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The region responsible for replication of Vibrio cholerae chromosome II (chrII) resembles those of plasmids that have repeated initiator binding sites (iterons) and an autorepressed initiator gene. ChrII has additional features: Its iterons require full methylation for initiator (RctB) binding, which makes them inactive for a part of the cell cycle when they are hemi-methylated. RctB also binds to a second kind of site, called 39-mers, in a methylation independent manner. This binding is inhibitory to chrII replication. The site that RctB uses for autorepression has not been identified. Here we show that a 29-mer sequence, similar to the 39-mers, serves as that site, as we find that it binds RctB in vitro and suffices to repress the rctB promoter in vivo. The site is not subject to methylation and is likely to be active throughout the cell cycle. The 29-mer, like the 39-mers, could inhibit RctB-dependent mini-chrII replication in Escherichia coli, possibly by coupling with iterons via RctB bridges, as was seen in vitro. The 29-mer thus appears to play a dual role in regulating chrII replication: one independent of the cell cycle, the other dependent upon iteron methylation, hence responsive to the cell cycle.

Rationale: There is limited evidence of the effect of exposure to heat on chronic obstructive pulmonary disease (COPD) morbidity, and the interactive effect between indoor heat and air pollution has not been established. Objectives:To determine the effect of indoor and outdoor heat exposure on COPD morbidity and to determine whether air pollution concentrations modify the effect of temperature.Methods: Sixty-nine participants with COPD were enrolled in a longitudinal cohort study, and data from the 601 participant days that occurred during the warm weather season were included in the analysis. Participants completed home environmental monitoring with measurement of temperature, relative humidity, and indoor air pollutants and simultaneous daily assessment of respiratory health with questionnaires and portable spirometry.Measurements and Main Results: Participants had moderate to severe COPD and spent the majority of their time indoors. Increases in maximal indoor temperature were associated with worsening of daily Breathlessness, Cough, and Sputum Scale scores and increases in rescue inhaler use. The effect was detected on the same day and lags of 1 and 2 days. The detrimental effect of temperature on these outcomes increased with higher concentrations of indoor fine particulate matter and nitrogen dioxide (P , 0.05 for interaction terms). On days during which participants went outdoors, increases in maximal daily outdoor temperature were associated with increases in Breathlessness, Cough, and Sputum Scale scores after adjusting for outdoor pollution concentrations.Conclusions: For patients with COPD who spend the majority of their time indoors, indoor heat exposure during the warmer months represents a modifiable environmental exposure that may contribute to respiratory morbidity. In the context of climate change, adaptive strategies that include optimization of indoor environmental conditions are needed to protect this high-risk group from the adverse health effects of heat.

In geostatistics, inference on spatial covariance parameters of the Gaussian process is often critical to scientists for understanding structural dependence in data. Finite-sample inference customarily proceeds either using posterior distributions from fully a Bayesian approach or via resampling/subsampling techniques in a frequentist setting. Resampling methods, in particular, the bootstrap, have become more attractive in the modern age of big data as, unlike Bayesian models that require sequential sampling from Markov chain Monte Carlo, they naturally lend themselves to parallel computing resources. However, a spatial bootstrap involves an expensive Cholesky decomposition to decorrelate the data. In this manuscript, we develop a highly scalable parametric spatial bootstrap that uses sparse Cholesky factors for parameter estimation and decorrelation. The proposed bootstrap for rapid inference on spatial covariances (BRISC) algorithm requires linear memory and computations and is embarrassingly parallel, thereby delivering substantial scalability. Simulation studies highlight the accuracy and computational efficiency of our approach. Analysing large satellite temperature data, BRISC produces inference that closely matches that delivered from a state-of-the-art Bayesian approach, while being several times faster. The R package BRISC is now available for download from GitHub (https://github.com/ArkajyotiSaha/BRISC) and will be available on CRAN soon.where 2 controls the variance of the spatial component, denotes the decay in spatial correlation, controls the process smoothness and K denotes the Bessel function of the second kind with order . If y denotes the vector of observations for all the locations and X is the corresponding covariate matrix, then marginalizing out w, the model for the observed data is given by y N.Xˇ, C.Â/ C 2 I/.

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Landslides are known as the world’s most dangerous threat in mountainous regions and pose a critical obstacle for both economic and infrastructural progress. It is, therefore, quite relevant to discuss the pattern of spatial incidence of this phenomenon. The current research manifests a set of individual and ensemble of machine learning and probabilistic approaches like an artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LR), and their ensembles such as ANN-RF, ANN-SVM, SVM-RF, SVM-LR, LR-RF, LR-ANN, ANN-LR-RF, ANN-RF-SVM, ANN-SVM-LR, RF-SVM-LR, and ANN-RF-SVM-LR for mapping landslide susceptibility in Rudraprayag district of Garhwal Himalaya, India. A landslide inventory map along with sixteen landslide conditioning factors (LCFs) was used. Randomly partitioned sets of 70%:30% were used to ascertain the goodness of fit and predictive ability of the models. The contribution of LCFs was analyzed using the RF model. The altitude and drainage density were found to be the responsible factors in causing the landslide in the study area according to the RF model. The robustness of models was assessed through three threshold dependent measures, i.e., receiver operating characteristic (ROC), precision and accuracy, and two threshold independent measures, i.e., mean-absolute-error (MAE) and root-mean-square-error (RMSE). Finally, using the compound factor (CF) method, the models were prioritized based on the results of the validation methods to choose best model. Results show that ANN-RF-LR indicated a realistic finding, concentrating only on 17.74% of the study area as highly susceptible to landslide. The ANN-RF-LR ensemble demonstrated the highest goodness of fit and predictive capacity with respective values of 87.83% (area under the success rate curve) and 93.98% (area under prediction rate curve), and the highest robustness correspondingly. These attempts will play a significant role in ensemble modeling, in building reliable and comprehensive models. The proposed ANN-RF-LR ensemble model may be used in the other geographic areas having similar geo-environmental conditions. It may also be used in other types of geo-hazard modeling.

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