Crème de la Créature: Dietary Influences on Behavior in Animal Models

In humans, alterations in cognitive, motivated, and affective behaviors have been described with consumption of processed diets high in refined sugars and saturated fats and with high body mass index, but the causes, mechanisms, and consequences of these changes remain poorly understood. Animal models have provided an opportunity to answer these questions and illuminate the ways in which diet composition, especially high-levels of added sugar and saturated fats, contribute to brain physiology, plasticity, and behavior. Here we review findings from invertebrate (flies) and vertebrate models (rodents, zebrafish) that implicate these diets with changes in multiple behaviors, including eating, learning and memory, and motivation, and discuss limitations, open questions, and future opportunities.

A behavioral propagation and competition model based on pressure

The influence of node behavior by the relevant group behavior in complex networks is a topic of recent interest. In order to measure the direct and indirect influence of the neighborhoods, the behavioral propagation and competition model was established based on the pressure. The pressure is described by the impact of group behavior on nodes, which is related to the length and number of reachable paths between two nodes for measuring the nodal behavioral influence. In addition, the pressure range has an effect on the pressure. By modeling and analyzing the change of nodes motivation and the rules of behavioral propagation, and numerical simulations are performed on the small-world networks and the scale-free networks. The results show that pressure is the major factor in the node behavioral motivation, where the pressure generated from behavior in related group network is dependent on the relative location and number of participators. At the same time, network structure also plays an important role at behavior propagation process. Further, competition arises when multiple behaviors are spread among people, while winning behaviors are widely spread among people.

Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems

Recently, several new metaheuristic schemes have been introduced in the literature. Although all these approaches consider very different phenomena as metaphors, the search patterns used to explore the search space are very similar. On the other hand, second-order systems are models that present different temporal behaviors depending on the value of their parameters. Such temporal behaviors can be conceived as search patterns with multiple behaviors and simple configurations. In this paper, a set of new search patterns are introduced to explore the search space efficiently. They emulate the response of a second-order system. The proposed set of search patterns have been integrated as a complete search strategy, called Second-Order Algorithm (SOA), to obtain the global solution of complex optimization problems. To analyze the performance of the proposed scheme, it has been compared in a set of representative optimization problems, including multimodal, unimodal, and hybrid benchmark formulations. Numerical results demonstrate that the proposed SOA method exhibits remarkable performance in terms of accuracy and high convergence rates.

The slip behavior of velocity-weakening fault barriers

<p>Subduction earthquakes are among the most devastating natural hazards across the planet and yet the factors controlling their size remain poorly understood. It is thus important to investigate the mechanisms controlling rupture arrest and runaway, in particular the nature of rupture barriers (areas where earthquakes tend to stop). Geodetic and seismic observations along several faults suggest that barriers are mostly creeping (low seismic coupling). It is often interpreted that creeping barriers are governed by velocity-strengthening friction (VS), which is a sufficient condition for stable slip. However, some barriers have been observed to host intermediate magnitude earthquakes or to be completely ruptured by a large earthquake. Therefore, the frictional properties of seismic barriers may not be restricted to VS. In particular, the possibility of velocity-weakening (VW) areas behaving as barriers needs to be further explored.</p><p>In this work, we characterize the multiple behaviors of seismic barriers on faults governed by velocity-weakening (VW) rate-and-state friction, using earthquake cycle simulations. We consider a 2D model, where a central VW area has a larger critical slip distance (Dc) or higher normal stress (σ) than the surrounding VW areas. We found that the central areas can behave as permanent or temporal barriers to earthquake propagation if their Dc or σ are large enough. On permanent barriers, creep occurs steadily. However, on temporary barriers, the locking degree changes throughout the cycle, despite frictional properties remaining constant.</p><p>To understand the efficiency of VW barriers (that is, to determine under what conditions they can stop ruptures), we use fracture mechanics theory. We found that barrier efficiency depends mainly on the ratio between the fracture energy of the barrier, which is proportional to Dc and normal stress, and the energy release rate of the neighboring seismic segment, which is proportional to its stress drop squared and length. If geological features of the overriding and subducting plates affect Dc and σ on the megathrust, our results support the idea of structural controls on the seismic behavior of megathrusts. Thus, understanding how geological features are linked to fracture energy may contribute to seismic hazard assessment by constraining rupture arrest and multi-segment ruptures in earthquake scenarios.</p>

The homotopy perturbation method for fractional differential equations: part 1 Mohand transform

Purpose This study aims that very lately, Mohand transform is introduced to solve the ordinary and partial differential equations (PDEs). In this paper, the authors modify this transformation and associate it with a further analytical method called homotopy perturbation method (HPM) for the fractional view of Newell–Whitehead–Segel equation (NWSE). As Mohand transform is restricted to linear obstacles only, as a consequence, HPM is used to crack the nonlinear terms arising in the illustrated problems. The fractional derivatives are taken into the Caputo sense. Design/methodology/approach The specific objective of this study is to examine the problem which performs an efficient role in the form of stripe orders of two dimensional systems. The authors achieve the multiple behaviors and properties of fractional NWSE with different positive integers. Findings The main finding of this paper is to analyze the fractional view of NWSE. The obtain results perform very good in agreement with exact solution. The authors show that this strategy is absolutely very easy and smooth and have no assumption for the constriction of this approach. Research limitations/implications This paper invokes these two main inspirations: first, Mohand transform is associated with HPM, secondly, fractional view of NWSE with different positive integers. Practical implications In this paper, the graph of approximate solution has the excellent promise with the graphs of exact solutions. Social implications This paper presents valuable technique for handling the fractional PDEs without involving any restrictions or hypothesis. Originality/value The authors discuss the fractional view of NWSE by a Mohand transform. The work of the present paper is original and advanced. Significantly, to the best of the authors’ knowledge, no such work has yet been published in the literature.

COVID-19-related psychological distress and engagement in preventative behaviors among individuals with severe mental illnesses

Individuals with severe mental illnesses (SMIs) may be disproportionately vulnerable to COVID-19 infection and psychological distress. This study investigated the prevalence of engagement in COVID-19 preventative behaviors, predictors of these behaviors, and COVID-19-related psychological distress. One hundred and sixty-three individuals with SMIs (94 with schizophrenia spectrum illnesses and 69 with affective disorders) and 27 psychiatrically healthy comparison participants were recruited from ongoing studies across 3 sites, to complete a phone survey querying implementation of 8 specific COVID-19 preventative behaviors that participants engaged in at least once in the past month as well as standard assessments of depression, anxiety, perceived stress, loneliness, and coping. Data were collected between 3 April 2020 and 4 June 2020. The large majority of our SMI sample, which consisted of outpatients with relatively mild symptom severity, endorsed engaging in multiple preventative behaviors. Relatively few differences were found between groups; however, individuals with SMI were less likely to work remotely than healthy individuals and individuals with schizophrenia spectrum illness were less likely to stay home as a preventative measure, wear face masks, and work remotely than individuals with affective disorders. Differences in staying home remained after controlling for potential confounds. Although individuals with SMI reported more psychological distress related to COVID-19, this distress was largely unrelated to engagement in preventative behaviors. The large majority of individuals with SMI in this outpatient sample, regardless of broad diagnostic category, reported performing multiple behaviors intended to prevent COVID-19 infection at least once a month and reported distress associated with the pandemic. These findings suggest a good level of awareness of COVID-19 among stable outpatients with SMI. The degree to which more acutely ill persons with SMI engage in such preventative behaviors, however, remains to be examined.

Development of Multiple Behaviors in Evolving Robots

We investigate whether standard evolutionary robotics methods can be extended to support the evolution of multiple behaviors by forcing the retention of variations that are adaptive with respect to all required behaviors. This is realized by selecting the individuals located in the first Pareto fronts of the multidimensional fitness space in the case of a standard evolutionary algorithms and by computing and using multiple gradients of the expected fitness in the case of a modern evolutionary strategies that move the population in the direction of the gradient of the fitness. The results collected on two extended versions of state-of-the-art benchmarking problems indicate that the latter method permits to evolve robots capable of producing the required multiple behaviors in the majority of the replications and produces significantly better results than all the other methods considered.

SIPEC: the deep-learning Swiss knife for behavioral data analysis

ABSTRACTAnalysing the behavior of individuals or groups of animals in complex environments is an important, yet difficult computer vision task. Here we present a novel deep learning architecture for classifying animal behavior and demonstrate how this end-to-end approach can significantly outperform pose estimation-based approaches, whilst requiring no intervention after minimal training. Our behavioral classifier is embedded in a first-of-its-kind pipeline (SIPEC) which performs segmentation, identification, pose-estimation and classification of behavior all automatically. SIPEC successfully recognizes multiple behaviors of freely moving mice as well as socially interacting nonhuman primates in 3D, using data only from simple mono-vision cameras in home-cage setups.

‘We don’t want to pull away from the initial purpose’: Program stakeholder concerns about expanding their childhood obesity program to focus on multiple behaviors

The main objective of this qualitative study was to understand stakeholder perspectives about expanding their childhood obesity program from a single-behavior (physical activity) to a multi-behavior (addition of nutrition) program. Qualitative data from semi-structured, face-to-face interviews with nine program stakeholders were analyzed with the framework method. Findings revealed stakeholder concerns about program expansion. Specifically, results indicated SES and lack of parental involvement remain significant barriers to nutrition education and reinforcement of healthy dietary behaviors at home. Innovative methods using existing, low-cost approaches such as behavioral texts or other low-cost technologiescan assist health educators overcome many of these obstacles.

MicroRNA-210 regulates placental adaptation to maternal hypoxic stress during pregnancy†

MicroRNA (miR)-210 is a well-known hypoxia-inducible small RNA. Increasing in vitro evidence demonstrates its involvement in regulating multiple behaviors of placental trophoblasts. However, direct in vivo evidence remains lacking. In the present study, we generated a miR-210-deficient mouse strain using CRISPR/Cas9 technology, in which miR-210 expression was markedly deficient in various tissues. Little influence on fertility rate and litter size was observed after the deletion of miR-210 in mice. Continuous exposure of pregnant mice to hypoxia (10.5% O2) from E6.5 to E10.5 or to E18.5 led to reduction in fetal weight, and such fetal weight loss was markedly worsened in miR-210-knockout dams. Analysis of the placental structure demonstrated the reduced expansion of placental spongiotrophoblast layer and hampered development of labyrinth fetal blood vessels in knockout mice compared to the wild-type controls upon hypoxia stimulation. The findings indicate that miR-210 participates in regulating placental adaptation to hypoxic stress during pregnancy.