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Pulmonary exposure to micro- and nanoscaled particles has been widely linked to adverse health effects and high concentrations of respirable particles are expected to occur within and around many industrial settings. In this study, a field-measurement campaign was performed at an industrial manufacturer, during the production of paints. Spatial and personal measurements were conducted and results were used to estimate the mass flows in the facility and the airborne particle release to the outdoor environment. Airborne particle number concentration (1 × 103-1.0 × 104 cm-3), respirable mass (0.06-0.6 mg m-3), and PM10 (0.3-6.5 mg m-3) were measured during pouring activities. In overall; emissions from pouring activities were found to be dominated by coarser particles >300 nm. Even though the raw materials were not identified as nanomaterials by the manufacturers, handling of TiO2 and clays resulted in release of nanometric particles to both workplace air and outdoor environment, which was confirmed by TEM analysis of indoor and stack emission samples. During the measurement period, none of the existing exposure limits in force were exceeded. Particle release to the outdoor environment varied from 6 to 20 g ton-1 at concentrations between 0.6 and 9.7 mg m-3 of total suspended dust depending on the powder. The estimated release of TiO2 to outdoors was 0.9 kg per year. Particle release to the environment is not expected to cause any major impact due to atmospheric dilution.The present aim was to compare the accuracy of several algorithms in classifying data collected from food scent samples. Measurements using an electronic nose (eNose) can be used for classification of different scents. An eNose was used to measure scent samples from seven food scent sources, both from an open plate and a sealed jar. The k-Nearest Neighbour (k-NN) classifier provides reasonable accuracy under certain conditions and uses traditionally the Euclidean distance for measuring the similarity of samples. Therefore, it was used as a baseline distance metric for the k-NN in this paper. Its classification accuracy was compared with the accuracies of the k-NN with 66 alternative distance metrics. In addition, 18 other classifiers were tested with raw eNose data. For each classifier various parameter settings were tried and compared. Overall, 304 different classifier variations were tested, which differed from each other in at least one parameter value. The results showed that Quadratic Discriminant Analysis, MLPClassifier, C-Support Vector Classification (SVC), and several different single hidden layer Neural Networks yielded lower misclassification rates applied to the raw data than k-NN with Euclidean distance. Both MLP Classifiers and SVC yielded misclassification rates of less than 3% when applied to raw data. Furthermore, when applied both to the raw data and the data preprocessed by principal component analysis that explained at least 95% or 99% of the total variance in the raw data, Quadratic Discriminant Analysis outperformed the other classifiers. The findings of this study can be used for further algorithm development. They can also be used, for example, to improve the estimation of storage times of fruit.Protein-protein interactions (PPIs) are the vital engine of cellular machinery. After virus entry in host cells the global organization of the viral life cycle is strongly regulated by the formation of virus-host protein interactions. With the advent of high-throughput -omics platforms, the mirage to obtain a "high resolution" view of virus-host interactions has come true. In fact, the rapidly expanding approaches of mass spectrometry (MS)-based proteomics in the study of PPIs provide efficient tools to identify a significant number of potential drug targets. Generation of PPIs maps by affinity purification-MS and by the more recent proximity labeling-MS may help to uncover cellular processes hijacked and/or altered by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), providing promising therapeutic targets. The possibility to further validate putative key targets from high-confidence interactions between viral bait and host protein through follow-up MS-based multi-omics experiments offers an unprecedented opportunity in the drug discovery pipeline. In particular, drug repurposing, making use of already existing approved drugs directly targeting these identified and validated host interactors, might shorten the time and reduce the costs in comparison to the traditional drug discovery process. This route might be promising for finding effective antiviral therapeutic options providing a turning point in the fight against the coronavirus disease-2019 (COVID-19) outbreak.Cell survival and normal cell function require a highly coordinated and precise regulation of basal cytosolic Ca2+ concentrations. The primary source of Ca2+ entry into the cell is mediated by the Ca2+ release-activated Ca2+ (CRAC) channel. Its action is stimulated in response to internal Ca2+ store depletion. The fundamental constituents of CRAC channels are the Ca2+ sensor, stromal interaction molecule 1 (STIM1) anchored in the endoplasmic reticulum, and a highly Ca2+-selective pore-forming subunit Orai1 in the plasma membrane. The precise nature of the Orai1 pore opening is currently a topic of intensive research. mTOR inhibitor This review describes how Orai1 gating checkpoints in the middle and cytosolic extended transmembrane regions act together in a concerted manner to ensure an opening-permissive Orai1 channel conformation. In this context, we highlight the effects of the currently known multitude of Orai1 mutations, which led to the identification of a series of gating checkpoints and the determination of their role in diverse steps of the Orai1 activation cascade. The synergistic action of these gating checkpoints maintains an intact pore geometry, settles STIM1 coupling, and governs pore opening. We describe the current knowledge on Orai1 channel gating mechanisms and summarize still open questions of the STIM1-Orai1 machinery.Nutritional strategies can be employed to mitigate greenhouse emissions from ruminants. This article investigates the effects of polyphenols extracted from the involucres of Castanea mollissima Blume (PICB) on in vitro rumen fermentation. Three healthy Angus bulls (350 ± 50 kg), with permanent rumen fistula, were used as the donors of rumen fluids. A basic diet was supplemented with five doses of PICB (0%-0.5% dry matter (DM)), replicated thrice for each dose. Volatile fatty acids (VFAs), ammonia nitrogen concentration (NH3-N), and methane (CH4) yield were measured after 24 h of in vitro fermentation, and gas production was monitored for 96 h. The trial was carried out over three runs. The results showed that the addition of PICB significantly reduced NH3-N (p less then 0.05) compared to control. The 0.1%-0.4% PICB significantly decreased acetic acid content (p less then 0.05). Addition of 0.2% and 0.3% PICB significantly increased the propionic acid content (p less then 0.05) and reduced the acetic acid/propionic acid ratio, CH4 content, and yield (p less then 0.