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PubMed1979

Texture analysis of cervical cell nuclei by segmentation of chromatin patterns.

Texture parameters of the nuclear chromatin pattern can contribute to the automated classification of specimens on the basis of single cell analysis in cervical cytology. Current texture parameters are abstract and therefore hamper understanding. In this paper texture parameters are described that can be derived from the chromatin pattern after segmentation of the nuclear image. These texture parameters are more directly related to the visual properties of the chromatin pattern. The image segmentation procedure is based on a region grow algorithm which specifically isolates high chromatin density. The texture analysis method has been tested on a data set of images of 112 cervical nuclei on photographic negatives digitized with a step size of 0.125 micron. The preliminary results of a classification trial indicate that these visually interpretable parameters have promising discriminatory power for the distinction between negative and positive specimens.

Carcinoma↗
PubMed1992

Numerical classification and identification of Aeromonas genospecies.

A total of 176 Aeromonas strains representing all currently characterized genospecies were tested for 329 biochemical characters. Overall similarities of all strains were determined by numerical taxonomic techniques, the UPGMA algorithm and the SSM and the SJ coefficients as measures of similarity. Sixteen clusters (two or more strains) and seven unclustered strains were recovered at the 93.5% similarity level (SSM). Genospecies 1, 4, 5, 6, 7, 9, 12 and 13 were largely represented by single phena, whereas strains of genospecies 2 and 3 were found in closely-related phena. Strains belonging to genospecies 8 formed two distinct biotypes. Strains belonging to genospecies 11 formed a subcluster within a cluster representing different genospecies. In general, similar groupings were obtained with the Jaccard coefficient at a similarity level of 80.0% (SJ) with minor changes in the definition of clusters. The phenetic data showed good correlation with the taxa defined by DNA/DNA hybridization and those obtained by multilocus enzyme analysis. For all genospecies (independent from cluster assignment) 30 diagnostic characters were selected to construct a matrix for probabilistic identification. The correct identification rate of the matrix was 71.51% taking a Willcox probability greater than 0.99, and 83.7% taking a Willcox probability greater than 0.9 as identification threshold levels.

Aeromonas↗
PubMed1990

Fuzzy sets-based classification of electron microscopy images of biological macromolecules with an application to ribosomal particles.

Pattern recognition methods based on the theory of fuzzy sets are tested for their ability to classify electron microscopy images of biological specimens. The concept of fuzzy sets was chosen for its ability to represent classes of objects that are vaguely described from the measured data. A number of partitional clustering algorithms and an extensive set of cluster-validity functionals (some already reported and some newly developed) have been applied to a test-data set and to two real-data sets of images. One of the real-data sets corresponded to images of the Escherichia coli 50S ribosomal subunits depleted of proteins L7/L12 and the other set to images of the E. coli 70S monosome in the range of overlap views. These two latter sets had been previously studied by another clustering methodology. The new results obtained by the application of fuzzy clustering techniques will be compared to those previously obtained and some conclusions about the consistency of these classifications will be drawn from this comparison.

Algorithms↗
PubMed1989

Progress in application of the image analysing system QUANTIMET 720 in the histomorphometry of the microcirculatory system.

The application of automated image analysing methods in the field of histomorphometry has been improved in the last decades. In this paper a new method is presented for the investigation of morphological reactions of the microcirculatory system. It is based on the measurement of quantitative changes in microvessel wall tissue by using the image analysing system QUANTIMET 720. In the measurement model, a microvessel intersection figure is considered as an ellipsis. A procedure is presented allowing the estimation of the wall and lumen area of the microvessel section and the calculation of its radius for a totally collapsed state. From this data, further morphometric parameters are derived. Using this method, the analysis of 500 microvessels including classification and parameter derivation takes about 10 min.

Algorithms↗
PubMed1988

A unified framework for connectionist systems.

Pattern classification using connectionist (i.e., neural network) models is viewed within a statistical framework. A connectionist network's subjective beliefs about its statistical environment are derived. This belief structure is the network's "subjective" probability distribution. Stimulus classification is interpreted as computing the "most probable" response for a given stimulus with respect to the subjective probability distribution. Given the subjective probability distribution, learning algorithms can be analyzed and designed using maximum likelihood estimation techniques, and statistical tests can be developed to evaluate and compare network architectures. The framework is applicable to many connectionist networks including those of Hopfield (1982, 1984), Cohen and Grossberg (1983), Anderson et al. (1977), and Rumelhart et al. (1986b).

Animals↗
PubMed1990

Automatic definition of recurrent local structure motifs in proteins.

An automatic procedure for defining recurrent folding motifs in proteins of known structure is described. These motifs are formed by short polypeptide fragments of equal size containing between four and seven residues. The method applies a classical clustering algorithm that operates on distances between selected backbone atoms. In one application, we use it to cluster all protein fragments into only four structural classes. This classification is rough considering the observed diversity of local structures, but comparable in homogeneity to the four classes of secondary structure (alpha-helix, beta-strand, turn and coil). Yet, it discriminates between extended and curved coil and distinguishes beta-bulges from beta-strands. In a second application, the clustering procedure is combined with assignment of backbone dihedral angles to allowed regions in the Ramachandran map. This produces an exhaustive repertoire of highly homogeneous families of structural motifs that contains all the beta-hairpins, beta alpha- and alpha beta-loops previously defined by manual procedures, and new structural families of which two examples, a beta alpha-loop and an alpha-helix beginning, are analyzed in detail. The described automatic procedures should be useful in categorizing structure information in proteins, thereby increasing our ability to analyze relations between structure and sequence.

Amino Acid Sequence↗
PubMed1990

Schedule for operationalized diagnosis according to the Leonhard classification of endogenous psychoses.

Psychiatric diagnoses in endogenous psychoses are still largely based on clinical psychopathology. Despite intensive efforts including progress to more reliability in classification, no definite and causally relevant biological abnormalities have been identified so far. This failure might be due to the present standard classification systems like DSM or ICD being too crude to allow the distinction of homogeneous nosological entities. The classification developed by K. Leonhard offers a more subtle alternative. In order to facilitate the handling of this system and in the interest of reliability, an algorithm for computer-assisted diagnostic decision making based on similarities to Leonhard's ideal types is presented.

Bipolar Disorder↗
PubMed2026

Strategies for mosaic variant calling in brain disorders.

The human brain is a genomic mosaic, where postzygotic mutations arising from embryogenesis to senescence drive diverse neurodevelopmental and neurodegenerative diseases. Because of numerous sequencing artifacts at ultralow variant allele frequencies (VAFs), detecting these variants remains a significant analytical challenge. This review focuses on single-nucleotide variants and small indels, summarizing current strategies for aligning sampling methods, including bulk, laser capture microdissection, and single-cell genomics, with the expected clonal architecture of the brain. It emphasizes that mosaic detection sensitivity is fundamentally constrained by sequencing depth, since even the most advanced algorithms cannot identify variants not physically represented in the sequencing library. The review further recommends the selection of variant calling algorithms based on validated VAF detection performance, matching tools like MuTect2 and MosaicForecast to their optimal performance ranges. Furthermore, we discuss how multitissue sampling, as emphasized by the SMaHT project, addresses the matched-control dilemma and supports accurate variant classification via cross-tissue VAF gradients. Integrating these established pipelines with multiomics modalities, including transcriptomic and epigenetic data, could advance the field toward a functional understanding of how the somatic genome impacts human brain health and disease.

Humans↗
PubMed1987

Single sweep analysis of visual evoked potentials through a model of parametric identification.

An original method is presented for the single sweep analysis of visual evoked potentials (VEP's). The introduced algorithm bases upon an AutoRegressive with eXogenous input (ARX) modeling. A Least Squares procedure estimates the coefficients of the model and allows to obtain a complete black-box description of the signal generation mechanism, besides providing a filtered version of the single sweep potential. The performance of the algorithm is verified on proper simulation tests and the experimental results put into evidence the noticeable improvement of signal-to-noise ratio with a consequent better recognition of the classical parameters of the peaks (latencies and amplitudes). The possibility of measuring these parameters on a single sweep basis enables to evaluate the dynamics of the Central Nervous System response during the entire course of the examination. A classification of the estimated evoked potentials in a small number of subsets, on the basis of their morphology, is also possible.

Algorithms↗
PubMed1990

Acoustic classification of alarm calls by vervet monkeys (Cercopithecus aethiops) and humans (Homo sapiens): II. Synthetic calls.

In 2 experiments classification of synthetic versions of species-typical snake and eagle alarm calls by vervet monkeys (Cercopithecus aethiops) and human (Homo sapiens) control subjects was investigated. In a 2-choice, operant-conditioning-based procedure, this work followed up acoustic analyses that had used various digitally based algorithms (Owren & Bernacki, 1988). All subjects were first tested with alarm-call replicas that were based on analysis data. These models were classified in the same manner as natural stimuli, which verified the appropriateness of the acoustic characterizations. Synthetic stimuli were then presented to test the importance of specific acoustic cues. Spectral patterning was found to be the most salient cue for classification by the monkeys, whereas results from the human subjects were mixed. Implications for the study of nonhuman primate vocalizations and Lieberman's (1984) theory of speech evolution are discussed.

Adult↗
PubMed2026

DeepLabCut-based automated system reveals diverse temperature tolerance among medaka strains and related Oryzias species.

Temperature is a critical environmental factor influencing the physiology and behavior of ectothermic animals, yet conventional methods for evaluating thermal tolerance in fish rely on subjective manual observation of loss of equilibrium (LOE), limiting experimental throughput and introducing observer bias. Here, we developed an automated temperature tolerance evaluation system integrating DeepLabCut-based pose estimation with custom image processing algorithms to objectively quantify the timing of LOE during thermal stress tests. Our system incorporated region partitioning and color transformation preprocessing to improve keypoint detection accuracy, followed by a classification model combining ResNet34-based frame features with keypoint coordinates to objectively determine the timing of LOE without manual observation. Validation against manual annotation showed that the automated system achieved an accuracy comparable to the natural variability between trained investigators, and outperformed naive human observers, supporting its validity as an objective and reproducible alternative to manual scoring. Using this system, we characterized cold and heat tolerance across six medaka strains (Oryzias latipes: d-rR/TOKYO, HB11A, OK-Cab, HO5 and HdrR-II1; O. sakaizumii: HNI-II). Cold and heat tolerance assessment revealed inter-strain variation, with HdrR-II1 among the most cold- and heat-tolerant strains and HNI-II the least tolerant of both cold and heat stress. We further evaluated cold tolerance in medaka-related species (O. sinensis, O. cabaranensis, O. curvinotus, O. luzonensis, O. celebensis, and O. javanicus) and zebrafish (Danio rerio), revealing substantial interspecific variation that broadly corresponded with latitudinal distribution. O. latipes, distributed at the highest latitudes among the tested species, exhibited the greatest cold tolerance, whereas O. celebensis, O. javanicus, and other tropical or low-latitude species showed comparatively low cold tolerance. Our automated system provides a robust, high-throughput platform for thermal tolerance evaluation and, combined with the genetic and genomic resources available in medaka, establishes a foundation for elucidating the molecular mechanisms underlying temperature adaptation in fish.

Animals↗
PubMed1992

Treatment of lower extremity infections in diabetics.

The infected diabetic lower extremity has enjoyed a surge in popularity in the medical literature. There have been numerous papers outlining classification systems for ulcer depth, surgical approaches, and microbiology. Discussions on antibiotic use have usually been directed toward therapy of the "diabetic foot infections" as a group, without regard to differences in severity and location of these infections. These infections can vary from the most superficial of processes to a severe life- and limb-threatening sepsis. The author presents a review of the processes involved in the diabetic lower extremity infection and suggests a classification system for selection of empiric antibiotic therapy based on the severity of the infection.

Algorithms↗
PubMed2026

AniAnn's: alignment-free annotation of tandem repeat arrays using fast average nucleotide identity estimates.

MOTIVATION: Satellite DNA has long posed challenges for genome assembly and analysis due to its low sequence complexity and poor mappability. These large heterochromatic arrays of tandem repeats are ubiquitous across eukaryotic genomes, yet remain understudied. Current methods for annotating satellite regions, and other classes of tandem repeat arrays, are limited in their ability to annotate divergent or novel sequences. RESULTS: In this work, we introduce AniAnn's, an algorithm for annotating large blocks of tandemly repeating DNAs. AniAnn's exploits the high Average Nucleotide Identity (ANI) shared between repeat units of the same array to quickly and accurately infer the boundaries of such arrays. We show that AniAnn's improves the annotation of satellites and other tandem repeats within a variety of plant and animal genomes, while requiring only a fraction of the runtime compared to previous approaches. We conclude by exploring several use cases of AniAnn's as a lightweight method for masking repeats prior to whole-genome alignment as well as the de novo annotation and classification of satellite repeats. AVAILABILITY: AniAnn's is open source software and available at github.com/marbl/anianns.

Algorithms↗
PubMed1988

Comparison of the performances of an automated arrhythmia detector working on original and virtual ECG tracings.

Two approaches can be taken to improve the performance of an automatic arrhythmia detector: perfecting the detection algorithms or improving the quality of the investigated traces by preprocessing the original traces. This paper reports on the results of a data preprocessing approach. Preprocessing consists in constructing new traces, which we call virtual. They are mathematically obtained from the original traces and referred to the dominant cardiac electric axis. The classifications obtained with an arrhythmia detector using both virtual and original traces are presented and discussed. By comparing the performance indices obtained under the two different conditions, it can be seen that a diagnosis based on the virtual traces is as acceptable as one based on the original traces. This result should be judged as favorable, since the algorithm was not adjusted or calibrated to the virtual traces, while those who developed it had certainly calibrated the parameters to the original traces.

Algorithms↗
PubMed1985

Tests of homogeneity and trend with medians.

Algorithms for tests of homogeneity and trend with medians are presented. Under certain conditions the tests are more efficient than many standard nonparametric tests on one-way classification designs. Computationally the tests are very simple and could be performed by hand or with the help of a calculator. Application to certain types of data derived in cytogenetics is pointed out with an example.

Animals↗
PubMed1990

Cases of alleged asbestos-related disease: a radiologic re-evaluation.

Chest radiographs were re-evaluated from 439 active and retired tireworkers previously designated as having a condition consistent with an asbestiform mineral exposure. The review was performed in an independent manner by three board-certified radiologists according to guidelines from an international classification system. The percentage of cases with abnormalities consistent with an asbestiform mineral exposure found separately by the three radiologists was 3.7, 3.0, and 2.7%. Application of an algorithm to form a consensus evaluation indicated that approximately 3.6% (16) of the subjects evaluated may have a condition consistent with an asbestos exposure. A more detailed review, however, revealed that only 11 workers, or 2.5% of the total, would have a reasonable likelihood of having such a condition. Most cases were normal and the majority of abnormalities present on the radiographs evaluated were nonoccupational in origin. Prevalent conditions identified included healed tuberculosis, histoplasmosis, emphysema, discoid atelectasis, effusions, healed rib fractures, scarring due to infection or old inflammatory disease, possible cancer, miscellaneous nonspecific linear markings consistent with cigarette smoking and aging, and heart and vascular system diseases--the latter evidenced by an abnormally large number of subjects with healed coronary artery bypass surgery and pacemaker implants. In summary, the best estimate from this study indicates that possibly 16 (3.6%), but more realistically 11 (2.5%), of the 439 tireworkers evaluated may have a condition consistent with exposure to an asbestiform mineral. This represents a 40-fold difference between the re-evaluation results and the original survey work.

Asbestosis↗
PubMed2025

Predicting host tropism in influenza a viruses: insights from multi-segment nucleotide signatures.

BACKGROUND: Influenza A virus (IAV) poses a significant public health threat due to its cross-species transmission and complex host adaptation mechanisms. This study integrated whole-genome data from avian, human, swine, and bovine IAV strains, using machine learning to predict viral host tropism based on nucleotide site features and to identify key sites driving host adaptation along with their synergistic effects. METHODS: A total of 64,000 IAV sequences from avian, human, swine, and bovine hosts were analyzed to build host-prediction models. A four-class classification framework (avian, human, swine, bovine) was constructed using nucleotide site features from all eight genomic segments (PB2, PB1, PA, HA, NP, NA, MP, NS). Eight machine learning algorithms (logistic regression, decision tree, random forest, SVM, KNN, gradient boosting, XGBoost, LightGBM) were benchmarked via 10-fold stratified cross-validation. Model performance was evaluated using accuracy, precision, recall, F1-score, AUPRC, and AUC. SHAP (SHapley Additive exPlanations) analysis prioritized critical nucleotide sites, while bivariate association tests identified synergistic/antagonistic interactions between sites. Nucleotide composition profiles were compared across host groups using hierarchical clustering and heatmap visualization. RESULTS: The XGBoost algorithm demonstrated the best and most stable performance, achieving an AUC value of over 0.95 in distinguishing human-derived sequences from non-human ones. SHAP analysis identified the top 20 critical nucleotide sites for each gene segment, such as sites 46 and 698 in the NS segment. Nucleotide composition analysis revealed high similarity between human and swine sequences in the HA and PB2 segments, and between avian and bovine sequences. The HA segment was particularly challenging in differentiating human from swine strains. Bivariate site association analysis uncovered significant synergistic or antagonistic effects between key sites within gene segments, forming complex networks. For instance, in the NS segment, a positive prediction contribution was observed when sites 371, 698, and 419 were all G. CONCLUSIONS: This study advances our mechanistic understanding of IAV host adaptation, identifies molecular determinants for zoonotic risk stratification, and establishes a scalable machine learning framework for predicting viral host tropism through nucleotide signature analysis, thereby enhancing surveillance strategies and informing preventive measures against emerging viral threats.

Influenza A virus↗