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Materials in car cabins contain performance-enhancing semi-volatile organic compounds (SVOCs). As these SVOCs are not chemically bound to the materials, they can emit from the materials at slow rates to the surrounding, causing human exposure. This study aimed at increasing the understanding on abundance of SVOCs in car cabins by studying 18 potential endocrine disrupting chemicals in car cabin air (gas phase and airborne particles) and dust. We also studied how levels of these chemicals varied by temperature inside the car cabin along with ventilation settings, relevant to human exposure. A positive correlation was observed between temperature and SVOC concentration in both the gas and the particle phase, where average gas phase levels at 80 °C were a factor of 18-16,000 higher than average levels at 25 °C, while average particle phase levels were a factor of 4.6-40,000 higher for the studied substances. This study also showed that levels were below the limit of detection for several SVOCs during realistic driving conditions, i.e., with the ventilation activated. To limit human exposure to SVOCs in car cabins, it is recommended to ventilate a warm car before entering and have the ventilation on during driving, as both temperature and ventilation have a significant impact on SVOC levels.

Increasing consumption of pharmaceuticals and the respective consequences for the aquatic environment have been the focus of many studies over the last thirty years. Various aspects in this field were investigated, considering diverse pharmaceutical groups and employing a wide range of research methodologies. Various questions from the perspectives of different research areas were devised and answered, resulting in a large mix of individual findings and conclusions. Collectively, the results of the studies offer a comprehensive overview. The large variety of methods and strategies, however, demands close attention when comparing and combining information from heterogeneous projects. This review critically examines the application of diverse sampling techniques as well as analytical methods in investigations concerning the behavior of pharmaceutically active compounds (PhACs) and contrast agents (CAs) in wastewater treatment plants (WWTPs). The combination of sampling and analysis is discussed with regard to its suitability for specific scientific problems. Different research focuses need different methods and answer different questions. An overview of studies dealing with the fate and degradation of PhACs and CAs in WWTPs is presented, discussing their strategic approaches and findings. This review includes surveys of anticancer drugs, antibiotics, analgesics and anti-inflammatory drugs, antidiabetics, beta blockers, hormonal contraceptives, lipid lowering agents, antidepressants as well as contrast agents for X-ray and magnetic resonance imaging.

BACKGROUND: Population viral load (VL), the most comprehensive measure of the HIV transmission potential, cannot be directly measured due to lack of complete sampling of all people with HIV.
OBJECTIVE: A given HIV clinic\'s electronic health record (EHR), a biased sample of this population, may be used to attempt to impute this measure.
METHODS: We simulated a population of 10,000 individuals with VL calibrated to surveillance data with a geometric mean of 4449 copies/mL. We sampled 3 hypothetical EHRs from (A) the source population, (B) those diagnosed, and (C) those retained in care. Our analysis imputed population VL from each EHR using sampling weights followed by Bayesian adjustment. These methods were then tested using EHR data from an HIV clinic in Delaware.
RESULTS: Following weighting, the estimates moved in the direction of the population value with correspondingly wider 95% intervals as follows: clinic A: 4364 (95% interval 1963-11,132) copies/mL; clinic B: 4420 (95% interval 1913-10,199) copies/mL; and clinic C: 242 (95% interval 113-563) copies/mL. Bayesian-adjusted weighting further improved the estimate.
CONCLUSIONS: These findings suggest that methodological adjustments are ineffective for estimating population VL from a single clinic\'s EHR without the resource-intensive elucidation of an informative prior.

Most field corn in the United States receives a neonicotinoid seed treatment for the management of early-season, soil-dwelling insect pests. Grubs of Maladera formosae (Brenske) (Coleoptera: Scarabaeidae) have been reported feeding on young field corn with both low and high rates of clothianidin seed treatments in Indiana, Michigan, and Ohio. Anecdotally, these infestations are restricted to sandy soils in the region. The purpose of this study was to (1) evaluate whether grub populations in corn are restricted to sandy soils, (2) assess whether soil type influences M. formosae survival, and (3) determine whether soil type affects clothianidin uptake by the plant, possibly explaining the observed differences in M. formosae abundance by soil type. We observed nearly 10-times more grubs in sand (>80% sand content) than loam (<80% sand content) soil within a single corn field. Grub survival to adult was not influenced by soil type. We then compared the concentrations of clothianidin seed treatment in the roots and shoots of corn seedlings grown in either sand or loam soil over time. Similar amounts of the active ingredient were found in the roots and shoots of corn grown in both soil types. Within 2 week, the clothianidin concentrations in both soil types had significantly declined in roots and shoots and were no different from the no-insecticide control. These findings suggest that factors other than insecticide exposure contribute to the higher abundance of M. formosae larvae in sand relative to loam soils, even within the same field.

Recent years witnessed the development of powerful generative models based on flows, diffusion, or autoregressive neural networks, achieving remarkable success in generating data from examples with applications in a broad range of areas. A theoretical analysis of the performance and understanding of the limitations of these methods remain, however, challenging. In this paper, we undertake a step in this direction by analyzing the efficiency of sampling by these methods on a class of problems with a known probability distribution and comparing it with the sampling performance of more traditional methods such as the Monte Carlo Markov chain and Langevin dynamics. We focus on a class of probability distribution widely studied in the statistical physics of disordered systems that relate to spin glasses, statistical inference, and constraint satisfaction problems. We leverage the fact that sampling via flow-based, diffusion-based, or autoregressive networks methods can be equivalently mapped to the analysis of a Bayes optimal denoising of a modified probability measure. Our findings demonstrate that these methods encounter difficulties in sampling stemming from the presence of a first-order phase transition along the algorithm\'s denoising path. Our conclusions go both ways: We identify regions of parameters where these methods are unable to sample efficiently, while that is possible using standard Monte Carlo or Langevin approaches. We also identify regions where the opposite happens: standard approaches are inefficient while the discussed generative methods work well.

Assessment of occupational exposure to viruses is crucial to identify virus reservoirs and sources of dissemination at an early stage and to help prevent spread between employees and to the general population. Measuring workers\' exposure can facilitate assessment of the effectiveness of protective and mitigation measures in place. The aim of this scoping review is to give an overview of available methods and those already implemented for airborne virus\' exposure assessment in different occupational and indoor environments. The results retrieved from the different studies may contribute to the setting of future standards and guidelines to ensure a reliable risk characterization in the occupational environments crucial for the implementation of effective control measures. The search aimed at selecting studies between January 1st 2010 and June 30th 2023 in the selected databases. Fifty papers on virus exposure assessment fitted the eligibility criteria and were selected for data extraction. Overall, this study identified gaps in knowledge regarding virus assessment and pinpointed the needs for further research. Several discrepancies were found (transport temperatures, elution steps, …), as well as a lack of publication of important data related to the exposure conditions (contextual information). With the available information, it is impossible to compare results between studies employing different methods, and even if the same methods are used, different conclusions/recommendations based on the expert judgment have been reported due to the lack of consensus in the contextual information retrieved and/or data interpretation. Future research on the field targeting sampling methods and in the laboratory regarding the assays to employ should be developed bearing in mind the different goals of the assessment.

BACKGROUND: Educational neuroscience research, which investigates the neurobiological mechanisms of learning, has historically incorporated samples drawn mostly from white, middle-class, and/or suburban populations. However, sampling in research without attending to representation can lead to biased interpretations and results that are less generalizable to an intended target population. Prior research revealing differences in neurocognitive outcomes both within- and across-groups further suggests that such practices may obscure significant effects with practical implications.
UNASSIGNED: Negative attitudes among historically marginalized communities, stemming from historical mistreatment, biased research outcomes, and implicit or explicit attitudes among research teams, can hinder diverse participation. Qualities of the research process including language requirements, study locations, and time demands create additional barriers.
METHODS: Flexible data collection approaches, community engaugement, and transparent reporting could build trust and enhance sampling diversity. Longer-term solutions include prioritizing research questions relevant to marginalized communities, increasing workforce diversity, and detailed reporting of sample demographics. Such concerted efforts are essential for robust educational neuroscience research to maximize positive impacts broadly across learners.

Stereology, the gold standard of lung morphometry, critically depends on sampling of tissue for analysis. Random sampling approaches guarantee each part of the organ an equal chance of being included in the analysis, hence they guarantee a representative sample of the whole. However, when biological or pathological structures of interest are rare and/or heterogeneously distributed over the whole lung, the random sampling approach can be inefficient or even result in meaningless data. In such cases, a targeted sampling approach can be useful which helps to relate the analytical items to an appropriate reference space. Targeted stereology greatly benefits from the increasing availability of multi-resolution imaging techniques at macroscopic and microscopic level as well as digital tools of segmentation. As such, the present article outlines two basic sampling scenarios: 1. In the first scenario, computed tomography and microscopy are subsequently used to segment the airway/arterial tree and perform stereological measurements on specific branches of the tree. 2. The second scenario deals with heterogeneous distribution of pathological lesions. This type of analysis can be divided into two stages: assessment of lesions of interest (LOI) within the lung and assessment of subcompartments within LOI. Taken together, targeted stereology has a thorough foundation in stereological theory and is not only able to significantly increase the efficiency of the analysis but also to yield new types of information that would be lost with the classical random sampling approach.

The two main things needed to fulfill the world\'s impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management.