How to identify sex chromosomes and their turnover

Although sex is a fundamental component of eukaryotic reproduction, the genetic systems that control sex determination are highly variable. In many organisms the presence of sex chromosomes is associated with female or male development. Although certain groups possess stable and conserved sex chromosomes, others exhibit rapid sex chromosome evolution including transitions between male and female heterogamety, and turnover in the chromosome pair recruited to determine sex. These turnover events have important consequences for multiple facets of evolution, as sex chromosomes are predicted to play a central role in adaptation, sexual dimorphism, and speciation. However, our understanding of the processes driving the formation and turnover of new sex chromosome systems is limited, in part because we lack a complete understanding of inter‐specific variation in the mechanisms by which sex is determined. New bioinformatic methods are making it possible to identify and characterize sex chromosomes in a diverse array of non‐model species, rapidly filling in the numerous gaps in our knowledge of sex chromosome systems across the tree of life. In turn, this growing dataset is facilitating and fueling efforts to address many of the unanswered questions in sex chromosome evolution. Here, we synthesize the available bioinformatic approaches to produce a guide for characterizing sex chromosome system and identity simultaneously across clades of organisms. Furthermore, we survey our current understanding of the processes driving sex chromosome turnover, and highlight important avenues for future research

Last months of life of people with intellectual disabilities: A UK population-based study of death and dying in intellectual disability community services.

BACKGROUND: Population-based data are presented on the nature of dying in intellectual disability services. METHODS: A retrospective survey was conducted over 18 months with a sample of UK-based intellectual disability service providers that supported over 12,000. Core data were obtained for 222 deaths within this population. For 158 (71%) deaths, respondents returned a supplemented and modified version of VOICES-SF. RESULTS: The observed death was 12.2 deaths per 1,000 people supported per year, but just over a third deaths had been deaths anticipated by care staff. Mortality patterns, place of usual care and availability of external support exerted considerable influence over outcomes at the end of life. CONCLUSION: Death is not a common event in intellectual disability services. A major disadvantage experienced by people with intellectual disabilities was that their deaths were relatively unanticipated. People with intellectual disabilities living in supported living settings, even when their dying was anticipated, experienced poorer outcomes

Supplemental Ascorbate Diminishes DNA Damage Yet Depletes Glutathione and Increases Acute Liver Failure in a Mouse Model of Hepatic Antioxidant System Disruption

Cellular oxidants are primarily managed by the thioredoxin reductase-1 (TrxR1)- and glutathione reductase (Gsr)-driven antioxidant systems. In mice having hepatocyte-specific codisruption of TrxR1 and Gsr (TrxR1/Gsr-null livers), methionine catabolism sustains hepatic levels of reduced glutathione (GSH). Although most mice with TrxR1/Gsr-null livers exhibit long-term survival, ~25% die from spontaneous liver failure between 4- and 7-weeks of age. Here we tested whether liver failure was ameliorated by ascorbate supplementation. Following ascorbate, dehydroascorbate, or mock treatment, we assessed survival, liver histology, or hepatic redox markers including GSH and GSSG, redox enzyme activities, and oxidative damage markers. Unexpectedly, rather than providing protection, ascorbate (5 mg/mL, drinking water) increased the death-rate to 43%. In adults, ascorbate (4 mg/g × 3 days i.p.) caused hepatocyte necrosis and loss of hepatic GSH in TrxR1/Gsr-null livers but not in wildtype controls. Dehydroascorbate (0.3 mg/g i.p.) also depleted hepatic GSH in TrxR1/Gsr-null livers, whereas GSH levels were not significantly affected by either treatment in wildtype livers. Curiously, however, despite depleting GSH, ascorbate treatment diminished basal DNA damage and oxidative stress markers in TrxR1/Gsr-null livers. This suggests that, although ascorbate supplementation can prevent oxidative damage, it also can deplete GSH and compromise already stressed livers

The NICE COVID‐19 rapid guideline on haematopoietic stem cell transplantation: development, implementation and impact

A new coronavirus SARS‐CoV‐2 emerged at the start of 2020 with rapid worldwide spread. It is the causative agent of Coronavirus Disease 2019 (COVID‐19). The resulting pandemic has been challenging for centres undertaking autologous and allogeneic haematopoietic stem cell transplants (HSCT). One of the main risks in HSCT is infection susceptibility, including viral infections, which are often more severe and life‐threatening.1 In response to the COVID‐19 pandemic, the National Institute for Health and Care Excellence (NICE) published the first version of the COVID‐19 rapid guideline for HSCT (NG164) on 1 April 20202; this was updated on 29 July 2020 in response to the changing context of the pandemic. The British Society of Blood and Marrow Transplantation and Cellular Therapy (BSBMTCT) and the European Society for Blood and Marrow Transplantation (EBMT) also developed detailed guidance to support transplant centres.3-5 This paper describes the development and update processes for NICE NG164, detailing the rationale behind the recommendations, implementation and the impact of COVID‐19 on HSCT activity in the UK compared with 2019, based on registrations in the BSBMTCT registry. The full NICE guidance can be obtained from: https://www.nice.org.uk/guidance/ng164

National Evaluation of the Preventing and Tackling Mental Ill Health through Green Social Prescribing Project: Interim Report - September 2021 to September 2022

This is the final version. This report, and the accompanying briefing, summary and appendices documents, are published by Defra (Defra Project Code BE0191) and are available from the Department’s Science and Research Projects Database at https://randd.defra.gov.ukDepartment for Environment, Food and Rural Affair

Discovering cancer genes by integrating network and functional properties

<p>Abstract</p> <p>Background</p> <p>Identification of novel cancer-causing genes is one of the main goals in cancer research. The rapid accumulation of genome-wide protein-protein interaction (PPI) data in humans has provided a new basis for studying the topological features of cancer genes in cellular networks. It is important to integrate multiple genomic data sources, including PPI networks, protein domains and Gene Ontology (GO) annotations, to facilitate the identification of cancer genes.</p> <p>Methods</p> <p>Topological features of the PPI network, as well as protein domain compositions, enrichment of gene ontology categories, sequence and evolutionary conservation features were extracted and compared between cancer genes and other genes. The predictive power of various classifiers for identification of cancer genes was evaluated by cross validation. Experimental validation of a subset of the prediction results was conducted using siRNA knockdown and viability assays in human colon cancer cell line DLD-1.</p> <p>Results</p> <p>Cross validation demonstrated advantageous performance of classifiers based on support vector machines (SVMs) with the inclusion of the topological features from the PPI network, protein domain compositions and GO annotations. We then applied the trained SVM classifier to human genes to prioritize putative cancer genes. siRNA knock-down of several SVM predicted cancer genes displayed greatly reduced cell viability in human colon cancer cell line DLD-1.</p> <p>Conclusion</p> <p>Topological features of PPI networks, protein domain compositions and GO annotations are good predictors of cancer genes. The SVM classifier integrates multiple features and as such is useful for prioritizing candidate cancer genes for experimental validations.</p

Phase transition in Random Circuit Sampling

Quantum computers hold the promise of executing tasks beyond the capability of classical computers. Noise competes with coherent evolution and destroys long-range correlations, making it an outstanding challenge to fully leverage the computation power of near-term quantum processors. We report Random Circuit Sampling (RCS) experiments where we identify distinct phases driven by the interplay between quantum dynamics and noise. Using cross-entropy benchmarking, we observe phase boundaries which can define the computational complexity of noisy quantum evolution. We conclude by presenting an RCS experiment with 70 qubits at 24 cycles. We estimate the computational cost against improved classical methods and demonstrate that our experiment is beyond the capabilities of existing classical supercomputers