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At least 91 records · Page 5Linked to original sources
PubMed1987

Event-related potentials: a critical review of methods for single-trial detection.

The analysis of ERP data has followed several lines over the last 20 years. The most prevalent method is simply to average ERPs for a given class of stimuli. The ERPs are compared for differences across classes of stimuli. Little other special data processing is used. The ERP comparisons are usually performed using visual examination of the wave-shapes. Sometimes statistics are calculated such as means, variances, and confidence limits. Linear filtering is used to reduce interference. Another approach is to model or analyze the ERP as a sequence of vectors or frames of data samples. These samples may be of the ERP time waveform or they may be of the frequency transform of the ERP waveform. The frames of data vary in length from the entire ERP waveform (500 to 1000 msec) to frames as short as ten sample points (100 msec). Recognition of an event in the ERP is achieved by computing a distance measure between parameter vectors for one class of stimuli and corresponding parameter vectors for another class of stimuli. Recognition is achieved by selecting the ERP with the lowest distance score. This approach is "pattern matching" and relies on two assumptions: adjacent frames of data are uncorrelated, and the variability of the data can be accounted for by the distance measured for all stimuli in the classes presented. Subject variability is generally not accounted for, other than to assume it is the same for all classes of stimuli. The data are clustered into a variety of reference patterns that represent particular manifestations of a particular stimulus. Another approach is "feature-based" recognition. The idea is to identify and automatically extract features of the data that can provide a characterization of stimuli. The features selected may be abstract. They are calculated from the data or transforms of the data.

Biometry↗
PubMed2025

CoxKAN: Kolmogorov-Arnold networks for interpretable, high-performance survival analysis.

MOTIVATION: Survival analysis is a branch of statistics that is crucial in medicine for modeling the time to critical events such as death or relapse, in order to improve treatment strategies and patient outcomes. Selecting survival models often involves a trade-off between performance and interpretability; deep learning models offer high performance but lack the transparency of more traditional approaches. This poses a significant issue in medicine, where practitioners are reluctant to use black-box models for critical patient decisions. RESULTS: We introduce CoxKAN, a Cox proportional hazards Kolmogorov-Arnold Network for interpretable, high-performance survival analysis. Kolmogorov-Arnold Networks (KANs) were recently proposed as an interpretable and accurate alternative to multi-layer perceptrons. We evaluated CoxKAN on four synthetic and nine real datasets, including five cohorts with clinical data and four with genomics biomarkers. In synthetic experiments, CoxKAN accurately recovered interpretable hazard function formulae and excelled in automatic feature selection. Evaluations on real datasets showed that CoxKAN consistently outperformed the traditional Cox proportional hazards model (by up to 4% in C-index) and matched or surpassed the performance of deep learning-based models. Importantly, CoxKAN revealed complex interactions between predictor variables and uncovered symbolic formulae, which are key capabilities that other survival analysis methods lack, to provide clear insights into the impact of key biomarkers on patient risk. AVAILABILITY AND IMPLEMENTATION: CoxKAN is available at GitHub and Zenodo.

Humans↗
PubMed1985

Biological factors predisposing to traumatic posterior dislocation of the hip. A selection process in the mechanism of injury.

The factors involved in the mechanism leading to traumatic posterior dislocation of the hip are examined. In 47 adult patients who had previously suffered such a dislocation, ultrasound scans were used to measure femoral anteversion on both the affected and the uninjured side. In 36 normal adult volunteers, used as controls, similar measurements were made. Femoral anteversion on both the injured and uninjured side was significantly reduced in the patients compared with the volunteers. These findings are discussed in the light of previous work which indicates that medial rotation is a factor in the mechanism of posterior dislocation of the hip. It is suggested that reduced anteversion acts like medial rotation to make the hip more susceptible to posterior dislocation, and that the less the anteversion the more likely is the injury to be a dislocation rather than a fracture-dislocation. It is concluded that patients who suffer such dislocated hips belong at one extreme of the normal population, having either reduced femoral anteversion or even retroversion, and that this anatomical feature selects towards hip dislocation rather than to injury of the femoral shaft, knee or tibia during the appropriate type of accident.

Adult↗
PubMed1992

Acoustic-phonetic contrasts and intelligibility in the dysarthria associated with mixed cerebral palsy.

This study evaluated the relationship between specific acoustic features of speech and perceptual judgments of word intelligibility of adults with cerebral palsy-dysarthria. Use of a contrasting word task allowed for intelligibility analysis and correlated acoustic analysis according to specified spectral and temporal features. Selected phonemic contrasts included syllable-initial voicing; syllable-final voicing; stop-nasal; fricative-affricate; front-back, high-low, and tense-lax vowels. Speech materials included a set of CVC stimulus words. Acoustic data are reported on vowel duration, formant frequency locations, voice onset times, amplitude rise times, and frication durations. Listeners' perceptual assessment of intelligibility of the 16 dysarthric adults by transcription and rating tasks is also presented. All but one acoustic contrast was successfully made as evidenced by measured acoustic differences between contrast pairs. However, the generally successful acoustic contrasts stood in marked contrast to the poorly rated intelligibility scores and high error percentages that were ascribed to the opposite pair members. A second analysis examined the contribution of these acoustic features towards estimates and prediction of intelligibility deficits in speakers with dysarthria. The scaled intelligibility was predicted by multiple regression analysis with 62.6% accuracy by acoustic measures related to one consonant contrast (fricative-affricate) and three vowel contrasts (front-back, high-low, and tense-lax). Other measured contrasts, such as those related to contrast voicing effects and stop-nasal distinctions, did not seem to contribute in a significant way to variability in the intelligibility estimates. These findings are discussed in relation to specific areas of production deficiency that are consistent across different types of dysarthria with cerebral palsy as the etiology.

Adult↗
PubMed2026

Transfer Learning across Material Properties Using Center-Environment Features: From Energetics to Mechanical Properties in Multicomponent Mo Alloys.

Transfer learning (TL) provides a viable approach to mitigate data scarcity in materials informatics. While conventional TL focuses on predicting identical properties across different systems, this work demonstrates a cross-property extension of TL from energy to mechanical properties via end-to-end model weight pre-training and fine-tuning: knowledge learned from predicting substitution energies is transferred to predict distinctly different mechanical properties, substantially improving computational efficiency given the typically higher cost of acquiring target-domain data. To accelerate computational alloy design, machine learning models using center-environment (CE) features were first developed to predict substitution energies of alloying elements in molybdenum (Mo)-based alloys. The Random Forest models achieved the optimal performance and transferability-R2 = 0.97, 〈MAE〉 = 0.11 eV, and 〈RMSE〉 = 0.16 eV-against the density functional theory (DFT) benchmark. The model dependency of feature selection and importance analysis was discussed. The transferability of the energy models was validated on unknown systems with new elements. Subsequently, the energy models were fine-tuned using limited mechanical property data to construct energy-to-property (E2P) TL models capable of predicting elastic properties, including bulk modulus, Young's modulus, shear modulus, and elastic constants, achieving an improved accuracy over the non-transferred ML by ∼10-30%, with its transferability verified by additional DFT calculations. This cross-property E2P transfer learning framework opens a new avenue for accelerating computational materials discovery and may be extended to other multiproperty predictions governed by similar physical principles.

center-environment feature↗
PubMed1991

Three-dimensional reconstruction of temporal bone from computed tomographic scans on a personal computer.

The advantages of computer reconstruction of anatomical structures from computed tomographic scans are common knowledge by now. Unfortunately, to date most reconstructions have required the use of large computers and/or have entailed tedious manual contour tracing. The system described here allows largely automatic detection of surfaces in computed tomographic scans plus the usual display capabilities including feature selection, magnification, rotation, shading, and slicing as well as measurement of lengths and angles. It runs on a normal International Business Machines AT-compatible computer with a medium-resolution video card.

Child↗
PubMed1989

On fully automatic feature measurement for banded chromosome classification.

Procedures for fully automatic location of chromosome axis and centromere in metaphase chromosomes are described for a practical interactive chromosome analysis system that omits the usual stages of interactive axis and centromere correction. Accuracy of centromere finding and consequential determination of a chromosome's polarity, i.e., which end is which, is measured experimentally. The saving in interaction by not correcting centromeres is compared to the increase in errors at the classification stage and the consequent increase in interaction needed to correct these errors. Some previously unreported features for banded chromosome classification are described, and in particular a set of global shape features is introduced. The discrimination capability of the feature measurements is evaluated by use of simple statistics and by reference to the performance of classifiers trained with various feature subsets. Class discrimination capability of the global shape feature set is shown to be comparable to that of centromere position, a widely used local shape feature. The variability of feature measurements that might occur in data from different laboratories on account of differing tissue, preparation methods, and digitiser hardware is assessed using three data bases of G-banded human metaphase cells. It is shown that the differences can be considerable and that appropriate feature selection and classifier training substantially improve classification performance.

Chromosomes↗
PubMed1978

Structure-activity studies of barbiturates using pattern recognition techniques.

The relationship between molecular structure and duration of depressant effect for barbiturates was investigated. A data set of 160 5,5'-disubstituted barbiturates with various acyclic substituents was coded using 47 numerical descriptors including fragments, substructures, environmental descriptors, and molecular connectivity indexes. All descriptors were derived directly from the connection tables of the barbiturates. Using an interactive error-correction feedback algorithm, linear discriminant functions were developed that could dichotomize the data set with respect to several thresholds separating longer from shorter acting compounds. Feature selection was used to focus on the relatively few structural descriptors sufficient to support linear separability. For three specific thresholds, nine, 11, and nine descriptors were sufficient. The importance of these descriptors and the utility of the technique are discussed. Predictive abilities of approximately 94% were obtained for known barbiturates of the same general molecular types.

Animals↗
PubMed1987

A comparison of the chemical analyses of cell lipids with their complete proton NMR spectrum.

Whole cells are made up of molecules in different environments to which NMR spectroscopy is sensitive. In particular, malignant and transformed cells contain lipids not only in bilayers but in isotropically tumbling domains which give rise to high-resolution spectra. We have recently developed a technique for simultaneously analyzing broadline and high-resolution signals (M. Bloom, K. T. Holmes, C. E. Mountford, and P. G. Williams, J. Magn. Reson., in press) and we report here its application to a range of rat, mouse, and human cell lines. Some selected features of the NMR spectra were compared with the chemical analysis of the whole-cell lipid. We found that in general the proportion of protons in the narrow methylene resonance at 1.3 ppm increased with the neutral lipid content of the cells. This peak was chosen because its T2 relaxation behavior correlates with metastatic potential in a rat model system. This new technique could be applied to other high-resolution components both in healthy and in diseased states.

Animals↗
PubMed1989

Eigenimage filtering in MR imaging: an application in the abnormal chest wall.

A postprocessing linear filter was applied to spin-echo images on 10 patients with known or suspected chest wall invasion due to bronchogenic carcinoma. This technique known as eigenimage filtering allows selective feature extraction of suspected abnormalities from conventional MR images. The final result is an image with marked increased contrast range through enhancement of a desired process (tumor) with suppression of an interfering process (e.g., normal surrounding tissue). This preliminary work demonstrates the ease with which the technique may be implemented, the contrast enhancement obtained between the desired and the interfering feature in the final eigenimage, and its ability to correct for partial volume averaging effects. Also demonstrated are artifacts that can interfere with the interpretation of the eigenimage and a method for minimizing these artifacts in the final eigenimage.

Carcinoma, Bronchogenic↗
PubMed2025

Hierarchical Multi-Label Classification With Gene-Environment Interactions in Disease Modeling.

In biomedical studies, gene-environment (G-E) interactions have been demonstrated to have important implications for analyzing disease outcomes beyond the main G and main E effects. Many approaches have been developed for G-E interaction analysis, yielding important findings. However, hierarchical multi-label classification, which provides insightful information on disease outcomes, remains unexplored in G-E analysis literature. Moreover, unlabeled data are commonly observed in practical settings but omitted by many existing methods of hierarchical multi-label classification. In this study, we consider a semi-supervised scenario and develop a novel approach for the two-layer hierarchical response with G-E interactions. A two-step penalized estimation is then proposed using an efficient expectation-maximization (EM) algorithm. Simulation shows that it has superior performance in classification and feature selection. The analysis of The Cancer Genome Atlas (TCGA) data on lung cancer demonstrates the practical utility of the proposed method. Overall, this study can fill the important knowledge gap in G-E interaction analysis by providing a widely applicable framework for hierarchical multi-label classification of complex disease outcomes.

Humans↗
PubMed2025

Digital Immunophenotyping of Lung Atypical Carcinoids and Large Cell Neuroendocrine Carcinomas Identifies Three Subtypes With Specific Tumor-Immune Microenvironment Features.

Atypical carcinoids (ACs) and large cell neuroendocrine carcinomas (LCNECs) are defined by the WHO as intermediate- and high-grade lung neuroendocrine neoplasms, respectively, based on morphological criteria; however, treatment strategies remain debated. Given the emerging role of the tumor microenvironment (TME) and tumor-infiltrating lymphocytes (TILs) in cancer prognosis and therapy response, this study aimed to characterize the immune landscape of ACs and LCNECs comprehensively. Immunohistochemistry for T-cell markers (CD3, CD8), immune checkpoints (PD-1, PD-L1), HLA molecules (HLA-DR, HLA-I), and fibroblasts (&#x3b1;-SMA) was performed on a re-evaluated cohort of 56 ACs and 104 LCNECs. Digital image analysis quantified intra-tumor (iTILs) and stromal (sTILs) CD3 and CD8 TILs in the whole slide and in specific tumor regions (invasive margin [IM] and central tumor [CT]). LCNECs exhibited significantly higher stromal T-cell infiltration, immune checkpoint expression, and HLA compared to ACs (p&#x2009;<&#x2009;0.001), while &#x3b1;-SMA was more prominent in ACs. No ACs showed PD-L1 tumor expression. Digital quantification confirmed greater iTILs and sTILs in LCNECs across all regions, with moderate concordance to manual counts. Interestingly, TIL parameters were higher at the IM than in the CT (p&#x2009;<&#x2009;0.001). Using Boruta feature selection algorithm, Principal Component Analysis and Hierarchical Clustering, three patient clusters were identified: Cluster 1 (mainly ACs, low TILs, favorable prognosis), Cluster 2 (mixed histology, intermediate TILs, moderate prognosis), and Cluster 3 (mostly LCNECs, high TILs, poor prognosis), with distinct TME marker profiles. PD-L1 tumor expression was strongly linked to Cluster 3. These findings suggest that ACs and LCNECs may be stratified into three distinct immune clusters, highlighting the heterogeneity of their tumor microenvironment and providing a rationale for further translational studies.

Humans↗
PubMed1992

On AR modelling for MEG spectral estimation, data compression and classification.

The use of the autoregressive (AR) model for magnetoencephalogram (MEG) processing is examined and compared to other methods. Spectral estimation, classification and data compression of MEG signals are studied. In application to spectral estimation the AR model is compared to the classical modified periodogram method. Also, AR modelling appears to perform very successfully when used for the classification of normal and epileptic MEG signals. Finally, the 17:1 to 23:1 data compression achieved by AR modelling, along with the above-mentioned advantages, render it suitable for storage applications. For comparison, the method of feature selection via orthogonal expansion is used as a tool to achieve data reduction. It is seen that while effective, this is less drastic than the compression of data volume achieved by AR modelling.

Brain Mapping↗
PubMed1976

Dynamic decision models for clinical diagnosis.

A unified approach to clinical decision-making is presented. This combines partially observable Markovian decision processes (Markov or semi-Markov) with cause-effect models as a probabilistic representation of the diagnostic process. Pattern recognition techniques are used in a first stage of system state identification. This new class of dynamic models has a direct application to medical diagnosis and treatment and specific physiological examples are emphasised. The methodology is given for combining the patient state of health, the clinician's state of knowledge of the cause-effect representation from the observation space (measurements), feature selection using pattern recognition techniques and, finally, the treatment decisions with which to restore the patient to a more desirable state of health. A cost functional for the decision process has then to be optimised according to some pre-assigned objective function (social return from the patient state of health or treatment cost for the patient), when the process has an infinite time horizon.

Computers↗
PubMed1985

Becoming a nurse: the ethical perspective.

This paper examines selected features of neophyte nursing students' approach to the moral dimension of their future practice. It argues that rapid changes in medical technology and in social perceptions of morality necessitate a critical examination of the content, sequence and methods of introductory ethics education in nursing. It suggests that greater emphasis should be placed upon individual moral development; and that Codes of Ethics and Bills of Rights should be presented as ideal statements, to serve as guides in assessing the ethical situation of the client. Some reactions of students are reported.

Curriculum↗
PubMed1988

Evoked potential estimates of the time course of adaptation and recovery to counterphase gratings.

Scalp-recorded evoked potentials (VEP) were sequentially sampled in humans during adaptation to and recovery from prolonged viewing of counterphase sinusoidal grating targets. The sum of the power at the first and second harmonics of the Fourier-transformed VEP components was found to decrease during adaptation and increase during recovery. Time constants (T) for the adaptation and recovery processes as estimated from exponential functions ranged from 2.9 to 19 sec, varying non-monotonically with the spatial frequency and contrast of the stimulus. The observed T values are shorter than those reported in psychophysical studies of adaptation but overlap estimates derived from single cell studies. An unexpected finding was the occurrence of a 3-6 sec delay in the appearance of the maximum VEP response after the onset of the adaptation stimulus. The delay occurred in all subjects and at all spatial frequencies when moderate to high adapting contrasts (e.g. greater than 0.2) were used. The data support a feature-selective, multi-channel lateral inhibitory model of spatial vision and suggest the presence of tonic inhibition between the channels.

Adaptation, Ocular↗
PubMed1986

BMDP program for piecewise linear regression.

Piecewise linear regression has potentially broad applications in medical data analysis as well as other types of regression. Various kinds of algorithms have been proposed for finding optimum piecewise linear regressions. This paper presents a BMDP program for obtaining near optimum piecewise linear regression equations. An idea intrinsic to the method is that restricting parameter space to a discrete set makes the difficult problems become standard problems. Any software having the variable selection feature in the multiple linear regression can be used to apply the method.

Computers↗