Genetic signatures of parental contribution in black and white populations in Brazil

Two hundred and three individuals classified as white were tested for 11 single nucleotide polymorphisms plus two insertion/deletions in their Y-chromosomes. A subset of these individuals (n = 172) was also screened for sequences in the first hypervariable segment of their mitochondrial DNA (mtDNA). In addition, complementary studies were done for 11 of the 13 markers indicated above in 54 of 107 black subjects previously investigated in this southern Brazilian population. The prevalence of Y-chromosome haplogroups among whites was similar to that found in the Azores (Portugal) or Spain, but not to that of other European countries. About half of the European or African mtDNA haplogroups of these individuals were related to their places of origin, but not their Amerindian counterparts. Persons classified in these two categories of skin color and related morphological traits showed distinct genomic ancestries through the country. These findings emphasize the need to consider in Brazil, despite some general trends, a notable heterogeneity in the pattern of admixture dynamics within and between populations/groups

The global burden of cancer attributable to risk factors, 2010–19: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND: Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. METHODS: The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk–outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. FINDINGS: Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01–4·94) deaths and 105 million (95·0–116) DALYs for both sexes combined, representing 44·4% (41·3–48·4) of all cancer deaths and 42·0% (39·1–45·6) of all DALYs. There were 2·88 million (2·60–3·18) risk-attributable cancer deaths in males (50·6% [47·8–54·1] of all male cancer deaths) and 1·58 million (1·36–1·84) risk-attributable cancer deaths in females (36·3% [32·5–41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6–28·4) and DALYs by 16·8% (8·8–25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9–42·8] and 33·3% [25·8–42·0]). INTERPRETATION: The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden

Coal-based Industrial Complex at Ramgarh

A resource-development project based chiefly on the large coking coal deposits discovered between Ramgarh and contiguous areas of the Bokaro Steel Project along the banks of the Damodar River is discussed in the paper. Starting with Carbonization of 4000 t/d of coking blend, the integrated project envisages the production of pig iron in low-shaft furnaces on the one hand and finally urea with surplus coke oven gas on the other. Other raw materials e.g. iron ore, limestone, manganese ore, etc. will be transported from a short distance and utilities like water and power resources will be made available from nearby sources

Autism, Alzheimer disease, and fragile X: APP, FMRP, and mGluR5 are molecular links

The present review highlights an association between autism, Alzheimer disease (AD), and fragile X syndrome (FXS). We propose a conceptual framework involving the amyloid-β peptide (Aβ), Aβ precursor protein (APP), and fragile X mental retardation protein (FMRP) based on experimental evidence. The anabolic (growth-promoting) effect of the secreted α form of the amyloid-β precursor protein (sAPPα) may contribute to the state of brain overgrowth implicated in autism and FXS. Our previous report demonstrated that higher plasma sAPPα levels associate with more severe symptoms of autism, including aggression. This molecular effect could contribute to intellectual disability due to repression of cell–cell adhesion, promotion of dense, long, thin dendritic spines, and the potential for disorganized brain structure as a result of disrupted neurogenesis and migration. At the molecular level, APP and FMRP are linked via the metabotropic glutamate receptor 5 (mGluR5). Specifically, mGluR5 activation releases FMRP repression of APP mRNA translation and stimulates sAPP secretion. The relatively lower sAPPα level in AD may contribute to AD symptoms that significantly contrast with those of FXS and autism. Low sAPPα and production of insoluble Aβ would favor a degenerative process, with the brain atrophy seen in AD. Treatment with mGluR antagonists may help repress APP mRNA translation and reduce secretion of sAPP in FXS and perhaps autism

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Not AvailableThe balanced application of fertilizer nutrients particularly the major nutrients, N P and K in optimum quantity, based on soil test and crop requirement is one of the most vital aspects for sustaining higher agricultural production. This requires the application of optimally balanced quantity of fertilizers in right proportion through correct method and time of application for a specific soil-crop-climate situation. It ensures increased quantity of produce, maintenance of soil productivity and the most efficient and judicious use of applied fertilizers. Thus in this content the soil fertility evolution and refined fertilizer prescription for sustained agricultural production is of great importance to any country of the world in general and farming community in particular. Hence the soils have to be tested precisely for their available nutrient status for making fertilizer recommendation based on crop response and economic circumstances. The determination of the amount of fertilizer that should be applied to a crop would be delightfully simple if a chemist could analyze the soil, and then use the analyses to measure the amount of plant nutrients in the soil and to calculate the amounts that should be applied to correct deficiencies. It is unfortunate that the determination of fertilizer requirements is not as simple as this. As every soil chemist knows, there are basic problems in interpreting soil test values in terms of nutrient availability to crops due to the interacting effects of other soil constituents, surface reactions, the changes that may occur in test values both laterally across farmers’ fields and vertically down the soil profile, and to all these factors may be added the uncertainties of weather, effects of crop variety, disease, pests etc. Any suggestion therefore that fertilizer requirement can be determined solely on the basis of a simple laboratory analysis of a few grams of soil, represents a vast oversimplification of a highly complex system. Nevertheless soil analysis can provide useful information on the effect that fertilizers are likely to have on yields, and it is important to use this information for the estimation of fertilizer requirements. Soil tests can provide a valuable piece of information and as such should be used in conjunction with such other information that is available for the estimation of fertilizer requirements. Soil test crop-response studies has been going on for a quite a long period of time both in India and abroad. the All India Coordinated Research Project on Soil Test Crop Response Correlation was initiated during the year 1967-68. Currently, STCR project is having seventeen cooperating centres. Under the STCR project multiple regression approach is being used to calculate the dose of nutrient (s) required to obtain the maximum yield of crops under given set of experimental conditions. It can further be used to calculate the economic dose of fertilizer nutrients by incorporating a constant factor i.e. per unit cost of input (fertilizer) in the original equation. In this approach yield is regressed with soil nutrients, fertilizer nutrients, their quadratic terms and the interaction term of soil and fertilizer nutrients. For this the following criteria should be fulfilled. (a) Soil test crop response calibration for economic yield of a crop is possible only when the response to added nutrients follow the law of diminishing returns. i.e. the signs of partial regression coefficients of linear, quadratic terms of nutrients and their interaction with available soil nutrients should in general be positive, negative and negative ( + , _ , _ ) respectively. (b) The coefficient of determination should be high. (c) The partial regression coefficients should be statistically significant. (d) The experiment should have sufficient design points i.e. the number of treatments should be at least two or more than the number of variables in the model. The above criterions are seldom fulfilled under the STCR project data. In such cases the optimum values of the nutrients cannot be derived or if they could be derived, they are either too high or too low. Keeping in view of the above problems and for better analysis of data, their interpretation and improvement in soil test calibration, the projector coordinator (STCR) Indian Institute of Soil Science, Bhopal, formally approached IASRI, New Delhi for collaboration. As large amount of data have also been gathered under the project, the creation of a database under the project was also solicited. Consequently a project entitled Planning, designing and analysis of experiments relating to AICRP on soil test crop response correlation was under taken at IASRI w.e.f. March 01, 2000 with the following objectives: (1) To improve the existing methodology for analysis of data of ongoing STCR experiments. (2) To carry out planning, design for the conduct of new set of experiments and subsequently to carry out the analysis of data. (3) To develop a database for the project. In this report, the first two chapters contain introduction and review of literature. In the third chapter Analytical techniques has been discussed along with a method, which has been developed at IASRI based on Response surface methodology has been discussed. In this method, the optimal values of N,P and K fertilizer nutrients can be derived if the soil test values for a particular site is available. Chapter four deals with designs for future STCR experimentation. In this, a number of designs have been proposed with different designs points, based on the requirements of STCR project, from designs of type (5 x 4 x 3), ( 4 x 4 x 3), (4 x 4 x 4) etc. Chapter five deals with results and discussion. Although we have received the data from a number of centres but due to pending query for discrepancies, only data of seven centres (totalling about 12 experiments) have been discussed in detail. The common result is that in almost all the cases the response surface methodology produced the stationery point as saddle points i.e. neither maxima nor minima. In such cases exploration of the response surface in the vicinity of the stationery point has been attempted. The optimal values of the fertilizer nutrients N, P and K obtained by Response Surface Methodology, has been found to be closely related to that obtained by Targeted yield approach adopted by the STCR project. Thus one could advocate for the adoption of the Targeted yield approach as has been tested by sound statistical system of Response Surface Methodology. A number of models have been tried for all the experiments but the models with 15 variables and 18 variables have been mostly found to be better. One model with 15 variables, which includes the interactions (FN x FP), (FN x FK) and (FP x FK) also gives higher values of R-Square. In some cases it was possible to find the optimum values from the Multiple Regression equations. Lastly, in Chapter six we have given the sketch of the database prepared for storing the STCR data. In this, number of queries can be prepared and the data can be retrieved.Not Availabl