Effects of Storage Structures and Moisture Contents on Seed Quality Attributes of Quality Protein Maize

The study was aimed to examine the effects of various storage structures and moisture contents on seed quality attributes of quality protein maize seed. The quality protein maize (QPM-1) seed was tested in conventional seed storage containers (Fertilizer sack and earthen pot) and the improved hermetic ones (Metal bin, Super grain bag, and Purdue Improved Crop Storage (PICS) bag) at Seed Science and Technology Division, Khumaltar, Nepal during February, 2015 to January 2016. Ten treatments comprising 5 storage devices in two moisture regimes (11% and 9%) replicated thrice and laid out in Completely Randomized Design (CRD). Data on temperature, relative humidity (RH), germination, electrical conductivity (EC), seed moisture content (MC) were collected bimonthly. The conventional containers were found liable to the external environmental condition whereas the hermetic structures observed with controlled RH level below 40% in all combinations. Electrical conductivity (EC) for seed vigor showed that hermetic containers provide higher seed vigor than the conventional ones. Up to 4 months all treatments were found statistically at par for germination. A significant difference was observed in each treatment after 4 months where PICS bag & Super grain bag showed best germination followed by metal bin while fertilizer bag & earthen-pot showed poorer and poorest germination respectively till one year. Almost all treatments with lower MC showed better results than the treatments with higher MC. A negative correlation (R2=69.7%) was found between EC and Germination. All six figures from 2 to 12 months on MC showed statistically different where hermetic plastic bags were found maintaining MC as initial whereas MC of fertilizer bags and earthen pot was spiked than the basal figure. The finding evidenced that the hermetic containers and low MC are the seed storage approaches for retaining the quality of seed even in an ambient environmental condition for more than a year

Prediction of overall survival for patients with metastatic castration-resistant prostate cancer : development of a prognostic model through a crowdsourced challenge with open clinical trial data

Background Improvements to prognostic models in metastatic castration-resistant prostate cancer have the potential to augment clinical trial design and guide treatment strategies. In partnership with Project Data Sphere, a not-for-profit initiative allowing data from cancer clinical trials to be shared broadly with researchers, we designed an open-data, crowdsourced, DREAM (Dialogue for Reverse Engineering Assessments and Methods) challenge to not only identify a better prognostic model for prediction of survival in patients with metastatic castration-resistant prostate cancer but also engage a community of international data scientists to study this disease. Methods Data from the comparator arms of four phase 3 clinical trials in first-line metastatic castration-resistant prostate cancer were obtained from Project Data Sphere, comprising 476 patients treated with docetaxel and prednisone from the ASCENT2 trial, 526 patients treated with docetaxel, prednisone, and placebo in the MAINSAIL trial, 598 patients treated with docetaxel, prednisone or prednisolone, and placebo in the VENICE trial, and 470 patients treated with docetaxel and placebo in the ENTHUSE 33 trial. Datasets consisting of more than 150 clinical variables were curated centrally, including demographics, laboratory values, medical history, lesion sites, and previous treatments. Data from ASCENT2, MAINSAIL, and VENICE were released publicly to be used as training data to predict the outcome of interest-namely, overall survival. Clinical data were also released for ENTHUSE 33, but data for outcome variables (overall survival and event status) were hidden from the challenge participants so that ENTHUSE 33 could be used for independent validation. Methods were evaluated using the integrated time-dependent area under the curve (iAUC). The reference model, based on eight clinical variables and a penalised Cox proportional-hazards model, was used to compare method performance. Further validation was done using data from a fifth trial-ENTHUSE M1-in which 266 patients with metastatic castration-resistant prostate cancer were treated with placebo alone. Findings 50 independent methods were developed to predict overall survival and were evaluated through the DREAM challenge. The top performer was based on an ensemble of penalised Cox regression models (ePCR), which uniquely identified predictive interaction effects with immune biomarkers and markers of hepatic and renal function. Overall, ePCR outperformed all other methods (iAUC 0.791; Bayes factor >5) and surpassed the reference model (iAUC 0.743; Bayes factor >20). Both the ePCR model and reference models stratified patients in the ENTHUSE 33 trial into high-risk and low-risk groups with significantly different overall survival (ePCR: hazard ratio 3.32, 95% CI 2.39-4.62, p Interpretation Novel prognostic factors were delineated, and the assessment of 50 methods developed by independent international teams establishes a benchmark for development of methods in the future. The results of this effort show that data-sharing, when combined with a crowdsourced challenge, is a robust and powerful framework to develop new prognostic models in advanced prostate cancer.Peer reviewe

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Production Performance and Nutrient Composition of Fodder Triticale (X Triticosecale W.)

A study was undertaken to compare the productivity and nutrient compositions of different varieties of fodder triticale (xTriticosecale W.) from 2019 to 2021. The experiments were laid-out in a Randomized Complete Block Design with four treatments consisting three varieties of triticale (Winter Max, Crack Jack, and Bolt) and one local wheat variety (as a check), with three replications. The fodder dry matter (DM) yields of evaluated varieties significantly varied (p<0.05) in 2020 and in 2021, although it was non-significant in pooled data analysis of three years. The interaction effects of the varieties and locations on fodder dry matter yield were non-significant in 2019, 2020 and pooled data analysis of three years but was significantly different in 2021. The seed yield was statistically different for the varieties in different years and also in pooled data analysis. Similarly, the interaction effects of varieties and locations were significantly different in seed yields in all three years. The seed yields were significantly different for the fodder triticale varieties in both the locations and pooled data analysis. The interaction effects of varieties and years were significant for seed yields. The average protein percentage was ranged from 8.88 to 10.39%. Bolt performed well in terms of dry matter and Winter Max did well in terms of seed production in different years while Crack Jack was found to be best for the protein percentage. The temporal and spatial effects on varieties indicate the need of the further niche or region-specific studies

Robot-assisted partial nephrectomy in morbidly obese patients: a VCQI database study

To compare perioperative outcomes following robot-assisted partial nephrectomy (RAPN) in patients with morbid obesity (body mass index (BMI \u3e 40 kg/m(2))) and non-obese patients. Using the Vattikuti Collective quality initiative (VCQI) database for RAPN, data for morbidly obese and non-obese patients was obtained. Propensity scores were calculated for two treatment groups (morbidly obese vs. non-obese) for the following variables i.e. age, sex, tumor size, RNS, surgical access (retroperitoneal/transperitoneal) and estimated glomerular filtration rate (eGFR) to ensure comparability. The primary outcome for the study was comparison of trifecta between the two groups. In this study, 158 morbidly obese patients were matched with 158 non-obese patients undergoing RAPN. Two groups matched well for age, sex, tumor size, eGFR and RNS. There was no difference between two groups for ischemia time, blood loss, blood transfusion, conversion to radical nephrectomy, length of stay, intraoperative and postoperative complications. Operative time was longer in morbidly obese patients (median 210 min vs. 120 min, p = 0.000). On pathological analysis, malignant tumors were more likely in the morbidly obese group (83.1% vs.73.4%, p = 0.018). Trifecta outcomes were comparable between the two groups (60.1% vs. 63.3%, p = 0.563). The Median duration of follow-up was 12 months (1-96 months). The morbidly obese group had significantly higher day one creatinine (1.25 ± 0.7 vs. 1.07 ± 0.37, p = 0.001) and significantly lower day one eGFR (62.1 ± 19 vs. 69.2 ± 21, p = 0.018). However, there was no difference between the two groups for the last follow-up creatinine and eGFR. RAPN in morbidly obese patients is associated with equivalent perioperative outcomes compared to non-obese patients