An interesting case of suicidal poisoning

Aconite is one of the most poisonous known herbs. It has been known to be used as a homicidal poison from long time in history. However this is rarely known to be used as suicidal poison. Poisoning with aconite is usually fatal and death commonly occurs due to arrhythmias and cardiotoxicity. We report a case of attempted suicidal poisoning by aconite where patient survived in spite of documented cardiotoxic effects of the poison

Super Chief Tomato Hybird

Super Chief is the F 1 hybrid resulting from a cross of SD 82- 106 x SD 85-048-1. Both parents were developed in the South Dakota fresh-market tomato breeding program. The hybrid was evaluated as 87-13

HFRAS : design of a high-density feature representation model for effective augmentation of satellite images

Efficiently extracting features from satellite images is crucial for classification and post-processing activities. Many feature representation models have been created for this purpose. However, most of them either increase computational complexity or decrease classification efficiency. The proposed model in this paper initially collects a set of available satellite images and represents them via a hybrid of long short-term memory (LSTM) and gated recurrent unit (GRU) features. These features are processed via an iterative genetic algorithm, identifying optimal augmentation methods for the extracted feature sets. To analyse the efficiency of this optimization process, we model an iterative fitness function that assists in incrementally improving the classification process. The fitness function uses an accuracy & precision-based feedback mechanism, which helps in tuning the hyperparameters of the proposed LSTM & GRU feature extraction process. The suggested model used 100 k images, 60% allocated for training and 20% each designated for validation and testing purposes. The proposed model can increase classification precision by 16.1% and accuracy by 17.1% compared to conventional augmentation strategies. The model also showcased incremental accuracy enhancements for an increasing number of training image sets.© The Author(s) 2023. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.fi=vertaisarvioitu|en=peerReviewed

Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms : Principles and Perspectives

Funding Information: This work was supported in part by the National Research Foundation of Korea grant funded by the Korean Government, Ministry of Science and ICT, under Grant NRF-2020R1A2B5B02002478, and in part by Sejong University through its Faculty Research Program.Peer reviewe

Patterns of physiologic diversity of Puccinia triticina on wheat in Indian subcontinent during 2008-2013

Brown (leaf) rust of wheat, caused by Puccinia triticina was widely distributed in all the wheat growing areas. To identify new pathotypes and determine the virulence pattern of Puccinia triticina, rust samples were analyzed from the wheat growing areas in India, Bangladesh, Bhutan and Nepal. A new pathotype 93R57 (104-4=NHKSP) was identified from Arki area of Solan district in Himachal Pradesh. Among the 37 pathotypes identified in 2124 samples analyzed during 2008-2013, four predominant pathotypes 121R63-1(77-5=THTTS), 21R55 (104-2=PHTTL), 121R60-1(77-9=MHTTS) and 21R63 (104-3=PHTKL) accounted for 68% of the population. These four pathotypes have virulence to Lr1, Lr3, Lr10, Lr11, Lr12, Lr13, Lr14a, Lr16, Lr17, Lr23, Lr26. Virulence on Lr19 was found in 7 samples only whereas the proportion of pathotype 121R60-1 (77-9=MHTTS) has increased recently in Tamil Nadu and was identified in about 40% of the samples from that area. Virulent pathotypes on Lr9, Lr24, Lr25, Lr32, Lr39 and Lr45 were not observed in the field population of brown rust in the Indian subcontinent during the last five cropping seasons

Heritability of ocular component dimensions in chickens: genetic variants controlling susceptibility to experimentally induced myopia and pretreatment eye size are distinct

Purpose. To investigate the extent to which shared genetic variants control (1) multiple ocular component dimensions and (2) both normal eye length and susceptibility to visually induced myopic eye growth. Methods. Two laboratory-reared populations of chicks were examined. The first was a three-generation pedigree of White Leghorn (WL) birds used in a selective breeding experiment testing susceptibility to monocular deprivation of sharp vision (DSV). The chicks were assessed before (age, 4 days) and after 4 days of treatment with diffusers. The second was the 10th generation of an advanced intercross line (AIL) derived from a broiler-layer cross (age, 3 weeks). Variance components analysis was used to estimate heritability and to assess the evidence for shared genetic determination. Results. All measured ocular components were moderately or highly heritable (range, 0.36–0.61; all P < 0.001) in both chick populations, and there were strong genetic correlations across the traits, corneal curvature, vitreous chamber depth, and axial length. The genetic correlations between eye size and myopia susceptibility traits were not significantly different from 0. Conclusions. The genetic variants controlling ocular component dimensions in chicks are shared across some ocular traits (corneal curvature, vitreous chamber depth, and axial length) but distinct for others (lens thickness and corneal thickness). The genetic variants controlling susceptibility to visually induced myopia in chicks are different from those controlling normal eye siz

Deep learning approach for discovery of in silico drugs for combating COVID-19

Early diagnosis of pandemic diseases such as COVID-19 can prove beneficial in dealing with difficult situations and helping radiologists and other experts manage staffing more effectively. The application of deep learning techniques for genetics, microscopy, and drug discovery has created a global impact. It can enhance and speed up the process of medical research and development of vaccines, which is required for pandemics such as COVID-19. However, current drugs such as remdesivir and clinical trials of other chemical compounds have not shown many impressive results. Therefore, it can take more time to provide effective treatment or drugs. In this paper, a deep learning approach based on logistic regression, SVM, Random Forest, and QSAR modeling is suggested. QSAR modeling is done to find the drug targets with protein interaction along with the calculation of binding affinities. Then deep learning models were used for training the molecular descriptor dataset for the robust discovery of drugs and feature extraction for combating COVID-19. Results have shown more significant binding affinities (greater than -18) for many molecules that can be used to block the multiplication of SARS-CoV-2, responsible for COVID-19. [Abstract copyright: Copyright © 2021 Nishant Jha et al.