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Abstract: C-di-GMP [Bis-(3′-5′)-cyclic-dimeric-guanosine monophosphate], a second messenger is involved in intracellular communication in the bacterial species. As a result several multi-cellular behaviors in both Gram-positive and Gram-negative bacteria are directly linked to the intracellular level of c-di-GMP. The cellular concentration of c-di-GMP is maintained by two opposing activities, diguanylate cyclase (DGC) and phosphodiesterase (PDE-A). In Mycobacterium smegmatis, a single bifunctional protein MSDGC-1 is responsible for the cellular concentration of c-di-GMP. A better understanding of the regulation of c-di-GMP at the genetic level is necessary to control the function of above two activities. In this work, we have characterized the promoter element present in msdgc-1 along with the +1 transcription start site and identified the sigma factors that regulate the transcription of msdgc-1. Interestingly, msdgc-1 utilizes SigA during the initial phase of growth, whereas near the stationary phase SigB containing RNA polymerase takes over the expression of msdgc-1. We report here that the promoter activity of msdgc-1 increases during starvation or depletion of carbon source like glucose or glycerol. When msdgc-1 is deleted, the numbers of viable cells are ~10 times higher in the stationary phase in comparison to that of the wild type. We propose here that msdgc-1 is involved in the regulation of cell population density. [Copyright &y& Elsevier]

• A setup containing a smartphone and a dark chamber was used for predicting soil texture. • Soil images were acquired using the setup and analyzed via computer vision, RF, and CNN models. • Both RF and CNN showed high prediction accuracy for clay and sand, with moderate prediction accuracy for silt. • Image-extracted color features showed the maximum influence on the RF model performance. • An Android app using the calibrated CNN model was able to predicted soil textural values with satisfactory accuracy. The rapid and non-invasive prediction of soil sand, silt, and clay is becoming increasingly attractive given the laborious nature of traditional soil textural analysis. This study proposed a novel and cheap setup comprising a smartphone, a custom-made dark chamber, and a smartphone application for predicting soil texture of the dried, ground, and sieved samples. The image acquisition system was used to capture triplicate images from 90 mineral soil samples, representing a wide textural variability from sand to clay. Local features, color features, and texture features were extracted from the cropped images and subsequently used in different combinations to predict laboratory-measured clay, silt, and sand via random forest (RF) and convolutional neural network (CNN) algorithms. Results indicated high prediction accuracy for clay (R2 = 0.97–0.98) and sand (R2 = 0.96–0.98) and moderate prediction accuracy for silt (R2 = 0.62–0.75) using both algorithms. Color features outperformed all other image-extracted features and showed the maximum influence on RF model performance. The better performance of the color features can be attributed to the color features of mineral matter and soil organic matter (SOM). An Android-based smartphone application based on the calibrated CNN model was able to predict and return soil textural values. These results exhibited the potential of the proposed system as a proximal sensor for rapid, cost-effective, and eco-friendly soil textural analysis using computer-vision and deep learning. More research is warranted to augment the setup design, develop a standalone mobile application, and measure the impacts of soil moisture and high SOM on the model prediction performance to extend the approach for on-site prediction of soil texture. [ABSTRACT FROM AUTHOR]