VeNet: Hybrid Stacked Autoencoder Learning for Cooperative Edge Intelligence in IoV

Emerging applications of the Internet of Vehicles (IoV) require the wireless transmission of growing amounts of data, e.g., vehicle location and sensor data, over unreliable and increasingly congested wireless links between the mobile vehicles and the Road Side Units (RSUs); also, urban areas are becoming increasingly congested with vehicle road traffic. Road traffic management and data network traffic management to address these challenges require accurate representations of the road and network traffic, which are difficult due to the wide temporal and spatial correlations in the road and network traffic. We address this representation problem by designing, implementing, and evaluating the VeNet deep learning system to exploit the wirelessly transmitted data to predict future vehicle locations and network traffic. We develop the novel VeNet hybrid learning system that employs a stacked autoencoder (AE) consisting of a central AE and multiple local AEs that jointly feed into a Long-Short Term Memory (LSTM). We propose a new training algorithm for the hybrid VeNet learning system. The novel VeNet hybrid learning system conducts spatial learning that accounts for the spatial and temporal correlations in the dataset gathered from the mobile vehicles. Evaluations that involve measurements with custom-made Raspberry Pi vehicles indicate that the VeNet learning model significantly reduces the required signalling network traffic and prediction errors (down to approx. three quarters) compared to existing prediction models. At the same time, VeNet reduces the energy consumption on the vehicles as well as the learning delay

PAC-learning is Undecidable

The problem of attempting to learn the mapping between data and labels is the crux of any machine learning task. It is, therefore, of interest to the machine learning community on practical as well as theoretical counts to consider the existence of a test or criterion for deciding the feasibility of attempting to learn. We investigate the existence of such a criterion in the setting of PAC-learning, basing the feasibility solely on whether the mapping to be learnt lends itself to approximation by a given class of hypothesis functions. We show that no such criterion exists, exposing a fundamental limitation in the decidability of learning. In other words, we prove that testing for PAC-learnability is undecidable in the Turing sense. We also briefly discuss some of the probable implications of this result to the current practice of machine learning

Role of Oxygen Free Radicals, Nitric Oxide and Mitochondria in Mediating Cardiac Alterations During Liver Cirrhosis Induced by Thioacetamide

Thioacetamide (TAA) administration is widely used for induction of liver cirrhosis in rats, where reactive oxygen radicals (ROS) and nitric oxide (NO) participate in development of liver damage. Cardiac dysfunction is an important complication of liver cirrhosis, but the role of ROS or NO in cardiac abnormalities during liver cirrhosis is not well understood. This was investigated in animals after TAA-induced liver cirrhosis and temporal changes in oxidative stress, NO and mitochondrial function in the heart evaluated. TAA induced elevation in cardiac levels of nitrate before development of frank liver cirrhosis, without gross histological alterations. This was accompanied by an early induction of P38 MAP kinase, which is influenced by ROS and plays an important signaling role for induction of iNOS. Increased nitrotyrosine, protein oxidation and lipid peroxidation in the heart and cardiac mitochondria, suggestive of oxidative stress, also preceded frank liver cirrhosis. However, compromised cardiac mitochondrial function with a decrease in respiratory control ratio and increased mitochondrial swelling was seen later, when cirrhosis was evident. In conclusion, TAA induces elevations in ROS and NO in the heart in parallel to early liver damage. This leads to later development of functional deficits in cardiac mitochondria after development of liver cirrhosis

Molecularly Guided Drug Repurposing for Cholangiocarcinoma: An Integrative Bioinformatic Approach.

Cholangiocarcinoma (CCA) has a complex immune microenvironment architecture, thus possessing challenges in its characterization and treatment. This study aimed to repurpose FDA-approved drugs for cholangiocarcinoma by transcriptomic-driven bioinformatic approach. Cox-proportional univariate regression was applied to 3017 immune-related genes known a priori to identify a list of mortality-associated genes, so-called immune-oncogenic gene signature, in CCA tumor-derived RNA-seq profiles of two independent cohorts. Unsupervised clustering stratified CCA tumors into two groups according to the immune-oncogenic gene signature expression, which then confirmed its clinical relevance by Kaplan-Meier curve. Molecularly guided drug repurposing was performed by an integrative connectivity map-prioritized drug-gene network analysis. The immune-oncogenic gene signature consists of 26 mortality-associated immune-related genes. Patients with high-expression signature had a poorer overall survival (log-rank p < 0.001), while gene enrichment analysis revealed cell-cycle checkpoint regulation and inflammatory-immune response signaling pathways affected this high-risk group. The integrative drug-gene network identified eight FDA-approved drugs as promising candidates, including Dasatinib a multi-kinase inhibitor currently investigated for advanced CCA with isocitrate-dehydrogenase mutations. This study proposes the use of the immune-oncogenic gene signature to identify high-risk CCA patients. Future preclinical and clinical studies are required to elucidate the therapeutic efficacy of the molecularly guided drugs as the adjunct therapy, aiming to improve the survival outcome

ADAMTS13 missense variants associated with defective activity and secretion of ADAMTS13 in a patient with non-cirrhotic portal hypertension

BACKGROUND: Non-cirrhotic intrahepatic portal hypertension (NCIPH) is characterized by thrombotic microangiopathy of the portal venous system, low ADAMTS13 (a disintegrin-like and metalloproteinase with thrombospondin type 1 motifs-13), and high vWF (von Willebrand factor) levels. This study aimed to screen for ADAMTS13 mutations, focusing on the CUB domain, in these patients. METHODS: Prospectively recruited NCIPH patients and healthy volunteers underwent tests for plasma vWF-ADAMTS13 balance. Sanger sequencing of the CUB domain of ADAMTS13 was done in a subset of the NCIPH patients, and the detected mutation was screened for in all the study participants. Next-generation sequencing of clinically relevant exome and liver immunostaining for ADAMTS13 was done in patients with detected ADAMTS13 mutation. RESULTS: Plasma vWF-ADAMTS13 balance was significantly altered in 24 NCIPH patients (Child's class A:23, B:1) as compared to 22 controls. On initial sequencing of the CUB domain (17 cases and 3 controls), one NCIPH patient showed a rare missense variant (SNV) at position c.3829C >T resulting in p.R1277W (rs14045669). Subsequent RFLP analysis targeted to the R1277W variant did not detect this in any other NCIPH patient, nor in any of the 22 controls. The NCIPH patient with the R1277W variant had severe ADAMTS13 deficiency, consistently high vWF, other missense SNVs in ADAMTS13, vWF, and complement genes. Immunostaining of his liver biopsy revealed globules of ADAMTS13 within stellate cells. CONCLUSIONS: We report missense variants in ADAMTS13, vWF, and complement genes in a patient with NCIPH who had decreased secretion and activity of ADAMTS13 protein. Further studies are needed in NCIPH patients in this regard