Variations in the 6.2 μ\mum emission profile in starburst-dominated galaxies: a signature of polycyclic aromatic nitrogen heterocycles (PANHs)?

Analyses of the polycyclic aromatic hydrocarbon (PAH) feature profiles, especially the 6.2 $\mu$m feature, could indicate the presence of nitrogen incorporated in their aromatic rings. In this work, 155 predominantly starburst-dominated galaxies (including HII regions and Seyferts, for example), extracted from the Spitzer/IRS ATLAS project (Hern\'an-Caballero & Hatziminaoglou 2011), have their 6.2 $\mu$m profiles fitted allowing their separation into the Peeters' A, B and C classes (Peeters et al. 2002). 67% of these galaxies were classified as class A, 31% were as class B and 2% as class C. Currently class A sources, corresponding to a central wavelength near 6.22 $\mu$m, seem only to be explained by polycyclic aromatic nitrogen heterocycles (PANH, Hudgins et al. 2005), whereas class B may represent a mix between PAHs and PANHs emissions or different PANH structures or ionization states. Therefore, these spectra suggest a significant presence of PANHs in the interstellar medium (ISM) of these galaxies that could be related to their starburst-dominated emission. These results also suggest that PANHs constitute another reservoir of nitrogen in the Universe, in addition to the nitrogen in the gas phase and ices of the ISM

Comment on "Ab initio calculations of the lattice parameter and elastic stiffness coefficients of bcc Fe with solutes" Comp. Mat. Sci. v.126 pp.503-513 (2017)

In a recent paper, the authors propose to separately calculate the volumetric and chemical contributions to the elastic stiffness coefficients of systems containing solutes, as it is "computationally more efficient". We show that this is not the case and further that their methodology and hence their results are incorrect. There is no short cut for performing the desired calculations, if done rigorously, as we show in our 2012 work

EXA-2017-1S-FUNDAMENTOS DE PROGRAMACIÓN-13-Mejora.pdf

Distribution of promoter powers with strong consensus. (PDF 157 kb

Additional file 5: of Therapy-induced stress response is associated with downregulation of pre-mRNA splicing in cancer cells

Results of the enrichment analysis of genes that were differentially expressed in response to different stress factors. (XLSX 171 kb

Additional file 3: of Therapy-induced stress response is associated with downregulation of pre-mRNA splicing in cancer cells

(A) Description of all alternative splicing events in cancer cell lines after therapy. (B) Identification of stop codons in transcripts with retained introns, which were detected in at least half of cancer cell lines before and after chemotherapy. (C) Description of all alternative splicing events in PDX tumors after different types of therapy. (D) Identification of stop codons in transcripts with retained introns, which were detected in PDX tumors before and after chemotherapy. (E) Description of alternative splicing events in spliceosomal genes in PDX tumors after different types of therapy. (F) Description of insertions which were detected in 7 cell lines (A375, A549, H3122, N87, PC9, RT112, H358) used in our analysis of alternative splicing changes. (XLSX 716 kb

Additional file 4: of Therapy-induced stress response is associated with downregulation of pre-mRNA splicing in cancer cells

Figure S1. PCA clustering of splicing inclusion level differences between treated and untreated PDX tumors. Figure S2: Graph representing the common transcription factors GFI1B (A) and TARDBP (B) that may induce concerted changes in the expression of pairs of splicing- and mitotic-related genes after a course of chemotherapy. Solid black lines connect a pair of co-expressed genes and red lines connect transcription factors with their target genes. Figure S3: Western blotting analysis of U87MG cells and their concentrated secretomes before and after treatment with 30 μM Cisplatin (CP). Figure S4: Pladienolide B increases the sensitivity of cancer cells to Cisplatin. (A) Viability assay of U87MG, Hela and MCF-7 cells that were pretreated with 2 nM Pladienolide B (2 days) following treatment with different concentrations of Cisplatin (4 days). (B) FACS analysis of caspase 3/7 and SYTOX staining of SKOV3 cells treated with 0.5 nM Pladienolide B, 10 μM Cisplatin or both drugs together. (C) Cell cycle analysis of SKOV3 and HT29 cells treated for 3 days with 0.5 nM and 1 nM Pladienolide B, respectively. (D) FACS analysis of phospho ATM staining in Hela, A549 and HT29 cells that were cultivated with 1 nM Pladienolide B (2 days) and subsequently treated with the indicated concentrations of Cisplatin (1 day). (PDF 855 kb

Additional file 6: of Therapy-induced stress response is associated with downregulation of pre-mRNA splicing in cancer cells

Description of gene clusters identified by the time clusterization analysis. (XLSX 415 kb

Additional file 2: of Therapy-induced stress response is associated with downregulation of pre-mRNA splicing in cancer cells

Supplementary materials and methods. (PDF 151 kb

Additional file 1: of Therapy-induced stress response is associated with downregulation of pre-mRNA splicing in cancer cells

Full information regarding the mRNA microarray gene expression datasets used in this study. The dataset title is used in the text as a dataset identifier. (PDF 171 kb

Agent Based Modeling of Human Gut Microbiome Interactions and Perturbations

<div><p>Background</p><p>Intestinal microbiota plays an important role in the human health. It is involved in the digestion and protects the host against external pathogens. Examination of the intestinal microbiome interactions is required for understanding of the community influence on host health. Studies of the microbiome can provide insight on methods of improving health, including specific clinical procedures for individual microbial community composition modification and microbiota correction by colonizing with new bacterial species or dietary changes.</p><p>Methodology/Principal Findings</p><p>In this work we report an agent-based model of interactions between two bacterial species and between species and the gut. The model is based on reactions describing bacterial fermentation of polysaccharides to acetate and propionate and fermentation of acetate to butyrate. Antibiotic treatment was chosen as disturbance factor and used to investigate stability of the system. System recovery after antibiotic treatment was analyzed as dependence on quantity of feedback interactions inside the community, therapy duration and amount of antibiotics. Bacterial species are known to mutate and acquire resistance to the antibiotics. The ability to mutate was considered to be a stochastic process, under this suggestion ratio of sensitive to resistant bacteria was calculated during antibiotic therapy and recovery.</p><p>Conclusion/Significance</p><p>The model confirms a hypothesis of feedbacks mechanisms necessity for providing functionality and stability of the system after disturbance. High fraction of bacterial community was shown to mutate during antibiotic treatment, though sensitive strains could become dominating after recovery. The recovery of sensitive strains is explained by fitness cost of the resistance. The model demonstrates not only quantitative dynamics of bacterial species, but also gives an ability to observe the emergent spatial structure and its alteration, depending on various feedback mechanisms. Visual version of the model shows that spatial structure is a key factor, which helps bacteria to survive and to adapt to changed environmental conditions.</p></div