Energy-Dissipation Performance of Combined Low Yield Point Steel Plate Damper Based on Topology Optimization and Its Application in Structural Control

In view of the disadvantages such as higher yield stress and inadequate adjustability, a combined low yield point steel plate damper involving low yield point steel plates and common steel plates is proposed. Three types of combined plate dampers with new hollow shapes are proposed, and the specific forms include interior hollow, boundary hollow, and ellipse hollow. The “maximum stiffness” and “full stress state” are used as the optimization objectives, and the topology optimization of different hollow forms by alternating optimization method is to obtain the optimal shape. Various combined steel plate dampers are calculated by finite element simulation, the results indicate that the initial stiffness of the boundary optimized damper and interior optimized damper is lager, the hysteresis curves are full, and there is no stress concentration. These two types of optimization models made in different materials rations are studied by numerical simulation, and the adjustability of yield stress of these combined dampers is verified. The nonlinear dynamic responses, seismic capacity, and damping effect of steel frame structures with different combined dampers are analyzed. The results show that the boundary optimized damper has better energy-dissipation capacity and is suitable for engineering application

Age and gender differences and construct of the children's emotional intelligence

With respect to the age and gender the children’s emotional intelligence construct is still being developed with little empirical support. Hence, this research follows a specific objective for determination of the differences between construct of the children’s’ emotional intelligence (EI) and their personal characteristics such as age and gender. The present study was carried out among 107 Iranian students in the Iranian primary schools in Kuala Lumpur, Malaysia. The students (girls and boys) were clustered in three different age groups, 8, 9, and 10 years old. Data were collected using the Emotional Quotient Inventory Youth Version (Bar- on EQ-i; YV, 2000) and demographic questionnaire. The statistical findings, with respect to gender and ages, indicated that there was a noticeable difference between emotional intelligence of girls and boys in groups of ages

Age-Related Differences in Cortical Thickness Vary by Socioeconomic Status

Recent findings indicate robust associations between socioeconomic status (SES) and brain structure in children, raising questions about the ways in which SES may modify structural brain development. In general, cortical thickness and surface area develop in nonlinear patterns across childhood and adolescence, with developmental patterns varying to some degree by cortical region. Here, we examined whether age-related nonlinear changes in cortical thickness and surface area varied by SES, as indexed by family income and parental education. We hypothesized that SES disparities in age-related change may be particularly evident for language- and literacy-supporting cortical regions. Participants were 1148 typically-developing individuals between 3 and 20 years of age. Results indicated that SES factors moderate patterns of age-associated change in cortical thickness but not surface area. Specifically, at lower levels of SES, associations between age and cortical thickness were curvilinear, with relatively steep age-related decreases in cortical thickness earlier in childhood, and subsequent leveling off during adolescence. In contrast, at high levels of SES, associations between age and cortical thickness were linear, with consistent reductions across the age range studied. Notably, this interaction was prominent in the left fusiform gyrus, a region that is critical for reading development. In a similar pattern, SES factors significantly moderated linear age-related change in left superior temporal gyrus, such that higher SES was linked with steeper age-related decreases in cortical thickness in this region. These findings suggest that SES may moderate patterns of age-related cortical thinning, especially in language- and literacy-supporting cortical regions

The <i>Sinocyclocheilus</i> cavefish genome provides insights into cave adaptation

BACKGROUND: An emerging cavefish model, the cyprinid genus Sinocyclocheilus, is endemic to the massive southwestern karst area adjacent to the Qinghai-Tibetan Plateau of China. In order to understand whether orogeny influenced the evolution of these species, and how genomes change under isolation, especially in subterranean habitats, we performed whole-genome sequencing and comparative analyses of three species in this genus, S. grahami, S. rhinocerous and S. anshuiensis. These species are surface-dwelling, semi-cave-dwelling and cave-restricted, respectively. RESULTS: The assembled genome sizes of S. grahami, S. rhinocerous and S. anshuiensis are 1.75 Gb, 1.73 Gb and 1.68 Gb, respectively. Divergence time and population history analyses of these species reveal that their speciation and population dynamics are correlated with the different stages of uplifting of the Qinghai-Tibetan Plateau. We carried out comparative analyses of these genomes and found that many genetic changes, such as gene loss (e.g. opsin genes), pseudogenes (e.g. crystallin genes), mutations (e.g. melanogenesis-related genes), deletions (e.g. scale-related genes) and down-regulation (e.g. circadian rhythm pathway genes), are possibly associated with the regressive features (such as eye degeneration, albinism, rudimentary scales and lack of circadian rhythms), and that some gene expansion (e.g. taste-related transcription factor gene) may point to the constructive features (such as enhanced taste buds) which evolved in these cave fishes. CONCLUSION: As the first report on cavefish genomes among distinct species in Sinocyclocheilus, our work provides not only insights into genetic mechanisms of cave adaptation, but also represents a fundamental resource for a better understanding of cavefish biology. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12915-015-0223-4) contains supplementary material, which is available to authorized users

A Hierarchical Probabilistic Model for Rapid Object Categorization in Natural Scenes

Humans can categorize objects in complex natural scenes within 100–150 ms. This amazing ability of rapid categorization has motivated many computational models. Most of these models require extensive training to obtain a decision boundary in a very high dimensional (e.g., ∼6,000 in a leading model) feature space and often categorize objects in natural scenes by categorizing the context that co-occurs with objects when objects do not occupy large portions of the scenes. It is thus unclear how humans achieve rapid scene categorization

Neuroimaging-Based Classification of PTSD Using Data-Driven Computational Approaches:A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.</p

Amygdala Atrophy and Its Functional Disconnection with the Cortico-Striatal-Pallidal-Thalamic Circuit in Major Depressive Disorder in Females

Background Major depressive disorder (MDD) is approximately twice as common in females than males. Furthermore, female patients with MDD tend to manifest comorbid anxiety. Few studies have explored the potential anatomical and functional brain changes associated with MDD in females. Therefore, the purpose of the present study was to investigate the anatomical and functional changes underlying MDD in females, especially within the context of comorbid anxiety. Methods In this study, we recruited antidepressant-free females with MDD (N = 35) and healthy female controls (HC; N = 23). The severity of depression and anxiety were evaluated by the Hamilton Depression Rating Scale (HAM-D) and the Hamilton Anxiety Rating Scale (HAM-A), respectively. Structural and resting-state functional images were acquired on a Siemens 3.0 Tesla scanner. We compared the structural volumetric differences between patients and HC with voxel-based morphometry (VBM) analyses. Seed-based voxel-wise correlative analyses were used to identify abnormal functional connectivity. Regions with structural deficits showed a significant correlation between gray matter (GM) volume and clinical variables that were selected as seeds. Furthermore, voxel-wise functional connectivity analyses were applied to identify the abnormal connectivity relevant to seed in the MDD group. Results Decreased GM volume in patients was observed in the insula, putamen, amygdala, lingual gyrus, and cerebellum. The right amygdala was selected as a seed to perform connectivity analyses, since its GM volume exhibited a significant correlation with the clinical anxiety scores. We detected regions with disrupted connectivity relevant to seed primarily within the cortico-striatal-pallidal-thalamic circuit. Conclusions Amygdaloid atrophy, as well as decreased functional connectivity between the amygdala and the cortico-striatal-pallidal-thalamic circuit, appears to play a role in female MDD, especially in relation to comorbid anxiety

Assessment of Brain Age in Posttraumatic Stress Disorder: Findings from the ENIGMA PTSD and Brain Age Working Groups

Background Posttraumatic stress disorder (PTSD) is associated with markers of accelerated aging. Estimates of brain age, compared to chronological age, may clarify the effects of PTSD on the brain and may inform treatment approaches targeting the neurobiology of aging in the context of PTSD. Method Adult subjects (N = 2229; 56.2% male) aged 18–69 years (mean = 35.6, SD = 11.0) from 21 ENIGMA-PGC PTSD sites underwent T1-weighted brain structural magnetic resonance imaging, and PTSD assessment (PTSD+, n = 884). Previously trained voxel-wise (brainageR) and region-of-interest (BARACUS and PHOTON) machine learning pipelines were compared in a subset of control subjects (n = 386). Linear mixed effects models were conducted in the full sample (those with and without PTSD) to examine the effect of PTSD on brain predicted age difference (brain PAD; brain age − chronological age) controlling for chronological age, sex, and scan site. Results BrainageR most accurately predicted brain age in a subset (n = 386) of controls (brainageR: ICC = 0.71, R = 0.72, MAE = 5.68; PHOTON: ICC = 0.61, R = 0.62, MAE = 6.37; BARACUS: ICC = 0.47, R = 0.64, MAE = 8.80). Using brainageR, a three-way interaction revealed that young males with PTSD exhibited higher brain PAD relative to male controls in young and old age groups; old males with PTSD exhibited lower brain PAD compared to male controls of all ages. Discussion Differential impact of PTSD on brain PAD in younger versus older males may indicate a critical window when PTSD impacts brain aging, followed by age-related brain changes that are consonant with individuals without PTSD. Future longitudinal research is warranted to understand how PTSD impacts brain aging across the lifespan