Climate change and the associated risk for the occurrence of extreme temperature events or permanent changes in ambient temperature are important in the husbandry of farm animals. The aim of our study was to investigate the effects of permanent cultivation temperatures below (35 °C) and above (39 °C, 41 °C) the standard cultivation temperature (37 °C) on porcine muscle development. Therefore, we used our porcine primary muscle cell culture derived from satellite cells as an in vitro model. Neonatal piglets have limited thermoregulatory stability, and several days after birth are required to maintain their body temperature. To consider this developmental step, we used myoblasts originating from thermolabile (five days of age) and thermostable piglets (twenty days of age).

The efficiency of myoblast proliferation using real-time monitoring via electrical impedance was comparable at all temperatures with no difference in the cell index, slope or doubling time. Both temperatures of 37 °C and 39 °C led to simi increased HSP expression, but it also accelerates myogenic development. Cultivation at 35 °C, however, leads to less differentiated myoblasts with distinct thermogenetic activity. The adaptive behavior of derived primary muscle cells to different cultivation temperatures seems to be determined by the thermoregulatory stability of the donor piglets.

We conclude that 37 °C to 39 °C is the best physiological temperature range for adequate porcine myoblast development. Corresponding to the body temperatures of piglets, it is therefore possible to culture primary muscle cells at 39 °C. Only the highest temperature of 41 °C acts as a thermal stressor for myoblasts with increased HSP expression, but it also accelerates myogenic development. Cultivation at 35 °C, however, leads to less differentiated myoblasts with distinct thermogenetic activity. The adaptive behavior of derived primary muscle cells to different cultivation temperatures seems to be determined by the thermoregulatory stability of the donor piglets.

Low conception rate (CR) despite insemination with morphologically normal spermatozoa is a common reproductive restraint that limits buffalo productivity. read more This accounts for a significant loss to the farmers and the dairy industry, especially in agriculture-based economies. The immune-related proteins on the sperm surface are known to regulate fertility by assisting the spermatozoa in their survival and performance in the female reproductive tract (FRT). Regardless of their importance, very few studies have specifically catalogued the buffalo sperm surface proteome. The study was designed to determine the identity of sperm surface proteins and to ascertain if the epididymal expressed beta-defensins (BDs), implicated in male fertility, are translated and applied onto buffalo sperm surface along with other immune-related proteins.

The raw mass spectra data searched against an in-house generated proteome database from UniProt using Comet search engine identified more than 300 proteins on the ejaculated buffal have a role in buffalo sperm function. Further studies elucidating its exact physiological function are required to better understand its role in the regulation of male fertility.

Plasmids are mobile genetic elements that often carry accessory genes, and are vectors for horizontal transfer between bacterial genomes. Plasmid detection in large genomic datasets is crucial to analyze their spread and quantify their role in bacteria adaptation and particularly in antibiotic resistance propagation. Bioinformatics methods have been developed to detect plasmids. However, they suffer from low sensitivity (i.e., most plasmids remain undetected) or low precision (i.e., these methods identify chromosomes as plasmids), and are overall not adapted to identify plasmids in whole genomes that are not fully assembled (contigs and scaffolds).

We developed PlasForest, a homology-based random forest classifier identifying bacterial plasmid sequences in partially assembled genomes. Without knowing the taxonomical origin of the samples, PlasForest identifies contigs as plasmids or chromosomes with a F1 score of 0.950. Notably, it can detect 77.4% of plasmid contigs below 1kb with 2.8% of false positives and 99.9% of plasmid contigs over 50kb with 2.2% of false positives.

PlasForest outperforms other currently available tools on genomic datasets by being both sensitive and precise. The performance of PlasForest on metagenomic assemblies are currently well below those of other k-mer-based methods, and we discuss how homology-based approaches could improve plasmid detection in such datasets.

PlasForest outperforms other currently available tools on genomic datasets by being both sensitive and precise. The performance of PlasForest on metagenomic assemblies are currently well below those of other k-mer-based methods, and we discuss how homology-based approaches could improve plasmid detection in such datasets.

The Interrupted Time Series (ITS) is a quasi-experimental design commonly used in public health to evaluate the impact of interventions or exposures. Multiple statistical methods are available to analyse data from ITS studies, but no empirical investigation has examined how the different methods compare when applied to real-world datasets.

A random sample of 200 ITS studies identified in a previous methods review were included. Time series data from each of these studies was sought. Each dataset was re-analysed using six statistical methods. Point and confidence interval estimates for level and slope changes, standard errors, p-values and estimates of autocorrelation were compared between methods.

From the 200 ITS studies, including 230 time series, 190 datasets were obtained. We found that the choice of statistical method can importantly affect the level and slope change point estimates, their standard errors, width of confidence intervals and p-values. Statistical significance (categorised at the 5% level) often differed across the pairwise comparisons of methods, ranging from 4 to 25% disagreement. Estimates of autocorrelation differed depending on the method used and the length of the series.

The choice of statistical method in ITS studies can lead to substantially different conclusions about the impact of the interruption. Pre-specification of the statistical method is encouraged, and naive conclusions based on statistical significance should be avoided.

The choice of statistical method in ITS studies can lead to substantially different conclusions about the impact of the interruption. Pre-specification of the statistical method is encouraged, and naive conclusions based on statistical significance should be avoided.