Anomaly in numerical integrations of the Kardar-Parisi-Zhang equation

Author name used in this publication: F. G. ShinVersion of RecordPublishe

Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach. Data scientists mainly attempt to find an advanced way to analyze data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale data sets, Curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies are successfully tackled some application domains, such as image processing, sentiment analysis, network traffics / anomalies analysis, credit score analysis and other benchmark functions/data sets analysis

The breadth of primary care: a systematic literature review of its core dimensions

Background: Even though there is general agreement that primary care is the linchpin of effective health care delivery, to date no efforts have been made to systematically review the scientific evidence supporting this supposition. The aim of this study was to examine the breadth of primary care by identifying its core dimensions and to assess the evidence for their interrelations and their relevance to outcomes at (primary) health system level. Methods: A systematic review of the primary care literature was carried out, restricted to English language journals reporting original research or systematic reviews. Studies published between 2003 and July 2008 were searched in MEDLINE, Embase, Cochrane Library, CINAHL, King's Fund Database, IDEAS Database, and EconLit. Results: Eighty-five studies were identified. This review was able to provide insight in the complexity of primary care as a multidimensional system, by identifying ten core dimensions that constitute a primary care system. The structure of a primary care system consists of three dimensions: 1. governance; 2. economic conditions; and 3. workforce development. The primary care process is determined by four dimensions: 4. access; 5. continuity of care; 6. coordination of care; and 7. comprehensiveness of care. The outcome of a primary care system includes three dimensions: 8. quality of care; 9. efficiency care; and 10. equity in health. There is a considerable evidence base showing that primary care contributes through its dimensions to overall health system performance and health. Conclusions: A primary care system can be defined and approached as a multidimensional system contributing to overall health system performance and health

Overexpression of IL-7 enhances cisplatin resistance in glioma

Cisplatin is one of the most commonly used chemotherapeutic agents for glioma patients. In this study, array comparative genomic hybridization (aCGH) was used to identify genes associated with cisplatin resistance in a human glioma cell line. The cisplatin-resistant U251/CP2 cell line was derived by stepwise selection using cisplatin. The genetic aberrations of the U251 parental cell line and the U251/CP2 cells were analyzed using aCGH. RT-PCR was used to detect the expression of the altered genes revealed by aCGH. The sensitivity of glioma cells to cisplatin was determined by using the MTT assay. Apoptosis was detected using flow cytometry and western blot analysis. The IC50 value of cisplatin in U251/CP2 cells was five times higher than its IC50 in U251 cells. The U251 cells lost at least one copy each of the CFHR1 and CFHR3 genes, and both CFHR1 and CFHR3 were homozygously deleted in U251/CP2 cells. The U251/CP2 cells gained two to three copies of C8orf70 and IL-7 genes. IL-7 mRNA expression was studied in 12 glioma cell lines, and expression was positively correlated with the IC50 of cisplatin. Furthermore, IL-7 mRNA expression was also positively correlated with the IC50 of cisplatin in 91 clinical glioma specimens. Additionally, treatment with recombinant human IL-7 (rhIL-7) enhanced cisplatin resistance and increased the relative growth rate of the glioma cells. Moreover, the apoptosis induced by cisplatin could be inhibited by IL-7. In conclusion, our results suggest that IL-7 may play an important role in cisplatin resistance in glioma

Evolutionary Computation, Optimization and Learning Algorithms for Data Science

A large number of engineering, science and computational problems have yet to be solved in a computationally efficient way. One of the emerging challenges is how evolving technologies grow towards autonomy and intelligent decision making. This leads to collection of large amounts of data from various sensing and measurement technologies, e.g., cameras, smart phones, health sensors, smart electricity meters, and environment sensors. Hence, it is imperative to develop efficient algorithms for generation, analysis, classification, and illustration of data. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. We focus on data science as a crucial area, specifically focusing on a curse of dimensionality (CoD) which is due to the large amount of generated/sensed/collected data. This motivates researchers to think about optimization and to apply nature-inspired algorithms, such as evolutionary algorithms (EAs) to solve optimization problems. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. Researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms

Finite element analysis and calculation method of residual flexural capacity of post-fire RC beams

Fire tests and subsequent bending tests of fourreinforced concrete (RC) beamswere performed. Based on these tests, the post-fire performance of RCbeams was further studied using finite element simulation through reasonable selection of suitable thermal and thermodynamic parameters of steel and concrete materials. A thermodynamic model of RC beams with three sides under fire was built using finite element analysis(FEA)software ABAQUS. The FEA model was validated with the results of fire tests. Different factors were taken into account for further parametric studies in fire using the proposed FE model.The results show that the main factors affecting the fire resistance of the beamsare the thickness of the concretecover, reinforcement ratio of longitudinal steel,the fire exposure timeandthe fire exposure sides. Based on the strength reduction formula at high temperature of steel and concrete, animproved section method was proposed to develop a calculation formula to calculate the flexural capacity of RC beams after fire. The theoretical calculation method proposed in this paper shows good agreement with FEA results, which can be used to calculate the flexuralcapacity of RC beams after fire

Effect of B7.1 Costimulation on T-Cell Based Immunity against TAP-Negative Cancer Can Be Facilitated by TAP1 Expression

Tumors deficient in expression of the transporter associated with antigen processing (TAP) usually fail to induce T-cell-mediated immunity and are resistant to T-cell lysis. However, we have found that introduction of the B7.1 gene into TAP-negative (TAP−) or TAP1-transfected (TAP1+) murine lung carcinoma CMT.64 cells can augment the capacity of the cells to induce a protective immune response against wild-type tumor cells. Differences in the strength of the protective immune responses were observed between TAP− and TAP1+ B7.1 expressing CMT.64 cells depending on the doses of γ-irradiated cell immunization. While mice immunized with either high or low dose of B7.1-expressing TAP1+ cells rejected TAP− tumors, only high dose immunization with B7.1-expressing TAP− cells resulted in tumor rejection. The induced protective immunity was T-cell dependent as demonstrated by dramatically reduced antitumor immunity in mice depleted of CD8 or CD4 cells. Augmentation of T-cell mediated immune response against TAP− tumor cells was also observed in a virally infected tumor cell system. When mice were immunized with a high dose of γ-irradiated CMT.64 cells infected with vaccinia viruses carrying B7.1 and/or TAP1 genes, we found that the cells co-expressing B7.1 and TAP1, but not those expressing B7.1 alone, induced protective immunity against CMT.64 cells. In addition, inoculation with live tumor cells transfected with several different gene(s) revealed that only B7.1- and TAP1-coexpressing tumor cells significantly decreased tumorigenicity. These results indicate that B7.1-provoked antitumor immunity against TAP− cancer is facilitated by TAP1-expression, and thus both genes should be considered for cancer therapy in the future

Development of novel molecularly imprinted solid-phase microextraction fibers and their application for the determination of antibiotic drugs in biological samples by SPME-LC/MSn

Novel molecularly imprinted polymer (MIP)-coated fibers for solid-phase microextraction (SPME) fibers were prepared by using linezolid as the template molecule. The characteristics and application of these fibers were investigated. The polypyrrole, polythiophene, and poly(3-methylthiophene) coatings were prepared in the electrochemical polymerization way. The molecularly imprinted SPME coatings display a high selectivity toward linezolid. Molecularly imprinted coatings showed a stable and reproducible response without any influence of interferents commonly existing in biological samples. High-performance liquid chromatography with spectroscopic UV and mass spectrometry (MS) detectors were used for the determination of selected antibiotic drugs (linezolid, daptomycin, amoxicillin). The isolation and preconcentration of selected antibiotic drugs from new types of biological samples (acellular and protein-free simulated body fluid) and human plasma samples were performed. The SPME MIP-coated fibers are suitable for the selective extraction of antibiotic drugs in biological samples

Phylogeny of Echinoderm Hemoglobins

Recent genomic information has revealed that neuroglobin and cytoglobin are the two principal lineages of vertebrate hemoglobins, with the latter encompassing the familiar myoglobin and α-globin/β-globin tetramer hemoglobin, and several minor groups. In contrast, very little is known about hemoglobins in echinoderms, a phylum of exclusively marine organisms closely related to vertebrates, beyond the presence of coelomic hemoglobins in sea cucumbers and brittle stars. We identified about 50 hemoglobins in sea urchin, starfish and sea cucumber genomes and transcriptomes, and used Bayesian inference to carry out a molecular phylogenetic analysis of their relationship to vertebrate sequences, specifically, to assess the hypothesis that the neuroglobin and cytoglobin lineages are also present in echinoderms.The genome of the sea urchin Strongylocentrotus purpuratus encodes several hemoglobins, including a unique chimeric 14-domain globin, 2 androglobin isoforms and a unique single androglobin domain protein. Other strongylocentrotid genomes appear to have similar repertoires of globin genes. We carried out molecular phylogenetic analyses of 52 hemoglobins identified in sea urchin, brittle star and sea cucumber genomes and transcriptomes, using different multiple sequence alignment methods coupled with Bayesian and maximum likelihood approaches. The results demonstrate that there are two major globin lineages in echinoderms, which are related to the vertebrate neuroglobin and cytoglobin lineages. Furthermore, the brittle star and sea cucumber coelomic hemoglobins appear to have evolved independently from the cytoglobin lineage, similar to the evolution of erythroid oxygen binding globins in cyclostomes and vertebrates.The presence of echinoderm globins related to the vertebrate neuroglobin and cytoglobin lineages suggests that the split between neuroglobins and cytoglobins occurred in the deuterostome ancestor shared by echinoderms and vertebrates