The emission of X-rays by fission fragments

Approximately 5% of the γ-ray transitions in fission fragments are internally converted, giving rise to X-rays. X-ray energies are characteristic of Z, but yields depend strongly and not smoothly on Z and A of the fragments and on time. If these biases can be accepted, X-rays can be used to identify the atomic number of fission fragments. More generally, internal conversion and X-ray emission must be interpreted in the context of detailed knowledge of γ-ray transitions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/43110/1/10967_2005_Article_BF02039468.pd

Automated Structure Solution with the PHENIX Suite

Significant time and effort are often required to solve and complete a macromolecular crystal structure. The development of automated computational methods for the analysis, solution and completion of crystallographic structures has the potential to produce minimally biased models in a short time without the need for manual intervention. The PHENIX software suite is a highly automated system for macromolecular structure determination that can rapidly arrive at an initial partial model of a structure without significant human intervention, given moderate resolution and good quality data. This achievement has been made possible by the development of new algorithms for structure determination, maximum-likelihood molecular replacement (PHASER), heavy-atom search (HySS), template and pattern-based automated model-building (RESOLVE, TEXTAL), automated macromolecular refinement (phenix.refine), and iterative model-building, density modification and refinement that can operate at moderate resolution (RESOLVE, AutoBuild). These algorithms are based on a highly integrated and comprehensive set of crystallographic libraries that have been built and made available to the community. The algorithms are tightly linked and made easily accessible to users through the PHENIX Wizards and the PHENIX GUI

Universal DNA methylation age across mammalian tissues

DATA AVAILABILITY STATEMENT : The individual-level data from the Mammalian Methylation Consortium can be accessed from several online locations. All data from the Mammalian Methylation Consortium are posted on Gene Expression Omnibus (complete dataset, GSE223748). Subsets of the datasets can also be downloaded from accession numbers GSE174758, GSE184211, GSE184213, GSE184215, GSE184216, GSE184218, GSE184220, GSE184221, GSE184224, GSE190660, GSE190661, GSE190662, GSE190663, GSE190664, GSE174544, GSE190665, GSE174767, GSE184222, GSE184223, GSE174777, GSE174778, GSE173330, GSE164127, GSE147002, GSE147003, GSE147004, GSE223943 and GSE223944. Additional details can be found in Supplementary Note 2. The mammalian data can also be downloaded from the Clock Foundation webpage: https://clockfoundation.org/MammalianMethylationConsortium. The mammalian methylation array is available through the non-profit Epigenetic Clock Development Foundation (https://clockfoundation.org/). The manifest file of the mammalian array and genome annotations of CpG sites can be found on Zenodo (10.5281/zenodo.7574747). All other data supporting the findings of this study are available from the corresponding author upon reasonable request. The chip manifest files, genome annotations of CpG sites and the software code for universal pan-mammalian clocks can be found on GitHub95 at https://github.com/shorvath/MammalianMethylationConsortium/tree/v2.0.0. The individual R code for the universal pan-mammalian clocks, EWAS analysis and functional enrichment studies can be also found in the Supplementary Code.SUPPLEMENTARY MATERIAL 1 : Supplementary Tables 1–3 and Notes 1–6.SUPPLEMENTARY MATERIAL 2 : Reporting SummarySUPPLEMENTARY MATERIAL 3 : Supplementary Data 1–14.SUPPLEMENTARY MATERIAL 4 : Supplementary Code.Aging, often considered a result of random cellular damage, can be accurately estimated using DNA methylation profiles, the foundation of pan-tissue epigenetic clocks. Here, we demonstrate the development of universal pan-mammalian clocks, using 11,754 methylation arrays from our Mammalian Methylation Consortium, which encompass 59 tissue types across 185 mammalian species. These predictive models estimate mammalian tissue age with high accuracy (r > 0.96). Age deviations correlate with human mortality risk, mouse somatotropic axis mutations and caloric restriction. We identified specific cytosines with methylation levels that change with age across numerous species. These sites, highly enriched in polycomb repressive complex 2-binding locations, are near genes implicated in mammalian development, cancer, obesity and longevity. Our findings offer new evidence suggesting that aging is evolutionarily conserved and intertwined with developmental processes across all mammals.https://www.nature.com/nataginghj2024Zoology and EntomologySDG-15:Life on lan