Epidemiological trends of subarachnoid hemorrhage at global, regional, and national level: a trend analysis study from 1990 to 2021.

BACKGROUND: Subarachnoid hemorrhage (SAH) is a subtype of hemorrhagic stroke characterized by high mortality and low rates of full recovery. This study aimed to investigate the epidemiological characteristics of SAH between 1990 and 2021. METHODS: Data on SAH incidence, mortality, and disability-adjusted life-years (DALYs) from 1990 to 2021 were obtained from the Global Burden of Disease Study (GBD) 2021. Estimated annual percentage changes (EAPCs) were calculated to evaluate changes in the age-standardized rate (ASR) of incidence and mortality, as well as trends in SAH burden. The relationship between disease burden and sociodemographic index (SDI) was also analyzed. RESULTS: In 2021, the incidence of SAH was found to be 37.09% higher than that in 1990; however, the age-standardized incidence rates (ASIRs) showed a decreased [EAPC: -1.52; 95% uncertainty interval (UI) -1.66 to -1.37]. Furthermore, both the number and rates of deaths and DALYs decreased over time. It was observed that females had lower rates compared to males. Among all regions, the high-income Asia Pacific region exhibited the highest ASIR (14.09/100,000; 95% UI 12.30/100,000 - 16.39/100,000) in 2021, with an EPAC for ASIR < 0 indicating decreasing trend over time for SAH ASIR. Oceania recorded the highest age-standardized mortality rates (ASMRs) and age-standardized DALYs rates among all regions in 2021 at values of respectively 8.61 (95% UI 6.03 - 11.95) and 285.62 (95% UI 209.42 - 379.65). The burden associated with SAH primarily affected individuals aged between 50 - 69 years old. Metabolic risks particularly elevated systolic blood pressure were identified as the main risk factors contributing towards increased disease burden associated with SAH when compared against environmental or occupational behavioral risks evaluated within the GBD framework. CONCLUSIONS: The burden of SAH varies by gender, age group, and geographical region. Although the ASRs have shown a decline over time, the burden of SAH remains significant, especially in regions with middle and low-middle SDI levels. High systolic blood pressure stands out as a key risk factor for SAH. More specific supportive measures are necessary to alleviate the global burden of SAH.

Prokaryotic virus host prediction with graph contrastive augmentaion.

Prokaryotic viruses, also known as bacteriophages, play crucial roles in regulating microbial communities and have the potential for phage therapy applications. Accurate prediction of phage-host interactions is essential for understanding the dynamics of these viruses and their impacts on bacterial populations. Numerous computational methods have been developed to tackle this challenging task. However, most existing prediction models can be constrained due to the substantial number of unknown interactions in comparison to the constrained diversity of available training data. To solve the problem, we introduce a model for prokaryotic virus host prediction with graph contrastive augmentation (PHPGCA). Specifically, we construct a comprehensive heterogeneous graph by integrating virus-virus protein similarity and virus-host DNA sequence similarity information. As the backbone encoder for learning node representations in the virus-prokaryote graph, we employ LGCN, a state-of-the-art graph embedding technique. Additionally, we apply graph contrastive learning to augment the node representations without the need for additional labels. We further conducted two case studies aimed at predicting the host range of multi-species phages, helping to understand the phage ecology and evolution.

scPML: pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data.

Recent developments in single-cell technology have enabled the exploration of cellular heterogeneity at an unprecedented level, providing invaluable insights into various fields, including medicine and disease research. Cell type annotation is an essential step in its omics research. The mainstream approach is to utilize well-annotated single-cell data to supervised learning for cell type annotation of new singlecell data. However, existing methods lack good generalization and robustness in cell annotation tasks, partially due to difficulties in dealing with technical differences between datasets, as well as not considering the heterogeneous associations of genes in regulatory mechanism levels. Here, we propose the scPML model, which utilizes various gene signaling pathway data to partition the genetic features of cells, thus characterizing different interaction maps between cells. Extensive experiments demonstrate that scPML performs better in cell type annotation and detection of unknown cell types from different species, platforms, and tissues.

Knowledge graph embedding for profiling the interaction between transcription factors and their target genes.

Interactions between transcription factor and target gene form the main part of gene regulation network in human, which are still complicating factors in biological research. Specifically, for nearly half of those interactions recorded in established database, their interaction types are yet to be confirmed. Although several computational methods exist to predict gene interactions and their type, there is still no method available to predict them solely based on topology information. To this end, we proposed here a graph-based prediction model called KGE-TGI and trained in a multi-task learning manner on a knowledge graph that we specially constructed for this problem. The KGE-TGI model relies on topology information rather than being driven by gene expression data. In this paper, we formulate the task of predicting interaction types of transcript factor and target genes as a multi-label classification problem for link types on a heterogeneous graph, coupled with solving another link prediction problem that is inherently related. We constructed a ground truth dataset as benchmark and evaluated the proposed method on it. As a result of the 5-fold cross experiments, the proposed method achieved average AUC values of 0.9654 and 0.9339 in the tasks of link prediction and link type classification, respectively. In addition, the results of a series of comparison experiments also prove that the introduction of knowledge information significantly benefits to the prediction and that our methodology achieve state-of-the-art performance in this problem.

GraphTGI: an attention-based graph embedding model for predicting TF-target gene interactions.

MOTIVATION: Interaction between transcription factor (TF) and its target genes establishes the knowledge foundation for biological researches in transcriptional regulation, the number of which is, however, still limited by biological techniques. Existing computational methods relevant to the prediction of TF-target interactions are mostly proposed for predicting binding sites, rather than directly predicting the interactions. To this end, we propose here a graph attention-based autoencoder model to predict TF-target gene interactions using the information of the known TF-target gene interaction network combined with two sequential and chemical gene characters, considering that the unobserved interactions between transcription factors and target genes can be predicted by learning the pattern of the known ones. To the best of our knowledge, the proposed model is the first attempt to solve this problem by learning patterns from the known TF-target gene interaction network. RESULTS: In this paper, we formulate the prediction task of TF-target gene interactions as a link prediction problem on a complex knowledge graph and propose a deep learning model called GraphTGI, which is composed of a graph attention-based encoder and a bilinear decoder. We evaluated the prediction performance of the proposed method on a real dataset, and the experimental results show that the proposed model yields outstanding performance with an average AUC value of 0.8864 +/- 0.0057 in the 5-fold cross-validation. It is anticipated that the GraphTGI model can effectively and efficiently predict TF-target gene interactions on a large scale. AVAILABILITY: Python code and the datasets used in our studies are made available at https://github.com/YanghanWu/GraphTGI.

catena-Poly[[bis-(dimethyl sulfoxide-κO)cerium(III)]tris-(µ(2)-3,5-dinitro-benzoato-κO:O')].

The polymeric title compound, [Ce(C(7)H(3)N(2)O(6))(3)(C(2)H(6)OS)(2)](n), exists as a linear chain along [111] as the three dinitro-benzoate anions each engages in bridging adjacent dimethyl sulfoxide (DMSO) coordinated Ce(III) atoms. The metal atoms are surrounded by eight O atoms in a square-anti-prismatic environment. There are two independent formula units in the asymmetric unit. The S atoms of two of the four DMSO mol-ecules are disordered in a 0.926 (3):0.074 (3) ratio.