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Background The limited proliferation capacity of β-cells contributes to insulin insufficiency, which promotes the onset of diabetes. β-cells secrete multiple beneficial substances, except for insulin; therefore, the regeneration mechanism of β-cells requires further study. Previously, ubiquitin-binding enzyme 2C (UBE2C) was reported to be downstream of the key β-cell transcription factor NKX6.1, but the specific function and mechanism of UBE2C in pancreatic islets remain unknown. Methods We performed islet perfusion, immunofluorescence staining, and transmission electron microscopy (TEM) to examine the role of UBE2C in mice with a Cre-loxP-constructed β-cell-specific knockout (βUbe2cKO) or overexpression through lentivirus infusion within the pancreatic ducts. Single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq, tandem mass tag (TMT) quantification proteomics, immunoprecipitation, and immunoblotting were conducted to explore the mechanisms by which UBE2C regulates β-cell proliferation. Results We discovered that ubiquitin-conjugating enzyme E2C (UBE2C) was highly expressed in a β-cell subgroup, which exhibited active proliferation capacity. We confirmed that UBE2C promotes β-cell compensatory proliferation by activating cell cycle renewal during a high-fat diet (HFD) and weaning. Consequently, β-cell-specific Ube2c knockout (βUbe2cKO) mice developed typical type 2 diabetes owing to β-cell loss caused by insulin insufficiency. Mechanistically, UBE2C combines with CUL1 to regulate PER1 degradation through ubiquitination. Notably, restored Ube2c expression in islets activated β-cell regeneration in streptozotocin (STZ)-induced diabetic mice, and alleviated diabetic hyperglycaemia and glucose intolerance. Conclusion This study indicates that UBE2C positively regulates β-cell proliferation by promoting ubiquitination and degradation of the biological clock suppressor PER1. The UBE2C-mediated beneficial effects on β-cells suggest a promising application in the treatment of diabetic patients with β-cell deficiency.

Type 1 diabetes (T1D) is a T cell-mediated autoimmune disease. Increased incidence of T1D was reported in patients receiving IFN-α treatment. However, the exact mechanisms of IFN-α that facilitate the pathogenesis of T1D are not fully understood. To explore the mechanism of IFN-α on the immune system and islets, non-obese diabetic (NOD) mice were injected with IFN-α and the progression of autoimmune insulitis was assessed by haematoxylin and eosin (HE) staining, immunohistochemical and flow cytometry analysis. Transcriptional profiling of islets treated with IFN-α was explored by RNA-seq. IFN-α induced antigen presentation was evaluated by qRT-PCR, western blot and immunofluorescence, and key transcription factors were inhibited by small interfering RNAs (siRNAs). Our data show that IFN-α contributed to the progression of autoimmune insulitis in NOD mice by promoting the proliferation of CD8+ T cells. IFN-α upregulated antigen presentation related genes MHC I, TAP1, B2M, PSMB8, NLRC5 and transcriptional regulator STAT1, STAT2, IRF7 at a time and dose-dependent manner. The silence of STAT1 or STAT2 both weakened IFN-α-induced increase of antigen presenting related molecules. IRF7 was also merely influenced by STAT1 silence. The knockdown of IRF7 decreased the IFN-α induced expressions of TAP1, PSMB8 and MHC I and prevented the expression of STAT2 but not STAT1. Our study demonstrated that STAT1-IRF7-MHC I complex axis were crucial for IFN-α signalling in islets, and created positive feedback through IRF7-STAT2 cascade amplifying signals which accelerated the process of T1D.

Islets have complex heterogeneity and subpopulations. Cell surface markers representing alpha, beta and delta cell subpopulations are urgently needed for investigations to explore the compositional changes of each subpopulation in obesity progress and diabetes onset, and the adaptation mechanism of islet metabolism induced by a high-fat diet (HFD).Single-cell RNA sequencing (scRNA-seq) was applied to identify alpha, beta and delta cell subpopulation markers in an HFD-induced mouse model of glucose intolerance. Flow cytometry and immunostaining were used to sort and assess the proportion of each subpopulation. Single-cell proteomics was performed on sorted cells, and the functional status of each alpha, beta and delta cell subpopulation in glucose intolerance was deeply elucidated based on protein expression.A total of 33,999 cells were analysed by scRNA-seq and clustered into eight populations, including alpha, beta and delta cells. For alpha cells, scRNA-seq revealed that the Ace2We identified ACE2, CD81 and GLUT2 as surface markers to distinguish, respectively, alpha, beta and delta cell subpopulations with heterogeneous maturation and function. The changes in the proportion and functional status of islet endocrine subpopulations reflect the metabolic adaptation of islets to high-fat stress, which weakened the function of alpha cells and enhanced the function of beta and delta cells to bring about glycaemic homeostasis. Our findings provide a fundamental resource for exploring the mechanisms maintaining each islet endocrine subpopulation's fate and function in health and disease.The scRNA-seq analysis datasets from the current study are available in the Gene Expression Omnibus (GEO) repository under the accession number GSE203376.

Depth estimation from stereo images is essential to many applications such as robotics and autonomous vehicles, most of which ask for the real-time response, high energy and storage efficiency. Recent work has shown deep neural networks (DNN) perform extremely well for stereo estimation. However, these state-of-the-art DNN based algorithms are challenging to be deployed into real-world applications due to the high computational complexities of DNNs. Most of them are too slow for real-time inference and require several seconds of GPU computation to process image frames. In this article, we address the problem of fast stereo estimation and propose an efficient and light-weighted stereo matching system, called StereoBit, to produce a disparity map in a real-time manner while achieving close to state-of-the-art accuracy. To achieve this goal, we propose a binary neural network to generate weighted Hamming distance for an efficient similarity join in stereo estimation. In addition, we propose a novel approximation approach to derive StereoBit network directly from the well-trained network with the cosine similarity. Our approximation strategies enable a significant speedup while maintaining almost the same accuracy compared to the network with the cosine similarity. Furthermore, we present an optimization framework for fully exploiting the computing power of StereoBit. The framework provides a significant speedup of stereo estimation routines, and at the same time, reduces the memory usage for storing parameters. The effectiveness of StereoBit is evaluated by comprehensive experiments. StereoBit can achieve 60 frames per second on an NVIDIA TITAN Xp GPU on KITTI 2012 benchmark while achieving 3-pixel non-occluded stereo error 3.56 percent.

Background China, which used to be an export country for migrants, has become a new destination for international migrants due to its rapid economic growth. However, little empirical data is available on the health status of and health service access barriers faced by these international migrants. Methods Foreigners who visited the Guangzhou Municipal Exit-Entry Administration Office to extend their visas were invited to participate in the study. Quantitative data were collected using electronic questionnaire in 13 languages. The participants were characterised by the income level of their country of origin (high-, middle- and low-income countries (HICs, MICs and LICs, respectively)), and the key factors associated with their health status, medical insurance coverage and perceptions of health services in China were examined. Results Overall, 1146 participants from 119 countries participated in the study, 57.1, 25.1 and 17.8% of whom were from MICs, HICs and LICs, respectively. Over one fifth of the participants experienced health problems while staying in China, and about half had no health insurance. Although the participants from HICs were more likely than those from MICs and LICs to have medical insurance, they were also more likely to have health problems. Furthermore, 43.0, 45.0 and 12.0% of the participants thought that the health services in China were good, fair and poor, respectively. Among the participants, those from HICs were less likely to have positive feedback. Conclusions Our study is the first to report a quantitative survey of the health status, health insurance coverage, and health service perceptions of a diverse and surging population of international migrants in China. The findings call for more in-depth studies on the challenges presented by the increasing global migration to the health system.

Individual forecasts of economic variables show widespread overreaction to news, but laboratory experiments on belief updating typically find underinference from signals. We provide new experimental evidence to connect these two seemingly inconsistent phenomena. Building on a classic experimental paradigm, we study how people make inferences and revise forecasts in the same information environment. Subjects underreact to signals when inferring about underlying states, but overreact to signals when revising forecasts about future outcomes. This gap in belief updating is largely driven by the use of different simplifying heuristics between the two tasks. Additional treatments link our results to the difficulty of recognizing the conceptual connection between making inferences and revising forecasts.

New workers often compare themselves to their high-achieving senior coworkers, but they often do so without knowing how senior workers performed in the early stages of their careers. This upward social comparison under incomplete information can have adverse effects on new workers’ well-being and employee turnover. We study whether providing performance trajectory information to new workers mitigates the negative consequences of performance comparison. In a large-scale randomized control trial at a leading multinational spa chain in China, we sent workers twice-weekly messages on the performance trajectories of their high-performing senior coworkers. This information treatment reduces the attrition rate of new workers by 12%, and the effect is most pronounced for the more productive workers. The lower attrition rate is mostly driven by an improvement in new workers’ stress levels and mental health due to the lowering of their beliefs about senior coworkers’ past performance. Overall, this study demonstrates that showing junior workers the “Curricula Vitae” of senior workers mitigates social comparison costs within firms.

Online communication platforms like Slack and Microsoft teams have become increasingly crucial for a digitized workplace to improve business efficiency and growth. However, these chat platforms can overwhelm the users with unstructured long streams of back and forth discussions scattered in various places. Thus, discussions become challenging to follow, leading to an increased likelihood of missing valuable information. Moreover, with the unsatisfying keyword-based chat search, users spend a significant amount of time to read, digest, and recall information from the conversations at the cost of productivity. In this paper, we present Vertext, an end-to-end AI system that ingests user conversations and automatically extracts information such as announcements, task assignments, and conversation summary. Moreover, Vertext gives a unique search experience to the users by providing search results along with their context, with an improved performance enabled by semantic search. For the ease of user interaction, all the information is consolidated on a single dashboard provided by Vertext.

The field of recommender systems has rapidly evolved over the last few years, with significant advances made due to the in-flux of deep learning techniques. However, as a result of this rapid progress, escalating barriers-to-entry for new researchers is emerging. In particular, state-of-the-art approaches have fragmented into a large number of code-bases, often requiring different input formats, pre-processing stages and evaluating with different metric packages. Hence, it is time-consuming for new researchers to reach the point of having both an effective baseline set and a sound comparative environment. As a step towards elevating this problem, we have developed BETA-Rec, an open source project for Building, Evaluating and Tuning Automated Recommender Systems. BETA-Rec aims to provide a practical data toolkit for building end-to-end recommendation systems in a standardized way. It provides means for dataset preparation and splitting using common strategies, a generalized model engine for implementing recommender models using Pytorch with 9 models available out-of-the-box, as well as a unified training, validation, tuning and testing pipeline. Furthermore, BETA-Rec is designed to be both modular and extensible, enabling new models to be quickly added to the framework. It is deployable in a wide range of environments via pre-built docker containers and supports distributed parameter tuning using Ray. In this demo, we will illustrate the deployment and use of BETA-Rec for researchers and practitioners on a number of standard recommendation datasets. The source code of the project is available at github: https://github.com/beta-team/beta-recsys.

Subject motion during MRI scan can result in severe degradation of image quality. Existing motion correction algorithms rely on the assumption that no information is missing during motions. However, this assumption does not hold when out-of-FOV motion happens. Currently available algorithms are not able to correct for image artifacts introduced by out-of-FOV motion. The purpose of this study is to demonstrate the feasibility of incorporating convolutional neural network (CNN) derived prior image into solving the out-of-FOV motion problem.A modified U-net network was proposed to correct out-of-FOV motion artifacts by incorporating motion parameters into the loss function. A motion model based data fidelity term was applied in combination with the CNN prediction to further improve the motion correction performance. We trained the CNN on 1113 MPRAGE images with simulated oscillating and sudden motion trajectories, and compared our algorithm to a gradient-based autofocusing (AF) algorithm in both 2D and 3D images. Additional experiment was performed to demonstrate the feasibility of transferring the networks to different dataset. We also evaluated the robustness of this algorithm by adding Gaussian noise to the motion parameters. The motion correction performance was evaluated using mean square error (NMSE), peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM).The proposed algorithm outperformed AF-based algorithm for both 2D (NMSE: 0.0066 ± 0.0009 vs 0.0141 ± 0.008, P .01; PSNR: 29.60 ± 0.74 vs 21.71 ± 0.27, P .01; SSIM: 0.89 ± 0.014 vs 0.73 ± 0.004, P .01) and 3D imaging (NMSE: 0.0067 ± 0.0008 vs 0.070 ± 0.021, P .01; PSNR: 32.40 ± 1.63 vs 22.32 ± 2.378, P .01; SSIM: 0.89 ± 0.01 vs 0.62 ± 0.03, P .01). Robust reconstruction was achieved with 20% data missed due to the out-of-FOV motion.In conclusion, the proposed CNN-based motion correction algorithm can significantly reduce out-of-FOV motion artifacts and achieve better image quality compared to AF-based algorithm.

When an agent receives information from a source whose accuracy might be either high or low, standard theory dictates that she update as if the source has medium accuracy. In a lab experiment, subjects deviate from this benchmark by reacting less to uncertain sources, especially when the sources release good news. This pattern is validated using observational data on stock price reactions to analyst earnings forecasts, where analysts with no forecast records are classified as uncertain sources. A theory of belief updating where agents are insensitive and averse to uncertainty in information accuracy can explain these results.

In this article, we provide a comprehensive examination of the spatial and career mobility of China's labor population. We integrate theories on stratification and social change and exploit the innovative design and measurement of the China Labor-force Dynamics Survey to minimize the undercoverage problem of the rural-urban migratory experience. Our analysis provides several fresh findings: (1) at-birth rural household registration (hukou) status leads to a greater probability of spatial mobility and career advancement than at-birth urban hukou status does; (2) education and gender differentiates rural-origin people, increasing the heterogeneity of urban labor and decreasing the heterogeneity of rural labor; (3) hukou policy relaxation favors later cohorts over earlier cohorts; and (4) among demographically comparable people, having experienced spatial mobility is correlated with having career advancement experience. Work organizations are found to be the arena where the two dimensions of mobility can happen jointly. Our findings provide a rich context for understanding the management and organization of Chinese labor.

In decision making under uncertainty, the subjective evaluation of an outcome often depends on the information content of its very own realization. We accommodate this dependence by introducing information-dependent utility to the Anscombe-Aumann expected utility framework. In our first representation, the utility of an outcome can flexibly depend on its information content. However, subjective beliefs are not identified from preference over acts. In our second representation, the utility of consumption and utility of information are additively separable and the subjective belief is point identified. Our representations allow for both information seeking and information averse preferences, as well as a comparative theory of information preferences. For information seeking preferences, we introduce a Hidden Acts representation where the value of information is as if induced from the expected utility of the optimal choice in a fictitious future decision problem given that information.

This study tests international migration theory, especially cumulative causation theory, by looking into the causal mechanisms of international migration behavior among African immigrants in China by using the respondent-driven sampling method since African immigrants in China belong to a small hidden population. This method collected a representative sample (N = 648) from two locations in 2011. The paper reveals that the immigration behaviors of African immigrants in China from 2005 to 2011 have characteristics similar to international immigrants in initial stages - the cumulative causal effects of immigrants' social capital was continually strengthened during the reproduction of migration behavior in the sending countries. Consequently, given the sustainable economic growth and maintenance of a stable society in China, the scale of future transnational immigration (including illegal migration) will continue to expand. The paper proposes that at the present stage, existing policies should raise the entry threshold of African immigrants into China in order to mitigate the speed and scale of migrants' social class decline.

Gender Diagnosticity questionnaires are created by determining which questions best predict membership in gender groups. The UBC Gender Diagnosticity Questionnaire was both created by and administered to a single group of 60 students (50 female, 10 male) enrolled in an undergraduate personality psychology course. Each student was instructed to contribute questions that they believed would differentiate between genders, resulting in a 390-item questionnaire completed by the class. Results mirror other gender diagnosticity studies demonstrating that occupation, hobbies, and activity preferences predicted gender groups with large effects. The final 23-item questionnaire covered a greater range of questions, including sexual preferences, emotional responses, and concern for appearance. Results indicated the questionnaire had good reliability and factorial validity. The only Big Five personality trait that demonstrated a significant correlation with the questionnaire was agreeableness (-.34). This is consistent with past research that Gender Diagnosticity is mostly independent of the Big Five traits.