scholarly journals Mild Cystic Fibrosis Lung Disease Is Associated with Bacterial Community Stability

2021 ◽  
Author(s):  
Thomas H. Hampton ◽  
Devin Thomas ◽  
Christopher van der Gast ◽  
George A. O’Toole ◽  
Bruce A. Stanton
Keyword(s):  
Cystic Fibrosis ◽  
Bacterial Community ◽  
Bacterial Communities ◽  
Large Data ◽  
Model Systems ◽  
Community Stability ◽  
Controlled Experiments ◽  
Data Set ◽  
Cystic Fibrosis Lung Disease ◽  
Starting Point

While much research supports a polymicrobial view of the CF airway, one in which the community is seen as the pathogenic unit, only controlled experiments using model bacterial communities can unravel the mechanistic role played by different communities. This report uses a large data set to identify a small number of communities as a starting point in the development of tractable model systems.

Gender Differences in the Provision of Job-Search Help

2019 ◽  
Vol 33 (5) ◽  
pp. 746-771
Author(s):  
Min Zhou
Keyword(s):  
Gender Differences ◽  
Gender Inequality ◽  
Job Search ◽  
Large Data ◽  
Direct Influence ◽  
Data Set ◽  
Social Contacts ◽  
Force Dynamics ◽  
Hiring Process ◽  
Starting Point

The existing literature has well studied the use of social contacts in job search, including gender inequality, in using social contacts. What is missing is the perspective of social contacts who help others find jobs. Using a large data set from the 2012 China Labor-Force Dynamics Survey, this study reveals significant gender differences in the provision of job-search help. Compared with women, men are more likely to provide job-search help and especially show a greater likelihood of exerting direct influence on the hiring process. While women are gender neutral in their choice of help recipients, men display a selective preference for helping other men. This men’s advantage of providing job-search help, especially influence-based help, and men’s selective preference for helping other men, imply another prominent gender inequality in informal hiring in the labor market. This study suggests several theoretical propositions to explain the revealed gender differences in both “whether to help” and “whom to help,” providing a starting point for further research.

Role of airway surface liquid and submucosal glands in cystic fibrosis lung disease

2003 ◽  
Vol 284 (1) ◽  
pp. C2-C15 ◽  
Author(s):  
A. S. Verkman ◽  
Yuanlin Song ◽  
Jay R. Thiagarajah
Keyword(s):  
Cystic Fibrosis ◽  
Lung Disease ◽  
Model Systems ◽  
Airway Surface Liquid ◽  
Cystic Fibrosis Lung Disease ◽  
Submucosal Glands ◽  
Cftr Protein ◽  
Surface Liquid ◽  
Gland Function ◽  
Cf Transmembrane Conductance Regulator

Cystic fibrosis (CF) is caused by mutations in the CF transmembrane conductance regulator (CFTR) protein, an epithelial chloride channel expressed in the airways, pancreas, testis, and other tissues. A central question is how defective CFTR function in CF leads to chronic lung infection and deterioration of lung function. Several mechanisms have been proposed to explain lung disease in CF, including abnormal airway surface liquid (ASL) properties, defective airway submucosal gland function, altered inflammatory response, defective organellar acidification, loss of CFTR regulation of plasma membrane ion transporters, and others. This review focuses on the physiology of the ASL and submucosal glands with regard to their proposed role in CF lung disease. Experimental evidence for defective ASL properties and gland function in CF is reviewed, and deficiencies in understanding ASL/gland physiology are identified as areas for further investigation. New model systems and measurement technologies are being developed to make progress in establishing lung disease mechanisms in CF, which should facilitate mechanism-based design of therapies for CF.

scholarly journals Geographical Distribution and Diversity of Bacteria Associated with Natural Populations of Drosophila melanogaster

2007 ◽  
Vol 73 (11) ◽  
pp. 3470-3479 ◽  
Author(s):  
Vanessa Corby-Harris ◽  
Ana Clara Pontaroli ◽  
Lawrence J. Shimkets ◽  
Jeffrey L. Bennetzen ◽  
Kristin E. Habel ◽  
...  
Keyword(s):  
Drosophila Melanogaster ◽  
Bacterial Community ◽  
Bacterial Communities ◽  
Gene Sequence ◽  
Abiotic Factors ◽  
Natural Populations ◽  
Host Population ◽  
Model Systems ◽  
Rrna Gene ◽  
Significant Spatial Variation

ABSTRACT Drosophila melanogaster is one of the most widely used model systems in biology. However, little is known about its associated bacterial community. As a first step towards understanding these communities, we compared bacterial 16S rRNA gene sequence libraries recovered from 11 natural populations of adult D. melanogaster. Bacteria from these sequence libraries were grouped into 74 distinct taxa, spanning the phyla Proteobacteria, Bacteroidetes, and Firmicutes, which were unevenly spread across host populations. Summed across populations, the distribution of abundance of genera was closely fit by a power law. We observed differences among host population locations both in bacterial community richness and in composition. Despite this significant spatial variation, no relationship was observed between species richness and a variety of abiotic factors, such as temperature and latitude. Overall, bacterial communities associated with adult D. melanogaster hosts are diverse and differ across host populations.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals The capacity of wastewater treatment plants drives bacterial community structure and its assembly

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Young Kyung Kim ◽  
Keunje Yoo ◽  
Min Sung Kim ◽  
Il Han ◽  
Minjoo Lee ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Community Structure ◽  
Bacterial Community ◽  
Activated Sludge ◽  
Community Assembly ◽  
Bacterial Communities ◽  
Wastewater Treatment Plants ◽  
High Capacity ◽  
Operating Conditions ◽  
Rrna Gene

Abstract Bacterial communities in wastewater treatment plants (WWTPs) affect plant functionality through their role in the removal of pollutants from wastewater. Bacterial communities vary extensively based on plant operating conditions and influent characteristics. The capacity of WWTPs can also affect the bacterial community via variations in the organic or nutrient composition of the influent. Despite the importance considering capacity, the characteristics that control bacterial community assembly are largely unknown. In this study, we discovered that bacterial communities in WWTPs in Korea and Vietnam, which differ remarkably in capacity, exhibit unique structures and interactions that are governed mainly by the capacity of WWTPs. Bacterial communities were analysed using 16S rRNA gene sequencing and exhibited clear differences between the two regions, with these differences being most pronounced in activated sludge. We found that capacity contributed the most to bacterial interactions and community structure, whereas other factors had less impact. Co-occurrence network analysis showed that microorganisms from high-capacity WWTPs are more interrelated than those from low-capacity WWTPs, which corresponds to the tighter clustering of bacterial communities in Korea. These results will contribute to the understanding of bacterial community assembly in activated sludge processing.

scholarly journals Co-Occurrence of Regulated and Emerging Mycotoxins in Corn Silage: Relationships with Fermentation Quality and Bacterial Communities

Toxins ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 232
Author(s):  
Antonio Gallo ◽  
Francesca Ghilardelli ◽  
Alberto Stanislao Atzori ◽  
Severino Zara ◽  
Barbara Novak ◽  
...  
Keyword(s):  
Bacterial Community ◽  
Bacterial Communities ◽  
Dry Matter ◽  
Kojic Acid ◽  
Corn Silage ◽  
Fungal Metabolites ◽  
Fusarium Mycotoxins ◽  
Low Concentrations ◽  
Fermentation Quality ◽  
Low Levels

Sixty-four corn silages were characterized for chemicals, bacterial community, and concentrations of several fungal metabolites. Silages were grouped in five clusters, based on detected mycotoxins, and they were characterized for being contaminated by (1) low levels of Aspergillus- and Penicillium-mycotoxins; (2) low levels of fumonisins and other Fusarium-mycotoxins; (3) high levels of Aspergillus-mycotoxins; (4) high levels of non-regulated Fusarium-mycotoxins; (5) high levels of fumonisins and their metabolites. Altersetin was detected in clusters 1, 3, and 5. Rugulusovin or brevianamide F were detected in several samples, with the highest concentration in cluster 3. Emodin was detected in more than 50.0% of samples of clusters 1, 3 and 5, respectively. Kojic acid occurred mainly in clusters 1 and 2 at very low concentrations. Regarding Fusarium mycotoxins, high occurrences were observed for FB3, FB4, FA1, whereas the average concentrations of FB6 and FA2 were lower than 12.4 µg/kg dry matter. Emerging Fusarium-produced mycotoxins, such as siccanol, moniliformin, equisetin, epiequisetin and bikaverin were detected in the majority of analyzed corn silages. Pestalotin, oxaline, phenopirrozin and questiomycin A were detected at high incidences. Concluding, this work highlighted that corn silages could be contaminated by a high number of regulated and emerging mycotoxins.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

Sign in / Sign up

Export Citation Format

Share Document