Artificial neural networks and player recruitment in professional soccer

The aim was to objectively identify key performance indicators in professional soccer that influence outfield players’ league status using an artificial neural network. Mean technical performance data were collected from 966 outfield players’ (mean SD; age: 25 ± 4 yr, 1.81 ±) 90-minute performances in the English Football League. ProZone’s MatchViewer system and online databases were used to collect data on 347 indicators assessing the total number, accuracy and consistency of passes, tackles, possessions regained, clearances and shots. Players were assigned to one of three categories based on where they went on to complete most of their match time in the following season: group 0 (n = 209 players) went on to play in a lower soccer league, group 1 (n = 637 players) remained in the Football League Championship, and group 2 (n = 120 players) consisted of players who moved up to the English Premier League. The models created correctly predicted between 61.5% and 78.8% of the players’ league status. The model with the highest average test performance was for group 0 v 2 (U21 international caps, international caps, median tackles, percentage of first time passes unsuccessful upper quartile, maximum dribbles and possessions gained minimum) which correctly predicted 78.8% of the players’ league status with a test error of 8.3%. To date, there has not been a published example of an objective method of predicting career trajectory in soccer. This is a significant development as it highlights the potential for machine learning to be used in the scouting and recruitment process in a professional soccer environment

Source attribution of poly- and perfluoroalkyl substances (PFASs) in surface waters from Rhode Island and the New York Metropolitan Area

Exposure to poly- and perfluoroalkyl substances (PFASs) has been associated with adverse health effects in humans and wildlife. Understanding pollution sources is essential for environmental regulation, but source attribution for PFASs has been confounded by limited information about industrial releases and rapid changes in chemical production. Here we use principal component analysis (PCA), hierarchical clustering, and geospatial analysis to understand source contributions to 14 PFASs measured across 37 sites in the northeastern United States in 2014. PFASs are significantly elevated in urban areas compared to rural sites except for perfluorobutanesulfonate, N-methyl perfluorooctanesulfonamidoacetic acid, perfluoroundecanate, and perfluorododecanate. The highest PFAS concentrations across sites were those of perfluorooctanate (PFOA, 56 ng L−1) and perfluorohexanesulfonate (PFHxS, 43 ng L−1), and perfluorooctanesulfonate (PFOS) levels are lower than earlier measurements of U.S. surface waters. PCA and cluster analysis indicate three main statistical groupings of PFASs. Geospatial analysis of watersheds reveals the first component/cluster originates from a mixture of contemporary point sources such as airports and textile mills. Atmospheric sources from the waste sector are consistent with the second component, and the metal smelting industry plausibly explains the third component. We find this source-attribution technique is effective for better understanding PFAS sources in urban areas

An Improved Global Model for Air-Sea Exchange of Mercury: High Concentrations over the North Atlantic

We develop an improved treatment of the surface ocean in the GEOS-Chem global 3-D biogeochemical model for mercury (Hg). We replace the globally uniform subsurface ocean Hg concentrations used in the original model with basin-specific values based on measurements. Updated chemical mechanisms for Hg0/HgII redox reactions in the surface ocean include both photochemical and biological processes, and we improved the parametrization of particle-associated Hg scavenging. Modeled aqueous Hg concentrations are consistent with limited surface water observations. Results more accurately reproduce high-observed MBL concentrations over the North Atlantic (NA) and the associated seasonal trends. High seasonal evasion in the NA is driven by inputs from Hg enriched subsurface waters through entrainment and Ekman pumping. Globally, subsurface waters account for 40% of Hg inputs to the ocean mixed layer, and 60% is from atmospheric deposition. Although globally the ocean is a net sink for 3.8 Mmol Hg y−1, the NA is a net source to the atmosphere, potentially due to enrichment of subsurface waters with legacy Hg from historical anthropogenic sources.Engineering and Applied Science

Wind Energy Harvesting and Conversion Systems: A Technical Review

Wind energy harvesting for electricity generation has a significant role in overcoming the challenges involved with climate change and the energy resource implications involved with population growth and political unrest. Indeed, there has been significant growth in wind energy capacity worldwide with turbine capacity growing significantly over the last two decades. This confidence is echoed in the wind power market and global wind energy statistics. However, wind energy capture and utilisation has always been challenging. Appreciation of the wind as a resource makes for difficulties in modelling and the sensitivities of how the wind resource maps to energy production results in an energy harvesting opportunity. An opportunity that is dependent on different system parameters, namely the wind as a resource, technology and system synergies in realizing an optimal wind energy harvest. This paper presents a thorough review of the state of the art concerning the realization of optimal wind energy harvesting and utilisation. The wind energy resource and, more specifically, the influence of wind speed and wind energy resource forecasting are considered in conjunction with technological considerations and how system optimization can realise more effective operational efficiencies. Moreover, non-technological issues affecting wind energy harvesting are also considered. These include standards and regulatory implications with higher levels of grid integration and higher system non-synchronous penetration (SNSP). The review concludes that hybrid forecasting techniques enable a more accurate and predictable resource appreciation and that a hybrid power system that employs a multi-objective optimization approach is most suitable in achieving an optimal configuration for maximum energy harvesting

Nitrifying Microorganisms Linked to Biotransformation of Perfluoroalkyl Sulfonamido Precursors from Legacy Aqueous Film-Forming Foams

Drinking water supplies across the United States have been contaminated by firefighting and fire-training activities that use aqueous film-forming foams (AFFF) containing per- and polyfluoroalkyl substances (PFAS). Much of the AFFF is manufactured using electrochemical fluorination by 3M. Precursors with six perfluorinated carbons (C6) and non-fluorinated amine substituents make up approximately one-third of the PFAS in 3M AFFF. C6 precursors can be transformed through nitrification (microbial oxidation) of amine moieties into perfluorohexane sulfonate (PFHxS), a compound of regulatory concern. Here, we report biotransformation of the most abundant C6 sulfonamido precursors in 3M AFFF with available commercial standards (FHxSA, PFHxSAm, and PFHxSAmS) in microcosms representative of the groundwater/surface water boundary. Results show rapid (\u3c1 day) biosorption to living cells by precursors but slow biotransformation into PFHxS (1–100 pM day–1). The transformation pathway includes one or two nitrification steps and is supported by the detection of key intermediates using high-resolution mass spectrometry. Increasing nitrate concentrations and total abundance of nitrifying taxa occur in parallel with precursor biotransformation. Together, these data provide multiple lines of evidence supporting microbially limited biotransformation of C6 sulfonamido precursors involving ammonia-oxidizing archaea (Nitrososphaeria) and nitrite-oxidizing bacteria (Nitrospina). Further elucidation of interrelationships between precursor biotransformation and nitrogen cycling in ecosystems would help inform site remediation efforts

Health-care guidelines and policies during the COVID-19 pandemic in Mexico: a case of health-inequalities

Background Heterogeneous government responses have been reported in reaction to COVID-19. The aim of this study is to generate an exploratory review of healthcare policies published during COVID-19 by health-care institutions in Mexico. Analyzing policies within different health sub-systems becomes imperative in the Mexican case due to the longstanding fragmentation of the health-care system and health inequalities. Data and Methods Policies purposely included in the analysis were published by four public health institutions (IMSS, ISSSTE, SSA and PEMEX) during the COVID-19 epidemic in Mexico (from February 29th to June 15th, 2020) on official institutional websites. Researchers reviewed each document and classified them into seven policy categories set by the Rapid Research Evaluation and Appraisal Lab (RREAL): public health response, health-care delivery, human resources, health-system infrastructure and supplies, clinical response, health-care management, and epidemiological surveillance. Results Policy types varied by health institution. The largest number of policies were aimed at public health responses followed by health-care delivery and human resources. Policies were mainly published during the community transmission phase. Conclusions The pandemic exposed underlying health-care system inequalities and a reactive rather than prepared response to the outbreak. Additionally, this study outlines potential policy gaps and delays in the response that could be avoided in the future

Hematopoietic stem cell gene therapy for brain metastases using myeloid cell-specific gene promoters

Background: Brain metastases (BrM) develop in 20-40% of cancer patients and represent an unmet clinical need. Limited access of drugs into the brain due to the blood-brain barrier is at least partially responsible for therapeutic failure, necessitating improved drug delivery systems. Methods: Green fluorescent protein (GFP)-transduced murine and non-transduced human hematopoietic stem cells (HSCs) were administered into mice (n = 10 and 3). The HSC progeny in mouse BrM and in patient-derived BrM tissue (n = 6) was characterized by flow cytometry and immunofluorescence. Promoters driving gene expression, specifically within the BrM-infiltrating HSC progeny, were identified through differential gene expression analysis and subsequent validation of a series of promoter-GFP-reporter constructs in mice (n = 5). One of the promoters was used to deliver TNF-related apoptosis-inducing ligand (TRAIL) to BrM in mice (n = 17/21 for TRAIL versus control group). Results: HSC progeny (consisting mostly of macrophages) efficiently homed to macrometastases (37.6% [SD = 7.2%] of all infiltrating cells for murine HSC progeny; 27.9% [SD = 4.9%] of infiltrating CD45+ hematopoietic cells for human HSC progeny) and micrometastases in mice (19.3-53.3% of all macrophages for murine HSCs). Macrophages were also abundant in patient-derived BrM tissue (8.8%, SD = 7.8%). Collectively, this provided a rationale to optimize the delivery of gene therapy to BrM within myeloid cells. MMP14 promoter emerged as the strongest promoter construct capable of limiting gene expression to BrM-infiltrating myeloid cells in mice TRAIL delivered under MMP14 promoter statistically significantly prolonged survival in mice (19.0 [SD = 3.4] versus 15.0 [SD = 2.0] days for TRAIL versus control group; two-sided p = 0.006), demonstrating therapeutic and translational potential of our approach. Conclusions: Our study establishes HSC gene therapy using a myeloid cell-specific promoter as a new strategy to target BrM. This approach, with strong translational value, has potential to overcome the blood-brain barrier, target micrometastases, and control multifocal lesions

HOMMEXX 1.0: a performance-portable atmospheric dynamical core for the Energy Exascale Earth System Model

We present an architecture-portable and performant implementation of the atmospheric dynamical core (High-Order Methods Modeling Environment, HOMME) of the Energy Exascale Earth System Model (E3SM). The original Fortran implementation is highly performant and scalable on conventional architectures using the Message Passing Interface (MPI) and Open MultiProcessor (OpenMP) programming models. We rewrite the model in C++ and use the Kokkos library to express on-node parallelism in a largely architecture-independent implementation. Kokkos provides an abstraction of a compute node or device, layout-polymorphic multidimensional arrays, and parallel execution constructs. The new implementation achieves the same or better performance on conventional multicore computers and is portable to GPUs. We present performance data for the original and new implementations on multiple platforms, on up to 5400 compute nodes, and study several aspects of the single- and multi-node performance characteristics of the new implementation on conventional CPU (e.g., Intel Xeon), many core CPU (e.g., Intel Xeon Phi Knights Landing), and Nvidia V100 GPU.</p