Effect of different herbicide spray volumes on weed control efficiency of a battery-operated Unmanned aerial vehicle sprayer in transplanted rice (Oryza sativa L.)

The effect of spray volume on weed control in transplanted rice ecosystems using the Unmanned aerial vehicle (UAV) needs to be better understood for management in the advancements of UAV-based spraying technology. The present study aimed to find out the influence of varied spray volumes of 15 L/ha, 20 L/ha and 25 L/ha using the UAV and 500 L/ha using a Knapsack sprayer (KS) to compare the weed density, weed dry matter and weed control efficiency and yield in transplanted rice (Oryza sativa L.). Pre-emergence (PE) application of Pyrazosulfuron-ethyl at 25 g a.i./ha at three days after transplanting (DAT) and post-emergence (PoE) application of Bis-pyribac sodium at 25 g a.i./ha at 25 DAT were used as herbicide treatments. The results revealed that varied spray volumes significantly influenced the weed density, dry matter, and weed control efficiency of the UAV and KS. Application of herbicides using KS (500 L/ha) and UAV (25 L/ha) had better control on the weeds by reducing weed density and dry matter at 20, 40, and 60 DAT, with no significant difference. Higher grain yield and straw yield were recorded in KS (500 L/ha) and UAV (25 L/ha), with no significant difference. However, applying 25 L/ha had better weed control efficiency and higher yield, possibly due to optimum deposition. Considering the low volume application of UAV (25 L/ha) as compared with KS (500 L/ha), it is better to go for the optimal application of 25 L/ha, which is an energy-efficient and cost-effective, labour-saving approach compared to KS

Ayurvedic Management of Kitibha Kushta

Psoriasis is long lasting, non-contagious autoimmune disease. Characterized by scaly patches on skin with dry and reddish discoloration. In India incidence rate of psoriasis about 2% to 4%, steroids and immunosuppressive drugs are the treatment option in contemporary science. Due to their limitation and high chance of recurrence. Present case report, A 50 years old female patient presented with a 1 month history of itching and whitish scaly patches on the skin diagnosed as Kitibha kushta (psoriasis). The patient was treated with 15 day course of Shamanaushadi, including Tab. Gandakarasayana, Arogyavardini vati, Cap. Guggulu tiktaka ghrita, and Bactimo ointment. Significant improvement was observed within 15 days, with no signs of recurrence. The treatment protocol adopted based on the Ayurvedic principles, targeting the Vatakapha dosha predominance in Kitibhakushta. This case report highlights the effectiveness of Shamanaushadi in managing psoriasis and support further research to explore its potential as a complementary therapy

Assessment of land use and land cover mapping using object-based classification techniques for the eastern districts of Tamil Nadu

LULC (Land use and land cover) mapping is crucial for understanding environmental monitoring, supporting sustainable development, and managing natural resources. This study evaluated the accuracy of object-based LULC classification using Sentinel-2 data and machine learning classifiers in the Ariyalur, Perambalur, and Mayiladuthurai districts of Tamil Nadu during the kharif season of 2023. OBIA (Object-based image analysis) clusters pixels based on their spectral and spatial characteristics, utilizing segmentation to generate masks that effectively represent the image content. The OBIA methodology involves multiresolution segmentation using eCognition software to delineate homogeneous image objects based on spectral, spatial, and contextual characteristics. Several widely used machine learning algorithms, including Random forest (RF), Support vector machine (SVM), Decision Tree (DT), Naive bayes (NB) and k-nearest Neighbor (k-NN), were evaluated to improve classification accuracy. The classification results varied across the districts, with the RF algorithm consistently demonstrating high performance. The Perambalur and Mayiladuthurai RF achieved an overall accuracy of 88 %, with a kappa coefficient of 0.76 and 83 % and a kappa coefficient of 0.66. In Ariyalur, the DT model was used, with an accuracy of 85 % and a kappa coefficient of 0.70. The NB and k-NN classifiers achieved lower accuracies in all districts. In contrast, the RF algorithm was the most reliable for LULC classification in these areas, highlighting its strength and efficiency in accurately identifying complex land cover patterns

Analysis of cholinergic pathway-induced alterations at the intestinal mucosa and their association with enhanced resistance to lethal bacterial infection

Abstract The mucosal innate immune system represents the first line of defense against microorganisms invading the intestinal tract. Increasing evidence suggests that mucosal immune defenses are tightly regulated by immunological and neural pathways. We previously demonstrated that activation of the cholinergic pathway through the administration of a highly specific acetylcholinesterase inhibitor (AChEI), paraoxon, confers resistance to an oral infection by virulent S. typhimurium. This protection is mediated by enhanced innate anti-microbial mucosal defenses in the GI tract. In the present study, we investigated the involvement of different immune cell populations in the GI tract in the modulation of AChEI-mediated anti-Salmonella resistance. Morphological evaluation of intestinal tissue from control- or AChEI-treated mice revealed a significant thickening of the mucin layer and increased B and T lymphocytic infiltration into intestinal mucosa in the AChEI group. Multi-color flow cytometric analysis of iliac intraepithelial lymphocytes (IELs) showed that cholinergic stimulation increased the percentage of CD3+CD8+gamma/delta TCR+ cells. No significant alterations were observed in lamina propria cells. Our findings indicate that cholinergic pathway is able to modulate not only mucin-secreting cells but more importantly gamma/delta T cells, the cell population known to be involved in tissue homeostasis and repair as well as surveillance of the epithelial barrier.</jats:p

Bioinformatics mining and experimental validation of prognostic biomarkers in colorectal cancer

Abstract Colorectal cancer (CRC) is a prevalent condition with increasing incidence and mortality rates. The identification of robust prognostic gene signatures remains an unmet clinical need in CRC treatment. In this study, data from the GEO and TCGA databases were utilized to identify 2,779 upregulated and 2,629 downregulated genes in CRC tissues compared to adjacent normal tissues. WGCNA analysis highlighted the MEbrown module, which comprised 1,639 genes that exhibited strong correlations with CRC progression. Subsequently, an intersection analysis was conducted to further refine the candidate gene set, resulting in the selection of 926 differentially expressed CRC-related genes for subsequent analysis. Through univariate Cox regression, LASSO regularization, and multivariate Cox regression, a five-gene prognostic signature (TIMP1, PCOLCE2, MEIS2, HDC, CXCL13) was established, demonstrating consistent predictive accuracy in external (GSE32323) and internal validation cohorts. Mutational profiling showed predominant missense mutations across signature genes, with TIMP1 exhibiting the highest variant allele frequency. Functional enrichment analysis linked TIMP1 to critical CRC pathways including type I interferon receptor binding, oxidative phosphorylation, and Notch signaling pathways. High expression of TIMP1 was associated with poor prognosis in patients with CRC. Additionally, using siRNA technology, the impact of TIMP1 on cellular proliferation, metastasis and apoptosis in CRC cell lines (HCT116 and HT29) was investigated, showing that TIMP1 knockdown significantly inhibited CRC cell proliferation, metastasis, and promoted apoptosis. These experimental results were consistent with the conclusions drawn from the bioinformatics analysis. This research presents a prognostic risk model for CRC, further highlights TIMP1 as a potential biomarker and therapeutic target for the disease