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A crucial rule for both rapid identification and classification of toxins by the relative content of the five most informative amino acids (glycine, cysteine, threonine, alanine and tryptophane) was established by using a computer algorithm of pattern recognition based on amino acid sequences of three families of toxic polypeptides.
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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.
Using mathematical analysis methods, the authors try to develop algorithm of the diagnosis and early treatment of acute purulent pyelonephritis (APP). On the basis of 107 case histories (84 surgical, 12 conservative APP cases, 11 patients without the inflammation) characteristic symptoms were established. These may serve as parameters for the classification analysis and plotting curves indicative of the kidney involvement in the pyodestructive process. The analysis of the curves elicited that the pattern of the parameter changes reflects stages of purulent pyelonephritis in the kidney.
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.
We describe a method for discovering active motifs in a set of related protein sequences. The method is an automatic two step process: (1) find candidate motifs in a small sample of the sequences; (2) test whether these motifs are approximately present in all the sequences. To reduce the running time, we develop two optimization heuristics based on statistical estimation and pattern matching techniques. Experimental results obtained by running these algorithms on generated data and functionally related proteins demonstrate the good performance of the presented method compared with visual method of O'Farrell and Leopold. By combining the discovered motifs with an existing fingerprint technique, we develop a protein classifier. When we apply the classifier to the 698 groups of related proteins in the PROSITE catalog, it gives information that is complementary to the BLOCKS protein classifier of Henikoff and Henikoff. Thus, using our classifier in conjunction with theirs, one can obtain high confidence classifications (if BLOCKS and our classifier agree) or suggest a new hypothesis (if the two disagree).
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.
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.
A new measure of dissimilarity between two EEG segments is proposed. It is derived from the application of the mathematical concept of distance between series of one-step predictions according to the estimated non-linear autoregressive functions. The non-linear autoregressive estimation is performed by non-parametric regression using kernel estimators. The possibility of applying this measure for automatic classification of EEG segments is explored. For this purpose multidimensional scaling and cluster analyses are applied on the basis of the calculated dissimilarity measures. In particular, its application to different EEG segments with delta activity and also with alpha waves reveals high agreement with visual classification by EEG specialists.
The shape of myocardial electrogram complexes can change gradually in response to electrical and physiological transients. These changes could affect the reliability of morphologic-based electrogram classifiers proposed for use in implantable cardioverters. In this report, we present a method of detecting gradual changes in the shape of electrogram complexes and evaluate the method by incorporating it into a simple adaptive classification scheme. Of the six subjects recruited to take part in a previous comparative study of myocardial electrogram features, we observed extensive morphologic drift of normal sinus beats in two subjects. Our results indicate that the adaptive classification scheme proposed here can reduce observed classification error rates compared to rates obtained without adaptation.
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.
Molecular sequence megaclassification is a technique for automated protein sequence analysis and annotation. Implementation of the method has been limited by the need to store and randomly access a database of all the sequence pair similarities. More than 80,000 protein sequences are now present in the public databases, and the pair similarity data table for the full protein sequence database requires over 1 gigabyte of storage. In this paper we present a computationally efficient representation of groups based on a graph theory approach where sequence clusters are described by a minimal spanning tree of highest scoring similarity pairs. This representation allows a classification of N proteins to be stored in order(N) memory. The use of this minimal spanning tree representation simplifies analysis of groups, the description of group characteristics and the manual correction of artifacts resulting from false hits. The new tree representation also introduces new possibilities for artifact generation in sequence classification. Methods for detecting and removing these artifacts are discussed.
PURPOSE: To assess magnification error in digital subtraction angiography as it pertains to arteriovenous malformation (AVM) size. METHODS: A rectangular grid phantom with equally spaced markers mounted in a stereotactic frame was imaged with digital angiographic equipment. The location and orientation of the grid was altered relative to the central plane of the phantom. Both linear and area measurements were made according to the perceived location of phantom markers using a standard catheter calibration technique and compared with stereotactically derived estimates. Finally, a single case example of an angiographically imaged rolandic AVM was used to compare linear dimensions obtained with both described techniques. RESULTS: The determination of location and size with standard angiographic imaging is subject to error because of the divergent geometry of the incident x-ray beam. The resulting nonconstant geometric magnification causes errors in linear measurements of 10% to 13% at depths of 7 cm from the calibration plane. Errors in area measurements at the same position increase by 20% to 25%. Measurements of maximum diameter or cross-sectional area may have an additional error when nonspherical objects are inclined to the viewing direction (40% at 45 degrees inclination). These errors are reduced to less than 1 mm using the stereotactic technique. Some commercial angiographic systems have internal software to enable a spatial calibration based on known distances in the image or on the diameter of a catheter. The catheter technique was accurate in the calibration direction (perpendicular to the catheter axis) but had a 12% error in the direction parallel to the catheter because of a nonunity aspect ratio in the video system. Measurement of the dimensions of a rolandic AVM using the catheter calibration technique had an error that ranged from -3% to +26% (standard error, 20%) with respect to the stereotactic technique. CONCLUSIONS: Numerous nonstereotactic referential systems for determining linear distances are inherently erroneous by varying degrees compared with the stereotactic technique. Area and volume determinations naturally increase this error further. To the extent that no standardized method for determining linear distances exists, significant variations in estimation of AVM size result. Classification schemes for AVMs have been hampered by this technical error.
The novel method for classification of P450s has been proposed basing on consecutive multiple alignments of consensus sequences. A code can be assigned to each P450 amino acid sequence reflecting its relative distance from the corresponding consensus sequences at three hierarchical levels. Thus, only the principle of sequence similarity is chosen for classification without involving such criteria as enzymatic properties, phylogenetic assignment or chronology.
The paper presents the results of using an experimental version of the automatic classifying system (ACS) based on unstatistical methods of recognizing the images with estimate calculation algorithms. According to 21 blood parameters obtained on an automatic analyser, ACS could divide, at higher than 90% significance, groups of persons living in the polluted areas of the towns of Klintsy and Zlynka who have accumulated mean radiation dose of 15 and 63 mZv, respectively. The results suggest that ACS may be used to divide subjects into risk groups in terms of exposures to unfavorable environmental factors.
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.
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 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.