A Transcultural Model of the Centrality of “Thinking a Lot” in Psychopathologies Across the Globe and the Process of Localization: A Cambodian Refugee Example

We present a general model of why “thinking a lot” is a key presentation of distress in many cultures and examine how “thinking a lot” plays out in the Cambodian cultural context. We argue that the complaint of “thinking a lot” indicates the presence of a certain causal network of psychopathology that is found across cultures, but that this causal network is localized in profound ways. We show, using a Cambodian example, that examining “thinking a lot” in a cultural context is a key way of investigating the local bio-cultural ontology of psychopathology. Among Cambodian refugees, a typical episode of “thinking a lot” begins with ruminative-type negative cognitions, in particular worry and depressive thoughts. Next these negative cognitions may induce mental symptoms (e.g., poor concentration, forgetfulness, and “zoning out”) and somatic symptoms (e.g., migraine headache, migraine-like blurry vision such as scintillating scotomas, dizziness, palpitations). Subsequently the very fact of “thinking a lot” and the induced symptoms may give rise to multiple catastrophic cognitions. Soon, as distress escalates, in a kind of looping, other negative cognitions such as trauma memories may be triggered. All these processes are highly shaped by the Cambodian socio-cultural context. The article shows that Cambodian trauma survivors have a locally specific illness reality that centers on dynamic episodes of “thinking a lot,” or on what might be called the “thinking a lot” causal network.     

Uncoupled Analysis of Stochastic Reaction Networks in Fluctuating Environments

The dynamics of stochastic reaction networks within cells are inevitably modulated by factors considered extrinsic to the network such as, for instance, the fluctuations in ribosome copy numbers for a gene regulatory network. While several recent studies demonstrate the importance of accounting for such extrinsic components, the resulting models are typically hard to analyze. In this work we develop a general mathematical framework that allows to uncouple the network from its dynamic environment by incorporating only the environment''s effect onto the network into a new model. More technically, we show how such fluctuating extrinsic components (e.g., chemical species) can be marginalized in order to obtain this decoupled model. We derive its corresponding process- and master equations and show how stochastic simulations can be performed. Using several case studies, we demonstrate the significance of the approach.     

The heritability of life history strategy: the K-factor, covitality, and personality

Archival data from the MIDUS survey (Brim et al., 2000), a nationally representative sample, on 309 MZ and 333 DZ twin pairs aged 25-74 years were used to test the psychometrics and behavioral genetics of life history strategy. We organized 253 of the originally administered 2,000 questions into 30 scales measuring life history traits (e.g., quality of family relationships and altruism towards kin), medical symptoms (e.g., thyroid problems), personality traits (e.g., neuroticism, extraversion, conscientiousness), and social background (e.g., financial security). A single higher-order factor, indicating a general life history strategy, composed of three lower-order factors, was replicated. Factor analyses were then performed on the genetic variance-covariance matrices. We found that (a) a single higher-order factor explained the preponderance of the genetic correlations among the scales and (b) this higher-order factor was itself 68 percent heritable and accounted for 82 percent of the genetic variance among the three component lower-order factors.     

The heritability of life history strategy: The k‐factor,covitality, and personality

Abstract

Archival data from the MIDUS survey (Brim et al., 2000), a nationally representative sample, on 309 MZ and 333 DZ twin pairs aged 25–74 years were used to test the psychometrics and behavioral genetics of life history strategy. We organized 253 of the originally administered 2,000 questions into 30 scales measuring life history traits (e.g., quality of family relationships and altruism towards kin), medical symptoms (e.g., thyroid problems), personality traits (e.g., neuroticism, extraversion, conscientiousness), and social background (e.g., financial security). A single higher‐order factor, indicating a general life history strategy, composed of three lower‐order factors, was replicated. Factor analyses were then performed on the genetic variance‐covariance matrices. We found that (a) a single higher‐order factor explained the preponderance of the genetic correlations among the scales and (b) this higher‐order factor was itself 68 percent heritable and accounted for 82 percent of the genetic variance among the three component lower‐order factors.     

Multilevel modeling of chronotype and weekdays versus weekends to predict nonrestorative sleep

Nonrestorative sleep, a form of subjective sleep disturbance that has been largely neglected in the literature, is newly accessible to researchers via the validated restorative sleep questionnaire (RSQ). The daily version of the RSQ allows for analysis of within-subjects variation in restorative sleep across repeated samplings, and such day-to-day regularity in sleep variables has been highlighted as an important new direction for research. The present study used a sophisticated statistical approach, multilevel modeling, to examine the contributions of circadian chronotype, calendar day of questionnaire completion (weekends versus weekdays), and their interaction in explaining both interindividual and intraindividual variance in restorative sleep. Analyses were conducted using an archival dataset of college undergraduates who continuously completed daily RSQs over a 14-day sampling period. In the final multilevel model, possessing an evening type predicted lower restorative sleep between subjects, while sampling on weekdays predicted lower restorative sleep within subjects. Furthermore, a cross-level interaction was observed, such that the difference in restorative sleep on weekends versus weekdays was more pronounced among those with greater evening circadian preference. All of the effects were maintained after accounting for the significant influence of gender (women had less restorative sleep than men). These results are theoretically consistent with findings that evening types display stronger disparities in sleep schedules across free and workdays (i.e., social jet lag), and attest to the usefulness of multilevel models for statistically investigating how stable traits interact with factors that vary day to day (e.g., work or school schedules) in influencing sleep outcomes.     

How do high-level specifications of the brain relate to variational approaches?

High-level specification of how the brain represents and categorizes the causes of its sensory input allows to link "what is to be done" (perceptual task) with "how to do it" (neural network calculation). In this article, we describe how the variational framework, which encountered a large success in modeling computer vision tasks, has some interesting relationships, at a mesoscopic scale, with computational neuroscience. We focus on cortical map computations such that "what is to be done" can be represented as a variational approach, i.e., an optimization problem defined over a continuous functional space. In particular, generalizing some existing results, we show how a general variational approach can be solved by an analog neural network with a given architecture and conversely. Numerical experiments are provided as an illustration of this general framework, which is a promising framework for modeling macro-behaviors in computational neuroscience.     

Network stability is a balancing act of personality, power, and conflict dynamics in rhesus macaque societies

Stability in biological systems requires evolved mechanisms that promote robustness. Cohesive primate social groups represent one example of a stable biological system, which persist in spite of frequent conflict. Multiple sources of stability likely exist for any biological system and such robustness, or lack thereof, should be reflected and thus detectable in the group's network structure, and likely at multiple levels. Here we show how network structure and group stability are linked to the fundamental characteristics of the individual agents in groups and to the environmental and social contexts in which these individuals interact. Both internal factors (e.g., personality, sex) and external factors (e.g., rank dynamics, sex ratio) were considered from the level of the individual to that of the group to examine the effects of network structure on group stability in a nonhuman primate species. The results yielded three main findings. First, successful third-party intervention behavior is a mechanism of group stability in rhesus macaques in that successful interventions resulted in less wounding in social groups. Second, personality is the primary factor that determines which individuals perform the role of key intervener, via its effect on social power and dominance discrepancy. Finally, individuals with high social power are not only key interveners but also key players in grooming networks and receive reconciliations from a higher diversity of individuals. The results from this study provide sound evidence that individual and group characteristics such as personality and sex ratio influence network structures such as patterns of reconciliation, grooming and conflict intervention that are indicators of network robustness and consequent health and well-being in rhesus macaque societies. Utilizing this network approach has provided greater insight into how behavioral and social processes influence social stability in nonhuman primate groups.     

Achieving Harmony among Different Social Identities within the Self-Concept: The Consequences of Internalising a Group-Based Philosophy of Life

It can be hard for individuals to manage multiple group identities within their self-concept (e.g., being a Christian and a woman). We examine how the inter-identity fit between potentially conflicting identities can become more harmonious through a self-defining group philosophy for life. Specifically, we test the hypothesis that holistic group identities (based in group philosophies for life that prescribe the behavior of their members in any situation, such as religion) become more strongly related to other identities in the self-concept (e.g., gender) when they are strongly self-defining (i.e., devotedly applied to daily life). In three studies we investigated the inter-identity fit between individuals’ (highly holistic) religious identity and (less holistic) gender identity. Results provided converging support for our hypothesis across diverging methods (explicit questionnaires, more implicit associations, and a novel network analysis of group traits). We discuss the importance of understanding how some (i.e., holistic and self-defining) group identities may harmonize otherwise less harmonious group identities within one’s self-concept.     

Molecular modelling methods for prediction of sequence-selectivity in DNA recognition

We describe how one can apply molecular modelling methods, based on the molecular mechanics/generalised Born (MM/GB) approach, to the prediction of the relative affinity of DNA minor groove binding ligands for different DNA sequences. We discuss the theoretical background to the technique, some variations in the methodology that can be employed, and illustrate its application through a case study: analysis of the energetics of binding of Hoechst 33258 to the minor groove of various A/T-rich DNA duplexes. We show how the underpinning molecular dynamics (MD) simulations can be set up, how they can be analysed for satisfactory behaviour, and various approaches to extracting thermodynamics of drug binding from them. We find that while certain elaborations to the basic MM/GB method can improve the agreement with experimental data (e.g., calculating the DNA perturbation energy), others have to be analysed with more caution (e.g., calculating configurational entropy changes). Overall, these methodologies can rank the affinity of a ligand for the minor groove of different DNA sequences fairly well, but the calculation of absolute binding affinities is not very reliable.     

Behavior and Performance of Interactive Multi-Player Game Servers

With the recent explosion in deployment of services to large numbers of customers over the Internet and in global services in general, issues related to the architecture of scalable servers are becoming increasingly important. However, our understanding of these types of applications is currently limited, especially on how well they scale to support large numbers of users. One such, novel, commercial class of applications, are interactive, multi-player game servers. Multi-player games are both an important class of commercial applications (in the entertainment industry) and they can be valuable in understanding the architectural requirements of scalable services. They impose requirements on system performance, scalability, and availability, stressing multiple aspects of the system architecture (e.g., compute cycles and network I/O). Recently there has been a lot of interest on client side issues with respect to games. However, there has beenlittle or no work on the server side. In this paper we use a commercial game server to gain insight in this class of applications and the requirements they impose on modern architectures. We find that: (1) In terms of the benchmarking methodology, interactive game servers are very different from scientific workloads. We propose a methodology that deals with the related issues in benchmarking this class of applications. Our methodology bears many similarities with methodologies used in benchmarking online transaction processing (OLTP) systems. (2) Current, sequential game servers can support at most up to a few tens of users (60–100) on existing processors. (3) The bottleneck in the server is both game-related as well as network-related processing (about 50–50). (4) Network bandwidth requirements are not an important issue for the numbers of players we are interested in. (5) The processor achieves a surprisingly low IPC of 0.416.     

Formidable females redux:male social integration into female networks and the value of dynamic multilayer networks

The development of multilayer network techniques is a boon for researchers who wish to understand how different interaction layers might influence each other,and how these in turn might influence group dynamics.Here,we investigate how integration between male and female grooming and aggression interaction networks influences male power trajectories in vervet monkeys Chlorocebus pygerythrus.Our previous analyses of this phenomenon used a monolayer approach,and our aim here is to extend these analyses using a dynamic multilayer approach.To do so,we constructed a temporal series of male and female interaction layers.We then used a multivariate multilevel autoregression model to compare cross-lagged associations between a male's centrality in the female grooming layer and changes in male Elo ratings.Our results confirmed our original findings:changes in male centrality within the female grooming network were weakly but positively tied to changes in their Elo ratings.However,the multilayer network approach offered additional insights into this social process,identifying how changes in a male's centrality cascade through the other network layers.This dynamic view indicates that the changes in Elo ratings are likely to be short-lived,but that male centrality within the female network had a much stronger impact throughout the multilayer network as a whole,especially on reducing intermale aggression(i.e.,aggression directed by males toward other males).We suggest that multilayer social network approaches can take advantage of increased amounts of social data that are more commonly collected these days,using a variety of methods.Such data are inherently multilevel and multilayered,and thus offer the ability to quantify more precisely the dynamics of animal social behaviors.     

An architecture for a shop-floor controller using colored Petri nets

In a dynamic and flexible manufacturing environment, a shop-floor controller must be designed so that it automatically (or with minimum human intervention) and quickly responds to the changes (e.g., in part type or part routing) in the system. Such a performance may be achieved provided that the controller is simple and sufficiently general in its scope of application. In this article, we present an architecture for such a shop-floor controller. The architecture is based on colored Petri nets with ordered colored sets and structured input and output functions.     

Neuronal chains for actions in the parietal lobe: a computational model

The inferior part of the parietal lobe (IPL) is known to play a very important role in sensorimotor integration. Neurons in this region code goal-related motor acts performed with the mouth, with the hand and with the arm. It has been demonstrated that most IPL motor neurons coding a specific motor act (e.g., grasping) show markedly different activation patterns according to the final goal of the action sequence in which the act is embedded (grasping for eating or grasping for placing). Some of these neurons (parietal mirror neurons) show a similar selectivity also during the observation of the same action sequences when executed by others. Thus, it appears that the neuronal response occurring during the execution and the observation of a specific grasping act codes not only the executed motor act, but also the agent's final goal (intention).In this work we present a biologically inspired neural network architecture that models mechanisms of motor sequences execution and recognition. In this network, pools composed of motor and mirror neurons that encode motor acts of a sequence are arranged in form of action goal-specific neuronal chains. The execution and the recognition of actions is achieved through the propagation of activity bursts along specific chains modulated by visual and somatosensory inputs.The implemented spiking neuron network is able to reproduce the results found in neurophysiological recordings of parietal neurons during task performance and provides a biologically plausible implementation of the action selection and recognition process.Finally, the present paper proposes a mechanism for the formation of new neural chains by linking together in a sequential manner neurons that represent subsequent motor acts, thus producing goal-directed sequences.     

Identification of Neural Networks That Contribute to Motion Sickness through Principal Components Analysis of Fos Labeling Induced by Galvanic Vestibular Stimulation

Motion sickness is a complex condition that includes both overt signs (e.g., vomiting) and more covert symptoms (e.g., anxiety and foreboding). The neural pathways that mediate these signs and symptoms are yet to identified. This study mapped the distribution of c-fos protein (Fos)-like immunoreactivity elicited during a galvanic vestibular stimulation paradigm that is known to induce motion sickness in felines. A principal components analysis was used to identify networks of neurons activated during this stimulus paradigm from functional correlations between Fos labeling in different nuclei. This analysis identified five principal components (neural networks) that accounted for greater than 95% of the variance in Fos labeling. Two of the components were correlated with the severity of motion sickness symptoms, and likely participated in generating the overt signs of the condition. One of these networks included neurons in locus coeruleus, medial, inferior and lateral vestibular nuclei, lateral nucleus tractus solitarius, medial parabrachial nucleus and periaqueductal gray. The second included neurons in the superior vestibular nucleus, precerebellar nuclei, periaqueductal gray, and parabrachial nuclei, with weaker associations of raphe nuclei. Three additional components (networks) were also identified that were not correlated with the severity of motion sickness symptoms. These networks likely mediated the covert aspects of motion sickness, such as affective components. The identification of five statistically independent component networks associated with the development of motion sickness provides an opportunity to consider, in network activation dimensions, the complex progression of signs and symptoms that are precipitated in provocative environments. Similar methodology can be used to parse the neural networks that mediate other complex responses to environmental stimuli.     

Affected by smells? Environmental chemical responsivity predicts odor perception

Strong negative reactions, physical symptoms, and behavioral disruptions due to environmental odors are common in the adult population. We investigated relationships among such environmental chemosensory responsivity (CR), personality traits, affective states, and odor perception. Study 1 showed that CR and neuroticism were positively correlated in a sample of young adults (n = 101), suggesting that persons high in neuroticism respond more negatively to environmental odors. Study 2 explored the relationships among CR, noise responsivity (NR), neuroticism, and odor perception (i.e., pleasantness and intensity) in a subset of participants (n = 40). High CR was associated with high NR. Regression analyses indicated that high CR predicted higher odor intensity ratings and low olfactory threshold (high sensitivity) predicted lower pleasantness ratings. However, neuroticism was not directly associated with odor ratings or thresholds. Overall, the results suggest that CR and odor thresholds predict perceptual ratings of odors and that high CR is associated with nonchemosensory affective traits.     

Hierarchical ordering of reticular networks

The structure of hierarchical networks in biological and physical systems has long been characterized using the Horton-Strahler ordering scheme. The scheme assigns an integer order to each edge in the network based on the topology of branching such that the order increases from distal parts of the network (e.g., mountain streams or capillaries) to the "root" of the network (e.g., the river outlet or the aorta). However, Horton-Strahler ordering cannot be applied to networks with loops because they they create a contradiction in the edge ordering in terms of which edge precedes another in the hierarchy. Here, we present a generalization of the Horton-Strahler order to weighted planar reticular networks, where weights are assumed to correlate with the importance of network edges, e.g., weights estimated from edge widths may correlate to flow capacity. Our method assigns hierarchical levels not only to edges of the network, but also to its loops, and classifies the edges into reticular edges, which are responsible for loop formation, and tree edges. In addition, we perform a detailed and rigorous theoretical analysis of the sensitivity of the hierarchical levels to weight perturbations. In doing so, we show that the ordering of the reticular edges is more robust to noise in weight estimation than is the ordering of the tree edges. We discuss applications of this generalized Horton-Strahler ordering to the study of leaf venation and other biological networks.     

Network dynamics and synchronous activity in cultured cortical neurons

Neurons extracted from specific areas of the Central Nervous System (CNS), such as the hippocampus, the cortex and the spinal cord, can be cultured in vitro and coupled with a micro-electrode array (MEA) for months. After a few days, neurons connect each other with functionally active synapses, forming a random network and displaying spontaneous electrophysiological activity. In spite of their simplified level of organization, they represent an useful framework to study general information processing properties and specific basic learning mechanisms in the nervous system. These experimental preparations show patterns of collective rhythmic activity characterized by burst and spike firing. The patterns of electrophysiological activity may change as a consequence of external stimulation (i.e., chemical and/or electrical inputs) and by partly modifying the "randomness" of the network architecture (i.e., confining neuronal sub-populations in clusters with micro-machined barriers). In particular we investigated how the spontaneous rhythmic and synchronous activity can be modulated or drastically changed by focal electrical stimulation, pharmacological manipulation and network segregation. Our results show that burst firing and global synchronization can be enhanced or reduced; and that the degree of synchronous activity in the network can be characterized by simple parameters such as cross-correlation on burst events.