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PubMed2006

Evolution of biogeographic patterns, ploidy levels, and breeding systems in a diploid-polyploid species complex of Primula.

Primula sect. Aleuritia subsect. Aleuritia (Aleuritia) includes diploid, self-incompatible heterostyles and polyploid, self-compatible homostyles, the latter generally occurring at higher latitudes than the former. This study develops a phylogenetic hypothesis for Aleuritia to elucidate the interactions between Pleistocene glacial cycles, biogeographic patterns, ploidy levels and breeding systems. Sequences from five chloroplast DNA loci were analyzed with parsimony to reconstruct a phylogeny, haplotype network, and ancestral states for ploidy levels and breeding systems.The results supported the monophyly of Aleuritia and four major biogeographic lineages: an amphi-Pacific, a South American, an amphi-Atlantic and a European/North American lineage. At least four independent switches to homostyly and five to polyploidy were inferred. An Asian ancestor probably gave origin to an amphi-Pacific clade and to a lineage that diversified on the European and American continents. Switches to homostyly occurred exclusively in polyploid lineages, which mainly occupy previously glaciated areas. The higher success of the autogamous polyploid species at recolonizing habitats freed by glacial retreat might be explained in terms of selection for reproductive assurance.

Biological Evolution↗

PubMed1999

How to design a connectionist holistic parser.

Connectionist holistic parsing offers a viable and attractive alternative to traditional algorithmic parsers. With exposure to a limited subset of grammatical sentences and their corresponding parse trees only, a holistic parser is capable of learning inductively the grammatical regularity underlying the training examples that affects the parsing process. In the past, various connectionist parsers have been proposed. Each approach had its own unique characteristics, and yet some techniques were shared in common. In this article, various dimensions underlying the design of a holistic parser are explored, including the methods to encode sentences and parse trees, whether a sentence and its corresponding parse tree share the same representation, the use of confluent inference, and the inclusion of phrases in the training set. Different combinations of these design factors give rise to different holistic parsers. In succeeding discussions, we scrutinize these design techniques and compare the performances of a few parsers on language parsing, including the confluent preorder parser, the backpropagation parsing network, the XERIC parser of Berg (1992), the modular connectionist parser of Sharkey and Sharkey (1992), Reilly's (1992) model, and their derivatives. Experiments are performed to evaluate their generalization capability and robustness. The results reveal a number of issues essential for building an effective holistic parser.

Artificial Intelligence↗

PubMed2005

RSPOP: rough set-based pseudo outer-product fuzzy rule identification algorithm.

System modeling with neuro-fuzzy systems involves two contradictory requirements: interpretability verses accuracy. The pseudo outer-product (POP) rule identification algorithm used in the family of pseudo outer-product-based fuzzy neural networks (POPFNN) suffered from an exponential increase in the number of identified fuzzy rules and computational complexity arising from high-dimensional data. This decreases the interpretability of the POPFNN in linguistic fuzzy modeling. This article proposes a novel rough set-based pseudo outer-product (RSPOP) algorithm that integrates the sound concept of knowledge reduction from rough set theory with the POP algorithm. The proposed algorithm not only performs feature selection through the reduction of attributes but also extends the reduction to rules without redundant attributes. As many possible reducts exist in a given rule set, an objective measure is developed for POPFNN to correctly identify the reducts that improve the inferred consequence. Experimental results are presented using published data sets and real-world application involving highway traffic flow prediction to evaluate the effectiveness of using the proposed algorithm to identify fuzzy rules in the POPFNN using compositional rule of inference and singleton fuzzifier (POPFNN-CRI(S)) architecture. Results showed that the proposed rough set-based pseudo outer-product algorithm reduces computational complexity, improves the interpretability of neuro-fuzzy systems by identifying significantly fewer fuzzy rules, and improves the accuracy of the POPFNN.

Algorithms↗

PubMed2026

Phylogeographic epidemiology of Dabie bandavirus in East Asia: divergent transmission networks and genotype‑linked clinical severity.

BACKGROUND: Severe fever with thrombocytopenia syndrome (SFTS), caused by Dabie bandavirus (SFTSV), exhibits geographically decoupled incidence and fatality patterns across East Asia. We aimed to elucidate the distinct ecological drivers and phylogeographic dynamics underlying this inland-coastal epidemiological divergence. METHODS: Integrating 1820 high-quality global genomes of SFTSV with well-characterized clinical cohorts (936 patients) and nationwide surveillance data (27,457 cases) from China, we constructed a comprehensive analytical framework. Ecological modeling, Bayesian phylogeography, and genotype-phenotype association analyses were employed to trace the evolutionary trajectories and clinical implications of the virus. RESULTS: A pronounced "inland-high-incidence vs. coastal-high-fatality" pattern of SFTS was identified. The incidence of SFTS exhibited divergent sensitivities to meteorological factors; inland transmission was sensitive to thermal fluctuations, whereas coastal dynamics were constrained by a sunshine threshold (> 200 h/month). In contrast, spatial divergence in clinical severity correlated with the distribution of regional viral genetic structures. Inland regions mainly co-circulated genotypes A, C, and D, while coastal regions were dominated by genotype B. Zhejiang province was identified as a genetic hub with significantly higher recombination frequencies than inland regions (11.0% vs. 3.5%, P < 0.001). Bayesian phylogeographic inference indicated frequent lineage exchange of Zhejiang province in China with the Republic of Korea and Japan. Clinically, genotypes B and D were associated with elevated mortality in coastal and inland regions, respectively, suggesting that the severe coastal phenotype is shaped by its genotype B-dominated structure. Additionally, the RdRp-N828S mutation emerged as a robust molecular correlate of fatal outcomes, warranting further functional validation. CONCLUSIONS: Divergent meteorological factors and plausible maritime transmission networks may underlie the geographically decoupled epidemiology of SFTS. These findings highlight that risk assessment must extend beyond incidence alone and provide a phylogeographically informed framework for targeted surveillance and genotype-specific interventions in high-risk hotspots.

Humans↗

PubMed2026

Identifying JAK2 and ANXA5 as Key Genes Linking Obstructive Sleep Apnea and Oxidative Stress via Machine Learning and Multilayer Transcriptomic Integration With Functional Validation.

Obstructive sleep apnea (OSA) is a common and severe sleep disorder closely associated with oxidative stress (OS). This study aims to identify and validate potential OS-related genes associated with OSA through bioinformatics methods. We successfully identified OS-related differentially expressed genes (OS-DEGs) by combining the limma test, weighted correlation network analysis (WGCNA), and OS-related genes from the GeneCards database. Key genes and potential biological roles were further identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), enrichment analysis, protein-protein interaction (PPI) network analysis, Lasso regression analysis, random forest algorithm, and support vector machine recursive feature elimination (SVM-RFE) method. Evaluate and validate the accuracy of key genes through receiver operating characteristic (ROC) curve analysis. The human single-cell RNA sequencing (scRNA-seq) dataset is used for cell classification annotation, analysis of key gene single-cell expression profiles, and virtual gene knockout experiments based on the scTenifoldKnk algorithm. Integrating scRNA-seq sequencing, pseudotime trajectory inference, cell-cell communication analysis, and bulk immune infiltration deconvolution reveals monocyte subtype remodeling in OSA. Finally, the expression levels of key genes in clinical samples were validated using real-time quantitative PCR (RT-qPCR) and Western blotting. A total of 57 common DEGs, indicating significant enrichment in OS, inflammation, and tumor pathways, particularly prominent in the immunometabolism pathway. By integrating DEGs, WGCNA, PPI results, and machine learning methods, key genes Janus kinase 2 (JAK2) and ANXA5 were screened out. JAK2 was significantly upregulated under disease conditions, while ANXA5 was significantly downregulated. ROC curve exhibited high accuracy (area under the curve [AUC] > 0.85). Human scRNA-seq analysis revealed that key genes were predominantly highly expressed in monocytes. Virtual knockout experiments demonstrated that these key genes play a crucial role in regulating immune responses and inflammatory reactions. PPI networks and enrichment analysis verified that downstream genes S100P, ALOX5AP, PROK2, and PADI4 may collaboratively participate in immune response and inflammation regulation. Finally, clinical sample experiment further validated the results of bioinformatics analysis. This study provides new research insights for the diagnosis, mechanism research, and treatment development of OSA in the future by integrating multilayer transcriptomic and machine learning techniques.

Humans↗

PubMed2001

Multilocus nested haplotype networks extended with DNA fingerprints show common origin and fine-scale, ongoing genetic divergence in a wild microbial metapopulation.

Nested haplotype networks for three loci in a haploid, fungal plant pathogen, Sclerotinia sclerotiorum, in two natural, Norwegian populations of the woodland buttercup, Ranunculus ficaria, were extended with DNA fingerprints to determine fine-scale population divergence. To preserve the cladistic structure in the network for both nonrecombinant and postrecombinant haplotypes in highly recombinant clades, recombinant events were not removed ('peeled off'), but instead were examined in alternative (marginal) networks. Fungi from both sampling locations share a common origin with subsequent genetic divergence, consistent with expectations for metapopulation structure. Evidence for divergence includes (i) lack of shared fingerprints between the two locations, (ii) evolution of new fingerprints, via transposition and recombination, within 2 years on a fine spatial scale within one sampling location, and (iii) increase in the size of the intergenic spacer (IGS) in both sampling locations. Sites of microsatellite repeat expansion and of an insertion were consistent with the boundaries of two recombination blocks in the IGS. Both alternative networks based on the recombination blocks were essential to finding all associations of DNA fingerprints with IGS size, sampling site, sampling year and mycelial compatibility group. Variation in the elongation factor 1alpha and calmodulin loci supported the topologies and the recurrent, ongoing polarity of change in fingerprints and IGS size inferred from the IGS.

Ascomycota↗

PubMed2003

From immunogenetics to immunomics: functional prospecting of genes and transcripts.

Human and mouse genome and transcriptome projects have expanded the field of 'immunogenetics' beyond the traditional study of the genetics and evolution of MHC, TCR and Ig loci into the new interdisciplinary area of 'immunomics'. Immunomics is the study of the molecular functions associated with all immune-related coding and non-coding mRNA transcripts. To unravel the function, regulation and diversity of the immunome requires that we identify and correctly categorize all immune-related transcripts. The importance of intercalated genes, antisense transcripts and non-coding RNAs and their potential role in regulation of immune development and function are only just starting to be appreciated. To better understand immune function and regulation, transcriptome projects (e.g. Functional Annotation of the Mouse, FANTOM), that focus on sequencing full-length transcripts from multiple tissue sources, ideally should include specific immune cells (e.g. T cell, B cells, macrophages, dendritic cells) at various states of development, in activated and unactivated states and in different disease contexts. Progress in deciphering immune regulatory networks will require the cooperative efforts of immunologists, immunogeneticists, molecular biologists and bioinformaticians. Although primary sequence analysis remains useful for annotation of new transcripts it is less useful for identifying novel functions of known transcripts in a new context (protein interaction network or pathway). The most efficient approach to mine useful information from the vast a priori knowledge contained in biological databases and the scientific literature, is to use a combination of computational and expert-driven knowledge discovery strategies. This paper will illustrate the challenges posed in attempts to functionally infer transcriptional regulation and interaction of immune-related genes from text and sequence-based data sources.

Alternative Splicing↗

PubMed2026

High-accuracy SNV calling for bacterial isolates using deep learning with AccuSNV.

Accurate detection of mutations within bacterial species is critical for fundamental studies of microbial evolution, reconstruction of transmission events, and identification of antimicrobial resistance mutations. Although many tools have been developed to identify single-nucleotide variants (SNVs) from whole-genome sequencing, they often suffer from high false-positive rates owing to the complexity of bacterial genomes and the need for different filtering cutoffs across sample types and sequencing depths. As data sets increase in size, the manual filtering required for high accuracy presents a significant obstacle. Here, we present AccuSNV, a novel deep learning-based tool for high-precision and automated bacterial SNV calling. Unlike traditional methods that process one sample at a time, AccuSNV leverages a convolutional neural network (CNN) that integrates alignment information across multiple samples, enhancing precision through learned across-sample patterns. We evaluate AccuSNV against seven popular SNV-calling tools using simulated data from six bacterial species with varied sequencing depths, numbers of isolates, mutations, and divergence levels. To further validate its real-world utility, we test AccuSNV on multiple curated bacterial data sets containing reported SNVs. In both simulated and real-world scenarios, AccuSNV consistently achieves the best performance. Moreover, AccuSNV provides comprehensive user-friendly downstream analysis modules and outputs, including mutation annotation information, phylogenetic inference, d N/d S calculations, and optional manual filtering. Together with the automated deep learning-based calling, these features make AccuSNV broadly accessible to users with different levels of computational expertise.

Deep Learning↗

PubMed2000

Behavior analysis and revaluation.

Revaluation refers to phenomena in which the strength of an operant is altered by reinforcer-related manipulations that take place outside the conditioning situation in which the operant was selected. As an example, if lever pressing is acquired using food as a reinforcer and food is later paired with an aversive stimulus, the frequency of lever pressing decreases when subsequently tested. Associationist psychology infers from such findings that conditioning produces a response-outcome (i.e., reinforcer) association and that the operant decreased in strength because pairing the reinforcer with the aversive stimulus changed the value of the outcome. Here, we present an approach to the interpretation of these and related findings that employs neural network simulations grounded in the experimental analysis of behavior and neuroscience. In so doing, we address some general issues regarding the relations among behavior analysis, neuroscience, and associationism.

Animals↗

PubMed2004

From a phylogenetic tree to a reticulated network.

In many phylogenetic problems, assuming that species have evolved from a common ancestor by a simple branching process is unrealistic. Reticulate phylogenetic models, however, have been largely neglected because the concept of reticulate evolution have not been supported by using appropriate analytical tools and software. The reticulate model can adequately describe such complicated mechanisms as hybridization between species or lateral gene transfer in bacteria. In this paper, we describe a new algorithm for inferring reticulate phylogenies from evolutionary distances among species. The algorithm is capable of detecting contradictory signals encompassed in a phylogenetic tree and identifying possible reticulate events that may have occurred during evolution. The algorithm produces a reticulate phylogeny by gradually improving upon the initial solution provided by a phylogenetic tree model. The new algorithm is compared to the popular SplitsGraph method in a reanalysis of the evolution of photosynthetic organisms. A computer program to construct and visualize reticulate phylogenies, called T-Rex (Tree and Reticulogram Reconstruction), is available to researchers at the following URL: www.fas.umontreal.ca/biol/casgrain/en/labo/t-rex.

Algorithms↗

PubMed2003

Heterogeneity of rhythmic suprachiasmatic nucleus neurons: Implications for circadian waveform and photoperiodic encoding.

Circadian rhythms in neuronal ensemble, subpopulations, and single unit activity were recorded in the suprachiasmatic nuclei (SCN) of rat hypothalamic slices. Decomposition of the ensemble pattern revealed that neuronal subpopulations and single units within the SCN show surprisingly short periods of enhanced electrical activity of approximately 5 h and show maximal activity at different phases of the circadian cycle. The summed activity accounts for the neuronal ensemble pattern of the SCN, indicating that circadian waveform of electrical activity is a composed tissue property. The recorded single unit activity pattern was used to simulate the responsiveness of SCN neurons to different photoperiods. We inferred predictions on changes in peak width, amplitude, and peak time in the multiunit activity pattern and confirmed these predictions with hypothalamic slices from animals that had been kept in a short or long photoperiod. We propose that the animals' ability to code for day length derives from plasticity in the neuronal network of oscillating SCN neurons.

Animals↗

PubMed2005

Temporal changes in U.S. benzene emissions inferred from atmospheric measurements.

The 1990 Clean Air Act Amendments required the United States Environmental Protection Agency (U.S. EPA) to enact stricter regulations aimed at reducing benzene emissions. In an effort to determine whether these new regulations have been successful in reducing atmospheric benzene concentrations, we have evaluated benzene-to-acetylene ratios from data sets spanning nearlythree decades, collected during several field studies and from the U.S. EPA's Photochemical Assessment Monitoring Station (PAMS) network. The field-study data indicate a decrease in benzene relative to acetylene of approximately 40% from 1994 to 2002. This corresponds to a decrease in benzene alone of approximately 56% over the same period. In contrast, the PAMS data exhibit high interannual variability with no discernible trend. This discrepancy is attributed to measurement problems in the PAMS data sets.

Air Pollutants↗

PubMed1988

Conformational properties of the guanine-binding site of ribonuclease T1 inferred from the X-ray structure and protein engineering.

Recognition by ribonuclease T1 of guanine bases via multidentate hydrogen bonding and stacking interactions appears to be mediated mainly by a short peptide segment formed by one stretch of a heptapeptide, Tyr42-Asn43-Asn44-Tyr45-Glu46-Gly47- Phe48. The segment displays a unique folding of the polypeptide chain--consisting of a reverse turn, Asn44-Tyr45-Glu46-Gly47, stabilized by a hydrogen-bond network involving the side chain of Asn44, the main-chain atoms of Asn44, Gly47 and Phe48 and one water molecule. The segment is connected to the C terminus of a beta-strand and expands into a loop region between Asn43 and Ser54. Low values for the crystallographic thermal parameters of the segment indicate that the structure has a rigidity comparable to that of a beta-pleated sheet. Replacement of Asn44 with alanine leads to a far lower enzymatic activity and demonstrates that the side chain of Asn44 plays a key role in polypeptide folding in addition to a role in maintaining the segment structure. Substitution of Asn43 by alanine to remove a weak hydrogen bond to the guanine base destabilized the transition state of the complex by 6.3 kJ/mol at 37 degrees C. In contrast, mutation of Glu46 to alanine to remove a strong hydrogen bond to the guanine base caused a destabilization of the complex by 14.0 kJ/mol. A double-mutant enzyme with substitutions of Asn43 by a histidine and Asn44 by an aspartic acid, to reproduce the natural substitutions found in ribonuclease Ms, showed an activity and base specificity similar to that of the wild-type ribonuclease Ms. The segment therefore appears to be well conserved in several fungal ribonucleases.

Amino Acid Sequence↗

PubMed2004

Learning mixture models with the regularized latent maximum entropy principle.

This paper presents a new approach to estimating mixture models based on a recent inference principle we have proposed: the latent maximum entropy principle (LME). LME is different from Jaynes' maximum entropy principle, standard maximum likelihood, and maximum aposteriori probability estimation. We demonstrate the LME principle by deriving new algorithms for mixture model estimation, and show how robust new variants of the expectation maximization (EM) algorithm can be developed. We show that a regularized version of LME (RLME), is effective at estimating mixture models. It generally yields better results than plain LME, which in turn is often better than maximum likelihood and maximum a posterior estimation, particularly when inferring latent variable models from small amounts of data.

Algorithms↗

PubMed2003

Clinical application of artificial neural network (ANN) modeling to predict pharmacokinetic parameters of severely ill patients.

Artificial neural network (ANN) modeling was used to evaluate the pharmacokinetics of aminoglycosides (arbekacin sulfate and amikacin sulfate) in severely ill patients. The plasma level was predicted by ANN modeling using parameters related to the severity of the patient's condition and the predictive performance was shown to be better than could be achieved using multiple regression analysis. These results indicate that there is a non-linear relationship between the pharmacokinetics of aminoglycosides and the severity of the patient's condition, and this should be taken into account when determining the dose for severely ill patients. Patients whose plasma levels are likely to fall below the effective level can be identified by ANN modeling with a predictive sensitivity and specificity superior to multivariate logistic regression analysis. The predictable range should be inferred from the data structure before the modeling in order to improve the predictive performance. The volume of distribution (Vd) in the normal range was weakly predicted by ANN modeling from the patients' data. Prediction of clearance by ANN modeling was poorer than that obtained from serum creatinine concentration by linear regression analysis. These results suggest that the input-output relationship (linear or non-linear) should be taken into account in selecting the modeling method. Linear modeling can give better predictive performance for linear systems and non-linear modeling can give better predictive performance for non-linear systems. In general, the performance of ANN modeling was superior to linear modeling for PK/PD prediction. For accurate modeling, a predictable range should be inferred from the data structure before the analysis. Restriction of the predictable region, as determined from the data structure, produced an increase in prediction performance. When applying ANN modeling in clinical settings, the predictive performance and predictable region should be investigated in detail to avoid the risk of harm to severely ill patients.

Critical Illness↗

PubMed2005

Primate sociality in evolutionary context.

Much work has been done to further our understanding of the mechanisms that underlie the diversity of primate social organizations, but none has addressed the limits to that diversity or the question of what causes species to either form or not form social networks. The fact that all living primates typically live in social networks makes it highly likely that the last common ancestor of living primates already lived in social networks, and that sociality formed an integral part of the adaptive nature of primate origins. A characterization of primate sociality within the wider mammalian context is therefore essential to further our understanding of the adaptive nature of primate origins. Here we determine correlates of sociality and nonsociality in rodents as a model to infer causes of sociality in primates. We found sociality to be most strongly associated with large-bodied arboreal species that include a significant portion of fruit in their diet. Fruits and other plant products, such as flowers, seeds, and young leaves, are patchily distributed in time and space and are therefore difficult to find. These food resources are, however, predictable and dependable when their location is known. Hence, membership in a social unit can maximize food exploitation if information on feeding sites is shared. Whether sociality evolved in the primate stem lineage or whether it was already present earlier in the evolution of Euarchontoglires remains uncertain, although tentative evidence points to the former scenario. In either case, frugivory is likely to have played an important role in maintaining the presence of a social lifestyle throughout primate evolution.

Animals↗

PubMedDate not supplied

Data input module for Birth Defects Systems Manager.

The need for a computational bioinformatics infrastructure to manage the vast digital information from functional genomics and proteomics motivated us to develop Birth Defects Systems Manager (BDSM) as an open resource to facilitate analysis and discovery in developmental biology and developmental toxicity. This report describes the design, development and implementation of the data loading module of BDSM, referred to as LoadBDSM. It includes a shared data directory resource that can be granted various levels of security for different research groups or investigators to manage experimental datasets individually or in groups. LoadBDSM allows the upload of data and experiment details using controlled semantics for developmental exposure (toxicant, dosing scenario, intervention), biological sample (species, tissue, stage) and disease outcome (time, risk, phenotype). It adheres to existing controlled vocabulary plus rules of inference (ontologies) for experiment, data and metadata annotations. LoadBDSM extends the capabilities of BDSM to support the emergence of "embryo-formatics" defined here as the data, information and knowledge from genomic sciences applied to, or derived from, an embryological context. This includes, but is not limited to, delineating pathways and biological regulatory networks for specific chemicals or classes of developmental toxicants, developing novel biomarkers indicative of exposure and/or predictive of adverse effects, and integrating modern computing and information technology with data from molecular biology.

Abnormalities, Drug-Induced↗

PubMed2001

The neural processing of complex sounds.

This paper considers the temporal processing of complex sounds relevant to musical analysis. Functional imaging studies, using positron emission tomography (PET), functional magnetic resonance imaging (fMRI), and magnetoencephalography (MEG), and the psychophysical assessment of patients with lesions allow two different approaches to this. Functional imaging allows the determination of structures normally involved in temporal analysis, while patient studies allow inference about the necessary structures for temporal analysis. Both approaches suggest a hierarchal organization in the brain corresponding to the processing of music. The features of individual notes are analyzed in the pathway up to and including the auditory cortices, while higher-order patterns formed by those features are analyzed by distributed networks in the temporal lobe and frontal lobes distinct from the auditory cortices.

Auditory Perception↗