generalize. This suggests that honeybees are able to discriminate Monet paintings from Picasso ones by extracting and learning the characteristic visual information inherent in each painting style. Our study further suggests that discrimination of artistic styles is not a higher cognitive function that is unique to humans, but simply due to the capacity of animals-from insects to humans-to extract and categorize the visual characteristics of complex images.

The zebrafish world of colors and shapes: preference and discrimination.

PubMed

Oliveira, Jessica; Silveira, Mayara; Chacon, Diana; Luchiari, Ana

2015-04-01

Natural environment imposes many challenges to animals, which have to use cognitive abilities to cope with and exploit it to enhance their fitness. Since zebrafish is a well-established model for cognitive studies and high-throughput screening for drugs and diseases that affect cognition, we tested their ability for ambient color preference and 3D objects discrimination to establish a protocol for memory evaluation. For the color preference test, zebrafish were observed in a multiple-chamber tank with different environmental color options. Zebrafish showed preference for blue and green, and avoided yellow and red. For the 3D objects discrimination, zebrafish were allowed to explore two equal objects and then observed in a one-trial test in which a new color, size, or shape of the object was presented. Zebrafish showed discrimination for color, shape, and color+shape combined, but not size. These results imply that zebrafish seem to use some categorical system to discriminate items, and distracters affect their ability for discrimination. The type of variables available (color and shape) may favor zebrafish objects perception and facilitate discrimination processing. We suggest that this easy and simple memory test could serve as a useful screening tool for cognitive dysfunction and neurotoxicological studies.

Fourier transform infrared spectroscopy combined with chemometrics for discrimination of Curcuma longa, Curcuma xanthorrhiza and Zingiber cassumunar

NASA Astrophysics Data System (ADS)

Rohaeti, Eti; Rafi, Mohamad; Syafitri, Utami Dyah; Heryanto, Rudi

2015-02-01

Turmeric (Curcuma longa), java turmeric (Curcuma xanthorrhiza) and cassumunar ginger (Zingiber cassumunar) are widely used in traditional Indonesian medicines (jamu). They have similar color for their rhizome and possess some similar uses, so it is possible to substitute one for the other. The identification and discrimination of these closely-related plants is a crucial task to ensure the quality of the raw materials. Therefore, an analytical method which is rapid, simple and accurate for discriminating these species using Fourier transform infrared spectroscopy (FTIR) combined with some chemometrics methods was developed. FTIR spectra were acquired in the mid-IR region (4000-400 cm-1). Standard normal variate, first and second order derivative spectra were compared for the spectral data. Principal component analysis (PCA) and canonical variate analysis (CVA) were used for the classification of the three species. Samples could be discriminated by visual analysis of the FTIR spectra by using their marker bands. Discrimination of the three species was also possible through the combination of the pre-processed FTIR spectra with PCA and CVA, in which CVA gave clearer discrimination. Subsequently, the developed method could be used for the identification and discrimination of the three closely-related plant species.

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

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

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.

Robust and Accurate Discrimination of Self/Non-Self Antigen Presentations by Regulatory T Cell Suppression.

PubMed

Furusawa, Chikara; Yamaguchi, Tomoyuki

The immune response by T cells usually discriminates self and non-self antigens, even though the negative selection of self-reactive T cells is imperfect and a certain fraction of T cells can respond to self-antigens. In this study, we construct a simple mathematical model of T cell populations to analyze how such self/non-self discrimination is possible. The results demonstrate that the control of the immune response by regulatory T cells enables a robust and accurate discrimination of self and non-self antigens, even when there is a significant overlap between the affinity distribution of T cells to self and non-self antigens. Here, the number of regulatory T cells in the system acts as a global variable controlling the T cell population dynamics. The present study provides a basis for the development of a quantitative theory for self and non-self discrimination in the immune system and a possible strategy for its experimental verification.

Robust and Accurate Discrimination of Self/Non-Self Antigen Presentations by Regulatory T Cell Suppression

PubMed Central

Furusawa, Chikara; Yamaguchi, Tomoyuki

2016-01-01

The immune response by T cells usually discriminates self and non-self antigens, even though the negative selection of self-reactive T cells is imperfect and a certain fraction of T cells can respond to self-antigens. In this study, we construct a simple mathematical model of T cell populations to analyze how such self/non-self discrimination is possible. The results demonstrate that the control of the immune response by regulatory T cells enables a robust and accurate discrimination of self and non-self antigens, even when there is a significant overlap between the affinity distribution of T cells to self and non-self antigens. Here, the number of regulatory T cells in the system acts as a global variable controlling the T cell population dynamics. The present study provides a basis for the development of a quantitative theory for self and non-self discrimination in the immune system and a possible strategy for its experimental verification. PMID:27668873

Design of an efficient music-speech discriminator.

PubMed

Tardón, Lorenzo J; Sammartino, Simone; Barbancho, Isabel

2010-01-01

In this paper, the problem of the design of a simple and efficient music-speech discriminator for large audio data sets in which advanced music playing techniques are taught and voice and music are intrinsically interleaved is addressed. In the process, a number of features used in speech-music discrimination are defined and evaluated over the available data set. Specifically, the data set contains pieces of classical music played with different and unspecified instruments (or even lyrics) and the voice of a teacher (a top music performer) or even the overlapped voice of the translator and other persons. After an initial test of the performance of the features implemented, a selection process is started, which takes into account the type of classifier selected beforehand, to achieve good discrimination performance and computational efficiency, as shown in the experiments. The discrimination application has been defined and tested on a large data set supplied by Fundacion Albeniz, containing a large variety of classical music pieces played with different instrument, which include comments and speeches of famous performers.

A simple solubility tests for the discrimination of acrylic and modacrylic fibers.

PubMed

Suga, Keisuke; Narita, Yuji; Suzuki, Shinichi

2014-05-01

In a crime scene investigation, single fibers play an important role as significant trace physical evidence. Acrylic fibers are frequently encountered in forensic analysis. Currently, acrylic and modacrylic are not discriminated clearly in Japan. Only results of FT-IR, some of acrylics were difficult to separate clearly to acrylic and modacrylic fibers. Solubility test is primitive but convenient useful method, and Japan Industrial Standards (JIS) recommends FT-IR and solubility test to distinguish acrylic and modacrylic fibers. But recommended JIS dissolving test using 100% N,N-dimethylformamide (DMF) as a solvent, some acrylics could not be discriminated. In this report, we used DMF and ethanol (90:10, v/v) solvent. The JIS method could not discriminate 6 acrylics in 60 acrylics; hence, DMF and ethanol (90:10, v/v) solvent discriminated 59 of the 60 fibers (43 acrylic and 16 modacrylic fibers) clearly, but only one modacrylic fiber incorrectly identified as acrylic. © 2014 American Academy of Forensic Sciences.

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.

How Things Work: Metal Locators and Related Devices.

ERIC Educational Resources Information Center

Crane, H. Richard, Ed.

1984-01-01

Describes a simple form of metal detector, discussing the principles of signal generation, and the detection and discrimination of induced eddy current signals from the located objects. Includes a rough schematic of the detector. (JM)

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.

Pseudorandom Noise Code-Based Technique for Cloud and Aerosol Discrimination Applications

NASA Technical Reports Server (NTRS)

Campbell, Joel F.; Prasad, Narasimha S.; Flood, Michael A.; Harrison, Fenton Wallace

2011-01-01

NASA Langley Research Center is working on a continuous wave (CW) laser based remote sensing scheme for the detection of CO2 and O2 from space based platforms suitable for ACTIVE SENSING OF CO2 EMISSIONS OVER NIGHTS, DAYS, AND SEASONS (ASCENDS) mission. ASCENDS is a future space-based mission to determine the global distribution of sources and sinks of atmospheric carbon dioxide (CO2). A unique, multi-frequency, intensity modulated CW (IMCW) laser absorption spectrometer (LAS) operating at 1.57 micron for CO2 sensing has been developed. Effective aerosol and cloud discrimination techniques are being investigated in order to determine concentration values with accuracies less than 0.3%. In this paper, we discuss the demonstration of a PN code based technique for cloud and aerosol discrimination applications. The possibility of using maximum length (ML)-sequences for range and absorption measurements is investigated. A simple model for accomplishing this objective is formulated, Proof-of-concept experiments carried out using SONAR based LIDAR simulator that was built using simple audio hardware provided promising results for extension into optical wavelengths. Keywords: ASCENDS, CO2 sensing, O2 sensing, PN codes, CW lidar

Action Centered Contextual Bandits.

PubMed

Greenewald, Kristjan; Tewari, Ambuj; Klasnja, Predrag; Murphy, Susan

2017-12-01

Contextual bandits have become popular as they offer a middle ground between very simple approaches based on multi-armed bandits and very complex approaches using the full power of reinforcement learning. They have demonstrated success in web applications and have a rich body of associated theoretical guarantees. Linear models are well understood theoretically and preferred by practitioners because they are not only easily interpretable but also simple to implement and debug. Furthermore, if the linear model is true, we get very strong performance guarantees. Unfortunately, in emerging applications in mobile health, the time-invariant linear model assumption is untenable. We provide an extension of the linear model for contextual bandits that has two parts: baseline reward and treatment effect. We allow the former to be complex but keep the latter simple. We argue that this model is plausible for mobile health applications. At the same time, it leads to algorithms with strong performance guarantees as in the linear model setting, while still allowing for complex nonlinear baseline modeling. Our theory is supported by experiments on data gathered in a recently concluded mobile health study.

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.

Kinetics of DSB rejoining and formation of simple chromosome exchange aberrations

NASA Technical Reports Server (NTRS)

Cucinotta, F. A.; Nikjoo, H.; O'Neill, P.; Goodhead, D. T.

2000-01-01

PURPOSE: To investigate the role of kinetics in the processing of DNA double strand breaks (DSB), and the formation of simple chromosome exchange aberrations following X-ray exposures to mammalian cells based on an enzymatic approach. METHODS: Using computer simulations based on a biochemical approach, rate-equations that describe the processing of DSB through the formation of a DNA-enzyme complex were formulated. A second model that allows for competition between two processing pathways was also formulated. The formation of simple exchange aberrations was modelled as misrepair during the recombination of single DSB with undamaged DNA. Non-linear coupled differential equations corresponding to biochemical pathways were solved numerically by fitting to experimental data. RESULTS: When mediated by a DSB repair enzyme complex, the processing of single DSB showed a complex behaviour that gives the appearance of fast and slow components of rejoining. This is due to the time-delay caused by the action time of enzymes in biomolecular reactions. It is shown that the kinetic- and dose-responses of simple chromosome exchange aberrations are well described by a recombination model of DSB interacting with undamaged DNA when aberration formation increases with linear dose-dependence. Competition between two or more recombination processes is shown to lead to the formation of simple exchange aberrations with a dose-dependence similar to that of a linear quadratic model. CONCLUSIONS: Using a minimal number of assumptions, the kinetics and dose response observed experimentally for DSB rejoining and the formation of simple chromosome exchange aberrations are shown to be consistent with kinetic models based on enzymatic reaction approaches. A non-linear dose response for simple exchange aberrations is possible in a model of recombination of DNA containing a DSB with undamaged DNA when two or more pathways compete for DSB repair.

Different Neuroplasticity for Task Targets and Distractors

PubMed Central

Spingath, Elsie Y.; Kang, Hyun Sug; Plummer, Thane; Blake, David T.

2011-01-01

Adult learning-induced sensory cortex plasticity results in enhanced action potential rates in neurons that have the most relevant information for the task, or those that respond strongly to one sensory stimulus but weakly to its comparison stimulus. Current theories suggest this plasticity is caused when target stimulus evoked activity is enhanced by reward signals from neuromodulatory nuclei. Prior work has found evidence suggestive of nonselective enhancement of neural responses, and suppression of responses to task distractors, but the differences in these effects between detection and discrimination have not been directly tested. Using cortical implants, we defined physiological responses in macaque somatosensory cortex during serial, matched, detection and discrimination tasks. Nonselective increases in neural responsiveness were observed during detection learning. Suppression of responses to task distractors was observed during discrimination learning, and this suppression was specific to cortical locations that sampled responses to the task distractor before learning. Changes in receptive field size were measured as the area of skin that had a significant response to a constant magnitude stimulus, and these areal changes paralleled changes in responsiveness. From before detection learning until after discrimination learning, the enduring changes were selective suppression of cortical locations responsive to task distractors, and nonselective enhancement of responsiveness at cortical locations selective for target and control skin sites. A comparison of observations in prior studies with the observed plasticity effects suggests that the non-selective response enhancement and selective suppression suffice to explain known plasticity phenomena in simple spatial tasks. This work suggests that differential responsiveness to task targets and distractors in primary sensory cortex for a simple spatial detection and discrimination task arise from nonselective increases in response over a broad cortical locus that includes the representation of the task target, and selective suppression of responses to the task distractor within this locus. PMID:21297962

Simple quasi-analytical holonomic homogenization model for the non-linear analysis of in-plane loaded masonry panels: Part 1, meso-scale

NASA Astrophysics Data System (ADS)

Milani, G.; Bertolesi, E.

2017-07-01

A simple quasi analytical holonomic homogenization approach for the non-linear analysis of masonry walls in-plane loaded is presented. The elementary cell (REV) is discretized with 24 triangular elastic constant stress elements (bricks) and non-linear interfaces (mortar). A holonomic behavior with softening is assumed for mortar. It is shown how the mechanical problem in the unit cell is characterized by very few displacement variables and how homogenized stress-strain behavior can be evaluated semi-analytically.

A Two-Stage Process Model of Sensory Discrimination: An Alternative to Drift-Diffusion

PubMed Central

Landy, Michael S.

2016-01-01

Discrimination of the direction of motion of a noisy stimulus is an example of sensory discrimination under uncertainty. For stimuli that are extended in time, reaction time is quicker for larger signal values (e.g., discrimination of opposite directions of motion compared with neighboring orientations) and larger signal strength (e.g., stimuli with higher contrast or motion coherence, that is, lower noise). The standard model of neural responses (e.g., in lateral intraparietal cortex) and reaction time for discrimination is drift-diffusion. This model makes two clear predictions. (1) The effects of signal strength and value on reaction time should interact multiplicatively because the diffusion process depends on the signal-to-noise ratio. (2) If the diffusion process is interrupted, as in a cued-response task, the time to decision after the cue should be independent of the strength of accumulated sensory evidence. In two experiments with human participants, we show that neither prediction holds. A simple alternative model is developed that is consistent with the results. In this estimate-then-decide model, evidence is accumulated until estimation precision reaches a threshold value. Then, a decision is made with duration that depends on the signal-to-noise ratio achieved by the first stage. SIGNIFICANCE STATEMENT Sensory decision-making under uncertainty is usually modeled as the slow accumulation of noisy sensory evidence until a threshold amount of evidence supporting one of the possible decision outcomes is reached. Furthermore, it has been suggested that this accumulation process is reflected in neural responses, e.g., in lateral intraparietal cortex. We derive two behavioral predictions of this model and show that neither prediction holds. We introduce a simple alternative model in which evidence is accumulated until a sufficiently precise estimate of the stimulus is achieved, and then that estimate is used to guide the discrimination decision. This model is consistent with the behavioral data. PMID:27807167

A Two-Stage Process Model of Sensory Discrimination: An Alternative to Drift-Diffusion.

PubMed

Sun, Peng; Landy, Michael S

2016-11-02

Discrimination of the direction of motion of a noisy stimulus is an example of sensory discrimination under uncertainty. For stimuli that are extended in time, reaction time is quicker for larger signal values (e.g., discrimination of opposite directions of motion compared with neighboring orientations) and larger signal strength (e.g., stimuli with higher contrast or motion coherence, that is, lower noise). The standard model of neural responses (e.g., in lateral intraparietal cortex) and reaction time for discrimination is drift-diffusion. This model makes two clear predictions. (1) The effects of signal strength and value on reaction time should interact multiplicatively because the diffusion process depends on the signal-to-noise ratio. (2) If the diffusion process is interrupted, as in a cued-response task, the time to decision after the cue should be independent of the strength of accumulated sensory evidence. In two experiments with human participants, we show that neither prediction holds. A simple alternative model is developed that is consistent with the results. In this estimate-then-decide model, evidence is accumulated until estimation precision reaches a threshold value. Then, a decision is made with duration that depends on the signal-to-noise ratio achieved by the first stage. Sensory decision-making under uncertainty is usually modeled as the slow accumulation of noisy sensory evidence until a threshold amount of evidence supporting one of the possible decision outcomes is reached. Furthermore, it has been suggested that this accumulation process is reflected in neural responses, e.g., in lateral intraparietal cortex. We derive two behavioral predictions of this model and show that neither prediction holds. We introduce a simple alternative model in which evidence is accumulated until a sufficiently precise estimate of the stimulus is achieved, and then that estimate is used to guide the discrimination decision. This model is consistent with the behavioral data. Copyright © 2016 the authors 0270-6474/16/3611259-16$15.00/0.

The Fisher-Markov selector: fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data.

PubMed

Cheng, Qiang; Zhou, Hongbo; Cheng, Jie

2011-06-01

Selecting features for multiclass classification is a critically important task for pattern recognition and machine learning applications. Especially challenging is selecting an optimal subset of features from high-dimensional data, which typically have many more variables than observations and contain significant noise, missing components, or outliers. Existing methods either cannot handle high-dimensional data efficiently or scalably, or can only obtain local optimum instead of global optimum. Toward the selection of the globally optimal subset of features efficiently, we introduce a new selector--which we call the Fisher-Markov selector--to identify those features that are the most useful in describing essential differences among the possible groups. In particular, in this paper we present a way to represent essential discriminating characteristics together with the sparsity as an optimization objective. With properly identified measures for the sparseness and discriminativeness in possibly high-dimensional settings, we take a systematic approach for optimizing the measures to choose the best feature subset. We use Markov random field optimization techniques to solve the formulated objective functions for simultaneous feature selection. Our results are noncombinatorial, and they can achieve the exact global optimum of the objective function for some special kernels. The method is fast; in particular, it can be linear in the number of features and quadratic in the number of observations. We apply our procedure to a variety of real-world data, including mid--dimensional optical handwritten digit data set and high-dimensional microarray gene expression data sets. The effectiveness of our method is confirmed by experimental results. In pattern recognition and from a model selection viewpoint, our procedure says that it is possible to select the most discriminating subset of variables by solving a very simple unconstrained objective function which in fact can be obtained with an explicit expression.

Understanding the Implications of Neural Population Activity on Behavior

NASA Astrophysics Data System (ADS)

Briguglio, John

Learning how neural activity in the brain leads to the behavior we exhibit is one of the fundamental questions in Neuroscience. In this dissertation, several lines of work are presented to that use principles of neural coding to understand behavior. In one line of work, we formulate the efficient coding hypothesis in a non-traditional manner in order to test human perceptual sensitivity to complex visual textures. We find a striking agreement between how variable a particular texture signal is and how sensitive humans are to its presence. This reveals that the efficient coding hypothesis is still a guiding principle for neural organization beyond the sensory periphery, and that the nature of cortical constraints differs from the peripheral counterpart. In another line of work, we relate frequency discrimination acuity to neural responses from auditory cortex in mice. It has been previously observed that optogenetic manipulation of auditory cortex, in addition to changing neural responses, evokes changes in behavioral frequency discrimination. We are able to account for changes in frequency discrimination acuity on an individual basis by examining the Fisher information from the neural population with and without optogenetic manipulation. In the third line of work, we address the question of what a neural population should encode given that its inputs are responses from another group of neurons. Drawing inspiration from techniques in machine learning, we train Deep Belief Networks on fake retinal data and show the emergence of Garbor-like filters, reminiscent of responses in primary visual cortex. In the last line of work, we model the state of a cortical excitatory-inhibitory network during complex adaptive stimuli. Using a rate model with Wilson-Cowan dynamics, we demonstrate that simple non-linearities in the signal transferred from inhibitory to excitatory neurons can account for real neural recordings taken from auditory cortex. This work establishes and tests a variety of hypotheses that will be useful in helping to understand the relationship between neural activity and behavior as recorded neural populations continue to grow.

Time-over-threshold for pulse shape discrimination in a time-of-flight phoswich PET detector

PubMed Central

Chang, Chen-Ming; Cates, Joshua W.; Levin, Craig S.

2016-01-01

It is well known that a PET detector capable of measuring both photon time-of-flight (TOF) and depth-of-interaction (DOI) improves the image quality and accuracy. Phoswich designs have been realized in PET detectors to measure DOI for more than a decade. However, PET detectors based on phoswich designs put great demand on the readout circuits, which have to differentiate the pulse shape produced by different crystal layers. A simple pulse shape discrimination approach is required to realize the phoswich designs in a clinical PET scanner, which consists of thousands of scintillation crystal elements. In this work, we studied time-over-threshold (ToT) as a pulse shape parameter for DOI. The energy, timing and DOI performance were evaluated for a phoswich detector design comprising 3 mm × 3 mm × 10 mm LYSO:Ce crystal optically coupled to 3 mm × 3 mm × 10 mm calcium co-doped LSO:Ce,Ca(0.4%) crystal read out by a silicon photomultiplier (SiPM). A DOI accuracy of 97.2% has been achieved for photopeak events using the proposed time-over-threshold (ToT) processing. The energy resolution without correction for SiPM non-linearity was 9.7 ± 0.2% and 11.3 ± 0.2% FWHM at 511 keV for LYSO and LSO crystal layers, respectively. The coincidence time resolution for photopeak events ranges from 164.6 ps to 183.1 ps FWHM, depending on the layer combinations. The coincidence time resolution for inter-crystal scatter events ranges from 214.6 ps to 418.3 ps FWHM, depending on the energy windows applied. These results show great promises of using ToT for pulse shape discrimination in a TOF phoswich detector since a ToT measurement can be easily implemented in readout electronics. PMID:27991437

Linear and Non-Linear Visual Feature Learning in Rat and Humans

PubMed Central

Bossens, Christophe; Op de Beeck, Hans P.

2016-01-01

The visual system processes visual input in a hierarchical manner in order to extract relevant features that can be used in tasks such as invariant object recognition. Although typically investigated in primates, recent work has shown that rats can be trained in a variety of visual object and shape recognition tasks. These studies did not pinpoint the complexity of the features used by these animals. Many tasks might be solved by using a combination of relatively simple features which tend to be correlated. Alternatively, rats might extract complex features or feature combinations which are nonlinear with respect to those simple features. In the present study, we address this question by starting from a small stimulus set for which one stimulus-response mapping involves a simple linear feature to solve the task while another mapping needs a well-defined nonlinear combination of simpler features related to shape symmetry. We verified computationally that the nonlinear task cannot be trivially solved by a simple V1-model. We show how rats are able to solve the linear feature task but are unable to acquire the nonlinear feature. In contrast, humans are able to use the nonlinear feature and are even faster in uncovering this solution as compared to the linear feature. The implications for the computational capabilities of the rat visual system are discussed. PMID:28066201

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.

A concordance index for matched case-control studies with applications in cancer risk.

PubMed

Brentnall, Adam R; Cuzick, Jack; Field, John; Duffy, Stephen W

2015-02-10

In unmatched case-control studies, the area under the receiver operating characteristic (ROC) curve (AUC) may be used to measure how well a variable discriminates between cases and controls. The AUC is sometimes used in matched case-control studies by ignoring matching, but it lacks interpretation because it is not based on an estimate of the ROC for the population of interest. We introduce an alternative measure of discrimination that is the concordance of risk factors conditional on the matching factors. Parametric and non-parametric estimators are given for different matching scenarios, and applied to real data from breast and lung cancer case-control studies. Diagnostic plots to verify the constancy of discrimination over matching factors are demonstrated. The proposed simple measure is easy to use, interpret, more efficient than unmatched AUC statistics and may be applied to compare the conditional discrimination performance of risk factors. Copyright © 2014 John Wiley & Sons, Ltd.

Taxonomic discrimination of higher plants by pyrolysis mass spectrometry.

PubMed

Kim, S W; Ban, S H; Chung, H J; Choi, D W; Choi, P S; Yoo, O J; Liu, J R

2004-02-01

Pyrolysis mass spectrometry (PyMS) is a rapid, simple, high-resolution analytical method based on thermal degradation of complex material in a vacuum and has been widely applied to the discrimination of closely related microbial strains. Leaf samples of six species and one variety of higher plants (Rosa multiflora, R. multiflora var. platyphylla, Sedum kamtschaticum, S. takesimense, S. sarmentosum, Hepatica insularis, and H. asiatica) were subjected to PyMS for spectral fingerprinting. Principal component analysis of PyMS data was not able to discriminate these plants in discrete clusters. However, canonical variate analysis of PyMS data separated these plants from one another. A hierarchical dendrogram based on canonical variate analysis was in agreement with the known taxonomy of the plants at the variety level. These results indicate that PyMS is able to discriminate higher plants based on taxonomic classification at the family, genus, species, and variety level.

An integrtated approach to the use of Landsat TM data for gold exploration in west central Nevada

NASA Technical Reports Server (NTRS)

Mouat, D. A.; Myers, J. S.; Miller, N. L.

1987-01-01

This paper represents an integration of several Landsat TM image processing techniques with other data to discriminate the lithologies and associated areas of hydrothermal alteration in the vicinity of the Paradise Peak gold mine in west central Nevada. A microprocessor-based image processing system and an IDIMS system were used to analyze data from a 512 X 512 window of a Landsat-5 TM scene collected on June 30, 1984. Image processing techniques included simple band composites, band ratio composites, principal components composites, and baseline-based composites. These techniques were chosen based on their ability to discriminate the spectral characteristics of the products of hydrothermal alteration as well as of the associated regional lithologies. The simple band composite, ratio composite, two principal components composites, and the baseline-based composites separately can define the principal areas of alteration. Combined, they provide a very powerful exploration tool.

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.

Development and validation of a brief, descriptive Danish pain questionnaire (BDDPQ).

PubMed

Perkins, F M; Werner, M U; Persson, F; Holte, K; Jensen, T S; Kehlet, H

2004-04-01

A new pain questionnaire should be simple, be documented to have discriminative function, and be related to previously used questionnaires. Word meaning was validated by using bilingual Danish medical students and asking them to translate words taken from the Danish version of the McGill pain questionnaire into English. Evaluative word value was estimated using a visual analog scale (VAS). Discriminative function was assessed by having patients with one of six painful conditions (postherpetic neuralgia, phantom limb pain, rheumatoid arthritis, ankle fracture, appendicitis, or labor pain) complete the questionnaire. We were not able to find Danish words that were reliably back-translated to the English words 'splitting' or 'gnawing'. A simple three-word set of evaluative terms had good separation when rated on a VAS scale ('let' 17.5+/-6.5 mm; 'moderat' 42.7+/-8.6 mm; and 'staerk' 74.9+/-9.7 mm). The questionnaire was able to discriminate among the six painful conditions with 77% accuracy by just using the descriptive words. The accuracy of the questionnaire increased to 96% with the addition of evaluative terms (for pain at rest and with activity), chronicity (acute vs. chronic), and location of the pain. A Danish pain questionnaire that subjects and patients can self-administer has been developed and validated relative to the words used in the English McGill Pain questionnaire. The discriminative ability of the questionnaire among some common painful conditions has been tested and documented. The questionnaire may be of use in patient care and research.

Development of a simple and practical method of discrimination between Vibrio furnissii and V. fluvialis based on single-nucleotide polymorphisms of 16S rRNA genes observed in V. furnissii but not in V. fluvialis.

PubMed

Takajo, Ichiro; Yamada, Akiteru; Umeki, Kazumi; Saeki, Yuji; Hashikura, Yuuki; Yamamoto, Ikuo; Umekita, Kunihiko; Urayama-Kawano, Midori; Yamasaki, Shogo; Taniguchi, Takako; Misawa, Naoaki; Okayama, Akihiko

2018-01-01

Vibrio furnissii and V. fluvialis are closely related, the discrimination of which by conventional biochemical assay remains a challenge. Investigation of the sequence of the 16S rRNA genes in a clinical isolate of V. furnissii by visual inspection of a sequencing electropherogram revealed two sites of single-nucleotide polymorphisms (SNPs; positions 460 A/G and 1261 A/G) in these genes. A test of 12 strains each of V. fluvialis and V. furnissii revealed these SNPs to be common in V. furnissii but not in V. fluvialis. Divergence of SNP frequency was observed among the strains of V. furnissii tested. Because the SNPs described in V. furnissii produce a difference in the target sequence of restriction enzymes, a combination of polymerase chain reaction (PCR) of the 16S rRNA genes using conventional primers and restriction fragment length polymorphism analysis using Eco RV and Eae I was shown to discriminate between V. fluvialis and V. furnissii. This method is simple and alleviates the need for expensive equipment or primer sets specific to these bacteria. Therefore, we believe that this method can be useful, alongside specific PCR and mass spectrometry, when there is a need to discriminate between V. fluvialis and V. furnissii. Copyright © 2017 Elsevier B.V. All rights reserved.

Pregnenolone sulphate enhances spatial orientation and object discrimination in adult male rats: evidence from a behavioural and electrophysiological study.

PubMed

Plescia, Fulvio; Sardo, Pierangelo; Rizzo, Valerio; Cacace, Silvana; Marino, Rosa Anna Maria; Brancato, Anna; Ferraro, Giuseppe; Carletti, Fabio; Cannizzaro, Carla

2014-01-01

Neurosteroids can alter neuronal excitability interacting with specific neurotransmitter receptors, thus affecting several functions such as cognition and emotionality. In this study we investigated, in adult male rats, the effects of the acute administration of pregnenolone-sulfate (PREGS) (10mg/kg, s.c.) on cognitive processes using the Can test, a non aversive spatial/visual task which allows the assessment of both spatial orientation-acquisition and object discrimination in a simple and in a complex version of the visual task. Electrophysiological recordings were also performed in vivo, after acute PREGS systemic administration in order to investigate on the neuronal activation in the hippocampus and the perirhinal cortex. Our results indicate that, PREGS induces an improvement in spatial orientation-acquisition and in object discrimination in the simple and in the complex visual task; the behavioural responses were also confirmed by electrophysiological recordings showing a potentiation in the neuronal activity of the hippocampus and the perirhinal cortex. In conclusion, this study demonstrates that PREGS systemic administration in rats exerts cognitive enhancing properties which involve both the acquisition and utilization of spatial information, and object discrimination memory, and also correlates the behavioural potentiation observed to an increase in the neuronal firing of discrete cerebral areas critical for spatial learning and object recognition. This provides further evidence in support of the role of PREGS in exerting a protective and enhancing role on human memory. Copyright © 2013. Published by Elsevier B.V.

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.

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.

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.

Multi-Mode Analysis of Dual Ridged Waveguide Systems for Material Characterization

DTIC Science & Technology

2015-09-17

characterization is the process of determining the dielectric, magnetic, and magnetoelectric properties of a material. For simple (i.e., linear ...field expressions in terms of elementary functions (sines, cosines, exponentials and Bessel functions) and corresponding propagation constants of the...with material parameters 0 and µ0. • The MUT is simple ( linear , isotropic, homogeneous), and the sample has a uniform thickness. • The waveguide

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.

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.

Exploiting Acoustic and Syntactic Features for Automatic Prosody Labeling in a Maximum Entropy Framework

PubMed Central

Sridhar, Vivek Kumar Rangarajan; Bangalore, Srinivas; Narayanan, Shrikanth S.

2009-01-01

In this paper, we describe a maximum entropy-based automatic prosody labeling framework that exploits both language and speech information. We apply the proposed framework to both prominence and phrase structure detection within the Tones and Break Indices (ToBI) annotation scheme. Our framework utilizes novel syntactic features in the form of supertags and a quantized acoustic–prosodic feature representation that is similar to linear parameterizations of the prosodic contour. The proposed model is trained discriminatively and is robust in the selection of appropriate features for the task of prosody detection. The proposed maximum entropy acoustic–syntactic model achieves pitch accent and boundary tone detection accuracies of 86.0% and 93.1% on the Boston University Radio News corpus, and, 79.8% and 90.3% on the Boston Directions corpus. The phrase structure detection through prosodic break index labeling provides accuracies of 84% and 87% on the two corpora, respectively. The reported results are significantly better than previously reported results and demonstrate the strength of maximum entropy model in jointly modeling simple lexical, syntactic, and acoustic features for automatic prosody labeling. PMID:19603083

A Quantum Chemical and Statistical Study of Phenolic Schiff Bases with Antioxidant Activity against DPPH Free Radical

PubMed Central

Anouar, El Hassane

2014-01-01

Phenolic Schiff bases are known as powerful antioxidants. To select the electronic, 2D and 3D descriptors responsible for the free radical scavenging ability of a series of 30 phenolic Schiff bases, a set of molecular descriptors were calculated by using B3P86 (Becke’s three parameter hybrid functional with Perdew 86 correlation functional) combined with 6-31 + G(d,p) basis set (i.e., at the B3P86/6-31 + G(d,p) level of theory). The chemometric methods, simple and multiple linear regressions (SLR and MLR), principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to reduce the dimensionality and to investigate the relationship between the calculated descriptors and the antioxidant activity. The results showed that the antioxidant activity mainly depends on the first and second bond dissociation enthalpies of phenolic hydroxyl groups, the dipole moment and the hydrophobicity descriptors. The antioxidant activity is inversely proportional to the main descriptors. The selected descriptors discriminate the Schiff bases into active and inactive antioxidants. PMID:26784873

Precise fabrication of a 5 nm graphene nanopore with a helium ion microscope for biomolecule detection

NASA Astrophysics Data System (ADS)

Deng, Yunsheng; Huang, Qimeng; Zhao, Yue; Zhou, Daming; Ying, Cuifeng; Wang, Deqiang

2017-01-01

We report a scalable method to fabricate high-quality graphene nanopores for biomolecule detection using a helium ion microscope (HIM). HIM milling shows promising capabilities for precisely controlling the size and shape, and may allow for the potential production of nanopores at wafer scale. Nanopores could be fabricated at different sizes ranging from 5 to 30 nm in diameter in few minutes. Compared with the current solid-state nanopore fabrication techniques, e.g. transmission electron microscopy, HIM is fast. Furthermore, we investigated the exposure-time dependence of graphene nanopore formation: the rate of pore expansion did not follow a simple linear relationship with exposure time, but a fast expansion rate at short exposure time and a slow rate at long exposure time. In addition, we performed biomolecule detection with our patterned graphene nanopore. The ionic current signals induced by 20-base single-stranded DNA homopolymers could be used as a basis for homopolymer differentiation. However, the charge interaction of homopolymer chains with graphene nanopores, and the conformations of homopolymer chains need to be further considered to improve the accuracy of discrimination.

Oral cancer detection based on fluorescence polarization of blood plasma at excitation wavelength 405 nm

NASA Astrophysics Data System (ADS)

Pachaiappan, Rekha; Prakasarao, Aruna; Manoharan, Yuvaraj; Dornadula, Koteeswaran; Singaravelu, Ganesan

2017-02-01

During metabolism the metabolites such as hormones, proteins and enzymes were released in to the blood stream by the cells. These metabolites reflect any change that occurs due to any disturbances in normal metabolic function of the human system. This was well observed with the altered spectral signatures observed with fluorescence spectroscopic technique. Previously many have reported on the significance of native fluorescence spectroscopic method in the diagnosis of cancer. As fluorescence spectroscopy is sensitive and simple, it has complementary techniques such as excitation-emission matrix, synchronous and polarization. The fluorescence polarization measurement provides details about any association or binding reactions and denaturing effects that occurs due to change in the micro environment of cells and tissues. In this study, we have made an attempt in the diagnosis of oral cancer at 405 nm excitation using fluorescence polarization measurement. The fluorescence anisotropic values calculated from polarized fluorescence spectral data of normal and oral cancer subjects yielded a good accuracy when analyzed with linear discriminant analysis based artificial neural network. The results will be discussed in detail.

Error quantification of a high-resolution coupled hydrodynamic-ecosystem coastal-ocean model: Part 2. Chlorophyll-a, nutrients and SPM

NASA Astrophysics Data System (ADS)

Allen, J. Icarus; Holt, Jason T.; Blackford, Jerry; Proctor, Roger

2007-12-01

Marine systems models are becoming increasingly complex and sophisticated, but far too little attention has been paid to model errors and the extent to which model outputs actually relate to ecosystem processes. Here we describe the application of summary error statistics to a complex 3D model (POLCOMS-ERSEM) run for the period 1988-1989 in the southern North Sea utilising information from the North Sea Project, which collected a wealth of observational data. We demonstrate that to understand model data misfit and the mechanisms creating errors, we need to use a hierarchy of techniques, including simple correlations, model bias, model efficiency, binary discriminator analysis and the distribution of model errors to assess model errors spatially and temporally. We also demonstrate that a linear cost function is an inappropriate measure of misfit. This analysis indicates that the model has some skill for all variables analysed. A summary plot of model performance indicates that model performance deteriorates as we move through the ecosystem from the physics, to the nutrients and plankton.

In-Depth Two-Year Study of Phenolic Profile Variability among Olive Oils from Autochthonous and Mediterranean Varieties in Morocco, as Revealed by a LC-MS Chemometric Profiling Approach.

PubMed

Bajoub, Aadil; Medina-Rodríguez, Santiago; Olmo-García, Lucía; Ajal, El Amine; Monasterio, Romina P; Hanine, Hafida; Fernández-Gutiérrez, Alberto; Carrasco-Pancorbo, Alegría

2016-12-28

Olive oil phenolic fraction considerably contributes to the sensory quality and nutritional value of this foodstuff. Herein, the phenolic fraction of 203 olive oil samples extracted from fruits of four autochthonous Moroccan cultivars ("Picholine Marocaine", "Dahbia", "Haouzia" and "Menara"), and nine Mediterranean varieties recently introduced in Morocco ("Arbequina", "Arbosana", "Cornicabra", "Frantoio", "Hojiblanca", "Koroneiki", "Manzanilla", "Picholine de Languedoc" and "Picual"), were explored over two consecutive crop seasons (2012/2013 and 2013/2014) by using liquid chromatography-mass spectrometry. A total of 32 phenolic compounds (and quinic acid), belonging to five chemical classes (secoiridoids, simple phenols, flavonoids, lignans and phenolic acids) were identified and quantified. Phenolic profiling revealed that the determined phenolic compounds showed variety-dependent levels, being, at the same time, significantly affected by the crop season. Moreover, based on the obtained phenolic composition and chemometric linear discriminant analysis, statistical models were obtained allowing a very satisfactory classification and prediction of the varietal origin of the studied oils.

Robust mislabel logistic regression without modeling mislabel probabilities.

PubMed

Hung, Hung; Jou, Zhi-Yu; Huang, Su-Yun

2018-03-01

Logistic regression is among the most widely used statistical methods for linear discriminant analysis. In many applications, we only observe possibly mislabeled responses. Fitting a conventional logistic regression can then lead to biased estimation. One common resolution is to fit a mislabel logistic regression model, which takes into consideration of mislabeled responses. Another common method is to adopt a robust M-estimation by down-weighting suspected instances. In this work, we propose a new robust mislabel logistic regression based on γ-divergence. Our proposal possesses two advantageous features: (1) It does not need to model the mislabel probabilities. (2) The minimum γ-divergence estimation leads to a weighted estimating equation without the need to include any bias correction term, that is, it is automatically bias-corrected. These features make the proposed γ-logistic regression more robust in model fitting and more intuitive for model interpretation through a simple weighting scheme. Our method is also easy to implement, and two types of algorithms are included. Simulation studies and the Pima data application are presented to demonstrate the performance of γ-logistic regression. © 2017, The International Biometric Society.

Spectral mineral mapping for characterization of subtle geothermal prospects using ASTER data

NASA Astrophysics Data System (ADS)

Abubakar, A. J.; Hashim, M.; Pour, A. B.

2017-05-01

In this study, the performance of ASTER data is evaluated for mapping subtle geothermal prospects in an unexplored tropical region having a number of thermal springs. The study employed a simple Decorrelation stretch with specific absorptions to highlight possible alteration zones of interest related to Geothermal (GT) systems. Hydrothermal alteration minerals are subsequently mapped using Spectral Angle Mapper (SAM) and Linear Spectral Unmixing (LSU) algorithms to target representative minerals such as clays, carbonates and AL-OH minerals as indicators of GT activity. The results were validated through field GPS survey, rock sampling and laboratory analysis using latest smart lab X-Ray Diffractometer technology. The study indicates that ASTER broadband satellite data could be used to map subtle GT prospects with the aid of an in-situ verification. However, it also shows that ASTER could not discriminate within specie minerals especially for clays using SWIR bands. Subsequent studies are aimed at looking at both ASTER and Hyperion hyperspectral data in the same area as this could have significant implications for GT resource detection in unmapped aseismic and inaccessible tropical regions using available spaceborne data.

Fourier transform infrared spectroscopy combined with chemometrics for discrimination of Curcuma longa, Curcuma xanthorrhiza and Zingiber cassumunar.

PubMed

Rohaeti, Eti; Rafi, Mohamad; Syafitri, Utami Dyah; Heryanto, Rudi

2015-02-25

Turmeric (Curcuma longa), java turmeric (Curcuma xanthorrhiza) and cassumunar ginger (Zingiber cassumunar) are widely used in traditional Indonesian medicines (jamu). They have similar color for their rhizome and possess some similar uses, so it is possible to substitute one for the other. The identification and discrimination of these closely-related plants is a crucial task to ensure the quality of the raw materials. Therefore, an analytical method which is rapid, simple and accurate for discriminating these species using Fourier transform infrared spectroscopy (FTIR) combined with some chemometrics methods was developed. FTIR spectra were acquired in the mid-IR region (4000-400 cm(-1)). Standard normal variate, first and second order derivative spectra were compared for the spectral data. Principal component analysis (PCA) and canonical variate analysis (CVA) were used for the classification of the three species. Samples could be discriminated by visual analysis of the FTIR spectra by using their marker bands. Discrimination of the three species was also possible through the combination of the pre-processed FTIR spectra with PCA and CVA, in which CVA gave clearer discrimination. Subsequently, the developed method could be used for the identification and discrimination of the three closely-related plant species. Copyright © 2014 Elsevier B.V. All rights reserved.

Gender discrimination in the elderly and its impact on the elderly health.

PubMed

Keskinoglu, Pembe; Ucuncu, Tugba; Yildirim, Idris; Gurbuz, Turgut; Ur, Ismail; Ergor, Gul

2007-01-01

The aim of this study was to determine gender discrimination and risk factors in the elderly population and to assess the impact of that discrimination on elderly health. One hundred and sixty-eight elderly individuals who were selected from the records by simple randomized sampling were enrolled in the study. Data were obtained by face-to-face interviews at the residence of the elderly individuals. Chi(2)-Analysis, t-test, Mann-Whitney U-test, and logistic regression were used for data analysis. 81.1% of the elderly were married and 40.5% were middle or high school graduates, and 93.9% of the subjects had at least one living child. It was determined that 51.7% of the females, and 21.3% of the males were exposed to negative gender discrimination. This discrimination was higher among women in all sub-groups. In fact, older women and elderly individuals with only primary school education or less were significantly more exposed to gender discrimination (p=0.008 and p=0.043, respectively). It was found that only economical variables were related to poor health status, without gender discrimination. Despite the fact that the freedom has been obtained in some areas such as participation in household decision-making and dressing, the patriarchal family structure and sexual inequality continue in older age.

Security on the Internet: is your system vulnerable?

PubMed

Neray, P

1997-07-01

Internet technology does not discriminate. Whether or not your system is an intentional target really doesn't matter; you have a duty to ensure its safekeeping. Ten simple steps are given to protect your system from viruses, hackers, etc.

The Role of Ethnic and National Identifications in Perceived Discrimination for Asian Americans: Toward a Better Understanding of the Buffering Effect of Group Identifications on Psychological Distress

PubMed Central

Huynh, Que-Lam; Devos, Thierry; Goldberg, Robyn

2013-01-01

A robust relationship between perceived racial discrimination and psychological distress has been established. Yet, mixed evidence exists regarding the extent to which ethnic identification moderates this relationship, and scarce attention has been paid to the moderating role of national identification. We propose that the role of group identifications in the perceived discrimination–psychological distress relationship is best understood by simultaneously and interactively considering ethnic and national identifications. A sample of 259 Asian American students completed measures of perceived discrimination, group identifications (specific ethnic identification stated by respondents and national or “mainstream American” identification), and psychological distress (anxiety and depression symptoms). Regression analyses revealed a significant three-way interaction of perceived discrimination, ethnic identification, and national identification on psychological distress. Simple-slope analyses indicated that dual identification (strong ethnic and national identifications) was linked to a weaker relationship between perceived discrimination and psychological distress compared with other group identification configurations. These findings underscore the need to consider the interconnections between ethnic and national identifications to better understand the circumstances under which group identifications are likely to buffer individuals against the adverse effects of racial discrimination. PMID:25258674

Do rats use shape to solve “shape discriminations”?

PubMed Central

Minini, Loredana; Jeffery, Kathryn J.

2006-01-01

Visual discrimination tasks are increasingly used to explore the neurobiology of vision in rodents, but it remains unclear how the animals solve these tasks: Do they process shapes holistically, or by using low-level features such as luminance and angle acuity? In the present study we found that when discriminating triangles from squares, rats did not use shape but instead relied on local luminance differences in the lower hemifield. A second experiment prevented this strategy by using stimuli—squares and rectangles—that varied in size and location, and for which the only constant predictor of reward was aspect ratio (ratio of height to width: a simple descriptor of “shape”). Rats eventually learned to use aspect ratio but only when no other discriminand was available, and performance remained very poor even at asymptote. These results suggest that although rats can process both dimensions simultaneously, they do not naturally solve shape discrimination tasks this way. This may reflect either a failure to visually process global shape information or a failure to discover shape as the discriminative stimulus in a simultaneous discrimination. Either way, our results suggest that simultaneous shape discrimination is not a good task for studies of visual perception in rodents. PMID:16705141

Effect of americium-241 alpha-particles on the dose-response of chromosome aberrations in human lymphocytes analysed by fluorescence in situ hybridization.

PubMed

Barquinero, J F; Stephan, G; Schmid, E

2004-02-01

To evaluate by the fluorescent in-situ hybridization (FISH) technique the dose-response and intercellular distribution of alpha-particle-induced chromosome aberrations. In particular, the validity of using the yield of characteristic types of chromosome abnormalities in stable cells as quantitative indicators for retrospective dose reconstruction has been evaluated. Monolayers of human peripheral lymphocytes were exposed at doses from 0.02 to 1 Gy to alpha-particles emitted from a source of americium-241. The most probable energy of the alpha-particles entering the cells was 2.7 MeV. FISH painting was performed using DNA probes for chromosomes 2, 4 and 8 in combination with a pan-centromeric probe. In complete first-division cells, identified by harlequin staining, aberrations involving painted target chromosomal material were recorded as well as aberrations involving only unpainted chromosomal material. In total, the percentage of complex aberrations was about 35% and no dose dependence was observed. When complex-type exchanges were reduced to simple base types, the different cell distributions were clearly over-dispersed, and the linear coefficients of the dose-effect curves for translocations were significantly higher than for dicentrics. For past dose reconstruction, only a few complex aberrations were in stable cells. The linear coefficient obtained for transmissible aberrations in stable cells was more than seven times lower than that obtained in all analysed cells, i.e. including unstable cells. FISH-based analysis of complex rearrangements allows discrimination between partial-body exposures to low-linear energy transfer radiation and high-linear energy transfer exposures. In assessing past or chronic exposure to alpha-particles, the use of a dose-effect curve obtained by FISH-based translocation data, which had not excluded data determined in unstable cells, would underestimate the dose. Insertions are ineffective biomarkers because their frequency is too low.

Mining Distance Based Outliers in Near Linear Time with Randomization and a Simple Pruning Rule

NASA Technical Reports Server (NTRS)

Bay, Stephen D.; Schwabacher, Mark

2003-01-01

Defining outliers by their distance to neighboring examples is a popular approach to finding unusual examples in a data set. Recently, much work has been conducted with the goal of finding fast algorithms for this task. We show that a simple nested loop algorithm that in the worst case is quadratic can give near linear time performance when the data is in random order and a simple pruning rule is used. We test our algorithm on real high-dimensional data sets with millions of examples and show that the near linear scaling holds over several orders of magnitude. Our average case analysis suggests that much of the efficiency is because the time to process non-outliers, which are the majority of examples, does not depend on the size of the data set.

Self-reported discrimination and mental health among Asian Indians: Cultural beliefs and coping style as moderators

PubMed Central

Nadimpalli, Sarah B.; Kanaya, Alka M.; McDade, Thomas W.; Kandula, Namratha R.

2016-01-01

The South Asian (SA) population has been underrepresented in research linking discrimination with health indicators; studies that focus on the unique cultural and psychosocial experiences of different SA subgroups are needed. The purpose of this study was to examine associations between self-reported discrimination and mental health among Asian Indians (AIs), and whether traditional cultural beliefs (believing that South Asian cultural traditions should be practiced in the US), coping style, and social support moderated these relationships. Asian Indians (N = 733) had been recruited from community-based sampling frames for the Mediators of Atherosclerosis in South Asians Living in America (MASALA) study were included in this analysis. Multiple linear regression analyses were employed to evaluate relationships between discrimination and depressive symptoms, anger, and anxiety. Participants (men = 54%) were on average 55 years of age and had high levels of English proficiency, education, and income. Higher reports of discrimination were significantly associated with higher depressive symptoms, B = .27 (.05) p < .001, anger, B = .08 (.01), p < .001, and anxiety, B = .10 (.01), p < .001. Associations between discrimination and anger, B = −.005 (.002), p = .02, were weakest among those with stronger cultural beliefs. The link between discrimination and anxiety was attenuated by an active coping style, B = −.05 (.03), p = .04. In sum, self-reported discrimination appeared to adversely impact the mental health of AIs. Discrimination may be better coped with by having strong traditional cultural beliefs and actively managing experiences of discrimination. PMID:27668066

Chronic exposure to everyday discrimination and sleep in a multiethnic sample of middle-aged women.

PubMed

Lewis, Tené T; Troxel, Wendy M; Kravitz, Howard M; Bromberger, Joyce T; Matthews, Karen A; Hall, Martica H

2013-07-01

Researchers have suggested that poor sleep may play a role in the association between discrimination and health, but studies linking experiences of discrimination to sleep are limited. The authors examined associations between reports of everyday discrimination over 4 years (chronic everyday discrimination) and subjective and objective indicators of poor sleep. Participants were 368 African American, Caucasian, and Chinese women from the Study of Women's Health Across the Nation Sleep Study. Everyday discrimination was assessed each year from baseline through the third follow-up exam via questionnaire with the Everyday Discrimination Scale (intraclass correlation coefficient over 4 years = .90). Subjective sleep complaints were measured beginning in Year 5 with the Pittsburgh Sleep Quality Index. Objective indices of sleep continuity, duration, and architecture were assessed via in-home polysomnography, beginning in Year 5. In linear regression analyses adjusted for age, race/ethnicity, and financial strain, chronic everyday discrimination was associated with more subjective sleep complaints (Estimate = 1.52, p < .001) and polysomnography-assessed wakefulness after sleep onset (Estimate = .19, p < .02), a marker of sleep continuity. Findings did not differ by race/ethnicity and remained significant after adjusting for menopausal status, body mass index, medication use, and depressive symptoms. Experiences of chronic everyday discrimination are independently associated with both subjective and objective indices of poor sleep. Findings add to the growing literature linking discrimination to key markers of biobehavioral health. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Racial discrimination associated with higher diastolic blood pressure in a sample of American Indian adults.

PubMed

Thayer, Zaneta M; Blair, Irene V; Buchwald, Dedra S; Manson, Spero M

2017-05-01

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. 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. 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 = .02), and with a similar non-significant trend toward higher systolic blood pressure (β = 0.25, SE = 0.15, p = .09). 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. © 2017 Wiley Periodicals, Inc.

Socioeconomic Status Discrimination is Associated with Poor Sleep in African-Americans, but not Whites

PubMed Central

Van Dyke, Miriam E.; Vaccarino, Viola; Quyyumi, Arshed A.; Lewis, Tené T.

2016-01-01

Rationale Research on self-reported experiences of discrimination and health has grown in recent decades, but has largely focused on racial discrimination or overall mistreatment. Less is known about reports of discrimination on the basis of socioeconomic status (SES), despite the fact that SES is one of the most powerful social determinants of health. Objective We sought to examine the cross-sectional association between self-reported SES discrimination and subjective sleep quality, an emerging risk factor for disease. We further examined whether associations differed by race or SES. Methods We used logistic and linear regression to analyze data from a population-based cohort of 425 African-American and White middle-aged adults (67.5% female) in the Southeastern United States. SES discrimination was assessed with a modified Experiences of Discrimination Scale and poor subjective sleep quality was assessed with the Pittsburgh Sleep Quality Index. Results In logistic regression models adjusted for age, gender, and education, reports of SES discrimination were associated with poor sleep quality among African-Americans (OR=2.39, 95% CI =1.35, 4.24), but not Whites (OR=1.03, 95% CI= 0.57, 1.87), and the race × SES discrimination interaction was significant at p=0.04. After additional adjustments for reports of racial and gender discrimination, other psychosocial stressors, body mass index and depressive symptoms, SES discrimination remained a significant predictor of poor sleep among African-Americans, but not Whites. In contrast to findings by race, SES discrimination and sleep associations did not significantly differ by SES. Conclusion Findings suggest that reports of SES discrimination may be an important risk factor for subjective sleep quality among African-Americans and support the need to consider the health impact of SES-related stressors in the context of race. PMID:26896878

Socioeconomic status discrimination is associated with poor sleep in African-Americans, but not Whites.

PubMed

Van Dyke, Miriam E; Vaccarino, Viola; Quyyumi, Arshed A; Lewis, Tené T

2016-03-01

Research on self-reported experiences of discrimination and health has grown in recent decades, but has largely focused on racial discrimination or overall mistreatment. Less is known about reports of discrimination on the basis of socioeconomic status (SES), despite the fact that SES is one of the most powerful social determinants of health. We sought to examine the cross-sectional association between self-reported SES discrimination and subjective sleep quality, an emerging risk factor for disease. We further examined whether associations differed by race or SES. We used logistic and linear regression to analyze data from a population-based cohort of 425 African-American and White middle-aged adults (67.5% female) in the Southeastern United States. SES discrimination was assessed with a modified Experiences of Discrimination Scale and poor subjective sleep quality was assessed with the Pittsburgh Sleep Quality Index. In logistic regression models adjusted for age, gender, and education, reports of SES discrimination were associated with poor sleep quality among African-Americans (OR = 2.39 95%, CI = 1.35, 4.24), but not Whites (OR = 1.03, 95% CI = 0.57, 1.87), and the race × SES discrimination interaction was significant at p = 0.04. After additional adjustments for reports of racial and gender discrimination, other psychosocial stressors, body mass index and depressive symptoms, SES discrimination remained a significant predictor of poor sleep among African-Americans, but not Whites. In contrast to findings by race, SES discrimination and sleep associations did not significantly differ by SES. Findings suggest that reports of SES discrimination may be an important risk factor for subjective sleep quality among African-Americans and support the need to consider the health impact of SES-related stressors in the context of race. Copyright © 2016 Elsevier Ltd. All rights reserved.

Motor Oil Classification using Color Histograms and Pattern Recognition Techniques.

PubMed

Ahmadi, Shiva; Mani-Varnosfaderani, Ahmad; Habibi, Biuck

2018-04-20

Motor oil classification is important for quality control and the identification of oil adulteration. In thiswork, we propose a simple, rapid, inexpensive and nondestructive approach based on image analysis and pattern recognition techniques for the classification of nine different types of motor oils according to their corresponding color histograms. For this, we applied color histogram in different color spaces such as red green blue (RGB), grayscale, and hue saturation intensity (HSI) in order to extract features that can help with the classification procedure. These color histograms and their combinations were used as input for model development and then were statistically evaluated by using linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM) techniques. Here, two common solutions for solving a multiclass classification problem were applied: (1) transformation to binary classification problem using a one-against-all (OAA) approach and (2) extension from binary classifiers to a single globally optimized multilabel classification model. In the OAA strategy, LDA, QDA, and SVM reached up to 97% in terms of accuracy, sensitivity, and specificity for both the training and test sets. In extension from binary case, despite good performances by the SVM classification model, QDA and LDA provided better results up to 92% for RGB-grayscale-HSI color histograms and up to 93% for the HSI color map, respectively. In order to reduce the numbers of independent variables for modeling, a principle component analysis algorithm was used. Our results suggest that the proposed method is promising for the identification and classification of different types of motor oils.

A CCA+ICA based model for multi-task brain imaging data fusion and its application to schizophrenia.

PubMed

Sui, Jing; Adali, Tülay; Pearlson, Godfrey; Yang, Honghui; Sponheim, Scott R; White, Tonya; Calhoun, Vince D

2010-05-15

Collection of multiple-task brain imaging data from the same subject has now become common practice in medical imaging studies. In this paper, we propose a simple yet effective model, "CCA+ICA", as a powerful tool for multi-task data fusion. This joint blind source separation (BSS) model takes advantage of two multivariate methods: canonical correlation analysis and independent component analysis, to achieve both high estimation accuracy and to provide the correct connection between two datasets in which sources can have either common or distinct between-dataset correlation. In both simulated and real fMRI applications, we compare the proposed scheme with other joint BSS models and examine the different modeling assumptions. The contrast images of two tasks: sensorimotor (SM) and Sternberg working memory (SB), derived from a general linear model (GLM), were chosen to contribute real multi-task fMRI data, both of which were collected from 50 schizophrenia patients and 50 healthy controls. When examining the relationship with duration of illness, CCA+ICA revealed a significant negative correlation with temporal lobe activation. Furthermore, CCA+ICA located sensorimotor cortex as the group-discriminative regions for both tasks and identified the superior temporal gyrus in SM and prefrontal cortex in SB as task-specific group-discriminative brain networks. In summary, we compared the new approach to some competitive methods with different assumptions, and found consistent results regarding each of their hypotheses on connecting the two tasks. Such an approach fills a gap in existing multivariate methods for identifying biomarkers from brain imaging data.

A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

PubMed Central

Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.

PubMed

Sweeney, Elizabeth M; Vogelstein, Joshua T; Cuzzocreo, Jennifer L; Calabresi, Peter A; Reich, Daniel S; Crainiceanu, Ciprian M; Shinohara, Russell T

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.

Testing for nonlinearity in time series: The method of surrogate data

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

Theiler, J.; Galdrikian, B.; Longtin, A.

1991-01-01

We describe a statistical approach for identifying nonlinearity in time series; in particular, we want to avoid claims of chaos when simpler models (such as linearly correlated noise) can explain the data. The method requires a careful statement of the null hypothesis which characterizes a candidate linear process, the generation of an ensemble of surrogate'' data sets which are similar to the original time series but consistent with the null hypothesis, and the computation of a discriminating statistic for the original and for each of the surrogate data sets. The idea is to test the original time series against themore » null hypothesis by checking whether the discriminating statistic computed for the original time series differs significantly from the statistics computed for each of the surrogate sets. We present algorithms for generating surrogate data under various null hypotheses, and we show the results of numerical experiments on artificial data using correlation dimension, Lyapunov exponent, and forecasting error as discriminating statistics. Finally, we consider a number of experimental time series -- including sunspots, electroencephalogram (EEG) signals, and fluid convection -- and evaluate the statistical significance of the evidence for nonlinear structure in each case. 56 refs., 8 figs.« less

Integrated InP frequency discriminator for Phase-modulated microwave photonic links.

PubMed

Fandiño, J S; Doménech, J D; Muñoz, P; Capmany, J

2013-02-11

We report the design, fabrication and characterization of an integrated frequency discriminator on InP technology for microwave photonic phase modulated links. The optical chip is, to the best of our knowledge, the first reported in an active platform and the first to include the optical detectors. The discriminator, designed as a linear filter in intensity, features preliminary SFDR values the range between 67 and 79 dB.Hz(2/3) for signal frequencies in the range of 5-9 GHz limited, in principle, by the high value of the optical losses arising from the use of several free space coupling devices in our experimental setup. As discussed, these losses can be readily reduced by the use of integrated spot-size converters improving the SFDR by 17.3 dB (84-96 dB.Hz(2/3)). Further increase up to a range of (104-116 dB.Hz(2/3)) is possible by reducing the system noise eliminating the EDFA employed in the setup and using a commercially available laser source providing higher output power and lower relative intensity noise. Other paths for improvement requiring a filter redesign to be linear in the optical field are also discussed.

Kernel-based discriminant feature extraction using a representative dataset

NASA Astrophysics Data System (ADS)

Li, Honglin; Sancho Gomez, Jose-Luis; Ahalt, Stanley C.

2002-07-01

Discriminant Feature Extraction (DFE) is widely recognized as an important pre-processing step in classification applications. Most DFE algorithms are linear and thus can only explore the linear discriminant information among the different classes. Recently, there has been several promising attempts to develop nonlinear DFE algorithms, among which is Kernel-based Feature Extraction (KFE). The efficacy of KFE has been experimentally verified by both synthetic data and real problems. However, KFE has some known limitations. First, KFE does not work well for strongly overlapped data. Second, KFE employs all of the training set samples during the feature extraction phase, which can result in significant computation when applied to very large datasets. Finally, KFE can result in overfitting. In this paper, we propose a substantial improvement to KFE that overcomes the above limitations by using a representative dataset, which consists of critical points that are generated from data-editing techniques and centroid points that are determined by using the Frequency Sensitive Competitive Learning (FSCL) algorithm. Experiments show that this new KFE algorithm performs well on significantly overlapped datasets, and it also reduces computational complexity. Further, by controlling the number of centroids, the overfitting problem can be effectively alleviated.

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.

Local linear discriminant analysis framework using sample neighbors.

PubMed

Fan, Zizhu; Xu, Yong; Zhang, David

2011-07-01

The linear discriminant analysis (LDA) is a very popular linear feature extraction approach. The algorithms of LDA usually perform well under the following two assumptions. The first assumption is that the global data structure is consistent with the local data structure. The second assumption is that the input data classes are Gaussian distributions. However, in real-world applications, these assumptions are not always satisfied. In this paper, we propose an improved LDA framework, the local LDA (LLDA), which can perform well without needing to satisfy the above two assumptions. Our LLDA framework can effectively capture the local structure of samples. According to different types of local data structure, our LLDA framework incorporates several different forms of linear feature extraction approaches, such as the classical LDA and principal component analysis. The proposed framework includes two LLDA algorithms: a vector-based LLDA algorithm and a matrix-based LLDA (MLLDA) algorithm. MLLDA is directly applicable to image recognition, such as face recognition. Our algorithms need to train only a small portion of the whole training set before testing a sample. They are suitable for learning large-scale databases especially when the input data dimensions are very high and can achieve high classification accuracy. Extensive experiments show that the proposed algorithms can obtain good classification results.

A GPS Phase-Locked Loop Performance Metric Based on the Phase Discriminator Output

PubMed Central

Stevanovic, Stefan; Pervan, Boris

2018-01-01

We propose a novel GPS phase-lock loop (PLL) performance metric based on the standard deviation of tracking error (defined as the discriminator’s estimate of the true phase error), and explain its advantages over the popular phase jitter metric using theory, numerical simulation, and experimental results. We derive an augmented GPS phase-lock loop (PLL) linear model, which includes the effect of coherent averaging, to be used in conjunction with this proposed metric. The augmented linear model allows more accurate calculation of tracking error standard deviation in the presence of additive white Gaussian noise (AWGN) as compared to traditional linear models. The standard deviation of tracking error, with a threshold corresponding to half of the arctangent discriminator pull-in region, is shown to be a more reliable/robust measure of PLL performance under interference conditions than the phase jitter metric. In addition, the augmented linear model is shown to be valid up until this threshold, which facilitates efficient performance prediction, so that time-consuming direct simulations and costly experimental testing can be reserved for PLL designs that are much more likely to be successful. The effect of varying receiver reference oscillator quality on the tracking error metric is also considered. PMID:29351250

Classification of sodium MRI data of cartilage using machine learning.

PubMed

Madelin, Guillaume; Poidevin, Frederick; Makrymallis, Antonios; Regatte, Ravinder R

2015-11-01

To assess the possible utility of machine learning for classifying subjects with and subjects without osteoarthritis using sodium magnetic resonance imaging data. Theory: Support vector machine, k-nearest neighbors, naïve Bayes, discriminant analysis, linear regression, logistic regression, neural networks, decision tree, and tree bagging were tested. Sodium magnetic resonance imaging with and without fluid suppression by inversion recovery was acquired on the knee cartilage of 19 controls and 28 osteoarthritis patients. Sodium concentrations were measured in regions of interests in the knee for both acquisitions. Mean (MEAN) and standard deviation (STD) of these concentrations were measured in each regions of interest, and the minimum, maximum, and mean of these two measurements were calculated over all regions of interests for each subject. The resulting 12 variables per subject were used as predictors for classification. Either Min [STD] alone, or in combination with Mean [MEAN] or Min [MEAN], all from fluid suppressed data, were the best predictors with an accuracy >74%, mainly with linear logistic regression and linear support vector machine. Other good classifiers include discriminant analysis, linear regression, and naïve Bayes. Machine learning is a promising technique for classifying osteoarthritis patients and controls from sodium magnetic resonance imaging data. © 2014 Wiley Periodicals, Inc.

Musically tone-deaf individuals have difficulty discriminating intonation contours extracted from speech.

PubMed

Patel, Aniruddh D; Foxton, Jessica M; Griffiths, Timothy D

2005-12-01

Musically tone-deaf individuals have psychophysical deficits in detecting pitch changes, yet their discrimination of intonation contours in speech appears to be normal. One hypothesis for this dissociation is that intonation contours use coarse pitch contrasts which exceed the pitch-change detection thresholds of tone-deaf individuals (). We test this idea by presenting intonation contours for discrimination, both in the context of the original sentences in which they occur and in a "pure" form dissociated from any phonetic context. The pure form consists of gliding-pitch analogs of the original intonation contours which exactly follow their pattern of pitch and timing. If the spared intonation perception of tone-deaf individuals is due to the coarse pitch contrasts of intonation, then such individuals should discriminate the original sentences and the gliding-pitch analogs equally well. In contrast, we find that discrimination of the gliding-pitch analogs is severely degraded. Thus it appears that the dissociation between spoken and musical pitch perception in tone-deaf individuals is due to a deficit at a higher level than simple pitch-change detection.

Spatial mode discriminator based on leaky waveguides

NASA Astrophysics Data System (ADS)

Xu, Jing; Liu, Jialing; Shi, Hongkang; Chen, Yuntian

2018-06-01

We propose a conceptually simple and experimentally compatible configuration to discriminate the spatial mode based on leaky waveguides, which are inserted in-between the transmission link. The essence of such a spatial mode discriminator is to introduce the leakage of the power flux on purpose for detection. Importantly, the leaky angle of each individual spatial mode with respect to the propagation direction are different for non-degenerated modes, while the radiation patterns of the degenerated spatial modes in the plane perpendicular to the propagation direction are also distinguishable. Based on these two facts, we illustrate the operation principle of the spatial mode discriminators via two concrete examples; a w-type slab leaky waveguide without degeneracy, and a cylindrical leaky waveguide with degeneracy. The correlation between the leakage angle and the spatial mode distribution for a slab leaky waveguide, as well as differences between the in-plane radiation patterns of degenerated modes in a cylindrical leaky waveguide, are verified numerically and analytically. Such findings can be readily useful in discriminating the spatial modes for optical communication or optical sensing.

Indonesian palm civet coffee discrimination using UV-visible spectroscopy and several chemometrics methods

NASA Astrophysics Data System (ADS)

Yulia, M.; Suhandy, D.

2017-05-01

Indonesian palm civet coffee or kopi luwak (Indonesian words for coffee and palm civet) is well known as the world’s priciest and rarest coffee. To protect the authenticity of luwak coffee and protect consumer from luwak coffee adulteration, it is very important to develop a simple and inexpensive method to discriminate between civet and non-civet coffee. The discrimination between civet and non-civet coffee in ground roasted (powder) samples is very challenging since it is very difficult to distinguish between the two by using conventional method. In this research, the use of UV-Visible spectra combined with two chemometric methods, SIMCA and PLS-DA, was evaluated to discriminate civet and non-civet ground coffee samples. The spectral data of civet and non-civet coffee were acquired using UV-Vis spectrometer (Genesys™ 10S UV-Vis, Thermo Scientific, USA). The result shows that using both supervised discrimination methods: SIMCA and PLS-DA, all samples were correctly classified into their corresponding classes with 100% rate for accuracy, sensitivity and specificity, respectively.

Prediction of the Main Engine Power of a New Container Ship at the Preliminary Design Stage

NASA Astrophysics Data System (ADS)

Cepowski, Tomasz

2017-06-01

The paper presents mathematical relationships that allow us to forecast the estimated main engine power of new container ships, based on data concerning vessels built in 2005-2015. The presented approximations allow us to estimate the engine power based on the length between perpendiculars and the number of containers the ship will carry. The approximations were developed using simple linear regression and multivariate linear regression analysis. The presented relations have practical application for estimation of container ship engine power needed in preliminary parametric design of the ship. It follows from the above that the use of multiple linear regression to predict the main engine power of a container ship brings more accurate solutions than simple linear regression.

Quantification of the cerebrospinal fluid from a new whole body MRI sequence

NASA Astrophysics Data System (ADS)

Lebret, Alain; Petit, Eric; Durning, Bruno; Hodel, Jérôme; Rahmouni, Alain; Decq, Philippe

2012-03-01

Our work aims to develop a biomechanical model of hydrocephalus both intended to perform clinical research and to assist the neurosurgeon in diagnosis decisions. Recently, we have defined a new MR imaging sequence based on SPACE (Sampling Perfection with Application optimized Contrast using different flip-angle Evolution). On these images, the cerebrospinal fluid (CSF) appears as a homogeneous hypersignal. Therefore such images are suitable for segmentation and for volume assessment of the CSF. In this paper we present a fully automatic 3D segmentation of such SPACE MRI sequences. We choose a topological approach considering that CSF can be modeled as a simply connected object (i.e. a filled sphere). First an initial object which must be strictly included in the CSF and homotopic to a filled sphere, is determined by using a moment-preserving thresholding. Then a priority function based on an Euclidean distance map is computed in order to control the thickening process that adds "simple points" to the initial thresholded object. A point is called simple if its addition or its suppression does not result in change of topology neither for the object, nor for the background. The method is validated by measuring fluid volume of brain phantoms and by comparing our volume assessments on clinical data to those derived from a segmentation controlled by expert physicians. Then we show that a distinction between pathological cases and healthy adult people can be achieved by a linear discriminant analysis on volumes of the ventricular and intracranial subarachnoid spaces.

Differential effect of race, education, gender, and language discrimination on glycemic control in adults with type 2 diabetes.

PubMed

Reynolds, D Brice; Walker, Rebekah J; Campbell, Jennifer A; Egede, Leonard E

2015-04-01

Discrimination has been linked to negative health outcomes, but little research has investigated different types of discrimination to determine if some have a greater impact on outcomes. We examined the differential effect of discrimination based on race, level of education, gender, and language on glycemic control in adults with type 2 diabetes. Six hundred two patients with type 2 diabetes from two adult primary care clinics in the southeastern United States completed validated questionnaires. Questions included perceived discrimination because of race/ethnicity, level of education, sex/gender, or language. A multiple linear regression model assessed the differential effect of each type of perceived discrimination on glycemic control while adjusting for relevant covariates, including race, site, gender, marital status, duration of diabetes, number of years in school, number of hours worked per week, income, and health status. The mean age was 61.5 years, and the mean duration of diabetes was 12.3 years. Of the sample, 61.6% were men, and 64.9% were non-Hispanic black. In adjusted models, education discrimination remained significantly associated with glycemic control (β=0.47; 95% confidence interval, 0.03, 0.92). Race, gender and language discrimination were not significantly associated with poor glycemic control in either unadjusted or adjusted analyses. Discrimination based on education was found to be significantly associated with poor glycemic control. The findings suggest that education discrimination may be an important social determinant to consider when providing care to patients with type 2 diabetes and should be assessed separate from other types of discrimination, such as that based on race.

Darker Skin Tone Increases Perceived Discrimination among Male but Not Female Caribbean Black Youth.

PubMed

Assari, Shervin; Caldwell, Cleopatra Howard

2017-12-12

Among most minority groups, males seem to report higher levels of exposure and vulnerability to racial discrimination. Although darker skin tone may increase exposure to racial discrimination, it is yet unknown whether skin tone similarly influences perceived discrimination among male and female Caribbean Black youth. The current cross-sectional study tests the role of gender on the effects of skin tone on perceived discrimination among Caribbean Black youth. Data came from the National Survey of American Life-Adolescent Supplement (NSAL-A), 2003-2004, which included 360 Caribbean Black youth (ages 13 to 17). Demographic factors (age and gender), socioeconomic status (SES; family income, income to needs ratio, and subjective SES), skin tone, and perceived everyday discrimination were measured. Linear regressions were used for data analysis. In the pooled sample, darker skin tone was associated with higher levels of perceived discrimination among Caribbean Black youth ( b = 0.48; 95% Confidence Interval (CI) = 0.07-0.89). A significant interaction was found between gender and skin tone ( b = 1.17; 95% CI = 0.49-1.86), suggesting a larger effect of skin tone on perceived discrimination for males than females. In stratified models, darker skin tone was associated with more perceived discrimination for males ( b = 1.20; 95% CI = 0.69-0.72) but not females ( b = 0.06; 95% CI = -0.42-0.55). Similar to the literature documenting male gender as a vulnerability factor to the effects of racial discrimination, we found that male but not female Caribbean Black youth with darker skin tones perceive more discrimination.

The association between discrimination and PTSD in African Americans: exploring the role of gender.

PubMed

Brooks Holliday, Stephanie; Dubowitz, Tamara; Haas, Ann; Ghosh-Dastidar, Bonnie; DeSantis, Amy; Troxel, Wendy M

2018-02-28

Research has demonstrated the adverse impact that discrimination has on physical and mental health. However, few studies have examined the association between discrimination and symptoms of posttraumatic stress disorder (PTSD). There is evidence that African Americans experience higher rates of PTSD and are more likely to develop PTSD following trauma exposure than Whites, and discrimination may be one reason for this disparity. To examine the association between discrimination and PTSD among a cross-sectional sample largely comprising African American women, controlling for other psychosocial stressors (psychological distress, neighborhood safety, crime). A sample of 806 participants was recruited from two low-income predominantly African American neighborhoods. Participants completed self-report measures of PTSD symptoms, perceived discrimination, perceived safety, and psychological distress. Information on neighborhood crime was obtained through data requested from the city. Multivariate linear regression models were estimated to assess adjusted relationships between PTSD symptoms and discrimination. Discrimination was significantly associated with PTSD symptoms with a small effect size, controlling for relevant sociodemographic variables. This association remained consistent after controlling for psychological distress, perceived safety, and total neighborhood crime. There was no evidence of a gender by discrimination interaction. Participants who experienced any discrimination were significantly more likely to screen positive for PTSD. Discrimination may contribute to the disparate rates of PTSD experienced by African Americans. PTSD is associated with a range of negative consequences, including poorer physical health, mental health, and quality of life. These results suggest the importance of finding ways to promote resilience in this at-risk population.

Fast classification of hazelnut cultivars through portable infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Manfredi, Marcello; Robotti, Elisa; Quasso, Fabio; Mazzucco, Eleonora; Calabrese, Giorgio; Marengo, Emilio

2018-01-01

The authentication and traceability of hazelnuts is very important for both the consumer and the food industry, to safeguard the protected varieties and the food quality. This study investigates the use of a portable FTIR spectrometer coupled to multivariate statistical analysis for the classification of raw hazelnuts. The method discriminates hazelnuts from different origins/cultivars based on differences of the signal intensities of their IR spectra. The multivariate classification methods, namely principal component analysis (PCA) followed by linear discriminant analysis (LDA) and partial least square discriminant analysis (PLS-DA), with or without variable selection, allowed a very good discrimination among the groups, with PLS-DA coupled to variable selection providing the best results. Due to the fast analysis, high sensitivity, simplicity and no sample preparation, the proposed analytical methodology could be successfully used to verify the cultivar of hazelnuts, and the analysis can be performed quickly and directly on site.

Discrimination of Temperature and Strain in Brillouin Optical Time Domain Analysis Using a Multicore Optical Fiber

PubMed Central

Zaghloul, Mohamed A. S.; Wang, Mohan; Milione, Giovanni; Li, Ming-Jun; Li, Shenping; Huang, Yue-Kai; Wang, Ting; Chen, Kevin P.

2018-01-01

Brillouin optical time domain analysis is the sensing of temperature and strain changes along an optical fiber by measuring the frequency shift changes of Brillouin backscattering. Because frequency shift changes are a linear combination of temperature and strain changes, their discrimination is a challenge. Here, a multicore optical fiber that has two cores is fabricated. The differences between the cores’ temperature and strain coefficients are such that temperature (strain) changes can be discriminated with error amplification factors of 4.57 °C/MHz (69.11 μϵ/MHz), which is 2.63 (3.67) times lower than previously demonstrated. As proof of principle, using the multicore optical fiber and a commercial Brillouin optical time domain analyzer, the temperature (strain) changes of a thermally expanding metal cylinder are discriminated with an error of 0.24% (3.7%). PMID:29649148

DISCRIMINATION OF GRANITOIDS AND MINERALIZED GRANITOIDS IN THE MIDYAN REGION, NORTHWESTERN ARABIAN SHIELD, SAUDI ARABIA, BY LANDSAT MSS DATA-ANALYSIS.

USGS Publications Warehouse

Davis, Philip A.; Grolier, Maurice J.

1984-01-01

Landsat multispectral scanner (MSS) band and band-ratio databases of two scenes covering the Midyan region of northwestern Saudi Arabia were examined quantitatively and qualitatively to determine which databases best discriminate the geologic units of this semi-arid and arid region. Unsupervised, linear-discriminant cluster-analysis was performed on these two band-ratio combinations and on the MSS bands for both scenes. The results for granitoid-rock discrimination indicated that the classification images using the MSS bands are superior to the band-ratio classification images for two reasons, discussed in the paper. Yet, the effects of topography and material type (including desert varnish) on the MSS-band data produced ambiguities in the MSS-band classification results. However, these ambiguities were clarified by using a simulated natural-color image in conjunction with the MSS-band classification image.

Discrimination of Temperature and Strain in Brillouin Optical Time Domain Analysis Using a Multicore Optical Fiber.

PubMed

Zaghloul, Mohamed A S; Wang, Mohan; Milione, Giovanni; Li, Ming-Jun; Li, Shenping; Huang, Yue-Kai; Wang, Ting; Chen, Kevin P

2018-04-12

Brillouin optical time domain analysis is the sensing of temperature and strain changes along an optical fiber by measuring the frequency shift changes of Brillouin backscattering. Because frequency shift changes are a linear combination of temperature and strain changes, their discrimination is a challenge. Here, a multicore optical fiber that has two cores is fabricated. The differences between the cores' temperature and strain coefficients are such that temperature (strain) changes can be discriminated with error amplification factors of 4.57 °C/MHz (69.11 μ ϵ /MHz), which is 2.63 (3.67) times lower than previously demonstrated. As proof of principle, using the multicore optical fiber and a commercial Brillouin optical time domain analyzer, the temperature (strain) changes of a thermally expanding metal cylinder are discriminated with an error of 0.24% (3.7%).

Are V1 Simple Cells Optimized for Visual Occlusions? A Comparative Study

PubMed Central

Bornschein, Jörg; Henniges, Marc; Lücke, Jörg

2013-01-01

Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex. PMID:23754938

Racial and ethnic health disparities: evidence of discrimination's effects across the SEP spectrum.

PubMed

D'Anna, Laura Hoyt; Ponce, Ninez A; Siegel, Judith M

2010-04-01

Perceived discrimination is a psychosocial stressor that plays a role in explaining racial/ethnic disparities in self-reported physical and mental health. The purpose of this paper is: (1) to investigate the association between perceived discrimination in receiving healthcare and racial/ethnic disparities in self-rated health status, physical, and emotional functional limitations among a diverse sample of California adults; (2) to assess whether discrimination effects vary by racial/ethnic group and gender; and (3) to evaluate how the effects of discrimination on health are manifest across the socioeconomic position (SEP) spectrum. Data were drawn from the 2001 California Health Interview Survey adult file (n=55,428). The analytic approach employed multivariate linear and logistic regressions. Discrimination is qualitatively identified into two types: (1) discrimination due to race/ethnicity, language, or accent, and (2) other discrimination. Findings show that both types of discrimination negatively influenced self-rated health, and were associated with a two to three-fold odds of limitations in physical and emotional health. Further, these effects varied by racial/ethnic group and gender, and the effects were mixed. Most notably, for emotional health, racial/ethnic discrimination penalized Latinas more than non-Latina Whites, but for physical health, other discrimination was less detrimental to Latinas than it was to non-Latina Whites. At higher levels of SEP, the effects of racial/ethnic discrimination on self-rated health and other discriminations' effects on physical health were attenuated. Higher SEP may serve as an important mitigator, particularly when comparing the medium to the low SEP categories. It is also possible that SEP effects cannot be extracted from the relationships of interest in that SEP is an expression of social discrimination. In fact, negative health effects associated with discrimination are evident across the SEP spectrum. This study highlights the complexity of the relationships between discrimination and racial/ethnic identity, gender, and SEP.

Multispectral imaging burn wound tissue classification system: a comparison of test accuracies between several common machine learning algorithms

NASA Astrophysics Data System (ADS)

Squiers, John J.; Li, Weizhi; King, Darlene R.; Mo, Weirong; Zhang, Xu; Lu, Yang; Sellke, Eric W.; Fan, Wensheng; DiMaio, J. Michael; Thatcher, Jeffrey E.

2016-03-01

The clinical judgment of expert burn surgeons is currently the standard on which diagnostic and therapeutic decisionmaking regarding burn injuries is based. Multispectral imaging (MSI) has the potential to increase the accuracy of burn depth assessment and the intraoperative identification of viable wound bed during surgical debridement of burn injuries. A highly accurate classification model must be developed using machine-learning techniques in order to translate MSI data into clinically-relevant information. An animal burn model was developed to build an MSI training database and to study the burn tissue classification ability of several models trained via common machine-learning algorithms. The algorithms tested, from least to most complex, were: K-nearest neighbors (KNN), decision tree (DT), linear discriminant analysis (LDA), weighted linear discriminant analysis (W-LDA), quadratic discriminant analysis (QDA), ensemble linear discriminant analysis (EN-LDA), ensemble K-nearest neighbors (EN-KNN), and ensemble decision tree (EN-DT). After the ground-truth database of six tissue types (healthy skin, wound bed, blood, hyperemia, partial injury, full injury) was generated by histopathological analysis, we used 10-fold cross validation to compare the algorithms' performances based on their accuracies in classifying data against the ground truth, and each algorithm was tested 100 times. The mean test accuracy of the algorithms were KNN 68.3%, DT 61.5%, LDA 70.5%, W-LDA 68.1%, QDA 68.9%, EN-LDA 56.8%, EN-KNN 49.7%, and EN-DT 36.5%. LDA had the highest test accuracy, reflecting the bias-variance tradeoff over the range of complexities inherent to the algorithms tested. Several algorithms were able to match the current standard in burn tissue classification, the clinical judgment of expert burn surgeons. These results will guide further development of an MSI burn tissue classification system. Given that there are few surgeons and facilities specializing in burn care, this technology may improve the standard of burn care for patients without access to specialized facilities.

Highly sensitive molecular diagnosis of prostate cancer using surplus material washed off from biopsy needles

PubMed Central

Bermudo, R; Abia, D; Mozos, A; García-Cruz, E; Alcaraz, A; Ortiz, Á R; Thomson, T M; Fernández, P L

2011-01-01

Introduction: Currently, final diagnosis of prostate cancer (PCa) is based on histopathological analysis of needle biopsies, but this process often bears uncertainties due to small sample size, tumour focality and pathologist's subjective assessment. Methods: Prostate cancer diagnostic signatures were generated by applying linear discriminant analysis to microarray and real-time RT–PCR (qRT–PCR) data from normal and tumoural prostate tissue samples. Additionally, after removal of biopsy tissues, material washed off from transrectal biopsy needles was used for molecular profiling and discriminant analysis. Results: Linear discriminant analysis applied to microarray data for a set of 318 genes differentially expressed between non-tumoural and tumoural prostate samples produced 26 gene signatures, which classified the 84 samples used with 100% accuracy. To identify signatures potentially useful for the diagnosis of prostate biopsies, surplus material washed off from routine biopsy needles from 53 patients was used to generate qRT–PCR data for a subset of 11 genes. This analysis identified a six-gene signature that correctly assigned the biopsies as benign or tumoural in 92.6% of the cases, with 88.8% sensitivity and 96.1% specificity. Conclusion: Surplus material from prostate needle biopsies can be used for minimal-size gene signature analysis for sensitive and accurate discrimination between non-tumoural and tumoural prostates, without interference with current diagnostic procedures. This approach could be a useful adjunct to current procedures in PCa diagnosis. PMID:22009027

Prediction of Depression in Cancer Patients With Different Classification Criteria, Linear Discriminant Analysis versus Logistic Regression.

PubMed

Shayan, Zahra; Mohammad Gholi Mezerji, Naser; Shayan, Leila; Naseri, Parisa

2015-11-03

Logistic regression (LR) and linear discriminant analysis (LDA) are two popular statistical models for prediction of group membership. Although they are very similar, the LDA makes more assumptions about the data. When categorical and continuous variables used simultaneously, the optimal choice between the two models is questionable. In most studies, classification error (CE) is used to discriminate between subjects in several groups, but this index is not suitable to predict the accuracy of the outcome. The present study compared LR and LDA models using classification indices. This cross-sectional study selected 243 cancer patients. Sample sets of different sizes (n = 50, 100, 150, 200, 220) were randomly selected and the CE, B, and Q classification indices were calculated by the LR and LDA models. CE revealed the a lack of superiority for one model over the other, but the results showed that LR performed better than LDA for the B and Q indices in all situations. No significant effect for sample size on CE was noted for selection of an optimal model. Assessment of the accuracy of prediction of real data indicated that the B and Q indices are appropriate for selection of an optimal model. The results of this study showed that LR performs better in some cases and LDA in others when based on CE. The CE index is not appropriate for classification, although the B and Q indices performed better and offered more efficient criteria for comparison and discrimination between groups.

Event Recognition for Contactless Activity Monitoring Using Phase-Modulated Continuous Wave Radar.

PubMed

Forouzanfar, Mohamad; Mabrouk, Mohamed; Rajan, Sreeraman; Bolic, Miodrag; Dajani, Hilmi R; Groza, Voicu Z

2017-02-01

The use of remote sensing technologies such as radar is gaining popularity as a technique for contactless detection of physiological signals and analysis of human motion. This paper presents a methodology for classifying different events in a collection of phase modulated continuous wave radar returns. The primary application of interest is to monitor inmates where the presence of human vital signs amidst different, interferences needs to be identified. A comprehensive set of features is derived through time and frequency domain analyses of the radar returns. The Bhattacharyya distance is used to preselect the features with highest class separability as the possible candidate features for use in the classification process. The uncorrelated linear discriminant analysis is performed to decorrelate, denoise, and reduce the dimension of the candidate feature set. Linear and quadratic Bayesian classifiers are designed to distinguish breathing, different human motions, and nonhuman motions. The performance of these classifiers is evaluated on a pilot dataset of radar returns that contained different events including breathing, stopped breathing, simple human motions, and movement of fan and water. Our proposed pattern classification system achieved accuracies of up to 93% in stationary subject detection, 90% in stop-breathing detection, and 86% in interference detection. Our proposed radar pattern recognition system was able to accurately distinguish the predefined events amidst interferences. Besides inmate monitoring and suicide attempt detection, this paper can be extended to other radar applications such as home-based monitoring of elderly people, apnea detection, and home occupancy detection.

A standard test case suite for two-dimensional linear transport on the sphere: results from a collection of state-of-the-art schemes

NASA Astrophysics Data System (ADS)

Lauritzen, P. H.; Ullrich, P. A.; Jablonowski, C.; Bosler, P. A.; Calhoun, D.; Conley, A. J.; Enomoto, T.; Dong, L.; Dubey, S.; Guba, O.; Hansen, A. B.; Kaas, E.; Kent, J.; Lamarque, J.-F.; Prather, M. J.; Reinert, D.; Shashkin, V. V.; Skamarock, W. C.; Sørensen, B.; Taylor, M. A.; Tolstykh, M. A.

2013-09-01

Recently, a standard test case suite for 2-D linear transport on the sphere was proposed to assess important aspects of accuracy in geophysical fluid dynamics with a "minimal" set of idealized model configurations/runs/diagnostics. Here we present results from 19 state-of-the-art transport scheme formulations based on finite-difference/finite-volume methods as well as emerging (in the context of atmospheric/oceanographic sciences) Galerkin methods. Discretization grids range from traditional regular latitude-longitude grids to more isotropic domain discretizations such as icosahedral and cubed-sphere tessellations of the sphere. The schemes are evaluated using a wide range of diagnostics in idealized flow environments. Accuracy is assessed in single- and two-tracer configurations using conventional error norms as well as novel diagnostics designed for climate and climate-chemistry applications. In addition, algorithmic considerations that may be important for computational efficiency are reported on. The latter is inevitably computing platform dependent, The ensemble of results from a wide variety of schemes presented here helps shed light on the ability of the test case suite diagnostics and flow settings to discriminate between algorithms and provide insights into accuracy in the context of global atmospheric/ocean modeling. A library of benchmark results is provided to facilitate scheme intercomparison and model development. Simple software and data-sets are made available to facilitate the process of model evaluation and scheme intercomparison.

A standard test case suite for two-dimensional linear transport on the sphere: results from a collection of state-of-the-art schemes

NASA Astrophysics Data System (ADS)

Lauritzen, P. H.; Ullrich, P. A.; Jablonowski, C.; Bosler, P. A.; Calhoun, D.; Conley, A. J.; Enomoto, T.; Dong, L.; Dubey, S.; Guba, O.; Hansen, A. B.; Kaas, E.; Kent, J.; Lamarque, J.-F.; Prather, M. J.; Reinert, D.; Shashkin, V. V.; Skamarock, W. C.; Sørensen, B.; Taylor, M. A.; Tolstykh, M. A.

2014-01-01

Recently, a standard test case suite for 2-D linear transport on the sphere was proposed to assess important aspects of accuracy in geophysical fluid dynamics with a "minimal" set of idealized model configurations/runs/diagnostics. Here we present results from 19 state-of-the-art transport scheme formulations based on finite-difference/finite-volume methods as well as emerging (in the context of atmospheric/oceanographic sciences) Galerkin methods. Discretization grids range from traditional regular latitude-longitude grids to more isotropic domain discretizations such as icosahedral and cubed-sphere tessellations of the sphere. The schemes are evaluated using a wide range of diagnostics in idealized flow environments. Accuracy is assessed in single- and two-tracer configurations using conventional error norms as well as novel diagnostics designed for climate and climate-chemistry applications. In addition, algorithmic considerations that may be important for computational efficiency are reported on. The latter is inevitably computing platform dependent. The ensemble of results from a wide variety of schemes presented here helps shed light on the ability of the test case suite diagnostics and flow settings to discriminate between algorithms and provide insights into accuracy in the context of global atmospheric/ocean modeling. A library of benchmark results is provided to facilitate scheme intercomparison and model development. Simple software and data sets are made available to facilitate the process of model evaluation and scheme intercomparison.

Luminescent screen composition and apparatus

NASA Technical Reports Server (NTRS)

Hilborn, E. H.

1970-01-01

Ultraviolet light projects photographically produced images on a screen composed of a mixture of linear and nonlinear phosphors whose spectral emissions are different. This allows the display of polychromatic luminescent images, which gives better discrimination of the objects being viewed.

Electronic circuit delivers pulse of high interval stability

NASA Technical Reports Server (NTRS)

Fisher, B.

1966-01-01

Circuit generates a pulse of high interval stability with a complexity level considerably below systems of comparable stability. This circuit is being used as a linear frequency discriminator in the signal conditioner of the Apollo command module.

Automated discrimination of dementia spectrum disorders using extreme learning machine and structural T1 MRI features.

PubMed

Jongin Kim; Boreom Lee

2017-07-01

The classification of neuroimaging data for the diagnosis of Alzheimer's Disease (AD) is one of the main research goals of the neuroscience and clinical fields. In this study, we performed extreme learning machine (ELM) classifier to discriminate the AD, mild cognitive impairment (MCI) from normal control (NC). We compared the performance of ELM with that of a linear kernel support vector machine (SVM) for 718 structural MRI images from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The data consisted of normal control, MCI converter (MCI-C), MCI non-converter (MCI-NC), and AD. We employed SVM-based recursive feature elimination (RFE-SVM) algorithm to find the optimal subset of features. In this study, we found that the RFE-SVM feature selection approach in combination with ELM shows the superior classification accuracy to that of linear kernel SVM for structural T1 MRI data.

Principle component analysis and linear discriminant analysis of multi-spectral autofluorescence imaging data for differentiating basal cell carcinoma and healthy skin

NASA Astrophysics Data System (ADS)

Chernomyrdin, Nikita V.; Zaytsev, Kirill I.; Lesnichaya, Anastasiya D.; Kudrin, Konstantin G.; Cherkasova, Olga P.; Kurlov, Vladimir N.; Shikunova, Irina A.; Perchik, Alexei V.; Yurchenko, Stanislav O.; Reshetov, Igor V.

2016-09-01

In present paper, an ability to differentiate basal cell carcinoma (BCC) and healthy skin by combining multi-spectral autofluorescence imaging, principle component analysis (PCA), and linear discriminant analysis (LDA) has been demonstrated. For this purpose, the experimental setup, which includes excitation and detection branches, has been assembled. The excitation branch utilizes a mercury arc lamp equipped with a 365-nm narrow-linewidth excitation filter, a beam homogenizer, and a mechanical chopper. The detection branch employs a set of bandpass filters with the central wavelength of spectral transparency of λ = 400, 450, 500, and 550 nm, and a digital camera. The setup has been used to study three samples of freshly excised BCC. PCA and LDA have been implemented to analyze the data of multi-spectral fluorescence imaging. Observed results of this pilot study highlight the advantages of proposed imaging technique for skin cancer diagnosis.

Geographical identification of saffron (Crocus sativus L.) by linear discriminant analysis applied to the UV-visible spectra of aqueous extracts.

PubMed

D'Archivio, Angelo Antonio; Maggi, Maria Anna

2017-03-15

We attempted geographical classification of saffron using UV-visible spectroscopy, conventionally adopted for quality grading according to the ISO Normative 3632. We investigated 81 saffron samples produced in L'Aquila, Città della Pieve, Cascia, and Sardinia (Italy) and commercial products purchased in various supermarkets. Exploratory principal component analysis applied to the UV-vis spectra of saffron aqueous extracts revealed a clear differentiation of the samples belonging to different quality categories, but a poor separation according to the geographical origin of the spices. On the other hand, linear discriminant analysis based on 8 selected absorbance values, concentrated near 279, 305 and 328nm, allowed a good distinction of the spices coming from different sites. Under severe validation conditions (30% and 50% of saffron samples in the evaluation set), correct predictions were 85 and 83%, respectively. Copyright © 2016 Elsevier Ltd. All rights reserved.

Combining features from ERP components in single-trial EEG for discriminating four-category visual objects.

PubMed

Wang, Changming; Xiong, Shi; Hu, Xiaoping; Yao, Li; Zhang, Jiacai

2012-10-01

Categorization of images containing visual objects can be successfully recognized using single-trial electroencephalograph (EEG) measured when subjects view images. Previous studies have shown that task-related information contained in event-related potential (ERP) components could discriminate two or three categories of object images. In this study, we investigated whether four categories of objects (human faces, buildings, cats and cars) could be mutually discriminated using single-trial EEG data. Here, the EEG waveforms acquired while subjects were viewing four categories of object images were segmented into several ERP components (P1, N1, P2a and P2b), and then Fisher linear discriminant analysis (Fisher-LDA) was used to classify EEG features extracted from ERP components. Firstly, we compared the classification results using features from single ERP components, and identified that the N1 component achieved the highest classification accuracies. Secondly, we discriminated four categories of objects using combining features from multiple ERP components, and showed that combination of ERP components improved four-category classification accuracies by utilizing the complementarity of discriminative information in ERP components. These findings confirmed that four categories of object images could be discriminated with single-trial EEG and could direct us to select effective EEG features for classifying visual objects.

Perceived discrimination and social networks among older African Americans and Caribbean blacks.

PubMed

Marshall, Gillian L; Rue, Tessa C

2012-01-01

The relationship between perceived discrimination and depressive symptoms among older black American populations is poorly understood. Although a small number of studies have examined the relationship between stress and social support, few have examined the association between perceived discrimination, social networks, and depressive symptoms among a representative sample of older racial and ethnic groups. This study examines (a) the relationship between sociodemographic factors, perceived discrimination and depressive symptoms and (b) social networks as a potential moderator in the perceived discrimination and depressive symptom relationship between 2 groups of older black Americans. This was a cross-sectional study using data from the National Survey of American Life with a sample of older African Americans (N = 837) and Caribbean blacks (N = 271). Depressive symptoms were assessed using the 12-item Center for Epidemiological Studies Depression scale. Linear regression analyses were used to predict depressive symptoms. The relationship between perceived discrimination and depressive symptoms was significant in both groups. Social networks contributed as a protective factor for depressive symptoms for both groups. However, there was no significant moderation effect. Results suggest that regardless of ethnic affiliation, the experience of perceived discrimination is similar in both groups and is a risk factor for depressive symptoms. Future research is needed in this area to better understand the associations between sociodemographic factors, perceived discrimination, social networks, and their impact on depressive symptoms.

Environmental discrimination of wines using the content of lithium, potassium and rubidium.

PubMed

Del Signore, Antonella

2003-01-01

56 wine samples were analysed to determine their content of Li, K and Rb. These samples came from 28 species of vine grown on two plots of land, each of which had different pedo-climatic characteristics. The data collected were elaborated using Linear Discriminant Analysis (LDA); this statistical approach showed that it was possible to net differentiate both the soil where the different species of vines were grown and the colour of wines. The variable "species of vine nationality", instead, has not been discriminated by LDA. These results point out that it is possible to identify the place of origin of wines and that the "environment" variable prevails over the others, using the content of Li, K and Rb.

Assessment of sampling stability in ecological applications of discriminant analysis

USGS Publications Warehouse

Williams, B.K.; Titus, K.

1988-01-01

A simulation study was undertaken to assess the sampling stability of the variable loadings in linear discriminant function analysis. A factorial design was used for the factors of multivariate dimensionality, dispersion structure, configuration of group means, and sample size. A total of 32,400 discriminant analyses were conducted, based on data from simulated populations with appropriate underlying statistical distributions. A review of 60 published studies and 142 individual analyses indicated that sample sizes in ecological studies often have met that requirement. However, individual group sample sizes frequently were very unequal, and checks of assumptions usually were not reported. The authors recommend that ecologists obtain group sample sizes that are at least three times as large as the number of variables measured.

Commande optimale minimisant la consommation d'energie d'un drone utilise comme relai de communication

NASA Astrophysics Data System (ADS)

Mechirgui, Monia

The purpose of this project is to implement an optimal control regulator, particularly the linear quadratic regulator in order to control the position of an unmanned aerial vehicle known as a quadrotor. This type of UAV has a symmetrical and simple structure. Thus, its control is relatively easy compared to conventional helicopters. Optimal control can be proven to be an ideal controller to reconcile between the tracking performance and energy consumption. In practice, the linearity requirements are not met, but some elaborations of the linear quadratic regulator have been used in many nonlinear applications with good results. The linear quadratic controller used in this thesis is presented in two forms: simple and adapted to the state of charge of the battery. Based on the traditional structure of the linear quadratic regulator, we introduced a new criterion which relies on the state of charge of the battery, in order to optimize energy consumption. This command is intended to be used to monitor and maintain the desired trajectory during several maneuvers while minimizing energy consumption. Both simple and adapted, linear quadratic controller are implemented in Simulink in discrete time. The model simulates the dynamics and control of a quadrotor. Performance and stability of the system are analyzed with several tests, from the simply hover to the complex trajectories in closed loop.

Discrimination among populations of sockeye salmon fry with Fourier analysis of otolith banding patterns formed during incubation

USGS Publications Warehouse

Finn, James E.; Burger, Carl V.; Holland-Bartels, Leslie E.

1997-01-01

We used otolith banding patterns formed during incubation to discriminate among hatchery- and wild-incubated fry of sockeye salmon Oncorhynchus nerka from Tustumena Lake, Alaska. Fourier analysis of otolith luminance profiles was used to describe banding patterns: the amplitudes of individual Fourier harmonics were discriminant variables. Correct classification of otoliths to either hatchery or wild origin was 83.1% (cross-validation) and 72.7% (test data) with the use of quadratic discriminant function analysts on 10 Fourier amplitudes. Overall classification rates among the six test groups (one hatchery and five wild groups) were 46.5% (cross-validation) and 39.3% (test data) with the use of linear discriminant function analysis on 16 Fourier amplitudes. Although classification rates for wild-incubated fry from any one site never exceeded 67% (cross-validation) or 60% (test data), location-specific information was evident for all groups because the probability of classifying an individual to its true incubation location was significantly greater than chance. Results indicate phenotypic differences in otolith microstructure among incubation sites separated by less than 10 km. Analysis of otolith luminance profiles is a potentially useful technique for discriminating among and between various populations of hatchery and wild fish.

Discriminative illumination: per-pixel classification of raw materials based on optimal projections of spectral BRDF.

PubMed

Liu, Chao; Gu, Jinwei

2014-01-01

Classifying raw, unpainted materials--metal, plastic, ceramic, fabric, and so on--is an important yet challenging task for computer vision. Previous works measure subsets of surface spectral reflectance as features for classification. However, acquiring the full spectral reflectance is time consuming and error-prone. In this paper, we propose to use coded illumination to directly measure discriminative features for material classification. Optimal illumination patterns--which we call "discriminative illumination"--are learned from training samples, after projecting to which the spectral reflectance of different materials are maximally separated. This projection is automatically realized by the integration of incident light for surface reflection. While a single discriminative illumination is capable of linear, two-class classification, we show that multiple discriminative illuminations can be used for nonlinear and multiclass classification. We also show theoretically that the proposed method has higher signal-to-noise ratio than previous methods due to light multiplexing. Finally, we construct an LED-based multispectral dome and use the discriminative illumination method for classifying a variety of raw materials, including metal (aluminum, alloy, steel, stainless steel, brass, and copper), plastic, ceramic, fabric, and wood. Experimental results demonstrate its effectiveness.

A simple, stable, and accurate linear tetrahedral finite element for transient, nearly, and fully incompressible solid dynamics: A dynamic variational multiscale approach [A simple, stable, and accurate tetrahedral finite element for transient, nearly incompressible, linear and nonlinear elasticity: A dynamic variational multiscale approach

DOE PAGES

Scovazzi, Guglielmo; Carnes, Brian; Zeng, Xianyi; ...

2015-11-12

Here, we propose a new approach for the stabilization of linear tetrahedral finite elements in the case of nearly incompressible transient solid dynamics computations. Our method is based on a mixed formulation, in which the momentum equation is complemented by a rate equation for the evolution of the pressure field, approximated with piece-wise linear, continuous finite element functions. The pressure equation is stabilized to prevent spurious pressure oscillations in computations. Incidentally, it is also shown that many stabilized methods previously developed for the static case do not generalize easily to transient dynamics. Extensive tests in the context of linear andmore » nonlinear elasticity are used to corroborate the claim that the proposed method is robust, stable, and accurate.« less

A simple, stable, and accurate linear tetrahedral finite element for transient, nearly, and fully incompressible solid dynamics: A dynamic variational multiscale approach [A simple, stable, and accurate tetrahedral finite element for transient, nearly incompressible, linear and nonlinear elasticity: A dynamic variational multiscale approach

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

Scovazzi, Guglielmo; Carnes, Brian; Zeng, Xianyi

Here, we propose a new approach for the stabilization of linear tetrahedral finite elements in the case of nearly incompressible transient solid dynamics computations. Our method is based on a mixed formulation, in which the momentum equation is complemented by a rate equation for the evolution of the pressure field, approximated with piece-wise linear, continuous finite element functions. The pressure equation is stabilized to prevent spurious pressure oscillations in computations. Incidentally, it is also shown that many stabilized methods previously developed for the static case do not generalize easily to transient dynamics. Extensive tests in the context of linear andmore » nonlinear elasticity are used to corroborate the claim that the proposed method is robust, stable, and accurate.« less

Asymptotic Linear Spectral Statistics for Spiked Hermitian Random Matrices

NASA Astrophysics Data System (ADS)

Passemier, Damien; McKay, Matthew R.; Chen, Yang

2015-07-01

Using the Coulomb Fluid method, this paper derives central limit theorems (CLTs) for linear spectral statistics of three "spiked" Hermitian random matrix ensembles. These include Johnstone's spiked model (i.e., central Wishart with spiked correlation), non-central Wishart with rank-one non-centrality, and a related class of non-central matrices. For a generic linear statistic, we derive simple and explicit CLT expressions as the matrix dimensions grow large. For all three ensembles under consideration, we find that the primary effect of the spike is to introduce an correction term to the asymptotic mean of the linear spectral statistic, which we characterize with simple formulas. The utility of our proposed framework is demonstrated through application to three different linear statistics problems: the classical likelihood ratio test for a population covariance, the capacity analysis of multi-antenna wireless communication systems with a line-of-sight transmission path, and a classical multiple sample significance testing problem.

Development and validation of a clinically applicable score to classify cachexia stages in advanced cancer patients

PubMed Central

Zhou, Ting; Wang, Bangyan; Liu, Huiquan; Yang, Kaixiang; Thapa, Sudip; Zhang, Haowen; Li, Lu

2018-01-01

Abstract Background Cachexia is a multifactorial syndrome that is highly prevalent in advanced cancer patients and leads to progressive functional impairments. The classification of cachexia stages is essential for diagnosing and treating cachexia. However, there is a lack of simple tools with good discrimination for classifying cachexia stages. Therefore, our study aimed to develop a clinically applicable cachexia staging score (CSS) and validate its discrimination of clinical outcomes for different cachexia stages. Methods Advanced cancer patients were enrolled in our study. A CSS comprising the following five components was developed: weight loss, a simple questionnaire of sarcopenia (SARC‐F), Eastern Cooperative Oncology Group, appetite loss, and abnormal biochemistry. According to the CSS, patients were classified into non‐cachexia, pre‐cachexia, cachexia, and refractory cachexia stages, and clinical outcomes were compared among the four groups. Results Of the 297 participating patients, data from 259 patients were ultimately included. Based on the CSS, patients were classified into non‐cachexia (n = 69), pre‐cachexia (n = 68), cachexia (n = 103), and refractory cachexia (n = 19) stages. Patients with more severe cachexia stages had lower skeletal muscle indexes (P = 0.002 and P = 0.004 in male and female patients, respectively), higher prevalence of sarcopenia (P = 0.017 and P = 0.027 in male and female patients, respectively), more severe symptom burden (P < 0.001), poorer quality of life (P < 0.001 for all subscales except social well‐being), and shorter survival times (P < 0.001). Conclusions The CSS is a simple and clinically applicable tool with excellent discrimination for classifying cachexia stages. This score is extremely useful for the clinical treatment and prognosis of cachexia and for designing clinical trials. PMID:29372594

Implementation of projective measurements with linear optics and continuous photon counting

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

Takeoka, Masahiro; Sasaki, Masahide; Loock, Peter van

2005-02-01

We investigate the possibility of implementing a given projection measurement using linear optics and arbitrarily fast feedforward based on the continuous detection of photons. In particular, we systematically derive the so-called Dolinar scheme that achieves the minimum-error discrimination of binary coherent states. Moreover, we show that the Dolinar-type approach can also be applied to projection measurements in the regime of photonic-qubit signals. Our results demonstrate that for implementing a projection measurement with linear optics, in principle, unit success probability may be approached even without the use of expensive entangled auxiliary states, as they are needed in all known (near-)deterministic linear-opticsmore » proposals.« less

Intergenerational Consequences: Women's Experiences of Discrimination in Pregnancy Predict Infant Social-Emotional Development at 6 Months and 1 Year.

PubMed

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

2018-04-01

Racial/ethnic and socioeconomic disparities in infant development in the United States have lifelong consequences. Discrimination predicts poorer health and academic outcomes. This study explored for the first time intergenerational consequences of women's experiences of discrimination reported during pregnancy for their infants' social-emotional development in the first year of life. Data come from a longitudinal study with predominantly Black and Latina, socioeconomically disadvantaged, urban young women (N = 704, Mage = 18.53) across pregnancy through 1 year postpartum. Women were recruited from community hospitals and health centers in a Northeastern US city. Linear regression analyses examined whether women's experiences of everyday discrimination reported during pregnancy predicted social-emotional development outcomes among their infants at 6 months and 1 year of age, controlling for potentially confounding medical and sociodemographic factors. Path analyses tested if pregnancy distress, anxiety, or depressive symptoms mediated significant associations. Everyday discrimination reported during pregnancy prospectively predicted greater inhibition/separation problems and greater negative emotionality, but did not predict attention skills or positive emotionality, at 6 months and 1 year. Depressive symptoms mediated the association of discrimination with negative emotionality at 6 months, and pregnancy distress, anxiety, and depressive symptoms mediated the association of discrimination with negative emotionality at 1 year. Findings support that there are intergenerational consequences of discrimination, extending past findings to infant social-emotional development outcomes in the first year of life. It may be important to address discrimination before and during pregnancy and enhance support to mothers and infants exposed to discrimination to promote health equity across the life span.

Contextual and Serial Discriminations: A New Learning Paradigm to Assess Simultaneously the Effects of Acute Stress on Retrieval of Flexible or Stable Information in Mice

PubMed Central

Célérier, Aurélie; Piérard, Christophe; Rachbauer, Dagmar; Sarrieau, Alain; Béracochéa, Daniel

2004-01-01

The present study was aimed at simultaneously determining on the same subject, the effects of stress on retrieval of flexible (contextual or temporal) or stable (spatial) information. Three behavioral paradigms carried out in a four-hole board were designed as follows: (1) Simple Discrimination (SD), in which mice learned a single discrimination; (2) Contextual and Serial Discriminations (CSD), in which mice learned two successive discriminations on two different internal contexts; (3) Spatial Serial Discriminations (SSD), in which mice learned two successive discriminations on an identical internal context. The stressor (three inescapable electric footshocks) was delivered 5 min before retention, occurring 5 min or 24 h after acquisition. Results showed that this stressor increased plasmatic corticosterone levels and fear reactivity in an elevated-plus-maze, as compared with nonstressed mice. The stressor reversed the normal pattern of retrieval observed in nonstressed controls in the CSD task, this effect being context dependent, as it was not observed in the SSD task. Overall, our study shows that stress affected the retrieval of flexible and old information, but spared the retrieval of stable or recent ones. Therefore, these behavioral paradigms allow us to study simultaneously, on the same animal, the effects of stress on distinct forms of memory retrieval. PMID:15054135

Sex differences in audiovisual discrimination learning by Bengalese finches (Lonchura striata var. domestica).

PubMed

Seki, Yoshimasa; Okanoya, Kazuo

2008-02-01

Both visual and auditory information are important for songbirds, especially in developmental and sexual contexts. To investigate bimodal cognition in songbirds, the authors conducted audiovisual discrimination training in Bengalese finches. The authors used two types of stimulus: an "artificial stimulus," which is a combination of simple figures and sound, and a "biological stimulus," consisting of video images of singing males along with their songs. The authors found that while both sexes predominantly used visual cues in the discrimination tasks, males tended to be more dependent on auditory information for the biological stimulus. Female responses were always dependent on the visual stimulus for both stimulus types. Only males changed their discrimination strategy according to stimulus type. Although males used both visual and auditory cues for the biological stimulus, they responded to the artificial stimulus depending only on visual information, as the females did. These findings suggest a sex difference in innate auditory sensitivity. (c) 2008 APA.

Activation in the Right Inferior Parietal Lobule Reflects the Representation of Musical Structure beyond Simple Pitch Discrimination.

PubMed

Royal, Isabelle; Vuvan, Dominique T; Zendel, Benjamin Rich; Robitaille, Nicolas; Schönwiesner, Marc; Peretz, Isabelle

2016-01-01

Pitch discrimination tasks typically engage the superior temporal gyrus and the right inferior frontal gyrus. It is currently unclear whether these regions are equally involved in the processing of incongruous notes in melodies, which requires the representation of musical structure (tonality) in addition to pitch discrimination. To this aim, 14 participants completed two tasks while undergoing functional magnetic resonance imaging, one in which they had to identify a pitch change in a series of non-melodic repeating tones and a second in which they had to identify an incongruous note in a tonal melody. In both tasks, the deviants activated the right superior temporal gyrus. A contrast between deviants in the melodic task and deviants in the non-melodic task (melodic > non-melodic) revealed additional activity in the right inferior parietal lobule. Activation in the inferior parietal lobule likely represents processes related to the maintenance of tonal pitch structure in working memory during pitch discrimination.

Activation in the Right Inferior Parietal Lobule Reflects the Representation of Musical Structure beyond Simple Pitch Discrimination

PubMed Central

Royal, Isabelle; Vuvan, Dominique T.; Zendel, Benjamin Rich; Robitaille, Nicolas; Schönwiesner, Marc; Peretz, Isabelle

2016-01-01

Pitch discrimination tasks typically engage the superior temporal gyrus and the right inferior frontal gyrus. It is currently unclear whether these regions are equally involved in the processing of incongruous notes in melodies, which requires the representation of musical structure (tonality) in addition to pitch discrimination. To this aim, 14 participants completed two tasks while undergoing functional magnetic resonance imaging, one in which they had to identify a pitch change in a series of non-melodic repeating tones and a second in which they had to identify an incongruous note in a tonal melody. In both tasks, the deviants activated the right superior temporal gyrus. A contrast between deviants in the melodic task and deviants in the non-melodic task (melodic > non-melodic) revealed additional activity in the right inferior parietal lobule. Activation in the inferior parietal lobule likely represents processes related to the maintenance of tonal pitch structure in working memory during pitch discrimination. PMID:27195523

Odor Discrimination in Drosophila: From Neural Population Codes to Behavior

PubMed Central

Parnas, Moshe; Lin, Andrew C.; Huetteroth, Wolf; Miesenböck, Gero

2013-01-01

Summary Taking advantage of the well-characterized olfactory system of Drosophila, we derive a simple quantitative relationship between patterns of odorant receptor activation, the resulting internal representations of odors, and odor discrimination. Second-order excitatory and inhibitory projection neurons (ePNs and iPNs) convey olfactory information to the lateral horn, a brain region implicated in innate odor-driven behaviors. We show that the distance between ePN activity patterns is the main determinant of a fly’s spontaneous discrimination behavior. Manipulations that silence subsets of ePNs have graded behavioral consequences, and effect sizes are predicted by changes in ePN distances. ePN distances predict only innate, not learned, behavior because the latter engages the mushroom body, which enables differentiated responses to even very similar odors. Inhibition from iPNs, which scales with olfactory stimulus strength, enhances innate discrimination of closely related odors, by imposing a high-pass filter on transmitter release from ePN terminals that increases the distance between odor representations. PMID:24012006

Optimization of single-base-pair mismatch discrimination in oligonucleotide microarrays

NASA Technical Reports Server (NTRS)

Urakawa, Hidetoshi; El Fantroussi, Said; Smidt, Hauke; Smoot, James C.; Tribou, Erik H.; Kelly, John J.; Noble, Peter A.; Stahl, David A.

2003-01-01

The discrimination between perfect-match and single-base-pair-mismatched nucleic acid duplexes was investigated by using oligonucleotide DNA microarrays and nonequilibrium dissociation rates (melting profiles). DNA and RNA versions of two synthetic targets corresponding to the 16S rRNA sequences of Staphylococcus epidermidis (38 nucleotides) and Nitrosomonas eutropha (39 nucleotides) were hybridized to perfect-match probes (18-mer and 19-mer) and to a set of probes having all possible single-base-pair mismatches. The melting profiles of all probe-target duplexes were determined in parallel by using an imposed temperature step gradient. We derived an optimum wash temperature for each probe and target by using a simple formula to calculate a discrimination index for each temperature of the step gradient. This optimum corresponded to the output of an independent analysis using a customized neural network program. These results together provide an experimental and analytical framework for optimizing mismatch discrimination among all probes on a DNA microarray.

Experimental variability and data pre-processing as factors affecting the discrimination power of some chemometric approaches (PCA, CA and a new algorithm based on linear regression) applied to (+/-)ESI/MS and RPLC/UV data: Application on green tea extracts.

PubMed

Iorgulescu, E; Voicu, V A; Sârbu, C; Tache, F; Albu, F; Medvedovici, A

2016-08-01

The influence of the experimental variability (instrumental repeatability, instrumental intermediate precision and sample preparation variability) and data pre-processing (normalization, peak alignment, background subtraction) on the discrimination power of multivariate data analysis methods (Principal Component Analysis -PCA- and Cluster Analysis -CA-) as well as a new algorithm based on linear regression was studied. Data used in the study were obtained through positive or negative ion monitoring electrospray mass spectrometry (+/-ESI/MS) and reversed phase liquid chromatography/UV spectrometric detection (RPLC/UV) applied to green tea extracts. Extractions in ethanol and heated water infusion were used as sample preparation procedures. The multivariate methods were directly applied to mass spectra and chromatograms, involving strictly a holistic comparison of shapes, without assignment of any structural identity to compounds. An alternative data interpretation based on linear regression analysis mutually applied to data series is also discussed. Slopes, intercepts and correlation coefficients produced by the linear regression analysis applied on pairs of very large experimental data series successfully retain information resulting from high frequency instrumental acquisition rates, obviously better defining the profiles being compared. Consequently, each type of sample or comparison between samples produces in the Cartesian space an ellipsoidal volume defined by the normal variation intervals of the slope, intercept and correlation coefficient. Distances between volumes graphically illustrates (dis)similarities between compared data. The instrumental intermediate precision had the major effect on the discrimination power of the multivariate data analysis methods. Mass spectra produced through ionization from liquid state in atmospheric pressure conditions of bulk complex mixtures resulting from extracted materials of natural origins provided an excellent data basis for multivariate analysis methods, equivalent to data resulting from chromatographic separations. The alternative evaluation of very large data series based on linear regression analysis produced information equivalent to results obtained through application of PCA an CA. Copyright © 2016 Elsevier B.V. All rights reserved.

Fat discrimination: A phenotype with potential implications for studying fat intake behaviors and obesity

PubMed Central

Liang, Lisa C.H.; Sakimura, Johannah; May, Daniel; Breen, Cameron; Driggin, Elissa; Tepper, Beverly J.; Chung, Wendy K.; Keller, Kathleen L.

2013-01-01

Variations in fat preference and intake across humans are poorly understood in part because of difficulties in studying this behavior. The objective of this study was to develop a simple procedure to assess fat discrimination, the ability to accurately perceive differences in the fat content of foods, and assess the associations between this phenotype and fat ingestive behaviors and adiposity. African-American adults (n=317) were tested for fat discrimination using 7 forced choice same/different tests with Italian salad dressings that ranged in fat-by-weight content from 5–55%. Performance on this procedure was determined by tallying the number of trials in which a participant correctly identified the pair of samples as “same” or “different” across all test pairs (ranging from 1–7). Individuals who received the lowest scores on this task (≤3 out of 7 correct) were classified as fat non-discriminators (n=33) and those who received the highest scores (7 out of 7 correct) were classified as fat discriminators (n=59). These 2 groups were compared for the primary outcome variables: reported food intake, preferences, and adiposity. After adjusting for BMI, sex, age, and dietary restraint and disinhibition, fat non-discriminators reported greater consumption of both added fats and reduced fat foods (p<0.05 for both). Fat non-discriminators also had greater abdominal adiposity compared to fat discriminators (p<0.05). Test-retest scores performed in a subset of participants (n=40) showed moderate reliability of the fat discrimination test (rho=0.53;p<0.01). If these results are replicated, fat discrimination may serve as clinical research tool to identify participants who are at risk for obesity and other chronic diseases due to increased fat intake. PMID:21925524

Fat discrimination: a phenotype with potential implications for studying fat intake behaviors and obesity.

PubMed

Liang, Lisa C H; Sakimura, Johannah; May, Daniel; Breen, Cameron; Driggin, Elissa; Tepper, Beverly J; Chung, Wendy K; Keller, Kathleen L

2012-01-18

Variations in fat preference and intake across humans are poorly understood in part because of difficulties in studying this behavior. The objective of this study was to develop a simple procedure to assess fat discrimination, the ability to accurately perceive differences in the fat content of foods, and assess the associations between this phenotype and fat ingestive behaviors and adiposity. African-American adults (n=317) were tested for fat discrimination using 7 forced choice same/different tests with Italian salad dressings that ranged in fat-by-weight content from 5 to 55%. Performance on this procedure was determined by tallying the number of trials in which a participant correctly identified the pair of samples as "same" or "different" across all test pairs (ranging from 1 to 7). Individuals who received the lowest scores on this task (≤3 out of 7 correct) were classified as fat non-discriminators (n=33) and those who received the highest scores (7 out of 7 correct) were classified as fat discriminators (n=59). These 2 groups were compared for the primary outcome variables: reported food intake, preferences, and adiposity. After adjusting for BMI, sex, age, and dietary restraint and disinhibition, fat non-discriminators reported greater consumption of both added fats and reduced fat foods (p<0.05 for both). Fat non-discriminators also had greater abdominal adiposity compared to fat discriminators (p<0.05). Test-retest scores performed in a subset of participants (n=40) showed moderate reliability of the fat discrimination test (rho=0.53; p<0.01). If these results are replicated, fat discrimination may serve as clinical research tool to identify participants who are at risk for obesity and other chronic diseases due to increased fat intake. Copyright © 2011 Elsevier Inc. All rights reserved.

Fully optimized discrimination of physiological responses to auditory stimuli

PubMed Central

Kruglikov, Stepan Y; Chari, Sharmila; Rapp, Paul E; Weinstein, Steven L; Given, Barbara K; Schiff, Steven J

2008-01-01

The use of multivariate measurements to characterize brain activity (electrical, magnetic, optical) is widespread. The most common approaches to reduce the complexity of such observations include principal and independent component analyses (PCA and ICA), which are not well suited for discrimination tasks. We addressed two questions: first, how do the neurophysiological responses to elongated phonemes relate to tone and phoneme responses in normal children, and, second, how discriminable are these responses. We employed fully optimized linear discrimination analysis to maximally separate the multi-electrode responses to tones and phonemes, and classified the response to elongated phonemes. We find that discrimination between tones and phonemes is dependent upon responses from associative regions of the brain apparently distinct from the primary sensory cortices typically emphasized by PCA or ICA, and that the neuronal correlates corresponding to elongated phonemes are highly variable in normal children (about half respond with neural correlates of tones and half as phonemes). Our approach is made feasible by the increase in computational power of ordinary personal computers and has significant advantages for a wide range of neuronal imaging modalities. PMID:18430975

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.

Discriminant analysis of resting-state functional connectivity patterns on the Grassmann manifold

NASA Astrophysics Data System (ADS)

Fan, Yong; Liu, Yong; Jiang, Tianzi; Liu, Zhening; Hao, Yihui; Liu, Haihong

2010-03-01

The functional networks, extracted from fMRI images using independent component analysis, have been demonstrated informative for distinguishing brain states of cognitive functions and neurological diseases. In this paper, we propose a novel algorithm for discriminant analysis of functional networks encoded by spatial independent components. The functional networks of each individual are used as bases for a linear subspace, referred to as a functional connectivity pattern, which facilitates a comprehensive characterization of temporal signals of fMRI data. The functional connectivity patterns of different individuals are analyzed on the Grassmann manifold by adopting a principal angle based subspace distance. In conjunction with a support vector machine classifier, a forward component selection technique is proposed to select independent components for constructing the most discriminative functional connectivity pattern. The discriminant analysis method has been applied to an fMRI based schizophrenia study with 31 schizophrenia patients and 31 healthy individuals. The experimental results demonstrate that the proposed method not only achieves a promising classification performance for distinguishing schizophrenia patients from healthy controls, but also identifies discriminative functional networks that are informative for schizophrenia diagnosis.

Screening analysis of biodiesel feedstock using UV-vis, NIR and synchronous fluorescence spectrometries and the successive projections algorithm.

PubMed

Insausti, Matías; Gomes, Adriano A; Cruz, Fernanda V; Pistonesi, Marcelo F; Araujo, Mario C U; Galvão, Roberto K H; Pereira, Claudete F; Band, Beatriz S F

2012-08-15

This paper investigates the use of UV-vis, near infrared (NIR) and synchronous fluorescence (SF) spectrometries coupled with multivariate classification methods to discriminate biodiesel samples with respect to the base oil employed in their production. More specifically, the present work extends previous studies by investigating the discrimination of corn-based biodiesel from two other biodiesel types (sunflower and soybean). Two classification methods are compared, namely full-spectrum SIMCA (soft independent modelling of class analogies) and SPA-LDA (linear discriminant analysis with variables selected by the successive projections algorithm). Regardless of the spectrometric technique employed, full-spectrum SIMCA did not provide an appropriate discrimination of the three biodiesel types. In contrast, all samples were correctly classified on the basis of a reduced number of wavelengths selected by SPA-LDA. It can be concluded that UV-vis, NIR and SF spectrometries can be successfully employed to discriminate corn-based biodiesel from the two other biodiesel types, but wavelength selection by SPA-LDA is key to the proper separation of the classes. Copyright © 2012 Elsevier B.V. All rights reserved.

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.

Analysis of Greek small coinage from the classic period

NASA Astrophysics Data System (ADS)

Šmit, Ž.; Šemrov, A.

2018-02-01

A series of 25 Greek coins from the 6th to 4th centuries BC was studied by PIXE for their trace element composition, with an aim to discover the origin of their silver ore. The procedure revealed a counterfeited coin, and then concentrated on distinguishing the coins minted from the ore of Laurion on the Attica peninsula and the coins minted from other sources. Linear discriminant analysis based on the impurities and alloying elements of copper, gold, lead and bismuth revealed that discrimination is indeed possible according to a single canonical variable.

A simple linear regression method for quantitative trait loci linkage analysis with censored observations.

PubMed

Anderson, Carl A; McRae, Allan F; Visscher, Peter M

2006-07-01

Standard quantitative trait loci (QTL) mapping techniques commonly assume that the trait is both fully observed and normally distributed. When considering survival or age-at-onset traits these assumptions are often incorrect. Methods have been developed to map QTL for survival traits; however, they are both computationally intensive and not available in standard genome analysis software packages. We propose a grouped linear regression method for the analysis of continuous survival data. Using simulation we compare this method to both the Cox and Weibull proportional hazards models and a standard linear regression method that ignores censoring. The grouped linear regression method is of equivalent power to both the Cox and Weibull proportional hazards methods and is significantly better than the standard linear regression method when censored observations are present. The method is also robust to the proportion of censored individuals and the underlying distribution of the trait. On the basis of linear regression methodology, the grouped linear regression model is computationally simple and fast and can be implemented readily in freely available statistical software.

Perceived discrimination and depressive symptoms among US Latinos: the modifying role of educational attainment.

PubMed

Ward, Julia B; Feinstein, Lydia; Vines, Anissa I; Robinson, Whitney R; Haan, Mary N; Aiello, Allison E

2017-04-12

Despite growing evidence that discrimination may contribute to poor mental health, few studies have assessed this association among US Latinos. Furthermore, the interaction between discrimination and educational attainment in shaping Latino mental health is virtually unexplored. This study aims to examine the association between perceived discrimination and depressive symptoms and the modifying role of education among a population of Mexican-origin adults. We utilized population-based data from 629 Mexican-origin adults (mean age = 52.8 years) participating the Niños Lifestyle and Diabetes Study (2013-2014). Perceived discrimination was defined as responding 'sometimes' or 'often' to at least one item on the 9-item Everyday Discrimination Scale. High depressive symptoms were defined as scoring ≥10 on the CESD-10. We used log-binomial and linear-binomial models to estimate prevalence ratios (PR) and prevalence differences (PD), respectively, of high depressive symptoms for levels of perceived discrimination. Final models were adjusted for age, sex, education, cultural orientation, and nativity. General estimating equations were employed to account for within-family clustering. Prevalence of perceived discrimination and high depressive symptoms were 49.5% and 29.2%, respectively. Participants experiencing discrimination had higher depressive symptom prevalence than those never or rarely experiencing discrimination [PR = 1.94, 95% confidence interval (CI): 1.46-2.58; PD = 0.19, 95% CI: 0.12-0.27]. The strength of this association varied by education level. The association between discrimination and depressive symptoms was stronger among those with >12 years of education (PR = 2.69; PD = 0.24) compared to those with ≤12 years of education (PR = 1.36; PD = 0.09). US Latinos suffer a high burden of depressive symptoms, and discrimination may be an important driver of this burden. Our results suggest that effortful coping strategies, such as achieving high education despite high perceived discrimination, may magnify discrimination's adverse effect on Latino mental health.

Darker Skin Tone Increases Perceived Discrimination among Male but Not Female Caribbean Black Youth

PubMed Central

Caldwell, Cleopatra Howard

2017-01-01

Background: Among most minority groups, males seem to report higher levels of exposure and vulnerability to racial discrimination. Although darker skin tone may increase exposure to racial discrimination, it is yet unknown whether skin tone similarly influences perceived discrimination among male and female Caribbean Black youth. Objective: The current cross-sectional study tests the role of gender on the effects of skin tone on perceived discrimination among Caribbean Black youth. Methods: Data came from the National Survey of American Life-Adolescent Supplement (NSAL-A), 2003–2004, which included 360 Caribbean Black youth (ages 13 to 17). Demographic factors (age and gender), socioeconomic status (SES; family income, income to needs ratio, and subjective SES), skin tone, and perceived everyday discrimination were measured. Linear regressions were used for data analysis. Results: In the pooled sample, darker skin tone was associated with higher levels of perceived discrimination among Caribbean Black youth (b = 0.48; 95% Confidence Interval (CI) = 0.07–0.89). A significant interaction was found between gender and skin tone (b = 1.17; 95% CI = 0.49–1.86), suggesting a larger effect of skin tone on perceived discrimination for males than females. In stratified models, darker skin tone was associated with more perceived discrimination for males (b = 1.20; 95% CI = 0.69–0.72) but not females (b = 0.06; 95% CI = −0.42–0.55). Conclusion: Similar to the literature documenting male gender as a vulnerability factor to the effects of racial discrimination, we found that male but not female Caribbean Black youth with darker skin tones perceive more discrimination. PMID:29231903

Meta-analytic review of the development of face discrimination in infancy: Face race, face gender, infant age, and methodology moderate face discrimination.

PubMed

Sugden, Nicole A; Marquis, Alexandra R

2017-11-01

Infants show facility for discriminating between individual faces within hours of birth. Over the first year of life, infants' face discrimination shows continued improvement with familiar face types, such as own-race faces, but not with unfamiliar face types, like other-race faces. The goal of this meta-analytic review is to provide an effect size for infants' face discrimination ability overall, with own-race faces, and with other-race faces within the first year of life, how this differs with age, and how it is influenced by task methodology. Inclusion criteria were (a) infant participants aged 0 to 12 months, (b) completing a human own- or other-race face discrimination task, (c) with discrimination being determined by infant looking. Our analysis included 30 works (165 samples, 1,926 participants participated in 2,623 tasks). The effect size for infants' face discrimination was small, 6.53% greater than chance (i.e., equal looking to the novel and familiar). There was a significant difference in discrimination by race, overall (own-race, 8.18%; other-race, 3.18%) and between ages (own-race: 0- to 4.5-month-olds, 7.32%; 5- to 7.5-month-olds, 9.17%; and 8- to 12-month-olds, 7.68%; other-race: 0- to 4.5-month-olds, 6.12%; 5- to 7.5-month-olds, 3.70%; and 8- to 12-month-olds, 2.79%). Multilevel linear (mixed-effects) models were used to predict face discrimination; infants' capacity to discriminate faces is sensitive to face characteristics including race, gender, and emotion as well as the methods used, including task timing, coding method, and visual angle. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

The assessment of biases in the acoustic discrimination of individuals

PubMed Central

Šálek, Martin

2017-01-01

Animal vocalizations contain information about individual identity that could potentially be used for the monitoring of individuals. However, the performance of individual discrimination is subjected to many biases depending on factors such as the amount of identity information, or methods used. These factors need to be taken into account when comparing results of different studies or selecting the most cost-effective solution for a particular species. In this study, we evaluate several biases associated with the discrimination of individuals. On a large sample of little owl male individuals, we assess how discrimination performance changes with methods of call description, an increasing number of individuals, and number of calls per male. Also, we test whether the discrimination performance within the whole population can be reliably estimated from a subsample of individuals in a pre-screening study. Assessment of discrimination performance at the level of the individual and at the level of call led to different conclusions. Hence, studies interested in individual discrimination should optimize methods at the level of individuals. The description of calls by their frequency modulation leads to the best discrimination performance. In agreement with our expectations, discrimination performance decreased with population size. Increasing the number of calls per individual linearly increased the discrimination of individuals (but not the discrimination of calls), likely because it allows distinction between individuals with very similar calls. The available pre-screening index does not allow precise estimation of the population size that could be reliably monitored. Overall, projects applying acoustic monitoring at the individual level in population need to consider limitations regarding the population size that can be reliably monitored and fine-tune their methods according to their needs and limitations. PMID:28486488

Inverted-U Function Relating Cortical Plasticity and Task Difficulty

PubMed Central

Engineer, Navzer D.; Engineer, Crystal T.; Reed, Amanda C.; Pandya, Pritesh K.; Jakkamsetti, Vikram; Moucha, Raluca; Kilgard, Michael P.

2012-01-01

Many psychological and physiological studies with simple stimuli have suggested that perceptual learning specifically enhances the response of primary sensory cortex to task-relevant stimuli. The aim of this study was to determine whether auditory discrimination training on complex tasks enhances primary auditory cortex responses to a target sequence relative to non-target and novel sequences. We collected responses from more than 2,000 sites in 31 rats trained on one of six discrimination tasks that differed primarily in the similarity of the target and distractor sequences. Unlike training with simple stimuli, long-term training with complex stimuli did not generate target specific enhancement in any of the groups. Instead, cortical receptive field size decreased, latency decreased, and paired pulse depression decreased in rats trained on the tasks of intermediate difficulty while tasks that were too easy or too difficult either did not alter or degraded cortical responses. These results suggest an inverted-U function relating neural plasticity and task difficulty. PMID:22249158

DNA barcoding coupled to HRM analysis as a new and simple tool for the authentication of Gadidae fish species.

PubMed

Fernandes, Telmo J R; Costa, Joana; Oliveira, M Beatriz P P; Mafra, Isabel

2017-09-01

This work aimed to exploit the use of DNA mini-barcodes combined with high resolution melting (HRM) for the authentication of gadoid species: Atlantic cod (Gadus morhua), Pacific cod (Gadus macrocephalus), Alaska pollock (Theragra chalcogramma) and saithe (Pollachius virens). Two DNA barcode regions, namely cytochrome c oxidase subunit I (COI) and cytochrome b (cytb), were analysed in silico to identify genetic variability among the four species and used, subsequently, to develop a real-time PCR method coupled with HRM analysis. The cytb mini-barcode enabled best discrimination of the target species with a high level of confidence (99.3%). The approach was applied successfully to identify gadoid species in 30 fish-containing foods, 30% of which were not as declared on the label. Herein, a novel approach for rapid, simple and cost-effective discrimination/clustering, as a tool to authenticate Gadidae fish species, according to their genetic relationship, is proposed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Invariant approach to the character classification

NASA Astrophysics Data System (ADS)

Šariri, Kristina; Demoli, Nazif

2008-04-01

Image moments analysis is a very useful tool which allows image description invariant to translation and rotation, scale change and some types of image distortions. The aim of this work was development of simple method for fast and reliable classification of characters by using Hu's and affine moment invariants. Measure of Eucleidean distance was used as a discrimination feature with statistical parameters estimated. The method was tested in classification of Times New Roman font letters as well as sets of the handwritten characters. It is shown that using all Hu's and three affine invariants as discrimination set improves recognition rate by 30%.

Deep Hashing for Scalable Image Search.

PubMed

Lu, Jiwen; Liong, Venice Erin; Zhou, Jie

2017-05-01

In this paper, we propose a new deep hashing (DH) approach to learn compact binary codes for scalable image search. Unlike most existing binary codes learning methods, which usually seek a single linear projection to map each sample into a binary feature vector, we develop a deep neural network to seek multiple hierarchical non-linear transformations to learn these binary codes, so that the non-linear relationship of samples can be well exploited. Our model is learned under three constraints at the top layer of the developed deep network: 1) the loss between the compact real-valued code and the learned binary vector is minimized, 2) the binary codes distribute evenly on each bit, and 3) different bits are as independent as possible. To further improve the discriminative power of the learned binary codes, we extend DH into supervised DH (SDH) and multi-label SDH by including a discriminative term into the objective function of DH, which simultaneously maximizes the inter-class variations and minimizes the intra-class variations of the learned binary codes with the single-label and multi-label settings, respectively. Extensive experimental results on eight widely used image search data sets show that our proposed methods achieve very competitive results with the state-of-the-arts.

Predictive models reduce talent development costs in female gymnastics.

PubMed

Pion, Johan; Hohmann, Andreas; Liu, Tianbiao; Lenoir, Matthieu; Segers, Veerle

2017-04-01

This retrospective study focuses on the comparison of different predictive models based on the results of a talent identification test battery for female gymnasts. We studied to what extent these models have the potential to optimise selection procedures, and at the same time reduce talent development costs in female artistic gymnastics. The dropout rate of 243 female elite gymnasts was investigated, 5 years past talent selection, using linear (discriminant analysis) and non-linear predictive models (Kohonen feature maps and multilayer perceptron). The coaches classified 51.9% of the participants correct. Discriminant analysis improved the correct classification to 71.6% while the non-linear technique of Kohonen feature maps reached 73.7% correctness. Application of the multilayer perceptron even classified 79.8% of the gymnasts correctly. The combination of different predictive models for talent selection can avoid deselection of high-potential female gymnasts. The selection procedure based upon the different statistical analyses results in decrease of 33.3% of cost because the pool of selected athletes can be reduced to 92 instead of 138 gymnasts (as selected by the coaches). Reduction of the costs allows the limited resources to be fully invested in the high-potential athletes.

A Simple Piece of Apparatus to Aid the Understanding of the Relationship between Angular Velocity and Linear Velocity

ERIC Educational Resources Information Center

Unsal, Yasin

2011-01-01

One of the subjects that is confusing and difficult for students to fully comprehend is the concept of angular velocity and linear velocity. It is the relationship between linear and angular velocity that students find difficult; most students understand linear motion in isolation. In this article, we detail the design, construction and…

Improving validation methods for molecular diagnostics: application of Bland-Altman, Deming and simple linear regression analyses in assay comparison and evaluation for next-generation sequencing

PubMed Central

Misyura, Maksym; Sukhai, Mahadeo A; Kulasignam, Vathany; Zhang, Tong; Kamel-Reid, Suzanne; Stockley, Tracy L

2018-01-01

Aims A standard approach in test evaluation is to compare results of the assay in validation to results from previously validated methods. For quantitative molecular diagnostic assays, comparison of test values is often performed using simple linear regression and the coefficient of determination (R2), using R2 as the primary metric of assay agreement. However, the use of R2 alone does not adequately quantify constant or proportional errors required for optimal test evaluation. More extensive statistical approaches, such as Bland-Altman and expanded interpretation of linear regression methods, can be used to more thoroughly compare data from quantitative molecular assays. Methods We present the application of Bland-Altman and linear regression statistical methods to evaluate quantitative outputs from next-generation sequencing assays (NGS). NGS-derived data sets from assay validation experiments were used to demonstrate the utility of the statistical methods. Results Both Bland-Altman and linear regression were able to detect the presence and magnitude of constant and proportional error in quantitative values of NGS data. Deming linear regression was used in the context of assay comparison studies, while simple linear regression was used to analyse serial dilution data. Bland-Altman statistical approach was also adapted to quantify assay accuracy, including constant and proportional errors, and precision where theoretical and empirical values were known. Conclusions The complementary application of the statistical methods described in this manuscript enables more extensive evaluation of performance characteristics of quantitative molecular assays, prior to implementation in the clinical molecular laboratory. PMID:28747393

A simple real-time polymerase chain reaction (PCR)-based assay for authentication of the Chinese Panax ginseng cultivar Damaya from a local ginseng population.

PubMed

Wang, H; Wang, J; Li, G

2016-06-27

Panax ginseng is one of the most important medicinal plants in the Orient. Owing to its increasing demand in the world market, cultivated ginseng has become the main source of medicinal material. Among the Chinese ginseng cultivars, Damaya commands higher prices and is grown in significant proportions among the local ginseng population. Due to the lack of rapid and accurate authentication methods, Damaya is distributed among different cultivars in the local ginseng population in China. Here, we identified a unique, Damaya-specific single nucleotide polymorphism (SNP) site present in the second intron of mitochondrial cytochrome c oxidase subunit 2 (cox2). Based on this SNP, a Damaya cultivar-specific primer was designed and an allele-specific polymerase chain reaction (PCR) was optimized for the effective molecular authentication of Damaya. We designed a method by combining a simple DNA isolation method with real-time allele-specific PCR using SYBR Green I fluorescent dye, and proved its efficacy in clearly discriminated Damaya cultivar from other Chinese ginseng cultivars according to the allelic discrimination analysis. Hence, this study provides a simple and rapid assay for the differentiation and conservation of Damaya from the local Chinese ginseng population.

Pharmaceutical Raw Material Identification Using Miniature Near-Infrared (MicroNIR) Spectroscopy and Supervised Pattern Recognition Using Support Vector Machine

PubMed Central

Hsiung, Chang; Pederson, Christopher G.; Zou, Peng; Smith, Valton; von Gunten, Marc; O’Brien, Nada A.

2016-01-01

Near-infrared spectroscopy as a rapid and non-destructive analytical technique offers great advantages for pharmaceutical raw material identification (RMID) to fulfill the quality and safety requirements in pharmaceutical industry. In this study, we demonstrated the use of portable miniature near-infrared (MicroNIR) spectrometers for NIR-based pharmaceutical RMID and solved two challenges in this area, model transferability and large-scale classification, with the aid of support vector machine (SVM) modeling. We used a set of 19 pharmaceutical compounds including various active pharmaceutical ingredients (APIs) and excipients and six MicroNIR spectrometers to test model transferability. For the test of large-scale classification, we used another set of 253 pharmaceutical compounds comprised of both chemically and physically different APIs and excipients. We compared SVM with conventional chemometric modeling techniques, including soft independent modeling of class analogy, partial least squares discriminant analysis, linear discriminant analysis, and quadratic discriminant analysis. Support vector machine modeling using a linear kernel, especially when combined with a hierarchical scheme, exhibited excellent performance in both model transferability and large-scale classification. Hence, ultra-compact, portable and robust MicroNIR spectrometers coupled with SVM modeling can make on-site and in situ pharmaceutical RMID for large-volume applications highly achievable. PMID:27029624

Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis

PubMed Central

Cárdenas-Peña, David; Collazos-Huertas, Diego; Castellanos-Dominguez, German

2016-01-01

Dementia is a growing problem that affects elderly people worldwide. More accurate evaluation of dementia diagnosis can help during the medical examination. Several methods for computer-aided dementia diagnosis have been proposed using resonance imaging scans to discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and healthy controls (NC). Nonetheless, the computer-aided diagnosis is especially challenging because of the heterogeneous and intermediate nature of MCI. We address the automated dementia diagnosis by introducing a novel supervised pretraining approach that takes advantage of the artificial neural network (ANN) for complex classification tasks. The proposal initializes an ANN based on linear projections to achieve more discriminating spaces. Such projections are estimated by maximizing the centered kernel alignment criterion that assesses the affinity between the resonance imaging data kernel matrix and the label target matrix. As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of the 2014 CADDementia challenge. As a result, our proposal outperforms all the baselines (7% of classification accuracy and area under the receiver-operating-characteristic curve) at the time it reduces the class biasing. PMID:27148392

Component-based subspace linear discriminant analysis method for face recognition with one training sample

NASA Astrophysics Data System (ADS)

Huang, Jian; Yuen, Pong C.; Chen, Wen-Sheng; Lai, J. H.

2005-05-01

Many face recognition algorithms/systems have been developed in the last decade and excellent performances have also been reported when there is a sufficient number of representative training samples. In many real-life applications such as passport identification, only one well-controlled frontal sample image is available for training. Under this situation, the performance of existing algorithms will degrade dramatically or may not even be implemented. We propose a component-based linear discriminant analysis (LDA) method to solve the one training sample problem. The basic idea of the proposed method is to construct local facial feature component bunches by moving each local feature region in four directions. In this way, we not only generate more samples with lower dimension than the original image, but also consider the face detection localization error while training. After that, we propose a subspace LDA method, which is tailor-made for a small number of training samples, for the local feature projection to maximize the discrimination power. Theoretical analysis and experiment results show that our proposed subspace LDA is efficient and overcomes the limitations in existing LDA methods. Finally, we combine the contributions of each local component bunch with a weighted combination scheme to draw the recognition decision. A FERET database is used for evaluating the proposed method and results are encouraging.

A general soft label based linear discriminant analysis for semi-supervised dimensionality reduction.

PubMed

Zhao, Mingbo; Zhang, Zhao; Chow, Tommy W S; Li, Bing

2014-07-01

Dealing with high-dimensional data has always been a major problem in research of pattern recognition and machine learning, and Linear Discriminant Analysis (LDA) is one of the most popular methods for dimension reduction. However, it only uses labeled samples while neglecting unlabeled samples, which are abundant and can be easily obtained in the real world. In this paper, we propose a new dimension reduction method, called "SL-LDA", by using unlabeled samples to enhance the performance of LDA. The new method first propagates label information from the labeled set to the unlabeled set via a label propagation process, where the predicted labels of unlabeled samples, called "soft labels", can be obtained. It then incorporates the soft labels into the construction of scatter matrixes to find a transformed matrix for dimension reduction. In this way, the proposed method can preserve more discriminative information, which is preferable when solving the classification problem. We further propose an efficient approach for solving SL-LDA under a least squares framework, and a flexible method of SL-LDA (FSL-LDA) to better cope with datasets sampled from a nonlinear manifold. Extensive simulations are carried out on several datasets, and the results show the effectiveness of the proposed method. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

Intellectual Abilities That Discriminate Good and Poor Problem Solvers.

ERIC Educational Resources Information Center

Meyer, Ruth Ann

1981-01-01

This study compared good and poor fourth-grade problem solvers on a battery of 19 "reference" tests for verbal, induction, numerical, word fluency, memory, perceptual speed, and simple visualization abilities. Results suggest verbal, numerical, and especially induction abilities are important to successful mathematical problem solving.…

Multiplexed microsatellite recovery using massively parallel sequencing

Treesearch

T.N. Jennings; B.J. Knaus; T.D. Mullins; S.M. Haig; R.C. Cronn

2011-01-01

Conservation and management of natural populations requires accurate and inexpensive genotyping methods. Traditional microsatellite, or simple sequence repeat (SSR), marker analysis remains a popular genotyping method because of the comparatively low cost of marker development, ease of analysis and high power of genotype discrimination. With the availability of...

Novel jet observables from machine learning

NASA Astrophysics Data System (ADS)

Datta, Kaustuv; Larkoski, Andrew J.

2018-03-01

Previous studies have demonstrated the utility and applicability of machine learning techniques to jet physics. In this paper, we construct new observables for the discrimination of jets from different originating particles exclusively from information identified by the machine. The approach we propose is to first organize information in the jet by resolved phase space and determine the effective N -body phase space at which discrimination power saturates. This then allows for the construction of a discrimination observable from the N -body phase space coordinates. A general form of this observable can be expressed with numerous parameters that are chosen so that the observable maximizes the signal vs. background likelihood. Here, we illustrate this technique applied to discrimination of H\\to b\\overline{b} decays from massive g\\to b\\overline{b} splittings. We show that for a simple parametrization, we can construct an observable that has discrimination power comparable to, or better than, widely-used observables motivated from theory considerations. For the case of jets on which modified mass-drop tagger grooming is applied, the observable that the machine learns is essentially the angle of the dominant gluon emission off of the b\\overline{b} pair.

Discriminant Analysis of Time Series in the Presence of Within-Group Spectral Variability.

PubMed

Krafty, Robert T

2016-07-01

Many studies record replicated time series epochs from different groups with the goal of using frequency domain properties to discriminate between the groups. In many applications, there exists variation in cyclical patterns from time series in the same group. Although a number of frequency domain methods for the discriminant analysis of time series have been explored, there is a dearth of models and methods that account for within-group spectral variability. This article proposes a model for groups of time series in which transfer functions are modeled as stochastic variables that can account for both between-group and within-group differences in spectra that are identified from individual replicates. An ensuing discriminant analysis of stochastic cepstra under this model is developed to obtain parsimonious measures of relative power that optimally separate groups in the presence of within-group spectral variability. The approach possess favorable properties in classifying new observations and can be consistently estimated through a simple discriminant analysis of a finite number of estimated cepstral coefficients. Benefits in accounting for within-group spectral variability are empirically illustrated in a simulation study and through an analysis of gait variability.

Haptic shape discrimination and interhemispheric communication.

PubMed

Dowell, Catherine J; Norman, J Farley; Moment, Jackie R; Shain, Lindsey M; Norman, Hideko F; Phillips, Flip; Kappers, Astrid M L

2018-01-10

In three experiments participants haptically discriminated object shape using unimanual (single hand explored two objects) and bimanual exploration (both hands were used, but each hand, left or right, explored a separate object). Such haptic exploration (one versus two hands) requires somatosensory processing in either only one or both cerebral hemispheres; previous studies related to the perception of shape/curvature found superior performance for unimanual exploration, indicating that shape comparison is more effective when only one hemisphere is utilized. The current results, obtained for naturally shaped solid objects (bell peppers, Capsicum annuum) and simple cylindrical surfaces demonstrate otherwise: bimanual haptic exploration can be as effective as unimanual exploration, showing that there is no necessary reduction in ability when haptic shape comparison requires interhemispheric communication. We found that while successive bimanual exploration produced high shape discriminability, the participants' bimanual performance deteriorated for simultaneous shape comparisons. This outcome suggests that either interhemispheric interference or the need to attend to multiple objects simultaneously reduces shape discrimination ability. The current results also reveal a significant effect of age: older adults' shape discrimination abilities are moderately reduced relative to younger adults, regardless of how objects are manipulated (left hand only, right hand only, or bimanual exploration).

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.

Radar image processing for rock-type discrimination

NASA Technical Reports Server (NTRS)

Blom, R. G.; Daily, M.

1982-01-01

Image processing and enhancement techniques for improving the geologic utility of digital satellite radar images are reviewed. Preprocessing techniques such as mean and variance correction on a range or azimuth line by line basis to provide uniformly illuminated swaths, median value filtering for four-look imagery to eliminate speckle, and geometric rectification using a priori elevation data. Examples are presented of application of preprocessing methods to Seasat and Landsat data, and Seasat SAR imagery was coregistered with Landsat imagery to form composite scenes. A polynomial was developed to distort the radar picture to fit the Landsat image of a 90 x 90 km sq grid, using Landsat color ratios with Seasat intensities. Subsequent linear discrimination analysis was employed to discriminate rock types from known areas. Seasat additions to the Landsat data improved rock identification by 7%.

Discrimination of rectal cancer through human serum using surface-enhanced Raman spectroscopy

NASA Astrophysics Data System (ADS)

Li, Xiaozhou; Yang, Tianyue; Li, Siqi; Zhang, Su; Jin, Lili

2015-05-01

In this paper, surface-enhanced Raman spectroscopy (SERS) was used to detect the changes in blood serum components that accompany rectal cancer. The differences in serum SERS data between rectal cancer patients and healthy controls were examined. Postoperative rectal cancer patients also participated in the comparison to monitor the effects of cancer treatments. The results show that there are significant variations at certain wavenumbers which indicates alteration of corresponding biological substances. Principal component analysis (PCA) and parameters of intensity ratios were used on the original SERS spectra for the extraction of featured variables. These featured variables then underwent linear discriminant analysis (LDA) and classification and regression tree (CART) for the discrimination analysis. Accuracies of 93.5 and 92.4 % were obtained for PCA-LDA and parameter-CART, respectively.

A Simple Demonstration of Atomic and Molecular Orbitals Using Circular Magnets

ERIC Educational Resources Information Center

Chakraborty, Maharudra; Mukhopadhyay, Subrata; Das, Ranendu Sekhar

2014-01-01

A quite simple and inexpensive technique is described here to represent the approximate shapes of atomic orbitals and the molecular orbitals formed by them following the principles of the linear combination of atomic orbitals (LCAO) method. Molecular orbitals of a few simple molecules can also be pictorially represented. Instructors can employ the…

Discriminative functions and over-training as class-enhancing determinants of meaningful stimuli.

PubMed

Travis, Robert W; Fields, Lanny; Arntzen, Erik

2014-07-01

Likelihood of equivalence class formation (yield) was influenced by pre-class formation of simultaneous and successive discriminations, their mastery criteria, and overtraining of the successive discriminations. Each undergraduate in seven groups attempted to form two 3-node, 5-member equivalence classes (ABCDE). In the pictorial (PIC) group, meaningless nonsense syllables were used as the A, B, D, and E stimuli and meaningful pictures as the C stimuli. Nonsense syllables only were used in the other groups. The abstract (ABS) or 0-0-0 group involved no pre-class training. In the 84-0-0, 84-5-0 and 84-20-0 groups, simultaneous discriminations were trained among C stimuli to a mastery criterion of 84 trials, followed by successive discriminations trained to mastery criteria of 0, 5, and 20 trials, respectively. In the 84-20-0, 84-20-100, and 84-20-500 groups, simultaneous and successive discriminations were trained as noted, followed by overtraining with 0, 100, 500 successive-discrimination trials, respectively. The ABS group produced a 6% yield with the 84-0-0, 84-5-0, and 84-20-0 groups producing further modest increments. Overtraining produced a linear increase in yield, reaching 85% after 500 overtraining trials, a yield matching that produced by classes containing pictures as C stimuli (PIC). Thus, acquired discriminative functions and the overtraining of at least one function can account for class enhancement by meaningful stimuli. © Society for the Experimental Analysis of Behavior.

Simple linear and multivariate regression models.

PubMed

Rodríguez del Águila, M M; Benítez-Parejo, N

2011-01-01

In biomedical research it is common to find problems in which we wish to relate a response variable to one or more variables capable of describing the behaviour of the former variable by means of mathematical models. Regression techniques are used to this effect, in which an equation is determined relating the two variables. While such equations can have different forms, linear equations are the most widely used form and are easy to interpret. The present article describes simple and multiple linear regression models, how they are calculated, and how their applicability assumptions are checked. Illustrative examples are provided, based on the use of the freely accessible R program. Copyright © 2011 SEICAP. Published by Elsevier Espana. All rights reserved.

The development of global motion discrimination in school aged children

PubMed Central

Bogfjellmo, Lotte-Guri; Bex, Peter J.; Falkenberg, Helle K.

2014-01-01

Global motion perception matures during childhood and involves the detection of local directional signals that are integrated across space. We examine the maturation of local directional selectivity and global motion integration with an equivalent noise paradigm applied to direction discrimination. One hundred and three observers (6–17 years) identified the global direction of motion in a 2AFC task. The 8° central stimuli consisted of 100 dots of 10% Michelson contrast moving 2.8°/s or 9.8°/s. Local directional selectivity and global sampling efficiency were estimated from direction discrimination thresholds as a function of external directional noise, speed, and age. Direction discrimination thresholds improved gradually until the age of 14 years (linear regression, p < 0.05) for both speeds. This improvement was associated with a gradual increase in sampling efficiency (linear regression, p < 0.05), with no significant change in internal noise. Direction sensitivity was lower for dots moving at 2.8°/s than at 9.8°/s for all ages (paired t test, p < 0.05) and is mainly due to lower sampling efficiency. Global motion perception improves gradually during development and matures by age 14. There was no change in internal noise after the age of 6, suggesting that local direction selectivity is mature by that age. The improvement in global motion perception is underpinned by a steady increase in the efficiency with which direction signals are pooled, suggesting that global motion pooling processes mature for longer and later than local motion processing. PMID:24569985

Diagnosis of human malignancies using laser-induced breakdown spectroscopy in combination with chemometric methods

NASA Astrophysics Data System (ADS)

Chen, Xue; Li, Xiaohui; Yu, Xin; Chen, Deying; Liu, Aichun

2018-01-01

Diagnosis of malignancies is a challenging clinical issue. In this work, we present quick and robust diagnosis and discrimination of lymphoma and multiple myeloma (MM) using laser-induced breakdown spectroscopy (LIBS) conducted on human serum samples, in combination with chemometric methods. The serum samples collected from lymphoma and MM cancer patients and healthy controls were deposited on filter papers and ablated with a pulsed 1064 nm Nd:YAG laser. 24 atomic lines of Ca, Na, K, H, O, and N were selected for malignancy diagnosis. Principal component analysis (PCA), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and k nearest neighbors (kNN) classification were applied to build the malignancy diagnosis and discrimination models. The performances of the models were evaluated using 10-fold cross validation. The discrimination accuracy, confusion matrix and receiver operating characteristic (ROC) curves were obtained. The values of area under the ROC curve (AUC), sensitivity and specificity at the cut-points were determined. The kNN model exhibits the best performances with overall discrimination accuracy of 96.0%. Distinct discrimination between malignancies and healthy controls has been achieved with AUC, sensitivity and specificity for healthy controls all approaching 1. For lymphoma, the best discrimination performance values are AUC = 0.990, sensitivity = 0.970 and specificity = 0.956. For MM, the corresponding values are AUC = 0.986, sensitivity = 0.892 and specificity = 0.994. The results show that the serum-LIBS technique can serve as a quick, less invasive and robust method for diagnosis and discrimination of human malignancies.

Shape information from glucose curves: Functional data analysis compared with traditional summary measures

PubMed Central

2013-01-01

Background Plasma glucose levels are important measures in medical care and research, and are often obtained from oral glucose tolerance tests (OGTT) with repeated measurements over 2–3 hours. It is common practice to use simple summary measures of OGTT curves. However, different OGTT curves can yield similar summary measures, and information of physiological or clinical interest may be lost. Our mean aim was to extract information inherent in the shape of OGTT glucose curves, compare it with the information from simple summary measures, and explore the clinical usefulness of such information. Methods OGTTs with five glucose measurements over two hours were recorded for 974 healthy pregnant women in their first trimester. For each woman, the five measurements were transformed into smooth OGTT glucose curves by functional data analysis (FDA), a collection of statistical methods developed specifically to analyse curve data. The essential modes of temporal variation between OGTT glucose curves were extracted by functional principal component analysis. The resultant functional principal component (FPC) scores were compared with commonly used simple summary measures: fasting and two-hour (2-h) values, area under the curve (AUC) and simple shape index (2-h minus 90-min values, or 90-min minus 60-min values). Clinical usefulness of FDA was explored by regression analyses of glucose tolerance later in pregnancy. Results Over 99% of the variation between individually fitted curves was expressed in the first three FPCs, interpreted physiologically as “general level” (FPC1), “time to peak” (FPC2) and “oscillations” (FPC3). FPC1 scores correlated strongly with AUC (r=0.999), but less with the other simple summary measures (−0.42≤r≤0.79). FPC2 scores gave shape information not captured by simple summary measures (−0.12≤r≤0.40). FPC2 scores, but not FPC1 nor the simple summary measures, discriminated between women who did and did not develop gestational diabetes later in pregnancy. Conclusions FDA of OGTT glucose curves in early pregnancy extracted shape information that was not identified by commonly used simple summary measures. This information discriminated between women with and without gestational diabetes later in pregnancy. PMID:23327294