GraphTheta: A Distributed Graph Neural Network Learning System With Flexible Training Strategy

Graph neural networks (GNNs) have been demonstrated as a powerful tool for analysing non-Euclidean graph data. However, the lack of efficient distributed graph learning (GL) systems severely hinders applications of GNNs, especially when graphs are big and GNNs are relatively deep. Herein, we present GraphTheta, a novel distributed and scalable GL system implemented in vertex-centric graph programming model. GraphTheta is the first GL system built upon distributed graph processing with neural network operators implemented as user-defined functions. This system supports multiple training strategies, and enables efficient and scalable big graph learning on distributed (virtual) machines with low memory each. To facilitate graph convolution implementations, GraphTheta puts forward a new GL abstraction named NN-TGAR to bridge the gap between graph processing and graph deep learning. A distributed graph engine is proposed to conduct the stochastic gradient descent optimization with a hybrid-parallel execution. Moreover, we add support for a new cluster-batched training strategy besides global-batch and mini-batch. We evaluate GraphTheta using a number of datasets with network size ranging from small-, modest- to large-scale. Experimental results show that GraphTheta can scale well to 1,024 workers for training an in-house developed GNN on an industry-scale Alipay dataset of 1.4 billion nodes and 4.1 billion attributed edges, with a cluster of CPU virtual machines (dockers) of small memory each (5$\sim$12GB). Moreover, GraphTheta obtains comparable or better prediction results than the state-of-the-art GNN implementations, demonstrating its capability of learning GNNs as well as existing frameworks, and can outperform DistDGL by up to $2.02\times$ with better scalability. To the best of our knowledge, this work presents the largest edge-attributed GNN learning task conducted in the literature.Comment: 18 pages, 14 figures, 5 table

Nomograms predict prognosis and hospitalization time using non-contrast CT and CT perfusion in patients with ischemic stroke

BackgroundStroke is a major disease with high morbidity and mortality worldwide. Currently, there is no quantitative method to evaluate the short-term prognosis and length of hospitalization of patients.PurposeWe aimed to develop nomograms as prognosis predictors based on imaging characteristics from non-contrast computed tomography (NCCT) and CT perfusion (CTP) and clinical characteristics for predicting activity of daily living (ADL) and hospitalization time of patients with ischemic stroke.Materials and methodsA total of 476 patients were enrolled in the study and divided into the training set (n = 381) and testing set (n = 95). Each of them owned NCCT and CTP images. We propose to extract imaging features representing as the Alberta stroke program early CT score (ASPECTS) values from NCCT, ischemic lesion volumes from CBF, and TMAX maps from CTP. Based on imaging features and clinical characteristics, we addressed two main issues: (1) predicting prognosis according to the Barthel index (BI)–binary logistic regression analysis was employed for feature selection, and the resulting nomogram was assessed in terms of discrimination capability, calibration, and clinical utility and (2) predicting the hospitalization time of patients–the Cox proportional hazard model was used for this purpose. After feature selection, another specific nomogram was established with calibration curves and time-dependent ROC curves for evaluation.ResultsIn the task of predicting binary prognosis outcome, a nomogram was constructed with the area under the curve (AUC) value of 0.883 (95% CI: 0.781–0.985), the accuracy of 0.853, and F1-scores of 0.909 in the testing set. We further tried to predict discharge BI into four classes. Similar performance was achieved as an AUC of 0.890 in the testing set. In the task of predicting hospitalization time, the Cox proportional hazard model was used. The concordance index of the model was 0.700 (SE = 0.019), and AUCs for predicting discharge at a specific week were higher than 0.80, which demonstrated the superior performance of the model.ConclusionThe novel non-invasive NCCT- and CTP-based nomograms could predict short-term ADL and hospitalization time of patients with ischemic stroke, thus allowing a personalized clinical outcome prediction and showing great potential in improving clinical efficiency.SummaryCombining NCCT- and CTP-based nomograms could accurately predict short-term outcomes of patients with ischemic stroke, including whose discharge BI and the length of hospital stay.Key ResultsUsing a large dataset of 1,310 patients, we show a novel nomogram with a good performance in predicting discharge BI class of patients (AUCs > 0.850). The second nomogram owns an excellent ability to predict the length of hospital stay (AUCs > 0.800)

Dynamic Gust Load Analysis for Rotors

Dynamic load of helicopter rotors due to gust directly affects the structural stress and flight performance for helicopters. Based on a large deflection beam theory, an aeroelastic model for isolated helicopter rotors in the time domain is constructed. The dynamic response and structural load for a rotor under the impulse gust and slope-shape gust are calculated, respectively. First, a nonlinear Euler beam model with 36 degrees-of-freedoms per element is applied to depict the structural dynamics for an isolated rotor. The generalized dynamic wake model and Leishman-Beddoes dynamic stall model are applied to calculate the nonlinear unsteady aerodynamic forces on rotors. Then, we transformed the differential aeroelastic governing equation to an algebraic one. Hence, the widely used Newton-Raphson iteration algorithm is employed to simulate the dynamic gust load. An isolated helicopter rotor with four blades is studied to validate the structural model and the aeroelastic model. The modal frequencies based on the Euler beam model agree well with published ones by CAMRAD. The flap deflection due to impulse gust with the speed of 2m/s increases twice to the one without gust. In this numerical example, results indicate that the bending moment at the blade root is alleviated due to elastic effect

Alternate Wetting and Drying Irrigation Reduces P Availability in Paddy Soil Irrespective of Straw Incorporation

Crop production is highly impacted by soil phosphorus (P) availability which is poor and susceptibly affected by soil moisture. However, how water management and straw incorporation affect paddy soil P availability is still not well known. A 40-day incubation experiment was conducted to evaluate the effects of two water management regimes: continuous flooding irrigation (CF) and alternate wetting and drying irrigation (AWD) combined with different straw addition rates (equivalent to 0, 50%, 100%, 200%, and 300% straw incorporation rates in field) on P availability in paddy soil. Water management significantly affected soil available P, microbial biomass P, total reductant, and ferrous iron. However, straw addition showed no effect on soil P availability in the short term. Compared to CF, AWD consistently decreased the soil available P content under straw addition at different rates. The main reason was that AWD increased microbial biomass for immobilizing P and decreased ferrous iron content for increasing soil P absorption, reducing available P content. In conclusion, AWD reduces available P content in paddy soil compared to CF. Water management has a more significant regulatory effect on soil P availability than straw incorporation in the field management

The limiting effect of genome size on xylem vessel diameter is shifted by environmental pressures in seed plants

Abstract Current and previous studies have extensively studied the physiological and ecological consequences of genome size (GS) on plants because of the limiting effect of GS on cell size. However, it is still obscure whether such limiting effect could be shifted by environmental pressures, or not. Here, we compiled a global dataset comprised of GS, xylem vessel diameter (Vdia), xylem hydraulic conductivity (KS), P50 (xylem water potential at the loss of 50% maximum KS), and climate factors of 251 phylogeny and habitat divergent species from 59 families. The results showed that GS could limit the Vdia of the species from the same family sampled in the similar climate conditions. But the expected positive relationship between GS and Vdia became uncertain and even negative across different environmental conditions. Vdia was strongly positively coordinated with mean annual temperature (MAT), mean annual precipitation (MAP), and potential evapotranspiration (PET). Furthermore, Vdia as the anatomic foundation of plant hydraulic performance was strongly positively coordinated with KS and negatively coordinated with −P50. The strong environmental selection on KS and P50 explained the concerted regulation of Vdia by environmental factors. The findings revealed the combined regulation of GS and environmental pressures on xylem cell size and thus affected plant eco‐physiological performance. The shifted cell size limiting effect of GS by environmental factors manifests plants great plasticity under changed environmental conditions

Proteomic Analysis of Vero Cells Infected with Pseudorabies Virus

Suid herpesvirus 1 (SuHV-1), known as pseudorabies virus (PRV), is one of the most devastating swine pathogens in China, particularly the sudden occurrence of PRV variants in 2011. The higher pathogenicity and cross-species transmission potential of the newly emerged variants caused not only colossal economic losses, but also threatened public health. To uncover the underlying pathogenesis of PRV variants, Tandem Mass Tag (TMT)-based proteomic analysis was performed to quantitatively screen the differentially expressed cellular proteins in PRV-infected Vero cells. A total of 7072 proteins were identified and 960 proteins were significantly regulated: specifically 89 upregulated and 871 downregulated. To make it more credible, the expression of XRCC5 and XRCC6 was verified by western blot and RT-qPCR, and the results dovetailed with the proteomic data. The differentially expressed proteins were involved in various biological processes and signaling pathways, such as chaperonin-containing T-complex, NIK/NF-κB signaling pathway, DNA damage response, and negative regulation of G2/M transition of mitotic cell cycle. Taken together, our data holistically outline the interactions between PRV and host cells, and our results may shed light on the pathogenesis of PRV variants and provide clues for pseudorabies prevention

Molecular classification of human papilloma virus-negative head and neck squamous cell carcinomas: Cell cycle-based classifier and prognostic signature.

The molecular classification of human papillomavirus (HPV)-negative head and neck squamous cell carcinomas (HNSCCs) remains questionable. Differentially expressed genes were detected between tumor and normal tissues and GSEA showed they are associated with cell cycle pathways. This study aimed to classify HPV-negative HNSCCs based on cell cycle-related genes. The established gene pattern was correlated with tumor progression, clinical prognosis, and drug treatment efficacy. Biological analysis was performed using HNSCC patient sample data obtained from the Cancer Genome Atlas (TCGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Gene Expression Omnibus (GEO) databases. All samples included in this study contained survival information. RNA sequencing data from 740 samples were used for the analysis. Previously characterized cell cycle-related genes were included for unsupervised consensus clustering. Two subtypes of HPV-negative HNSCCs (C1, C2) were identified. Subtype C1 displayed low cell cycle activity, 'hot' tumor microenvironment (TME), earlier N stage, lower pathological grade, better prognosis, and higher response rate to the immunotherapy and targeted therapy. Subtype C2 was associated with higher cell cycle activity, 'cold' TME, later N stage, higher pathological grade, worse prognosis, and lower response rate to the treatment. According to the nearest template prediction method, classification rules were established and verified. Our work explored the molecular mechanism of HPV-negative HNSCCs in the view of cell cycle and might provide new sights for personalized anti-cancer treatment

Achieving High Selectivity in Photocatalytic Oxidation of Toluene on Amorphous BiOCl Nanosheets Coupled with TiO₂

The inert C(sp3)–H bond and easy overoxidation of toluene make the selective oxidation of toluene to benzaldehyde a great challenge. Herein, we present that a photocatalyst, constructed with a small amount (1 mol %) of amorphous BiOCl nanosheets assembled on TiO2 (denoted as 0.01BOC/TiO2), shows excellent performance in toluene oxidation to benzaldehyde, with 85% selectivity at 10% conversion, and the benzaldehyde formation rate is up to 1.7 mmol g–1 h–1, which is 5.6 and 3.7 times that of bare TiO2 and BOC, respectively. In addition to the charge separation function of the BOC/TiO2 heterojunction, we found that the amorphous structure of BOC endows its abundant surface oxygen vacancies (Ov), which can further promote the charge separation. Most importantly, the surface Ov of amorphous BOC can efficiently adsorb and activate O2, and amorphous BOC makes the product, benzaldehyde, easily desorb from the catalyst surface, which alleviates the further oxidation of benzaldehyde, and results in the high selectivity. This work highlights the importance of the microstructure based on heterojunctions, which is conducive to the rational design of photocatalysts with high performance in organic synthesis