Identification of QTLs for Arsenic Accumulation in Maize (Zea mays L.) Using a RIL Population

The Arsenic (As) concentration in different tissues of maize was analyzed using a set of RIL populations derived from an elite hybrid, Nongda108. The results showed that the trend of As concentration in the four measured tissues was leaves>stems>bracts>kernels. Eleven QTLs for As concentration were detected in the four tissues. Three QTLs for As concentration in leaves were mapped on chromosomes 1, 5, and 8, respectively. For As concentration in the bracts, two QTLs were identified, with 9.61% and 10.03% phenotypic variance. For As concentration in the stems, three QTLs were detected with 8.24%, 14.86%, and 15.23% phenotypic variance. Three QTLs were identified for kernels on chromosomes 3, 5, and 7, respectively, with 10.73%, 8.52%, and 9.10% phenotypic variance. Only one common chromosomal region between SSR marker bnlg1811 and umc1243 was detected for QTLs qLAV1 and qSAC1. The results implied that the As accumulation in different tissues in maize was controlled by different molecular mechanism. The study demonstrated that maize could be a useful plant for phytoremediation of As-contaminated paddy soil, and the QTLs will be useful for selecting inbred lines and hybrids with low As concentration in their kernels

Association between Alcohol Consumption and Cancers in the Chinese Population—A Systematic Review and Meta-Analysis

Alcohol consumption is increasing worldwide and is associated with numerous cancers. This systematic review examined the role of alcohol in the incidence of cancer in the Chinese population.Medline/PubMed, EMBASE, CNKI and VIP were searched to identify relevant studies. Cohort and case-control studies on the effect of alcohol use on cancers in Chinese were included. Study quality was evaluated using the Newcastle-Ottawa Scale. Data were independently abstracted by two reviewers. Odds ratios (OR) or relative risks (RR) were pooled using RevMan 5.0. Heterogeneity was evaluated using the Q test and I-squared statistic. P<.01 was considered statistically significant.Pooled results from cohort studies indicated that alcohol consumption was not associated with gastric cancer, esophageal cancers (EC) or lung cancer. Meta-analysis of case-control studies showed that alcohol consumption was a significant risk factor for five cancers; the pooled ORs were 1.79 (99% CI, 1.47–2.17) EC, 1.40 (99% CI, 1.19–1.64) gastric cancer, 1.56 (99% CI, 1.16–2.09) hepatocellular carcinoma, 1.21 (99% CI, 1.00–1.46) nasopharyngeal cancer and 1.71 (99% CI, 1.20–2.44) oral cancer. Pooled ORs of the case-control studies showed that alcohol consumption was protective for female breast cancer and gallbladder cancer: OR 0.76 (99% CI, 0.60–0.97) and 0.70 (99% CI, 0.49–1.00) respectively. There was no significant correlation between alcohol consumption and lung cancer, colorectal cancer, pancreatic cancer, cancer of the ampulla of Vater, prostate cancer or extrahepatic cholangiocarcinoma. Combined results of case-control and cohort studies showed that alcohol consumption was associated with 1.78- and 1.40-fold higher risks of EC and gastric cancer but was not significantly associated with lung cancer.Health programs focused on limiting alcohol intake may be important for cancer control in China. Further studies are needed to examine the interaction between alcohol consumption and other risk factors for cancers in Chinese and other populations

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Measuring progress from 1990 to 2017 and projecting attainment to 2030 of the health-related Sustainable Development Goals for 195 countries and territories: a systematic analysis for the Global Burden of Disease Study 2017

Background Efforts to establish the 2015 baseline and monitor early implementation of the UN Sustainable Development Goals (SDGs) highlight both great potential for and threats to improving health by 2030. To fully deliver on the SDG aim of “leaving no one behind”, it is increasingly important to examine the health-related SDGs beyond national-level estimates. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2017 (GBD 2017), we measured progress on 41 of 52 health-related SDG indicators and estimated the health-related SDG index for 195 countries and territories for the period 1990–2017, projected indicators to 2030, and analysed global attainment. Methods We measured progress on 41 health-related SDG indicators from 1990 to 2017, an increase of four indicators since GBD 2016 (new indicators were health worker density, sexual violence by non-intimate partners, population census status, and prevalence of physical and sexual violence [reported separately]). We also improved the measurement of several previously reported indicators. We constructed national-level estimates and, for a subset of health-related SDGs, examined indicator-level differences by sex and Socio-demographic Index (SDI) quintile. We also did subnational assessments of performance for selected countries. To construct the healthrelated SDG index, we transformed the value for each indicator on a scale of 0–100, with 0 as the 2·5th percentile and 100 as the 97·5th percentile of 1000 draws calculated from 1990 to 2030, and took the geometric mean of the scaled indicators by target. To generate projections through 2030, we used a forecasting framework that drew estimates from the broader GBD study and used weighted averages of indicator-specific and country-specific annualised rates of change from 1990 to 2017 to inform future estimates. We assessed attainment of indicators with defined targets in two ways: first, using mean values projected for 2030, and then using the probability of attainment in 2030 calculated from 1000 draws. We also did a global attainment analysis of the feasibility of attaining SDG targets on the basis of past trends. Using 2015 global averages of indicators with defined SDG targets, we calculated the global annualised rates of change required from 2015 to 2030 to meet these targets, and then identified in what percentiles the required global annualised rates of change fell in the distribution of country-level rates of change from 1990 to 2015. We took the mean of these global percentile values across indicators and applied the past rate of change at this mean global percentile to all health-related SDG indicators, irrespective of target definition, to estimate the equivalent 2030 global average value and percentage change from 2015 to 2030 for each indicator. Findings The global median health-related SDG index in 2017 was 59·4 (IQR 35·4–67·3), ranging from a low of 11·6 (95% uncertainty interval 9·6–14·0) to a high of 84·9 (83·1–86·7). SDG index values in countries assessed at the subnational level varied substantially, particularly in China and India, although scores in Japan and the UK were more homogeneous. Indicators also varied by SDI quintile and sex, with males having worse outcomes than females for non-communicable disease (NCD) mortality, alcohol use, and smoking, among others. Most countries were projected to have a higher health-related SDG index in 2030 than in 2017, while country-level probabilities of attainment by 2030 varied widely by indicator. Under-5 mortality, neonatal mortality, maternal mortality ratio, and malaria indicators had the most countries with at least 95% probability of target attainment. Other indicators, including NCD mortality and suicide mortality, had no countries projected to meet corresponding SDG targets on the basis of projected mean values for 2030 but showed some probability of attainment by 2030. For some indicators, including child malnutrition, several infectious diseases, and most violence measures, the annualised rates of change required to meet SDG targets far exceeded the pace of progress achieved by any country in the recent past. We found that applying the mean global annualised rate of change to indicators without defined targets would equate to about 19% and 22% reductions in global smoking and alcohol consumption, respectively; a 47% decline in adolescent birth rates; and a more than 85% increase in health worker density per 1000 population by 2030. Interpretation The GBD study offers a unique, robust platform for monitoring the health-related SDGs across demographic and geographic dimensions. Our findings underscore the importance of increased collection and analysis of disaggregated data and highlight where more deliberate design or targeting of interventions could accelerate progress in attaining the SDGs. Current projections show that many health-related SDG indicators, NCDs, NCD-related risks, and violence-related indicators will require a concerted shift away from what might have driven past gains—curative interventions in the case of NCDs—towards multisectoral, prevention-oriented policy action and investments to achieve SDG aims. Notably, several targets, if they are to be met by 2030, demand a pace of progress that no country has achieved in the recent past. The future is fundamentally uncertain, and no model can fully predict what breakthroughs or events might alter the course of the SDGs. What is clear is that our actions—or inaction—today will ultimately dictate how close the world, collectively, can get to leaving no one behind by 2030

Laser Interactions for the Synthesis and In Situ Diagnostics of Nanomaterials

Laser interactions have traditionall been at thec center of nanomaterials science, providing highly nonequilibrium growth conditions to enable the syn- thesis of novel new nanoparticles, nanotubes, and nanowires with metastable phases. Simultaneously, lasers provide unique opportunities for the remote char- acterization of nanomaterial size, structure, and composition through tunable laser spectroscopy, scattering, and imaging. Pulsed lasers offer the opportunity, there- fore, to supply the required energy and excitation to both control and understand the growth processes of nanomaterials, providing valuable views of the typically nonequilibrium growth kinetics and intermediates involved. Here we illustrate the key challenges and progress in laser interactions for the synthesis and in situ diagnostics of nanomaterials through recent examples involving primarily carbon nanomaterials, including the pulsed growth of carbon nanotubes and graphene