Genetic diversity of five goat breeds in China based on microsatellite markers

The genetic diversity of five goat breeds in China was surveyed using 15 microsatellites. The five goat breeds included Tangshan dairy goat (TSD), Liaoning cashmere goat (LNC), Nanjiang yellow goat (NJY), Chengde  polled goat (CDP) and Leizhou black goat (LZB). The mean polymorphism information content value (PIC) of  the populations ranged from 0.6606 to 0.8405. The mean heterozygosity (H) of the populations ranged from  0.7936 to 0.8202. The mean number of effective allele (Ne) of the populations ranged from 5.3373 to 5.8812 and the coefficient of genetic differentiation between breeds was 0.0620. It was suggested that the five goat  populations have abundant genetic diversity and extensive genetic basis, with limited inbreeding, especially in Leizhou black goat. The unweighted pair-group method with arithmetic averages (UPGMA) dendrogram based  on the Nei's standard genetic distance indicated that Tangshan dairy goat, Chengde polled goat and Liaoning  cashmere goat breeds / populations clustered together. The Nanjiang yellow goat and Leizhou black goat  populations clustered together, consistent with the geographical distribution of goat breeds.Key words: Goat, microsatellite, genetic diversity, clustering

Transcriptomic analysis reveals the molecular mechanisms underlying osteoclast differentiation in the estrogen-deficient pullets

Several previous reports have suggested that estrogen (E2) is a vital signal responsible for the reg-ulation of skeletal homeostasis and bone remodeling in mammals. E2 could efficiently accelerate the growth of medullary bone in pullets during sexual maturity. Fur-thermore, the low E2 level can strengthen the mechanical bone functions in female hens. However, mechanistic studies to describe the effects of E2 on bone in pullets during the initiation of the puberty period are remaining elusive. Therefore, the aim of this study was to explore the effect of inhibiting E2 biosynthesis on the biomechani-cal properties and its molecular mechanism during sexual maturity of pullets. In this study, a total of 90 Hy-line Sonia pullets with comparable body weight at 13 wk of age were selected and categorized into 2 separate groups. Daily, 0.5 mg/4 mL of letrozole (LZ) was orally adminis-tered to the treatment (TRT) group and 4 mL of saline to the control (CON) group of pullets for 6 wk. Com-pared with the CON group, a lower plasma E2 level was observed in the TRT group. Furthermore, plasma P, Gla protein (BGP), and 1,25-dihydroxy vitamin D3 (1,25-(OH)2D3) levels were markedly suppressed, whereas the plasma alkaline phosphatase (ALP) and tartrate-resistant acid phosphatase (TRAP) levels were signifi-cantly elevated. Moreover, the cortical bone thickness and breaking strength of the tibia and femur, the bone mineral density of the humerus, and the bone mineral content of the humerus as well as the femur were increased significantly. The expression levels of 340 dif-ferentially expressed genes (DEGs) differed signifi-cantly between the CON and TRT group in the tibia at 19 wk of age. Among them, 32 genes were up-regulated, whereas 308 were down-regulated in the TRT group. The variations in candidate genes associated with oste-oclast differentiation and cell adhesion may indicate that LZ inhibits E2 biosynthesis, consequently, reduces osteoclast differentiation by suppressing inter-cellular communication and cells attaching to extracellular matrix components. Taken together, the present study demonstrated that inhibiting E2 synthesis during sex-ual maturity of pullets decreased osteoclast differentia-tion and considerably enhanced bone quality

An eQTL in the cystathionine beta synthase gene is linked to osteoporosis in laying hens

Background Skeletal damage is a challenge for laying hens because the physiological adaptations required for egg laying make them susceptible to osteoporosis. Previously, we showed that genetic factors explain 40% of the variation in end of lay bone quality and we detected a quantitative trait locus (QTL) of large effect on chicken chromosome 1. The aim of this study was to combine data from the commercial founder White Leghorn population and the F2 mapping population to fine-map this QTL and understand its function in terms of gene expression and physiology. Results Several single nucleotide polymorphisms on chromosome 1 between 104 and 110 Mb (galGal6) had highly significant associations with tibial breaking strength. The alternative genotypes of markers of large effect that flanked the region had tibial breaking strengths of 200.4 vs. 218.1 Newton (P < 0.002) and, in a subsequent founder generation, the higher breaking strength genotype was again associated with higher breaking strength. In a subsequent generation, cortical bone density and volume were increased in individuals with the better bone genotype but with significantly reduced medullary bone quality. The effects on cortical bone density were confirmed in a further generation and was accompanied by increased mineral maturity of the cortical bone as measured by infrared spectrometry and there was evidence of better collagen cross-linking in the cortical bone. Comparing the transcriptome of the tibia from individuals with good or poor bone quality genotypes indicated four differentially-expressed genes at the locus, one gene, cystathionine beta synthase (CBS), having a nine-fold higher expression in the genotype for low bone quality. The mechanism was cis-acting and although there was an amino-acid difference in the CBS protein between the genotypes, there was no difference in the activity of the enzyme. Plasma homocysteine concentration, the substrate of CBS, was higher in the poor bone quality genotype. Conclusions Validated markers that predict bone strength have been defined for selective breeding and a gene was identified that may suggest alternative ways to improve bone health in addition to genetic selection. The identification of how genetic variants affect different aspects of bone turnover shows potential for translational medicine

Master data management dans le cadre de l'industrie du futur

Master data management dans le cadre de l'industrie du futu

Exploration des Opportunités et des Défis Apportés par l'Industrie 4.0 aux Chaînes d'Approvisionnement Mondiales et à la Macroéconomie en Intégrant l'Intelligence Artificielle et les Méthodes Traditionnelles

L'industrie 4.0 est un changement important et un défi de taille pour chaque segment industriel. La recherche utilise d'abord l'analyse de la littérature pour trier la littérature et énumérer la direction du développement et l'état d'application de différents domaines, ce qui se consacre à montrer un rôle de premier plan pour la théorie et la pratique de l'industrie 4.0. La recherche explore ensuite la tendance principale de l'offre à plusieurs niveaux dans l'industrie 4.0 en combinant l'apprentissage automatique et les méthodes traditionnelles. Ensuite, la recherche examine la relation entre l'investissement et l'emploi dans l'industrie 4.0 pour examiner la dépendance interrégionale de l'industrie 4.0 afin de présenter un regroupement raisonnable basé sur différents critères et de faire des suggestions et une analyse de la chaîne d'approvisionnement mondiale pour les entreprises et les organisations.De plus, notre système d'analyse jette un coup d'oeil sur la macroéconomie. La combinaison du traitement du langage naturel dans l'apprentissage automatique pour classer les sujets de recherche et de la revue de la littérature traditionnelle pour enquêter sur la chaîne d'approvisionnement à plusieurs niveaux améliore considérablement l'objectivité de l'étude et jette une base solide pour des recherches ultérieures. L'utilisation de réseaux et d'économétrie complexes pour analyser la chaîne d'approvisionnement mondiale et les problèmes macroéconomiques enrichit la méthodologie de recherche au niveau macro et politique. Cette recherche fournit des analyses et des références aux chercheurs, aux décideurs et aux entreprises pour leur prise de décision stratégique.Industry 4.0 is a significant shift and a tremendous challenge for every industrial segment, especially for the manufacturing industry that gave birth to the new industrial revolution. The research first uses literature analysis to sort out the literature, and focuses on the use of “core literature extension method” to enumerate the development direction and application status of different fields, which devotes to showing a leading role for theory and practice of industry 4.0. The research then explores the main trend of multi-tier supply in Industry 4.0 by combining machine learning and traditional methods. Next, the research investigates the relationship of industry 4.0 investment and employment to look into the inter-regional dependence of industry 4.0 so as to present a reasonable clustering based on different criteria and make suggestions and analysis of the global supply chain for enterprises and organizations. Furthermore, our analysis system takes a glance at the macroeconomy. The combination of natural language processing in machine learning to classify research topics and traditional literature review to investigate the multi-tier supply chain significantly improves the study's objectivity and lays a solid foundation for further research. Using complex networks and econometrics to analyze the global supply chain and macroeconomic issues enriches the research methodology at the macro and policy level. This research provides analysis and references to researchers, decision-makers, and companies for their strategic decision-making

HOW INDUSTRY 4.0 RESHAPES THE WORLD: RECOMMENDATIONS BASED ON COMPLEX GRAPH NETWORK ANALYSIS

International audienceAbstract Industry 4.0 is a great opportunity and a tremendous challenge for every role of society. Our study combines complex network and qualitative methods to analyze the Industry 4.0 macroeconomic issues and global supply chain, which enriches the qualitative analysis and machine learning in macroscopic and strategic research. Unsupervised complex graph network models are used to explore how industry 4.0 reshapes the world. Based on the in-degree and out-degree of the weighted and unweighted edges of each node, combined with the grouping results based on unsupervised learning, our study shows that the cooperation groups of Industry 4.0 are different from the previous traditional alliances. Macroeconomics issues also are studied. Finally, strong cohesive groups and recommendations for businessmen and policymakers are proposed

The main trends for multi-tier supply chain in Industry 4.0 based on Natural Language Processing

International audienceMulti-tier supply chains in Industry 4.0 are critical emerging issues today. This article briefly examines the Industry 4.0 policies in different countries. In order to decide on a better model and the number of topics in the model, a comparative test of the coherence value for two machine learning classification methods based on Latent Dirichlet Allocation was conducted. Subsequently, the article combines the traditional literature review method with a survey article referring to Industry 4.0 and multi-tier supply chain, indexed by science citation index expanded (SCI-EXPANDED) and social sciences citation index (SSCI) during 2009-2018. The research direction, research type, and research approaches of each paper were extracted, and the topics of all the articles were classified by machine learning, which provides feasible routes and valuable research directions for researchers in this field. Afterward, the research status and future research directions were identified. The combination of natural language processing in machine learning to classify research topics and traditional literature review to investigate article details greatly improved the objectivity and scientificity of the study and laid a solid foundation for further resear

Optimal 3D Angle of Arrival Sensor Placement with Gaussian Priors

Sensor placement is an important factor that may significantly affect the localization performance of a sensor network. This paper investigates the sensor placement optimization problem in three-dimensional (3D) space for angle of arrival (AOA) target localization with Gaussian priors. We first show that under the A-optimality criterion, the optimization problem can be transferred to be a diagonalizing process on the AOA-based Fisher information matrix (FIM). Secondly, we prove that the FIM follows the invariance property of the 3D rotation, and the Gaussian covariance matrix of the FIM can be diagonalized via 3D rotation. Based on this finding, an optimal sensor placement method using 3D rotation was created for when prior information exists as to the target location. Finally, several simulations were carried out to demonstrate the effectiveness of the proposed method. Compared with the existing methods, the mean squared error (MSE) of the maximum a posteriori (MAP) estimation using the proposed method is lower by at least 25% when the number of sensors is between 3 and 6, while the estimation bias remains very close to zero (smaller than 0.15 m)

Design of 3D-Printed Electronic Fiber Optic Sensor to Detect Rhodamine B Reagent: An Initiation to Potential Virus Detection

A fluorescence device based on ultraviolet light is proposed in this paper, which currently stands at the design stage with the eventual aim to potentially detect virus/antibody fluorescence reactions. The designed device is proposed to have the characteristics of high reflectivity, low power consumption, wide spectrum of light source, and proper silver coating. For fabrication and raising product quality, 3D printing technology and a sputtering test will be used. In this connection, this paper firstly introduces the design sources; then, the ideas of inventing fluorescence detection devices based on ultraviolet light, followed by the data analysis as well as discussing the results of computer simulations. The design process, materials, methods, and experiments are demonstrated following the reality work procedure. Instead of directly using a virus or antibodies for the experiment, at the current design stage, we focus on using this device to detect the rhodamine B reagent. Experiment shows that this reagent can be successfully detected. With this achievement, we logically believe that such type of an ultraviolet optical sensor, with further development and testing, may have the possible value to detect a single virus such as COVID-19, as well as other viruses or small molecules. Though there is long way to go to achieve such a goal, future works experimenting with the detection device on real virus or antibodies can take place more efficiently with a good foundation