Coordination of Tissue Cell Polarity by Auxin Transport and Signaling

Plants coordinate the polarity of hundreds of cells during vein formation, but how they do so is unclear. The prevailing hypothesis proposes that GNOM, a regulator of membrane trafficking, positions PIN-FORMED auxin transporters to the correct side of the plasma membrane; the resulting cell-to-cell, polar transport of auxin would coordinate tissue cell polarity and induce vein formation. Contrary to predictions of the hypothesis, we find that vein formation occurs in the absence of PIN-FORMED or any other intercellular auxin-transporter; that the residual auxin-transport-independent vein-patterning activity relies on auxin signaling; and that a GNOM-dependent signal acts upstream of both auxin transport and signaling to coordinate tissue cell polarity and induce vein formation. Our results reveal synergism between auxin transport and signaling, and their unsuspected control by GNOM in the coordination of tissue cell polarity during vein patterning, one of the most informative expressions of tissue cell polarization in plants

Effect of calcined clay on the improvement of compaction, swell and microstructural characteristics of expansive soil

Expansive soil is problematic soil because its alternate swell shrink behaviour depends on the presence of water. Soil stabilization technique was widely adopted to alter the characteristics of the expansive soil which is suitable for construction. Among the various soil stabilization techniques, chemical stabilization was found to be more suitable method of sustainable stabilizing the soil due to its effective and timely reaction with the chemical compound. Calcined form of clay material is used as an admixture to study the effects on the improvement of soil properties. Calcined Clay (CC) is added into the virgin soil with different percentages of 2%,4%,6%,8% and 10% under varying 1,3,7,14,28 and 60 days of curing by conducting experiments such as standard proctor test, Free Swell test to analyse the compaction characteristics and swelling behaviour of the soil. In addition to that the X-Ray Diffraction (XRD) and Scanning Electron Microscope (SEM) on virgin and treated soil were studied by varying 2% incremental of CC up to 10% at 28 days of curing. From the test results it shows the variation in the compaction characteristics by rising in Maximum Dry Density (MDD) and reduction in Optimum Moisture Content (OMC) that merges at 8% as an optimum to develop the soil behaviour and from the free swell test, it was found that the Free Swell Index (FSI) of the soil decrease from 210 to 80 at 10% calcined clay added soil and the Mineralogical studies also show the variation in the compounds. Thus, this naturally available calcined clay was used to improve the soil Compaction and swell characteristics that influences the reduction in deformation and increase in shear strength of soil which helped to minimize the environmental problem as well as one of the effective admixtures to improve the expansive soil characteristics

A Study on Marketing Behaviour of Rural Youth Entrepreneurs among Seven Different Ventures

The study was conducted among 210 rural youth entrepreneurs of seven different ventures in Krishnagiri district to assess their marketing behaviour. The entrepreneurial ventures selected for the study were Sericulture, Mushroom Production, Hi-tech nurseries (Polyhouse), Fruit and flower nursery, Fisheries, Poultry farming and Value addition (Tamarind processing and Millet based cookies).

Nanotechnology-Enabled Biosensors: A Review of Fundamentals, Design Principles, Materials, and Applications

Biosensors are modern engineering tools that can be widely used for various technological applications. In the recent past, biosensors have been widely used in a broad application spectrum including industrial process control, the military, environmental monitoring, health care, microbiology, and food quality control. Biosensors are also used specifically for monitoring environmental pollution, detecting toxic elements’ presence, the presence of bio-hazardous viruses or bacteria in organic matter, and biomolecule detection in clinical diagnostics. Moreover, deep medical applications such as well-being monitoring, chronic disease treatment, and in vitro medical examination studies such as the screening of infectious diseases for early detection. The scope for expanding the use of biosensors is very high owing to their inherent advantages such as ease of use, scalability, and simple manufacturing process. Biosensor technology is more prevalent as a large-scale, low cost, and enhanced technology in the modern medical field. Integration of nanotechnology with biosensors has shown the development path for the novel sensing mechanisms and biosensors as they enhance the performance and sensing ability of the currently used biosensors. Nanoscale dimensional integration promotes the formulation of biosensors with simple and rapid detection of molecules along with the detection of single biomolecules where they can also be evaluated and analyzed critically. Nanomaterials are used for the manufacturing of nano-biosensors and the nanomaterials commonly used include nanoparticles, nanowires, carbon nanotubes (CNTs), nanorods, and quantum dots (QDs). Nanomaterials possess various advantages such as color tunability, high detection sensitivity, a large surface area, high carrier capacity, high stability, and high thermal and electrical conductivity. The current review focuses on nanotechnology-enabled biosensors, their fundamentals, and architectural design. The review also expands the view on the materials used for fabricating biosensors and the probable applications of nanotechnology-enabled biosensors

Hierarchical Ternary Sulfides as Effective Photocatalyst for Hydrogen Generation Through Water Splitting: A Review on the Performance of ZnIn₂S₄

One of the major aspects and advantages of solar energy conversion is the photocatalytic hydrogen generation using semiconductor materials for an eco-friendly technology. Designing a low-cost efficient material to overcome limited light absorption as well as rapid recombination of photogenerated charge carriers is essential to achieve considerable hydrogen generation. In recent years, sulfide based semiconductors have attracted scientific research interest due to their excellent solar response and narrow band gap. The present review focuses on the recent approaches in the development of hierarchical ternary sulfide based photocatalysts with a special focus on ZnIn2S4. We also observe how the electronic structure of ZnIn2S4 is beneficial for water splitting and the various strategies involved for improving the material efficiency for photocatalytic hydrogen generation. The review places emphasis on the latest advancement/new insights on ZnIn2S4 being used as an efficient material for hydrogen generation through photocatalytic water splitting. Recent progress on essential aspects which govern light absorption, charge separation and transport are also discussed in detail

Machine Learning Models for Prediction of Xenobiotic Chemicals with High Propensity to Transfer into Human Milk

Breast milk serves as a vital source of essential nutrients for infants. However, human milk contamination via the transfer of environmental chemicals from maternal exposome is a significant concern for infant health. The milk to plasma concentration (M/P) ratio is a critical metric that quantifies the extent to which these chemicals transfer from maternal plasma into breast milk, impacting infant exposure. Machine learning-based predictive toxicology models can be valuable in predicting chemicals with a high propensity to transfer into human milk. To this end, we build such classification- and regression-based models by employing multiple machine learning algorithms and leveraging the largest curated data set, to date, of 375 chemicals with known milk-to-plasma concentration (M/P) ratios. Our support vector machine (SVM)-based classifier outperforms other models in terms of different performance metrics, when evaluated on both (internal) test data and an external test data set. Specifically, the SVM-based classifier on (internal) test data achieved a classification accuracy of 77.33%, a specificity of 84%, a sensitivity of 64%, and an F-score of 65.31%. When evaluated on an external test data set, our SVM-based classifier is found to be generalizable with a sensitivity of 77.78%. While we were able to build highly predictive classification models, our best regression models for predicting the M/P ratio of chemicals could achieve only moderate R2 values on the (internal) test data. As noted in the earlier literature, our study also highlights the challenges in developing accurate regression models for predicting the M/P ratio of xenobiotic chemicals. Overall, this study attests to the immense potential of predictive computational toxicology models in characterizing the myriad of chemicals in the human exposome

Machine Learning Models for Prediction of Xenobiotic Chemicals with High Propensity to Transfer into Human Milk

Breast milk serves as a vital source of essential nutrients for infants. However, human milk contamination via the transfer of environmental chemicals from maternal exposome is a significant concern for infant health. The milk to plasma concentration (M/P) ratio is a critical metric that quantifies the extent to which these chemicals transfer from maternal plasma into breast milk, impacting infant exposure. Machine learning-based predictive toxicology models can be valuable in predicting chemicals with a high propensity to transfer into human milk. To this end, we build such classification- and regression-based models by employing multiple machine learning algorithms and leveraging the largest curated data set, to date, of 375 chemicals with known milk-to-plasma concentration (M/P) ratios. Our support vector machine (SVM)-based classifier outperforms other models in terms of different performance metrics, when evaluated on both (internal) test data and an external test data set. Specifically, the SVM-based classifier on (internal) test data achieved a classification accuracy of 77.33%, a specificity of 84%, a sensitivity of 64%, and an F-score of 65.31%. When evaluated on an external test data set, our SVM-based classifier is found to be generalizable with a sensitivity of 77.78%. While we were able to build highly predictive classification models, our best regression models for predicting the M/P ratio of chemicals could achieve only moderate R2 values on the (internal) test data. As noted in the earlier literature, our study also highlights the challenges in developing accurate regression models for predicting the M/P ratio of xenobiotic chemicals. Overall, this study attests to the immense potential of predictive computational toxicology models in characterizing the myriad of chemicals in the human exposome