Downregulation of HLA Class I Renders Inflammatory Neutrophils More Susceptible to NK Cell-Induced Apoptosis

Neutrophils are potent effector cells and contain a battery of harmful substances and degrading enzymes. A silent neutrophil death, i.e., apoptosis, is therefore of importance to avoid damage to the surrounding tissue and to enable termination of the acute inflammatory process. There is a pile of evidence supporting the role for pro-inflammatory cytokines in extending the life-span of neutrophils, but relatively few studies have been devoted to mechanisms actively driving apoptosis induction in neutrophils. We have previously demonstrated that natural killer (NK) cells can promote apoptosis in healthy neutrophils. In this study, we set out to investigate how neutrophil sensitivity to NK cell-mediated cytotoxicity is regulated under inflammatory conditions. Using in vitro-activated neutrophils and a human skin chamber model that allowed collection of in vivo-transmigrated neutrophils, we performed a comprehensive characterization of neutrophil expression of ligands to NK cell receptors. These studies revealed a dramatic downregulation of HLA class I molecules in inflammatory neutrophils, which was associated with an enhanced susceptibility to NK cell cytotoxicity. Collectively, our data shed light on the complex regulation of interactions between NK cells and neutrophils during an inflammatory response and provide further support for a role of NK cells in the resolution phase of inflammation

Monitoring of water content in a porous reservoir by seismic data: A 3D simulation study

A potential framework to estimate the amount of water stored in a porous storage reservoir from seismic data is neural networks. In this study, the water storage reservoir system is modeled as a coupled poroviscoelastic-viscoelastic medium, and the underlying wave propagation problem is solved using a three-dimensional discontinuous Galerkin method coupled with an Adams-Bashforth time stepping scheme. The wave problem solver is used to generate databases for the neural network-based machine learning model to estimate the water content. In the numerical examples, we investigate a deconvolution-based approach to normalize the effect from the source wavelet in addition to the network's tolerance for noise levels. We also apply the SHapley Additive exPlanations method to obtain greater insight into which part of the input data contributes the most to the water content estimation. The numerical results demonstrate the capacity of the fully connected neural network to estimate the amount of water stored in the porous storage reservoir

Changes in physical activity by context and residential greenness among recent retirees : Longitudinal GPS and accelerometer study

This study examined the changes in accelerometer-measured physical activity by GPS-measured contexts among Finnish retirees (n = 45 (537 measurement days)) participating in a physical activity intervention. We also assessed whether residential greenness, measured with Normalized Difference Vegetation Index, moderated the changes. Moderate-to-vigorous physical activity (MVPA) increased at home by 7 min/day, (P < 0.001) and during active travel by 5 min/day (P = 0.03). The participants with the highest vs. lowest greenness had 25 min/ day greater increase in MVPA over the follow-up (P for Time*Greenness interaction = 0.04). In conclusion, retirees participating in the intervention increased their MVPA both at home and in active travel, and more so if they lived in a greener area.Peer reviewe

Shoulder Check:Investigating Shoulder Injury Rates, Types, Severity, Mechanisms, and Risk Factors in Canadian Youth Ice Hockey

Objective: To describe shoulder-related injury rates, types, severity, mechanisms, and risk factors in youth ice hockey players during games and practices. Study Design: Secondary analysis of data from a 5-year prospective cohort study Safeto-Play (2013-2018). Subjects: Overall, 4419 individual players (representing 6585 player-seasons; 3806 males: 613 females) participated. During this period, 118 primary shoulder-related game injuries and 12 practice injuries were reported. Outcome Measures: Injury surveillance data was collected from 2013-2018 (time-loss or medical attention injuries). Descriptive statistics were calculated, and injury rates with 95% CI were estimated using Poisson regression. An exploratory multivariable mixed-effects Poisson regression model (clustering by team and offset by exposure hours) examined risk factors. Results: The shoulder injury rate was 0.70 injuries/1000 game-hours (95% CI 0.371.33) and 0.07 injuries/1000 practice-hours (95% CI 0.04-0.12). Two-thirds of game injuries (n=79, 69%) resulted in &gt;8 days of time-loss, and more than one-third (n=44, 39%) resulted in &gt;28 days of time-loss. An 82% lower rate of shoulder injury was associated with policy prohibiting body checking compared to leagues allowing body checking [IRR=0.18 (95% CI 0.10-0.32)]. A higher shoulder injury rate was seen for those who reported any injury in the last 12-months compared to those with no history [IRR=2.32 (95% CI 1.57-3.41)]. Conclusions: Most shoulder injuries resulted in more than one week of time-loss. Risk factors for shoulder injury included participation in a body checking league and history of injury in the previous 12 months. Further study of prevention strategies specific to the shoulder may merit further consideration in ice hockey

Shoulder Check:Investigating Shoulder Injury Rates, Types, Severity, Mechanisms, and Risk Factors in Canadian Youth Ice Hockey

Objective: To describe shoulder-related injury rates, types, severity, mechanisms, and risk factors in youth ice hockey players during games and practices. Study Design: Secondary analysis of data from a 5-year prospective cohort study Safeto-Play (2013-2018). Subjects: Overall, 4419 individual players (representing 6585 player-seasons; 3806 males: 613 females) participated. During this period, 118 primary shoulder-related game injuries and 12 practice injuries were reported. Outcome Measures: Injury surveillance data was collected from 2013-2018 (time-loss or medical attention injuries). Descriptive statistics were calculated, and injury rates with 95% CI were estimated using Poisson regression. An exploratory multivariable mixed-effects Poisson regression model (clustering by team and offset by exposure hours) examined risk factors. Results: The shoulder injury rate was 0.70 injuries/1000 game-hours (95% CI 0.371.33) and 0.07 injuries/1000 practice-hours (95% CI 0.04-0.12). Two-thirds of game injuries (n=79, 69%) resulted in &gt;8 days of time-loss, and more than one-third (n=44, 39%) resulted in &gt;28 days of time-loss. An 82% lower rate of shoulder injury was associated with policy prohibiting body checking compared to leagues allowing body checking [IRR=0.18 (95% CI 0.10-0.32)]. A higher shoulder injury rate was seen for those who reported any injury in the last 12-months compared to those with no history [IRR=2.32 (95% CI 1.57-3.41)]. Conclusions: Most shoulder injuries resulted in more than one week of time-loss. Risk factors for shoulder injury included participation in a body checking league and history of injury in the previous 12 months. Further study of prevention strategies specific to the shoulder may merit further consideration in ice hockey

Multifrequency Observations of the Gamma-Ray Blazar 3C 279 in Low-State during Integral AO-1

We report first results of a multifrequency campaign from radio to hard X-ray energies of the prominent gamma-ray blazar 3C 279 during the first year of the INTEGRAL mission. The variable blazar was found at a low activity level, but was detected by all participating instruments. Subsequently a multifrequency spectrum could be compiled. The individual measurements as well as the compiled multifrequency spectrum are presented. In addition, this 2003 broadband spectrum is compared to one measured in 1999 during a high activity period of 3C 279.Comment: 4 pages including 6 figures, to appear in: 'Proc. of the 5th INTEGRAL Workshop', ESA SP-552, in pres

Liver safety assessment: required data elements and best practices for data collection and standardization in clinical trials.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access.A workshop was convened to discuss best practices for the assessment of drug-induced liver injury (DILI) in clinical trials. In a breakout session, workshop attendees discussed necessary data elements and standards for the accurate measurement of DILI risk associated with new therapeutic agents in clinical trials. There was agreement that in order to achieve this goal the systematic acquisition of protocol-specified clinical measures and lab specimens from all study subjects is crucial. In addition, standard DILI terms that address the diverse clinical and pathologic signatures of DILI were considered essential. There was a strong consensus that clinical and lab analyses necessary for the evaluation of cases of acute liver injury should be consistent with the US Food and Drug Administration (FDA) guidance on pre-marketing risk assessment of DILI in clinical trials issued in 2009. A recommendation that liver injury case review and management be guided by clinicians with hepatologic expertise was made. Of note, there was agreement that emerging DILI signals should prompt the systematic collection of candidate pharmacogenomic, proteomic and/or metabonomic biomarkers from all study subjects. The use of emerging standardized clinical terminology, CRFs and graphic tools for data review to enable harmonization across clinical trials was strongly encouraged. Many of the recommendations made in the breakout session are in alignment with those made in the other parallel sessions on methodology to assess clinical liver safety data, causality assessment for suspected DILI, and liver safety assessment in special populations (hepatitis B, C, and oncology trials). Nonetheless, a few outstanding issues remain for future consideration

Zearalenone production and growth in drinking water inoculated with Fusarium graminearum

Production of the mycotoxin zearalenone (ZEN) was examined in drinking water inoculated with Fusarium graminearum. The strain employed was isolated from a US water distribution system. ZEN was purified with an immunoaffinity column and quantified by high-performance liquid chromatography (HPLC) with fluorescence detection. The extracellular yield of ZEN was 15.0 ng l−1. Visual growth was observed. Ergosterol was also indicative of growth and an average of 6.2 μg l−1 was obtained. Other compounds were also detected although remain unidentified. There is no equivalent information available. More work is required on metabolite expression in water as mycotoxins have consequences for human and animal health. The levels detected in this study were low. Water needs to be accepted as a potential source as it attracts high quality demands in terms of purity.Fundação para a Ciência e a Tecnologia (FCT

Estimation of groundwater storage from seismic data using deep learning

We investigate the feasibility of the use of convolutional neural networks to estimate the amount of groundwater stored in the aquifer and delineate water-table level from active-source seismic data. The seismic data to train and test the neural networks are obtained by solving wave propagation in a coupled poroviscoelastic-elastic media. A discontinuous Galerkin method is used to model wave propagation whereas a deep convolutional neural network is used for the parameter estimation problem. In the numerical experiment, the primary unknowns, the amount of stored groundwater and water-table level, are estimated, while the remaining parameters, assumed to be of less of interest, are successfully marginalized in the convolutional neural networks-based solution