Mapping child growth failure across low- and middle-income countries

Childhood malnutrition is associated with high morbidity and mortality globally1. Undernourished children are more likely to experience cognitive, physical, and metabolic developmental impairments that can lead to later cardiovascular disease, reduced intellectual ability and school attainment, and reduced economic productivity in adulthood2. Child growth failure (CGF), expressed as stunting, wasting, and underweight in children under five years of age (0�59 months), is a specific subset of undernutrition characterized by insufficient height or weight against age-specific growth reference standards3�5. The prevalence of stunting, wasting, or underweight in children under five is the proportion of children with a height-for-age, weight-for-height, or weight-for-age z-score, respectively, that is more than two standard deviations below the World Health Organization�s median growth reference standards for a healthy population6. Subnational estimates of CGF report substantial heterogeneity within countries, but are available primarily at the first administrative level (for example, states or provinces)7; the uneven geographical distribution of CGF has motivated further calls for assessments that can match the local scale of many public health programmes8. Building from our previous work mapping CGF in Africa9, here we provide the first, to our knowledge, mapped high-spatial-resolution estimates of CGF indicators from 2000 to 2017 across 105 low- and middle-income countries (LMICs), where 99 of affected children live1, aggregated to policy-relevant first and second (for example, districts or counties) administrative-level units and national levels. Despite remarkable declines over the study period, many LMICs remain far from the ambitious World Health Organization Global Nutrition Targets to reduce stunting by 40 and wasting to less than 5 by 2025. Large disparities in prevalence and progress exist across and within countries; our maps identify high-prevalence areas even within nations otherwise succeeding in reducing overall CGF prevalence. By highlighting where the highest-need populations reside, these geospatial estimates can support policy-makers in planning interventions that are adapted locally and in efficiently directing resources towards reducing CGF and its health implications. © 2020, The Author(s)

Fludarabine, cytarabine, granulocyte colony-stimulating factor, and idarubicin with gemtuzumab ozogamicin improves event-free survival in younger patients with newly diagnosed aml and overall survival in patients with npm1 and flt3 mutations

Purpose To determine the optimal induction chemotherapy regimen for younger adults with newly diagnosed AML without known adverse risk cytogenetics. Patients and Methods One thousand thirty-three patients were randomly assigned to intensified (fludarabine, cytarabine, granulocyte colony-stimulating factor, and idarubicin [FLAG-Ida]) or standard (daunorubicin and Ara-C [DA]) induction chemotherapy, with one or two doses of gemtuzumab ozogamicin (GO). The primary end point was overall survival (OS). Results There was no difference in remission rate after two courses between FLAG-Ida + GO and DA + GO (complete remission [CR] + CR with incomplete hematologic recovery 93% v 91%) or in day 60 mortality (4.3% v 4.6%). There was no difference in OS (66% v 63%; P = .41); however, the risk of relapse was lower with FLAG-Ida + GO (24% v 41%; P < .001) and 3-year event-free survival was higher (57% v 45%; P < .001). In patients with an NPM1 mutation (30%), 3-year OS was significantly higher with FLAG-Ida + GO (82% v 64%; P = .005). NPM1 measurable residual disease (MRD) clearance was also greater, with 88% versus 77% becoming MRD-negative in peripheral blood after cycle 2 (P = .02). Three-year OS was also higher in patients with a FLT3 mutation (64% v 54%; P = .047). Fewer transplants were performed in patients receiving FLAG-Ida + GO (238 v 278; P = .02). There was no difference in outcome according to the number of GO doses, although NPM1 MRD clearance was higher with two doses in the DA arm. Patients with core binding factor AML treated with DA and one dose of GO had a 3-year OS of 96% with no survival benefit from FLAG-Ida + GO. Conclusion Overall, FLAG-Ida + GO significantly reduced relapse without improving OS. However, exploratory analyses show that patients with NPM1 and FLT3 mutations had substantial improvements in OS. By contrast, in patients with core binding factor AML, outcomes were excellent with DA + GO with no FLAG-Ida benefit

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dysregulated in tumors, but only a handful are known to play pathophysiological roles in cancer. We inferred lncRNAs that dysregulate cancer pathways, oncogenes, and tumor suppressors (cancer genes) by modeling their effects on the activity of transcription factors, RNA-binding proteins, and microRNAs in 5,185 TCGA tumors and 1,019 ENCODE assays. Our predictions included hundreds of candidate onco- and tumor-suppressor lncRNAs (cancer lncRNAs) whose somatic alterations account for the dysregulation of dozens of cancer genes and pathways in each of 14 tumor contexts. To demonstrate proof of concept, we showed that perturbations targeting OIP5-AS1 (an inferred tumor suppressor) and TUG1 and WT1-AS (inferred onco-lncRNAs) dysregulated cancer genes and altered proliferation of breast and gynecologic cancer cells. Our analysis indicates that, although most lncRNAs are dysregulated in a tumor-specific manner, some, including OIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergistically dysregulate cancer pathways in multiple tumor contexts. Chiu et al. present a pan-cancer analysis of lncRNA regulatory interactions. They suggest that the dysregulation of hundreds of lncRNAs target and alter the expression of cancer genes and pathways in each tumor context. This implies that hundreds of lncRNAs can alter tumor phenotypes in each tumor context

Not Available

Not AvailableThe present investigation was done on six pruning time’s i.e 15th May, 15th June, 15th July, 15th August, 15th Sept and Control and seven different genotypes such as. Sardar, RHR-Guv-58, RHR-Guv-60, RHR-Guv-14, RHR-Guv-16, RHR-Guv-3 and RHR-Guv-6. The experiment was laid out in factorial randomized block design with fourty two treatments replicated two times. Growth characters were significantly influenced by different genotypes. The plant spread, number of sprouted shoots, girth of shoot, shoot length was recorded maximum in Sardar. The Minimum time required for initiation of new shoots was observed in 15th May pruning time and in Sardar and also in their interactions. As well as, with respect to marketable yield 15th July pruning time was found to be better.Not Availabl

Histochemical and ultrastructural analysis of adhesive setae of lizards indicate that they contain lipids in addition to keratins

the ultrastructural analysis of the adhesive pad lamellae in geckos show oberhauvcthen cells contain lipids in addition to beta-proteins

Advancing real-time plant disease detection: A lightweight deep learning approach and novel dataset for pigeon pea crop

Plant disease detection and early disease treatment are essential for sustainable crop production. Computer vision for crop science is overgrowing with the advancement in deep learning. Real time plant disease detection poses a challenge due to the unpredictable spread of diseases within the plant, environmental factors, and the scarcity of real field datasets. The proposed work systematically addresses these issues through three key components: (a) Collaboratively generating the novel pigeon pea image dataset from agricultural fields, in partnership with 20 Agricultural Research Centers (ARS) and governmental agencies spanning 18 Indian states. (b) The design of lightweight and high-performance models for real-time plant disease detection in resource-constrained devices. (c) The extraction of multiscale feature of plant diseases using Multi-kernel Depthwise separable Convolutions. The proposed lightweight Lite-MDC architecture uses the Multi-kernel Depthwise separable Convolutions (MDsConv). The MDsConv module captures spatial features across various scales while maintaining a lightweight design. It effectively extracts multi-scale information to characterize plant diseases, accommodating their diverse scale. Proposed architectural approach significantly reduces computational complexity, employing only 2.2 million parameters, which is a 62% reduction compared to the standard VGG16 architecture. The proposed method outperforms the state-of-the-art networks such as InceptionV3, VGG16, ResNet50, DenseNet, MobileNet, MobileNetV3, NASNet, and EfficieNetB0 on the proposed pigeon pea dataset with 94.14% accuracy. Notably, the method achieves a 34 Frames Per Second (FPS) inference on an NVIDIA P100 GPU. Furthermore, its performance is validated across publicly available datasets, including the plant village dataset, Cassava, and apple leaf datasets, yielding 99.78%, 86.4%, and 97.2% accuracy, respectively. The Lite-MDC model exhibits the potential for real-time plant disease detection on resource-constrained edge devices such as Agriculture robots and drones

Not Available

This is the first report of the entomopathogenic nematode species Steinernema cholashanense from India. This EPN species have a biocontrol potential against insect pests of potato.Not AvailableNot Availabl