The nonnegative Q−matrix completion problem

In this paper, the nonnegative $Q$-matrix completion problem is studied. A real $n\times n$ matrix is a $Q$-matrix if for $k\in \{1,\ldots, n\}$, the sum of all $k \times k$ principal minors is positive. A digraph $D$ is said to have nonnegative $Q$-completion if every partial nonnegative $Q$-matrix specifying $D$ can be completed to a nonnegative $Q$-matrix. For nonnegative $Q$-completion problem, necessary conditions and sufficient conditions for a digraph to have nonnegative $Q$-completion are obtained. Further, the digraphs of order at most four that have nonnegative $Q$-completion have been studied

Pixel Count Based Yield Estimation Model, to Reduce Input feature required in Machine Learning System for Major Agricultural Crop

Traditionally, the crop analysis and agricultural production predictions were done based on statistical models. However, with the climate of the world changing to drastic degrees, these statistical models have become very ambiguous. Hence, it becomes prudent that we resort to other less vague methods. Through a traditional model, user interacts primarily with a mathematical computations and its results and helps to solve well-defined and structured problems. Whereas, in a data driven model, user interacts primarily with the data and helps to solve mainly unstructured problems. At this point, enters the concept of Machine Learning. In this work we tried to find a new approach to reduce the input feature to reduce the processing power needed. We have attempted at predicting the agricultural outputs of rice production in an area by implementing a pixel count based classification machine learning model. Through this model, we tried to predict the approximate crop yield based on NDVI values analyzed for a particular season and area

Evaluation of Antioxidant and Antifungal Activities of Polyphenol-rich Extracts of Dried Pulp of Garcinia pedunculata Roxb. and Garcinia morella Gaertn. (Clusiaceae)

Purpose: To evaluate the antioxidant and antifungal activities of polyphenol-rich extracts of the dried fruit pulp of Garcinia pedunculata (GP) and Garcinia morella (GM) to determine their traditional claims of therapeutic activity against certain diseases.Methods: Analysis of total phenolic (TP) and flavonoid (TF) contents of the extracts were performed by Folin-Ciocalteau and Arvouet-Grand methods. The antioxidant activity of the extracts was determined by 1,1-diphenyl-2-picrylhydrazyl (DPPH), hydrogen peroxide (H2O2) free radical scavenging activity, reducing power and in vitro lipid peroxidation (LPO). Antifungal activity was evaluated by agar-well diffusion method while mineral content was evaluated by atomic absorption spectrophotometry (AAS).Results: Significant amounts of TP (5.87 ± 0.06 and 5.46 ± 0.02 mg catechin eqivalents/g) and TF (5.61 ± 0.16 and 3.69 ± 0.04 mg quercetin equivalents/g) were found in the cold water (CW) extracts of GP and GM, respectively, along with DPPH free radical scavenging activity (50 % inhibitory concentration (IC50) = 3.53 ± 0.04 and 1 ± 0.03μg/mL) and H2O2-radical scavenging activity (IC50 = 1.4 ± 0.02 and 1.44 ± 0.01 μg/mL). Results indicated that the CW extracts of GP and GM were potent reducing agent than the HW extracts. CW extract of both species prevented in vitro LPO (IC50= 42 ± 0.01 and 30.36 ± 0.03 μg/mL) significantly. The antifungal activity of GP and GM extracts against some human dermatophytes was high. High concentrations of K and Fe were found in the extracts.Conclusion: GP and GM extracts have great potential as a source for useful antioxidant and antifungal agents.Keywords: Antioxidant, Phenolic, Flavonoid, Lipid peroxidation, Antifungal, Dermatophyte

Alzheimer Disease Detection using AI with Deep Learning based Features with Development and Validation based on Data Science

Alzheimer's disease (AD), a neurological condition that worsens over time, affects millions of individuals worldwide. Because of this, effective intervention and therapy depend on early and precise detection. In recent years, encouraging findings have been obtained using data science and artificial intelligence (AI) techniques in the field of medical diagnostics, particularly AD diagnosis. This work seeks to develop an accurate algorithm for diagnosing AD by identifying AI-based traits from neuroimaging and clinical data.The three key steps of the proposed methodology are data preprocessing, feature extraction, and model development and validation. To offer neuroimaging data, such as MRI and PET scans, as well as essential clinical information, a cohort of persons made up of AD patients and healthy controls is obtained. Throughout the preparation stage, the data are normalised, standardised, and quality-checked to ensure accuracy and consistency.The critical role of feature extraction in locating critical patterns and features potentially indicative of AD is critical. Advanced AI techniques like Convolutional Neural Networks and Recurrent Neural Networks are utilised to extract discriminative features from neuroimaging data after subjecting it to feature engineering methods.The retrieved features are then utilised to build a prediction model using state-of-the-art machine learning techniques such as Support Vector Machines (SVM), Random Forests, or Deep Learning architectures. Strict validation methods, such cross-validation and test datasets, are used to evaluate the model's performance in order to ensure generalizability and minimise overfitting.The project's objective is to identify AD with high specificity, sensitivity, and accuracy to support early diagnosis and tailored treatment planning. The results of this research contribute to the body of knowledge on AI-based diagnostics for neurodegenerative diseases and have the potential to significantly impact clinical practises by facilitating early interventions and improving patient outcomes. It is important to take into account the size and heterogeneity of the dataset as well as any prospective improvements and future expansions to the usage of AI in AD detection

Magnetic Resonance Imaging Appearance of Giant Intracerebral Tuberculoma: A Retrospective Analysis

Background: Giant intracerebral tuberculomas are rare lesions but should be considered in the differential diagnosis of intracranial space-occupying lesion in an endemic region. Objective: The purpose of this study is to analyze the clinical data and magnetic resonance imaging (MRI) findings of giant intracerebral tuberculomas to improve the diagnostic precision. Material and Methods: The clinical and MRI findings of 22 patients of giant intracerebral tuberculoma were analyzed retrospectively. For the statistical analysis independent sample Student t-test was used. Results: For 22 patients included in this sample the giant intracerebral tuberculoma was of size more than 2.5cm. The majority of the giant tuberculomas (19 patients (86.4%))was located in the supratentorial area.T2-weighted hypointense core of giant tuberculoma was observed in 12 patients (54.5%) and T1 hyperintensities were observed in peripheral (wall) of the giant tuberculoma in 14 patients (63.6%). The mean ADC value of the peripheral (wall) of the giant tuberculoma was 1.034± 0.466[SD] x 10-3mm2/s and the core was 0.994± 0.455[SD] x 10-3mm2/s with a statistically significant difference (p-value <0.0005) in between. MR spectroscopy showed raised lipid peak at 0.9 to 1.33 ppm in 10 patients (45.5%),raised lipid-lactate peak in 12 patients (54.5%),raised Choline/Cr ratio more than 1.2 in 14 patients(63.6%) and Choline/Cr ratio less than 1.2 in 5 patients (22.7%). Associated involvement of lung was observed in the 6patients (27.3%), cervical lymph node in 1 patient (4.5%) and spine in 1patient (4.5%). Conclusions: MRI plays a vital role in distinguishing giant intracerebral tuberculomas from other intracranial space-occupying lesions, thereby allows the early institution of anti-tubercular treatment (ATT), decreased patient morbidity, mortality, and prevents unnecessary neurosurgical excision

Novel peptides of therapeutic promise from Indian conidae

Highly structured small peptides are the major toxic constituents of the venom of cone snails, a family of widely distributed predatory marine molluscs. These animals use the venom for rapid prey immobilization. The peptide components in the venom target a wide variety of membrane-bound ion channels and receptors. Many have been found to be highly selective for a diverse range of mammalian ion channels and receptors associated with pain-signaling pathways. Their small size, structural stability, and target specificity make them attractive pharmacologic agents. A select number of laboratories mainly from the United States, Europe, Australia, Israel, and China have been engaged in intense drug discovery programs based on peptides from a few snail species. Coastal India has an estimated 20-30% of the known cone species; however, few serious studies have been reported so far. We have begun a comprehensive program for the identification and characterization of peptides from cone snails found in Indian Coastal waters. This presentation reviews our progress over the last 2 years. As expected from the evolutionary history of these venom components, our search has yielded novel peptides of therapeutic promise from the new species that we have studied

NMR assignment of 2H^2H, 13C^{13}C and 15N^{15}N labeled amino-terminal domain of apo-pantothenate synthetase from E. coli

Pantothenate (vitamin $b_5$) is an essential precursor for the biosynthesis of coenzyme A (CoA), an essential metabolite for many important cellular processes (Brown et al., 1987). Pantothenate Synthetase (PS) catalyzes the ATP dependent condensation of D-pantoate with $_\beta$-alanine to give rise to pantothenate. Our interest lies in studying the solution-state inter-domain interactions in E. coli PS that results in formation of the catalytic site. As a first step we have cloned and over-expressed an isotopically enriched sample of the dimeric amino-terminal domain (residues 1–176) of E. coli PS. Here we report the backbone and side-chain assignments for HN, ^{13}C (C^a, C^\beta & CO) and $^{15}N$ nuclei of the protein, obtained using triple resonance NMR experiments (Yamazaki et al., 1994). CSI and NOE data indicates that the protein is well folded containing both \alpha-helices and $_\beta$-sheet. Assignment for \sim 95% of backbone and side-chain resonances for the catalytic domain has been obtained and deposited in BMRB (accession # 6940)

NMR assignment of 2H^2H, 13C^{13}C and 15N^{15}N labeled amino-terminal domain of apo-pantothenate synthetase from E. coli

Pantothenate (vitamin $b_5$) is an essential precursor for the biosynthesis of coenzyme A (CoA), an essential metabolite for many important cellular processes (Brown et al., 1987). Pantothenate Synthetase (PS) catalyzes the ATP dependent condensation of D-pantoate with $_\beta$-alanine to give rise to pantothenate. Our interest lies in studying the solution-state inter-domain interactions in E. coli PS that results in formation of the catalytic site. As a first step we have cloned and over-expressed an isotopically enriched sample of the dimeric amino-terminal domain (residues 1–176) of E. coli PS. Here we report the backbone and side-chain assignments for HN, ^{13}C (C^a, C^\beta & CO) and $^{15}N$ nuclei of the protein, obtained using triple resonance NMR experiments (Yamazaki et al., 1994). CSI and NOE data indicates that the protein is well folded containing both \alpha-helices and $_\beta$-sheet. Assignment for \sim 95% of backbone and side-chain resonances for the catalytic domain has been obtained and deposited in BMRB (accession # 6940)