Testing predictions of inclusive fitness theory in inbreeding relatives with biparental care

Data accessibility Data are deposited in Dryad https:doi.org/10.5061/dryad.1zcrdfnf. R code supporting this article has been uploaded as part of the electronic supplementary material. Acknowledgements We thank the Tsawout and Tseycum First Nation bands for allowing access to Mandarte, numerous field assistants, graduate students and postdoctoral fellows who contributed to long-term data collection, and Brad Duthie for insightful discussions regarding underlying concepts. National Sciences and Engineering Research Council (P.A., E.A.G); Izaak Walton Killam Memorial Fund for Advanced Studies (E.A.G, J.M.R.), UK Natural Environment Research Council (R.J.S.) and the European Research Council (J.M.R.) provided funding.Peer reviewedPostprin

Aerosol and Surface Contamination of SARS-CoV-2 Observed in Quarantine and Isolation Care

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) originated in Wuhan, China in late 2019, and its resulting coronavirus disease, COVID-19, was declared a pandemic by the World Health Organization on March 11, 2020. The rapid global spread of COVID-19 represents perhaps the most significant public health emergency in a century. As the pandemic progressed, a continued paucity of evidence on routes of SARS-CoV-2 transmission has resulted in shifting infection prevention and control guidelines between classically-defined airborne and droplet precautions. During the initial isolation of 13 individuals with COVID-19 at the University of Nebraska Medical Center, we collected air and surface samples to examine viral shedding from isolated individuals. We detected viral contamination among all samples, supporting the use of airborne isolation precautions when caring for COVID-19 patients

Accelerometer-measured physical activity and sedentary time among children and their parents in the UK before and after COVID-19 lockdowns:a natural experiment

BACKGROUND: Restrictions due to the coronavirus disease 2019 (COVID-19) pandemic reduced physical activity provision for both children and their parents. Recent studies have reported decreases in physical activity levels during lockdown restrictions, but these were largely reliant on self-report methods, with data collected via unrepresentative self-report surveys. The post-pandemic impacts on children’s activity levels remain unknown. A key question is how active children become once lockdown restrictions are lifted. METHODS: Active-6 is a repeated cross-sectional natural experiment. Accelerometer data from 1296 children aged 10–11 and their parents were collected in 50 schools in the Greater Bristol area, UK in March 2017-May 2018 (pre-COVID-19 comparator group), and compared to 393 children aged 10–11 and parents in 23 of the same schools, collected in May-December 2021. Mean minutes of accelerometer-measured moderate-to-vigorous physical activity (MVPA) were derived for weekdays and weekend and compared pre- and post-lockdown via linear multilevel models. RESULTS: After adjusting for seasonality, accelerometer wear time and child/parent demographics, children’s mean weekday and weekend MVPA were 7.7 min (95% CI: 3.5 to 11.9) and 6.9 min (95% CI: 0.9 to 12.9) lower in 2021 than in 2018, respectively, while sedentary time was higher by 25.4 min (95% CI: 15.8 to 35.0) and 14.0 min (95% CI: 1.5 to 26.5). There was no evidence that differences varied by child gender or household education. There was no significant difference in parents’ MVPA or sedentary time, either on weekdays or weekends. CONCLUSIONS: Children’s MVPA was lower by 7–8 min/day in 2021 once restrictions were lifted than before the pandemic for all groups, on both weekdays and weekends. Previous research has shown that there is an undesirable age-related decline in children’s physical activity. The 8-min difference reported here would be broadly comparable to the decline that would have previously been expected to occur over a three-year period. Parents’ physical activity was similar to pre-pandemic levels. Our results suggest that despite easing of restrictions, children’s activity levels have not returned to pre-pandemic levels. There is an urgent need to understand why these changes have occurred and how long they are maintained. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12966-022-01290-4

Induction of stable human FOXP3⁺ Tregs by a parasite-derived TGF-β mimic

Immune homeostasis in the intestine is tightly controlled by FOXP3 + regulatory T cells (Tregs), defects of which are linked to the development of chronic conditions, such as inflammatory bowel disease (IBD). As a mechanism of immune evasion, several species of intestinal parasites boost Treg activity. The parasite Heligmosomoides polygyrus is known to secrete a molecule (Hp-TGM) that mimics the ability of TGF-β to induce FOXP3 expression in CD4 + T cells. The study aimed to investigate whether Hp-TGM could induce human FOXP3 + Tregs as a potential therapeutic approach for inflammatory diseases. CD4 + T cells from healthy volunteers were expanded in the presence of Hp-TGM or TGF-β. Treg induction was measured by flow cytometric detection of FOXP3 and other Treg markers, such as CD25 and CTLA-4. Epigenetic changes were detected using ChIP-Seq and pyrosequencing of FOXP3. Treg phenotype stability was assessed following inflammatory cytokine challenge and Treg function was evaluated by cellular co-culture suppression assays and cytometric bead arrays for secreted cytokines. Hp-TGM efficiently induced FOXP3 expression (&gt; 60%), in addition to CD25 and CTLA-4, and caused epigenetic modification of the FOXP3 locus to a greater extent than TGF-β. Hp-TGM-induced Tregs had superior suppressive function compared with TGF-β-induced Tregs, and retained their phenotype following exposure to inflammatory cytokines. Furthermore, Hp-TGM induced a Treg-like phenotype in in vivo differentiated Th1 and Th17 cells, indicating its potential to re-program memory cells to enhance immune tolerance. These data indicate Hp-TGM has potential to be used to generate stable human FOXP3 + Tregs to treat IBD and other inflammatory diseases. </p

Food Reservoir for Escherichia coli Causing Urinary Tract Infections

Closely related strains of Escherichia coli have been shown to cause extraintestinal infections in unrelated persons. This study tests whether a food reservoir may exist for these E. coli. Isolates from 3 sources over the same time period (2005–2007) and geographic area were compared. The sources comprised prospectively collected E. coli isolates from women with urinary tract infection (UTI) (n = 353); retail meat (n = 417); and restaurant/ready-to-eat foods (n = 74). E. coli were evaluated for antimicrobial drug susceptibility and O:H serotype and compared by using 4 different genotyping methods. We identified 17 clonal groups that contained E. coli isolates (n = 72) from >1 source. E. coli from retail chicken (O25:H4-ST131 and O114:H4-ST117) and honeydew melon (O2:H7-ST95) were indistinguishable from or closely related to E. coli from human UTIs. This study provides strong support for the role of food reservoirs or foodborne transmission in the dissemination of E. coli causing common community-acquired UTIs

Learning, Memory, and the Role of Neural Network Architecture

The performance of information processing systems, from artificial neural networks to natural neuronal ensembles, depends heavily on the underlying system architecture. In this study, we compare the performance of parallel and layered network architectures during sequential tasks that require both acquisition and retention of information, thereby identifying tradeoffs between learning and memory processes. During the task of supervised, sequential function approximation, networks produce and adapt representations of external information. Performance is evaluated by statistically analyzing the error in these representations while varying the initial network state, the structure of the external information, and the time given to learn the information. We link performance to complexity in network architecture by characterizing local error landscape curvature. We find that variations in error landscape structure give rise to tradeoffs in performance; these include the ability of the network to maximize accuracy versus minimize inaccuracy and produce specific versus generalizable representations of information. Parallel networks generate smooth error landscapes with deep, narrow minima, enabling them to find highly specific representations given sufficient time. While accurate, however, these representations are difficult to generalize. In contrast, layered networks generate rough error landscapes with a variety of local minima, allowing them to quickly find coarse representations. Although less accurate, these representations are easily adaptable. The presence of measurable performance tradeoffs in both layered and parallel networks has implications for understanding the behavior of a wide variety of natural and artificial learning systems

Justice Through a Multispecies Lens

The bushfires in Australia during the Summer of 2019–2020, in the midst of which we were writing this exchange, violently heightened the urgency of the task of rethinking justice through a multispecies lens for all of the authors in this exchange, and no doubt many of its readers. As I finish this introduction, still in the middle of the Australian summer, more than 10 million hectares (100,000 km2 or 24.7 million acres) of bushland have been burned and over a billion individual animals killed. This says nothing of the others who will die because their habitat and the relationships on which they depend no longer exist. People all around the world are mourning these deaths and the destruction of unique ecosystems. As humans on this planet, and specifically as political theorists facing the prospect that such devastating events will only become more frequent, the question before us is whether we can rethink what it means to be in ethical relationships with beings other than humans and what justice requires, in ways that mark these deaths as absolute wrongs that obligate us to act, and not simply as unfortunate tragedies that leave us bereft

Landscape of somatic single nucleotide variants and indels in colorectal cancer and impact on survival

Colorectal cancer (CRC) is a biologically heterogeneous disease. To characterize its mutational profile, we conduct targeted sequencing of 205 genes for 2,105 CRC cases with survival data. Our data shows several findings in addition to enhancing the existing knowledge of CRC. We identify PRKCI, SPZ1, MUTYH, MAP2K4, FETUB, and TGFBR2 as additional genes significantly mutated in CRC. We find that among hypermutated tumors, an increased mutation burden is associated with improved CRC-specific survival (HR=0.42, 95% CI: 0.21-0.82). Mutations in TP53 are associated with poorer CRC-specific survival, which is most pronounced in cases carrying TP53 mutations with predicted 0% transcriptional activity (HR=1.53, 95% CI: 1.21-1.94). Furthermore, we observe differences in mutational frequency of several genes and pathways by tumor location, stage, and sex. Overall, this large study provides deep insights into somatic mutations in CRC, and their potential relationships with survival and tumor features. Large scale sequencing study is of paramount importance to unravel the heterogeneity of colorectal cancer. Here, the authors sequenced 205 cancer genes in more than 2000 tumours and identified additional mutated driver genes, determined that mutational burden and specific mutations in TP53 are associated with survival odds

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14