This study reviews the current status and future prospective of knowledge on the use of artificial intelligence for the prediction of spontaneous preterm labor and birth ("preterm birth" hereafter). The summary of review suggests that different machine learning approaches would be optimal for different types of data regarding the prediction of preterm birth the artificial neural network, logistic regression and/or the random forest for numeric data; the support vector machine for electrohysterogram data; the recurrent neural network for text data; and the convolutional neural network for image data. The ranges of performance measures were 0.79-0.94 for accuracy, 0.22-0.97 for sensitivity, 0.86-1.00 for specificity, and 0.54-0.83 for the area under the receiver operating characteristic curve. The following maternal variables were reported to be major determinants of preterm birth delivery and pregestational body mass index, age, parity, predelivery systolic and diastolic blood pressure, twins, below high school graduation, infant sex, prior preterm birth, progesterone medication history, upper gastrointestinal tract symptom, gastroesophageal reflux disease, Helicobacter pylori, urban region, calcium channel blocker medication history, gestational diabetes mellitus, prior cone biopsy, cervical length, myomas and adenomyosis, insurance, marriage, religion, systemic lupus erythematosus, hydroxychloroquine sulfate, and increased cerebrospinal fluid and reduced cortical folding due to impaired brain growth.Disease is a global problem for animal farming industries causing tremendous economic losses (>USD 220 billion over the last decade) and serious animal welfare issues. The limitations and deficiencies of current non-selection disease control methods (e.g., vaccination, treatment, eradication strategy, genome editing, and probiotics) make it difficult to effectively, economically, and permanently eliminate the adverse influences of disease in the farm animals. These limitations and deficiencies drive animal breeders to be more concerned and committed to dealing with health problems in farm animals by selecting animals with favorable health traits. Both genetic selection and genomic selection contribute to improving the health of farm animals by selecting certain health traits (e.g., disease tolerance, disease resistance, and immune response), although both of them face some challenges. The objective of this review was to comprehensively review the potential of selecting health traits in coping with issues caused by diseases in farm animals. Within this review, we highlighted that selecting health traits can be applied as a method of disease control to help animal agriculture industries to cope with the adverse influences caused by diseases in farm animals. Certainly, the genetic/genomic selection solution cannot solve all the disease problems in farm animals. Therefore, management, vaccination, culling, medical treatment, and other measures must accompany selection solution to reduce the adverse impact of farm animal diseases on profitability and animal welfare.Commercial sparkling wine production represents a relatively low but important part of the Croatian wine production, especially in the Zagreb county. This study presents the results of volatile aroma compounds profile and organic acid composition of commercial sparkling wine samples from three vine-growing regions in Zagreb county. In total, 174 volatile aroma compounds were identified, separated between their chemical classes (aldehydes, higher alcohols, volatile phenols, terpenes, C13-norisoprenoids, lactones, esters, fatty acids, sulfur compounds, other compounds, other alcohols). Higher alcohols such as phenylethyl and isoamyl alcohol as well as 2-methyl-1-butanol, and esters such as diethyl succinate, ethyl hydrogensuccinate, and ethyl lactate had the strongest impact on the volatile compounds profile of Zagreb county sparkling wine. The presence of diethyl glutarate and diethyl malonate, compounds whose concentrations are influenced by yeast autolysis or caused by chemical esterification during the ageing process, was also noted. The influence of every single volatile aroma compound was evaluated by discriminant analysis using forward stepwise model. The volatile profiles of traditional sparkling wines from Croatia were presented for the first time. It is hoped the results will contribute to better understanding the quality potential and to evaluate possible differences on the bases of detected aroma concentrations and multivariate analysis.Noticing the regularity of the task is necessary to enhance motor performance. The experience of noticing further motivates improvement in motor performance. Motor control is explained by a comparator model that modifies the motor command to reduce discrepancies between sensory predictions and actual outcomes. A similar model could apply to sense of agency (SoA). SoA refers to the sensation of controlling one's own actions and, through them, the outcomes in the external world. SoA may also be enhanced by the experience of noticing errors. We recently reported gradual enhancement of SoA in participants with high perceptual-motor performance. However, what component of the motor task changed the SoA is unclear. In this study, we aimed to investigate the influence over time of the experience of noticing during a motor task on SoA. Participants performed an implicit regularity perceptual-motor task and an intentional binding task (a method that can quantitatively measure SoA) simultaneously. We separated participants into groups after the experiment based on noticing or not noticing the regularity.  https://www.selleckchem.com/products/bmh-21.html  SoA was gradually enhanced in the noticing group, compared with that of the non-noticing group. The results suggest that the experience of noticing may enhance SoA during perceptual-motor tasks.The pollution of urban soils by metals is a global problem. Prolonged exposure of habitants who are in contact with metals retained in soil poses a health risk. This particularly applies to industrialized cities with developed transport networks. The aim of the study was to determine the content and spatial distribution of mobile metal fractions in soils of the city of Łódź and to identify their load and sources. Multivariate statistical analysis (principal component analysis (PCA), cluster analysis (CA)), combined with GIS, were used to make a comprehensive evaluation of the soil contamination. Hot-spots and differences between urban and suburban areas were also investigated. Metals were determined by atomic absorption spectrometry (AAS) after soil extraction with 1 mol L-1 HCl. In most sites, the metal content changes in the following order Zn > Pb > Cu > Ni > Cd. About one-third of the samples are considerably (or very highly) contaminated, (contamination factor, CF > 3) with Cu, Pb, or Zn. In almost 40% of the samples, contaminated soils were found (pollution load index, PLI > 1).