Relations of stellar mass between electron temperature-based metallicity of star-forming galaxies in a wide mass range

We select 947 star-forming galaxies from SDSS-DR7 with [O~{\sc iii}]$\lambda$4363 emission lines detected at a signal-to-noise {ratio }larger than 5$\sigma$. Their electron temperatures and direct oxygen abundances are {then }determined. {W}e compare the results from different methods. $t_2${, the} electron temperature in {the }low ionization region{,} estimated from $t_3${, that} in {the }high ionization region{,} {is} compared {using} three analysis relations between $t_2-t_3${. These} show obvious differences, which result in some different ionic oxygen abundances. The results of $t_3$, $t_2$, {$\rm O^{++}$/$\rm H^+$} and {$\rm O^{+}$/$\rm H^+$} derived by using methods from IRAF and literature are also compared. The ionic abundances $\rm O^{++}$/$\rm H^+$ {are} higher than $\rm O^{+}$/$\rm H^+$ for most cases. The{ different} oxygen abundances derived from $T_{\rm e}$ and the strong-line ratios show {a }clear discrepancy, which is more obvious following increasing stellar mass and strong-line ratio $R_{23}$. The sample{ of} galaxies from SDSS {with} detected [O~{\sc iii}]$\lambda$4363 have lower metallicites and higher {star formation rates}, {so} they may not be typical representatives of the whole{ population of} galaxies. Adopting data objects from {Andrews \& Martini}, {Liang et al.} and {Lee et al.} data, we derive new relations of stellar mass and metallicity for star-forming galaxies in a much wider stellar mass range: from $10^6\,M_\odot$ to $10^{11}\,M_\odot$.Comment: 16 pages, 11 figures, Accepted by Research in Astronomy and Astrophysic

Characteristic Analysis of Salmonella Phage Pu29 and Its Application in Magnetic Separation and Enrichment of Salmonella

Salmonella pullorum phage Pu29 was comprehensively analyzed for its biological and genomic characteristics. A magnetic separation and enrichment technique for Salmonella was established using the phage Pu29 as a recognition element. The phage belonged to the genus Roufvirus and had an icosahedron head and an irreducible long tail. Pu29 had a wide host spectrum with an adsorption rate of 88.67% on host cells in 15 min, a latent period of 30 min, a rise period of 180 min, and a burst size of 115.74 PFU/cell. Meanwhile, Pu29 had good heat resistance (30–60 ℃) and pH tolerance (pH 4–11). Its genome was composed of 45 715 bp (GC content 46.08%) and 81 open reading frames (ORFs), including 18 ORFs with known functions that did not carry genes encoding toxicity or resistance factors. PhagePu29-MBs were prepared as a probe by coupling the phage with carboxylated nano-magnetic beads (MBs) through amide reaction. When 25 μg of the probe was incubated with Salmonella at 37 ℃ for 20 min, the highest capture rate of Salmonella of 83.93% and the lowest captured bacterial concentration of 45 CFU/mL were obtained. Transmission electron microscopy (TEM) was used to observe that PhagePu29-MBs could specifically capture Salmonella. In spiked samples, the highest capture rate of Salmonella separated and enriched by the probe reached 92.92%. The separation and enrichment process took approximately 30 min. Therefore, this study established a fast and highly specific magnetic separation method for Salmonella based on phage Pu29, which may lay the foundation for the development of a phage-based method for the rapid separation and enrichment of foodborne pathogens in food samples

Comparison of the RF-CL and CACS-CL models to estimate the pretest probability of obstructive coronary artery disease and predict prognosis in patients with stable chest pain and diabetes mellitus

BackgroundThe most appropriate tool for estimating the pretest probability (PTP) of obstructive coronary artery disease (CAD) in patients with diabetes mellitus (DM) and stable chest pain (SCP) remains unknown. Therefore, we aimed to validate and compare two recent models, namely, the risk factor-weighted clinical likelihood (RF-CL) model and coronary artery calcium score (CACS)-weighted clinical likelihood (CACS-CL) model, in these patient populations.MethodsA total of 1,245 symptomatic patients with DM, who underwent CACS and coronary computed tomographic angiography (CCTA) scan, were identified and followed up. PTP of obstructive CAD for each patient was estimated using the RF-CL model and CACS-CL model, respectively. Area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to assess the performance of models. The associations of major adverse cardiovascular events (MACE) with risk groups were evaluated using Cox proportional hazards regression.ResultsCompared with the RF-CL model, the CACS-CL model revealed a larger AUC (0.856 vs. 0.782, p = 0.0016), positive IDI (12%, p < 0.0001) and NRI (34%, p < 0.0001), stronger association to MACE (hazard ratio: 0.26 vs. 0.38) and less discrepancy between observed and predicted probabilities, resulting in a more effective risk assessment to optimize downstream clinical management.ConclusionAmong patients with DM and SCP, the incorporation of CACS into the CACS-CL model resulted in a more accurate estimation for PTP and prediction of MACE. Utilizing the CACS-CL model, instead of the RF-CL model, might have greater potential to avoid unnecessary and omissive cardiovascular imaging testing with minimal cost

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Phylogenomic relationships between amylolytic enzymes from 85 strains of fungi.

Fungal amylolytic enzymes, including α-amylase, gluocoamylase and α-glucosidase, have been extensively exploited in diverse industrial applications such as high fructose syrup production, paper making, food processing and ethanol production. In this paper, amylolytic genes of 85 strains of fungi from the phyla Ascomycota, Basidiomycota, Chytridiomycota and Zygomycota were annotated on the genomic scale according to the classification of glycoside hydrolase (GH) from the Carbohydrate-Active enZymes (CAZy) Database. Comparisons of gene abundance in the fungi suggested that the repertoire of amylolytic genes adapted to their respective lifestyles. Amylolytic enzymes in family GH13 were divided into four distinct clades identified as heterologous α-amylases, eukaryotic α-amylases, bacterial and fungal α-amylases and GH13 α-glucosidases. Family GH15 had two branches, one for gluocoamylases, and the other with currently unknown function. GH31 α-glucosidases showed diverse branches consisting of neutral α-glucosidases, lysosomal acid α-glucosidases and a new clade phylogenetically related to the bacterial counterparts. Distribution of starch-binding domains in above fungal amylolytic enzymes was related to the enzyme source and phylogeny. Finally, likely scenarios for the evolution of amylolytic enzymes in fungi based on phylogenetic analyses were proposed. Our results provide new insights into evolutionary relationships among subgroups of fungal amylolytic enzymes and fungal evolutionary adaptation to ecological conditions

Exploring the Subcellular Localization of <i>Monascus</i> Pigments Biosynthases: Preliminary Unraveling of the Compartmentalization Mechanism

Monascus pigments (MPs), a class of secondary metabolites produced by Monascus spp., can be classified into yellow, orange, and red MPs according to their differences in the wavelength of the maximum absorption. However, the biosynthetic sequence and cellular biosynthesis mechanism of different MPs components are still not yet completely clear in Monascus spp. In this study, the subcellular localization of five MPs synthases was investigated using fluorescent protein fusion expression. The results revealed that the proteins encoded by the MPs biosynthetic gene cluster were compartmentalized in various subcellular locations, including the mitochondrial polyketide synthase MrPigA, cytosolic enzymes consisting of the ketoreductase MrPigC, the oxidoreductase MrPigE, and the monooxygenase MrPigN, and the cell-wall-bound oxidoreductase MrPigF. Moreover, the correct localization of MrPigF to the cell wall was crucial for the synthesis of orange MPs. Lastly, we discussed the compartmentalized biosynthetic pathway of MPs. This study will not only be helpful in clarifying the biosynthetic sequence and biosynthesis mechanism of different MPs but also provides new insights into the cellular biosynthesis of secondary metabolites in filamentous fungi

Genome Mining and Analysis of PKS Genes in <i>Eurotium cristatum</i> E1 Isolated from Fuzhuan Brick Tea

Eurotium cristatum as the dominant fungi species of Fuzhuan brick tea in China, can produce multitudinous secondary metabolites (SMs) with various bioactivities. Polyketides are a very important class of SMs found in E. cristatum and have gained extensive attention in recent years due to their remarkable diversity of structures and multiple functions. Therefore, it is necessary to explore the polyketides produced by E. cristatum at the genomic level to enhance its application value. In this paper, 12 polyketide synthase (PKS) genes were found in the whole genome of E. cristatum E1 isolated from Fuzhuan brick tea. In addition, the qRT-PCR results further demonstrated that these genes were expressed. Moreover, metabolic analysis demonstrated E. cristatum E1 can produce a variety of polyketides, including citreorosein, emodin, physcion, isoaspergin, dihydroauroglaucin, iso-dihydroauroglaucin, aspergin, flavoglaucin and auroglaucin. Furthermore, based on genomic analysis, the putative secondary metabolites clusters for emodin and flavoglaucin were proposed. The results reported here will lay a good basis for systematically mining SMs resources of E. cristatum and broadening its application fields