Health Shocks, Village Elections, and Long-Term Income: Evidence from Rural China

Using a sample of households in 48 Chinese villages for the period 1986-2002, this paper studies the dynamic effects of major health shocks on household income and the role played by village elections in mitigating these effects. Our results show that in the first 15 years after a shock, a shock-hit household on average falls short of its normal income trajectory by 11.8% and its recovery would take 19 years. Based on the premise that shock-hit families impose negative externalities on richer families by borrowing from them, our political economy model predicts that the outcome of village elections would differ from that of a standard median voter model in that the elected village leaders tend to adopt pro-poor policies. Our empirical study finds that villages are more likely to establish a healthcare plan after the election is introduced. In addition, village elections reduce the probability of a household to borrow by 16.7% when one of its working adults is seriously sick. As a result, they reduce more than half of the negative effect of a health shock on household income.

Local elections and consumption insurance : evidence from Chinese villages

While the literature on consumption insurance is growing fast, little research has been conducted on how rural consumption insurance is affected by democracy. In this paper the authors examine how consumption insurance of Chinese rural residents is affected if the local leader is democratically elected. Exploring a unique panel data set of 1,400 households from 1987 to 2002, they find that consumption insurance is more complete when the households are in villages with elected village leaders. Furthermore, democracy improves consumption insurance only for the poor and middle-income farmers, but not for the rich. These findings underline the importance of democratic governance for ensuring better rural consumption insurance and poverty reduction.Rural Poverty Reduction,Consumption,Inequality,Services&Transfers to Poor,Economic Theory&Research

PYATB: An Efficient Python Package for Electronic Structure Calculations Using Ab Initio Tight-Binding Model

We present PYATB, a Python package designed for computing band structures and related properties of materials using the ab initio tight-binding Hamiltonian. The Hamiltonian is directly obtained after conducting self-consistent calculations with first-principles packages using numerical atomic orbital (NAO) bases, such as ABACUS. The package comprises three modules: Bands, Geometric, and Optical. In the Bands module, one can calculate essential properties of band structures, including the partial density of states (PDOS), fat bands, Fermi surfaces, and Weyl/Dirac points. The band unfolding method is utilized to obtain the energy band spectra of a supercell by projecting the electronic structure of the supercell onto the Brillouin zone of the primitive cell. With the Geometric module, one can compute the Berry phase and Berry curvature-related quantities, such as electric polarization, Wilson loops, Chern numbers, and anomalous Hall conductivities. The Optical module offers a range of optical property calculations, including optical conductivity and nonlinear optical responses, such as shift current and Berry curvature dipole

Influence of the Kuroshio interannual variability on the summertime precipitation over the East China Sea and adjacent area

Author Posting. © American Meteorological Society, 2019. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 32(8), (2019): 2185-2205. doi:10.1175/JCLI-D-18-0538.1.Much attention has been paid to the climatic impacts of changes in the Kuroshio Extension, instead of the Kuroshio in the East China Sea (ECS). This study, however, reveals the prominent influences of the lateral shift of the Kuroshio at interannual time scale in late spring [April–June (AMJ)] on the sea surface temperature (SST) and precipitation in summer around the ECS, based on high-resolution satellite observations and ERA-Interim. A persistent offshore displacement of the Kuroshio during AMJ can result in cold SST anomalies in the northern ECS and the Japan/East Sea until late summer, which correspondingly causes anomalous cooling of the lower troposphere. Consequently, the anomalous cold SST in the northern ECS acts as a key driver to robustly enhance the precipitation from the Yangtze River delta to Kyushu in early summer (May–August) and over the central ECS in late summer (July–September). In view of the moisture budget analysis, two different physical processes modulated by the lateral shift of the Kuroshio are identified to account for the distinct responses of precipitation in early and late summer, respectively. First, the anomalous cold SST in the northern ECS induced by the Kuroshio offshore shift is likely conducive to the earlier arrival of the mei-yu–baiu front at 30°–32°N and its subsequent slower northward movement, which may prolong the local rainy season, leading to the increased rain belt in early summer. Second, the persistent cold SST anomalies in late summer strengthen the near-surface baroclinicity and the associated strong atmospheric fronts embedded in the extratropical cyclones over the central ECS, which in turn enhances the local rainfall.We appreciate three anonymous reviewers for their thoughtful and constructive comments. This work is supported by the National Key Research and Development Program of China (2016YFA0601804), the National Natural Science Foundation of China (NSFC) Projects (91858102, 41490643, 41490640, 41506009, U1606402) and the OUC–WHOI joint research program (21366).2019-10-0

Efficient hybrid density functional calculation by deep learning

Hybrid density functional calculation is indispensable to accurate description of electronic structure, whereas the formidable computational cost restricts its broad application. Here we develop a deep equivariant neural network method (named DeepH-hybrid) to learn the hybrid-functional Hamiltonian from self-consistent field calculations of small structures, and apply the trained neural networks for efficient electronic-structure calculation by passing the self-consistent iterations. The method is systematically checked to show high efficiency and accuracy, making the study of large-scale materials with hybrid-functional accuracy feasible. As an important application, the DeepH-hybrid method is applied to study large-supercell Moir\'{e} twisted materials, offering the first case study on how the inclusion of exact exchange affects flat bands in the magic-angle twisted bilayer graphene

Sin1 phosphorylation impairs mTORC2 complex integrity and inhibits downstream Akt signaling to suppress tumorigenesis

The mechanistic target of rapamycin (mTOR) functions as a critical regulator of cellular growth and metabolism by forming multi-component, yet functionally distinct complexes mTORC1 and mTORC2. Although mTORC2 has been implicated in mTORC1 activation, little is known about how mTORC2 is regulated. Here we report that phosphorylation of Sin1 at T86 and T398 suppresses mTORC2 kinase activity by dissociating Sin1 from mTORC2. Importantly, Sin1 phosphorylation, triggered by S6K or Akt, in a cellular context-dependent manner, inhibits not only insulin/IGF-1-mediated, but also PDGF or EGF-induced Akt phosphorylation by mTORC2, demonstrating a negative regulation of mTORC2 independent of IRS-1 and Grb10. Lastly, a cancer patient-derived Sin1-R81T mutation impairs Sin1 phosphorylation, leading to hyper-mTORC2 activation via bypassing this negative regulation. Together, our work reveals a Sin1 phosphorylation-dependent mTORC2 regulation, providing a potential molecular mechanism by which mutations in the mTORC1/S6K/Sin1 signaling axis might cause aberrant hyper-activation of mTORC2/Akt that facilitates tumorigenesis

Glutamic acid decarboxylase autoantibodies are dominant but insufficient to identify most Chinese with adult-onset non-insulin requiring autoimmune diabetes: LADA China study 5.

AIMS: Adult-onset autoimmune diabetes is prevalent in China, in contrast to childhood-onset type 1 diabetes mellitus. Islet autoantibodies are the most important immune biomarkers to diagnose autoimmune diabetes. We assayed four different islet autoantibodies in recently diagnosed adult non-insulin-requiring diabetes Chinese subjects to investigate the best antibody assay strategy for the correct diagnosis of these subjects. METHODS: LADA China study is a nation-wide multicenter study conducted in diabetes patients from 46 university-affiliated hospitals in China. Non-insulin-treated newly diagnosed adult diabetes patients (n = 2388) were centrally assayed for glutamic acid decarboxylase autoantibody (GADA), protein tyrosine phosphatase-2 autoantibody (IA-2A), and zinc transporter 8 autoantibody (ZnT8A) by radioligand assay and insulin autoantibody (IAA) by microtiter plate radioimmunoassay. Clinical data were determined locally. RESULTS: Two hundred and six (8.63 %) subjects were autoantibody positive, of which GADA identified 5.78 % (138/2388) of the total, but only 67 % (138/206) of the autoimmune cases. IA-2A, ZnT8A, and IAA were found in 1.51, 1.84, and 1.26 % of the total study subjects, respectively. When assaying three islet autoantibodies, the most effective strategy was the combination of GADA, ZnT8A, and IAA, which could identify 92.2 % (190/206) autoimmune diabetes patients. The clinical data showed that those subjects with positive GADA had lower random C-peptide than autoantibody negative subjects (P < 0.05). CONCLUSIONS: As with Europeans, GADA is the dominant autoantibody in this form of autoimmune diabetes in China, but in contrast to Europeans, screening should include other diabetes-associated autoantibodies

Peculiar band geometry induced giant shift current in ferroelectric SnTe monolayer

Abstract The bulk photovoltaic effect (BPVE) occurs when homogeneous noncentrosymmetric materials generate photocurrent or photovoltage under illumination. The intrinsic contribution to this effect is known as the shift current effect. We calculate the shift current conductivities of the ferroelectric SnTe monolayer using first-principles methods. Our results reveal a giant shift-current conductivity near the valley points in the SnTe monolayer. More remarkably, the linear optical absorption coefficient at this energy is very small, resulting in an enormous Glass coefficient that is four orders of magnitude larger than that of BaTiO3. To understand these giant shift-current effects, we employ a three-band model and find that they arise from the nontrivial energy band geometries near the valley points, where the shift-vector diverges. This serves as a prominent example highlighting the crucial role of band geometry in determining the fundamental properties of solids

Webly-Supervised Video Recognition By Mutually Voting For Relevant Web Images And Web Video Frames

Video recognition usually requires a large amount of training samples, which are expensive to be collected. An alternative and cheap solution is to draw from the large-scale images and videos from the Web. With modern search engines, the top ranked images or videos are usually highly correlated to the query, implying the potential to harvest the labeling-free Web images and videos for video recognition. However, there are two key difficulties that prevent us from using the Web data directly. First, they are typically noisy and may be from a completely different domain from that of users’ interest (e.g. cartoons). Second, Web videos are usually untrimmed and very lengthy, where some query-relevant frames are often hidden in between the irrelevant ones. A question thus naturally arises: to what extent can such noisy Web images and videos be utilized for labeling-free video recognition? In this paper, we propose a novel approach to mutually voting for relevant Web images and video frames, where two forces are balanced, i.e. aggressive matching and passive video frame selection. We validate our approach on three large-scale video recognition datasets