Three Reagents for in-Solution Enrichment of Ancient Human DNA at More than a Million SNPs

In-solution enrichment for hundreds of thousands of single nucleotide polymorphisms (SNPs) has been the source of >70% of all genome-scale ancient human DNA data published to date. This approach has made it possible to generate data for one to two orders of magnitude lower cost than random shotgun sequencing, making it economical to study ancient samples with low proportions of human DNA, and increasing the rate of conversion of sampled remains into working data thereby facilitating ethical stewardship of human remains. So far, nearly all ancient DNA data obtained using in-solution enrichment has been generated using a set of bait sequences targeting about 1.24 million SNPs (the 1240k reagent). These sequences were published in 2015, but synthesis of the reagent has been cost-effective for only a few laboratories. In 2021, two companies made available reagents that target the same core set of SNPs along with supplementary content. Here, we test the properties of the three reagents on a common set of 27 ancient DNA libraries across a range of richness of DNA content and percentages of human molecules. All three reagents are highly effective at enriching many hundreds of thousands of SNPs. For all three reagents and a wide range of conditions, one round of enrichment produces data that is as useful as two rounds when tens of millions of sequences are read out as is typical for such experiments. In our testing, the Twist Ancient DNA reagent produces the highest coverages, greatest uniformity on targeted positions, and almost no bias toward enriching one allele more than another relative to shotgun sequencing. Allelic bias in 1240k enrichment has made it challenging to carry out joint analysis of these data with shotgun data, creating a situation where the ancient DNA community has been publishing two important bodes of data that cannot easily be co-analyzed by population genetic methods. To address this challenge, we introduce a subset of hundreds of thousands of SNPs for which 1240k data can be effectively co-analyzed with all other major data types.

Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm

Well parameterised epidemiological models including accurate representation of contacts, are fundamental to controlling epidemics. However, age-stratified contacts are typically estimated from pre-pandemic/peace-time surveys, even though interventions and public response likely alter contacts. Here we fit age-stratified models, including re-estimation of relative contact rates between age-classes, to public data describing the 2020-21 COVID-19 outbreak in England. This data includes age-stratified population size, cases, deaths, hospital admissions, and results from the Coronavirus Infection Survey (almost 9000 observations in all). Fitting stochastic compartmental models to such detailed data is extremely challenging, especially considering the large number of model parameters being estimated (over 150). An efficient new inference algorithm ABC-MBP combining existing Approximate Bayesian Computation (ABC) methodology with model-based proposals (MBP) is applied. Modified contact rates are inferred alongside time-varying reproduction numbers that quantify changes in overall transmission due to pandemic response, and age-stratified proportions of asymptomatic cases, hospitalisation rates and deaths. These inferences are robust to a range of assumptions including the values of parameters that cannot be estimated from available data. ABC-MBP is shown to enable reliable joint analysis of complex epidemiological data yielding consistent parametrisation of dynamic transmission models that can inform data-driven public health policy and interventions.

Joint Analysis and Reliability Test of Epoxy-Based Nano Silver Paste Under Different Pressure-Less Sintering Processes

Recent years, the sintered silver paste was introduced and further developed for power electronics packaging due to low processing temperature and high working temperature. The pressure-less sintering technology reduces the stress damage caused by the pressure to the chip, improves reliability, and is widely applied in manufacturing. Currently, most existed studies are focused on alcohol-based sintered silver pastes while resins have been demonstrated to improve the bonding properties of solder joints. Hence, the performance and sintering mechanisms with epoxy-based silver paste need to be further explored. In this work, a methodology for multi-factor investigation is settled on the epoxy-based silver paste to reveal the relationship between the strength and the different influence factors. We firstly analyzed the characteristics of commercialized epoxy-based silver paste samples, including silver content, silver particle size, organic paste composition, sample viscosity, and thermal conductivity. Samples were then prepared for shear tests and microstructure analysis under different pressure-less sintering temperatures, holding time, substrate surface, and chip size. Full factor analysis results were further discussed in detail for correlation. The influence factors were ranked from strong to weak as follows: sintering temperature, substrate surface, chip size, and holding time. Finally, a thermal cycling test was carried out for reliability analysis. Epoxy residues are one of the possible reasons which result in shear strength decreasing exponentially.

Systematic identification of ACE2 expression modulators reveals cardiomyopathy as a risk factor for mortality in COVID-19 patients

Background Angiotensin-converting enzyme 2 (ACE2) is the cell-entry receptor for SARS-CoV-2. It plays critical roles in both the transmission and the pathogenesis of COVID-19. Comprehensive profiling of ACE2 expression patterns could reveal risk factors of severe COVID-19 illness. While the expression of ACE2 in healthy human tissues has been well characterized, it is not known which diseases and drugs might be associated with ACE2 expression. Results We develop GENEVA (GENe Expression Variance Analysis), a semi-automated framework for exploring massive amounts of RNA-seq datasets. We apply GENEVA to 286,650 publicly available RNA-seq samples to identify any previously studied experimental conditions that could be directly or indirectly associated with ACE2 expression. We identify multiple drugs, genetic perturbations, and diseases that are associated with the expression of ACE2, including cardiomyopathy, HNF1A overexpression, and drug treatments with RAD140 and itraconazole. Our joint analysis of seven datasets confirms ACE2 upregulation in all cardiomyopathy categories. Using electronic health records data from 3936 COVID-19 patients, we demonstrate that patients with pre-existing cardiomyopathy have an increased mortality risk than age-matched patients with other cardiovascular conditions. GENEVA is applicable to any genes of interest and is freely accessible at http://genevatool.org. Conclusions This study identifies multiple diseases and drugs that are associated with the expression of ACE2. The effect of these conditions should be carefully studied in COVID-19 patients. In particular, our analysis identifies cardiomyopathy patients as a high-risk group, with increased ACE2 expression in the heart and increased mortality after SARS-COV-2 infection.

Guidelines for genetic testing of healthy adults who deposit samples and related data in bioresource collections and biobanks

Currently, a significant part of research in the fields of human and medical genetics is carried out using tissue samples, genealogical, population, medical and personal data. Their use is of particular relevance in the “genome era”, since only joint analysis of genomic data and health status of the population is crucial for understanding how genes are associated with health and disease. Genetic studies of adults without symptoms of diseases are carried out to obtain data on a possible predisposition to multifactorial diseases, to establish the carrier status of autosomal recessive mutations as part of preconception care and to assess individual sensitivity to drugs. In addition, healthy individuals can be tested to detect an inherited disease at presymptomatic stage. This situation increasingly emphasizes the importance of storing data on genome sequencing or any other patient tests for subsequent data reanalysis, as well as their safety, including biosamples from an individual and one’s family. The review article, based on international experience, summarizes guidelines for genetic testing of healthy individuals. The options for storing biological samples and related data are considered.

Leveraging pleiotropic association using sparse group variable selection in genomics data

Background Genome-wide association studies (GWAS) have identified genetic variants associated with multiple complex diseases. We can leverage this phenomenon, known as pleiotropy, to integrate multiple data sources in a joint analysis. Often integrating additional information such as gene pathway knowledge can improve statistical efficiency and biological interpretation. In this article, we propose statistical methods which incorporate both gene pathway and pleiotropy knowledge to increase statistical power and identify important risk variants affecting multiple traits. Methods We propose novel feature selection methods for the group variable selection in multi-task regression problem. We develop penalised likelihood methods exploiting different penalties to induce structured sparsity at a gene (or pathway) and SNP level across all studies. We implement an alternating direction method of multipliers (ADMM) algorithm for our penalised regression methods. The performance of our approaches are compared to a subset based meta analysis approach on simulated data sets. A bootstrap sampling strategy is provided to explore the stability of the penalised methods. Results Our methods are applied to identify potential pleiotropy in an application considering the joint analysis of thyroid and breast cancers. The methods were able to detect eleven potential pleiotropic SNPs and six pathways. A simulation study found that our method was able to detect more true signals than a popular competing method while retaining a similar false discovery rate. Conclusion We developed feature selection methods for jointly analysing multiple logistic regression tasks where prior grouping knowledge is available. Our method performed well on both simulation studies and when applied to a real data analysis of multiple cancers.

Identification of Phosphorus Stress Related Proteins in the Seedlings of Dongxiang Wild Rice (Oryza Rufipogon Griff.) Using Label-Free Quantitative Proteomic Analysis

Phosphorus (P) deficiency tolerance in rice is a complex character controlled by polygenes. Through proteomics analysis, we could find more low P tolerance related proteins in unique P-deficiency tolerance germplasm Dongxiang wild rice (Oryza Rufipogon, DXWR), which will provide the basis for the research of its regulation mechanism. In this study, a proteomic approach as well as joint analysis with transcriptome data were conducted to identify potential unique low P response genes in DXWR during seedlings. The results showed that 3589 significant differential accumulation proteins were identified between the low P and the normal P treated root samples of DXWR. The degree of change was more than 1.5 times, including 60 up-regulated and 15 downregulated proteins, 24 of which also detected expression changes of more than 1.5-fold in the transcriptome data. Through quantitative trait locus (QTLs) matching analysis, seven genes corresponding to the significantly different expression proteins identified in this study were found to be uncharacterized and distributed in the QTLs interval related to low P tolerance, two of which (LOC_Os12g09620 and LOC_Os03g40670) were detected at both transcriptome and proteome levels. Based on the comprehensive analysis, it was found that DXWR could increase the expression of purple acid phosphatases (PAPs), membrane location of P transporters (PTs), rhizosphere area, and alternative splicing, and it could decrease reactive oxygen species (ROS) activity to deal with low P stress. This study would provide some useful insights in cloning the P-deficiency tolerance genes from wild rice, as well as elucidating the molecular mechanism of low P resistance in DXWR.

Autowave Criteria of Fracture and Plastic Strain Localization of Zirconium Alloys

Plastic deformation and fracture of Zr–1% Nb alloys exposed to quasi-static tensile testing have been studied via a joint analysis of stress-strain curves, ultrasound velocity and double-exposure speckle photographs. The possibilities of ductility evaluation through the εxx strain distribution in thin-walled parts of zirconium alloys are shown in this paper. The stress-strain state of zirconium alloys in a cold rolling site is investigated considering the development of localized deformation bands and changes in ultrasound velocity. It is established that the transition from the upsetting to the reduction region is accompanied by the significant exhaustion of the plasticity margin of the material; therefore, the latter is more prone to fracture in this zone exactly. It is shown that traditional methods estimating the plasticity margin from the mechanical properties cannot reveal this region, requiring a comprehensive study of macroscopically localized plastic strain in combination with acoustic measurements. In particular, the multi-pass cold rolling of Zr alloys includes various localized deformation processes that can result in the formation of localized plasticity autowaves. Recommendations for strain distribution division over the deformation zone length in the alloy in the pilger roll grooves are provided as well.

Development and Validation of a Three-Gene-Based Prognostic Model for Predicting the Overall Survival of Head and Neck Squamous Cell Carcinoma Through Bioinformatics Analysis

Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancer worldwide and seriously threats public health safety. Despite the improvement of diagnostic and treatment methods, the overall survival for advanced patients has not improved yet. This study aimed to sort out prognosis-related molecular biomarkers for HNSCC and establish a prognostic model to stratify the risk hazards and predicate the prognosis for these patients, providing a theoretical basis for the formulation of individual treatment plans. We firstly identified differentially expressed genes (DEGs) between HNSCC tissues and normal tissues via joint analysis based on GEO databases. Then a total of 11 hub genes were selected for single-gene prognostic analysis to identify the prognostic genes. Later, the clinical information and transcription information of HNSCC were downloaded from the TCGA database. With the application of least absolute shrinkage and selection operator (LASSO) algorithm analyses for the prognostic genes on the TCGA cohort, a prognostic model consisting of three genes (COL4A1, PLAU and ITGA5) was successfully established and the survival analyses showed that the prognostic model possessed a robust performance in the overall survival prediction. Afterward, the univariate and multivariate regression analysis indicated that the prognostic model could be an independent prognostic factor. Finally, the predicative efficiency of this model was well confirmed in an independent external HNSCC cohort.