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In order to further improve the prediction accuracy, the network packet loss rate (PLR) prediction mathematical model based on the Internet of Things (IoTs) was proposed. First, the network data transmission module was established, and the network PLR prediction process was developed based on IoTs; second, the prediction framework of PLR was designed to obtain more accurate prior information. The relationship between PLR and user experience quality QoE is univariate and nonlinear. The mapping between PLR and user experience quality QoE is established using univariate nonlinear regression analysis; finally, a mathematical model of network PLR prediction is constructed to further improve the prediction accuracy. Experimental results show that the delays of network nodes are all within 5 s, which can ensure the real-time nature of data transmission. When the total number of packets and the number of lost packets are the same, the PLR predicted by the mathematical model designed by the authors is consistent with the actual PLR. Conclusion: The prediction effect of the model is better and has higher promotion value.

The purpose of this study is to quantify the relationship between fragmentized collective forestland, farmers investment and forest commodity output to examine the rationality of collective forest right reform after dispersed forestland operation. The data is based on the 2420 farmers’ survey data in nine provinces of China including Fujian, Jiangxi, Hunan, Sichuan, Zhejiang, Guangxi, Henan, Shandong and Liaoning. The results show that if S index is used, fragmentized forestland reaches 0.41 and can be ordered (from high to low) as Zhejiang, Jiangxi, Hunan, Liaoning, Sichuan, Fujian, Guangxi and Shandong. Under certain fragmentized intervals (forestland fragmentized ratio lower than 0.22 or higher than 0.51), the higher the forestland fragmentized, the lower the farmers investment. The investment-output model indicates that forest block reflects the negative impacts to forest output while other variables such as labor, tangible inputs and forestland have positive impacts. When bamboo is produced, level of fragmentation has negative impacts on forestland acreage and significant positive impacts on labor used. The study implies that after the land is divided to households, physical investment is the key factor to affect the commodity output and should be considered by the related policies to increase the incentives of farmers. Based on this study, we provide some information and guidance on policy of farmers’ forestland operation in large scale

Bridge crane and gantry crane are the equipments which are the most widely used in lifting field,and it is necessary to study its dynamic characteristics when the gantry crane is designed. Taking the L type gantry crane as an example,with the help of finite element method,the crane’s top six order natural frequency and vibration mode were obtained by modal analysis,and the frequency biggest impacting on the dynamic characteristics of crane was obtained by harmonic response analysis,then taking the frequency as index,the dynamic sensitivity analysis of its structural parameters was carried out. In the dynamic sensitivity analysis,the parameters which affect dynamic characteristics of the crane structure more greatly were obtained. By the way,the dynamic optimization model can be simplified. The results provide the basis for the further optimization of the structure and the reference for the dynamic design of other similar structures.

The improved one-pot synthesis of dimethyl carbonate and propylene glycol from propylene oxide, supercritical carbon dioxide, and methanol with potassium bicarbonate as the catalyst has been reported in this paper. As far as we know, it is the first time to use potassium bicarbonate only as the catalyst in the production process which is simple and cheap. Satisfactory conversion rate of propylene oxide and yield of the products could be achieved at the optimized conditions with quite a small amount of by-products. Our new method offers an attractive choice for the production of dimethyl carbonate in large-scale industry efficiently and environmental friendly.

Abstract Chinese indigenous pig breeds are recognized as an invaluable component of the world's pig genetic resources and are divided traditionally into six types. Twenty-six microsatellite markers recommended by the FAO (Food and Agriculture Organization) and ISAG (International Society of Animal Genetics) were employed to analyze the genetic diversity of 18 Chinese indigenous pig breeds with 1001 individuals representing five types, and three commercial breeds with 184 individuals. The observed heterozygosity, unbiased expected heterozygosity and the observed and effective number of alleles were used to estimate the genetic variation of each indigenous breed. The unbiased expected heterozygosity ranged between 0.700 (Mashen) and 0.876 (Guanling), which implies that there is an abundant genetic variation stored in Chinese indigenous pig breeds. Breed differentiation was shown by fixation indices (FIT, FIS, and FST). The FST per locus varied from 0.019 (S0090) to 0.170 (SW951), and the average FST of all loci was 0.077, which means that most of the genetic variation was kept within breeds and only a little of the genetic variation exists between populations. The Neighbor-Joining tree was constructed based on the Nei DA (1978) distances and one large cluster with all local breeds but the Mashen breed, was obtained. Four smaller sub-clusters were also found, which included two to four breeds each. These results, however, did not completely agree with the traditional type of classification. A Neighbor-Joining dendrogram of individuals was established from the distance of – ln(proportions of shared alleles); 92.14% of the individuals were clustered with their own breeds, which implies that this method is useful for breed demarcation. This extensive research on pig genetic diversity in China indicates that these 18 Chinese indigenous breeds may have one common ancestor, helps us to better understand the relative distinctiveness of pig genetic resources, and will assist in developing a national plan for the conservation and utilization of Chinese indigenous pig breeds.

Abstract Twelve Chinese indigenous goat populations were genotyped for twenty-six microsatellite markers recommended by the EU Sheep and Goat Biodiversity Project. A total of 452 goats were tested. Seventeen of the 26 microsatellite markers used in this analysis had four or more alleles. The mean expected heterozygosity and the mean observed heterozygosity for the population varied from 0.611 to 0.784 and 0.602 to 0.783 respectively. The mean FST (0.105) demonstrated that about 89.5% of the total genetic variation was due to the genetic differentiation within each population. A phylogenetic tree based on the Nei (1978) standard genetic distance displayed a remarkable degree of consistency with their different geographical origins and their presumed migration throughout China. The correspondence analysis did not only distinguish population groups, but also confirmed the above results, classifying the important populations contributing to diversity. Additionally, some specific alleles were shown to be important in the construction of the population structure. The study analyzed the recent origins of these populations and contributed to the knowledge and genetic characterization of Chinese indigenous goat populations. In addition, the seventeen microsatellites recommended by the EU Sheep and Goat Biodiversity Project proved to be useful for the biodiversity studies in goat breeds.

Abstract Background Cathepsin C (Cat C) functions as a central coordinator for activation of many serine proteases in inflammatory cells. It has been recognized that Cat C is responsible for neutrophil recruitment and production of chemokines and cytokines in many inflammatory diseases. However, Cat C expression and its functional role in the brain under normal conditions or in neuroinflammatory processes remain unclear. Our previous study showed that Cat C promoted the progress of brain demyelination in cuprizone-treated mice. The present study further investigated the Cat C expression and activity in lipopolysaccharide (LPS)-induced neuroinflammation in vivo and in vitro. Methods C57BL/6 J mice were intraperitoneally injected with either 0.9% saline or lipopolysaccharide (LPS, 5 mg/kg). Immunohistochemistry (IHC) and in situ hybridization (ISH) were used to analyze microglial activation, TNF-α, IL-1β, IL-6, iNOS mRNAs expressions and cellular localization of Cat C in the brain. Nitrite assay was used to examine microglial activation in vitro; RT-PCR and ELISA were used to determine the expression and release of Cat C. Cat C activity was analyzed by cellular Cat C assay kit. Data were evaluated for statistical significance with paired t test. Results Cat C was predominantly expressed in hippocampal CA2 neurons in C57BL/6 J mice under normal conditions. Six hours after LPS injection, Cat C expression was detected in cerebral cortical neurons; whereas, twenty-four hours later, Cat C expression was captured in activated microglial cells throughout the entire brain. The duration of induced Cat C expression in neurons and in microglial cells was ten days and three days, respectively. In vitro, LPS, IL-1β and IL-6 treatments increased microglial Cat C expression in a dose-dependent manner and upregulated Cat C secretion and its activity. Conclusions Taken together, these data indicate that LPS and proinflammatory cytokines IL-1β, IL-6 induce the expression, release and upregulate enzymatic activity of Cat C in microglial cells. Further investigation is required to determine the functional role of Cat C in the progression of neuroinflammation, which may have implications for therapeutics for the prevention of neuroinflammation-involved neurological disorders in the future.

Abstract Background Identification and culling of replacement gilts with poor skeletal conformation and feet and leg (FL) unsoundness is an approach used to reduce sow culling and mortality rates in breeding stock. Few candidate genes related to soundness traits have been identified in the pig. Methods In this study, 2066 commercial females were scored for 17 traits describing body conformation and FL structure, and were used for association analyses. Genotyping of 121 SNPs derived from 95 genes was implemented using Sequenom's MassARRAY system. Results Based on the association results from single trait and principal components using mixed linear model analyses and false discovery rate testing, it was observed that APOE, BMP8, CALCR, COL1A2, COL9A1, DKFZ, FBN1 and VDBP were very highly significantly (P < 0.001) associated with body conformation traits. The genes ALOX5, BMP8, CALCR, OPG, OXTR and WNT16 were very highly significantly (P < 0.001) associated with FL structures, and APOE, CALCR, COL1A2, GNRHR, IHH, MTHFR and WNT16 were highly significantly (P < 0.01) associated with overall leg action. Strong linkage disequilibrium between CALCR and COL1A2 on SSC9 was detected, and haplotype -ACGACC- was highly significantly (P < 0.01) associated with overall leg action and several important FL soundness traits. Conclusion The present findings provide a comprehensive list of candidate genes for further use in fine mapping and biological functional analyses.

Abstract Background MicroRNAs (miRNAs) are recognized as one of the most important families of non-coding RNAs that serve as important sequence-specific post-transcriptional regulators of gene expression. Identification of miRNAs is an important requirement for understanding the mechanisms of post-transcriptional regulation. Hundreds of miRNAs have been identified by direct cloning and computational approaches in several species. However, there are still many miRNAs that remain to be identified due to lack of either sequence features or robust algorithms to efficiently identify them. Results We have evaluated features valuable for pre-miRNA prediction, such as the local secondary structure differences of the stem region of miRNA and non-miRNA hairpins. We have also established correlations between different types of mutations and the secondary structures of pre-miRNAs. Utilizing these features and combining some improvements of the current pre-miRNA prediction methods, we implemented a computational learning method SVM (support vector machine) to build a high throughput and good performance computational pre-miRNA prediction tool called MiRFinder. The tool was designed for genome-wise, pair-wise sequences from two related species. The method built into the tool consisted of two major steps: 1) genome wide search for hairpin candidates and 2) exclusion of the non-robust structures based on analysis of 18 parameters by the SVM method. Results from applying the tool for chicken/human and D. melanogaster/D. pseudoobscura pair-wise genome alignments showed that the tool can be used for genome wide pre-miRNA predictions. Conclusion The MiRFinder can be a good alternative to current miRNA discovery software. This tool is available at http://www.bioinformatics.org/mirfinder/.