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Theoretical investigation of the dissociation dynamics of vibrationally excited vinyl bromide on an ab initio potential-energy surface obtained using modified novelty sampling and feed-forward neural networks.

The reaction dynamics of vibrationally excited vinyl bromide have been investigated using classical trajectory methods on a neural network potential surface that is fitted to an ab initio database of 12 122 configuration energies obtained from electronic structure calculations conducted at the MP4(SDQ) level of theory using a 6-31G(d,p) basis set for the carbon and hydrogen atoms and Huzinaga's (43334334) basis set augmented with split outer s and p orbitals (4332143214) and a polarization f orbital with an exponent of 0.5 for the bromine atom. The sampling of the 12-dimensional configuration hyperspace of vinyl bromide prior to execution of the electronic structure calculations is accomplished by combining novelty-sampling methods, chemical intuition, and trajectory sampling on empirical and neural network surfaces. The final potential is obtained using a two-layer feed-forward neural network comprising 38 and 1 neurons, respectively, with hyperbolic tangent sigmoid and linear transfer functions in the hidden and output layers, respectively. The fitting is accomplished using the Levenberg-Marquardt algorithm with early stopping and Bayesian regularization methods to avoid overfitting. The interpolated potentials have a standard deviation from the ab initio results of 0.0578 eV, which is within the range generally regarded as "chemical accuracy" for the purposes of electronic structure calculations. It is shown that the potential surface may be easily and conveniently transferred from one research group to another. The files required for transfer of the vinyl bromide surface can be obtained from the Electronic Physics Auxiliary Publication Service. Total dissociation rate coefficients for vinyl bromide are obtained at five different excitation energies between 4.50 and 6.44 eV. Branching ratios into each of the six open reaction channels are computed at 24 vibrational energies in the range between 4.00 and 6.44 eV. The distribution of vibrational energies in HBr formed via three-center dissociation from vinyl bromide is determined and compared with previous theoretical and experimental results. It is concluded that the combination of ab initio electronic structure calculations, novelty sampling with chemical intuition and trajectories on empirical analytic surfaces, and feed-forward neural networks provides a viable framework in which to execute purely ab initio molecular-dynamics studies on complex systems with multiple open reaction channels.

Journal Article↗

Statistical mechanics of learning with soft margin classifiers.

We study the typical learning properties of the recently introduced soft margin classifiers (SMCs), learning realizable and unrealizable tasks, with the tools of statistical mechanics. We derive analytically the behavior of the learning curves in the regime of very large training sets. We obtain exponential and power laws for the decay of the generalization error towards the asymptotic value, depending on the task and on general characteristics of the distribution of stabilities of the patterns to be learned. The optimal learning curves of the SMCs, which give the minimal generalization error, are obtained by tuning the coefficient controlling the trade-off between the error and the regularization terms in the cost function. If the task is realizable by the SMC, the optimal performance is better than that of a hard margin support vector machine and is very close to that of a Bayesian classifier.

Algorithms↗

Molecular and morphological incongruence in European species of Isothecium (Bryophyta).

During the identification of Moroccan samples a plant belonging to Isothecium with characteristics of I. alopecuroides (Dubois) Isov. and, to a smaller degree, I. algarvicum W.E. Nicholson and Dixon was found. Problems with attributing the plant to any of the European Isothecium species and the known large morphological variation in I. alopecuroides suggested that molecular studies were needed to evaluate patterns of relationships in this complex. We investigated one nuclear and one chloroplast marker from 66 samples (gametophytes) of Isothecium alopecuroides and from 18 samples of other Isothecium species. Parsimony and likelihood (via Bayesian analysis) were used as optimality criteria to compute phylogenetic trees. Bootstrapping and posterior probabilities were used, not only to quantify support, but also to evaluate competing phylogenetic alternatives in consensus networks. Finally, split decomposition and neighbour net analysis were used to compute distance based split networks, in order to avoid systematic error. The observed discrepancy among morphological and molecular data suggests that none of the European species Isothecium alopecuroides, I. holtii and I. myosuroides are monophyletic as defined by traditional morphological characters. Convergent morphological evolution cannot explain the discrepancy in this particular case; instead exchange of genetic material among Isothecium species is considered a potential explanation for the molecular diversity within morphospecies.

Base Sequence↗

A Bayesian committee machine.

The Bayesian committee machine (BCM) is a novel approach to combining estimators that were trained on different data sets. Although the BCM can be applied to the combination of any kind of estimators, the main foci are gaussian process regression and related systems such as regularization networks and smoothing splines for which the degrees of freedom increase with the number of training data. Somewhat surprisingly, we find that the performance of the BCM improves if several test points are queried at the same time and is optimal if the number of test points is at least as large as the degrees of freedom of the estimator. The BCM also provides a new solution for on-line learning with potential applications to data mining. We apply the BCM to systems with fixed basis functions and discuss its relationship to gaussian process regression. Finally, we show how the ideas behind the BCM can be applied in a non-Bayesian setting to extend the input-dependent combination of estimators.

Algorithms↗

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 (>&#x2009;200&#xa0;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↗

Discovering Neural Nets with Low Kolmogorov Complexity and High Generalization Capability.

Many neural net learning algorithms aim at finding "simple" nets to explain training data. The expectation is that the "simpler" the networks, the better the generalization on test data (--> Occam's razor). Previous implementations, however, use measures for "simplicity" that lack the power, universality and elegance of those based on Kolmogorov complexity and Solomonoff's algorithmic probability. Likewise, most previous approaches (especially those of the "Bayesian" kind) suffer from the problem of choosing appropriate priors. This paper addresses both issues. It first reviews some basic concepts of algorithmic complexity theory relevant to machine learing, and how the Solomonoff-Levin distribution (or universal prior) deals with the prior problem. The universal prior leads to a probabilistic method for finding "algorithmically simple" problem solutions with high generalization capability. The method is based on Levin complexity (a time-bounded generalization of Kolmogorov complexity) and inspired by Levin's optimal universal search algorithm. For a given problem, solution candidates are computed by efficient "self-sizing" programs that influence their own runtime and storage size. The probabilistic search algorithm finds the "good" programs (the ones quickly computing algorithmically probable solutions fitting the training data). Simulations focus on the task of discovering "algorithmically simple" neural networks with low Kolmogorov complexity and high generalization capability. It is demonstrated that the method, at least with certain toy problems where it is computationally feasible, can lead to generalization results unmatchable by previous neural network algorithms. Much remains to be done, however, to make large scale applications and "incremental learning" feasible. Copyright 1997 Elsevier Science Ltd.

Journal Article↗

Interval estimation of urban ozone level and selection of influential factors by employing automatic relevance determination model.

In this work, we focus on simulating the ground-level ozone (O3) time series and its daily maximum concentration in Hong Kong urban air by employing the multilayer perceptron (MLP) model combined with the automatic relevance determination (ARD) method (for simplicity, we name it as MLP-ARD model). Two air quality monitoring sites in Hong Kong, i.e., Tsuen Wan and Tung Chung, are selected for the numerical experiments. The MLP-ARD model based on Bayesian evidence framework can provide reliable interval estimation of real observation as well as offering efficient strategy to avoid over-fitting. The performance comparisons between MLP-ARD model and traditional artificial neural network (ANN) model based on maximum likelihood indicate that MLP-ARD model is more powerful to capture the wild fluctuation of O3 level especially during O3 episodes than the traditional model. Furthermore, it can assess and rank the input variables for the prediction according to their relative importance to the output variable, i.e., the daily maximum O3 concentration in this study. The preliminary experimental results indicate that nitric oxide (NO) and solar radiation are the most important input variables for O3 prediction at both selected sites. In addition, the previous daily maximum O3 level is also important for Tung Chung site. In this regard, MLP-ARD model is a feasible tool to interpret the real physical and chemical process of urban O3 variation.

Air Pollutants↗

An application of the holistochastic human exposure methodology to naturally occurring arsenic in Bangladesh drinking water.

The occurrence of arsenic in drinking water is an issue of considerable interest. In the case of Bangladesh, arsenic concentrations have been closely monitored since the early 1990s through an extensive sampling network. The focus of the present work is methodological. In particular, we propose the application of a holistochastic framework of human exposure to study lifetime population damage due to arsenic exposure across Bangladesh. The Bayesian Maximum Entropy theory is an important component of this framework, which possesses solid theoretical foundations and offers powerful tools to assimilate a variety of knowledge bases (physical, epidemiologic, toxicokinetic, demographic, etc.) and uncertainty sources (soft data, measurement errors, etc.). The holistochastic exposure approach leads to physically meaningful and informative spatial maps of arsenic distribution in Bangladesh drinking water. Global indicators of the adverse health effects on the population are generated, and valuable insight is gained by blending information from different scientific disciplines. The numerical results indicate an increased lifetime bladder cancer probability for the Bangladesh population due to arsenic. The health effect estimates obtained and the associated uncertainty assessments are valuable tools for a broad spectrum of end-users.

Arsenic↗

Integrative multi-omics identifies DOC2A as a novel pharmacological target for bipolar disorder.

BACKGROUND: Current bipolar disorder (BD) therapies suffer from limited efficacy and adverse effects, necessitating mechanistically grounded targets. METHODS: We integrated BD genome-wide association study data (158,036 cases; 2,796,499 controls) with brain proteomics (ROSMAP and Banner dorsolateral prefrontal cortex, n&#xa0;=&#xa0;376 and 152) to perform proteome-wide association studies (PWAS). Bayesian colocalization and summary-data-based Mendelian randomization (SMR) prioritized causal genes. Cell-type-specific transcriptomics validated dysregulation in iPSC-derived neurons, astrocytes, and postmortem hippocampus/prefrontal cortex. Weighted gene co-expression networks (WGCNAs), functional enrichment, and molecular docking assessed functional pathways and druggability. RESULTS: PWAS identified eight BD-associated genes (false discovery rate&#xa0;<&#xa0;0.05), with DOC2A emerging as the top candidate. Colocalization (H4&#xa0;>&#xa0;0.8) and SMR supported a causal association of DOC2A with BD, with no pleiotropy (heterogeneity in dependent instruments P&#xa0;>&#xa0;0.01); DOC2A expression decreased in BD across neurons (P&#xa0;=&#xa0;4.26&#xa0;&#xd7;&#xa0;10-2), astrocytes (P&#xa0;=&#xa0;2.09&#xa0;&#xd7;&#xa0;10-2), hippocampus (P&#xa0;=&#xa0;9.80&#xa0;&#xd7;&#xa0;10-3, t&#xa0;=&#xa0;-2.738), and prefrontal cortex (P&#xa0;=&#xa0;1.44&#xa0;&#xd7;&#xa0;10-2, t&#xa0;=&#xa0;-2.580); WGCNA positioned DOC2A as a key regulator (module membership/gene significance P&#xa0;<&#xa0;0.05) of co-expression networks enriched for BD-associated processes including neurotransmitter secretion and postsynaptic actin cytoskeleton organization (P&#xa0;<&#xa0;0.05); molecular docking revealed favorable-affinity binding (&#x394;G&#xa0;<&#xa0;-4&#xa0;kcal/mol) between DOC2A and BD-related drugs and neuroprotective compounds. CONCLUSIONS: Our convergent multi-omics framework highlights DOC2A dysregulation as a key contributor to synaptic dysfunction in BD and nominates it as a promising therapeutic target. The demonstrated interaction with existing neuroactive compounds provides immediate translational avenues.

Bipolar Disorder↗

Bayesian-based selection of metabolic objective functions.

MOTIVATION: A critical component of in silico analysis of underdetermined metabolic systems is the identification of the appropriate objective function. A common assumption is that the objective of the cell is to maximize growth. This objective function has been shown to be consistent in a few limited experimental cases, but may not be universally appropriate. Here a method is presented to quantitatively determine the most probable objective function. RESULTS: The genome-scale metabolism of Escherichia coli growing on succinate was used as a case-study for analysis. Five different objective functions, including maximization of growth rate, were chosen based on biological plausibility. A combination of flux balance analysis and linear programming was used to simulate cellular metabolism, which was then compared to independent experimental data using a Bayesian objective function discrimination technique. After comparing rates of oxygen uptake and acetate production, minimization of the production rate of redox potential was determined to be the most probable objective function. Given the appropriate reaction network and experimental data, the discrimination technique can be applied to any bacterium to test a variety of different possible objective functions. SUPPLEMENTARY INFORMATION: Additional files, code and a program for carrying out model discrimination are available at http://www.engr.uconn.edu/~srivasta/modisc.html.

Algorithms↗

A probabilistic approach to interpreting verbal autopsies: methodology and preliminary validation in Vietnam.

AIMS: Verbal autopsy (VA) has become an important tool in the past 20 years for determining cause of death in communities where there is no routine registration. In many cases, expert physicians have been used to interpret the VA findings and so assign individual causes of death. However, this is time consuming and not always repeatable. Other approaches such as algorithms and neural networks have been developed in some settings. This paper aims to develop a method that is simple, reliable and consistent, which could represent an advance in VA interpretation. METHODS: This paper describes the development of a Bayesian probability model for VA interpretation as an attempt to find a better approach. This methodology and a preliminary implementation are described, with an evaluation based on VA material from rural Vietnam. RESULTS: The new model was tested against a series of 189 VA interviews from a rural community in Vietnam. Using this very basic model, over 70% of individual causes of death corresponded with those determined by two physicians increasing to over 80% if those cases ascribed to old age or as being indeterminate by the physicians were excluded. DISCUSSION: Although there is a clear need to improve the preliminary model and to test more extensively with larger and more varied datasets, these preliminary results suggest that there may be good potential in this probabilistic approach.

Autopsy↗

Technical description of RODS: a real-time public health surveillance system.

This report describes the design and implementation of the Real-time Outbreak and Disease Surveillance (RODS) system, a computer-based public health surveillance system for early detection of disease outbreaks. Hospitals send RODS data from clinical encounters over virtual private networks and leased lines using the Health Level 7 (HL7) message protocol. The data are sent in real time. RODS automatically classifies the registration chief complaint from the visit into one of seven syndrome categories using Bayesian classifiers. It stores the data in a relational database, aggregates the data for analysis using data warehousing techniques, applies univariate and multivariate statistical detection algorithms to the data, and alerts users of when the algorithms identify anomalous patterns in the syndrome counts. RODS also has a Web-based user interface that supports temporal and spatial analyses. RODS processes sales of over-the-counter health care products in a similar manner but receives such data in batch mode on a daily basis. RODS was used during the 2002 Winter Olympics and currently operates in two states-Pennsylvania and Utah. It has been and continues to be a resource for implementing, evaluating, and applying new methods of public health surveillance.

Algorithms↗

Crossmodal integration for perception and action.

The integration of information from different sensory modalities has many advantages for human observers, including increase of salience, resolution of perceptual ambiguities, and unified perception of objects and surroundings. Several behavioral, electrophysiological and neuroimaging data collected in various tasks, including localization and detection of spatial events, crossmodal perception of object properties and scene analysis are reviewed here. All the results highlight the multiple faces of crossmodal interactions and provide converging evidence that the brain takes advantages of spatial and temporal coincidence between spatial events in the crossmodal binding of spatial features gathered through different modalities. Furthermore, the elaboration of a multimodal percept appears to be based on an adaptive combination of the contribution of each modality, according to the intrinsic reliability of sensory cue, which itself depends on the task at hand and the kind of perceptual cues involved in sensory processing. Computational models based on bayesian sensory estimation provide valuable explanations of the way perceptual system could perform such crossmodal integration. Recent anatomical evidence suggest that crossmodal interactions affect early stages of sensory processing, and could be mediated through a dynamic recurrent network involving backprojections from multimodal areas as well as lateral connections that can modulate the activity of primary sensory cortices, though future behavioral and neurophysiological studies should allow a better understanding of the underlying mechanisms.

Animals↗

Predictive Modular Neural Networks for Time Series Classification.

A predictive modular neural network (PREMONN) architecture for time series classification is presented. The PREMONN has a hierarchical structure. The bottom level consists of a bank of linear or nonlinear predictor modules. The top level is a decision module which employs Bayesian or nonprobabilistic decision rules. For various choices of prediction and decision modules, convergence to correct classification is proven. Also it is shown that PREMONN is robust to noise and the speed/accuracy tradeoff is investigated. The analysis is mainly mathematical; however, we also present classification experiments to corroborate our conclusions. Copyright 1996 Elsevier Science Ltd.

Journal Article↗

Stable behavior in a recurrent neural network for a finite state machine.

For the learning of a finite state machine (FSM) by a recurrent neural network (RNN), we think about how to train an RNN so as to stably mimic an FSM even for sequences having a long length. First, we consider the relationship between the stable behavior and the internal representation of states, that is, clusters of the internal units' outputs. As for this relationship, we prove that an RNN can get the stable cluster transitions when a neuron activation parameter is larger than a certain finite value micro0. Secondly, to acquire the stable behavior, we regard the internal representation for the stable behavior as prior knowledge. This produces a new target function of learning with internal representation term. We derive a Bayesian style method to estimate coefficients of the terms in the function, corresponding to hyperparameters. Finally, experiments show that RNNs readily acquire stable behavior by using our proposed method.

Artificial Intelligence↗

Host switch leads to emergence of Plasmodium vivax malaria in humans.

The geographical origin of Plasmodium vivax, the most widespread human malaria parasite, is controversial. Although genetic closeness to Asian primate malarias has been confirmed by phylogenetic analyses, genetic similarities between P. vivax and Plasmodium simium, a New World primate malaria, suggest that humans may have acquired P. vivax from New World monkeys or vice versa. Additionally, the near fixation of the Duffy-negative blood type (FY x B(null)/FY x B(null)) in West and Central Africa, consistent with directional selection, and the association of Duffy negativity with complete resistance to vivax malaria suggest a prolonged period of host-parasite coevolution in Africa. Here we use Bayesian and likelihood methods in conjunction with cophylogeny mapping to reconstruct the genetic and coevolutionary history of P. vivax from the complete mitochondrial genome of 176 isolates as well as several closely related Plasmodium species. Taken together, a haplotype network, parasite migration patterns, demographic history, and cophylogeny mapping support an Asian origin via a host switch from macaque monkeys.

Animals↗

Bayesian model assessment and comparison using cross-validation predictive densities.

In this work, we discuss practical methods for the assessment, comparison, and selection of complex hierarchical Bayesian models. A natural way to assess the goodness of the model is to estimate its future predictive capability by estimating expected utilities. Instead of just making a point estimate, it is important to obtain the distribution of the expected utility estimate because it describes the uncertainty in the estimate. The distributions of the expected utility estimates can also be used to compare models, for example, by computing the probability of one model having a better expected utility than some other model. We propose an approach using cross-validation predictive densities to obtain expected utility estimates and Bayesian bootstrap to obtain samples from their distributions. We also discuss the probabilistic assumptions made and properties of two practical cross-validation methods, importance sampling and k-fold cross-validation. As illustrative examples, we use multilayer perceptron neural networks and gaussian processes with Markov chain Monte Carlo sampling in one toy problem and two challenging real-world problems.

Arm↗

A Bayesian approach to modeling dynamic effective connectivity with fMRI data.

A state-space modeling approach for examining dynamic relationship between multiple brain regions was proposed in Ho, Ombao and Shumway (Ho, M.R., Ombao, H., Shumway, R., 2005. A State-Space Approach to Modelling Brain Dynamics to Appear in Statistica Sinica). Their approach assumed that the quantity representing the influence of one neuronal system over another, or effective connectivity, is time-invariant. However, more and more empirical evidence suggests that the connectivity between brain areas may be dynamic which calls for temporal modeling of effective connectivity. A Bayesian approach is proposed to solve this problem in this paper. Our approach first decomposes the observed time series into measurement error and the BOLD (blood oxygenation level-dependent) signals. To capture the complexities of the dynamic processes in the brain, region-specific activations are subsequently modeled, as a linear function of the BOLD signals history at other brain regions. The coefficients in these linear functions represent effective connectivity between the regions under consideration. They are further assumed to follow a random walk process so to characterize the dynamic nature of brain connectivity. We also consider the temporal dependence that may be present in the measurement errors. ML-II method (Berger, J.O., 1985. Statistical Decision Theory and Bayesian Analysis (2nd ed.). Springer, New York) was employed to estimate the hyperparameters in the model and Bayes factor was used to compare among competing models. Statistical inference of the effective connectivity coefficients was based on their posterior distributions and the corresponding Bayesian credible regions (Carlin, B.P., Louis, T.A., 2000. Bayes and Empirical Bayes Methods for Data Analysis (2nd ed.). Chapman and Hall, Boca Raton). The proposed method was applied to a functional magnetic resonance imaging data set and results support the theory of attentional control network and demonstrate that this network is dynamic in nature.

Attention↗