Insecticidal and antifeedant activity of Momordica charantia aqueous extract against cutworm, Spodoptera litura(f.) (lepidoptera: noctuidae) larvae

Antifeedant activity of aqueous extract of Momordica charantia (Cucurbitaceae) leaf or commonly known as ‘peria katak’ was tested on early 4th instar larvae of Spodoptera litura. The preliminary study was carried out to investigate the effect of feeding activity on body weight of S.litura. From the study, the early 4th larval instar was fed with fresh leaves of M. charantiaas well as fresh leaves of cabbage used as a control. The larvae fed on M. charantia leaves showed a high percentage value of reduction in weight, which is 91.81% after feeding at day-7. The aqueous extract of M. charantia was then prepared to determine the deterrent feeding activity of the larvae. The deterrent feeding activity is count based on percentage of starvation index (Percent starvation = (C-E) / (C-S) x 100; Where: C = Mean weight gain of control larvae within 24 hours, E = Mean weight gain of treated larvae at each tested concentration within 24 hours, S = Mean weight gain of starved control larvae within 24 hours). The 100% concentration of extracts was tested. From the experiment, the extract exhibited a significant antifeedant activity at the LC50 levels. We found that the extract of M. charantia has antifeedant activity against S. litura larvae, where 100% concentration gave 57.02% of starvation index

Design and fabrication of densely integrated silicon quantum dots using a VLSI compatible hydrogen silsesquioxane electron beam lithography process

Hydrogen silsesquioxane (HSQ) is a high resolution negative-tone electron beam resist allowing for direct transfer of nanostructures into silicon-on-insulator. Using this resist for electron beam lithography, we fabricate high density lithographically defined Silicon double quantum dot (QD) transistors. We show that our approach is compatible with very large scale integration, allowing for parallel fabrication of up to 144 scalable devices. HSQ process optimisation allowed for realisation of reproducible QD dimensions of 50 nm and tunnel junction down to 25 nm. We observed that 80% of the fabricated devices had dimensional variations of less than 5 nm. These are the smallest high density double QD transistors achieved to date. Single electron simulations combined with preliminary electrical characterisations justify the reliability of our device and process

Biallelic inheritance of hypomorphic PKD1 variants is highly prevalent in very early onset polycystic kidney disease

Purpose To investigate the prevalence of biallelic PKD1 and PKD2 variants underlying very early onset (VEO) polycystic kidney disease (PKD) in a large international pediatric cohort referred for clinical indications over a 10-year period (2010–2020). Methods All samples were tested by Sanger sequencing and multiplex ligation-dependent probe amplification (MLPA) of PKD1 and PKD2 genes and/or a next-generation sequencing panel of 15 additional cystic genes including PKHD1 and HNF1B. Two patients underwent exome or genome sequencing. Results Likely causative PKD1 or PKD2 variants were detected in 30 infants with PKD-VEO, 16 of whom presented in utero. Twenty-one of 30 (70%) had two variants with biallelic in trans inheritance confirmed in 16/21, 1 infant had biallelic PKD2 variants, and 2 infants had digenic PKD1/PKD2 variants. There was no known family history of ADPKD in 13 families (43%) and a de novo pathogenic variant was confirmed in 6 families (23%). Conclusion We report a high prevalence of hypomorphic PKD1 variants and likely biallelic disease in infants presenting with PKD-VEO with major implications for reproductive counseling. The diagnostic interpretation and reporting of these variants however remains challenging using current American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP) and Association of Clinical Genetic Science (ACGS) variant classification guidelines in PKD-VEO and other diseases affected by similar variants with incomplete penetrance

The anaerobic fungi: challenges and opportunities for industrial lignocellulosic biofuel production

Lignocellulose is a promising feedstock for biofuel production as a renewable, carbohydrate-rich and globally abundant source of biomass. However, challenges faced include environmental and/or financial costs associated with typical lignocellulose pretreatments needed to overcome the natural recalcitrance of the material before conversion to biofuel. Anaerobic fungi are a group of underexplored microorganisms belonging to the early diverging phylum Neocallimastigomycota and are native to the intricately evolved digestive system of mammalian herbivores. Anaerobic fungi have promising potential for application in biofuel production processes due to the combination of their highly effective ability to hydrolyse lignocellulose and capability to convert this substrate to H2 and ethanol. Furthermore, they can produce volatile fatty acid precursors for subsequent biological conversion to H2 or CH4 by other microorganisms. The complex biological characteristics of their natural habitat are described, and these features are contextualised towards the development of suitable industrial systems for in vitro growth. Moreover, progress towards achieving that goal is reviewed in terms of process and genetic engineering. In addition, emerging opportunities are presented for the use of anaerobic fungi for lignocellulose pretreatment; dark fermentation; bioethanol production; and the potential for integration with methanogenesis, microbial electrolysis cells and photofermentation

Ethnic differences translate to inadequacy of high-risk screening for gestational diabetes mellitus in an Asian population: a cohort study

Background: universal and high-risk screening for gestational diabetes mellitus (GDM) has been widely studied and debated. Few studies have assessed GDM screening in Asian populations and even fewer have compared Asian ethnic groups in a single multi-ethnic population.Methods: 1136 pregnant women (56.7% Chinese, 25.5% Malay and 17.8% Indian) from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) birth cohort study were screened for GDM by 75-g oral glucose tolerance test (OGTT) at 26–28 weeks of gestation. GDM was defined using the World Health Organization (WHO) criteria. High-risk screening is based on the guidelines of the UK National Institute for Health and Clinical Excellence.Results: universal screening detected significantly more cases than high-risk screening [crude OR 2.2 (95% CI 1.7-2.8)], particularly for Chinese women [crude OR = 3.5 (95% CI 2.5-5.0)]. Pre-pregnancy BMI > 30 kg/m2 (adjusted OR = 3.4, 95% CI 1.5-7.9) and previous GDM history (adjusted OR = 6.6, 95% CI 1.2-37.3) were associated with increased risk of GDM in Malay women while GDM history was the only significant risk factor for GDM in Chinese women (adjusted OR = 4.7, 95% CI 2.0-11.0).Conclusion: risk factors used in high-risk screening do not sufficiently predict GDM risk and failed to detect half the GDM cases in Asian women. Asian women, particularly Chinese, should be screened to avoid under-diagnosis of GDM and thereby optimize maternal and fetal outcome

Simultaneous Softening of sigma and rho Mesons associated with Chiral Restoration

Complex poles of the unitarized pi-pi scattering amplitude in nuclear matter are studied. Partial restoration of chiral symmetry is modeled by the decrease of in-medium pion decay constant f*_{pi}. For large chiral restoration (f*_{pi}/f_{pi} << 1), 2nd sheet poles in the scalar (sigma) and the vector (rho) mesons are both dictated by the Lambert W function and show universal softening as f*_{pi} decreases. In-medium pi-pi cross section receives substantial contribution from the soft mode and exhibits a large enhancement in low-energy region. Fate of this universality for small chiral restoration (f*_{pi}/f_{pi} ~ 1) is also discussed.Comment: 5 pages, 4-eps figures, version accepted by Phys. Rev. C (R) with minor modification

Novel Druggable Hot Spots in Avian Influenza Neuraminidase H5N1 Revealed by Computational Solvent Mapping of a Reduced and Representative Receptor Ensemble

The influenza virus subtype H5N1 has raised concerns of a possible human pandemic threat because of its high virulence and mutation rate. Although several approved anti-influenza drugs effectively target the neuraminidase, some strains have already acquired resistance to the currently available anti-influenza drugs. In this study, we present the synergistic application of extended explicit solvent molecular dynamics (MD) and computational solvent mapping (CS-Map) to identify putative ‘hot spots’ within flexible binding regions of N1 neuraminidase. Using representative conformations of the N1 binding region extracted from a clustering analysis of four concatenated 40-ns MD simulations, CS-Map was utilized to assess the ability of small, solvent-sized molecules to bind within close proximity to the sialic acid binding region. Mapping analyses of the dominant MD conformations reveal the presence of additional hot spot regions in the 150- and 430-loop regions. Our hot spot analysis provides further support for the feasibility of developing high-affinity inhibitors capable of binding these regions, which appear to be unique to the N1 strain

Angiopoietin-1 promotes functional neovascularization that relieves ischemia by improving regional reperfusion in a swine chronic myocardial ischemia model

10.1007/s11373-006-9082-xJournal of Biomedical Science134579-59

An integrated map of structural variation in 2,504 human genomes

Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association. © 2015 Macmillan Publishers Limited. All rights reserved

Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico, evaluation, but few have yet demonstrated real benefit to patient care. Early stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use, and pave the way to further large scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multistakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two round, modified Delphi process to collect and analyse expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 predefined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. 123 experts participated in the first round of Delphi, 138 in the second, 16 in the consensus meeting, and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI specific reporting items (made of 28 subitems) and 10 generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we have developed a guideline comprising key items that should be reported in early stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings