cies and offers translational relevance towards the design of novel vector control strategies. COVID-19 pandemic has resulted in significant strain on healthcare resources and this requires diligent resource re-allocation. We aim to describe the incidence and outcomes of in-hospital cardiac arrest (IHCA) during this period as compared to non-pandemic period. We conducted a retrospective study in a tertiary care hospital in Singapore. The study compared the incidence and outcomes of code blue activations over a 3-month period from March to May 2020 (COVID-19 period) with the same months in 2019 (pre-COVID-19 period). The primary outcome of the study was the rate of survival to hospital discharge for IHCA. The secondary outcomes included incidence of all code blue activation per 1000 hospital admissions, incidence of IHCA per 1000 hospital admissions. The rate of survival to hospital discharge for IHCA was 5.88% in the COVID-19 period as compared to 10.0% in the pre-COVID-19 period [odds ratio (OR), 0.72; 95% confidence interval (CI), 0.26-1.95]. Compared to pre-COVID-19 period, there were more IHCA incidences per 1000 hospital admissions in the COVID-19 period (1.86 vs 1.03; OR, 1.81; 95% CI, 0.78-4.41). The study observed a trend towards higher incidence of IHCA and lower rate of survival to hospital discharge during COVID-19 pandemic compared to pre-COVID-19 period. The study observed a trend towards higher incidence of IHCA and lower rate of survival to hospital discharge during COVID-19 pandemic compared to pre-COVID-19 period. The purpose of the study was to examine how bone mineral density (BMD) is related to body composition depending on the practiced sport (endurance, speed-power, throwing sports) in participants of the World Masters Athletics Championship. Dual-energy X-ray absorptiometry (DXA) was used to determine BMD and bone mass (BMC). Body composition was analyzed by means of the JAWON Medical X-scan analyzer using bioelectrical impedance methods. Percentage body fat (%BF), body fat mass (BFM), lean body mass (LBM), total body water (TBW), soft lean mass (SLM), intracellular water (ICW), and extracellular water (ECW) were evaluated. Among men, the most important variables affecting the BMD norm were LBM (OR = 32.578; p = 0.023), ECW (OR = 0.003; p = 0.016) and ICW (OR = 0.011; p = 0.031), in the distal part and SLM (OR = 5.008; p = 0.020) and ICW (0.354, p = 0.008) in the proximal part. In women, the most important predictors of normal BMD were ICW (OR = 10.174; p = 0.003) and LBM (OR = 0.470; p = 0.020) in the dist nature and mechanisms of these interactions. Pair bonding with a reproductive partner is rare among mammals but is an important feature of human social behavior. Decades of research on monogamous prairie voles (Microtus ochrogaster), along with comparative studies using the related non-bonding meadow vole (M. pennsylvanicus), have revealed many of the neural and molecular mechanisms necessary for pair-bond formation in that species. However, these studies have largely focused on just a few neuromodulatory systems. To test the hypothesis that neural gene expression differences underlie differential capacities to bond, we performed RNA-sequencing on tissue from three brain regions important for bonding and other social behaviors across bond-forming prairie voles and non-bonding meadow voles. We examined gene expression in the amygdala, hypothalamus, and combined ventral pallidum/nucleus accumbens in virgins and at three time points after mating to understand species differences in gene expression at baseline, in response to mating, and during bond formales but not promiscuous species such as meadow voles. Gene ontology analysis supports the hypothesis that pair-bond formation involves transcriptional regulation, and changes in neuronal structure. Together, our results expand knowledge of the genes involved in the pair bonding process and open new avenues of research in the molecular mechanisms of bond formation. These results reinforce the importance of pre-mating differences that confer the ability to form pair bonds in prairie voles but not promiscuous species such as meadow voles. Gene ontology analysis supports the hypothesis that pair-bond formation involves transcriptional regulation, and changes in neuronal structure. Together, our results expand knowledge of the genes involved in the pair bonding process and open new avenues of research in the molecular mechanisms of bond formation. The quality of gene annotation determines the interpretation of results obtained in transcriptomic studies. The growing number of genome sequence information calls for experimental and computational pipelines for de novo transcriptome annotation. Ideally, gene and transcript models should be called from a limited set of key experimental data. We developed TranscriptomeReconstructoR, an R package which implements a pipeline for automated transcriptome annotation. It relies on integrating features from independent and complementary datasets (i) full-length RNA-seq for detection of splicing patterns and (ii) high-throughput 5' and 3' tag sequencing data for accurate definition of gene borders. The pipeline can also take a nascent RNA-seq dataset to supplement the called gene model with transient transcripts. We reconstructed de novo the transcriptional landscape of wild type Arabidopsis thaliana seedlings and Saccharomyces cerevisiae cells as a proof-of-principle. A comparison to the existing transcriptome aWe combine the choice of library preparation methods and sequencing platforms with the dedicated computational pipeline implemented in the TranscriptomeReconstructoR package. The pipeline only requires prior knowledge on the reference genomic DNA sequence, but not the transcriptome. The package seamlessly integrates with Bioconductor packages for downstream analysis. DNA-Binding Proteins (DBP) plays a pivotal role in biological system. A mounting number of researchers are studying the mechanism and detection methods. To detect DBP, the tradition experimental method is time-consuming and resource-consuming. https://www.selleckchem.com/products/selnoflast.html In recent years, Machine Learning methods have been used to detect DBP. However, it is difficult to adequately describe the information of proteins in predicting DNA-binding proteins. In this study, we extract six features from protein sequence and use Multiple Kernel Learning-based on Centered Kernel Alignment to integrate these features. The integrated feature is fed into Support Vector Machine to build predictive model and detect new DBP. In our work, date sets of PDB1075 and PDB186 are employed to test our method. From the results, our model obtains better results (accuracy) than other existing methods on PDB1075 ([Formula see text]) and PDB186 ([Formula see text]), respectively. Multiple kernel learning could fuse the complementary information between different features.