Diseases of complex origin have a component of quantitative genetics that contributes to their susceptibility and phenotypic variability. However, after several studies, a major part of the genetic component of complex phenotypes has still not been found, a situation known as “missing heritability.” Although there have been many hypotheses put forward to explain the reasons for the missing heritability, its definitive causes remain unknown. Complex diseases are caused by multiple intermediate phenotypes involved in their pathogenesis and, very often, each one (...) of these intermediate phenotypes also has a component of quantitative inheritance. Here we propose that at least part of the missing heritability can be explained by the genetic component of intermediate phenotypes that is not detectable at the level of the main complex trait. At the same time, the identification of the genetic component of intermediate phenotypes provides an opportunity to identify part of the missing heritability of complex diseases. (shrink)

Because they are key components of aircraft, improving the safety, reliability and economy of engines is crucial. To ensure flight safety and reduce the cost of maintenance during aircraft engine operation, a prognostics and health management system that focuses on fault diagnosis, health assessment, and life prediction is introduced to solve the problems. Predicting the remaining useful life is the most important information for making decisions about aircraft engine operation and maintenance, and it relies largely on the selection of performance (...) degradation features. The choice of such features is highly significant, but there are some weaknesses in the current algorithm for RUL prediction, notably, the inability to obtain tendencies from the data. Especially with aircraft engines, extracting useful degradation features from multisensor data with complex correlations is a key technical problem that has hindered the implementation of degradation assessment. To solve these problems, deep learning has been proposed in recent years to exploit multiple layers of nonlinear information processing for unsupervised self-learning of features. This paper presents a deep learning approach to predict the RUL of an aircraft engine based on a stacked sparse autoencoder and logistic regression. The stacked sparse autoencoder is used to automatically extract performance degradation features from multiple sensors on the aircraft engine and to fuse multiple features through multilayer self-learning. Logistic regression is used to predict the remaining useful life. However, the hyperparameters of the deep learning, which significantly impact the feature extraction and prediction performance, are determined based on expert experience in most cases. The grid search method is introduced in this paper to optimize the hyperparameters of the proposed aircraft engine RUL prediction model. An application of this method of predicting the RUL of an aircraft engine with a benchmark dataset is employed to demonstrate the effectiveness of the proposed approach. (shrink)

主要内容为:文艺新学科的立科学研究,文艺新学科方法论、发生论、发展论、文艺新学科属性论等。.

主要内容为:文艺新学科的立科学研究,文艺新学科方法论、发生论、发展论、文艺新学科属性论等。.

Understanding the neural basis of schizophrenia (SZ) is important for shedding light on the neurobiological mechanisms underlying this mental disorder. Structural and functional alterations in the anterior cingulate cortex (ACC), dorsolateral prefrontal cortex (DLPFC), hippocampus, and medial prefrontal cortex (MPFC) have been implicated in the neurobiology of SZ. However, the effective connectivity among them in SZ remains unclear. The current study investigated how neuronal pathways involving these regions were affected in first-episode SZ using functional magnetic resonance imaging (fMRI). Forty-nine patients (...) with a first-episode of psychosis and diagnosis of SZ—according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision—were studied. Fifty healthy controls (HCs) were included for comparison. All subjects underwent resting state fMRI. We used spectral dynamic causal modeling (DCM) to estimate directed connections among the bilateral ACC, DLPFC, hippocampus, and MPFC. We characterized the differences using Bayesian parameter averaging (BPA) in addition to classical inference (t-test). In addition to common effective connectivity in these two groups, HCs displayed widespread significant connections predominantly involved in ACC not detected in SZ patients, but SZ showed few connections. Based on BPA results, SZ patients exhibited anterior cingulate cortico-prefrontal-hippocampal hyperconnectivity, as well as ACC-related and hippocampal-dorsolateral prefrontal-medial prefrontal hypoconnectivity. In summary, spectral DCM revealed the pattern of effective connectivity involving ACC in patients with first-episode SZ. This study provides a potential link between SZ and dysfunction of ACC, creating an ideal situation to associate mechanisms behind SZ with aberrant connectivity among these cognition and emotion-related regions. (shrink)

Post-stroke depression is a serious complication of stroke that significantly restricts rehabilitation. The use of immersive virtual reality for stroke survivors is promising. Herein, we investigated the effects of a novel immersive virtual reality training system on PSD and explored induced effective connectivity alterations in emotional networks using multivariate Granger causality analysis. Forty-four patients with PSD were equally allocated into an immersive-virtual reality group and a control group. In addition to their usual rehabilitation treatments, the participants in the immersive-virtual reality (...) group participated in an immersive-virtual reality rehabilitation program, while the patients in the control group received 2D virtual reality rehabilitation training. The Hamilton Depression Rating Scale, modified Barthel Index, and resting-state functional magnetic resonance imaging data were collected before and after a 4-week intervention. rsfMRI data were analyzed using multivariate GCA. We found that the immersive virtual reality training was more effective in improving depression in patients with PSD but had no statistically significant improvement in MBI scores compared to the control group. The GCA showed that the following causal connectivities were strengthened after immersive virtual reality training: from the amygdala, insula, middle temporal gyrus, and caudate nucleus to the dorsolateral prefrontal cortex; from the insula to the medial prefrontal cortex; and from the thalamus to the posterior superior temporal sulcus. These causal connectivities were weakened after treatment in the control group. Our results indicated the neurotherapeutic use of immersive virtual reality rehabilitation as an effective non-pharmacological intervention for PSD; the alteration of causal connectivity in emotional networks might constitute the neural mechanisms underlying immersive-virtual reality rehabilitation in PSD. (shrink)

Despite considerable disruption of social order caused by the COVID-19 pandemic, it has also been said to contribute to positive psychological changes and influence on the perception of public life satisfaction. The present study aimed to explore the association between the COVID-19 related posttraumatic growth and life satisfaction and the mediating role of personal values. A two-wave longitudinal design was used. 226 self-quarantined Chinese college students completed post traumatic growth inventory, satisfaction with life scale, personal values questionnaire between February 2020 (...) and May 2021. Results showed that more than half of self-quarantined Chinese college students reported moderate to high levels of the COVID-19 related posttraumatic growth. A structural equation model revealed that COVID-19 related posttraumatic growth was positively associated to life satisfaction, and self-transcendence and self-enhancement values partially mediated this association. These findings shed light on whether and how pandemic-related posttraumatic growth influenced personal life satisfaction, supporting the outcome and process perspectives of posttraumatic growth as well as Schwartz’s value theory. Based on the findings, some positive psychology interventions, such as online rumination activities and mindfulness practice, were proposed to enhance self-quarantined college students’ posttraumatic growth and life satisfaction. (shrink)

Ben shu jie shao le gong zi zhen, wei yuan, yan fu, kang you wei, tan si tong, liang qi chao, chen tian hua, zhang bing lin deng wan qing si xiang jia de zhe xue si xiang.

本书考察了颜钧思想产生的社会历史背景及颜钧生平;构建了表述颜钧“大中哲学”的理论框架,包括它的经典依据、它的心性论和工夫论、它的人生价值观和社会理想;阐释了颜钧思想的特征及其在当时和对后世的影响。.

本书主要从传统文化与民族精神、诚信的思考、传统与时代、德治与法制等八个方面,阐述了中国的传统文化与道德建设。.

本书分“德治论坛”、“全球化与伦理问题论坛”、“青少年道德教育论坛”三部分,收录了“儒家伦理与道德义务的权利性条件”、“生命伦理学的前沿问题”、“荀子的生态伦理思想”、“科技时代性伦理问题的向度”等文 章。.

本书内容包括什么是法社会学、多元化的法概念、社会·法律·法律文化、法与社会制度、法与社会发展、法与社会分层、法与社会问题、法与社会控制、法与社会化、法律职业、法的实现等11章。.

本书是一本研究建筑学的书籍, 全书包括有《现象学与建筑学的思考》、《建筑中的神性》等多篇文章.

本书受国家重点学科教育部人文社会科学重点研究基地——山西大学科学技术哲学研究中心基金资助.

本书内容包括:和谐文化思想的历史探析、建设中国特色社会主义与和谐文化、现代化进程中的和谐文化建设、云南和谐文化建设的探索与实践。.

本书主要讨论了先秦时期几种主要话语体系中“观”所具有的内涵结构，并进而讨论了“观”在这些话语体系以及后世几种重要理论中所呈现出来的诗性建构。.

本书分“历史篇”和“理论篇”两部分共6章。内容包括:马克思主义产生以前的文化观、马克思主义创始人的文化理念与文化研究方法等。.

本书是研究20世纪中国美学思想家宗白华的专论，阐述了宗白华美学思想的形成、发展，并兼及相关的思想文化和社会政治背景。.