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Berberis lycium Royle, a member of the Berberidaceae family, is a high-value medicinal plant with a documented history of usage in traditional medicine and has demonstrated significant therapeutic results among local populations throughout the globe. It is used traditionally in many parts of Pakistan to treat diarrhea, abdominal spasms, coughs, and chest problems.To investigate the antispasmodic, bronchodilator, and antidiarrheal effects of B. lycium and its possible underlying mechanisms through in silico, in vitro, and in vivo studies.LC ESI-MS/MS analysis was used to identify bioactive components within the hydromethanolic extract of B. lycium. In silico studies, including network pharmacology and molecular docking, were utilized to investigate the antispasmodic and bronchodilator properties of the extract's bioactive components. In vitro pharmacological studies were conducted using isolated rabbit jejunum, trachea, urinary bladder, and rat ileum preparations. In vivo antidiarrheal activities were conducted in mice, including castor oil-induced diarrhea, intestinal transit, and castor oil-induced enteropooling.The LC ESI-MS/MS analysis of the hydromethanolic extract of B. lycium identified 38 bioactive compounds. Network pharmacology study demonstrated that the mechanism of BLR for the treatment of diarrhea might involve IL1B, TLR4, PIK3R1, TNF, PTPRC, IL2, PIK3CD, and ABCB1, whereas, for respiratory ailments, it may involve PIK3CG, TRPV1, STAT3, ICAM1, ACE, PTGER2, PTGS2, TNF, MMP9, NOS2, IL2, CCR5, HRH1, and VDR. Molecular docking research revealed that chlorogenic acid, epigallocatechin, isorhamnetin, quinic acid, gallic acid, camptothecin, formononetin-7-O-glucoside, velutin, caffeic acid, and (S)-luteanine exhibited a higher docking score than dicyclomine with validated proteins of smooth muscle contractions such as CACB2_HUMAN, ACM3_HUMAN, MYLK_HUMAN, and PLCG1_HUMAN. In vitro investigations demonstrated that Blr.Cr, Blr.EtOAc, and Blr.Aq relaxed spontaneously contracting jejunum preparations; carbachol (1 μM)-induced and KThe dual blocking mechanism of muscarinic receptors and Ca

An advancement in Hyperspectral Imaging (HI) technology is creating important attraction among the researchers to develop better classification techniques. This technology is well known for its high spatial and spectral information due to which the discrimination of materials is much more accurate and efficient. The useful information is extracted in Hyperspectral Imaging technology after applying it in agriculture, biomedical, and disaster management studies. A review comparison has been carried out for air borne images using hyperspectral acquisition hardware for classification as well as segmentation purpose. Numerous approaches that have been focused for implementation namely semi-supervised technique used for hyperspectral imaging using active learning and multinomial logistic regression, Generalized Composite Kernels (GCKs) classification framework, classification of spectral-spatial based data on loopy belief propagation (LBP), multiple feature learning of HI classification, and semi-supervised GCKs with classification accuracy on AVIRIS dataset (59.97%, 92.89%, 81.45%, 75.84%, and 95.50) and segmentation accuracies using α-expansion method as (73.27%, 93.57%, 92.86%, 91.73% and 98.31), respectively.

A new methodology, the hybrid learning system (HLS), based upon semi-supervised learning is proposed. HLS categorizes hyperspectral images into segmented regions with discriminative features using reduced training size. The technique utilizes the modified breaking ties (MBT) algorithm for active learning and unsupervised learning-based regressors, viz. multinomial logistic regression, for hyperspectral image categorization. The probabilities estimated by multinomial logistic regression for each sample helps towards improved segregation. The high dimensionality leads to a curse of dimensionality, which ultimately deteriorates the performance of remote sensing data classification, and the problem aggravates further if labeled training samples are limited. Many studies have tried to address the problem and have employed different methodologies for remote sensing data classification, such as kernelized methods, because of insensitiveness towards the utilization of large dataset information and active learning (AL) approaches (breaking ties as a representative) to choose only prominent samples for training data. The HLS methodology proposed in the current study is a combination of supervised and unsupervised training with generalized composite kernels generating posterior class probabilities for classification. In order to retrieve the best segmentation labels, we employed Markov random fields, which make use of prior labels from the output of the multinomial logistic regression. The comparison of HLS was carried out with known methodologies, using benchmark hyperspectral imaging (HI) datasets, namely “Indian Pines” and “Pavia University”. Findings of this study show that the HLS yields the overall accuracy of {99.93% and 99.98%}Indian Pines and {99.14% and 99.42%}Pavia University for classification and segmentation, respectively.

Hyperspectral imaging (HI) is getting much more attention among researchers in different fields like agriculture, defense, medical, and geographical surveys. In this work, we have proposed a novel automated system for the classification and segmentation of landscapes using hyperspectral images. The proposed semi-supervised based approach has improved the extraction of spatial characteristics of the scene that has employed an extended multi-attribute profile (EMAP) by stacking of several attributes. The unlabeled data points located near the classifier boundaries are selected on the basis of entropy related to the corresponding class labels. In the next segmentation phase, MLR probabilities are computed against the output of classifier. Finally, maximum-a-posteriori segmentation is carried out on the multilevel logistic prior labels. The simulated results have obtained classification accuracy of 95.50% by comparing predicted labels with original ones. The segmentation accuracy, after developing regions on the output of classification, is 98.31%. A performance comparison of the proposed approach with several approaches has also been carried out.