On the Smarandache LCM dual function

The main purpose of this paper is using the elementary method to study the calculating problem of a Dirichlet series involving the Smarandache LCM dual function SL*(n) and the mean value distribution property of SL*(n), obtain an exact calculating formula and a sharper asymptotic formula for it

Role of salivary microbial enzymes and proline-rich proteins in celiac disease

INTRODUCTION: Human saliva contains a variety of microorganisms and salivary proteins implicated in oral health and disease. The oral bacterium R. mucilaginosa harbors glutamine endoprotease activity degrading salivary proline-rich proteins (PRPs) rich in glutamine residues. PRPs share structural features with dietary gluten proteins, which trigger celiac disease (CD). Their structural similarities and shared destination of the gastrointestinal tract raise the potential for the involvement of PRPs in CD pathogenesis. The aims of this study were to: 1) Investigate to what extent R. mucilaginosa cell-associated enzymes degrade gluten and abolish their immunogenicity; 2) Compare gluten-degrading enzyme activities and microbiomes in whole saliva (WS) from healthy and CD subjects; 3) Study the potential immunogenicity of salivary PRPs. METHODS: Studies on gluten degradation by R. mucilaginosa comprised SDS-PAGE, RP-HPLC, LC-ESI-MS/MS, and ELISA. Clinical studies were conducted with healthy and CD patient groups. Salivary hydrolytic activities were assessed towards Z-YPQ-pNA and gliadin-derived immunogenic 33-mer/26-mer peptides. Oral microbiomes were analyzed by 16S rDNA sequencing. Induction of cytokines (TNF-alpha, IL-10, IFN-gamma, and IL-21) by PRPs was studied in peripheral blood mononuclear cells (PBMCs) collected from CD patients. RESULTS: R. mucilaginosa cell-associated enzymes degraded gliadins/33-mer/26-mer, decreased their recognition by TG2 and abolished epitopes recognized by R5 antibody. WS showed no differences between healthy and CD patients with regard to activities relevant in gluten degradation, and salivary microbiome compositions were similar. PRPs protein patterns revealed minor differences that were not group-specific. Despite structural similarities, PRPs did not stimulate cytokines production by PBMCs, nor did they compete with gliadin-induced cytokine secretion. CONCLUSION: From a therapeutic view point, R. mucilaginosa cells and/or their gluten-degrading enzymes may offer novel perspectives for CD treatment. From an oral physiological perspective, endogenous WS gluten-degrading activities were low and comparable between healthy and CD groups, suggesting such activities may not be sufficient for gluten digestion in vivo, and further supporting the dietary supplementation concept. PRPs do not seem to harbor gliadin-like elements relevant in CD pathogenesis. Deciphering the structural basis for the lack of immunogenicity of salivary PRPs is of interest to inform development of gluten proteins lacking immunogenic epitopes, and their design is discussed

Novel optimisation methods for numerical inverse problems

Inverse problems involve the determination of one or more unknown quantities usually appearing in the mathematical formulation of a physical problem. These unknown quantities may be boundary heat flux, various source terms, thermal and material properties, boundary shape and size. Solving inverse problems requires additional information through in-situ data measurements of the field variables of the physical problems. These problems are also ill-posed because the solution itself is sensitive to random errors in the measured input data. Regularisation techniques are usually used in order to deal with the instability of the solution. In the past decades, many methods based on the nonlinear least squares model, both deterministic (CGM) and stochastic (GA, PSO), have been investigated for numerical inverse problems. The goal of this thesis is to examine and explore new techniques for numerical inverse problems. The background theory of population-based heuristic algorithm known as quantum-behaved particle swarm optimisation (QPSO) is re-visited and examined. To enhance the global search ability of QPSO for complex multi-modal problems, several modifications to QPSO are proposed. These include perturbation operation, Gaussian mutation and ring topology model. Several parameter selection methods for these algorithms are proposed. Benchmark functions were used to test the performance of the modified algorithms. To address the high computational cost of complex engineering optimisation problems, two parallel models of the QPSO (master-slave, static subpopulation) were developed for different distributed systems. A hybrid method, which makes use of deterministic (CGM) and stochastic (QPSO) methods, is proposed to improve the estimated solution and the performance of the algorithms for solving the inverse problems. Finally, the proposed methods are used to solve typical problems as appeared in many research papers. The numerical results demonstrate the feasibility and efficiency of QPSO and the global search ability and stability of the modified versions of QPSO. Two novel methods of providing initial guess to CGM with approximated data from QPSO are also proposed for use in the hybrid method and were applied to estimate heat fluxes and boundary shapes. The simultaneous estimation of temperature dependent thermal conductivity and heat capacity was addressed by using QPSO with Gaussian mutation. This combination provides a stable algorithm even with noisy measurements. Comparison of the performance between different methods for solving inverse problems is also presented in this thesis

Multi-wavelength Study of Transition Region Penumbral Subarcsecond Bright Dots Using IRIS and NST

Using high-resolution transition region (TR) observations taken by the Interface Region Imaging Spectrograph (IRIS) mission, Tian et al. (2014b) revealed numerous short-lived subarcsecond bright dots (BDs) above sunspots (mostly located in the penumbrae), which indicate yet unexplained small-scale energy releases. Moreover, whether these subarcsecond TR brightenings have any signature in the lower atmosphere and how they are formed are still not fully resolved. This paper presents a multi-wavelength study of the TR penumbral BDs using a coordinated observation of a near disk-center sunspot with IRIS and the 1.6 m New Solar Telescope (NST) at the Big Bear Solar Observatory. NST provides high-resolution chromospheric and photospheric observations with narrow-band H-alpha imaging spectroscopy and broad-band TiO images, respectively, complementary to IRIS TR observations. A total of 2692 TR penumbral BDs are identified from a 37-minute time series of IRIS 1400 A slitjaw images. Their locations tend to be associated more with downflowing and darker fibrils in the chromosphere, and weakly associated with bright penumbral features in the photosphere. However, temporal evolution analyses of the BDs show that there is no consistent and convincing brightening response in the chromosphere. These results are compatible with a formation mechanism of the TR penumbral BDs by falling plasma from coronal heights along more vertical and dense magnetic loops. The BDs may also be produced by small-scale impulsive magnetic reconnection taking place sufficiently high in the atmosphere that has no energy release in the chromosphere.Comment: 8 pages, 5 figures, accepted to Ap

LHX1 as a potential biomarker regulates EMT induction and cellular behaviors in uterine corpus endometrial carcinoma

Objectives: To investigate the expression of LHX1 and its role as a biomarker in the diagnosis and prognosis of Uterine Corpus Endometrial Carcinoma (UCEC). Methods: The Cancer Genome Atlas (TCGA) database was used to detect the expression level of LHX1 in UCEC cells and tissues, and to find out the effect of LHX1 on prognosis. Co-expressed genes were then identified by Spearman correlation analysis, and the protein-protein interaction network was constructed using Cytoscape software. The R “clusterProfiler” package was used to conduct Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. A series of in vitro experiments were performed to evaluate LHX1 expression and detect UCEC cell proliferation, invasion, and migration. Western blotting was used to determine the effect of LHX1 on expression levels of Epithelial-Mesenchymal Transition (EMT)-related proteins. Results: LHX1 was upregulated in UCEC tissues and correlated with poor overall survival and disease-specific survival outcomes. Functional enrichment analysis suggested that genes co-expressed with LHX1 were enriched in cell adhesion. The expression of LHX1 was positively correlated with the expression levels of genes related to EMT induction and invasion. LHX1 can enhance the proliferation, migration, and invasion activities of UCEC cells in vitro, and alter the expression levels of EMT-related proteins. Conclusion: LHX1 expression was highly upregulated in UCEC cells and tissues, which was correlated with the prognosis of patients with UCEC. LHX1 may regulate UCEC progression at least in part by modulating EMT induction

A Parallel FP-Growth Mining Algorithm with Load Balancing Constraints for Traffic Crash Data

Traffic safety is an important part of the roadway in sustainable development. Freeway traffic crashes typically cause serious casualties and property losses, being a serious threat to public safety. Figuring out the potential correlation between various risk factors and revealing their coupling mechanisms are of effective ways to explore and identity freeway crash causes. However, the existing association rule mining algorithms still have some limitations in both efficiency and accuracy. Based on this consideration, using the freeway traffic crash data obtained from WDOT (Washington Department of Transportation), this research constructed a multi-dimensional multilevel system for traffic crash analysis. Considering the load balancing, the FP-Growth (Frequent Pattern- Growth) algorithm was optimized parallelly based on Hadoop platform, to achieve an efficient and accurate association rule mining calculation for massive amounts of traffic crash data; then, according to the results of the coupling mechanism among the crash precursors, the causes of freeway traffic crashes were identified and revealed. The results show that the parallel FPgrowth algorithm with load balancing constraints has a better operating speed than both the conventional FP-growth algorithm and parallel FP-growth algorithm towards processing big data. This improved algorithm makes full use of Hadoop cluster resources and is more suitable for large traffic crash data sets mining while retaining the original advantages of conventional association rule mining algorithm. In addition, the mining association rules model with the improvement of multi-dimensional interaction proposed in this research can catch the occurrence mechanism of freeway traffic crash with serious consequences (lower support degree probably) accurately and efficiently

In vivo measurement of afferent activity with axon-specific calcium imaging.

In vivo calcium imaging from axons provides direct interrogation of afferent neural activity, informing the neural representations that a local circuit receives. Unlike in somata and dendrites, axonal recording of neural activity-both electrically and optically-has been difficult to achieve, thus preventing comprehensive understanding of neuronal circuit function. Here we developed an active transportation strategy to enrich GCaMP6, a genetically encoded calcium indicator, uniformly in axons with sufficient brightness, signal-to-noise ratio, and photostability to allow robust, structure-specific imaging of presynaptic activity in awake mice. Axon-targeted GCaMP6 enables frame-to-frame correlation for motion correction in axons and permits subcellular-resolution recording of axonal activity in previously inaccessible deep-brain areas. We used axon-targeted GCaMP6 to record layer-specific local afferents without contamination from somata or from intermingled dendrites in the cortex. We expect that axon-targeted GCaMP6 will facilitate new applications in investigating afferent signals relayed by genetically defined neuronal populations within and across specific brain regions