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Abstract Mannosylerythritol lipids (MEL) are microbial glycolipid biosurfactants with great potential for application in cosmetics and household detergents. In current biotechnological processes, they are produced by basidiomycetous fungi, the Ustilaginaceae, as a complex mixture of different chemical structures. It was the aim of this paper to study the influence of producer organisms and substrates on the resulting MEL structures with a novel high-resolution HPTLC–MALDI-TOF method. Given the seven different microbes and four plant oils, our analysis revealed that the product concentrations varied strongly between organisms, while they were similar for the different substrates. Coconut oil presented an exception, since only one organism was able to synthesize MEL from this substrate in considerable yields. Analysis by GC-FID further showed that the chain length pattern of hydrophobic fatty acid side-chains was very specific for individual organisms, while substrates had only a minor influence on the chain length. Our novel HPTLC–MALDI-TOF combination method finally demonstrated the presence of multiple MEL sub-variants with differing acetylation and fatty acid chain lengths. It also revealed the production of a more hydrophilic biosurfactant mannosylmannitol lipid (MML) as a side-product in certain fungi. Overall, it was concluded that the pattern of produced biosurfactant structures are mainly governed by producer organisms rather than substrates.

Manufacturing is struggling to find people to fill open positions in the various career opportunities they represent. The skills gap or skills needed by manufacturers has caused challenges in filling those open positions. Adding to this dilemma is that student interest in these careers has reduced, with the focus being on obtaining college degrees right out of high school. Due to these changes, manufacturing and education are partnering in ways to increase student interest and fill open positions. This study is looking to better understand one such partnership and how the activities students’ experience influence their career decision making. The Manufacturing and Engineering Partnership Program (MEPP), located in the Midwest partnered with local manufacturing companies to increase awareness and student interest in manufacturing. This research will add to the literature on whether partnerships like the MEPP program can add to the pipeline of people needed in the manufacturing industry. The purpose of this case study is to understand how the MEPP partnership’s activities shaped participating students’ interest in manufacturing careers. In specific, the goal is to capture students’ responses to the experiences in this partnership program and how they influenced their future career plans. These outcomes can help partnerships evaluate their activities or assist parties looking to create a partnership understand from a student perspective what activities most made an impact. Upon examining the results on the sixteen interviews, the partnership program does influence student interest in manufacturing careers. In the case of the sixteen students, ten of the 16 students are in or still pursuing careers in a manufacturing field. All sixteen students noted benefits in the career exploration process. Recommendations for future research is to have more studies to determine if students get into a manufacturing careers later in their lifetime. The implications are that the manufacturing industry and K-12 partnerships represent new opportunities to help in the career exploration and career decision making process. Providing more opportunities for students to understand and apply for the program like MEPP could be beneficial. As discussed in prior themes, the exposure MEPP provided further helped students to determine if a manufacturing related career is the right opportunity for them. This presents an opportunity for partnerships like this to investigate providing classes like MEPP as an elective course for exploring career opportunities. The research set out to understand how the activities in a K-12 and manufacturing partnership program influence career decision making. From the perspective of the students, there were multiple activities that were impactful and not all connected them to careers in manufacturing. While 10 of the 16 students currently chose careers in manufacturing, all 16 of the students found the Manufacturing and Engineering Partnership Program to be a beneficial part of their career decision making process. The exposure this partnership presented students helped them to determine a course of action for their future careers.

The temporal molecular changes that lead to disease onset and progression in Alzheimer’s disease (AD) are still unknown. Here we develop a temporal model for these unobserved molecular changes with a manifold learning method applied to RNA-Seq data collected from human postmortem brain samples collected within the ROS/MAP and Mayo Clinic RNA-Seq studies. We define an ordering across samples based on their similarity in gene expression and use this ordering to estimate the molecular disease stage–or disease pseudotime-for each sample. Disease pseudotime is strongly concordant with the burden of tau (Braak score, P  = 1.0 × 10 −5 ), Aβ (CERAD score, P  = 1.8 × 10 −5 ), and cognitive diagnosis ( P  = 3.5 × 10 −7 ) of late-onset (LO) AD. Early stage disease pseudotime samples are enriched for controls and show changes in basic cellular functions. Late stage disease pseudotime samples are enriched for late stage AD cases and show changes in neuroinflammation and amyloid pathologic processes. We also identify a set of late stage pseudotime samples that are controls and show changes in genes enriched for protein trafficking, splicing, regulation of apoptosis, and prevention of amyloid cleavage pathways. In summary, we present a method for ordering patients along a trajectory of LOAD disease progression from brain transcriptomic data. The limited understanding of the temporal molecular changes in late-onset Alzheimer’s disease hinder the development of therapeutic treatment. The authors use manifold learning to develop a molecular model for disease progression from RNASeq data from human postmortem brain samples.

Despite the dramatic underrepresentation of non-European populations in human genetics studies, researchers continue to exclude participants of non-European ancestry, as well as variants rare in European populations, even when these data are available. This practice perpetuates existing research disparities and can lead to important and large effect size associations being missed. Here, we conducted genome-wide association studies (GWAS) of 31 serum and urine biomarker quantitative traits in African (n = 9354), East Asian (n = 2559), and South Asian (n = 9823) ancestry UK Biobank (UKBB) participants. We adjusted for all known GWAS catalog variants for each trait, as well as novel signals identified in a recent European ancestry-focused analysis of UKBB participants. We identify 7 novel signals in African ancestry and 2 novel signals in South Asian ancestry participants (p < 1.61E−10). Many of these signals are highly plausible, including a cis pQTL for the gene encoding gamma-glutamyl transferase and PIEZO1 and G6PD variants with impacts on HbA1c through likely erythrocytic mechanisms. This work illustrates the importance of using the genetic data we already have in diverse populations, with novel discoveries possible in even modest sample sizes.

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry 1 – 3 . In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific 4 – 10 . Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations 11 , 12 . Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States—where minority populations have a disproportionately higher burden of chronic conditions 13 —the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities. Genetic analyses of ancestrally diverse populations show evidence of heterogeneity across ancestries and provide insights into clinical implications, highlighting the importance of including ancestrally diverse populations to maximize genetic discovery and reduce health disparities.

Background Circulating white blood cell and platelet traits are clinically linked to various disease outcomes and differ across individuals and ancestry groups. Genetic factors play an important role in determining these traits and many loci have been identified. However, most of these findings were identified in populations of European ancestry (EA), with African Americans (AA), Hispanics/Latinos (HL), and other races/ethnicities being severely underrepresented. Results We performed ancestry-combined and ancestry-specific genome-wide association studies (GWAS) for white blood cell and platelet traits in the ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) Study, including 16,201 AA, 21,347 HL, and 27,236 EA participants. We identified six novel findings at suggestive significance ( P < 5E-8), which need confirmation, and independent signals at six previously established regions at genome-wide significance ( P < 2E-9). We confirmed multiple previously reported genome-wide significant variants in the single variant association analysis and multiple genes using PrediXcan. Evaluation of loci reported from a Euro-centric GWAS indicated attenuation of effect estimates in AA and HL compared to EA populations. Conclusions Our results highlighted the potential to identify ancestry-specific and ancestry-agnostic variants in participants with diverse backgrounds and advocate for continued efforts in improving inclusion of racially/ethnically diverse populations in genetic association studies for complex traits.

Megabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS , ADM17 , PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis . A method that allows for the detection of mosaic chromosomal alterations from blood whole-genome sequencing data highlights ancestry-specific differences in the distribution of common and rare germline susceptibility variants.

IntroductionBinge eating (BE) behaviour is highly prevalent in adolescents, and can result in serious metabolic derangements and overweight in the long term. Weakened functioning of the behavioural inhibition system is one potential pathway leading to BE. Food cue exposure focusing on expectancy violation (CEEV) is a short intervention for BE that has proven effective in adults but has never been tested in adolescents. Thus, the current randomised pilot trial evaluates the feasibility of CEEV for adolescents and its efficacy in reducing eating in the absence of hunger (EAH) of binge food items.Methods and analysisThe trial will include N=76 female adolescents aged between 13 and 20 years with a diagnosis of bulimia nervosa, binge eating disorder (BED) or their subthreshold forms based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5). Participants will be randomly assigned to two sessions of CEEV or behavioural analysis (BA), a classical cognitive–behavioural therapy-based intervention. The primary endpoint is the change in EAH measured according to ad libitum consumption of personally preferred binge food in a bogus taste test at post-test based on the intention-to-treat population. Key secondary endpoints are changes in EAH of standardised binge food at post-test, in EAH at 3-month follow-up (FU) and in food craving after induction of food cue reactivity at post-test and FU. To identify further valid outcome parameters, we will assess effects of CEEV compared with BA on global ED psychopathology, BE frequency within the last 28 days, body weight, response inhibition and emotion regulation abilities. Treatment groups will be compared using analysis of covariance with intervention as fixed factor and body mass index at baseline as covariate.Ethics and disseminationThis clinical trial has been approved by the Ethics Review Committee of the Medical Association of Rhineland-Palatinate and the Medical Faculty of the Ruhr-University Bochum. The collected data will be disseminated locally and internationally through publications in relevant peer-reviewed journals and will be presented at scientific and clinical conferences. Participants data will only be published in an anonymised form.Trial registration numberDRKS00024009.

Background High mammographic density (MD) is a risk factor for the development of breast cancer (BC). Changes in MD are influenced by multiple factors such as age, BMI, number of full-term pregnancies and lactating periods. To learn more about MD, it is important to establish non-radiation-based, alternative examination methods to mammography such as ultrasound assessments. Methods We analyzed data from 168 patients who underwent standard-of-care mammography and performed additional ultrasound assessment of the breast using a high-frequency (12 MHz) linear probe of the VOLUSON ® 730 Expert system (GE Medical Systems Kretztechnik GmbH & Co OHG, Austria). Gray level bins were calculated from ultrasound images to characterize mammographic density. Percentage mammographic density (PMD) was predicted by gray level bins using various regression models. Results Gray level bins and PMD correlated to a certain extent. Spearman’s ρ ranged from − 0.18 to 0.32. The random forest model turned out to be the most accurate prediction model (cross-validated R 2 , 0.255). Overall, ultrasound images from the VOLUSON ® 730 Expert device in this study showed limited predictive power for PMD when correlated with the corresponding mammograms. Conclusions In our present work, no reliable prediction of PMD using ultrasound imaging could be observed. As previous studies showed a reasonable correlation, predictive power seems to be highly dependent on the device used. Identifying feasible non-radiation imaging methods of the breast and their predictive power remains an important topic and warrants further evaluation. Trial registration 325-19 B (Ethics Committee of the medical faculty at Friedrich Alexander University of Erlangen-Nuremberg, Erlangen, Germany).

The 1000 Genomes Project was launched as one of the largest distributed data collection and analysis projects ever undertaken in biology. In addition to the primary scientific goals of creating both a deep catalog of human genetic variation and extensive methods to accurately discover and characterize variation using new sequencing technologies, the project makes all of its data publicly available. Members of the project data coordination center have developed and deployed several tools to enable widespread data access.