Zinc whisker growth from electroplated finishes – a review

Electroplated zinc finishes have been associated with the electronics industry for many years as a result of their excellent corrosion resistance and relatively low cost. They are normally applied onto ferrous products to provide corrosion protection in a range of different environments. However, the formation of spontaneously grown whiskers on zinc-electroplated components, which are capable of resulting in electrical shorting or other damaging effects, can be highly problematic for the reliability of long life electrical and electronic equipment. The growth of zinc whiskers has been identified as the cause of some electrical and electronic failures in telecommunications and aerospace-based applications, with consequences ranging from mild inconvenience to complete system failures. Investigators have been striving to address the problems induced by whisker growth since 1940s. However, most research effort has been focused on tin whiskers, especially following European Union environmental legislation that restricted the use of lead (Pb), which when alloyed with tin (3–10% by weight) provided effective tin whisker mitigation. Compared with tin whisker research, much less attention has been paid to zinc whiskers. A number of mechanisms to explain zinc whisker growth have been proposed, but none of them are widely accepted and some are in conflict with each other. The aim of this paper is to review the available literature in regard to zinc whiskers, to discuss the reported growth mechanisms, to evaluate the effect of deposition parameters and to explore potential mitigation methods. This paper presents a chronologically ordered review of zinc whisker-related studies from 1946 to 2013. Some important early research, which investigated whisker growth in tin and cadmium, as well as zinc, has also been included

Telomere length in cystic fibrosis patients – Are patients with CF ageing too quickly?

Life expectancy for patients living with Cystic Fibrosis (CF) is increasing year on year and there is growing interest in the ageing process in CF. Telomeres are repetitive sequences of DNA that cap the ends of eukaryotic chromosomes and shorten with ongoing cell division, thus providing a marker of replicative history and biological ageing. We aimed to investigate whether telomere length as a function of age differs between patients with CF and healthy individuals and whether telomere length is associated with severity of the patient’s CF condition.Peripheral blood samples and demographic data were collected from 47 consenting patients (age 1 to 57 years) with CF attending their routine annual review appointment at the All Wales Adult CF Centre and Noah’s Ark Children’s’ Hospital in Cardiff, UK. Telomere length profiles were assessed from peripheral blood samples, using the high resolution single telomere length analysis technique (STELA) and compared to healthy control telomere length data.Patients with CF had significantly shorter telomere lengths than healthy individuals, when adjusting for age (

Telomere length in Cystic Fibrosis patients - are patients with CF ageing too quickly?

Life expectancy for patients living with Cystic Fibrosis (CF) is increasing year on year and there is growing interest in the ageing process in CF. Telomeres are repetitive sequences of DNA that cap the ends of eukaryotic chromosomes and shorten with ongoing cell division, thus providing a marker of replicative history and biological ageing. We aimed to investigate whether telomere length as a function of age differs between patients with CF and healthy individuals and whether telomere length is associated with severity of the patient’s CF condition. Peripheral blood samples and demographic data were collected from 47 consenting patients (age 1 to 57 years) with CF attending their routine annual review appointment at the All Wales Adult CF Centre and Noah’s Ark Children’s’ Hospital in Cardiff, UK. Telomere length profiles were assessed from peripheral blood samples, using the high resolution single telomere length analysis technique (STELA) and compared to healthy control telomere length data. Patients with CF had significantly shorter telomere lengths than healthy individuals, when adjusting for age (p<0.001). Telomere length is decreasing 70% more quickly in the CF cohort than healthy controls. Telomere length does not appear to correlate with markers of disease severity. Telomere lengths are significantly shorter in individuals with CF than in the age-adjusted healthy population. This is suggestive of premature biological ageing of peripheral blood leukocytes in CF patients

Cytochrome P450 Allele CYP3A7*1C Associates with Adverse Outcomes in Chronic Lymphocytic Leukemia, Breast, and Lung Cancer.

CYP3A enzymes metabolize endogenous hormones and chemotherapeutic agents used to treat cancer, thereby potentially affecting drug effectiveness. Here, we refined the genetic basis underlying the functional effects of a CYP3A haplotype on urinary estrone glucuronide (E1G) levels and tested for an association between CYP3A genotype and outcome in patients with chronic lymphocytic leukemia (CLL), breast, or lung cancers. The most significantly associated SNP was rs45446698, an SNP that tags the CYP3A7*1C allele; this SNP was associated with a 54% decrease in urinary E1G levels. Genotyping this SNP in 1,008 breast cancer, 1,128 lung cancer, and 347 CLL patients, we found that rs45446698 was associated with breast cancer mortality (HR, 1.74; P = 0.03), all-cause mortality in lung cancer patients (HR, 1.43; P = 0.009), and CLL progression (HR, 1.62; P = 0.03). We also found borderline evidence of a statistical interaction between the CYP3A7*1C allele, treatment of patients with a cytotoxic agent that is a CYP3A substrate, and clinical outcome (Pinteraction = 0.06). The CYP3A7*1C allele, which results in adult expression of the fetal CYP3A7 gene, is likely to be the functional allele influencing levels of circulating endogenous sex hormones and outcome in these various malignancies. Further studies confirming these associations and determining the mechanism by which CYP3A7*1C influences outcome are required. One possibility is that standard chemotherapy regimens that include CYP3A substrates may not be optimal for the approximately 8% of cancer patients who are CYP3A7*1C carriers

A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses

AbstractA large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis (https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317) for the historical (1981–2014) and future (2015–2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.</jats:p

Global-scale evaluation of precipitation datasets for hydrological modelling

Abstract. Precipitation is the most important driver of the hydrological cycle, but it is challenging to estimate it over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis version 5 (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR, hereafter PERCCDR) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against 1825 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly, and daily timescales, MSWEP followed by ERA5 demonstrated a higher correlation (CC) and Kling–Gupta efficiency (KGE) than other datasets for more than 50 % of the stations, whilst ERA5 was the second-highest-performing dataset, and it showed the highest error and bias for about 20 % of the stations. PERCCDR is the least-well-performing dataset, with a bias of up to 99 % and a normalised root mean square error of up to 247 %. PERCCDR only show a higher KGE and CC than the other products for less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region, or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products. </jats:p

Global scale evaluation of precipitation datasets for hydrological modelling

Abstract. Precipitation is the most important driver of the hydrological cycle but is challenging to estimate over large scales from satellites and models. Here, we assessed the performance of six global and quasi-global high-resolution precipitation datasets (ERA5 global reanalysis (ERA5), Climate Hazards group Infrared Precipitation with Stations version 2.0 (CHIRPS), Multi-Source Weighted-Ensemble Precipitation version 2.80 (MSWEP), TerraClimate (TERRA), Climate Prediction Centre Unified version 1.0 (CPCU) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERCCDR)) for hydrological modelling globally and quasi-globally. We forced the WBMsed global hydrological model with the precipitation datasets to simulate river discharge from 1983 to 2019 and evaluated the predicted discharge against more than 1800 hydrological stations worldwide, using a range of statistical methods. The results show large differences in the accuracy of discharge predictions when using different precipitation input datasets. Based on evaluation at annual, monthly and daily time scales, MSWEP followed by ERA5 demonstrated a higher CC and KGE than other datasets for more than 50 % of the stations. Whilst, ERA5 was the second-highest performing dataset, it showed the highest error and bias in about 20 % of the stations. The PERCCDR is the least well performing dataset with large bias (percentage of bias up to 99 %) and errors (normalised root mean square error up  to 247 %) with a higher KGE and CC than the other products in less than 10 % of the stations. Even though MSWEP provided the highest performance overall, our analysis reveals high spatial variability, meaning that it is important to consider other datasets in areas where MSWEP showed a lower performance. The results of this study provide guidance on the selection of precipitation datasets for modelling river discharge for a basin, region or climatic zone as there is no single best precipitation dataset globally. Finally, the large discrepancy in the performance of the datasets in different parts of the world highlights the need to improve global precipitation data products.  </jats:p

Identification of new genetic susceptibility loci for breast cancer through consideration of gene-environment interactions

Genes that alter disease risk only in combination with certain environmental exposures may not be detected in genetic association analysis. By using methods accounting for gene-environment (G × E) interaction, we aimed to identify novel genetic loci associated with breast cancer risk. Up to 34,475 cases and 34,786 controls of European ancestry from up to 23 studies in the Breast Cancer Association Consortium were included. Overall, 71,527 single nucleotide polymorphisms (SNPs), enriched for association with breast cancer, were tested for interaction with 10 environmental risk factors using three recently proposed hybrid methods and a joint test of association and interaction. Analyses were adjusted for age, study, population stratification, and confounding factors as applicable. Three SNPs in two independent loci showed statistically significant association: SNPs rs10483028 and rs2242714 in perfect linkage disequilibrium on chromosome 21 and rs12197388 in ARID1B on chromosome 6. While rs12197388 was identified using the joint test with parity and with age at menarche (P-values = 3 × 10(−07)), the variants on chromosome 21 q22.12, which showed interaction with adult body mass index (BMI) in 8,891 postmenopausal women, were identified by all methods applied. SNP rs10483028 was associated with breast cancer in women with a BMI below 25 kg/m(2) (OR = 1.26, 95% CI 1.15–1.38) but not in women with a BMI of 30 kg/m(2) or higher (OR = 0.89, 95% CI 0.72–1.11, P for interaction = 3.2 × 10(−05)). Our findings confirm comparable power of the recent methods for detecting G × E interaction and the utility of using G × E interaction analyses to identify new susceptibility loci

Common non-synonymous SNPs associated with breast cancer susceptibility: findings from the Breast Cancer Association Consortium.

Candidate variant association studies have been largely unsuccessful in identifying common breast cancer susceptibility variants, although most studies have been underpowered to detect associations of a realistic magnitude. We assessed 41 common non-synonymous single-nucleotide polymorphisms (nsSNPs) for which evidence of association with breast cancer risk had been previously reported. Case-control data were combined from 38 studies of white European women (46 450 cases and 42 600 controls) and analyzed using unconditional logistic regression. Strong evidence of association was observed for three nsSNPs: ATXN7-K264R at 3p21 [rs1053338, per allele OR = 1.07, 95% confidence interval (CI) = 1.04-1.10, P = 2.9 × 10(-6)], AKAP9-M463I at 7q21 (rs6964587, OR = 1.05, 95% CI = 1.03-1.07, P = 1.7 × 10(-6)) and NEK10-L513S at 3p24 (rs10510592, OR = 1.10, 95% CI = 1.07-1.12, P = 5.1 × 10(-17)). The first two associations reached genome-wide statistical significance in a combined analysis of available data, including independent data from nine genome-wide association studies (GWASs): for ATXN7-K264R, OR = 1.07 (95% CI = 1.05-1.10, P = 1.0 × 10(-8)); for AKAP9-M463I, OR = 1.05 (95% CI = 1.04-1.07, P = 2.0 × 10(-10)). Further analysis of other common variants in these two regions suggested that intronic SNPs nearby are more strongly associated with disease risk. We have thus identified a novel susceptibility locus at 3p21, and confirmed previous suggestive evidence that rs6964587 at 7q21 is associated with risk. The third locus, rs10510592, is located in an established breast cancer susceptibility region; the association was substantially attenuated after adjustment for the known GWAS hit. Thus, each of the associated nsSNPs is likely to be a marker for another, non-coding, variant causally related to breast cancer risk. Further fine-mapping and functional studies are required to identify the underlying risk-modifying variants and the genes through which they act