The influence of Arctic Oscillation (AO) on the frequency of wintertime fog days in eastern China is studied based on the winter AO index, the wintertime fog-day data of national stations in China, and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data from 1954 to 2007. The results show that heavy fog and light fog are more likely to occur during winter in eastern China with the strong interannual variability. During the winter with the positive-phase AO, there are more days of heavy fog in North China but less in South China, while light fog days become more in the whole of eastern China. It is mainly because that when AO is in the positive phase, the pressure in the polar region decreases at 500 hPa; the pressure in East Asia increases anomalously; the East Asian trough decreases; and the low-level westerly jet moves northward, preventing the northwesterly cold air from moving southward. Therefore, the whole eastern China gets warmer and wetter air, and there are more light fog days with the enhanced water vapor. However, the atmosphere merely becomes more towards unstable in South China, where the precipitation increases but the heavy fog days decreases. Nevertheless, heavy fog days increase with the water vapor in North China because of moving towards a stable atmosphere, which is formed by the anomalous downdrafts north of the precipitation center in South China. When AO is in the negative phase, the situation is basically opposite to that in the positive phase, but the variations of the corresponding fog days and circulations are weaker than those in the AO-positive-phase winter, which may be related to the nonlinear effect of AO on climate.
Graph neural networks (GNNs) have drawn significant research attention recently, mostly under the setting of semi-supervised learning. When task-agnostic representations are preferred or supervision is simply unavailable, the auto-encoder framework comes in handy with a natural graph reconstruction objective for unsupervised GNN training. However, existing graph auto-encoders are designed to reconstruct the direct links, so GNNs trained in this way are only optimized towards proximity-oriented graph mining tasks, and will fall short when the topological structures matter. In this work, we revisit the graph encoding process of GNNs which essentially learns to encode the neighborhood information of each node into an embedding vector, and propose a novel graph decoder to reconstruct the entire neighborhood information regarding both proximity and structure via Neighborhood Wasserstein Reconstruction (NWR). Specifically, from the GNN embedding of each node, NWR jointly predicts its node degree and neighbor feature distribution, where the distribution prediction adopts an optimal-transport loss based on the Wasserstein distance. Extensive experiments on both synthetic and real-world network datasets show that the unsupervised node representations learned with NWR have much more advantageous in structure-oriented graph mining tasks, while also achieving competitive performance in proximity-oriented ones. 1
Background: Alzheimer's disease (AD) is a widespread neurodegenerative disease that mostly affects the elderly population. Given its prevalence, a precise and efficient stratification system based on AD symptomology that uses functional magnetic resonance imaging (MRI) has great potential in the clinical diagnosis and prognosis estimation of AD patients. It was evident that deep learning methods have performed extremely well in the field of automated stratification of AD based on MRI because of their high predicting accuracy and reliability. Methods: We proposed a deep convolutional neural network (CNN) and iterated random forest (RF) architecture for MRI image stratification by both anatomical location and image modality using the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We employed 3 cross-sectional data sets from the ADNI to conduct our binary-stratification [AD and normal controls (NCs), or AD and mild cognitive impairment (MCI)], and multi-stratification (AD, MCI, and NCs) process using MRI. And the accuracy, recall, specificity, area under the curve of receiver operating characteristic curve (AUC), F1 and Matthew's correlation coefficient (MCC) scores to assess accuracy of auxiliary clinical diagnoses.Results: Compared to other combinations of algorithms, our model obtained remarkable overall stratification accuracies in all different classification sets. In terms of AD vs. MCI, the mean training AUC of the 3 runs were 85.1% in 95% confidence intervals (CIs). In terms of AD vs. NC, the mean training AUC of the 3 runs was 90.6% in 95% CIs. In terms of the 3 stratifications of AD, MCI, and NC, relative precision, recall, and specificity for each category in the training test (TS) were all near 89%, while the F1 and MCC scores of both sets were 59.9% and 59.5%, respectively.Conclusions: Using a deep CNN and iterated RF architecture, we showed that brain image stratification is a promising means for evaluating AD, and examining the underlying etiology of the disease, by applying computer and medical images to achieve the early auxiliary diagnosis of AD. However, we still have a long way to go from the discovery of image markers to clinical application.
Identification of juvenile and mature crustal sources in granite formation relies on radiogenic isotopic systems such as Sm-Nd and Lu-Hf and assumes isotope systems reach equilibrium between the melt and residual phases prior to melt extraction. However, we hypothesise disequilibrium melting and residual zircon result in preferential retention of 177 Hf in residues, generating partial melts with higher 176 Hf/ 177 Hf ratios. To test this hypothesis, we evaluate radiogenic isotopic signatures of strongly-peraluminous granites from the Chinese Altai. These granites show Nd-Hf isotopic decoupling and inherited zircons with negative ε Hf (t) values providing evidence for incomplete Hf release. This is consistent with the significant depletions in Zr and Hf. The Chinese data compilation shows that strongly-peraluminous and calcic to calc-alkalic, magnesian metaluminous or ferroan peraluminous (often respectively referred to as Sand I-type) granites show elevated ε Hf (t) relative to the terrestrial Hf-Nd isotopic array. Hf isotope disequilibrium marked by the preferential release of radiogenic Hf is likely ubiquitous during anatexis of zircon-rich protoliths.
C5a receptor 1 (C5aR1) is associated with various inflammatory processes, the pathogenesis of immune diseases, and tumor growth. However, its role in the tumor microenvironment of gastric cancer (GC) remains unclear. In this study, the expression of C5aR1 in GC and normal gastric mucosa tissues was compared using data retrieved from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, and the results were validated by in vitro qRT-PCR and immunohistochemical analyses. The relationship between C5aR1 expression and the overall survival of patients with GC was analyzed using the Kaplan–Meier method. Subsequently, enrichment analysis was performed, and the signaling pathways were screened. C5aR1 expression was also correlated with genes related to the immune checkpoint and immune cell infiltration. The results revealed that C5aR1 expression was enhanced in GC tissues compared to normal gastric tissues, and that patients with high expression of C5aR1 had a worse 10-year overall survival compared to those showing low expression of C5aR1. Functional analysis revealed that C5aR1 is a gene related to theimmune system and may play a crucial role in inflammatory and tumor immune responses. Additionally, C5aR1 showed a positive correlation with most immune checkpoint-related genes and a negative correlation with natural killer cells, dendritic cells, and CD8+ T cells. Immune evasion risk was observed to be significantly greater in patients with higher expression of C5aR1 than in those with lower expression. The results of this study reveal that C5aR1 shapes a non-inflammatory tumor microenvironment in GC and mediates immune evasion.
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