Baldwinclements5610

Chronic social defeat stress (CSDS) has been found to produce different impacts on anxiety-like behaviors, spatial cognitive function and memory in rodents with different susceptibilities. However, the impacts of chronic social defeat on social behaviors in adult male mice with different susceptibilities to social defeat and the underlying mechanisms in the brain remain unclear. In the present study, we found that ten days of social defeat reduced the tendency of susceptible adult male C57 mice to approach an unfamiliar individual and increased their avoidance of an unfamiliar CD-1 mouse but had no effects on resilient individuals. In addition, CSDS enhanced anxiety-like behavior in susceptible animals, but produced no effects in the resilient group. Meanwhile, CSDS increased the number of corticotropin-releasing factor (CRF)-positive neurons in the paraventricular nucleus of the hypothalamus and CRF-R2-positive neurons in the accumbens nucleus shell in both resilient and susceptible animals. CSDS increased the number of CRF-R1-positive neurons and CRF-R1 mRNA expression in the prelimbic cortex (PrL) and the number of CRF-R2-positive neurons in the basolateral amygdala, but reduced the number of CRF-R2-positive neurons and mRNA expression in the PrL in susceptible animals. Therefore, the different effects of CSDS on sociability and anxiety-like behavior in mice with different susceptibilities may be associated with region- and type-specific alterations in CRF receptor levels. These findings help us understand the underlying mechanism by which social stress affects emotion and social behavior and provides an important basis for the treatment of disorders of social and emotional behavior caused by social stress. Calorie restriction (CR) is the most potent, non-pharmacological intervention to support metabolic health. The effects of calorie restriction exceed weight loss. Consistent throughout many studies, calorie restriction induces a reduction in energy expenditure that is larger than the loss of metabolic mass, i.e. fat-free mass and fat mass, can explain. Per prevailing theories of mammalian aging, this disproportionate reduction in metabolic rate, defined as metabolic adaptation, reduces oxidative damage and thereby delays age-associated declines in physiological function. The aim of this narrative review is to investigate the origins of CR-induced metabolic adaptation. From a physiological standpoint this likely relates to the composition of body weight loss, reductions in insulin secretion, thyroid and leptin concentrations, and increased mitochondrial energy efficiency. Behavioral factors including physical activity and eating behaviors likely also play a role, specifically to prevent weight regain. Future studies are required to understand the interindividual differences in the response to CR, e.g. by sex, physical activity, or mitochondrial capacity, and to assess the long-term implications of CR for weight regain. There is still a paucity of longitudinal studies examining the relationships between objectively-assessed daily steps and cognitive performance in older adults. The current study aimed to explore whether there is a dose-response relationship between accelerometer-measured daily steps and subjective cognitive decline rate after 2 years in older adults. A total of 285 community-dwelling older adults (age = 74.52 ± 6.12 years, female = 55.4%) wore accelerometers for 7 consecutive days measuring daily steps in 2012. Subjective cognitive ability was measured using a Chinese version of the Ascertain Dementia 8-item Questionnaire (AD8). In total 274 (96.1%) participants completed the follow-up study in 2014. Multivariable negative binomial regression adjusted for confounders was undertaken. Daily steps were linearly related to a reduced decline rate in subjective cognitive ability after 2 years. When daily steps were categorized into groups ( less then 3500, 3500-6999, and ≥7000 steps/day), taking approximately 3500-6999 steps/day was associated with a reduced subjective cognitive decline rate (RR = 0.57, 95% CI = 0.37-0.89) after 2 years compared with less then 3500 steps/day. When accruing ≥7000 steps/day, the decline rate progressively decreased further (RR = 0.43, 95% CI = 0.23-0.82). Sensitivity analyses supported the stability of these findings. These results suggest that there is an inverse dose-response association of daily steps with subjective cognitive decline rate. Even as few as 3500-6999 steps/day was associated with a lower subjective cognitive decline rate after 2 years. Accumulating ≥7000 steps/day could provide greater protection for subjective cognitive ability. Computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data in exchange for time-intensive simulations. This paper reviews current trends in the use of machine learning for drug binding predictions, data sources to develop machine learning algorithms, and potential problems that may lead to overfitting and ungeneralizable models. A few popular datasets that can be used to develop virtual high-throughput screening models are characterized using spatial statistics to quantify potential biases. We can see from evaluating some common benchmarks that good performance correlates with models with high-predicted bias scores and models with low bias scores do not have much predictive power. A better understanding of the limits of available data sources and how to fix them will lead to more generalizable models that will lead to novel drug discovery. Swift-Gallant et al. (2020) provide a thought-provoking perspective on the topic of digit ratio research, research that has had some prominence in the journal Hormones and Behavior, and is research that has garnered much controversy. In this commentary on their paper, we add to the discussion of why there is skepticism of the use of digit ratios as a measure of individual differences in prenatal androgens, we comment on the mis-use of the facial width-to-height ratio as a measure of individual differences in testosterone, the grey areas in the interpretation of evidence, and we address the concern raised in their article regarding editorial policies at Hormones and Behavior (spoiler alert there are no secret policies). Animals continually assess their environment for cues associated with threats, competitors, allies, mates or prey, and experience is crucial for those associations. The auditory cortex is important for these computations to enable valence assignment and associative learning. The caudomedial nidopallium (NCM) is part of the songbird auditory association cortex and it is implicated in juvenile song learning, song memorization, and song perception. Like human auditory cortex, NCM is a site of action of estradiol (E2) and is enriched with the enzyme aromatase (E2-synthase). However, it is unclear how E2 modulates auditory learning and perception in the vertebrate auditory cortex. In this study we employ a novel, auditory-dependent operant task governed by social reinforcement to test the hypothesis that neuro-E2 synthesis supports auditory learning in adult male zebra finches. We show that local suppression of aromatase activity in NCM disrupts auditory association learning. By contrast, post-learning performance is unaffected by either NCM aromatase blockade or NCM pharmacological inactivation, suggesting that NCM E2 production and even NCM itself are not required for post-learning auditory discrimination or memory retrieval. Therefore, neuroestrogen synthesis in auditory cortex supports the association between sounds and behaviorally relevant consequences. Published by Elsevier Inc.Patients with thyroid dysfunction (31 hypothyroid, 32 subclinical hypothyroidism, 34 hyperthyroid, and 30 subclinical hyperthyroidism) and 37 euthyroid control subjects were recruited and performed the attention network test (ANT), which can simultaneously examine the alertness, orientation and execution control of the participants. Patients with hypothyroidism had abnormalities in the alerting network, and those with hyperthyroidism had impairments of the alerting and executive control networks. No attention networks deficit existed in patients with subclinical hyperthyroidism and subclinical hypothyroidism. The anxiety and depression scores of patients with thyroid dysfunction were significantly higher than those of the healthy control group. Covariance analysis demonstrated that interactions between group and Hamilton Anxiety Scale scores, group and HAMD score were not significant, but there was a significant main effect for group when analyzing the difference in values of the alerting network between groups. Further, the efficiency of the executive control network was negatively correlated with the T4 level in the hypothyroidism group, and positively correlated with the T4 level in the hyperthyroidism group. T4 or T3 level and efficiencies of the executive control network had a significant quadratic U-shaped relationship in all participants. In summary, the patients with four kinds of thyroid dysfunction exhibited different characteristics of ANT performance. Patients with thyroid dysfunction had various degrees of anxiety and depression disorders, but anxiety and depression disorders had no effect on the differences in the executive control network between the groups. Over the last twenty years advances in systems biology have changed our views on microbial communities and promise to revolutionize treatment of human diseases. In almost all scientific breakthroughs since time of Newton, mathematical modeling has played a prominent role. Regulatory networks emerged as preferred descriptors of how abundances of molecular species depend on each other. However, the central question on how cellular phenotypes emerge from dynamics of these network remains elusive. The principal reason is that differential equation models in the field of biology (while so successful in areas of physics and physical chemistry), do not arise from first principles, and these models suffer from lack of proper parameterization. In response to these challenges, discrete time models based on Boolean networks have been developed. In this review, we discuss an emerging modeling paradigm that combines ideas from differential equations and Boolean models, and has been developed independently within dynamical systems and computer science communities. The result is an approach that can associate a range of potential dynamical behaviors to a network, arrange the descriptors of the dynamics in a searchable database, and allows for multi-parameter exploration of the dynamics akin to bifurcation theory. Since this approach is computationally accessible for moderately sized networks, it allows, perhaps for the first time, to rationally compare different network topologies based on their dynamics. For decades already, the human fear conditioning paradigm has been used to study and develop treatments for anxiety disorders. This research is guided by theoretical assumptions that, in some cases indirectly, stem from the tradition of association formation models (e.g., the Rescorla-Wagner model). We argue that one of these assumptions - fear responding as a monotonic function of the associative activation of aversive memory representations - restricts the types of treatment that the research community currently considers. We discuss the importance of this assumption in the context of research on extinction-enhancing and reconsolidation interference techniques. While acknowledging the merit of this research, we argue that unstrapping the straitjacket of this assumption can lead to exploring new directions for utilizing fear conditioning procedures in treatment research. We discuss two determinants of fear responding other than associative memory activation. First, fear responding might also depend on relational information.