Learning gene functional classifications from multiple data types.

In our attempts to understand cellular function at the molecular level, we must be able to synthesize information from disparate types of genomic data. We consider the problem of inferring gene functional classifications from a heterogeneous data set consisting of DNA microarray expression measurements and phylogenetic profiles from whole-genome sequence comparisons. We demonstrate the application of the support vector machine (SVM) learning algorithm to this functional inference task. Our results suggest the importance of exploiting prior information about the heterogeneity of the data. In particular, we propose an SVM kernel function that is explicitly heterogeneous. In addition, we describe feature scaling methods for further exploiting prior knowledge of heterogeneity by giving each data type different weights.     

Exploring the Relation of Harsh Parental Discipline with Child Emotional and Behavioral Problems by Using Multiple Informants. The Generation R Study

Parental harsh disciplining, like corporal punishment, has consistently been associated with adverse mental health outcomes in children. It remains a challenge to accurately assess the consequences of harsh discipline, as researchers and clinicians generally rely on parent report of young children''s problem behaviors. If parents rate their parenting styles and their child''s behavior this may bias results. The use of child self-report on problem behaviors is not common but may provide extra information about the relation of harsh parental discipline and problem behavior. We examined the independent contribution of young children''s self-report above parental report of emotional and behavioral problems in a study of maternal and paternal harsh discipline in a birth cohort. Maternal and paternal harsh discipline predicted both parent reported behavioral and parent reported emotional problems, but only child reported behavioral problems. Associations were not explained by pre-existing behavioral problems at age 3. Importantly, the association with child reported outcomes was independent from parent reported problem behavior. These results suggest that young children''s self-reports of behavioral problems provide unique information on the effects of harsh parental discipline. Inclusion of child self-reports can therefore help estimate the effects of harsh parental discipline more accurately.     

Gene Regulatory Network Inferences Using a Maximum-Relevance and Maximum-Significance Strategy

Recovering gene regulatory networks from expression data is a challenging problem in systems biology that provides valuable information on the regulatory mechanisms of cells. A number of algorithms based on computational models are currently used to recover network topology. However, most of these algorithms have limitations. For example, many models tend to be complicated because of the “large p, small n” problem. In this paper, we propose a novel regulatory network inference method called the maximum-relevance and maximum-significance network (MRMSn) method, which converts the problem of recovering networks into a problem of how to select the regulator genes for each gene. To solve the latter problem, we present an algorithm that is based on information theory and selects the regulator genes for a specific gene by maximizing the relevance and significance. A first-order incremental search algorithm is used to search for regulator genes. Eventually, a strict constraint is adopted to adjust all of the regulatory relationships according to the obtained regulator genes and thus obtain the complete network structure. We performed our method on five different datasets and compared our method to five state-of-the-art methods for network inference based on information theory. The results confirm the effectiveness of our method.     

Identification of metabolic network models from incomplete high-throughput datasets

MOTIVATION: High-throughput measurement techniques for metabolism and gene expression provide a wealth of information for the identification of metabolic network models. Yet, missing observations scattered over the dataset restrict the number of effectively available datapoints and make classical regression techniques inaccurate or inapplicable. Thorough exploitation of the data by identification techniques that explicitly cope with missing observations is therefore of major importance. RESULTS: We develop a maximum-likelihood approach for the estimation of unknown parameters of metabolic network models that relies on the integration of statistical priors to compensate for the missing data. In the context of the linlog metabolic modeling framework, we implement the identification method by an Expectation-Maximization (EM) algorithm and by a simpler direct numerical optimization method. We evaluate performance of our methods by comparison to existing approaches, and show that our EM method provides the best results over a variety of simulated scenarios. We then apply the EM algorithm to a real problem, the identification of a model for the Escherichia coli central carbon metabolism, based on challenging experimental data from the literature. This leads to promising results and allows us to highlight critical identification issues.     

Ontogenetic approach reveals how cognitive capability and motivation shape prey‐searching behavior in Pholcus phalangioides cellar spiders

An important part of understanding the evolution of behavior is understanding how and why behavior develops and changes throughout ontogeny. Patterns of behavior are shaped by an animal's capabilities as well as its motivations, both of which are subject to selection. We ran an experiment to see how spiders' efforts to recover lost prey change with age and to determine the relative contributions of shifts in capability and motivation. We found that as spiders mature, they spend less time searching to recover lost prey, and they discriminate less between prey of different sizes. We also found that even the youngest, least experienced spiders are cognitively equipped to search for lost prey. Thus, predatory behavior in spiders fluctuated primarily with each age group's motivations to capture and consume prey, and did not seem to be hindered by behavioral or cognitive limitations at young ages.     

Imbalanced Decision Hierarchy in Addicts Emerging from Drug-Hijacked Dopamine Spiraling Circuit

Despite explicitly wanting to quit, long-term addicts find themselves powerless to resist drugs, despite knowing that drug-taking may be a harmful course of action. Such inconsistency between the explicit knowledge of negative consequences and the compulsive behavioral patterns represents a cognitive/behavioral conflict that is a central characteristic of addiction. Neurobiologically, differential cue-induced activity in distinct striatal subregions, as well as the dopamine connectivity spiraling from ventral striatal regions to the dorsal regions, play critical roles in compulsive drug seeking. However, the functional mechanism that integrates these neuropharmacological observations with the above-mentioned cognitive/behavioral conflict is unknown. Here we provide a formal computational explanation for the drug-induced cognitive inconsistency that is apparent in the addicts'' “self-described mistake”. We show that addictive drugs gradually produce a motivational bias toward drug-seeking at low-level habitual decision processes, despite the low abstract cognitive valuation of this behavior. This pathology emerges within the hierarchical reinforcement learning framework when chronic exposure to the drug pharmacologically produces pathologicaly persistent phasic dopamine signals. Thereby the drug hijacks the dopaminergic spirals that cascade the reinforcement signals down the ventro-dorsal cortico-striatal hierarchy. Neurobiologically, our theory accounts for rapid development of drug cue-elicited dopamine efflux in the ventral striatum and a delayed response in the dorsal striatum. Our theory also shows how this response pattern depends critically on the dopamine spiraling circuitry. Behaviorally, our framework explains gradual insensitivity of drug-seeking to drug-associated punishments, the blocking phenomenon for drug outcomes, and the persistent preference for drugs over natural rewards by addicts. The model suggests testable predictions and beyond that, sets the stage for a view of addiction as a pathology of hierarchical decision-making processes. This view is complementary to the traditional interpretation of addiction as interaction between habitual and goal-directed decision systems.     

Tropical dung beetle morphological traits predict functional traits and show intraspecific differences across land uses

Functional traits and functional diversity measures are increasingly being used to examine land use effects on biodiversity and community assembly rules. Morphological traits are often used directly as functional traits. However, behavioral characteristics are more difficult to measure. Establishing methods to derive behavioral traits from morphological measurements is necessary to facilitate their inclusion in functional diversity analyses. We collected morphometric data from over 1,700 individuals of 12 species of dung beetle to establish whether morphological measurements can be used as predictors of behavioral traits. We also compared morphology among individuals collected from different land uses (primary forest, logged forest, and oil palm plantation) to identify whether intraspecific differences in morphology vary among land use types. We show that leg and eye measurements can be used to predict dung beetle nesting behavior and period of activity and we used this information to confirm the previously unresolved nesting behavior for Synapsis ritsemae. We found intraspecific differences in morphological traits across different land use types. Phenotypic plasticity was found for traits associated with dispersal (wing aspect ratio and wing loading) and reproductive capacity (abdomen size). The ability to predict behavioral functional traits from morphology is useful where the behavior of individuals cannot be directly observed, especially in tropical environments where the ecology of many species is poorly understood. In addition, we provide evidence that land use change can cause phenotypic plasticity in tropical dung beetle species. Our results reinforce recent calls for intraspecific variation in traits to receive more attention within community ecology.     

When Does Reward Maximization Lead to Matching Law?

What kind of strategies subjects follow in various behavioral circumstances has been a central issue in decision making. In particular, which behavioral strategy, maximizing or matching, is more fundamental to animal''s decision behavior has been a matter of debate. Here, we prove that any algorithm to achieve the stationary condition for maximizing the average reward should lead to matching when it ignores the dependence of the expected outcome on subject''s past choices. We may term this strategy of partial reward maximization “matching strategy”. Then, this strategy is applied to the case where the subject''s decision system updates the information for making a decision. Such information includes subject''s past actions or sensory stimuli, and the internal storage of this information is often called “state variables”. We demonstrate that the matching strategy provides an easy way to maximize reward when combined with the exploration of the state variables that correctly represent the crucial information for reward maximization. Our results reveal for the first time how a strategy to achieve matching behavior is beneficial to reward maximization, achieving a novel insight into the relationship between maximizing and matching.     

Estimating animal behavior and residency from movement data

We present a process‐based approach to estimate residency and behavior from uncertain and temporally correlated movement data collected with electronic tags. The estimation problem is formulated as a hidden Markov model (HMM) on a spatial grid in continuous time, which allows straightforward implementation of barriers to movement. Using the grid to explicitly resolve space, location estimation can be supplemented by or based entirely on environmental data (e.g. temperature, daylight). The HMM method can therefore analyze any type of electronic tag data. The HMM computes the joint posterior probability distribution of location and behavior at each point in time. With this, the behavioral state of the animal can be associated to regions in space, thus revealing migration corridors and residence areas. We demonstrate the inferential potential of the method by analyzing satellite‐linked archival tag data from a southern bluefin tuna Thunnus maccoyii where longitudinal coordinates inferred from daylight are supplemented by latitudinal information in recorded sea surface temperatures.     

Bayesian networks as a novel tool to enhance interpretability and predictive power of ecological models

In today's world, it is becoming increasingly important to have the tools to understand, and ultimately to predict, the response of ecosystems to disturbance. However, understanding such dynamics is not simple. Ecosystems are a complex network of species interactions, and therefore any change to a population of one species will have some degree of community level effect. In recent years, the use of Bayesian networks (BNs) has seen successful applications in molecular biology and ecology, where they were able to recover plausible links in the respective systems they were applied to. The recovered network also comes with a quantifiable metric of interaction strength between variables. While the latter is an invaluable piece of information in ecology, an unexplored application of BNs would be using them as a novel variable selection tool in the training of predictive models. To this end, we evaluate the potential usefulness of BNs in two aspects: (1) we apply BN inference on species abundance data from a rocky shore ecosystem, a system with well documented links, to test the ecological validity of the revealed network; and (2) we evaluate BNs as a novel variable selection method to guide the training of an artificial neural network (ANN). Here, we demonstrate that not only was this approach able to recover meaningful species interactions networks from ecological data, but it also served as a meaningful tool to inform the training of predictive models, where there was an improvement in predictive performance in models with BN variable selection. Combining these results, we demonstrate the potential of this novel application of BNs in enhancing the interpretability and predictive power of ecological models; this has general applicability beyond the studied system, to ecosystems where existing relationships between species and other functional components are unknown.     

Context-sensitive data integration and prediction of biological networks

MOTIVATION: Several recent methods have addressed the problem of heterogeneous data integration and network prediction by modeling the noise inherent in high-throughput genomic datasets, which can dramatically improve specificity and sensitivity and allow the robust integration of datasets with heterogeneous properties. However, experimental technologies capture different biological processes with varying degrees of success, and thus, each source of genomic data can vary in relevance depending on the biological process one is interested in predicting. Accounting for this variation can significantly improve network prediction, but to our knowledge, no previous approaches have explicitly leveraged this critical information about biological context. RESULTS: We confirm the presence of context-dependent variation in functional genomic data and propose a Bayesian approach for context-sensitive integration and query-based recovery of biological process-specific networks. By applying this method to Saccharomyces cerevisiae, we demonstrate that leveraging contextual information can significantly improve the precision of network predictions, including assignment for uncharacterized genes. We expect that this general context-sensitive approach can be applied to other organisms and prediction scenarios. AVAILABILITY: A software implementation of our approach is available on request from the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at http://avis.princeton.edu/contextPIXIE/     

Social neuroendocrinology

In this paper we provide a critical review of research concerned with social/environmental mechanisms that modulate human neuroendocrine function. We survey research in four behavioral systems that have been shaped through evolution: competition, partnering, sex, and pregnancy/parenting. Generally, behavioral neuroendocrine research examines how hormones affect behavior. Instead, we focus on approaches that emphasize the effects of behavioral states on hormones (i.e., the “reverse relationship”), and their functional significance. We focus on androgens and estrogens because of their relevance to sexually selected traits. We conclude that the body of research employing a reversed or bidirectional perspective has an incomplete foundation: participants are mainly heterosexual men, and the functionality of induced shifts in neuroendocrine factors is generally unknown. This area of research is in its infancy, and opportunities abound for developing and testing intriguing research questions.     

趋流行为下斑马鱼幼鱼的全脑成像

目的 趋流,意即在水中调整身体方向并逆流而上的能力,是一种在大多数鱼类与两栖类动物中存在的保守行为。虽然关于趋流的研究已有一段很长的历史,并且近年来斑马鱼幼鱼趋流行为的理论机制也被提出,但是分布式的神经环路是如何整合多感知信息、做出决策、并实现行为控制仍然是个未知数。对自由运动的斑马鱼进行全脑神经活动成像为理解这一困难的问题提供了特殊的机会。方法 本文开发了一种微流控装置精确控制环境水流并激发斑马鱼的趋流行为。将该微流控芯片与扩增视野光场显微镜以及追踪系统整合,从而记录自由行为下斑马鱼全脑的神经活动。结果 在整合的微流控装置中稳定观察到了斑马鱼在水流中保持自身位置不变、逆流而上等刻板的趋流行为现象。与此同时,实现了对斑马鱼幼鱼趋流行为过程中的全脑钙活动记录。本文初步发现了几个脑区的神经活动与趋流行为相关。结论 本研究第一次展示了在斑马鱼幼鱼趋流行为的同时记录全脑神经活动的技术。接下来对神经活动和行为数据的分析与建模将有助于更好地理解一种重要自然行为背后的感觉运动转换机制。     

The vertebrate social behavior network: evolutionary themes and variations

Based on a wide variety of data, it is now clear that birds and teleost (bony) fish possess a core "social behavior network" within the basal forebrain and midbrain that is homologous to the social behavior network of mammals. The nodes of this network are reciprocally connected, contain receptors for sex steroid hormones, and are involved in multiple forms of social behavior. Other hodological features and neuropeptide distributions are likewise very similar across taxa. This evolutionary conservation represents a boon for experiments on phenotypic behavioral variation, as the extraordinary social diversity of teleost fish and songbirds can now be used to generate broadly relevant insights into issues of brain function that are not particularly tractable in other vertebrate groups. Two such lines of research are presented here, each of which addresses functional variation within the network as it relates to divergent patterns of social behavior. In the first set of experiments, we have used a sexually polymorphic fish to demonstrate that natural selection can operate independently on hypothalamic neuroendocrine functions that are relevant for (1) gonadal regulation and (2) sex-typical behavioral modulation. In the second set of experiments, we have exploited the diversity of avian social organizations and ecologies to isolate species-typical group size as a quasi-independent variable. These experiments have shown that specific areas and peptidergic components of the social behavior network possess functional properties that evolve in parallel with divergence and convergence in sociality.     

Network modeling of dynamic brain interactions predicts emergence of neural information that supports human cognitive behavior

How cognitive task behavior is generated by brain network interactions is a central question in neuroscience. Answering this question calls for the development of novel analysis tools that can firstly capture neural signatures of task information with high spatial and temporal precision (the “where and when”) and then allow for empirical testing of alternative network models of brain function that link information to behavior (the “how”). We outline a novel network modeling approach suited to this purpose that is applied to noninvasive functional neuroimaging data in humans. We first dynamically decoded the spatiotemporal signatures of task information in the human brain by combining MRI-individualized source electroencephalography (EEG) with multivariate pattern analysis (MVPA). A newly developed network modeling approach—dynamic activity flow modeling—then simulated the flow of task-evoked activity over more causally interpretable (relative to standard functional connectivity [FC] approaches) resting-state functional connections (dynamic, lagged, direct, and directional). We demonstrate the utility of this modeling approach by applying it to elucidate network processes underlying sensory–motor information flow in the brain, revealing accurate predictions of empirical response information dynamics underlying behavior. Extending the model toward simulating network lesions suggested a role for the cognitive control networks (CCNs) as primary drivers of response information flow, transitioning from early dorsal attention network-dominated sensory-to-response transformation to later collaborative CCN engagement during response selection. These results demonstrate the utility of the dynamic activity flow modeling approach in identifying the generative network processes underlying neurocognitive phenomena.

How is cognitive task behavior generated by brain network interactions? This study describes a novel network modeling approach and applies it to source electroencephalography data. The model accurately predicts future information dynamics underlying behavior and (via simulated lesioning) suggests a role for cognitive control networks as key drivers of response information flow.     

Tolerance of infection: A role for animal behavior,potential immune mechanisms,and consequences for parasite transmission

Infected organisms can resist or tolerate infection, with tolerance of infection defined as minimizing per-parasite reductions in fitness. Although tolerance is well studied in plants, researchers have only begun to probe the mechanisms and transmission consequences of tolerance in animals. Here we suggest that research on tolerance in animals would benefit from explicitly incorporating behavior as a component of tolerance, given the importance of behavior for host fitness and parasite transmission. We propose two distinct manifestations of tolerance in animals: tissue-specific tolerance, which minimizes fitness losses due to tissue damage during infection, and behavioral tolerance, which minimizes fitness losses by maintaining normal, fitness-enhancing behaviors during infection. Here we briefly review one set of potential immune mechanisms underlying both responses in vertebrate animals: inflammation and its associated signaling molecules. Inflammatory responses, including broadly effective resistance mechanisms like the production of reactive oxygen species, can incur severe costs in terms of damage to a host's own tissues, thereby reducing tissue-specific tolerance. In addition, signaling molecules involved in these responses facilitate stereotypical behavioral changes during infection, which include lethargy and anorexia, reducing normal behaviors and behavioral tolerance. We consider how tissue-specific and behavioral tolerance may vary independently or in conjunction and outline potential consequences of such covariation for the transmission of infectious diseases. We put forward the distinction between tissue-specific and behavioral tolerance not as a definitive framework, but to help stimulate and broaden future research by considering animal behavior as intimately linked to the mechanisms and consequences of tolerance in animals.     

Shaping embodied neural networks for adaptive goal-directed behavior

The acts of learning and memory are thought to emerge from the modifications of synaptic connections between neurons, as guided by sensory feedback during behavior. However, much is unknown about how such synaptic processes can sculpt and are sculpted by neuronal population dynamics and an interaction with the environment. Here, we embodied a simulated network, inspired by dissociated cortical neuronal cultures, with an artificial animal (an animat) through a sensory-motor loop consisting of structured stimuli, detailed activity metrics incorporating spatial information, and an adaptive training algorithm that takes advantage of spike timing dependent plasticity. By using our design, we demonstrated that the network was capable of learning associations between multiple sensory inputs and motor outputs, and the animat was able to adapt to a new sensory mapping to restore its goal behavior: move toward and stay within a user-defined area. We further showed that successful learning required proper selections of stimuli to encode sensory inputs and a variety of training stimuli with adaptive selection contingent on the animat's behavior. We also found that an individual network had the flexibility to achieve different multi-task goals, and the same goal behavior could be exhibited with different sets of network synaptic strengths. While lacking the characteristic layered structure of in vivo cortical tissue, the biologically inspired simulated networks could tune their activity in behaviorally relevant manners, demonstrating that leaky integrate-and-fire neural networks have an innate ability to process information. This closed-loop hybrid system is a useful tool to study the network properties intermediating synaptic plasticity and behavioral adaptation. The training algorithm provides a stepping stone towards designing future control systems, whether with artificial neural networks or biological animats themselves.     

Multilevel modeling analysis of dyadic network data with an application to Ye'kwana food sharing

Behavioral ecologists have recently begun using multilevel modeling for the analysis of social behavior. We present a multilevel modeling formulation of the Social Relations Model that is well suited for the analysis of dyadic network data. This model, which we adapt for count data and small datasets, can be fitted using standard multilevel modeling software packages. We illustrate this model with an analysis of meal sharing among Ye'kwana horticulturalists in Venezuela. In this setting, meal sharing among households is predicted by an association index, which reflects the amount of time that members of the households are interacting. This result replicates recent findings that interhousehold food sharing is especially prevalent among households that interact and cooperate in multiple ways. We discuss opportunities for human behavioral ecologists to expand their focus to the multiple currencies and cooperative behaviors that characterize interpersonal relationships in preindustrial societies. We discuss possible extensions to this statistical modeling approach and applications to research by human behavioral ecologists and primatologists. Am J Phys Anthropol 157:507–512, 2015. © 2015 Wiley Periodicals, Inc.