Technion – Israel Institute of Technology

About: Technion – Israel Institute of Technology is a education organization based out in Haifa, Israel. It is known for research contribution in the topics: Population & Upper and lower bounds. The organization has 31714 authors who have published 79377 publications receiving 2603976 citations. The organization is also known as: Technion Israel Institute of Technology & Ṭekhniyon, Makhon ṭekhnologi le-Yiśraʼel.

A Laser Surface Textured Parallel Thrust Bearing

Abstract: The potential use of a new technology of laser surface texturing (LST) in parallel thrust bearings is theoretically investigated. The surface texture has the form of micro-dimples with pre-selected diameter, depth, and area density. It can be applied to only a portion of the bearing area (partial LST) or the full bearing area (full LST). Optimum parameters of the dimples, and best LST mode, are found in order to obtain maximum load carrying capacity for a thrust bearing having parallel mating surfaces. A comparison is made with optimum linear and stepped sliders showing that parallel LST sliders can provide similar load carrying capacity. Scheduled for Presentation at the 58th Annual Meeting in New York City April 28–May 1, 2003

Microbial bile acid metabolites modulate gut RORγ+ regulatory T cell homeostasis.

Abstract: The metabolic pathways encoded by the human gut microbiome constantly interact with host gene products through numerous bioactive molecules1. Primary bile acids (BAs) are synthesized within hepatocytes and released into the duodenum to facilitate absorption of lipids or fat-soluble vitamins2. Some BAs (approximately 5%) escape into the colon, where gut commensal bacteria convert them into various intestinal BAs2 that are important hormones that regulate host cholesterol metabolism and energy balance via several nuclear receptors and/or G-protein-coupled receptors3,4. These receptors have pivotal roles in shaping host innate immune responses1,5. However, the effect of this host–microorganism biliary network on the adaptive immune system remains poorly characterized. Here we report that both dietary and microbial factors influence the composition of the gut BA pool and modulate an important population of colonic FOXP3+ regulatory T (Treg) cells expressing the transcription factor RORγ. Genetic abolition of BA metabolic pathways in individual gut symbionts significantly decreases this Treg cell population. Restoration of the intestinal BA pool increases colonic RORγ+ Treg cell counts and ameliorates host susceptibility to inflammatory colitis via BA nuclear receptors. Thus, a pan-genomic biliary network interaction between hosts and their bacterial symbionts can control host immunological homeostasis via the resulting metabolites. Both dietary and microbial factors influence the composition of the gut bile acid pool, which in turn modulates the frequencies and functionalities of RORγ-expressing colonic FOXP3+ regulatory T cells, contributing to protection from inflammatory colitis.

Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis.

Abstract: Aims Traditional prognostic risk assessment in patients undergoing non-invasive imaging is based upon a limited selection of clinical and imaging findings Machine learning (ML) can consider a greater number and complexity of variables Therefore, we investigated the feasibility and accuracy of ML to predict 5-year all-cause mortality (ACM) in patients undergoing coronary computed tomographic angiography (CCTA), and compared the performance to existing clinical or CCTA metrics Methods and results The analysis included 10 030 patients with suspected coronary artery disease and 5-year follow-up from the COronary CT Angiography EvaluatioN For Clinical Outcomes: An InteRnational Multicenter registry All patients underwent CCTA as their standard of care Twenty-five clinical and 44 CCTA parameters were evaluated, including segment stenosis score (SSS), segment involvement score (SIS), modified Duke index (DI), number of segments with non-calcified, mixed or calcified plaques, age, sex, gender, standard cardiovascular risk factors, and Framingham risk score (FRS) Machine learning involved automated feature selection by information gain ranking, model building with a boosted ensemble algorithm, and 10-fold stratified cross-validation Seven hundred and forty-five patients died during 5-year follow-up Machine learning exhibited a higher area-under-curve compared with the FRS or CCTA severity scores alone (SSS, SIS, DI) for predicting all-cause mortality (ML: 079 vs FRS: 061, SSS: 064, SIS: 064, DI: 062; P < 0001) Conclusions Machine learning combining clinical and CCTA data was found to predict 5-year ACM significantly better than existing clinical or CCTA metrics alone