Label Deconvolution for Node Representation Learning on Large-scale Attributed Graphs against Learning Bias

Node representation learning on attributed graphs -- whose nodes are associated with rich attributes (e.g., texts and protein sequences) -- plays a crucial role in many important downstream tasks. To encode the attributes and graph structures simultaneously, recent studies integrate pre-trained models with graph neural networks (GNNs), where pre-trained models serve as node encoders (NEs) to encode the attributes. As jointly training large NEs and GNNs on large-scale graphs suffers from severe scalability issues, many methods propose to train NEs and GNNs separately. Consequently, they do not take feature convolutions in GNNs into consideration in the training phase of NEs, leading to a significant learning bias from that by the joint training. To address this challenge, we propose an efficient label regularization technique, namely Label Deconvolution (LD), to alleviate the learning bias by a novel and highly scalable approximation to the inverse mapping of GNNs. The inverse mapping leads to an objective function that is equivalent to that by the joint training, while it can effectively incorporate GNNs in the training phase of NEs against the learning bias. More importantly, we show that LD converges to the optimal objective function values by thejoint training under mild assumptions. Experiments demonstrate LD significantly outperforms state-of-the-art methods on Open Graph Benchmark datasets

Integration of the Vegetation Phenology Module Improves Ecohydrological Simulation by the SWAT-Carbon Model

Vegetation phenology and hydrological cycles are closely interacted from leaf and species levels to watershed and global scales. As one of the most sensitive biological indicators of climate change, plant phenology is essential to be simulated accurately in hydrological models. Despite the Soil and Water Assessment Tool (SWAT) has been widely used for estimating hydrological cycles, its lack of integration with the phenology module has led to substantial uncertainties. In this study, we developed a process-based vegetation phenology module and coupled it with the SWAT-Carbon model to investigate the effects of vegetation dynamics on runoff in the upper reaches of Jinsha River watershed in China. The modified SWAT-Carbon model showed reasonable performance in phenology simulation, with root mean square error (RMSE) of 9.89 days for the start-of-season (SOS) and 7.51 days for the end-of-season (EOS). Simulations of both vegetation dynamics and runoff were also substantially improved compared to the original model. Specifically, the simulation of leaf area index significantly improved with the coefficient of determination (R2) increased by 0.62, the Nash–Sutcliffe efficiency (NSE) increased by 2.45, and the absolute percent bias (PBIAS) decreased by 69.0 % on average. Additionally, daily runoff simulation also showed notably improvement, particularly noticeable in June and October, with R2 rising by 0.22 and NSE rising by 0.43 on average. Our findings highlight the importance of integrating vegetation phenology into hydrological models to enhance modeling performance

Bovine PrPC directly interacts with αB-crystalline

AbstractWe used a bovine brain cDNA library to perform a yeast two-hybrid assay with bovine mature PrPC as bait. The screening result showed that αB-crystalline interacted with PrPC. The interaction was further evaluated both in vivo and in vitro with different methods, such as immunofluorescent colocalization, native polyacrylamide-gel electrophoresis, and IAsys biosensor assays. The results suggested that αB-crystalline may have the ability to refold denatured prion proteins, and provided first evidence that αB-crystalline is directly associated with prion protein

ACCESS-OM2 v1.0: a global ocean-sea ice model at three resolutions

We introduce ACCESS-OM2, a new version of the ocean–sea ice model of the Australian Community Climate and Earth System Simulator. ACCESS-OM2 is driven by a prescribed atmosphere (JRA55-do) but has been designed to form the ocean–sea ice component of the fully coupled (atmosphere–land–ocean–sea ice) ACCESS-CM2 model. Importantly, the model is available at three different horizontal resolutions: a coarse resolution (nominally 1∘ horizontal grid spacing), an eddy-permitting resolution (nominally 0.25∘), and an eddy-rich resolution (0.1∘ with 75 vertical levels); the eddy-rich model is designed to be incorporated into the Bluelink operational ocean prediction and reanalysis system. The different resolutions have been developed simultaneously, both to allow for testing at lower resolutions and to permit comparison across resolutions. In this paper, the model is introduced and the individual components are documented. The model performance is evaluated across the three different resolutions, highlighting the relative advantages and disadvantages of running ocean–sea ice models at higher resolution. We find that higher resolution is an advantage in resolving flow through small straits, the structure of western boundary currents, and the abyssal overturning cell but that there is scope for improvements in sub-grid-scale parameterizations at the highest resolution

A New 3D Shaping Method for Low-Thrust Trajectories between Non-Intersect Orbits

This paper proposes a new shape-based method in spherical coordinates to solve three-dimensional rendezvous problems. Compared with the existing shape-based methods, the proposed method does not need parameter optimization. Moreover, it improves the flexibility of orbit fitting, greatly reduces the velocity increment and maximum thrust acceleration, and ensures the orbit safety to a certain extent. The shaping function can provide the initial estimate for numerical trajectory optimization and improve the convergence rate in a certain range when combined with the normalization method. The superiority of the proposed method over the existing methods is demonstrated by two numerical examples. Its effectiveness at initial estimation generation in the indirect optimization of a low-thrust trajectory is demonstrated by the third example

Application of Rapid Rehabilitation Nursing in Perioperative Period of Laparoscopic Radical Prostatectomy for Prostate Cancer Patients

The purpose of the study is to explore the application of rapid rehabilitation nursing strategy in the perioperative period of laparoscopic radical prostatectomy for patients with prostate cancer. A total of 120 patients with prostate cancer undergoing laparoscopic radical prostatectomy were randomly divided into two groups, with 60 cases per group. The control group was given routine nursing care, and the experimental group received rapid rehabilitation nursing strategies. The stress hormone (cortisol and norepinephrine) levels, patient satisfaction, length of hospitalization, hospitalization costs, and postoperative complication were compared between the two groups before and after nursing. The serum cortisol and norepinephrine levels in the control group before nursing were similar to those in the experimental group (P>0.05). The stress hormone levels in the experimental group were lower than those in the control group (P<0.05). It was found that the experimental group had reduced operation time, less intraoperative blood loss, shortened exhaust time, and hospitalization stay and was earlier to eat and to get out of bed than the control group (P<0.05). The time for the patients in the experimental group to pull out the drainage tube was significantly shorter than that of the control group (P<0.05), and the hospitalization costs were fewer than the control group (P<0.05). The rates of postoperative complications including nausea, vomiting, bleeding, and fever in the experimental group were significantly lower than those in the control group (P<0.05). In conclusion, the study suggests that rapid rehabilitation nursing strategies can reduce the stress hormone levels, shorten the length of hospitalization, reduce hospitalization costs, reduce postoperative complication rates, and improve patient satisfaction for prostate cancer patients undergoing laparoscopic radical prostatectomy, in support of clinical application

Spatial and Temporal Variations of Compound Droughts and Hot Extremes in China

Droughts and hot extremes may lead to tremendous impacts on the ecosystem and different sectors of the society. A variety of studies have been conducted on the variability of the individual drought or hot extreme in China. However, the evaluation of compound droughts and hot extremes, which may induce even larger impacts than the individual drought or hot extreme, is still lacking. The aim of this study is to investigate changes in the frequency and spatial extent of compound droughts and hot extremes during summer in China using monthly precipitation and daily temperature data from 1953 to 2012. Results show that a high frequency of compound droughts and hot extremes mostly occur in the regions stretching from northeast to southwest of China. There is an overall increase in the frequency of co-occurrence of droughts and hot extremes across most parts of China with distinct regional patterns. In addition, an increasing trend in the areas covered by compound extremes has been observed, especially after the 1990s. At regional scales, the increase of the frequency and spatial extent of compound extremes has been shown to be most profound in North China (NC), South China (SC), and Southwest China (SWC), while the decrease of compound extremes was found in Central China (CC). These results show the variability of compound droughts and hot extremes and could provide useful insights into the mitigation efforts of extreme events in China

Deletion the C-terminal peptides of Vibrio harveyi phospholipase D significantly improved its enzymatic properties

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Dry-hot magnitude index: a joint indicator for compound event analysis

Weather and climate extremes, such as droughts and heat waves, have been commonly characterized by different properties, including frequency, duration, and magnitude. The magnitude is among the most important properties that determine the impact of extremes. Compound dry and hot events may cause detrimental impacts on water resources, energy security, crop production and food security, and have been receiving increasing attention in recent years. Although extensive studies have been conducted to investigate the magnitude of individual droughts or hot extremes, evaluation of the magnitude of compound dry and hot events has received limited attention. In this study, we develop a dry-hot magnitude index (DHMI) to characterize the magnitude of compound dry and hot events, using monthly precipitation and daily maximum temperature, which takes into account both dry and hot conditions. The DHMI is used to analyze the spatial and temporal patterns of the magnitude of compound dry and hot events in China during summer (June, July, and August) for the period of 1961–2013. Results show that high magnitudes of compound dry and hot events mainly occur in northeastern and southwestern China, with higher magnitudes mostly observed in recent decades since the 1990s. The proposed magnitude index has potential to be a useful tool for analyzing compound dry and hot events and their impacts