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PubMedDate not supplied

A comparative investigation on subspace dimension determination.

It is well-known that constrained Hebbian self-organization on multiple linear neural units leads to the same k-dimensional subspace spanned by the first k principal components. Not only the batch PCA algorithm has been widely applied in various fields since 1930s, but also a variety of adaptive algorithms have been proposed in the past two decades. However, most studies assume a known dimension k or determine it heuristically, though there exist a number of model selection criteria in the literature of statistics. Recently, criteria have also been obtained under the framework of Bayesian Ying-Yang (BYY) harmony learning. This paper further investigates the BYY criteria in comparison with existing typical criteria, including Akaike's information criterion (AIC), the consistent Akaike's information criterion (CAIC), the Bayesian inference criterion (BIC), and the cross-validation (CV) criterion. This comparative study is made via experiments not only on simulated data sets of different sample sizes, noise variances, data space dimensions, and subspace dimensions, but also on two real data sets from air pollution problem and sport track records, respectively. Experiments have shown that BIC outperforms AIC, CAIC, and CV while the BYY criteria are either comparable with or better than BIC. Therefore, BYY harmony learning is a more preferred tool for subspace dimension determination by further considering that the appropriate subspace dimension k can be automatically determined during implementing BYY harmony learning for the principal subspace while the selection of subspace dimension k by BIC, AIC, CAIC, and CV has to be made at the second stage based on a set of candidate subspaces with different dimensions which have to be obtained at the first stage of learning.

Air Pollution↗
PubMed2003

Dynamic causal modelling.

In this paper we present an approach to the identification of nonlinear input-state-output systems. By using a bilinear approximation to the dynamics of interactions among states, the parameters of the implicit causal model reduce to three sets. These comprise (1) parameters that mediate the influence of extrinsic inputs on the states, (2) parameters that mediate intrinsic coupling among the states, and (3) [bilinear] parameters that allow the inputs to modulate that coupling. Identification proceeds in a Bayesian framework given known, deterministic inputs and the observed responses of the system. We developed this approach for the analysis of effective connectivity using experimentally designed inputs and fMRI responses. In this context, the coupling parameters correspond to effective connectivity and the bilinear parameters reflect the changes in connectivity induced by inputs. The ensuing framework allows one to characterise fMRI experiments, conceptually, as an experimental manipulation of integration among brain regions (by contextual or trial-free inputs, like time or attentional set) that is revealed using evoked responses (to perturbations or trial-bound inputs, like stimuli). As with previous analyses of effective connectivity, the focus is on experimentally induced changes in coupling (cf., psychophysiologic interactions). However, unlike previous approaches in neuroimaging, the causal model ascribes responses to designed deterministic inputs, as opposed to treating inputs as unknown and stochastic.

Arousal↗
PubMed2005

Bottom-up and top-down dynamics in visual cortex.

A key emergent property of the primary visual cortex (V1) is the orientation selectivity of its neurons. Recent experiments demonstrate remarkable bottom-up and top-down plasticity in orientation networks of the adult cortex. The basis for such dynamics is the mechanism by which orientation tuning is created and maintained, by integration of thalamocortical and intracortical inputs. Intracellular measurements of excitatory and inhibitory synaptic conductances reveal that excitation and inhibition balance each other at all locations in the cortex. This balance is particularly critical at pinwheel centers of the orientation map, where neurons receive intracortical input from a wide diversity of local orientations. The orientation tuning of neurons in adult V1 changes systematically after short-term exposure to one stimulus orientation. Such reversible physiological shifts in tuning parallel the orientation tilt aftereffect observed psychophysically. Neurons at or near pinwheel centers show pronounced changes in orientation preference after adaptation with an oriented stimulus, while neurons in iso-orientation domains show minimal changes. Neurons in V1 of alert, behaving monkeys also exhibit short-term orientation plasticity after very brief adaptation with an oriented stimulus, on the time scale of visual fixation. Adaptation with stimuli that are orthogonal to a neuron's preferred orientation does not alter the preferred orientation but sharpens orientation tuning. Thus, successive fixation on dissimilar image patches, as happens during natural vision, combined with mechanisms of rapid cortical plasticity, actually improves orientation discrimination. Finally, natural vision involves judgements about where to look next, based on an internal model of the visual world. Experiments in behaving monkeys in which information about future stimulus locations can be acquired in one set of trials but not in another demonstrate that V1 neurons signal the acquisition of internal representations. Such Bayesian updating of responses based on statistical learning is fundamental for higher level vision, for deriving inferences about the structure of the visual world, and for the regulation of eye movements.

Adaptation, Psychological↗
PubMedDate not supplied

[Meta-analysis of the Italian studies on short-term effects of air pollution--MISA 1996-2002].

INTRODUCTION: the Italian Meta-analysis of short-term effects of air pollution for the period 1996-2002 (MISA-2) is a planned study on 15 Italian cities, among the larger country towns summing up 9 millions and one hundred thousand inhabitants at 2001 census. HEALTH OUTCOMES DATA: mortality for all natural causes (362254 deaths), for respiratory causes (22317) and cardiovascular causes (146830), and hospital admissions for acute conditions, respiratory (278028 admissions), cardiac (455540) and cerebrovascular (60960), have been considered. Mortality data came from Regional or Local Health Unit Registries, while hospital admissions data have been selected from Regional or Hospital Archives (exclusion percentages range for all admissions between 45% and 82%). For each participating city daily series averaged about 4.3 years, with a minimum of three consecutive years. AIR POLLUTANTS DATA: daily pollutants concentration series (SO2, NO2, CO, PM10, O3) came from air quality monitoring networks of Regional Environmental Protection Agencies, of Environmental Offices of Provinces or Municipalities. Monitors' selection has been done by a working group composed by representatives of monitoring network Agencies. The selection criteria are the representativeness of general population exposure for each specific pollutant, avoiding as possible monitors close to high traffic roads; and the number, quality and location of monitors, selecting around 3-4 monitors with continuous data flow in the period (at least 75% of valid hourly data). The final series has been created averaging over monitors and imputing missing values under proportionality assumptions. Median of Pearson correlation coefficients between pairs of monitors of the each city was 0.62, interquartile range 0.42-0.77. STATISTICAL METHODS: A generalized linear model on daily counts of health events has been fitted for each city. Linear pollutant effect has been specified and bi-pollutant models have been fitted for PM10+NO2 and PMO+O3. Temperature has been modelled parametrically using a change point at 21 degrees C and lagged effects. Humidity, day of the week, national holidays and influenza epidemics (using data from the National Surveillance Programs from 1999) are the other considered confounders. An age-specific natural cubic spline on season has been specified with 5 degree of freedom (on average) per year for mortality and 7 degree of freedom per year for hospital admission data. The base model is age-stratified (0-64, 65-74, 75+ years). Gender, age, season specific models have been fitted, too. Five sensitivity analyses have been done, varying the degree of freedom for the seasonality spline and specifying non parametric functions on temperature. Constrained distributed lag models have been fitted on mortality data to study potential harvesting effects. City-specific results have been meta-analyzed by random effects hierarchical Bayesian model. Four different models have been fitted in the sensitivity analyses, assuming different priors on heterogeneity variance and outlier-resistant prior on city-specific effects. Bayesian meta-regressions have been fitted on base model, bi-pollutant and season-specific city-specific results. Attributable deaths have been estimated by Monte Carlo methods using effect, pollutant, baseline rate distributions. Fourteen different scenarios have been considered for PM10 and ten for NO2 and CO, using meta-analitic and posterior city-specific effect estimates RESULTS: Pollutants effects are reported as percent increase on mortality or hospital admissions for an increase of 10 microg/m3 of SO2, NO2 and PM10, and 1 mg/m3 of CO. We found an increase on mortality for all natural causes associated to increase of air pollutants concentration (for NO2 0.6% 95%CrI 0.3,0.9; CO 1.2% 0.6,1.7; PM10 0.31% -0.2,0.7). Similar findings were found for cardiorespiratory mortality and hospital admissions for respiratory and cardiac diseases. We found no difference by gender. There was a weak evidence of greater effect size in extreme age groups (0-24 months and over 85 years where we found a percent increase in mortality for all natural causes for PM10 of 0.39% CrI95% 0.0,0.8). There was a strong evidence for each pollutant of greater effects in the warm season (1st May-30th September) on mortality and hospital admissions (we found a percent increase in mortality for all natural causes for PM10 in the warm season of 1.95% CrI95% 0.6,3.3). The associations between pollutants concentration and health events were present at different time lags, depending on outcome and exposure. For mortality, the excess risk peaked within few days from the exposure increase (two days for PM10, up to four days for NO2 and CO). Mortality displacement was minor and ended within two weeks. Cumulative effects at fifteen days showed higher risks for respiratory diseases (PM10 1.65% CI95% 0.3,3.0). The results of meta-regressions showed associations between PM10 effects on mortality and hospital admissions, and mortality for all causes (SMR) and PM10/NO2 ratio. The effect modification of temperature was very consistent, and also using bi-pollutant models. Such effect modification was greater during the cold season. We found and overall impact on mortality for all natural causes in the period 1996-2002 between 1.4% and 4.1% of all deaths for gaseous pollutants (NO2 and CO). The estimates were more imprecise for PM10, due to the variability among cities of the effect estimates (0.1%; 3.3%). The limits stated in the European Union directives for 2010 would have been saved about 900 deaths (1.4%) for PM10 or 1400 deaths for NO2 (1.7%) among all the MISA cities, applying posterior city-specific effect estimates.

Adolescent↗
PubMed2000

Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks.

By using an unsupervised cluster analyzer, we have identified a local structural alphabet composed of 16 folding patterns of five consecutive C(alpha) ("protein blocks"). The dependence that exists between successive blocks is explicitly taken into account. A Bayesian approach based on the relation protein block-amino acid propensity is used for prediction and leads to a success rate close to 35%. Sharing sequence windows associated with certain blocks into "sequence families" improves the prediction accuracy by 6%. This prediction accuracy exceeds 75% when keeping the first four predicted protein blocks at each site of the protein. In addition, two different strategies are proposed: the first one defines the number of protein blocks in each site needed for respecting a user-fixed prediction accuracy, and alternatively, the second one defines the different protein sites to be predicted with a user-fixed number of blocks and a chosen accuracy. This last strategy applied to the ubiquitin conjugating enzyme (alpha/beta protein) shows that 91% of the sites may be predicted with a prediction accuracy larger than 77% considering only three blocks per site. The prediction strategies proposed improve our knowledge about sequence-structure dependence and should be very useful in ab initio protein modelling.

Artificial Intelligence↗
PubMed2002

Sensor fusion by pseudo information measure: a mobile robot application.

In any autonomous mobile robot, one of the most important issues to be designed and implemented is environment perception. In this paper, a new approach is formulated in order to perform sensory data integration for generation of an occupancy grid map of the environment. This method is an extended version of the Bayesian fusion method for independent sources of information. The performance of the proposed method of fusion and its sensitivity are discussed. Map building simulation for a cylindrical robot with eight ultrasonic sensors and mapping implementation for a Khepera robot have been separately tried in simulation and experimental works. A new neural structure is introduced for conversion of proximity data that are given by Khepera IR sensors to occupancy probabilities. Path planning experiments have also been applied to the resulting maps. For each map, two factors are considered and calculated: the fitness and the augmented occupancy of the map with respect to the ideal map. The length and the least distance to obstacles were the other two factors that were calculated for the routes that are resulted by path planning experiments. Experimental and simulation results show that by using the new fusion formulas, more informative maps of the environment are obtained. By these maps more appropriate routes could be achieved. Actually, there is a tradeoff between the length of the resulting routes and their safety and by choosing the proper fusion function, this tradeoff is suitably tuned for different map building applications.

Algorithms↗
PubMed2026

Plasmacytoid dendritic cell-mediated L-glutamate catabolism links gut microbiota to male infertility.

Emerging evidence suggests that gut microbiota composition influences male reproductive health; however, the immunometabolic mechanisms underlying this association remain insufficiently characterized. We investigated whether specific immune cell-mediated metabolic pathways, particularly plasmacytoid dendritic cell (pDC)-driven L-glutamate catabolism via the hydroxyglutarate pathway, contribute to the causal link between gut microbiota and male infertility. We conducted a 2-sample, 2-step Mendelian randomization (MR) analysis using inverse-variance weighting as the primary estimator and Bayesian weighted MR for robustness. Exposure data comprised 412 gut microbial taxa/metabolic pathways and 731 immune cell phenotypes from large European-ancestry genome-wide association studies. Male infertility genome-wide association studies data (1429 cases; 128,710 controls) were obtained from FinnGen R10. Only exposure-mediator-outcome pairs meeting stringent pleiotropy, heterogeneity, and reverse-causality criteria were retained for mediation analysis. Nine microbial taxa/metabolic pathways and 18 immune traits exhibited putative causal associations with male infertility. The L-glutamate degradation V pathway via hydroxyglutarate was linked to reduced infertility risk (inverse-variance weighting odds ratio [OR] = 0.68; 95% confidence interval, 0.52-0.89; P = .005). Two-step MR suggested that forward scatter area on pDCs may mediate this association, although the mediation effect was imprecise (effect = 0.0277; 95% confidence interval, -0.0348 to 0.0903). This study provides suggestive genetic evidence that pDC-mediated glutamate catabolism may connect gut microbial metabolic activity to male infertility. These findings highlight immunometabolic pathways as testable targets for mechanistic validation and microbiota-directed interventions.

Male↗
PubMed2006

Candidate genes, pathways and mechanisms for alcoholism: an expanded convergent functional genomics approach.

We describe a comprehensive translational approach for identifying candidate genes for alcoholism. The approach relies on the cross-matching of animal model brain gene expression data with human genetic linkage data, as well as human tissue data and biological roles data, an approach termed convergent functional genomics. An analysis of three animal model paradigms, based on inbred alcohol-preferring (iP) and alcohol-non-preferring (iNP) rats, and their response to treatments with alcohol, was used. A comprehensive analysis of microarray gene expression data from five key brain regions (frontal cortex, amygdala, caudate-putamen, nucleus accumbens and hippocampus) was carried out. The Bayesian-like integration of multiple independent lines of evidence, each by itself lacking sufficient discriminatory power, led to the identification of high probability candidate genes, pathways and mechanisms for alcoholism. These data reveal that alcohol has pleiotropic effects on multiple systems, which may explain the diverse neuropsychiatric and medical pathology in alcoholism. Some of the pathways identified suggest avenues for pharmacotherapy of alcoholism with existing agents, such as angiotensin-converting enzyme (ACE) inhibitors. Experiments we carried out in alcohol-preferring rats with an ACE inhibitor show a marked modulation of alcohol intake. Other pathways are new potential targets for drug development. The emergent overall picture is that physical and physiological robustness may permit alcohol-preferring individuals to withstand the aversive effects of alcohol. In conjunction with a higher reactivity to its rewarding effects, they may able to ingest enough of this nonspecific drug for a strong hedonic and addictive effect to occur.

Alcohol Drinking↗
PubMedDate not supplied

BYY harmony learning, structural RPCL, and topological self-organizing on mixture models.

The Bayesian Ying-Yang (BYY) harmony learning acts as a general statistical learning framework, featured by not only new regularization techniques for parameter learning but also a new mechanism that implements model selection either automatically during parameter learning or via a new class of model selection criteria used after parameter learning. In this paper, further advances on BYY harmony learning by considering modular inner representations are presented in three parts. One consists of results on unsupervisedmixture models, ranging from Gaussian mixture based Mean Square Error (MSE) clustering, elliptic clustering, subspace clustering to NonGaussian mixture based clustering not only with each cluster represented via either Bernoulli-Gaussian mixtures or independent real factor models, but also with independent component analysis implicitly made on each cluster. The second consists of results on supervised mixture-of-experts (ME) models, including Gaussian ME, Radial Basis Function nets, and Kernel regressions. The third consists of two strategies for extending the above structural mixtures into self-organized topological maps. All these advances are introduced with details on three issues, namely, (a) adaptive learning algorithms, especially elliptic, subspace, and structural rival penalized competitive learning algorithms, with model selection made automatically during learning; (b) model selection criteria for being used after parameter learning, and (c) how these learning algorithms and criteria are obtained from typical special cases of BYY harmony learning.

Bayes Theorem↗
PubMed2024

Rapid spread of the SARS-CoV-2 Omicron XDR lineage derived from recombination between XBB and BA.2.86 subvariants circulating in Brazil in late 2023.

Recombination plays a crucial role in the evolution of SARS-CoV-2. The Omicron XBB* recombinant lineages are a noteworthy example, as they have been the dominant SARS-CoV-2 variant worldwide in the first half of 2023. Since November 2023, a new recombinant lineage between Omicron subvariants XBB and BA.2.86, designated XDR, has been detected mainly in Brazil. In this study, we reconstructed the spatiotemporal dynamics and estimated the absolute and relative transmissibility of the XDR lineage. The XDR lineage displayed a recombination breakpoint in the ORF1a-coding region, and the most closely related sequences to the 5' and 3' ends of the recombinant correspond to JD.1.1 and JN.1.1 lineages, respectively. The first XDR sequences were detected in November 2023 in the Northeastern Brazilian region, and their prevalence rapidly surged from <1% to 25% by February 2024. The Bayesian phylogeographic analysis supports that the XDR lineage likely emerged in the Northeastern Brazilian region around late October 2023 and rapidly disseminated within and outside Brazilian borders from mid-November onward. The median effective reproductive number of the XDR lineage in Brazil during the initial expansion phase was estimated to be around 1.5, and the average relative instantaneous reproduction numbers of XDR and JN* lineages were estimated to be 1.37 and 1.29 higher than that of co-circulating XBB* lineages. In summary, these findings support that the recombinant lineage XDR arose in the Northeastern Brazilian region in October 2023, shortly after the first detection of JN.1 sequences in the country. In Brazil, the XDR lineage exhibited a higher transmissibility level than its parental XBB.* lineages and is spreading at a rate similar to or slightly faster than the JN.1* lineages.IMPORTANCEThis study highlights the emergence and rapid dissemination of the recombinant SARS-CoV-2 XDR lineage, derived from the Omicron lineages JD.1.1 and JN.1.1. The XDR lineage exhibited equivalent transmissibility to its JN.1* parental lineages and quickly spread across Brazil in late 2023. The findings underscore the critical role of real-time genomic surveillance in detecting novel variants with higher transmission potential. By utilizing phylogenetic and epidemiological methods, this research provides important insights into the molecular dynamics of XDR, which could inform public health responses and vaccine composition updates. The study's significance lies in its ability to document the impact of recombination on viral evolution, offering valuable information to the field of virology and pandemic preparedness.

Brazil↗