We herein describe an ultrasensitive isothermal method to detect microRNA (miRNA) by utilizing target-induced chain amplification reaction (CAR). The hairpin probe (HP) employed in this strategy is designed to be opened upon binding to target miRNA. The exponential amplification reaction (EXPAR) template (ET) then binds to the exposed stem of HP and DNA polymerase (DP) promotes the extension reactions for both HP and ET, consequently producing intermediate double-stranded DNA product (IP) and concomitantly recycling target miRNA to open another intact HP. The IPs would produce a large number of target-mimicking probes (TMPs) and trigger probes (TPs) through the continuously repeated nicking and extension reactions at the two separated nicking sites within the IP. TMP triggers another CAR cycle by binding to intact HP as target miRNA did while TP promotes conventional EXPAR by independently binding to free ET. As a consequence of these interconnected reaction systems, a large number of final double-stranded DNA products (FPs) are produced, which can be monitored by measuring the fluorescent signal produced from duplex-specific fluorescent dye. Based on this unique design principle, the target miRNA was successfully determined down to even a single copy with high selectivity against non-specific miRNAs. The practical applicability of this method was also verified by reliably detecting target miRNA included in the total RNA extracted from the human cancer cell.The introduction, transmission, and persistence of Listeria monocytogenes in Belgian beef slaughterhouses was investigated using genetic characterization. During slaughter, samples were taken of the hide, carcass, and environment to detect the pathogen. Remarkably, L. monocytogenes was massively present on the hide of incoming animals (93%; 112/120), regardless of their visual cleanliness, which implies high contamination pressure levels entering the slaughterhouses. Pathogen transfer via cross-contamination was conclusively confirmed in this study, with the same pulsotypes isolated from the hide, carcass, and environmental samples. Despite the important bacterial presence on the hide of incoming animals, most slaughterhouses succeeded in limiting the transfer as cause of carcass contamination. Persistence along the slaughter line seemed to be a more significant problem, as it was clearly linked to most of the L. monocytogenes positive carcasses. In one slaughterhouse, whole genome sequencing (WGS) revealed that the carcass splitter had been contaminating carcasses with the same strain belonging to CC9 for more than one year.In this study, gas chromatography coupled to an ion mobility spectrometry (GC-IMS) was used for analyzing some volatile components and flavor fingerprint in samples from Jingyuan lambs of different ages (2, 6, and 12 months). The data obtained from ion mobility were processed using laboratory analysis view processing software for fingerprint recognition, and the principal component analysis (PCA) was performed. GC-IMS provided information on the characteristics and strength of 66 volatile flavor compounds (monomers and dimers). The differences in flavoring substances between lambs of different ages were observed. The compounds with higher intensity peaks in the lamb meat samples were alcohols (1-octen-3-ol, ethanol, (E)-2-hexen-1-ol, 1-pentanol, and 2-propanol), ketones (2-pentanone, 2-heptanone, 3-hydroxy-2-butanone, 2-hexanone, 2-butanone, 2-propanone, and 4-methyl-2-pentanone), aldehydes (n-nonanal, octanal, heptanal, 3-methylbutanal, hexanal, pentanal, 2-methylbutanal, (E)-2-octenal, (E)-2-nonenal, methional, and phenylacetaldehyde), esters (methyl benzoate), furan (2-pentylfuran), and thiazole (trimethylthiazole). The results showed that the flavor fingerprint in samples from Jingyuan lambs of different ages (2, 6, and 12 months) can be established by GC-IMS and PCA based on the identified volatile compounds. This method might be used for the rapid and comprehensive analysis of volatile components in lamb meat.Psychological flexibility (PF) is a popular construct in clinical psychology. However, similar constructs have existed since the mid-20th century, resulting in different terms, definitions and measures of flexibility, hindering the advancement of the field. The main measure of PF - the Acceptance and Action Questionnaire (AAQ-II; Bond et al., 2011) - has also been heavily criticized. To move towards definitional consensus and improved measurement, we surveyed the literature to map PF and related-terms, examine definitional overlaps, and assessthe psychometric quality of prominent flexibility measures. A scoping review was conducted in two databases (PsycNET and SCOPUS). Twenty-three flexibility constructs appeared across 220 articles, and twelve measures were included and rated for quality. PF, psychological inflexibility (PI), and cognitive flexibility (CF) were most prominent. Definitional similarities among prominent flexibility constructs emerged, namely handling distress or interference, taking action, and meeting goals or values. The Personalized Psychological Flexibility Index (PPFI; Kashdan, Disabato, Goodman, Doorley, & McKnight, 2020) appears to be the best measure available to assess PF. Problems with the current use of the AAQ-II were apparent, hindering current knowledge of PF. Definitional consensus and measurement development are vital to advance the field. To this end, recommendations and next steps for researchers and practitioners are outlined.The protein disulfide bond is a covalent bond that forms during post-translational modification by the oxidation of a pair of cysteines. In protein, the disulfide bond is the most frequent covalent link between amino acids after the peptide bond. It plays a significant role in three-dimensional (3D) ab initio protein structure prediction (aiPSP), stabilizing protein conformation, post-translational modification, and protein folding. In aiPSP, the location of disulfide bonds can strongly reduce the conformational space searching by imposing geometrical constraints. Existing experimental techniques for the determination of disulfide bonds are time-consuming and expensive. Thus, developing sequence-based computational methods for disulfide bond prediction becomes indispensable. This study proposed a stacking-based machine learning approach for disulfide bond prediction (diSBPred).  https://www.selleckchem.com/Bcl-2.html  Various useful sequence and structure-based features are extracted for effective training, including conservation profile, residue solvent accessibility, torsion angle flexibility, disorder probability, a sequential distance between cysteines, and more.