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The identification of plant disease is an imperative part of crop monitoring systems. Computer vision and deep learning (DL) techniques have been proven to be state-of-the-art to address various agricultural problems. This research performed the complex tasks of localization and classification of the disease in plant leaves. In this regard, three DL meta-architectures including the Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Network (RCNN), and Region-based Fully Convolutional Networks (RFCN) were applied by using the TensorFlow object detection framework. All the DL models were trained/tested on a controlled environment dataset to recognize the disease in plant species. Moreover, an improvement in the mean average precision of the best-obtained deep learning architecture was attempted through different state-of-the-art deep learning optimizers. The SSD model trained with an Adam optimizer exhibited the highest mean average precision (mAP) of 73.07%. The successful identification of 26 different types of defected and 12 types of healthy leaves in a single framework proved the novelty of the work. In the future, the proposed detection methodology can also be adopted for other agricultural applications. Moreover, the generated weights can be reused for future real-time detection of plant disease in a controlled/uncontrolled environment.

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly.

Recently, plant disease classification has been done by various state-of-the-art deep learning (DL) architectures on the publicly available/author generated datasets. This research proposed the deep learning-based comparative evaluation for the classification of plant disease in two steps. Firstly, the best convolutional neural network (CNN) was obtained by conducting a comparative analysis among well-known CNN architectures along with modified and cascaded/hybrid versions of some of the DL models proposed in the recent researches. Secondly, the performance of the best-obtained model was attempted to improve by training through various deep learning optimizers. The comparison between various CNNs was based on performance metrics such as validation accuracy/loss, F1-score, and the required number of epochs. All the selected DL architectures were trained in the PlantVillage dataset which contains 26 different diseases belonging to 14 respective plant species. Keras with TensorFlow backend was used to train deep learning architectures. It is concluded that the Xception architecture trained with the Adam optimizer attained the highest validation accuracy and F1-score of 99.81% and 0.9978 respectively which is comparatively better than the previous approaches and it proves the novelty of the work. Therefore, the method proposed in this research can be applied to other agricultural applications for transparent detection and classification purposes.

Access to medicines without prescription is a major contributing factor for self-medication practices. This study was designed to examine the ratio of non-prescribed medicines sales and self-medication practices in Punjab, Pakistan. This study also evaluates the reasons for self-medication within its communities. An observational study was conducted in 272 systemically selected pharmacies to analyze medicines-related sales, with or without prescription. A cross-sectional survey was performed between June 2015 and November 2016. Consumers were interviewed about their self-medication practices. Of the pharmacies surveyed, 65.3% participated in the study. A total of 4348 medicines were purchased for self-medication by 3037 consumers (15.2% of all study participants), of which 873 (28.7%) participated in an interview. Majority (81.2%) medicine purchaser, (90.9%) interview participants, and (59.4%) drug users were male. On average, each community pharmacy sold 7.9 medicines without prescription each day, to an average of 5.5 customers. Many participants (28.9%) had matriculation in their formal education. The medicines most often sold for self-medication were analgesics and antipyretics(39.4%). More than 25% of participants reported fever symptoms and 47.8% assumed their illness was too trivial to consult a doctor. Media advertisements were the most common source of information for participants (46.7%). Many types of medicines were often sold without prescription from community pharmacies. Self-medication was common practice for a wide range of illnesses. Pakistan also needs effective implementation of policies to monitor medication sales. Public education about rational medication and limits to advertising medicine are very necessary.

Recently, agriculture has gained much attention regarding automation by artificial intelligence techniques and robotic systems. Particularly, with the advancements in machine learning (ML) concepts, significant improvements have been observed in agricultural tasks. The ability of automatic feature extraction creates an adaptive nature in deep learning (DL), specifically convolutional neural networks to achieve human-level accuracy in various agricultural applications, prominent among which are plant disease detection and classification, weed/crop discrimination, fruit counting, land cover classification, and crop/plant recognition. This review presents the performance of recent uses in agricultural robots by the implementation of ML and DL algorithms/architectures during the last decade. Performance plots are drawn to study the effectiveness of deep learning over traditional machine learning models for certain agricultural operations. The analysis of prominent studies highlighted that the DL-based models, like RCNN (Region-based Convolutional Neural Network), achieve a higher plant disease/pest detection rate (82.51%) than the well-known ML algorithms, including Multi-Layer Perceptron (64.9%) and K-nearest Neighbour (63.76%). The famous DL architecture named ResNet-18 attained more accurate Area Under the Curve (94.84%), and outperformed ML-based techniques, including Random Forest (RF) (70.16%) and Support Vector Machine (SVM) (60.6%), for crop/weed discrimination. Another DL model called FCN (Fully Convolutional Networks) recorded higher accuracy (83.9%) than SVM (67.6%) and RF (65.6%) algorithms for the classification of agricultural land covers. Finally, some important research gaps from the previous studies and innovative future directions are also noted to help propel automation in agriculture up to the next level.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder marked by oxidative stress, inflammation, and the degeneration of dopaminergic neurons. Current treatments focus more on symptom management rather than disease prevention. Acacia jacquemontii , rich in antioxidants, may offer a novel therapeutic approach for PD. This study aims to investigate the phytochemical composition, antioxidant capacity, anti-Parkinsonian efficacy, and in-silico validation of Acacia jacquemontii methanol extract (AJME) using liquid chromatography-mass spectrometry (LC-MS). Secondary metabolites were identified, and total alkaloid, phenolic, and flavonoid contents were quantified. LC-MS was used for detailed compound profiling. Antioxidant activity was evaluated using the DPPH assay. In vivo tests on Wistar rats modeled PD through haloperidol administration. AJME’s anti-Parkinsonian effects were assessed via histological, biochemical, and behavioral analyses. In-silico techniques, including molecular docking, structural interaction fingerprinting, ADME prediction, DFT, MESP studies, and molecular dynamics (MD) simulations, were employed to understand AJME molecules’ binding interactions and electronic properties. In vivo , AJME improved locomotor activity, memory, exploratory behavior, oxidative stress markers (SOD, CAT, GSH, MDA), and neurotransmitter levels (dopamine, noradrenaline, serotonin) in rats. In-silico validation identified CP21 as a potent ligand. MD simulations indicated stable AJME-AChE complexes, with enhanced binding affinity through hydrophobic and van der Waals interactions. A. jacquemontii exhibits significant phytochemical, antioxidant, and anti-Parkinsonian properties. The combined in vitro, in vivo , and in silico studies, supported by LC-MS analysis, suggest that AJME could provide a promising option for developing new therapeutic approaches for PD. However, clinical evaluation is necessary to establish its efficacy and safety in human subjects.

The epidermal growth factor receptor (EGFR) presents a crucial target for combatting cancer mortality. This study employs a suite of computational techniques, including 3D-QSAR, ligand-based virtual screening, molecular docking, fingerprinting analysis, ADME, and DFT-based analyses (MESP, HOMO, LUMO), supplemented by molecular dynamics simulations and MMGB/PBSA free energy calculations, to explore the binding dynamics of quinazoline derivatives with EGFR. With strong q2 and r2 values from CoMFA and CoMSIA models, our 3D- QSAR models reliably predict EGFR inhibitors' efficacy. Utilizing a potent model compound as a reference, an E-pharmacophore model was developed to sift through the eMolecules database, identifying 19 virtual screening hits based on ShapeTanimoto, ColourTanimoto, and TanimotoCombo scores. These hits, assessed via 3D- QSAR, showed pIC predictions consistent with experimental data. Our analyses elucidate key features essential for EGFR inhibition, reinforced by ADME studies that reveal favorable pharmacokinetic profiles for most compounds. Among the primary phytochemicals examined, potential EGFR inhibitors were identified. Detailed MD simulation analyses on three select ligands-1Q1, 2Q17, and VS1-demonstrated their stability and consistent interaction over 200 ns, with MM/GBSA values corroborating their docking scores and highlighting 1Q1 and VS1's superior EGFR1 affinity. These results position VS1 as an especially promising lead in EGFR1 inhibitor development, contributing valuable insights towards crafting novel, effective EGFR1 inhibitors.

[This corrects the article DOI: 10.1371/journal.pone.0334312.].

Pseudomonas aeruginosa (P. aeruginosa ), a member of the ESKAPE family, is the major cause of infections leading to increased morbidity and mortality due to multidrug resistance (MDR). One of the main proteins involved in the Raetz pathway is LpxC, which plays a significant role in anti-microbial resistance (AMR). Our study aimed to identify a novel compound to combat MDR due to the LpxC protein. It involved in silico methods comprising molecular docking, simulations, ADMET profiling, and DFT calculations. First, an ADMET and bioactivity evaluation of the 25 top-hit compounds retrieved from ligand-based virtual screening was performed, followed by molecular docking. The results revealed compound P-2 as the lead compound, which was further subjected to DFT analysis and molecular dynamics (MD) simulations. With these analyses, our in silico study identified P-2, 3-[(dimethylamino)methyl]-N-[(2 S)-1-(hydroxyamino)-1-oxobutan-2-yl]benzamide as a potential lead compound that may behave as a very potent inhibitor of LpxC for the development of targeted therapies against MDR P. aeruginosa .

To apply weed control treatments effectively, the weeds must be accurately detected. Deep learning (DL) has been quite successful in performing the weed identification task. However, various aspects of the DL have not been explored in previous studies. This research aimed to achieve a high average precision (AP) of eight classes of weeds and a negative (non-weed) class, using the DeepWeeds dataset. In this regard, a DL-based two-step methodology has been proposed. This article is the second stage of the research, while the first stage has already been published. The former phase presented a weed detection pipeline and consisted of the evaluation of various neural networks, image resizers, and weight optimization techniques. Although a significant improvement in the mean average precision (mAP) was attained. However, the Chinee apple weed did not reach a high average precision. This result provided a solid ground for the next stage of the study. Hence, this paper presents an in-depth analysis of the Faster Region-based Convolutional Neural Network (RCNN) with ResNet-101, the best-obtained model in the past step. The architectural details of the Faster RCNN model have been thoroughly studied to investigate each class of weeds. It was empirically found that the generation of anchor boxes affects the training and testing performance of the Faster RCNN model. An enhancement to the anchor box scales and aspect ratios has been attempted by various combinations. The final results, with the addition of 64 × 64 scale size, and aspect ratio of 1:3 and 3:1, produced the best classification and localization of all classes of weeds and a negative class. An enhancement of 24.95% AP was obtained in Chinee apple weed. Furthermore, the mAP was improved by 2.58%. The robustness of the approach has been shown by the stratified k-fold cross-validation technique and testing on an external dataset.