Granular computing, rough entropy and object extraction

The problem of image object extraction in the framework of rough sets and granular computing is addressed. A measure called "rough entropy of image" is defined based on the concept of image granules. Its maximization results in minimization of roughness in both object and background regions; thereby determining the threshold of partitioning. Methods of selecting the appropriate granule size and efficient computation of rough entropy are described

“EVALUATION OF GALPHIMIA GLAUCA STEM METHANOL EXTRACT FRACTIONS FOR ANALGESIC AND ANTI-INFLAMMATORY ACTIVITIES”

Objective: This current investigation assesses in vivo central and peripheral analgesic effects and anti-inflammatory properties of fractions obtained from Galphimia glauca (GG) stem methanol extract. Methods: The laboratory models such as Swiss albino mice and Wistar albino rats were employed in the studies. The GG stem methanol extract was subjected to fractionation with solvents such as hexane, chloroform, ethyl acetate, and methanol. Orally, the dose range of 100, 200, and 400 mg/kg was given for 1 day for evaluating analgesic (hotplate test, tail clip test, writhing test, and formalin test) and weekdays for assessing anti-inflammatory activity (carrageenan and cotton pellet test methods), respectively. The experimental studies were further conducted for determining the involvement of central and peripheral receptor actions in the analgesic activity of the extract by prechallenging it with naloxone and acetic acid, respectively. The in vivo anti-inflammatory studies were conducted using carrageenan-induced rat paw edema model and cotton pellet granuloma test. Results: The LD50 of the extract was found to be >2000 mg/kg b.w. The methanol fraction of 400 mg/kg dose exhibited significant (p≤0.001) and dose-dependent analgesic and anti-inflammatory activity. It also exhibited central and peripheral analgesic actions when treated with naloxone and acetic acid, respectively. Conclusion: The results revealed that the stem methanol fraction has more potential in terms of analgesic and anti-inflammatory properties

Predictive Analysis of Tuberculosis Treatment Outcomes Using Machine Learning: A Karnataka TB Data Study at a Scale

Tuberculosis (TB) remains a global health threat, ranking among the leading causes of mortality worldwide. In this context, machine learning (ML) has emerged as a transformative force, providing innovative solutions to the complexities associated with TB treatment.This study explores how machine learning, especially with tabular data, can be used to predict Tuberculosis (TB) treatment outcomes more accurately. It transforms this prediction task into a binary classification problem, generating risk scores from patient data sourced from NIKSHAY, India's national TB control program, which includes over 500,000 patient records. Data preprocessing is a critical component of the study, and the model achieved an recall of 98% and an AUC-ROC score of 0.95 on the validation set, which includes 20,000 patient records.We also explore the use of Natural Language Processing (NLP) for improved model learning. Our results, corroborated by various metrics and ablation studies, validate the effectiveness of our approach. The study concludes by discussing the potential ramifications of our research on TB eradication efforts and proposing potential avenues for future work. This study marks a significant stride in the battle against TB, showcasing the potential of machine learning in healthcare

Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation

Due to advances in the acquisition and analysis of medical imaging, it is currently possible to quantify the tumor phenotype. The emerging field of Radiomics addresses this issue by converting medical images into minable data by extracting a large number of quantitative imaging features. One of the main challenges of Radiomics is tumor segmentation. Where manual delineation is time consuming and prone to inter-observer variability, it has been shown that semi-automated approaches are fast and reduce inter-observer variability. In this study, a semiautomatic region growing volumetric segmentation algorithm, implemented in the free and publicly available 3D-Slicer platform, was investigated in terms of its robustness for quantitative imaging feature extraction. Fifty-six 3D-radiomic features, quantifying phenotypic differences based on tumor intensity, shape and texture, were extracted from the computed tomography images of twenty lung cancer patients. These radiomic features were derived from the 3D-tumor volumes defined by three independent observers twice using 3D-Slicer, and compared to manual slice-by-slice delineations of five independent physicians in terms of intra-class correlation coefficient (ICC) and feature range. Radiomic features extracted from 3D-Slicer segmentations had significantly higher reproducibility (ICC = 0.85±0.15, p = 0.0009) compared to the features extracted from the manual segmentations (ICC = 0.77±0.17). Furthermore, we found that features extracted from 3D-Slicer segmentations were more robust, as the range was significantly smaller across observers (p = 3.819e-07), and overlapping with the feature ranges extracted from manual contouring (boundary lower: p = 0.007, higher: p = 5.863e-06). Our results show that 3D-Slicer segmented tumor volumes provide a better alternative to the manual delineation for feature quantification, as they yield more reproducible imaging descriptors. Therefore, 3D-Slicer can be employed for quantitative image feature extraction and image data mining research in large patient cohorts

Integrating livelihoods and conservation in protected areas: Understanding the role and stakeholder views on prospects for non-timber forest products, a Bangladesh case study

Protected areas (PAs) represent a key global strategy in biodiversity conservation. In tropical developing countries, the management of PAs is a great challenge as many contain resources on which local communities rely. Collection and trading of non-timber forest products (NTFPs) is a well-established forest-based livelihood strategy, which has been promoted as a potential means for enhanced conservation and improved rural livelihoods in recent years, even though the sustainability or ecological implications have rarely been tested. We conducted an exploratory survey to understand the role and stakeholder views on conservation prospects and perceived ecological feasibility of NTFPs and harvesting schemes in a northeastern PA of Bangladesh, namely the Satchari National Park. Households (n = 101) were interviewed from three different forest dependency categories, adopting a stratified random sampling approach and using a semi-structured questionnaire. The study identified 13 locally important NTFPs, with five being critically important to supporting local livelihoods. Our study suggests that collection, processing and trading in NTFPs constitutes the primary occupation for about 18% of local inhabitants and account for an estimated 19% of their cash annual income. The household consensus on issues relating to NTFPs and their prospective role in conservation was surprisingly high, with 48% of respondents believing that promotion of NTFPs in the PA could have positive conservation value. The majority (71%) of households also had some understanding of the ecological implications of NTFP harvesting, sustainability (53%) and possible management and monitoring regimes (100%). With little known about their real application in the field, our study suggests further investigations are required to understand the ecological compatibility of traditional NTFP harvesting patterns and management. © 2010 Taylor & Francis

Development of early maturing salt-tolerant rice variety KKL(R) 3 using a combination of conventional and molecular breeding approaches

Introduction: Soil salinity poses a severe threat to rice production, resulting in stunted growth, leaf damage, and substantial yield losses. This study focuses on developing an early maturing seedling stage salinity tolerant rice variety by integrating conventional breeding methods with marker assisted breeding (MAB) approaches.Methods: Seedling-stage salinity tolerance Quantitative Trait Locus (QTL) “Saltol” from the salt-tolerant parent FL478 was introduced into the high-yielding but salt-sensitive rice variety ADT 45. This was achieved through a combination of conventional breeding and MAB. The breeding process involved rigorous selection, screening, and physiological parameter assessments.Results: KKL(R) 3 (KR 15066) identified as the top performing Recombinant Inbred Line (RIL), consistently demonstrating maximum mean grain yields under both salinity (3435.6 kg/ha) and normal (6421.8 kg/ha) conditions. In comparison to the early maturing, salt-tolerant national check variety CSR 10, KKL(R) 3 exhibited a substantial yield increase over 50%.Discussion: The notable improvement observed in KKL(R) 3 positions it as a promising variety for release, offering a reliable solution to maximize yields, ensure food security, and promote agricultural sustainability in both saline and non-saline environments. The study highlights the effectiveness of MAB in developing salt-tolerant rice varieties and emphasizes the significance of the Saltol QTL in enhancing seedling stage salinity tolerance. The potential release of KKL(R) 3 has the capacity to revolutionize rice production in salt affected regions, providing farmers with a reliable solution to maximize yields and contribute to food security while ensuring agricultural sustainability

Structure-Activity Determinants in Antifungal Plant Defensins MsDef1 and MtDef4 with Different Modes of Action against Fusarium graminearum

Plant defensins are small cysteine-rich antimicrobial proteins. Their three-dimensional structures are similar in that they consist of an α-helix and three anti-parallel β-strands stabilized by four disulfide bonds. Plant defensins MsDef1 and MtDef4 are potent inhibitors of the growth of several filamentous fungi including Fusarium graminearum. However, they differ markedly in their antifungal properties as well as modes of antifungal action. MsDef1 induces prolific hyperbranching of fungal hyphae, whereas MtDef4 does not. Both defensins contain a highly conserved γ-core motif (GXCX3–9C), a hallmark signature present in the disulfide-stabilized antimicrobial peptides, composed of β2 and β3 strands and the interposed loop. The γ-core motifs of these two defensins differ significantly in their primary amino acid sequences and in their net charge. In this study, we have found that the major determinants of the antifungal activity and morphogenicity of these defensins reside in their γ-core motifs. The MsDef1-γ4 variant in which the γ-core motif of MsDef1 was replaced by that of MtDef4 was almost as potent as MtDef4 and also failed to induce hyperbranching of fungal hyphae. Importantly, the γ-core motif of MtDef4 alone was capable of inhibiting fungal growth, but that of MsDef1 was not. The analysis of synthetic γ-core variants of MtDef4 indicated that the cationic and hydrophobic amino acids were important for antifungal activity. Both MsDef1 and MtDef4 induced plasma membrane permeabilization; however, kinetic studies revealed that MtDef4 was more efficient in permeabilizing fungal plasma membrane than MsDef1. Furthermore, the in vitro antifungal activity of MsDef1, MsDef1-γ4, MtDef4 and peptides derived from the γ-core motif of each defensin was not solely dependent on their ability to permeabilize the fungal plasma membrane. The data reported here indicate that the γ-core motif defines the unique antifungal properties of each defensin and may facilitate de novo design of more potent antifungal peptides

Slow slip source characterized by lithological and geometric heterogeneity

Slow slip events (SSEs) accommodate a significant proportion of tectonic plate motion at subduction zones, yet little is known about the faults that actually host them. The shallow depth (<2 km) of well-documented SSEs at the Hikurangi subduction zone offshore New Zealand offers a unique opportunity to link geophysical imaging of the subduction zone with direct access to incoming material that represents the megathrust fault rocks hosting slow slip. Two recent International Ocean Discovery Program Expeditions sampled this incoming material before it is entrained immediately down-dip along the shallow plate interface. Drilling results, tied to regional seismic reflection images, reveal heterogeneous lithologies with highly variable physical properties entering the SSE source region. These observations suggest that SSEs and associated slow earthquake phenomena are promoted by lithological, mechanical, and frictional heterogeneity within the fault zone, enhanced by geometric complexity associated with subduction of rough crust