Second-order closures for compressible turbulence

This viewgraph presentation discusses project description, turbulence models, and computational engine and results for second-order closures for compressible turbulence

An Efficient Approach to Extrapolate the Data Falling in the Zone not Covered by Measurements in a Near-Field Spherical Facility

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Role of Non-Caloric Carbonated Beverage Preload During a Standardized Solid and Liquid Meal on Colecistokinin and Ghrelin Levels in Healthy Subjects

Background and Aim: The effects of beverages with carbon dioxide on the gastrointestinal system mainly involve the upper digestive tract, with a possible modification of gastric physiology and change in food intake. No data are available on the relationship between non caloric carbonated beverages intake and gastrointestinal hormones levels. We aimed to verify the effect of a sugar-free carbonated beverage (CB) preload compared to a CB without CO2 (DCB) and water (W), during a standardized solid (SM) and liquid (LM) meal, on colecistokinin (CCK) and ghrelin (Gh) release. Subjects & Methods: After 300 ml of CB, DCB and W, a standardized SM or LM was administered at constant rate (100 kcal/5 min) to ten healthy subjects (4 females, aged 22-30 years; BMI 21-24) on six days in a random order (D1: CB+SM; D2: DCB+SM; D3: W+SM; D4: CB+LM; D5: DCB+LM; D6: W+LM). Eating perceptions (desire to eat, hunger, prospective of food consumption) and maximum satiety (MS) as total kcals intake were measured. CCK and Gh were evaluated on blood samples collected at 0, 10 (after beverage), 30, 60 and 120 min. Hormones values are expressed as ratio with body area surface (BSA) and as peak and nadir for CCK and Gh respectively. All data are expressed as mean±SD. Results: Desire to eat, hunger and prospective of food consumption were not different among beverages and meals. Total kcal intakes at MS were significant increased during SM respect to LM for CB (774±209, 585±299, p<0.01), DCB (837±208, 585±280, p<0.01) andW(783±244, 630±353, p<0.01) respectively, without differences among beverages. No differences were found for CCK and Gh among all beverages during SM or LM. Instead, CCK after CB was higher during SM than LM (1.004±0.514, 0.513±0.243, p<0.05) but not after DCB and W (0.790±0.604, 0.849±0.595, n.s.; 0.712±0.473, 0.873±0.431, n.s.) respectively. Moreover, after all beverages, Gh was higher during SM than LM (CB: 0.314±0.100, 0.206±0.099, p<0.05; DCB: 0.288±0.060, 0.145±0.051, p<0.01; W: 0.307±0.083, 0.170±0.085, p<0.01). Conclusions: Liquid meal determined an earlier satiety respect to a solid meal with a parallel decrease of Ghrelin independently of the kind of beverage preload. A CCK decrease was found only during liquid meal after carbonated beverage preload without influence on kcal intake compared with DCB and W. Studies on the influence of carbon dioxide on CCK release nutrients related need to explain this data

CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning

Program synthesis or code generation aims to generate a program that satisfies a problem specification. Recent approaches using large-scale pretrained language models (LMs) have shown promising results, yet they have some critical limitations. In particular, they often follow a standard supervised fine-tuning procedure to train a code generation model only from the pairs of natural-language problem descriptions and ground-truth programs. Such paradigm largely ignores some important but potentially useful signals in the problem specification such as unit tests, which thus often results in poor performance when solving complex unseen coding tasks. To address the limitations, we propose "CodeRL", a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning (RL). Specifically, during training, we treat the code-generating LM as an actor network, and introduce a critic network that is trained to predict the functional correctness of generated programs and provide dense feedback signals to the actor. During inference, we introduce a new generation procedure with a critical sampling strategy that allows a model to automatically regenerate programs based on feedback from example unit tests and critic scores. For the model backbones, we extended the encoder-decoder architecture of CodeT5 with enhanced learning objectives, larger model sizes, and better pretraining data. Our method not only achieves new SOTA results on the challenging APPS benchmark, but also shows strong zero-shot transfer capability with new SOTA results on the simpler MBPP benchmark

Single-row vs. double-row arthroscopic rotator cuff repair: clinical and 3 Tesla MR arthrography results.

Background Arthroscopic rotator cuff repair has become popular in the last few years because it avoids large skin incisions and deltoid detachment and dysfunction. Earlier arthroscopic single-row (SR) repair methods achieved only partial restoration of the original footprint of the tendons of the rotator cuff, while double-row (DR) repair methods presented many biomechanical advantages and higher rates of tendon-to-bone healing. However, DR repair failed to demonstrate better clinical results than SR repair in clinical trials. MR imaging at 3 Tesla, especially with intra-articular contrast medium (MRA), showed a better diagnostic performance than 1.5 Tesla in the musculoskeletal setting. The objective of this study was to retrospectively evaluate the clinical and 3 Tesla MRA results in two groups of patients operated on for a medium-sized full-thickness rotator cuff tear with two different techniques. Methods The first group consisted of 20 patients operated on with the SR technique; the second group consisted of 20 patients operated on with the DR technique. All patients were evaluated at a minimum of 3 years after surgery. The primary end point was the re-tear rate at 3 Tesla MRA. The secondary end points were the Constant-Murley Scale (CMS), the Simple Shoulder Test (SST) scores, surgical time and implant expense. Results The mean follow-up was 40 months in the SR group and 38.9 months in the DR group. The mean postoperative CMS was 70 in the SR group and 68 in the DR group. The mean SST score was 9.4 in the SR group and 10.1 in the DR group. The re-tear rate was 60% in the SR group and 25% in the DR group. Leakage of the contrast medium was observed in all patients. Conclusions To the best of our knowledge, this is the first report on 3 Tesla MRA in the evaluation of two different techniques of rotator cuff repair. DR repair resulted in a statistically significant lower re-tear rate, with longer surgical time and higher implant expense, despite no difference in clinical outcomes. We think that leakage of the contrast medium is due to an incomplete tendon-to-bone sealing, which is not a re-tear. This phenomenon could have important medicolegal implications. Level of evidence III. Treatment study: Case–control study

LAVIS: A Library for Language-Vision Intelligence

We introduce LAVIS, an open-source deep learning library for LAnguage-VISion research and applications. LAVIS aims to serve as a one-stop comprehensive library that brings recent advancements in the language-vision field accessible for researchers and practitioners, as well as fertilizing future research and development. It features a unified interface to easily access state-of-the-art image-language, video-language models and common datasets. LAVIS supports training, evaluation and benchmarking on a rich variety of tasks, including multimodal classification, retrieval, captioning, visual question answering, dialogue and pre-training. In the meantime, the library is also highly extensible and configurable, facilitating future development and customization. In this technical report, we describe design principles, key components and functionalities of the library, and also present benchmarking results across common language-vision tasks. The library is available at: https://github.com/salesforce/LAVIS.Comment: Preprint of LAVIS technical repor

Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration

We present a method for learning a human-robot collaboration policy from human-human collaboration demonstrations. An effective robot assistant must learn to handle diverse human behaviors shown in the demonstrations and be robust when the humans adjust their strategies during online task execution. Our method co-optimizes a human policy and a robot policy in an interactive learning process: the human policy learns to generate diverse and plausible collaborative behaviors from demonstrations while the robot policy learns to assist by estimating the unobserved latent strategy of its human collaborator. Across a 2D strategy game, a human-robot handover task, and a multi-step collaborative manipulation task, our method outperforms the alternatives in both simulated evaluations and when executing the tasks with a real human operator in-the-loop. Supplementary materials and videos at https://sites.google.com/view/co-gail-web/homeComment: CoRL 202

Cell cultures harbouring constructs of different pig promoter polymorphisms show different transcriptional efficiency in gene reporter systems

AbstractProduction traits variability among and within breeds, differences among developmental stages or the response to different environments are in part due to genetic factors that affect gene expression. Within the context of an Italian FIRB project, whose objective is to identify genes and molecular mechanisms affecting meat quality and production traits in pig, we studied the promoter regions of candidate genes selected on the basis of their physiological role in animal tissue development or composition. Genomic DNA was isolated from liver or muscle tissue of individuals belonging to Large White and Casertana breed. PCR primers were designed to amplify 5' upstream region of SCD (Stearoyl-CoA Desaturase), LDLR (Low Density Lipoprotein Receptor), LEP (Leptin), MSTN (Myostatin), ACTA1 (Alpha-actin) and HFABP (Heart Fatty Acid Binding Protein) genes using sequences available at NCBI. A total of 19 single nucleotide polymorphisms (SNPs) not previously described were characterised. Some haplotypes, harbou..

Children on the Autism Spectrum and the Use of Virtual Reality for Supporting Social Skills

Background: Autism spectrum disorders (ASDs) are characterized by differences in socio-pragmatic communication. These conditions are allocated within a “spectrum” of phenotypic variability. Virtual reality (VR) is a useful tool for healthcare intervention and particularly safely advancing social abilities in children with ASD. Methods: In our study two types of intervention for improving social skills were compared: (i) emotional training obtained by the use of virtual reality (Gr1), (ii) traditional emotional training performed individually with a therapist (Gr2). We aimed to identify the intervention with the shortest acquisition time for the proposed social tasks. Results: Our findings show that both types of intervention had the same acquisition time for the recognition of primary emotions. However, for the use of primary and secondary emotions, the group using VR showed shorter acquisition times. Conclusions: These findings together with previous preliminary datasuggest that VR can be a promising, dynamic and effective practice for the support of basic and complex social skills of these individuals

Association between use of novel glucose-lowering drugs and COVID-19 hospitalization and death in patients with type 2 diabetes: a nationwide registry analysis

Aims Type 2 diabetes (T2DM) in patients with coronavirus disease-19 (COVID-19) is associated with a worse prognosis. We separately investigated the associations between the use of sodium-glucose cotransporter 2 inhibitors (SGLT2i), glucagon-like peptide-1 receptor agonists (GLP-1 RA), and dipeptidyl peptidase-4 inhibitors (DPP-4i), and the risk of COVID-19 hospitalization and death. Methods and results Patients with T2DM registered in the Swedish National Patient Registry and alive on 1 February 2020 were included. 'Incident severe COVID-19' was defined as the first hospitalization and/or death from COVID-19. A modified Poisson regression approach was applied to a 1:1 propensity score-matched population receiving vs. not receiving SGLT2i, GLP-1 RA, and DPP-4i to analyse the associations between their use and (I) incident severe COVID-19 and (II) risk of 30-day mortality in patients hospitalized for COVID-19. Among 344 413 patients, 39 172 (11%) were treated with SGLT2i, 34 290 (10%) with GLP-1 RA, and 53 044 (15%) with DPP-4i; 9538 (2.8%) had incident severe COVID-19 by 15 May 2021. SGLT2i and DPP-4i were associated with a 10% and 11% higher risk of incident severe COVID-19, respectively, whereas there was no association for GLP-1 RA. DPP-4i was also associated with a 10% higher 30-day mortality in patients hospitalized for COVID-19, whereas there was no association for SGLT2i and GLP-1 RA. Conclusion SGLT2i and DPP-4i use were associated with a higher risk of incident severe COVID-19. DPP-4i use was associated with higher 30-day mortality in patients with COVID-19, whereas SGLT2i use was not. No increased risk for any outcome was observed with GLP-1 RA