Gail Hershatter, The Gender of Memory: Rural Women and China’s Collective Past

Gail Hershatter’s book The Gender of Memory: Rural Women and China’s Collective Past is based on more than one decade of research she carried out with Gao Xiaoxian (高小贤), a native of their research site, Shaanxi Province, and both a research office director of the Shaanxi Provincial Women’s Federation and Secretary General of the Shaanxi Research Association for Women and Family. When the two first met in Beijing in 1992, they discovered a common interest in early socialism in rural China and..

Gail Hershatter, The Gender of Memory: Rural Women and China’s Collective Past

Le livre de Gail Hershatter, The Gender of Memory: Rural Women and China’s Collective Past, est basé sur plus d’une décennie de recherches entreprises avec Gao Xiaoxian (高小贤), née dans la province du Shaanxi – leur terrain d'études – et cumulant les fonctions de directrice du Bureau d’études de la Fédération des femmes de la province du Shaanxi et de secrétaire générale de l’Association de recherche sur les femmes et la famille de la province. Quand les deux femmes se rencontrent à Pékin en 1..

Gail Hershatter, The Gender of Memory: Rural Women and China’s Collective Past

Gail Hershatter’s book The Gender of Memory: Rural Women and China’s Collective Past is based on more than one decade of research she carried out with Gao Xiaoxian (高小贤), a native of their research site, Shaanxi Province, and both a research office director of the Shaanxi Provincial Women’s Federation and Secretary General of the Shaanxi Research Association for Women and Family. When the two first met in Beijing in 1992, they discovered a common interest in early socialism in rural China and..

Searching for the gut microbial contributing factors to social behavior in rodent models of autism spectrum disorder

Social impairment is one of the major symptoms in multiple psychiatric disorders, including autism spectrum disorder (ASD). Accumulated studies indicate a crucial role for the gut microbiota in social development, but these mechanisms remain unclear. This review focuses on two strategies adopted to elucidate the complicated relationship between gut bacteria and host social behavior. In a top-down approach, researchers have attempted to correlate behavioral abnormalities with altered gut microbial profiles in rodent models of ASD, including BTBR mice, maternal immune activation (MIA), maternal valproic acid (VPA) and maternal high-fat diet (MHFD) offspring. In a bottom-up approach, researchers use germ-free (GF) animals, antibiotics, probiotics or pathogens to manipulate the intestinal environment and ascertain effects on social behavior. The combination of both approaches will hopefully pinpoint specific bacterial communities that control host social behavior. Further discussion of how brain development and circuitry is impacted by depletion of gut microbiota is also included. The converging evidence strongly suggests that gut microbes affect host social behavior through the alteration of brain neural circuits. Investigation of intestinal microbiota and host social behavior will unveil any bidirectional communication between the gut and brain and provide alternative therapeutic targets for ASD

Improving Representation Learning for Histopathologic Images with Cluster Constraints

Recent advances in whole-slide image (WSI) scanners and computational capabilities have significantly propelled the application of artificial intelligence in histopathology slide analysis. While these strides are promising, current supervised learning approaches for WSI analysis come with the challenge of exhaustively labeling high-resolution slides - a process that is both labor-intensive and time-consuming. In contrast, self-supervised learning (SSL) pretraining strategies are emerging as a viable alternative, given that they don't rely on explicit data annotations. These SSL strategies are quickly bridging the performance disparity with their supervised counterparts. In this context, we introduce an SSL framework. This framework aims for transferable representation learning and semantically meaningful clustering by synergizing invariance loss and clustering loss in WSI analysis. Notably, our approach outperforms common SSL methods in downstream classification and clustering tasks, as evidenced by tests on the Camelyon16 and a pancreatic cancer dataset.Comment: Accepted by ICCV202

Stacking up electron-rich and electron-deficient monolayers to achieve extraordinary mid- to far-infrared excitonic absorption: Interlayer excitons in the C3B/C3N bilayer

Our ability to efficiently detect and generate far-infrared (i.e., terahertz) radiation is vital in areas spanning from biomedical imaging to interstellar spectroscopy. Despite decades of intense research, bridging the terahertz gap between electronics and optics remains a major challenge due to the lack of robust materials that can efficiently operate in this frequency range, and two-dimensional (2D) type-II heterostructures may be ideal candidates to fill this gap. Herein, using highly accurate many-body perturbation theory within the GW plus Bethe-Salpeter equation approach, we predict that a type-II heterostructure consisting of an electron rich C3N and an electron deficient C3B monolayers can give rise to extraordinary optical activities in the mid- to far-infrared range. C3N and C3B are two graphene-derived 2D materials that have attracted increasing research attention. Although both C3N and C3B monolayers are moderate gap 2D materials, and they only couple through the rather weak van der Waals interactions, the bilayer heterostructure surprisingly supports extremely bright, low-energy interlayer excitons with large binding energies of 0.2 ~ 0.4 eV, offering an ideal material with interlayer excitonic states for mid-to far-infrared applications at room temperature. We also investigate in detail the properties and formation mechanism of the inter- and intra-layer excitons.Comment: 15 pages, 6 figure

Association between cardiovascular risk factors and atrial fibrillation

BackgroundThe most prevalent sustained arrhythmia in medical practice, atrial fibrillation (AF) is closely associated with a high risk of cardiovascular disease. Nevertheless, the risk of AF associated with cardiovascular risk factors has not been well elucidated. We pooled all published studies to provide a better depiction of the relationship among cardiovascular risk factors with AF.MethodsStudies were searched in the MEDLINE, Web of Science, and EMBASE databases since initiation until January 15, 2022. Prospective cohort studies assessing the relationship a minimum of single cardiovascular risk factors to AF incidence were included if they contained adequate data for obtaining relative risks (RR) and 95% confidence intervals (CI). Random-effects models were utilized to perform independent meta-analyses on each cardiovascular risk factor. PROSPERO registry number: CRD42022310882.ResultsA total of 17,098,955 individuals and 738,843 incident cases were reported for data from 101 studies included in the analysis. In all, the risk of AF was 1.39 (95% CI, 1.30–1.49) for obesity, 1.27 (95% CI, 1.22–1.32) per 5 kg/m2 for increase in body mass index, 1.19 (95% CI, 1.10–1.28) for former smokers, 1.23 (95% CI, 1.09–1.38) for current smokers, 1.31 (95% CI, 1.23–1.39) for diabetes mellitus, 1.68 (95% CI, 1.51–1.87) for hypertension, and 1.12 (95% CI, 0.95–1.32) for dyslipidemia.InterpretationAdverse cardiovascular risk factors correlate with an increased risk of AF, yet dyslipidemia does not increase the risk of AF in the general population, potentially providing new insights for AF screening strategies among patients with these risk factors.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/, PROSPERO identifier (CRD42022310882)