EGTSyn: Edge-based Graph Transformer for Anti-Cancer Drug Combination Synergy Prediction

Combination therapy with multiple drugs is a potent therapy strategy for complex diseases such as cancer, due to its therapeutic efficacy and potential for reducing side effects. However, the extensive search space of drug combinations makes it challenging to screen all combinations experimentally. To address this issue, computational methods have been developed to identify prioritized drug combinations. Recently, Convolutional Neural Networks based deep learning methods have shown great potential in this community. Although the significant progress has been achieved by existing computational models, they have overlooked the important high-level semantic information and significant chemical bond features of drugs. It is worth noting that such information is rich and it can be represented by the edges of graphs in drug combination predictions. In this work, we propose a novel Edge-based Graph Transformer, named EGTSyn, for effective anti-cancer drug combination synergy prediction. In EGTSyn, a special Edge-based Graph Neural Network (EGNN) is designed to capture the global structural information of chemicals and the important information of chemical bonds, which have been neglected by most previous studies. Furthermore, we design a Graph Transformer for drugs (GTD) that combines the EGNN module with a Transformer-architecture encoder to extract high-level semantic information of drugs.Comment: 15 pages,4 figures,6 table

Large mass-independent sulphur isotope anomalies link stratospheric volcanism to the Late Ordovician mass extinction

Volcanic eruptions are thought to be a key driver of rapid climate perturbations over geological time, such as global cooling, global warming, and changes in ocean chemistry. However, identification of stratospheric volcanic eruptions in the geological record and their causal link to the mass extinction events during the past 540 million years remains challenging. Here we report unexpected, large mass-independent sulphur isotopic compositions of pyrite with Δ33S of up to 0.91‰ in Late Ordovician sedimentary rocks from South China. The magnitude of the Δ33S is similar to that discovered in ice core sulphate originating from stratospheric volcanism. The coincidence between the large Δ33S and the first pulse of the Late Ordovician mass extinction about 445 million years ago suggests that stratospheric volcanic eruptions may have contributed to synergetic environmental deteriorations such as prolonged climatic perturbations and oceanic anoxia, related to the mass extinction

Large mass-independent sulphur isotope anomalies link stratospheric volcanism to the Late Ordovician mass extinction

Abstract: Volcanic eruptions are thought to be a key driver of rapid climate perturbations over geological time, such as global cooling, global warming, and changes in ocean chemistry. However, identification of stratospheric volcanic eruptions in the geological record and their causal link to the mass extinction events during the past 540 million years remains challenging. Here we report unexpected, large mass-independent sulphur isotopic compositions of pyrite with Δ33S of up to 0.91‰ in Late Ordovician sedimentary rocks from South China. The magnitude of the Δ33S is similar to that discovered in ice core sulphate originating from stratospheric volcanism. The coincidence between the large Δ33S and the first pulse of the Late Ordovician mass extinction about 445 million years ago suggests that stratospheric volcanic eruptions may have contributed to synergetic environmental deteriorations such as prolonged climatic perturbations and oceanic anoxia, related to the mass extinction

Genetic Heterogeneity of Oesophageal Cancer in High-Incidence Areas of Southern and Northern China

BACKGROUND AND OBJECTIVE: Oesophageal cancer is one of the most common and deadliest cancers worldwide. Our previous population-based study reported a high prevalence of oesophageal cancer in Chaoshan, Guangdong Province, China. Ancestors of the Chaoshan population migrated from the Taihang Mountain region of north-central China, which is another high-incidence area for oesophageal cancer. The purpose of the present study was to obtain evidence of inherited susceptibility to oesophageal cancer in the Chaoshan population, with reference to the Taihang Mountain population, with the eventual goal of molecular identification of the disease genes. METHODS: We conducted familial correlation, commingling, and complex segregation analyses of 224 families from the Chaoshan population and 403 families from the Taihang population using the FPMM program of S.A.G.E. version 5.3.0. A second analysis focused on specific families having large numbers of affected individuals or early onset of the disease. RESULTS: For the general population, moderate sib-sib correlation was noticed for esophageal cancer. Additionally, brother-brother correlation was even higher. Commingling analyses indicated that a three-component distribution model best accounts for the variation in age of onset of oesophageal cancer, and that a multifactorial model provides the best fit to the general population data. An autosomal dominant mode and a dominant or recessive major gene with polygenic inheritance were found to be the best models of inherited susceptibility to oesophageal cancer in some large families. CONCLUSIONS: The current results provide evidence for inherited susceptibility to oesophageal cancer in certain high-risk groups in China, and support efforts to identify the susceptibility genes

TNFRSF10C methylation is a new epigenetic biomarker for colorectal cancer

Background Abnormal methylation of TNFRSF10C was found to be associated with different types of cancers, excluding colorectal cancer (CRC). In this paper, the performance of TNFRSF10C methylation in CRC was studied in two stages. Method The discovery stage was involved with 38 pairs of CRC tumor and paired adjacent non-tumor tissues, and 69 pairs of CRC tumor and paired adjacent non-tumor tissues were used for the validation stage. Quantitative methylation specific PCR (qMSP) method and percentage of methylated reference (PMR) were used to test and represent the methylation level of TNFRSF10C, respectively. A dual-luciferase reporter gene experiment was conducted to evaluate the promoter activity of TNFRSF10C fragment. Results A significant association of TNFRSF10C promoter hypermethylation with CRC was found and validated (discovery stage: 24.67 ± 7.52 vs. 3.36 ± 0.89; P = 0.003; validation stage: 31.21 ± 12.48 vs. 4.52 ± 1.47; P = 0.0005). Subsequent analyses of TCGA data among 46 pairs of CRC samples further confirmed our findings (cg23965061: P = 4E − 6; cg14015044: P = 1E − 7). Dual-luciferase reporter gene assay revealed that TNFRSF10C fragment was able to significantly promote gene expression (Fold change = 2.375, P = 0.013). Our data confirmed that TNFRSF10C promoter hypermethylation can predict shorter overall survival of CRC patients (P = 0.032). Additionally, bioinformatics analyses indicated that TNFRSF10C hypermethylation was significantly associated with lower TNFRSF10C expression. Conclusion Our work suggested that TNFRSF10C hypermethylation was significantly associated with the risk of CRC

2023 Low-Power Computer Vision Challenge (LPCVC) Summary

This article describes the 2023 IEEE Low-Power Computer Vision Challenge (LPCVC). Since 2015, LPCVC has been an international competition devoted to tackling the challenge of computer vision (CV) on edge devices. Most CV researchers focus on improving accuracy, at the expense of ever-growing sizes of machine models. LPCVC balances accuracy with resource requirements. Winners must achieve high accuracy with short execution time when their CV solutions run on an embedded device, such as Raspberry PI or Nvidia Jetson Nano. The vision problem for 2023 LPCVC is segmentation of images acquired by Unmanned Aerial Vehicles (UAVs, also called drones) after disasters. The 2023 LPCVC attracted 60 international teams that submitted 676 solutions during the submission window of one month. This article explains the setup of the competition and highlights the winners' methods that improve accuracy and shorten execution time.Comment: LPCVC 2023, website: https://lpcv.ai