A Semiparametric Estimation for Regression Functions in the Partially Linear Autoregressive Time Series Model

In this paper, a semiparametric method is proposed for estimating regression function in the partially linear autoregressive time series model . Here, we consider a combination of parametric forms and nonlinear functions, in which the errors are independent. Semiparametric and nonparametric curve estimation provides a useful tool for exploring and understanding the structure of a nonlinear time series data set to make for a more efficient study in the partially linear autoregressive model. The unknown parameters are estimated using the conditional nonlinear least squares method, and the nonparametric adjustment is also estimated by defining and minimizing the local L2 -fitting criterion with respect to the nonparametric adjustment and, with smooth-kernel method , these estimates are corrected. Then, the autoregression function estimators, which can be calculated with the sample and simulation data , are obtained. In this case , some strong and weak consistency and simulated results for the semiparametric estimation in this model are presented . The root mean square error and the average square error criterions are also applied to verify the efficiency of the suggested model

Quality of life, Work ability and other important indicators of women's occupational health

Objectives: Work ability may be considered as an important aspect of well-being and health status. One of the most important factors in association with work ability is health-related quality of life (HRQoL). The aim of this study has been to determine the association between work ability, individual characteristics and HRQoL of female workers. Material and Methods: The design of this study has been cross-sectional. The work ability index (WAI) and Short-Form General Health Survey (SF-12) questionnaires were used to collect data. Three hundred and twenty female workers were selected from food supplier factories in Karaj. One-way analysis of variance, Pearson's correlation analysis, independent sample t-test and multiple linear regression methods were used to analyze data. Results: Mean (M) and standard deviation (SD) of the WAI stood at 35.02 and 5.57, respectively. The categories of the WAI for women being as follows: 8.8 poor, 62 moderate, 25.4 good and 3.7 excellent. Mean±SD for the physical component summary (PCS) and mental component summary (MCS) of quality of life was 58.84±11.12 and 57.45±9.94, respectively. There was a positive significant association between the PCS and MCS with the WAI (p = 0.0001). Workers with higher education had a better work ability (p = 0.002) and shift-work workers had a worse work ability (p = 0.03). Conclusions: Work ability of majority of women was moderate. Considering mean age of studied women (27.6 years old), this work ability is not satisfactory. Physical and mental components of the HRQoL were the important factors associated with work ability

Structure of soot-containing laminar jet diffusion flames

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76650/1/AIAA-1993-708-697.pd

Nutrient and herbivore alterations cause uncoupled changes in producer diversity, biomass and ecosystem function, but not in overall multifunctionality

Altered nutrient cycles and consumer populations are among the top anthropogenic influences on ecosystems. However, studies on the simultaneous impacts of human-driven environmental alterations on ecosystem functions, and the overall change in system multifunctionality are scarce. We used estuarine tidal flats to study the effects of changes in herbivore density and nutrient availability on benthic microalgae (diversity, abundance and biomass) and ecosystem functions (N2-fixation, denitrification, extracellular polymeric substances -EPS- as a proxy for sediment cohesiveness, sediment water content as a proxy of water retention capacity and sediment organic matter). We found consistent strong impacts of modified herbivory and weak effects of increased nutrient availability on the abundance, biomass and diversity of benthic microalgae. However, the effects on specific ecosystem functions were disparate. Some functions were independently affected by nutrient addition (N2-fixation), modified herbivory (sediment organic matter and water content), or their interaction (denitrification), while others were not affected (EPS). Overall system multifunction remained invariant despite changes in specific functions. This study reveals that anthropogenic pressures can induce decoupled effects between community structure and specific ecosystem functions. Our results highlight the need to address several ecosystem functions simultaneously for better ecosystem characterization and management.Instituto de Limnología "Dr. Raul A. Ringuelet

Plasmon resonances of highly doped two-dimensional MoS2

The exhibition of plasmon resonances in two-dimensional (2D) semiconductor compounds is desirable for many applications. Here, by electrochemically intercalating lithium into 2D molybdenum disulfide (MoS2) nanoflakes, plasmon resonances in the visible and near UV wavelength ranges are achieved. These plasmon resonances are controlled by the high doping level of the nanoflakes after the intercalation, producing two distinct resonance peak areas based on the crystal arrangements. The system is also benchmarked for biosensing using bovine serum albumin. This work provides a foundation for developing future 2D MoS2 based biological and optical units

Next maSigPro: updating maSigPro Bioconductor package for RNA-seq time series

[EN] Motivation: The widespread adoption of RNA-seq to quantitatively measure gene expression has increased the scope of sequencing experimental designs to include time-course experiments. maSigPro is an R package specifically suited for the analysis of time-course gene expression data, which was developed originally for microarrays and hence was limited in its application to count data. Results: We have updated maSigPro to support RNA-seq time series analysis by introducing generalized linear models in the algorithm to support the modeling of count data while maintaining the traditional functionalities of the package. We show a good performance of the maSigPro-GLM method in several simulated time-course scenarios and in a real experimental dataset. Availability and implementation: The package is freely available under the LGPL license from the Bioconductor Web site (http:// bioconductor.org)This work has been funded by the FP7 STATegra [GA-30600] project, EU FP7 [30600] and the Spanish MINECO [BIO2012-40244].Nueda, MJ.; Tarazona Campos, S.; Conesa, A. (2014). Next maSigPro: updating maSigPro Bioconductor package for RNA-seq time series. Bioinformatics. 30(18):2598-2602. https://doi.org/10.1093/bioinformatics/btu333S259826023018Anders, S., & Huber, W. (2010). Differential expression analysis for sequence count data. Genome Biology, 11(10). doi:10.1186/gb-2010-11-10-r106Bullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010). Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics, 11(1). doi:10.1186/1471-2105-11-94Conesa, A., Nueda, M. J., Ferrer, A., & Talon, M. 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Eukaryotic Cell, 6(12), 2311-2322. doi:10.1128/ec.00244-07Liu, Y., Zhou, J., & White, K. P. (2013). RNA-seq differential expression studies: more sequence or more replication? Bioinformatics, 30(3), 301-304. doi:10.1093/bioinformatics/btt688Maekawa, T., Kracher, B., Vernaldi, S., Ver Loren van Themaat, E., & Schulze-Lefert, P. (2012). Conservation of NLR-triggered immunity across plant lineages. Proceedings of the National Academy of Sciences, 109(49), 20119-20123. doi:10.1073/pnas.1218059109Medina, I., Carbonell, J., Pulido, L., Madeira, S. C., Goetz, S., Conesa, A., … Dopazo, J. (2010). Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling. Nucleic Acids Research, 38(suppl_2), W210-W213. doi:10.1093/nar/gkq388Mortazavi, A., Williams, B. A., McCue, K., Schaeffer, L., & Wold, B. (2008). Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nature Methods, 5(7), 621-628. doi:10.1038/nmeth.1226Nueda, M. j., Ferrer, A., & Conesa, A. (2011). ARSyN: a method for the identification and removal of systematic noise in multifactorial time course microarray experiments. Biostatistics, 13(3), 553-566. doi:10.1093/biostatistics/kxr042Risso, D., Schwartz, K., Sherlock, G., & Dudoit, S. (2011). GC-Content Normalization for RNA-Seq Data. BMC Bioinformatics, 12(1), 480. doi:10.1186/1471-2105-12-480Roberts, A., & Pachter, L. (2012). Streaming fragment assignment for real-time analysis of sequencing experiments. Nature Methods, 10(1), 71-73. doi:10.1038/nmeth.2251Robinson, M. D., & Oshlack, A. (2010). A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biology, 11(3), R25. doi:10.1186/gb-2010-11-3-r25Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2009). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139-140. doi:10.1093/bioinformatics/btp616Sims, D., Sudbery, I., Ilott, N. E., Heger, A., & Ponting, C. P. (2014). Sequencing depth and coverage: key considerations in genomic analyses. Nature Reviews Genetics, 15(2), 121-132. doi:10.1038/nrg3642Tarazona, S., Garcia-Alcalde, F., Dopazo, J., Ferrer, A., & Conesa, A. (2011). Differential expression in RNA-seq: A matter of depth. Genome Research, 21(12), 2213-2223. doi:10.1101/gr.124321.111Trapnell, C., Roberts, A., Goff, L., Pertea, G., Kim, D., Kelley, D. R., … Pachter, L. (2012). Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nature Protocols, 7(3), 562-578. doi:10.1038/nprot.2012.016Terol, J., Conesa, A., Colmenero, J. M., Cercos, M., Tadeo, F., Agustí, J., … Talon, M. (2007). BMC Genomics, 8(1), 31. doi:10.1186/1471-2164-8-3