Epidemiology and Impact of Abdominal Oblique Injuries in Major and Minor League Baseball.

BACKGROUND: Oblique injuries are known to be a common cause of time out of play for professional baseball players, and prior work has suggested that injury rates may be on the rise in Major League Baseball (MLB). PURPOSE: To better understand the current incidence of oblique injuries, determine their impact based on time out of play, and to identify common injury patterns that may guide future injury prevention programs. STUDY DESIGN: Descriptive epidemiological study. METHODS: Using the MLB Health and Injury Tracking System, all oblique injuries that resulted in time out of play in MLB and Minor League Baseball (MiLB) during the 2011 to 2015 seasons were identified. Player demographics such as age, position/role, and handedness were included. Injury-specific factors analyzed included the following: date of injury, timing during season, days missed, mechanism, side, treatment, and reinjury status. RESULTS: A total of 996 oblique injuries occurred in 259 (26%) MLB and 737 (74%) MiLB players. Although the injury rate was steady in MiLB, the MLB injury rate declined (P = .037). A total of 22,064 days were missed at a mean rate of 4413 days per season and 22.2 days per injury. The majority of these occurred during batting (n = 455, 46%) or pitching (n = 348, 35%), with pitchers losing 5 days more per injury than batters (P \u3c .001). The leading side was injured in 77% of cases and took 5 days longer to recover from than trailing side injuries (P = .009). Seventy-nine (7.9%) players received either a corticosteroid or platelet-rich plasma injection, and the mean recovery time was 11 days longer compared with those who did not receive an injection (P \u3c .001). CONCLUSION: Although the rate of abdominal oblique injuries is on the decline in MLB, this is not the case for MiLB, and these injuries continue to represent a significant source of time out of play in professional baseball. The vast majority of injuries occur on the lead side, and these injuries result in the greatest amount time out of play. The benefit of injections for the treatment of oblique injuries remains unknown

Mutations in the Poliovirus 3CD Proteinase S1-Specificity Pocket Affect Substrate Recognition and RNA Binding

AbstractSequence and structure comparisons with homologous trypsin-like serine proteases have predicted the S1-specificity pocket in picornavirus 3C proteinases. In this study, we examine the putative roles of such residues in poliovirus 3C substrate recognition. Single amino acid substitutions at 3C residues Thr-142, His-161, Gly-163, Gly-164, and Ala-172 were introduced into near full-length poliovirus cDNAs, and protein processing was examined in the context of authentic 3Cciscleavage activity. Our data are consistent with residues Thr-142, His-161, Gly-163, and Gly-164 acting as important determinants of 3C substrate specificity and support published models of 3C protein structure. Anin vivoanalysis of mutant viruses containing individual amino acid substitutions at 3C residues Thr-142 and Ala-172 suggests that such residues are important determinants for viral RNA replication. In addition, bacterially expressed, recombinant 3CD polypeptides containing amino acid substitutions at Thr-142 and Ala-172 show altered RNA binding properties in mobility shift assays that use a synthetic RNA corresponding to the poliovirus 5′-terminal sequences

A physics-aware deep learning model for energy localization in multiscale shock-to-detonation simulations of heterogeneous energetic materials

Predictive simulations of the shock-to-detonation transition (SDT) in heterogeneous energetic materials (EM) are vital to the design and control of their energy release and sensitivity. Due to the complexity of the thermo-mechanics of EM during the SDT, both macro-scale response and sub-grid mesoscale energy localization must be captured accurately. This work proposes an efficient and accurate multiscale framework for SDT simulations of EM. We employ deep learning to model the mesoscale energy localization of shock-initiated EM microstructures upon which prediction results are used to supply reaction progress rate information to the macroscale SDT simulation. The proposed multiscale modeling framework is divided into two stages. First, a physics-aware recurrent convolutional neural network (PARC) is used to model the mesoscale energy localization of shock-initiated heterogeneous EM microstructures. PARC is trained using direct numerical simulations (DNS) of hotspot ignition and growth within microstructures of pressed HMX material subjected to different input shock strengths. After training, PARC is employed to supply hotspot ignition and growth rates for macroscale SDT simulations. We show that PARC can play the role of a surrogate model in a multiscale simulation framework, while drastically reducing the computation cost and providing improved representations of the sub-grid physics. The proposed multiscale modeling approach will provide a new tool for material scientists in designing high-performance and safer energetic materials

Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions

Artificial intelligence (AI) is rapidly emerging as an enabling tool for solving various complex materials design problems. This paper aims to review recent advances in AI-driven materials-by-design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro-morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials-by-design, namely representation learning of microstructure morphology (i.e., shape descriptors), structure-property-performance (S-P-P) linkage estimation, and optimization/design exploration. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materials-by-design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials-by-design, such as meta-learning, active learning, Bayesian learning, and semi-/weakly-supervised learning, to bridge the gap between machine learning research and EM research

Deep learning for synthetic microstructure generation in a materials-by-design framework for heterogeneous energetic materials

The sensitivity of heterogeneous energetic (HE) materials (propellants, explosives, and pyrotechnics) is critically dependent on their microstructure. Initiation of chemical reactions occurs at hot spots due to energy localization at sites of porosities and other defects. Emerging multi-scale predictive models of HE response to loads account for the physics at the meso-scale, i.e. at the scale of statistically representative clusters of particles and other features in the microstructure. Meso-scale physics is infused in machine-learned closure models informed by resolved meso-scale simulations. Since microstructures are stochastic, ensembles of meso-scale simulations are required to quantify hot spot ignition and growth and to develop models for microstructure-dependent energy deposition rates. We propose utilizing generative adversarial networks (GAN) to spawn ensembles of synthetic heterogeneous energetic material microstructures. The method generates qualitatively and quantitatively realistic microstructures by learning from images of HE microstructures. We show that the proposed GAN method also permits the generation of new morphologies, where the porosity distribution can be controlled and spatially manipulated. Such control paves the way for the design of novel microstructures to engineer HE materials for targeted performance in a materials-by-design framework

Neonatal neurobehavioral abnormalities and MRI brain injury in encephalopathic newborns treated with hypothermia

Background Neonatal Encephalopathy (NE) is a prominent cause of infant mortality and neurodevelopmental disability. Hypothermia is an effective neuroprotective therapy for newborns with encephalopathy. Post-hypothermia functional–anatomical correlation between neonatal neurobehavioral abnormalities and brain injury findings on MRI in encephalopathic newborns has not been previously described. Aim To evaluate the relationship between neonatal neurobehavioral abnormalities and brain injury on magnetic resonance imaging (MRI) in encephalopathic newborns treated with therapeutic hypothermia. Study design Neonates with hypoxic ischemic encephalopathy (HIE) referred for therapeutic hypothermia were prospectively enrolled in this observational study. Neurobehavioral functioning was assessed with the NICU network neurobehavioral scale (NNNS) performed at target age 14 days. Brain injury was assessed by MRI at target age 7–10 days. NNNS scores were compared between infants with and without severe MRI injury. Subjects & outcome measures Sixty-eight term newborns (62% males) with moderate to severe encephalopathy underwent MRI at median 8 days (range 5–16) and NNNS at median 12 days of life (range 5–20). Fifteen (22%) had severe injury on MRI. Results Overall Total Motor Abnormality Score and individual summary scores for Non-optimal Reflexes and Asymmetry were higher, while Total NNNS Z-score across cognitive/behavioral domains was lower (reflecting poorer performance) in infants with severe MRI injury compared to those without (p \u3c 0.05). Conclusions Neonatal neurobehavioral abnormalities identified by the NNNS are associated with MRI brain injury in encephalopathic newborns post-hypothermia. The NNNS can provide an early functional assessment of structural brain injury in newborns, which may guide rehabilitative therapies in infants after perinatal brain injury

Pyrophosphate-Dependent ATP Formation from Acetyl Coenzyme A in Syntrophus aciditrophicus, a New Twist on ATP Formation.

UnlabelledSyntrophus aciditrophicus is a model syntrophic bacterium that degrades key intermediates in anaerobic decomposition, such as benzoate, cyclohexane-1-carboxylate, and certain fatty acids, to acetate when grown with hydrogen-/formate-consuming microorganisms. ATP formation coupled to acetate production is the main source for energy conservation by S. aciditrophicus However, the absence of homologs for phosphate acetyltransferase and acetate kinase in the genome of S. aciditrophicus leaves it unclear as to how ATP is formed, as most fermentative bacteria rely on these two enzymes to synthesize ATP from acetyl coenzyme A (CoA) and phosphate. Here, we combine transcriptomic, proteomic, metabolite, and enzymatic approaches to show that S. aciditrophicus uses AMP-forming, acetyl-CoA synthetase (Acs1) for ATP synthesis from acetyl-CoA. acs1 mRNA and Acs1 were abundant in transcriptomes and proteomes, respectively, of S. aciditrophicus grown in pure culture and coculture. Cell extracts of S. aciditrophicus had low or undetectable acetate kinase and phosphate acetyltransferase activities but had high acetyl-CoA synthetase activity under all growth conditions tested. Both Acs1 purified from S. aciditrophicus and recombinantly produced Acs1 catalyzed ATP and acetate formation from acetyl-CoA, AMP, and pyrophosphate. High pyrophosphate levels and a high AMP-to-ATP ratio (5.9 ± 1.4) in S. aciditrophicus cells support the operation of Acs1 in the acetate-forming direction. Thus, S. aciditrophicus has a unique approach to conserve energy involving pyrophosphate, AMP, acetyl-CoA, and an AMP-forming, acetyl-CoA synthetase.ImportanceBacteria use two enzymes, phosphate acetyltransferase and acetate kinase, to make ATP from acetyl-CoA, while acetate-forming archaea use a single enzyme, an ADP-forming, acetyl-CoA synthetase, to synthesize ATP and acetate from acetyl-CoA. Syntrophus aciditrophicus apparently relies on a different approach to conserve energy during acetyl-CoA metabolism, as its genome does not have homologs to the genes for phosphate acetyltransferase and acetate kinase. Here, we show that S. aciditrophicus uses an alternative approach, an AMP-forming, acetyl-CoA synthetase, to make ATP from acetyl-CoA. AMP-forming, acetyl-CoA synthetases were previously thought to function only in the activation of acetate to acetyl-CoA

Association Between Chronic Hepatitis C Virus Infection and Myocardial Infarction Among People Living With HIV in the United States.

Hepatitis C virus (HCV) infection is common among people living with human immunodeficiency virus (PLWH). Extrahepatic manifestations of HCV, including myocardial infarction (MI), are a topic of active research. MI is classified into types, predominantly atheroembolic type 1 MI (T1MI) and supply-demand mismatch type 2 MI (T2MI). We examined the association between HCV and MI among patients in the Centers for AIDS Research (CFAR) Network of Integrated Clinical Systems, a US multicenter clinical cohort of PLWH. MIs were centrally adjudicated and categorized by type using the Third Universal Definition of Myocardial Infarction. We estimated the association between chronic HCV (RNA+) and time to MI while adjusting for demographic characteristics, cardiovascular risk factors, clinical characteristics, and history of injecting drug use. Among 23,407 PLWH aged ≥18 years, there were 336 T1MIs and 330 T2MIs during a median of 4.7 years of follow-up between 1998 and 2016. HCV was associated with a 46% greater risk of T2MI (adjusted hazard ratio (aHR) = 1.46, 95% confidence interval (CI): 1.09, 1.97) but not T1MI (aHR = 0.87, 95% CI: 0.58, 1.29). In an exploratory cause-specific analysis of T2MI, HCV was associated with a 2-fold greater risk of T2MI attributed to sepsis (aHR = 2.01, 95% CI: 1.25, 3.24). Extrahepatic manifestations of HCV in this high-risk population are an important area for continued research

Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices

Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI’s spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models’ output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outper-formed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability