Enhanced Thermal Conductivity for Nanofluids Containing Silver Nanowires with Different Shapes

Nanofluids are the special agents to enhance the heat transfer property of the common fluids, and most of the thermal additives are the spherical nanoparticles. Up to now, the 1D thermal additives are not well exploited. In this paper, a kind of silver nanowires (AgNWs) with well-distributed shape and aspect ratio is synthesized. The results show that when we use the AgNWs prepared by the poly-vinyl-pyrrolidone (PVP) with a specific molecular weight of 40000, the thermal conductivity enhancement of nanofluids prepared by that kind of silver nanowires is as high as 13.42% when loading 0.46 vol.% AgNWs, and the value of the thermal conductivity is 0.2843 W/m·K, which is far more than the case when loading the same volume of spherical silver particles. Besides, we use H&C model to fit the experimental results and the experimental results are consistent with the model

Automatic Nonlinear Subspace Identification Using Clustering Judgment Based on Similarity Filtering

Accurately determining system order plays a vital role in system identification directly related to the accuracy of identification results, especially for nonlinear system identification. Due to the need for human subjective judgment, the traditional sequence determination method easily causes uncertainty in the results; and the phenomenon of the virtual mode or omission occurs. An automatic nonlinear subspace identification method is proposed to address the aforementioned problems. When the eigenvalue decomposition of the constructed Hankel matrix is performed, the calculation range of the modal order of the system is estimated. The similarity coefficient and distance function are introduced to cluster the identified modal results, the poles of the false modes are removed to obtain the cluster stabilization diagram, and the best order of the system is received. Then, the modal parameters and nonlinear coefficients are obtained. Simulation examples are carried out to verify the effectiveness and robustness of the proposed method. An experimental study is carried out on a multilayer building with nonlinear characteristics. Compared with the traditional stabilization graph, the accuracy of the automatic order determination proposed in this paper is proven

Inter-Procedural Diagnosis Path Generation for Automatic Confirmation of Program Suspected Faults

Static analysis plays an important role in the software testing field. However, the initial results of static analysis always have a large number of false positives, which need to be confirmed by manual or automatic tools. In this paper, a novel approach is proposed, which combines the demand-driven analysis and the inter-procedural dataflow analysis, and generates the inter-procedural diagnosis paths to help the testers confirm the suspected faults automatically. In our approach, first, the influencing nodes of suspected fault are calculated. Then, the CFG of each associated procedure is simplified according to the influencing nodes. Finally, the “section-whole” strategy is employed to generate the inter-procedural diagnosis path. In order to illustrate and verify our approach, an experimental study is performed on the five open source C language projects. The results show that compared with the traditional approach, our approach requires less time and can generate more inter-procedural diagnosis paths in the given suspected faults

Systematic identification of genes involved in divergent skeletal muscle growth rates of broiler and layer chickens

<p>Abstract</p> <p>Background</p> <p>The genetic closeness and divergent muscle growth rates of broilers and layers make them great models for myogenesis study. In order to discover the molecular mechanisms determining the divergent muscle growth rates and muscle mass control in different chicken lines, we systematically identified differentially expressed genes between broiler and layer skeletal muscle cells during different developmental stages by microarray hybridization experiment.</p> <p>Results</p> <p>Taken together, 543 differentially expressed genes were identified between broilers and layers across different developmental stages. We found that differential regulation of slow-type muscle gene expression, satellite cell proliferation and differentiation, protein degradation rate and genes in some metabolic pathways could give great contributions to the divergent muscle growth rates of the two chicken lines. Interestingly, the expression profiles of a few differentially expressed genes were positively or negatively correlated with the growth rates of broilers and layers, indicating that those genes may function in regulating muscle growth during development.</p> <p>Conclusion</p> <p>The multiple muscle cell growth regulatory processes identified by our study implied that complicated molecular networks involved in the regulation of chicken muscle growth. These findings will not only offer genetic information for identifying candidate genes for chicken breeding, but also provide new clues for deciphering mechanisms underlining muscle development in vertebrates.</p

Identification of long non-protein coding RNAs in chicken skeletal muscle using next generation sequencing

AbstractVertebrate genomes encode thousands of non-coding RNAs including short non-coding RNAs (such as microRNAs) and long non-coding RNAs (lncRNAs). Chicken (Gallus gallus) is an important model organism for developmental biology, and the recently assembled genome sequences for chicken will facilitate the understanding of the functional roles of non-coding RNA genes during development. The present study concerns the first systematic identification of lncRNAs using RNA-Seq to sample the transcriptome during chicken muscle development. A computational approach was used to identify 281 new intergenic lncRNAs in the chicken genome. Novel lncRNAs in general are less conserved than protein-coding genes and slightly more conserved than random non-coding sequences. The present study has provided an initial chicken lncRNA catalog and greatly increased the number of chicken ncRNAs in the non-protein coding RNA database. Furthermore, the computational pipeline presented in the current work will be useful for characterizing lncRNAs obtained from deep sequencing data

Differential expression profiling between the relative normal and dystrophic muscle tissues from the same LGMD patient

BACKGROUND: Limb-girdle muscular dystrophy (LGMD) is a group of heterogeneous muscular disorders with autosomal dominant and recessive inheritance, in which the pelvic or shoulder girdle musculature is predominantly or primarily involved. Although analysis of the defective proteins has shed some light onto their functions implicated in the etiology of LGMD, our understanding of the molecular mechanisms underlying muscular dystrophy remains incomplete. METHODS: To give insight into the molecular mechanisms of AR-LGMD, we have examined the differentially expressed gene profiling between the relative normal and pathological skeletal muscles from the same AR-LGMD patient with the differential display RT-PCR approach. The research subjects came from a Chinese AR-LGMD family with three affected sisters. RESULTS: In this report, we have identified 31 known genes and 12 unknown ESTs, which were differentially expressed between the relative normal and dystrophic muscle from the same LGMD patient. The expression of many genes encoding structural proteins of skeletal muscle fibers (such as titin, myosin heavy and light chains, and nebulin) were dramatically down-regulated in dystrophic muscles compared to the relative normal muscles. The genes, reticulocalbin 1, kinectin 1, fatty acid desaturase 1, insulin-like growth factor binding protein 5 (IGFBP5), Nedd4 family interacting protein 1 (NDFIP1), SMARCA2 (SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 2), encoding the proteins involved in signal transduction and gene expression regulation were up-regulated in the dystrophic muscles. CONCLUSION: The functional analysis of these expression-altered genes in the pathogenesis of LGMD could provide additional information for understanding possible molecular mechanisms of LGMD development

Influenza Virus Database (IVDB): an integrated information resource and analysis platform for influenza virus research

Frequent outbreaks of highly pathogenic avian influenza and the increasing data available for comparative analysis require a central database specialized in influenza viruses (IVs). We have established the Influenza Virus Database (IVDB) to integrate information and create an analysis platform for genetic, genomic, and phylogenetic studies of the virus. IVDB hosts complete genome sequences of influenza A virus generated by Beijing Institute of Genomics (BIG) and curates all other published IV sequences after expert annotation. Our Q-Filter system classifies and ranks all nucleotide sequences into seven categories according to sequence content and integrity. IVDB provides a series of tools and viewers for comparative analysis of the viral genomes, genes, genetic polymorphisms and phylogenetic relationships. A search system has been developed for users to retrieve a combination of different data types by setting search options. To facilitate analysis of global viral transmission and evolution, the IV Sequence Distribution Tool (IVDT) has been developed to display the worldwide geographic distribution of chosen viral genotypes and to couple genomic data with epidemiological data. The BLAST, multiple sequence alignment and phylogenetic analysis tools were integrated for online data analysis. Furthermore, IVDB offers instant access to pre-computed alignments and polymorphisms of IV genes and proteins, and presents the results as SNP distribution plots and minor allele distributions. IVDB is publicly available a

Thermal Conductivity of Composite Materials Containing Copper Nanowires

The development of thermal conductive polymer composite is necessary for the application in thermal management. In this paper, the experimental and theoretical investigations have been conducted to determine the effect of copper nanowires (CuNWs) and copper nanoparticles (CuNPs) on the thermal conductivity of dimethicone nanocomposites. The CuNWs and CuNPs were prepared by using a liquid phase reduction method, and they were characterized through scanning electron microscopy (SEM) and X-ray diffraction (XRD). The experimental data show that the thermal conductivity of composites increases with the increase of filler. With the addition of 10 vol.% CuNWs, the thermal conductivity of the composite is 0.41 W/m/K. The normalized thermal conductivity enhancement factor is 2.73, much higher than that of the analogue containing CuNPs (1.67). These experimental data are in agreement with Nan’s model prediction. Due to the high aspect ratio of 1D CuNWs, they can construct thermal networks more effectively than CuNPs in the composite, resulting in higher thermal conductivity

Efficacy and safety of belimumab for the treatment of refractory childhood-onset systemic lupus erythematosus: A single-center, real-world, retrospective study

ObjectiveThis study aimed to investigate the efficacy and safety of belimumab for treating children with refractory childhood-onset systemic lupus erythematosus (cSLE).MethodsTwenty-six cSLE patients who received belimumab treatment in our hospital from January 2020 to September 2021 (23 of them for more than 52 weeks) were enrolled in this study. Their clinical and laboratory data, assessment of disease activity, glucocorticoid dosage, and treatment-emergent adverse events (TEAEs) were retrieved for analysis. The paired samples t-test and the nonparametric test were used to compare the baseline and post-treatment data.ResultsThe mean age of onset was 10.3 ± 2.4 years old; the mean disease duration was 41.6 ± 37.4 months; the median Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K) score was 10 (P25, P75: 3, 17); and the mean Physician’s Global Assessment (PGA) score at baseline was 1.9 ± 1.0. Compared with the baseline values, there was a significant decrease in the 24-h urine protein quantifications at 24 and 52 weeks of treatment (P&lt;0.05) as well as an elevated complement (C) 3 and C4 levels at 4, 12, 24, and 52 weeks of treatment. In addition, the SLEDAI-2K and PGA scores as well as the percentage of CD19+ B cells were significantly decreased at 12, 24, and 52 weeks of treatment compared with the baseline values (P&lt;0.05). The dosage of glucocorticoid at 4, 12, 24, and 52 weeks of treatment was significantly less than that at baseline or the previous follow-up (P&lt;0.05). At 52 weeks, 14 subjects (53.8%) achieved Lupus Low Disease Activity State (LLDAS), and 4 subjects (15.4%) reached clinical remission (CR). At the last follow-up, 16 subjects (61.5%) achieved LLDAS, and 10 subjects (38.5%) reached CR.ConclusionsBelimumab treatment can significantly improve laboratory indicators, reduce disease activity, and decrease the dosage of glucocorticoid required in children with cSLE. Moreover, it has a good safety profile