a b s t r a c tThe outbreak of the 2019 novel coronavirus disease has caused more than 100,000 people to be infected and has caused thousands of deaths. Currently, the number of infections and deaths is still increasing rapidly. COVID-19 seriously threatens human health, production, life, social functioning and international relations, and has caused widespread concern around the globe. In the fight against COVID-19, geographic information systems (GIS) and big data technologies have played an important role in many aspects, including the rapid aggregation of multisource big data, rapid visualization of epidemic information, spatial tracking of COVID-19, prediction of regional transmission, identification of the spatial allocation of risk and selection of the control level, balance and management of the supply and demand of medical resources, social-emotional guidance and panic elimination, the provision of solid spatial information support for decision-making about COVID-19 prevention and control, measures formulation, and assessment of the effectiveness of COVID-19 prevention and control. GIS has developed and matured relatively quickly and has a complete technological route for data preparation, platform construction, model construction, and map production. However, for the struggle against COVID-19, the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy to provide accurate information for rapid social management. Additionally, in the era of big data, data no longer come mainly from the government but are gathered from more diverse enterprises. As a result, the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data, which requires governments, businesses, and academic institutions to jointly promote the formulation of relevant policies. At the technical level, spatial analysis methods for big data are in the ascendancy. Currently and for a long time in the future, the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition, which signifies that GIS should be used to reinforce the social operation parameterization of models and methods, especially when providing support for social management.

Learning informative representations of users and items from the interaction data is of crucial importance to collaborative filtering (CF). Present embedding functions exploit user-item relationships to enrich the representations, evolving from a single user-item instance to the holistic interaction graph. Nevertheless, they largely model the relationships in a uniform manner, while neglecting the diversity of user intents on adopting the items, which could be to pass time, for interest, or shopping for others like families. Such uniform approach to model user interests easily results in suboptimal representations, failing to model diverse relationships and disentangle user intents in representations. In this work, we pay special attention to user-item relationships at the finer granularity of user intents. We hence devise a new model, Disentangled Graph Collaborative Filtering (DGCF), to disentangle these factors and yield disentangled representations. Specifically, by modeling a distribution over intents for each user-item interaction, we iteratively refine the intent-aware interaction graphs and representations. Meanwhile, we encourage independence of different intents. This leads to disentangled representations, effectively distilling information pertinent to each intent. We conduct extensive experiments on three benchmark datasets, and DGCF achieves significant improvements over several state-of-the-art models like NGCF [40], DisenGCN [25], and MacridVAE [26]. Further analyses offer insights into the advantages of DGCF on the disentanglement of user intents and interpretability of representations. Our codes are available in https://github.com/ xiangwang1223/disentangled_graph_collaborative_filtering. CCS CONCEPTS • Information systems → Recommender systems.

This review summarizes the recent progress related to the field of doping regulation in transition metal compounds, aiming to give an overview of this strategy for designing high-performance catalysts towards electrocatalytic applications.

Engineering electronic properties by elemental doping is a direct strategy to design efficient catalysts towards CO electroreduction. Atomically thin SnS nanosheets were modified by Ni doping for efficient electroreduction of CO . The introduction of Ni into SnS nanosheets significantly enhanced the current density and Faradaic efficiency for carbonaceous product relative to pristine SnS nanosheets. When the Ni content was 5 atm %, the Ni-doped SnS nanosheets achieved a remarkable Faradaic efficiency of 93 % for carbonaceous product with a current density of 19.6 mA cm at -0.9 V vs. RHE. A mechanistic study revealed that the Ni doping gave rise to a defect level and lowered the work function of SnS nanosheets, resulting in the promoted CO activation and thus improved performance in CO electroreduction.

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The transient elevation of cytoplasmic calcium is essential for pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI). However, the calcium channels responsible for this process have remained unknown. Here, we show that rice CDS1 (CELL DEATH and SUSCEPTIBLE to BLAST 1) encoding OsCNGC9, a cyclic nucleotide-gated channel protein, positively regulates the resistance to rice blast disease. We show that OsCNGC9 mediates PAMP-induced Ca 2+ influx and that this event is critical for PAMPs-triggered ROS burst and induction of PTI-related defense gene expression. We further show that a PTI-related receptor-like cytoplasmic kinase OsRLCK185 physically interacts with and phosphorylates OsCNGC9 to activate its channel activity. Our results suggest a signaling cascade linking pattern recognition to calcium channel activation, which is required for initiation of PTI and disease resistance in rice.

BackgroundNeutrophil gelatinase-associated lipocalin (NGAL) has been identified as an early biomarker for prediction of acute kidney injury (AKI). However, the utility of NGAL to predict the occurrence of AKI in septic patients remains controversial. We performed a systematic review and meta-analysis to evaluate the evidence on diagnosis of sepsis AKI and the prediction of other clinical outcomes.MethodThe MEDLINE, EMBASE, Cochrane Library, Wanfang, and CNKI databases were systematically searched up to August 19, 2015. Quality assessment was applied by using the Quality Assessment for Studies of Diagnostic Accuracy (QUADAS-2) tool. The diagnostic performance of NGAL for the prediction of AKI in sepsis was evaluated using pooled estimates of sensitivity, specificity, likelihood ratio, and diagnostic odds ratio (DOR), as well as summary receiver operating characteristic curves (SROC).ResultsFifteen studies with a total of 1,478 patients were included in the meta-analysis. For plasma NGAL, the pooled sensitivity and specificity with corresponding 95 % confidence intervals (CI) were 0.83 (95 % CI: 0.77 − 0.88) and 0.57 (95 % CI: 0.54 − 0.61), respectively. The pooled positive likelihood ratio (PLR) was 3.10 (95 % CI: 1.57 − 6.11) and the pooled negative likelihood ratio (NLR) was 0.24 (95 % CI: 0.13 − 0.43). The pooled DOR was 14.72 (95 % CI: 6.55 − 33.10) using a random effects model. The area under the curve (AUC) for SROC to summarize diagnostic accuracy was 0.86. For urine NGAL, the pooled sensitivity, specificity, PLR, NLR, DOR, and AUC values were 0.80 (95 % CI: 0.77 − 0.83), 0.80 (95 % CI: 0.77 − 0.83), 4.42 (95 % CI: 2.84 − 6.89), 0.21 (95 % CI: 0.13 − 0.35), 24.20 (95 % CI: 9.92 − 59.05) and 0.90, respectively. Significant heterogeneity was explored as a potential source. There was no notable publication bias observed across the eligible studies. NGAL for prediction of renal replacement therapy (RRT) and mortality associated with AKI in septic patients were also evaluated.ConclusionTo a certain extent, NGAL is not only an effective predictive factor for AKI in the process of sepsis, but also shows potential predictive value for RRT and mortality. However, future trials are needed to clarify this controversial issue.

Electroreduction of CO is a sustainable approach to produce syngas with controllable ratios, which are required as specific reactants for the optimization of different industrial processes. However, it is challenging to achieve tunable syngas production with a wide ratio of CO/H , while maintaining a high current density. Herein, cadmium sulfoselenide (CdS Se ) alloyed nanorods are developed, which enable the widest range of syngas proportions ever reported at the current density above 10 mA cm in CO electroreduction. Among CdS Se nanorods, CdS nanorods exhibit the highest Faradaic efficiency (FE) of 81% for CO production with a current density of 27.1 mA cm at -1.2 V vs. reversible hydrogen electrode. With the increase of Se content in CdS Se nanorods, the FE for H production increases. At -1.2 V vs. RHE, the ratios of CO/H in products vary from 4:1 to 1:4 on CdS Se nanorods (x from 1 to 0). Notably, all proportions of syngas are achieved with current density higher than ≈25 mA cm . Mechanistic study reveals that the increased Se content in CdS Se nanorods strengthens the binding of H atoms, resulting in the increased coverage of H* and thus the enhanced selectivity for H production in CO electroreduction.