DISCO: Achieving Low Latency and High Reliability in Scheduling of Graph-Structured Tasks over Mobile Vehicular Cloud

To effectively process data across a fleet of dynamic and distributed vehicles, it is crucial to implement resource provisioning techniques that provide reliable, cost-effective, and real-time computing services. This article explores resource provisioning for computation-intensive tasks over mobile vehicular clouds (MVCs). We use undirected weighted graphs (UWGs) to model both the execution of tasks and communication patterns among vehicles in a MVC. We then study low-latency and reliable scheduling of UWG asks through a novel methodology named double-plan-promoted isomorphic subgraph search and optimization (DISCO). In DISCO, two complementary plans are envisioned to ensure effective task completion: Plan A and Plan B.Plan A analyzes the past data to create an optimal mapping ($\alpha$) between tasks and the MVC in advance to the practical task scheduling. Plan B serves as a dependable backup, designed to find a feasible mapping ($\beta$) in case $\alpha$ fails during task scheduling due to unpredictable nature of the network.We delve into into DISCO's procedure and key factors that contribute to its success. Additionally, we provide a case study that includes comprehensive comparisons to demonstrate DISCO's exceptional performance in regards to time efficiency and overhead. We further discuss a series of open directions for future research

Multi-Job Intelligent Scheduling with Cross-Device Federated Learning

Recent years have witnessed a large amount of decentralized data in various (edge) devices of end-users, while the decentralized data aggregation remains complicated for machine learning jobs because of regulations and laws. As a practical approach to handling decentralized data, Federated Learning (FL) enables collaborative global machine learning model training without sharing sensitive raw data. The servers schedule devices to jobs within the training process of FL. In contrast, device scheduling with multiple jobs in FL remains a critical and open problem. In this paper, we propose a novel multi-job FL framework, which enables the training process of multiple jobs in parallel. The multi-job FL framework is composed of a system model and a scheduling method. The system model enables a parallel training process of multiple jobs, with a cost model based on the data fairness and the training time of diverse devices during the parallel training process. We propose a novel intelligent scheduling approach based on multiple scheduling methods, including an original reinforcement learning-based scheduling method and an original Bayesian optimization-based scheduling method, which corresponds to a small cost while scheduling devices to multiple jobs. We conduct extensive experimentation with diverse jobs and datasets. The experimental results reveal that our proposed approaches significantly outperform baseline approaches in terms of training time (up to 12.73 times faster) and accuracy (up to 46.4% higher).Comment: To appear in TPDS; 22 pages, 17 figures, 8 tables. arXiv admin note: substantial text overlap with arXiv:2112.0592

Toxicology and efficacy of tumor-targeting Salmonella typhimurium A1-R compared to VNP 20009 in a syngeneic mouse tumor model in immunocompetent mice.

Salmonella typhimurium A1-R (S. typhimurium A1-R) attenuated by leu and arg auxotrophy has been shown to target multiple types of cancer in mouse models. In the present study, toxicologic and biodistribution studies of tumor-targeting S. typhimurium A1-R and S. typhimurium VNP20009 (VNP 20009) were performed in a syngeneic tumor model growing in immunocompetent BALB/c mice. Single or multiple doses of S. typhimurium A1-R of 2.5 × 105 and 5 × 105 were tolerated. A single dose of 1 × 106 resulted in mouse death. S. typhimurium A1-R (5 × 105 CFU) was eliminated from the circulation, liver and spleen approximately 3-5 days after bacterial administration via the tail vein, but remained in the tumor in high amounts. S. typhimurium A1-R was cleared from other organs much more rapidly. S. typhimurium A1-R and VNP 20009 toxicity to the spleen and liver was minimal. S. typhimurium A1-R showed higher selective targeting to the necrotic areas of the tumors than VNP20009. S. typhimurium A1-R inhibited the growth of CT26 colon carcinoma to a greater extent at the same dose of VNP20009. In conclusion, we have determined a safe dose and schedule of S. typhimurium A1-R administration in BALB/c mice, which is also efficacious against tumor growth. The results of the present report indicate similar toxicity of S. typhimurium A1-R and VNP20009, but greater antitumor efficacy of S. typhimurium A1-R in an immunocompetent animal. Since VNP2009 has already proven safe in a Phase I clinical trial, the present results indicate the high clinical potential of S. typhimurium A1-R

Exploration of the causal associations between circulating inflammatory proteins, immune cells, and neuromyelitis optica spectrum disorder: a bidirectional Mendelian randomization study and mediation analysis

BackgroundAn increasing body of research has demonstrated a robust correlation between circulating inflammatory proteins and neuromyelitis optica spectrum disorders (NMOSD). However, whether this association is causal or whether immune cells act as mediators currently remains unclear.MethodsWe employed bidirectional two-sample Mendelian randomization (TSMR) analysis to examine the potential causal association between circulating inflammatory proteins, immune cells, and NMOSD using data from genome-wide association studies (GWAS). Five different methods for Mendelian randomization analyses were applied, with the inverse variance-weighted (IVW) method being the primary approach. Sensitivity analyses were further performed to assess the presence of horizontal pleiotropy and heterogeneity in the results. Finally, a two-step Mendelian randomization (MR) design was employed to examine the potential mediating effects of immune cells.ResultsA notable causal relationship was observed between three circulating inflammatory proteins (CSF-1, IL-24, and TNFRSF9) and genetically predicted NMOSD. Furthermore, two immune cell phenotypes, genetically predicted CD8 on naive CD8+ T cells, and Hematopoietic Stem Cell Absolute Count were negatively and positively associated with genetically predicted NMOSD, respectively, although they did not appear to function as mediators.ConclusionCirculating inflammatory proteins and immune cells are causally associated with NMOSD. Immune cells do not appear to mediate the pathway linking circulating inflammatory proteins to NMOSD

Time-Specific Ecologic Niche Models Forecast the Risk of Hemorrhagic Fever with Renal Syndrome in Dongting Lake District, China, 2005–2010

Background: Hemorrhagic fever with renal syndrome (HFRS), a rodent-borne infectious disease, is one of the most serious public health threats in China. Increasing our understanding of the spatial and temporal patterns of HFRS infections could guide local prevention and control strategies. Methodology/Principal Findings: We employed statistical models to analyze HFRS case data together with environmental data from the Dongting Lake district during 2005–2010. Specifically, time-specific ecologic niche models (ENMs) were used to quantify and identify risk factors associated with HFRS transmission as well as forecast seasonal variation in risk across geographic areas. Results showed that the Maximum Entropy model provided the best predictive ability (AUC = 0.755). Time-specific Maximum Entropy models showed that the potential risk areas of HFRS significantly varied across seasons. High-risk areas were mainly found in the southeastern and southwestern areas of the Dongting Lake district. Our findings based on models focused on the spring and winter seasons showed particularly good performance. The potential risk areas were smaller in March, May and August compared with those identified for June, July and October to December. Both normalized difference vegetation index (NDVI) and land use types were found to be the dominant risk factors. Conclusions/Significance: Our findings indicate that time-specific ENMs provide a useful tool to forecast the spatial and temporal risk of HFRS

Common Core Genes Play Vital Roles in Gastric Cancer With Different Stages

Background: Owing to complex molecular mechanisms in gastric cancer (GC) oncogenesis and progression, existing biomarkers and therapeutic targets could not significantly improve diagnosis and prognosis. This study aims to identify the key genes and signaling pathways related to GC oncogenesis and progression using bioinformatics and meta-analysis methods.Methods: Eligible microarray datasets were downloaded and integrated using the meta-analysis method. According to the tumor stage, GC gene chips were classified into three groups. Thereafter, the three groups’ differentially expressed genes (DEGs) were identified by comparing the gene data of the tumor groups with those of matched normal specimens. Enrichment analyses were conducted based on common DEGs among the three groups. Then protein–protein interaction (PPI) networks were constructed to identify relevant hub genes and subnetworks. The effects of significant DEGs and hub genes were verified and explored in other datasets. In addition, the analysis of mutated genes was also conducted using gene data from The Cancer Genome Atlas database.Results: After integration of six microarray datasets, 1,229 common DEGs consisting of 1,065 upregulated and 164 downregulated genes were identified. Alpha-2 collagen type I (COL1A2), tissue inhibitor matrix metalloproteinase 1 (TIMP1), thymus cell antigen 1 (THY1), and biglycan (BGN) were selected as significant DEGs throughout GC development. The low expression of ghrelin (GHRL) is associated with a high lymph node ratio (LNR) and poor survival outcomes. Thereafter, we constructed a PPI network of all identified DEGs and gained 39 subnetworks and the top 20 hub genes. Enrichment analyses were performed for common DEGs, the most related subnetwork, and the top 20 hub genes. We also selected 61 metabolic DEGs to construct PPI networks and acquired the relevant hub genes. Centrosomal protein 55 (CEP55) and POLR1A were identified as hub genes associated with survival outcomes.Conclusion: The DEGs, hub genes, and enrichment analysis for GC with different stages were comprehensively investigated, which contribute to exploring the new biomarkers and therapeutic targets

Plio-Pleistocene establishment of Irtysh River in Junggar, Northwest China: implications for Siberian-Arctic river system evolution and resulting climate impact

The influence of Siberian freshwater input to the Arctic Ocean on Northern Hemisphere ice-sheet expansions remains poorly known due to the incomplete geologic record of Siberian-Arctic river systems during the late Pliocene. The Irtysh River is a major Siberian river, rising from the Altay Mountains, northwestern China, and flowing 4,282 km before joining the Ob River. Here, we present new field evidence and chronological data from a combination of cosmogenic 21Ne and 26Al/10Be measurements that constrain the establishment of the Irtysh River to ca. 2.77+0.39/-0.33 Ma. These first quantitative chronological results, together with previous sedimentological, geomorphological, and geochemical evidence, support a young Siberian-Arctic river system. Its coincidence with the late Pliocene ice-sheet expansions in the Northern Hemisphere implies a profound impact of Siberian freshwater input to the Arctic on the major ice advances that significantly affected global oceanographic and climatic systems