Molecular characterization, polymorphism of growth differentiation factor 5 gene and association with ultrasound measurement traits in native Chinese cattle breeds

Growth differentiation factor 5 (GDF5), involved in the development and maintenance of bone andcartilage, is a n important candidate gene for growth and carcass traits selection through markerassisted selection (MAS). Genomic structural analysis showed that bovine GDF5 shares much similarity with human GDF5. The latest findings demonstrate that the single nucleotide polymorphism (SNP) T586C in exon 1 is significantly associated with ultrasound marbling score (UMAR) and ultrasound backfat thickness (UBF). Furthermore, the analysis of T586C SNP marker shows there are significant effects on the UBF (P = 0.0498) and on the UMAR (P = 0.0058) in 465 individuals. These results clearly suggest that the GDF5 gene is among target genes for carcass traits in bovine reproduction and breeding.Keywords: Cattle, GDF5 gene, ultrasound measurement, polymorphism, association analysisAfrican Journal of Biotechnology Vol. 9(33), pp. 5269-5273, 16 August, 201

Modelling of a seasonally perturbed competitive three species impulsive system

The population of biological species in the ecosystem is known sensitive to the periodic fluctuations of seasonal change, food resources and climatic conditions. Research in the ecological management discipline conventionally models the behavior of such dynamic systems through specific impulsive response functions, but the results of such research are applicable only when the environments conform exactly to the conditions as defined by the specific response functions that have been implemented for specific scenarios. This means that the application of previous work may be somewhat limited. Moreover, the intra and inter competitions among species have been seldom studied for modelling the prey-predator ecosystem. To fill in the gaps this paper models the delicate balance of two-prey and one-predator system by addressing three main areas of: ⅰ) instead of using the specific impulse response this work models the ecosystem through a more general response function; ⅱ) to include the effects due to the competition between species and ⅲ) the system is subjected to the influences of seasonal factors. The seasonal factor has been implemented here in terms of periodic functions to represent the growth rates of predators. The sufficient condition for the local and global asymptotic stability of the prey-free periodic solution and the permanence of the system have been subsequently obtained by using the Comparison techniques and the Floquet theorems. Finally, the correctness of developed theories is verified by numerical simulation, and the corresponding biological explanation is given.2017005,2017019: Shanxi Agricultural University of Science and Technology Innovation Fund Projects

Clinical Study Metformin and Diammonium Glycyrrhizinate Enteric-Coated Capsule versus Metformin Alone versus Diammonium Glycyrrhizinate Enteric-Coated Capsule Alone in Patients with Nonalcoholic Fatty Liver Disease and Type 2 Diabetes Mellitus

Objective. The present study was conducted to compare the efficacy of metformin combined with diammonium glycyrrhizinate enteric-coated capsule (DGEC) versus metformin alone versus DGEC alone for the treatment of nonalcoholic fatty liver disease (NAFLD) in patients with type 2 diabetes mellitus (T2DM). Subjects and Methods. 163 patients with NAFLD and T2DM were enrolled in this 24-week study and were randomized to one of three groups: group 1 was treated with metformin alone; group 2 was treated with DGEC alone; group 3 received metformin plus DGEC combination therapy. Anthropometric parameters, liver function, lipid profile, serum ferritin (SF), metabolic parameters, liver/spleen computed tomography (CT) ratio, and fibroscan value were evaluated at baseline and after 8, 16, and 24 weeks of treatment. Results. After 24 weeks, significant improvements in all measured parameters were observed in three groups ( < 0.05) except for the improvements in low density lipoprotein cholesterol (LDL-C) and metabolic parameters in group 2 which did not reach statistical significance ( > 0.05). Compared with group 1 and group 2, the patients in group 3 had greater reductions in observed parameters apart from CB and TB ( < 0.05). Conclusions. This study showed that metformin plus DGEC was more effective than metformin alone or DGEC alone in reducing liver enzymes, lipid levels, and metabolic parameters and ameliorating the degree of hepatic fibrosis in patients with NAFLD and T2DM

Indole Alleviates Diet-induced Hepatic Steatosis and Inflammation in a Manner Involving Myeloid Cell PFKFB3

Background and aims: Indole is a microbiota metabolite that exerts anti-inflammatory responses. However, the relevance of indole to human non-alcoholic fatty liver disease (NAFLD) is not clear. It also remains largely unknown whether and how indole acts to protect against NAFLD. The present study sought to examine the association between the circulating levels of indole and liver fat content in human subjects and explore the mechanisms underlying indole actions in mice with diet-induced NAFLD. Approach and results: In a cohort of 137 subjects, the circulating levels of indole were reversely correlated with body mass index. In addition, the circulating levels of indole in obese subjects were significantly lower than those in lean subjects and were accompanied with increased liver fat content. At the whole-animal level, treatment of high-fat diet (HFD)-fed C57BL/6J mice with indole caused significant decreases in the severity of hepatic steatosis and inflammation. In cultured cells, indole treatment stimulated the expression of 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 3 (PFKFB3), a master regulatory gene of glycolysis, and suppressed macrophage proinflammatory activation in a PFKFB3-dependent manner. Moreover, myeloid cell-specific PFKFB3 disruption exacerbated the severity of HFD-induced hepatic steatosis and inflammation and blunted the effect of indole on alleviating diet-induced NAFLD phenotype. Conclusions: Taken together, our results demonstrate that indole is relevant to human NAFLD and capable of alleviating diet-induced NAFLD phenotypes in mice in a myeloid cell PFKFB3-dependent manner. Therefore, indole mimetic and/or macrophage-specific PFKFB3 activation may be the viable preventive and/or therapeutic approaches for inflammation-associated diseases including NAFLD

Analyzing the Impact of Climate Factors on GNSS-Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

A variety of climate factors influence the precision of the long-term Global Navigation Satellite System (GNSS) monitoring data. To precisely analyze the effect of different climate factors on long-term GNSS monitoring records, this study combines the extended seven-parameter Helmert transformation and a machine learning algorithm named Extreme Gradient boosting (XGboost) to establish a hybrid model. We established a local-scale reference frame called stable Puerto Rico and Virgin Islands reference frame of 2019 (PRVI19) using ten continuously operating long-term GNSS sites located in the rigid portion of the Puerto Rico and Virgin Islands (PRVI) microplate. The stability of PRVI19 is approximately 0.4 mm/year and 0.5 mm/year in the horizontal and vertical directions, respectively. The stable reference frame PRVI19 can avoid the risk of bias due to long-term plate motions when studying localized ground deformation. Furthermore, we applied the XGBoost algorithm to the postprocessed long-term GNSS records and daily climate data to train the model. We quantitatively evaluated the importance of various daily climate factors on the GNSS time series. The results show that wind is the most influential factor with a unit-less index of 0.013. Notably, we used the model with climate and GNSS records to predict the GNSS-derived displacements. The results show that the predicted displacements have a slightly lower root mean square error compared to the fitted results using spline method (prediction: 0.22 versus fitted: 0.31). It indicates that the proposed model considering the climate records has the appropriate predict results for long-term GNSS monitoring

New Acoustic Features for Synthetic and Replay Spoofing Attack Detection

With the rapid development of intelligent speech technologies, automatic speaker verification (ASV) has become one of the most natural and convenient biometric speaker recognition approaches. However, most state-of-the-art ASV systems are vulnerable to spoofing attack techniques, such as speech synthesis, voice conversion, and replay speech. Due to the symmetry distribution characteristic between the genuine (true) speech and spoof (fake) speech pair, the spoofing attack detection is challenging. Many recent research works have been focusing on the ASV anti-spoofing solutions. This work investigates two types of new acoustic features to improve the performance of spoofing attacks. The first features consist of two cepstral coefficients and one LogSpec feature, which are extracted from the linear prediction (LP) residual signals. The second feature is a harmonic and noise subband ratio feature, which can reflect the interaction movement difference of the vocal tract and glottal airflow of the genuine and spoofing speech. The significance of these new features has been investigated in both the t-stochastic neighborhood embedding space and the binary classification modeling space. Experiments on the ASVspoof 2019 database show that the proposed residual features can achieve from 7% to 51.7% relative equal error rate (EER) reduction on the development and evaluation set over the best single system baseline. Furthermore, more than 31.2% relative EER reduction on both the development and evaluation set shows that the proposed new features contain large information complementary to the source acoustic features

A fast sampling based evolutionary algorithm for million-dimensional multiobjective optimization

Li L, He C, Cheng R, Li H, Pan L, Jin Y. A fast sampling based evolutionary algorithm for million-dimensional multiobjective optimization. Swarm and Evolutionary Computation . 2022;75: 101181.With their complexity and vast search space, large-scale multiobjective optimization problems (LSMOPs) challenge existing multiobjective evolutionary algorithms (MOEAs). Recently, several large-scale multiobjective evolutionary algorithms have been developed to tackle LSMOPs. Unlike conventional MOEAs that concentrate on selection operations in the objective space, large-scale MOEAs emphasize operations in the decision space, such as offspring generation, to tackle the large number of decision variables. Nevertheless, most present large-scale MOEAs experience difficulty effectively and efficiently solving LSMOPs with tens of thousands or more decision variables or exhibit poor versatility in solving different LSMOPs. We propose a fast large-scale MOEA framework with reference-guided offspring generation, named FLEA, aiming at these issues. Generally, FLEA constructs several reference vectors in the decision space to steer the sampling of promising solutions during offspring generation. A parameter is used to allocate computation resources between the convergence and diversity of the offspring population adaptively. Without computationally expensive problem reformulation or decision variable analysis techniques, the proposed method can significantly accelerate the search speed of conventional MOEAs in solving LSMOPs. FLEA is examined on various LSMOPs with up to 1.6 million decision variables, demonstrating its superior effectiveness, efficiency, and versatility in large-scale multiobjective optimization

Association of polymorphisms in IGF2, CLU and STAT5A genes with milk production characteristics in Chinese Holstein cattle

Reflecting the quality of milk at the molecular level is a frontier technology. The aim of this study was to analyze the polymorphisms of bovine insulin-like growth factor 2 (IGF2), signal transducer and activator of transcription 5A (STAT5A) and clusterin (CLU) genes in the raw milk from 507 Chinese Holstein cow using polymerase chain reaction (PCR)-restriction fragment length polymorphism techniques and to evaluate their correlations with the milk protein content (MPC), milk fat content (MFC), milk lactose content (MLC) and milk total solids content (MTSC). In IGF2 gene, genotype GG was the most frequent genotype (51.68%) followed by the genotype GT (38.03%) and TT (10.29%). And the genotype TT of IGF2 gene was superior to the other genotypes in MPC. In CLU gene, genotype GG was the most common genotype (63.99%) followed by the genotype GA (34.45%) and AA (1.56%). And the genotype AA of CLU gene had greater MFC and MLC, but lower MTSC than GA genotype individuals. For STAT5A gene, the frequency of genotype CC and CT was similar (45.30% and 45.08%), while the genotype TT had lowest frequency (9.62%). And the genotype TT of STA5A gene had highest MPC and lowest MLC. Thus, screening for the IGF2, CLU and STAT5A genes were available for evaluating milk quality and raw milk samples were graded according to the different genotypes