Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The ...stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the 'Automatic and Intelligent Data Collector and Classifier' framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The 'Custom-Net' model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the 'Custom-Net'. Furthermore, the impact of transfer learning on the 'Custom-Net' and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the 'Custom-Net' extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the 'Custom-Net' model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of 'Custom-Net' is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.
We present an approach for preparing cryo-electron microscopy (cryo-EM) grids to study short-lived molecular states. Using piezoelectric dispensing, two independent streams of ~50-pl droplets of ...sample are deposited within 10 ms of each other onto the surface of a nanowire EM grid, and the mixing reaction stops when the grid is vitrified in liquid ethane ~100 ms later. We demonstrate this approach for four biological systems where short-lived states are of high interest.
The automatic detection, visualization, and classification of plant diseases through image datasets are key challenges for precision and smart farming. The technological solutions proposed so far ...highlight the supremacy of the Internet of Things in data collection, storage, and communication, and deep learning models in automatic feature extraction and feature selection. Therefore, the integration of these technologies is emerging as a key tool for the monitoring, data capturing, prediction, detection, visualization, and classification of plant diseases from crop images. This manuscript presents a rigorous review of the Internet of Things and deep learning models employed for plant disease monitoring and classification. The review encompasses the unique strengths and limitations of different architectures. It highlights the research gaps identified from the related works proposed in the literature. It also presents a comparison of the performance of different deep learning models on publicly available datasets. The comparison gives insights into the selection of the optimum deep learning models according to the size of the dataset, expected response time, and resources available for computation and storage. This review is important in terms of developing optimized and hybrid models for plant disease classification.
Membrane budding entails forces to transform flat membrane into vesicles essential for cell survival. Accumulated studies have identified coat-proteins (e.g., clathrin) as potential budding factors. ...However, forces mediating many non-coated membrane buddings remain unclear. By visualizing proteins in mediating endocytic budding in live neuroendocrine cells, performing in vitro protein reconstitution and physical modeling, we discovered how non-coated-membrane budding is mediated: actin filaments and dynamin generate a pulling force transforming flat membrane into Λ-shape; subsequently, dynamin helices surround and constrict Λ-profile's base, transforming Λ- to Ω-profile, and then constrict Ω-profile's pore, converting Ω-profiles to vesicles. These mechanisms control budding speed, vesicle size and number, generating diverse endocytic modes differing in these parameters. Their impact is widespread beyond secretory cells, as the unexpectedly powerful functions of dynamin and actin, previously thought to mediate fission and overcome tension, respectively, may contribute to many dynamin/actin-dependent non-coated-membrane buddings, coated-membrane buddings, and other membrane remodeling processes.
The increasing gap between the demand and productivity of maize crop is a point of concern for the food industry, and farmers. Its' susceptibility to diseases such as Turcicum Leaf Blight, and Rust ...is a major cause for reducing its production. Manual detection, and classification of these diseases, calculation of disease severity, and crop loss estimation is a time-consuming task. Also, it requires expertise in disease detection. Thus, there is a need to find an alternative for automatic disease detection, severity prediction, and crop loss estimation. The promising results of machine learning, and deep learning algorithms in pattern recognition, object detection, and data analysis motivate researchers to employ these techniques for disease detection, classification, and crop loss estimation in maize crop. The research works available in literature, have proven their potential in automatic disease detection using machine learning, and deep learning models. But, there is a lack none of these works a reliable and real-life labelled dataset for training these models. Also, none of the existing works focus on severity prediction, and crop loss estimation. The authors in this manuscript collect the real-life dataset labelled by plant pathologists. They propose a deep learning-based framework for pre-processing of dataset, automatic disease detection, severity prediction, and crop loss estimation. It uses the K-Means clustering algorithm for extracting the region of interest. Next, they employ the customized deep learning model ‘MaizeNet’ for disease detection, severity prediction, and crop loss estimation. The model reports the highest accuracy of 98.50%. Also, the authors perform the feature visualization using the Grad-CAM. Now, the proposed model is integrated with a web application to provide a user-friendly interface. The efficacy of the model in extracting the relevant features, a smaller number of parameters, low training time, high accuracy favors its importance as an assisting tool for plant pathology experts.The copyright for the associated web application ‘Maize-Disease-Detector’ is filed with diary number: 17006/2021-CO/SW.
•Collection and labelling of datasets in close supervision of plant pathologist.•Employing K-Means algorithm for segmentation of region of interest.•Using severity scale for estimation of crop loss in maize crop.•Deep learning model for disease classification, and severity prediction.•User friendly web application integrated with deep learning model.
This study investigates the truth behind conversations on stubble burning (SB) contribution to Delhi’s air pollution (DAP) using ground observations, geophysical models, and satellite-based ...measurements during 2019 and 2020. Pieces of evidence from ground-based measurements showed a drastic increase in the pollutant concentration during the SB episode (October–November of each year), which leads to the increased air quality index (AQI), confirming the significant contribution of SB in DAP along with internal sources. Geophysical models, including Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) back trajectories and Navy Aerosol Analysis and Prediction System (NAAPS), also indicated the contribution of regional SB in DAP. Measurements from Moderate Resolution Imaging Spectroradiometer (MODIS), Visible Infrared Imagine Radiometer Suite (VIIRS), and Sentinel-P5 satellites further strengthen our findings on the regional contribution of SB, majorly from Punjab and Haryana in DAP. Nevertheless, the meteorological conditions (derived both from ground and satellite) worsen the situation of pollution in Delhi during winter.
Graphical Abstract
Thermostable direct hemolysin (TDH) is a ~19 kDa, hemolytic pore‐forming toxin from the gram‐negative marine bacterium Vibrio parahaemolyticus, one of the causative agents of seafood‐borne acute ...gastroenteritis and septicemia. Previous studies have established that TDH exists as a tetrameric assembly in physiological state; however, there is limited knowledge regarding the molecular arrangement of its disordered N‐terminal region (NTR)—the absence of which has been shown to compromise TDH's hemolytic and cytotoxic abilities. In our current study, we have employed single‐particle cryo‐electron microscopy to resolve the solution‐state structures of wild‐type TDH and a TDH construct with deletion of the NTR (NTD), in order to investigate structural aspects of NTR on the overall tetrameric architecture. We observed that both TDH and NTD electron density maps, resolved at global resolutions of 4.5 and 4.2 Å, respectively, showed good correlation in their respective oligomeric architecture. Additionally, we were able to locate extra densities near the pore opening of TDH which might correspond to the disordered NTR. Surprisingly, under cryogenic conditions, we were also able to observe novel supramolecular assemblies of TDH tetramers, which we were able to resolve to 4.3 Å. We further investigated the tetrameric and inter‐tetrameric interaction interfaces to elaborate upon the key residues involved in both TDH tetramers and TDH super assemblies. Our current structural study will aid in understanding the mechanistic aspects of this pore‐forming toxin and the role of its disordered NTR in membrane interaction.
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