Experimental analysis of air vortex Impingement through porous screens

This paper presents the experimental analysis of air flow vortex propagation through porous screens. This study is conducted by a new and unique experimental setup for ve- locity measurements and visualization of air vortex interac- tion with porous screen. A custom-made, high-precision vortex generator provides a variety of velocity profiles for vortex generation with an unprecedented level of precision. The flow fields are captured with the use of a fog gener- ator and a high-speed CCD camera. The porous screens are constructed out of acrylic rods with various orientations, thickness, and porosities from rod separation. The results presented in this paper show the effect of porosity and air injection velocity on the behavior of air flow (separation, accumulation), and the transport phenomena of vortex flow while interacting with porous screens

Changing practice in dementia care in the community: developing and testing evidence-based interventions, from timely diagnosis to end of life (EVIDEM)

Background Dementia has an enormous impact on the lives of individuals and families, and on health and social services, and this will increase as the population ages. The needs of people with dementia and their carers for information and support are inadequately addressed at all key points in the illness trajectory. Methods The Unit is working specifically on an evaluation of the impact of the Mental Capacity Act 2005, and will develop practice guidance to enhance concordance with the Act. Phase One of the study has involved baseline interviews with practitioners across a wide range of services to establish knowledge and expectations of the Act, and to consider change processes when new policy and legislation are implemented. Findings Phase 1, involving baseline interviews with 115 practitioners, identified variable knowledge and understanding about the principles of the Act. Phase 2 is exploring everyday decision-making by people with memory problems and their carers

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers

HPLC-accelerator MS measurement of atrazine metabolites in human urine after dermal exposure

Metabolites of atrazine were measured in human urine after dermal exposure using HPLC to separate and identify metabolites and accelerator mass spectrometry (AMS) to quantify them. Ring-labeled [14C]atrazine was applied for 24 h with a dermal patch to human volunteers at low (0.167 mg, 6.45 μCi) and high (1.98 mg, 24.7 μCi) doses. Urine was collected for 7 days. The urine was centrifuged to remove solids, and the supernatant was measured by liquid scintillation counting prior to injection on the HPLC to ensure that \u3c0.17 Bq (4.5 pCi) was injected on the column. A reversed-phase gradient of 0.1% acetic acid in water and 0.1% acetic acid in acetonitrile became less polar with increasing time and separated the parent compound and major atrazine metabolites over 31 min on an octadecylsilane column. Peaks were identified by coelution with known standards. Elution fractions were collected in 1-min increments; half of each fraction was analyzed by AMS to obtain limits of quantitation of 14 amol. Mercapturate metabolites of atrazine and dealkylated atrazine dominated the early metabolic time points, accounting for ~90% of the 14C in the urine. No parent compound was detected. The excreted atrazine metabolites became more polar with increasing time, and an unidentified polar metabolite that was present in all samples became as prevalent as any of the known ring metabolites several days after the dose was delivered. Knowledge of metabolite dynamics is crucial to developing useful assays for monitoring atrazine exposure in agricultural workers