The concentration of particulate matter in the barn air and its influence on the content of heavy metals in milk

Heavy metals are one of the components of smog, which is mainly the product of burning fossil fuels in residential buildings. These elements, introduced into the body of cattle by inhalation, may enter the milk. The goal of this study was to assess the impact of particulate pollution in the atmospheric air on the concentration of particulate matter in the air of a dairy cattle barn and on the content of selected heavy metals in milk from cows present in the building. Measurements were taken between November and April (148 measurement days). The calculations carried out showed a high correlation (R$_{S}$ = + 0.95) between the concentrations of particulates measured outside and inside the barn, which is indicative of a significant impact of the atmospheric air on the particulate pollution level of the livestock building. The number of days in excess of the daily standard for PM$_{10}$ inside was 51. The conducted analysis of the chemical composition of the milk collected under high particulate pollution (February) showed that the permitted lead level had been exceeded—21.93 µg/kg (norm 20.00 µg/kg)

Assessment of the health risk associated with exposure to heavy metals present in particulate matter deposition in the Małopolska Province

The aim of the study was to investigate the content of trace elements in deposited particulate matter and to estimate the health risk to Kraków inhabitants, caused by the exposure to heavy metals in particulate matter deposition. The qualitative and quantitative assessments of selected heavy metals in deposited particulate matter have been carried out in the city of Kraków (Małopolska, southern Poland, 5 measuring points) for seven months, between February and September 2017. A comparative study was conducted at the same time in Małopolska (5 measuring points). The deposited particulate matter was collected gravitationally, using measurement plates covered with aluminum foil and paraffin jelly. The largest deposition of particulate matter was found in May and June. The highest amount of deposited particulate matter and metals present in it was determined in Kraków. The Hazard Quotient (HQ) evaluation for non-carcinogenic effect showed low risk for each metal. In the case of lead in particulate matter, the carcinogenic risk value did not reach 10−6 hence this risk is acceptable. The total carcinogenic risk for all routes of exposure to cadmium was higher, indicating the risk of cancer in children and adults, with children more exposed. However, the carcinogenic risk for cadmium was also acceptable. The study showed that the problem of poor air quality concerns not only the city of Kraków, but also the entire Małopolska region. Elevated metal concentrations in particulate matter indicate the need for monitoring it in the air

The aac(6')Ib gene in Proteus mirabilis strains resistant to aminoglycosides.

The aim of this study was to evaluate the presence of aac(6')-Ib gene conferring resistance to aminoglycosides in Proteus mirabilis strains. Five isolates had aac(6')-Ib gene. In one case the gene was no-expressed. Three isolates were resistant to all aminoglycosides and minimum inhibitory concentrations were > or = 256 microg/ml. Additionally, all positive strains were resistant to tetracycline and ciprofloxacin

Analysis of the Longest Distances Run by the Best Soccer Players at the FIFA World Cup in Brazil in 2014

The aim of the study was, among other things, to characterise the maximum distance run by the four best teams in the FIFA World Cup in Brazil, including individual tactical formations and players who played for at least 90 minutes in the whole tournament. In total, the highest results of 68 players were established. In the analysis data obtained using Castrol Performance Index method were used. The mean maximum distance run by players of the tournament semi-finalist teams in the 24 analysed games was 11.63 km. The mean by the analysed defenders was 11.75 km, whereas for midfielders it was 12.02 km, for forwards – 11.02 km, and for goalkeepers – 5.65 km. In group of all 68 respondents soccer players a distance of over 15 km in one game was obtained by Bastian Schweinsteiger, Thomas Müller and Lucas Biglia

CAN THE FACE-TO-FACE INTUBATION TECHNIQUE BE USED DURING CARDIOPULMONARY RESUSCITATION? A PROSPECTIVE, RANDOMIZED, CROSSOVER MANIKIN TRIAL

   BACKGROUND: Endotracheal intubation in cardiopulmonary resuscitation conditions is the gold standard for the protection of airway patency, allowing for both ventilation with positive pressures and continuous moni­toring of carbon dioxide concentration in the exhaled air, as well as enabling continuous chest compressions. AIM: The aim of the study was to compare the effectiveness of endotracheal intubation performed with the usage of Macintosh laryngoscope in two positions: behind the patient’s head and in the face-to-face position. METHODS: We included 54 students during their final year of medicine in the study. All of participants declared the ability to perform endotracheal intubation based on direct laryngoscopy. Prior to the study, all participants took part in the training in laryngoscopy and cardiopulmonary resuscitation. During the study, the participants performed intubation in the simulated resuscitation environment in two scenarios: Scenario A — intubation from behind the patient;s head, Scenario B — face-to-face intubation. Participants had a maximum of three intubation attempts. The chest compressions were paused during the procedure. RESULTS: The effectiveness of the first intubation attempt in the case of scenario A was 44.4%, while in the case of scenario B — 24.1%. The overall success ratios of intubation for scenarios A and B were 88.9% vs. 53.7%, respectively. The median intubation time during scenario A was 43.5 [IQR; 34–53.5] seconds, and 54.5 [IQR; 38.5–59.5] seconds for scenario B. CONCLUSIONS: In the study, intubation performed by final-year medical students while taking a position behind the head of the victim was of a higher efficiency when compared to the face-to-face position

Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm

Pruning techniques have been successfully used in neural networks to trade accuracy for sparsity. However, the impact of network pruning is not uniform: prior work has shown that the recall for underrepresented classes in a dataset may be more negatively affected. In this work, we study such relative distortions in recall by hypothesizing an intensification effect that is inherent to the model. Namely, that pruning makes recall relatively worse for a class with recall below accuracy and, conversely, that it makes recall relatively better for a class with recall above accuracy. In addition, we propose a new pruning algorithm aimed at attenuating such effect. Through statistical analysis, we have observed that intensification is less severe with our algorithm but nevertheless more pronounced with relatively more difficult tasks, less complex models, and higher pruning ratios. More surprisingly, we conversely observe a de-intensification effect with lower pruning ratios, which indicates that moderate pruning may have a corrective effect to such distortions

Think-aloud interviews: A tool for exploring student statistical reasoning

Research has shown that introductory statistics students hold many misconceptions, and that many of these are also present among practicing scientists. But statistics is becoming important to many new fields, and understanding how students learn statistics is more important than ever, if it is to be effectively taught. We describe an education research method intended to reveal how students think and to provide practical ways of measuring their understanding: a combination of think-aloud interviews and concept inventories. Think-aloud interviews give unprecedented insight into student thinking, while concept inventories can be administered to entire classes to measure learning. We demonstrate the method through insights gained from 42 think-aloud interviews with introductory students, plus large-scale concept inventory data from over 50 questions given as pre- and post-tests to hundreds of introductory statistics students at two institutions. Think-aloud interviews revealed previously under-reported misconceptions about sampling distributions and causation, while helping us refine conceptual questions to measure their prevalence at large scale. These insights, and the final questions, may help educators develop improved lessons, while suggesting directions for future statistics education research and providing practical tools for researchers to improve our understanding of student learning.Comment: 23 pages, 4 tables, 3 figures; supplemental data available online at https://doi.org/10.1184/R1/1005844

Assessing small area estimates via artificial populations from KBAABB: a kNN-based approximation to ABB

Comparing and evaluating small area estimation (SAE) models for a given application is inherently difficult. Typically, we do not have enough data in many areas to check unit-level modeling assumptions or to assess unit-level predictions empirically; and there is no ground truth available for checking area-level estimates. Design-based simulation from artificial populations can help with each of these issues, but only if the artificial populations (a) realistically represent the application at hand and (b) are not built using assumptions that could inherently favor one SAE model over another. In this paper, we borrow ideas from random hot deck, approximate Bayesian bootstrap (ABB), and k nearest neighbor (kNN) imputation methods, which are often used for multiple imputation of missing data. We propose a kNN-based approximation to ABB (KBAABB) for a different purpose: generating an artificial population when rich unit-level auxiliary data is available. We introduce diagnostic checks on the process of building the artificial population itself, and we demonstrate how to use such an artificial population for design-based simulation studies to compare and evaluate SAE models, using real data from the Forest Inventory and Analysis (FIA) program of the US Forest Service. We illustrate how such simulation studies may be disseminated and explored interactively through an online R Shiny application