Search

Agriculture plays a vital role in Bangladesh's economy. It is essential to ensure the proper growth and health of crops for the development of the agricultural sector. In the context of Bangladesh, crop diseases pose a significant threat to agricultural output and, consequently, food security. This necessitates the timely and precise identification of such diseases to ensure the sustainability of food production. This study focuses on building a hybrid deep learning model for the identification of three specific diseases affecting three major crops: late blight in potatoes, brown spot in rice, and common rust in corn. The proposed model leverages EfficientNetB0′s feature extraction capabilities, known for achieving rapid high learning rates, coupled with the classification proficiency of SVMs, a well-established machine learning algorithm. This unified approach streamlines data processing and feature extraction, potentially improving model generalizability across diverse crops and diseases. It also aims to address the challenges of computational efficiency and accuracy that are often encountered in precision agriculture applications. The proposed hybrid model achieved 97.29% accuracy. A comparative analysis with other models, CNN, VGG16, ResNet50, Xception, Mobilenet V2, Autoencoders, Inception v3, and EfficientNetB0 each achieving an accuracy of 86.57%, 83.29%, 68.79%, 94.07%, 90.71%, 87.90%, 94.14%, and 96.14% respectively, demonstrated the superior performance of our proposed model. [ABSTRACT FROM AUTHOR]

In this study, we automated the diagnostic procedure of autism spectrum disorder (ASD) with the help of anatomical alterations found in structural magnetic resonance imaging (sMRI) data of the ASD brain and machine learning tools. Initially, the sMRI data was preprocessed using the FreeSurfer toolbox. Further, the brain regions were segmented into 148 regions of interest using the Destrieux atlas. Features such as volume, thickness, surface area, and mean curvature were extracted for each brain region, and the morphological connectivity was computed using Pearson correlation. These morphological connections were fed to XGBoost for feature reduction and to build the diagnostic model. The results showed an average accuracy of 94.16% for the top 18 features. The frontal and limbic regions contributed more features to the classification model. Our proposed method is thus effective for the classification of ASD and can also be useful for the screening of other similar neurological disorders. [ABSTRACT FROM AUTHOR]

Background: Mutations in the postsynaptic transmembrane protein neuroligin-3 are highly correlative with autism spectrum disorders (ASDs) and intellectual disabilities (IDs). Fear learning is well studied in models of these disorders, however differences in fear response behaviours are often overlooked. We aim to examine fear behaviour and its cellular underpinnings in a rat model of ASD/ID lacking Nlgn3. Methods: This study uses a range of behavioural tests to understand differences in fear response behaviour in Nlgn3−/y rats. Following this, we examined the physiological underpinnings of this in neurons of the periaqueductal grey (PAG), a midbrain area involved in flight-or-freeze responses. We used whole-cell patch-clamp recordings from ex vivo PAG slices, in addition to in vivo local-field potential recordings and electrical stimulation of the PAG in wildtype and Nlgn3−/y rats. We analysed behavioural data with two- and three-way ANOVAS and electrophysiological data with generalised linear mixed modelling (GLMM). Results: We observed that, unlike the wildtype, Nlgn3−/y rats are more likely to response with flight rather than freezing in threatening situations. Electrophysiological findings were in agreement with these behavioural outcomes. We found in ex vivo slices from Nlgn3−/y rats that neurons in dorsal PAG (dPAG) showed intrinsic hyperexcitability compared to wildtype. Similarly, stimulating dPAG in vivo revealed that lower magnitudes sufficed to evoke flight behaviour in Nlgn3−/y than wildtype rats, indicating the functional impact of the increased cellular excitability. Limitations: Our findings do not examine what specific cell type in the PAG is likely responsible for these phenotypes. Furthermore, we have focussed on phenotypes in young adult animals, whilst the human condition associated with NLGN3 mutations appears during the first few years of life. Conclusions: We describe altered fear responses in Nlgn3−/y rats and provide evidence that this is the result of a circuit bias that predisposes flight over freeze responses. Additionally, we demonstrate the first link between PAG dysfunction and ASD/ID. This study provides new insight into potential pathophysiologies leading to anxiety disorders and changes to fear responses in individuals with ASD. [ABSTRACT FROM AUTHOR]

Stress response pathways protect the lung fromthe damaging effects of environmental toxicants. Here we investigate the role of the fragile X mental retardation protein (FMRP), a multifunctional protein implicated in stress responses, in the lung. We report that FMRP is expressed in murine and human lungs, in the airways and more broadly. Analysis of airway stress responses in mice and in a murine cell line ex vivo, using the well-established naphthalene injury model, reveals that FMRPdeficient cells exhibit increased expression of markers of oxidative and genotoxic stress and increased cell death. Further inquiry shows that FMRP-deficient cells fail to actuate the integrated stress response pathway (ISR) and upregulate the transcription factor ATF4. Knockdown of ATF4 expression phenocopies the loss of FMRP. We extend our analysis of the role of FMRP to human bronchial BEAS-2B cells, using a 9,10-phenanthrenequinone air pollutantmodel, to find that FMRP-deficient BEAS-2B cells also fail to actuate the ISR and exhibit greater susceptibility. Taken together, our data suggest that FMRP has a conserved role in protecting the airways by facilitating the ISR. [ABSTRACT FROM AUTHOR]