Jespersenebbesen7726

We developed an in vitro translation system from yeast, reconstituted with purified translation elongation and termination factors and programmed by CrPV IGR IRES-containing mRNA, which functions in the absence of initiation factors. The system is capable of synthesizing the active reporter protein, nanoLuciferase, with a molecular weight of 19 kDa. The protein synthesis by the system is appropriately regulated by controlling its composition, including translation factors, amino acids, and antibiotics. We found that a high eEF1A concentration relative to the ribosome concentration is critically required for efficient IRES-mediated translation initiation, to ensure its dominance over IRES-independent random internal translation initiation. © The Author(s) 2020. Published by Oxford University Press on behalf of the Japanese Biochemical Society. All rights reserved.The gut microbial community is known to influence the human health and disease state and is shaped by various factors since birth. It is now evident that understanding the alterations in these commensal microbes during crucial stages of life is of utmost importance to determine and predict the health status of an individual. To study the gut microbiota in two such vital stages, pregnancy and infancy, we analyzed gut microbial communities from 20 mother-infant dyads at different stages of pregnancy and early infancy. In total, we analyzed 80 fecal samples for profiling the gut microbial community using 16S rRNA gene-based sequencing. We observed no significant alterations in the gut bacterial diversity during pregnancy; however, significant alterations were observed during the period from birth to six months in infants, with a reduction in Staphylococcus and Enterococcus and an increase in Bifidobacterium and Streptococcus with a more stable microbial community at the age of six months. © FEMS 2020.Importance Subconcussive head impacts have emerged as a complex public health concern. The oculomotor system is sensitive to brain trauma; however, neuro-ophthalmologic response to subconcussive head impacts remains unclear. Objective To examine whether subconcussive head impacts cause impairments in neuro-ophthalmologic function as measured by the King-Devick test (KDT) and oculomotor function as measured by the near point of convergence. Design, Setting, and Participants In this randomized clinical trial, adult soccer players were randomized into either a heading group or kicking (control) group. The heading group executed 10 headers with soccer balls projected at a speed of 25 mph. The kicking-control group followed the same protocol but with 10 kicks. Peak linear and rotational head accelerations were assessed with a triaxial accelerometer. The KDT speed and error and near point of convergence were assessed at baseline (preheading or prekicking) and at 0, 2, and 24 hours after heading or kicking. Exposuree measures can be a useful clinical tool in detecting acute subconcussive injury. Trial Registration ClinicalTrials.gov Identifier NCT03488381.MOTIVATION Fast and accurate classification of ligand-binding sites in proteins with respect to the class of binding molecules is invaluable not only to the automatic functional annotation of large datasets of protein structures, but also to projects in protein evolution, protein engineering, and drug development. Deep learning techniques, which have already been successfully applied to address challenging problems across various fields, are inherently suitable to classify ligand-binding pockets. Our goal is to demonstrate that off-the-shelf deep learning models can be employed with minimum development effort to recognize nucleotide- and heme-binding sites with a comparable accuracy to highly specialized, voxel-based methods. RESULTS We developed BionoiNet, a new deep learning-based framework implementing a popular ResNet model for image classification. BionoiNet first transforms the molecular structures of ligand-binding sites to 2D Voronoi diagrams, which are then used as the input to a pretrained convolutional neural network classifier. The ResNet model generalizes well to unseen data achieving the accuracy of 85.6% for nucleotide- and 91.3% for heme-binding pockets. BionoiNet also computes significance scores of pocket atoms, called BionoiScores, to provide meaningful insights into their interactions with ligand molecules. BionoiNet is a lightweight alternative to computationally expensive 3D architectures. AVAILABILITY AND IMPLEMENTATION BionoiNet is implemented in Python with the source code freely available at https//github.com/CSBG-LSU/BionoiNet. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.BACKGROUND The burden of cardiovascular risk is distributed unequally between ethnic groups. It is uncertain to what extent this is attributable to ethnic differences in general and abdominal obesity. Therefore, we studied the contribution of general and abdominal obesity to metabolic cardiovascular risk among different ethnic groups. METHODS We used data of 21 411 participants of Dutch, South-Asian Surinamese, African-Surinamese, Ghanaian, Turkish or Moroccan origin in Healthy Life in an Urban Setting (Amsterdam, the Netherlands). Obesity was defined using body-mass-index (general) or waist-to-height-ratio (abdominal). High metabolic risk was defined as having at least two of the following triglycerides ≥1.7 mmol/l, fasting glucose ≥5.6 mmol/l, blood pressure ≥130 mmHg systolic and/or ≥85 mmHg diastolic and high-density lipoprotein cholesterol less then 1.03 mmol/l (men) or less then 1.29 mmol/l (women). RESULTS Among ethnic minority men, age-adjusted prevalence rates of high metabolic risk ranged from 32 to 59% vs. 33% among Dutch men. Contributions of general obesity to high metabolic risk ranged from 7.1 to 17.8%, vs. 10.1% among Dutch men, whereas contributions of abdominal obesity ranged from 52.1 to 92.3%, vs. 53.9% among Dutch men. Among ethnic minority women, age-adjusted prevalence rates of high metabolic risk ranged from 24 to 35% vs. 12% among Dutch women. Contributions of general obesity ranged from 14.6 to 41.8%, vs. 20% among Dutch women, whereas contributions of abdominal obesity ranged from 68.0 to 92.8%, vs. 72.1% among Dutch women. CONCLUSIONS Obesity, especially abdominal obesity, contributes significantly to the prevalence of high metabolic cardiovascular risk. Results suggest that this contribution varies substantially between ethnic groups, which helps explain ethnic differences in cardiovascular risk. © The Author(s) 2020. Published by Oxford University Press on behalf of the European Public Health Association. ZD1839 All rights reserved.