Megasphaera elsdenii Lactate Degradation Pattern Shifts in Rumen Acidosis Models

Background:Megasphaera elsdenii is an ecologically important rumen bacterium that metabolizes lactate and relieves rumen acidosis (RA) induced by a high-grain-diet. Understanding the regulatory mechanisms of the lactate metabolism of this species in RA conditions might contribute to developing dietary strategies to alleviate RA.Methods:Megasphaera elsdenii was co-cultured with four lactate producers (Streptococcus bovis, Lactobacilli fermentum, Butyrivibrio fibrisolvens, and Selenomonas ruminantium) and a series of substrate starch doses (1, 3, and 9 g/L) were used to induce one normal and two RA models (subacute rumen acidosis, SARA and acute rumen acidosis, ARA) under batch conditions. The associations between bacterial competition and the shift of organic acids’ (OA) accumulation patterns in both statics and dynamics manners were investigated in RA models. Furthermore, we examined the effects of substrate lactate concentration and pH on Megasphaera elsdenii’s lactate degradation pattern and genes related to the lactate utilizing pathways in the continuous culture.Results and Conclusion: The positive growth of M. elsdenii and B. fibrisolvens caused OA accumulation in the SARA model to shift from lactate to butyrate and resulted in pH recovery. Furthermore, both the quantities of substrate lactate and pH had remarkable effects on M. elsdenii lactate utilization due to the transcriptional regulation of metabolic genes, and the lactate utilization in M. elsdenii was more sensitive to pH changes than to the substrate lactate level. In addition, compared with associations based on statics data, associations discovered from dynamics data showed greater significance and gave additional explanations regarding the relationships between bacterial competition and OA accumulation

Parameter Estimation for PMSM based on a Back Propagation Neural Network Optimized by Chaotic Artificial Fish Swarm Algorithm

Permanent Magnet Synchronous Motor(PMSM) control system with strong nonlinearity makes it difficult to accurately identify motor parameters such as stator winding, dq axis inductance, and rotor flux linkage. Aiming at the premature convergence of traditional Back Propagation Neural Network(BPNN) in PMSM motor parameter identification, a new method of PMSM motor parameter identification is proposed. It uses Chaotic Artificial Fish Swarm Algorithm(CAFSA) to optimize the initial weights and thresholds of BPNN, and then strengthens training by BPNN algorithm. Thus, the global optimal network parameters are obtained by using the global optimization of CAFSA and the local search ability of BPNN. The simulation results and experimental data show that the initial value sensitivity of the network model optimized by CAFS-BPNN Algorithm is weak, the parameter setting is robust, and the system stability is good under complex conditions. Compared with other intelligent algorithms, such as RSL and PSO, CAFS-BPNNA has high identification accuracy and fast convergence speed for PMSM motor parameters

Spectral Methods in Spatial Statistics

When the spatial location area increases becoming extremely large, it is very difficult, if not possible, to evaluate the covariance matrix determined by the set of location distance even for gridded stationary Gaussian process. To alleviate the numerical challenges, we construct a nonparametric estimator called periodogram of spatial version to represent the sample property in frequency domain, because periodogram requires less computational operation by fast Fourier transform algorithm. Under some regularity conditions on the process, we investigate the asymptotic unbiasedness property of periodogram as estimator of the spectral density function and achieve the convergence rate

Association between temperament related traits and SNPs in the serotonin and oxytocin systems in Merino sheep

ABSTRACT Animal temperament is defined as the consistent behavioral and physiological differences that are seen between individuals in response to the same stressor. Neurotransmitter systems, like serotonin and oxytocin in the central nervous system, underlie variation in behavioral traits in humans and other animals. Variations like single nucleotide polymorphisms (SNPs) in the genes for tryptophan 5-hydroxylase (TPH2), the serotonin transporter (SLC6A4), the serotonin receptor (HTR2A), and the oxytocin receptor (OXTR) are associated with behavioral phenotype in humans. Thus, the objective of this study was to identify SNPs in those genes and to test if those variations are associated with the temperament in Merino sheep. Using ewes from the University of Western Australia temperament flock, that has been selected on emotional reactivity for more than 20 generations, eight SNPs (rs107856757, rs107856818, rs107856856 and rs107857156 in TPH2, rs20917091 in SLC6A4, rs17196799 and rs17193181 in HTR2A, and rs17664565 in OXTR) were found to be distributed differently between calm and nervous sheep. These eight SNPs were then genotyped in 260 sheep from a flock that has never been selected on emotional reactivity, followed by the estimation of the behavioral traits of those 260 sheep using an arena test and an isolation box test. We found that several SNPs in TPH2 (rs107856757, rs107856818, rs107856856 and rs107857156) were in strong linkage disequilibrium, and all were associated with behavioral phenotype in the non-selected sheep. Similarly, rs17196799 in HTR2A was also associated with the behavioral phenotype

A Benchmark of Genetic Variant Calling Pipelines Using Metagenomic Short-Read Sequencing

Microbes live in complex communities that are of major importance for environmental ecology, public health, and animal physiology and pathology. Short-read metagenomic shotgun sequencing is currently the state-of-the-art technique for exploring these communities. With the aid of metagenomics, our understanding of the microbiome is moving from composition toward functionality, even down to the genetic variant level. While the exploration of single-nucleotide variation in a genome is a standard procedure in genomics, and many sophisticated tools exist to perform this task, identification of genetic variation in metagenomes remains challenging. Major factors that hamper the widespread application of variant-calling analysis include low-depth sequencing of individual genomes (which is especially significant for the microorganisms present in low abundance), the existence of large genomic variation even within the same species, the absence of comprehensive reference genomes, and the noise introduced by next-generation sequencing errors. Some bioinformatics tools, such as metaSNV or InStrain, have been created to identify genetic variants in metagenomes, but the performance of these tools has not been systematically assessed or compared with the variant callers commonly used on single or pooled genomes. In this study, we benchmark seven bioinformatic tools for genetic variant calling in metagenomics data and assess their performance. To do so, we simulated metagenomic reads to mimic human microbial composition, sequencing errors, and genetic variability. We also simulated different conditions, including low and high depth of coverage and unique or multiple strains per species. Our analysis of the simulated data shows that probabilistic method-based tools such as HaplotypeCaller and Mutect2 from the GATK toolset show the best performance. By applying these tools to longitudinal gut microbiome data from the Human Microbiome Project, we show that the genetic similarity between longitudinal samples from the same individuals is significantly greater than the similarity between samples from different individuals. Our benchmark shows that probabilistic tools can be used to call metagenomes, and we recommend the use of GATK's tools as reliable variant callers for metagenomic samples

Exploration of serum sensitive biomarkers of fatty liver in dairy cows

Serum proteins are sensitive with diseases in dairy cows, and some of them could be used as biomarkers for fatty liver. This study aimed to explore serum biomarkers for fatty liver in dairy cows. A total of 28 early lactating dairy cows were chosen from a commercial dairy herds, liver samples were collected for determining concentration of triacylglycerol (TAG), and serum samples were collected for measuring fibroblast growth factor-21 (FGF-21), adiponectin, Lipoprotein-associated phospholipase A2 (LP-PLA2), and hemoglobin (Hb). Dairy cows were divided into fatty liver (liver TAG > 5%, wet weight) and control group (liver TAG < 5%, wet weight). Concentration of FGF-21 was greater in fatty liver cows, while the concentration of LP-PLA2 and Hb was less. The concentration of FGF-21 and total Hb had strong correlation with the liver TAG as well as good prediction power (kappa value = 0.79 and 0.58, respectively). These results suggested that the serum concentration of FGF-21 and total Hb could be potentially used as fatty liver biomarkers in lactating dairy cows