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Pre-exposure prophylaxis (PrEP) is highly effective at reducing HIV acquisition. We aimed to estimate usage of oral-PrEP, and factors associated with adherence among female sex workers (FSWs) in Nairobi, Kenya, using a novel point-of-care urine tenofovir lateral flow assay (LFA). The Maisha Fiti study randomly selected FSWs from Sex Worker Outreach Program clinics in Nairobi. Data were collected from 1003 FSWs from June-October 2019, including surveys on self-reported oral-PrEP adherence. Adherence was also measured using the LFA for HIV-negative FSWs currently taking oral-PrEP. Informed by a social-ecological theoretical framework, we used hierarchical multivariable logistic regression models to estimate associations between individual, interpersonal/community, and structural/institutional-level factors and either self-reported or LFA-assessed adherence. Overall, 746 HIV-negative FSWs aged 18-40 participated in the study, of whom 180 (24.1%) self-reported currently taking oral-PrEP. Of these, 56 (31.1%) were adherent to oral-PrEP as measured by LFA. In the multivariable analyses, associations with currently taking oral-PrEP included having completed secondary education, high alcohol/substance use, feeling empowered to use PrEP, current intimate partner, no recent intimate partner violence, having support from sex worker organisations, experiencing sex work-related stigma, and seeking healthcare services despite stigma. Associations with oral-PrEP LFA-measured adherence measured included having only primary education, experience of childhood emotional violence, belonging to a higher wealth tertile, and being nulliparous. Oral-PrEP adherence, measured by self-report or objectively, is low among FSWs in Nairobi. Programs to improve oral-PrEP usage among FSWs should work to mitigate social and structural barriers and involve collaboration between FSWs, healthcare providers and policymakers.

PURPOSE: Current clinical risk stratification methods for localized prostate cancer are suboptimal, leading to over- and undertreatment. Recently, machine learning approaches using digital histopathology have shown superior prognostic ability in phase III trials. This study aims to develop a clinically usable risk grouping system using multimodal artificial intelligence (MMAI) models that outperform current National Comprehensive Cancer Network (NCCN) risk groups. MATERIALS AND METHODS: The cohort comprised 9,787 patients with localized prostate cancer from eight NRG Oncology randomized phase III trials, treated with radiation therapy, androgen deprivation therapy, and/or chemotherapy. Locked MMAI models, which used digital histopathology images and clinical data, were applied to each patient. Expert consensus on cut points defined low-, intermediate-, and high-risk groups on the basis of 10-year distant metastasis rates of 3% and 10%, respectively. The MMAIs reclassification and prognostic performance were compared with the three-tier NCCN risk groups. RESULTS: The median follow-up for censored patients was 7.9 years. According to NCCN risk categories, 30.4% of patients were low-risk, 25.5% intermediate-risk, and 44.1% high-risk. The MMAI risk classification identified 43.5% of patients as low-risk, 34.6% as intermediate-risk, and 21.8% as high-risk. MMAI reclassified 1,039 (42.0%) patients initially categorized by NCCN. Despite the MMAI low-risk group being larger than the NCCN low-risk group, the 10-year metastasis risks were comparable: 1.7% (95% CI, 0.2 to 3.2) for NCCN and 3.2% (95% CI, 1.7 to 4.7) for MMAI. The overall 10-year metastasis risk for NCCN high-risk patients was 16.6%, with MMAI further stratifying this group into low-, intermediate-, and high-risk, showing metastasis rates of 3.4%, 8.2%, and 26.3%, respectively. CONCLUSION: The MMAI risk grouping system expands the population of men identified as having low metastatic risk and accurately pinpoints a high-risk subset with elevated metastasis rates. This approach aims to prevent both overtreatment and undertreatment in localized prostate cancer, facilitating shared decision making.

The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.

The Peruvian Province, from 6° S in Peru to 42° S in Chile, is a highly productive coastal marine region whose biology and fossil record have long been studied separately but never integrated. To understand how past events and conditions affected todays species composition and interactions, we examined the role of extinction, colonization, geologic changes to explain previously unrecognized peculiar features of the biota and to compare the Peruvian Provinces history to that of other climatically similar temperate coasts. We synthesized all available data on the benthic (or benthically feeding) biota, with emphasis on fossilizable taxa, for the interval from the Miocene (23-5.4 Ma) and Pliocene (5.4-2.5 Ma) to the present. We outline the history of ecological guilds including primary producers, herbivores, predators, and suspension-feeders and document patterns of extinction, colonization, and geographic restriction. We identify twelve unusual attributes of the biota, most of which are the result of repeated episodes of extinction. Several guilds present during the Miocene and Pliocene are not represented in the province today, while groups such as kelps and perhaps intertidal predatory sea stars are relative newcomers. Guilds on soft bottoms and in sheltered habitats were severely affected by extinction, whereas those on hard bottoms were most affected by colonists and held their own in diversity. The Peruvian Province has not served as a biogeographic refuge, in contrast to the coasts of Australasia and Argentina, where lineages no longer present in the Peruvian Province survive. The loss of sheltered habitats since the Pliocene explains many of the present-day peculiarities of the biota. The history of the provinces biota explains its unique attributes. High productivity, a rich Southern Hemisphere heritage, and colonization from the north account for the present-day composition and unusual characteristics of the biota.

Background: The status of the regional lymph node basin is of prognostic importance in patients with melanoma, making the performance of sentinel lymph node biopsies (SLNBs) a key component of patient care management, particularly with the advent of immunotherapy for adjuvant treatment. The primary goal of our study was to assess the false-negative rate of SLNBs among patients with melanoma. Methods: We conducted a retrospective review of patients with melanoma undergoing SLNB by a single surgeon between Jan. 1, 2005, and Dec. 31, 2020. We extracted and cross-referenced patient demographic and pathologic information. Results: During the study period, 501 patients underwent an SLNB. Of these, 97 (19.4%) patients had pathologically positive sentinel lymph nodes and 404 (80.6%) patients had negative results. The latter were subject to further review; 84 (20.8%) patients subsequently developed recurrence, with 25 (6.2%) recurrences within the primary nodal basin. Isolated regional recurrence occurred in 11 (2.7%) patients and conjunction with a false-negative rate was 10.2%. Unadjusted recurrence rates were similar across each lymph node basin, including the axilla (2.7%), groin (3.6%), and neck (1.4%). Conclusion: The false-negative SLNB rate was 10.2% for isolated regional recurrences. These findings need to be considered in the era of using adjuvant systemic therapy for patients with melanoma. Contexte: L' tat du bassin ganglionnaire r gional rev t une importance pronostique chez les personnes atteintes de m lanome, ce qui rend les biopsies des ganglions sentinelles (BGS) essentielles la prise en charge de la maladie, particuli rement depuis l'av nement de l'immunoth rapie en traitement adjuvant. Le principal objectif de notre tude tait d' valuer le taux de r sultats faussement n gatifs des BGS dans les cas de m lanome. M thodes: Nous avons proc d une revue r trospective des cas de m lanome soumis des BGS effectu es par 1 seul membre de l' quipe de chirurgie entre le 1 (er) janvier 2005 et le 31 d cembre 2020. Nous avons extrait et recoup les donn es d mographiques et anatomopathologiques. R sultats: Pendant la dur e de l' tude, 501 personnes ont subi des BGS. Parmi elles, 97 (19,4%) pr sentaient des ganglions sentinelles positifs la biopsie et 404 (80,6%) avaient des r sultats n gatifs. Ce dernier groupe a t soumis un examen plus approfondi; 84 (20,8%) ont par la suite pr sent des r cidives, dont 25 (6,2%) l'int rieur du bassin ganglionnaire primitif. Des r cidives r gionales isol es sont survenues chez 11 (2,7%) personnes et le recoupement avec le taux de r sultats faussement n gatifs a t de 10,2%. Les taux de r cidives non ajust s ont t similaires peu importe les bassins ganglionnaires, y compris axillaires (2,7%), inguinaux (3,6%) et cervicaux (1,4%). Conclusion: Le taux de r sultats faussement n gatifs des BGS a t de 10,2% dans le cas des r cidives r gionales isol es. Il faut interpr ter ces r sultats en tenant compte du fait qu'ils ont t recueillis une poque o les malades atteints de m lanome taient trait s par chimioth rapie adjuvante., The status of the regional lymph node basin is of prognostic significance in patients with melanoma, making sentinel lymph node biopsy (SLNB) a key component of patient care management. Although [...]

Aim: Ecological and anthropogenic factors shift the abundances of dominant and rare tree species within local forest communities, thus affecting species composition and ecosystem functioning. To inform forest and conservation management it is important to understand the drivers of dominance and rarity in local tree communities. We answer the following research questions: (1) What are the patterns of dominance and rarity in tree communities? (2) Which ecological and anthropogenic factors predict these patterns? And (3) what is the extinction risk of locally dominant and rare tree species?. Location: Global. Time period: 1990–2017. Major taxa studied: Trees. Methods: We used 1.2 million forest plots and quantified local tree dominance as the relative plot basal area of the single most dominant species and local rarity as the percentage of species that contribute together to the least 10% of plot basal area. We mapped global community dominance and rarity using machine learning models and evaluated the ecological and anthropogenic predictors with linear models. Extinction risk, for example threatened status, of geographically widespread dominant and rare species was evaluated. Results: Community dominance and rarity show contrasting latitudinal trends, with boreal forests having high levels of dominance and tropical forests having high levels of rarity. Increasing annual precipitation reduces community dominance, probably because precipitation is related to an increase in tree density and richness. Additionally, stand age is positively related to community dominance, due to stem diameter increase of the most dominant species. Surprisingly, we find that locally dominant and rare species, which are geographically widespread in our data, have an equally high rate of elevated extinction due to declining populations through large-scale land degradation. Main conclusions: By linking patterns and predictors of community dominance and rarity to extinction risk, our results suggest that also widespread species should be considered in large-scale management and conservation practices.

Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system1. Remote-sensing estimates to quantify carbon losses from global forests2-5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced6 and satellite-derived approaches2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151-363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.

Objective: Static cold storage with ice has been the mainstay of cardiac donor preservation. Early preclinical data suggest that allograft preservation at 10 °C may be beneficial. We tested this hypothesis by using a static 10 °C storage device to preserve and transport cardiac allografts. Methods: In total, 52 allografts were recovered between July 2023 and March 2024 and transported using a 10 °C storage cooler. Results were compared to a 3:1 propensity match of allografts transported on ice. Patients were excluded for the following reasons: dual viscera transplant, previous heart transplant, complex congenital heart disease, or allograft injury during procurement. Results: Among the 10 °C cooler cohort, median total ischemic time was 222 minutes at 10 °C versus 193 minutes on ice (P .99). 10 °C hearts demonstrated less change in lactate but no difference in vasoactive inotrope scores or cardiac index. In hearts with extended ischemic time, delta lactate was lower in 10 °C cooler hearts. There was no statistical difference in outcomes for donor hearts >40 years old. Conclusions: This is an early experience of static preservation in a 10 °C cooler. Postoperative allograft function was excellent, and lactate profiles lower in those allografts with extended ischemic times. Static cold storage targeting 10 °C may offer an inexpensive method for extended heart preservation. Further investigation is needed to assess long-term outcomes of 10 °C storage.

ABSTRACT: The objectives of this study were to determine the farm-level hyperketolactia (HKL) prevalence, as diagnosed from the milk BHB concentration, on dairy farms milking with an automated milking system (AMS) and to describe the farm-level housing, management, and nutritional risk factors associated with increased farm-average milk BHB and the within-herd HKL prevalence in the first 45 DIM. Canadian AMS farms (n = 162; eastern Canada, n = 8; Quebec, n = 24; Ontario, n = 75; western Canada n = 55) were visited once between April and September 2019 to record housing and herd management practices. The first test milk data for each cow under 45 DIM were collected, along with the final test of the previous lactations for all multiparous cows, from April 1, 2019, to September 30, 2020. The first test milk BHB was then used to classify each individual cow as having or not having HKL (milk BHB ≥0.15 mmol/L) at the time of testing. Milk fat and protein content, milk BHB, and HKL prevalence were summarized by farm and lactation group (all, primiparous, and multiparous). During this same time period, formulated diets for dry and lactating cows, including ingredients and nutrient composition, and AMS milking data were collected. Data from the AMS were used to determine milking behaviors and milk production of each herd during the first 45 DIM. Multivariable regression models were used to associate herd-level housing, feeding management practices, and formulated nutrient composition with first test milk BHB concentrations and within-herd HKL levels separately for primiparous and multiparous cows. The within-herd HKL prevalence for all cows was 21.8%, with primiparous cows having a lower mean prevalence (12.2 ± 9.2%) than multiparous cows (26.6 ± 11.3%). Milk BHB concentration (0.095 ± 0.018 mmol/L) and HKL prevalence for primiparous cows were positively associated with formulated prepartum DMI and forage content of the dry cow diet; however, they were negatively associated with formulated postpartum DMI, the major ingredient in the concentrate supplemented through the AMS, and the partially mixed ration to AMS concentrate ratio. Multiparous cows' milk BHB concentration (0.12 ± 0.023 mmol/L) and HKL prevalence were positively associated with the length of the previous lactation, milk BHB at dry-off, prepartum diet nonfiber carbohydrate content, and the major forage fed on farm, while tending to be negatively associated with feed bunk space during lactation. This study is the first to determine the farm-level risk factors associated with herd-level prevalence of HKL in AMS dairy herds. The results may help to optimize management and guide diet formulation and thus promote the reduction of HKL prevalence.

Abstract Background As precision medicine advances, polygenic scores (PGS) have become increasingly important for clinical risk assessment. Many methods have been developed to create polygenic models with increased accuracy for risk prediction. Our select and shrink with summary statistics (S4) PGS method has previously been shown to accurately predict the polygenic risk of epithelial ovarian cancer. Here, we applied S4 PGS to 12 phenotypes for UK Biobank participants, and compared it with the LDpred2 and a combined S4 + LDpred2 method. Results The S4 + LDpred2 method provided overall improved PGS accuracy across a variety of phenotypes for UK Biobank participants. Additionally, the S4 + LDpred2 method had the best estimated PGS accuracy in Finnish and Japanese populations. We also addressed the challenge of limited genotype level data by developing the PGS models using only GWAS summary statistics. Conclusions Taken together, the S4 + LDpred2 method represents an improvement in overall PGS accuracy across multiple phenotypes and populations.

We conducted a systematic review and meta-analysis to examine if individuals with reading difficulties (RD), mathematics difficulties (MD), or unspecified learning difficulties (ULD) experience internalizing problems (i.e., anxiety, depression, somatic complaints, and social withdrawal) to the same extent, and if the effect sizes are influenced by moderators (age, internalizing problems type, anxiety type, rater type, selection criteria, and attention control). We reviewed 2,806 studies published in English between January 2000 and April 2023. Our final sample consisted of 96 studies that reported effect sizes or data to calculate them (a total of 120 unique samples, 83,260 participants, age range = 7.3-34.8 years). Risk of bias and sensitivity were assessed. A random-effects model analysis revealed a significant and moderate overall effect size (Hedge's g = -0.54), indicating that individuals with RD, MD or ULD experience more internalizing problems than their chronological-age (CA) controls. Follow-up analyses showed that neither learning difficulties type nor age of participants were significant moderators, but selection criteria (diagnosis versus screening) and internalizing problems type were. These findings suggest that individuals with RD do not differ from those with MD or ULD in internalizing problems, and all fare poorly compared to CA controls.

Histamine is a biogenic amine that regulates multiple physiological functions in diverse organisms, specifically playing a central role in the mammalian immune response. The common bed bug, Cimex lectularius L. (Hemiptera: Cimicidae), excretes histamine in large amounts in its feces as a component of its aggregation pheromone, which contaminates homes. The potential health risks associated with the presence of indoor histamine are unclear, but to predict future exposure risks, it is critical that we understand if other arthropods excrete histamine in any discernible phylogenetic pattern. In the present study, we evaluated histamine excretion by various arthropods; specifically those commonly found in large numbers indoors, other hematophagous species, and other species in the order Hemiptera. To evaluate arthropods for histamine excretion, rearing containers for each arthropod were swabbed and/or the harborage substrates were collected. Samples were then analyzed for the presence of histamine using gas chromatography-mass spectrometry. For those arthropods where histamine was present above the method detection limit, total histamine excretion was quantified over a period of 2 wk. Our results indicate that histamine excretion is limited to hematophagous hemipterans (bed bugs, bat bugs, tropical bed bugs, and kissing bugs), suggesting that indoor histamine contamination in the United States can be primarily linked to bed bugs.

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17-34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling.

Abstract Background Although behavioral interventions show some promise for reducing stimulant use and achieving durable viral suppression in sexual minority men (SMM) with HIV, scalable mHealth applications are needed to optimize their reach and cost-effectiveness. Methods Supporting Treatment Adherence for Resilience and Thriving (START) is a randomized controlled trial (RCT) testing the efficacy and cost-effectiveness of a mHealth application that integrates evidence-based positive affect regulation skills with self-monitoring of adherence and mood. The primary outcome is detectable HIV viral load (i.e., > 300 copies/mL) from self-collected dried blood spot (DBS) specimens at 6 months. Secondary outcomes include detectable DBS viral load at 12 months, self-reported stimulant use severity, anti-retroviral therapy (ART) adherence, and positive affect over 12 months. A national sample of up to 250 SMM with HIV who screen positive for stimulant use disorder and reporting suboptimal ART adherence is being recruited via social networking applications through April of 2024. After providing informed consent, participants complete a run-in period (i.e., waiting period) including two baseline assessments with self-report measures and a self-collected DBS sample. Those who complete the run-in period are randomized to either the START mHealth application or access to a website with referrals to HIV care and substance use disorder treatment resources. Participants provide DBS samples at baseline, 6, and 12 months to measure HIV viral load as well as complete self-report measures for secondary outcomes at quarterly follow-up assessments over 12 months. Discussion To date, we have paid $117,500 to advertise START on social networking applications and reached 1,970 eligible participants ($59.77 per eligible participant). Although we identified this large national sample of potentially eligible SMM with HIV who screen positive for a stimulant use disorder and report suboptimal ART adherence, only one-in-four have enrolled in the RCT. The run-in period has proven to be crucial for maintaining scientific rigor and reproducibility of this RCT, such that only half of consented participants complete the required study enrollment activities and attended a randomization visit. Taken together, findings will guide adequate resource allocation to achieve randomization targets in future mHealth research SMM with HIV who use stimulants. Trial Registration This protocol was registered on clinicaltrials.gov (NCT05140876) on December 2, 2021.

Objective Using the modified Rodnan skin score (mRSS) as a surrogate for disease activity, a phase 2a study in patients with systemic sclerosis (SSc) measured efficacy of the autotaxin inhibitor ziritaxestat. Mathematical modeling of mRSS was used to predict disease progression, examine candidate trial designs, and predict the probability of successfully discriminating treatment effect. Methods Patients with SSc receiving 600 mg of ziritaxestat or placebo for 24 weeks were included, in addition to data up to week 52 of the open‐label extension (OLE). Longitudinal mRSS data were described using a disease progression model; drug effect was a binary variable. Parameters used to predict the OLE mRSS outcome were estimated using data from the 24‐week double‐blind phase and validated with observed data. Three trial designs were simulated to identify which had the highest probability of detecting a treatment effect. Power to detect a treatment effect was quantified using the simulations. Results Maximum decreases from baseline in mRSS were 50.4% (ziritaxestat) and 34.7% (placebo). Study designs based on 300 patients randomized 2:1 or 1:1 to 600 mg of ziritaxestat or placebo had similar probabilities of detecting a significant treatment effect. Power to detect a treatment effect was >80% for all simulations. Conclusion Disease progression and drug effect could be predicted beyond the range of observed data. This modeling and simulation approach may inform future trial design, including study duration, and predict the probability of success.

Abstract In recent years, demographers have parsed variation in survivorship into two distinct components: the shape and the pace of ageing. The pace of ageing is defined by measures of longevity such as mean life expectancy or maximum longevity, whereas measures of the shape of ageing attempt to classify different shapes of the survivorship curve. We recently published a paper pointing out that a commonly used discretization of a shape measure, Keyfitz' entropy, does not correctly classify survivorship curves into negatively and positively senescing curves (de Vries et al., 2023). In that paper, we also suggested an alternative, accurate discrete‐time version of Keyfitz' entropy. de Vries et al. (2023) ended with two open questions, both of which have been answered by Giaimo (2024) now: (1) Can a discrete‐time entropy measure of survivorship be generalized beyond age‐based population models? Giaimo (2024) introduce a new formula that achieves this. (2) Will a discretization of Keyfitz' derivation of his measure as the elasticity of lifespan to a uniform change in mortality lead to the same formula as a discretization of Keyfitz' result? Giaimo (2024) answers: no. Here, we briefly discuss the implications of the results obtained by Giaimo (2024), and the implementation of his new formula into the Rpackage Rage, which is widely used for comparative demographic studies. We showcase the strength of the new method by reanalysing a comparison of Keyfitz' entropy to another shape measure. We find that the comparison is significantly altered by using Giaimo's new Keyfitz' formula. This example strengthens Giaimo's (2004) words of warning in approaching discretizations with attentiveness.

ABSTRACT: The objective of this study was to describe the nutritional strategies used on Canadian dairy farms with automated milking systems (AMS), both at the feed bunk and the concentrate offered at the AMS, as well as to determine what dietary components and nutrients, as formulated, were associated with milk production and milking behaviors on those farms. Formulated diets (including ingredients and nutrient content) and AMS data were collected from April 1, 2019, until September 30, 2020, on 160 AMS farms (eastern Canada [East] = 8, Ontario [ON] = 76, Quebec [QC] = 22, and western Canada [West] = 54). Both partial mixed ration (PMR) and AMS concentrate samples were collected from May 1 to September 30, 2019, on 169 farms (East = 12, ON = 63, QC = 42, West = 52). We collected AMS milking data for 154 herds. For each farm (n = 161), milk recording data were collected and summarized by farm to calculate average milk yield and components. Multivariable regression models were used to associate herd-level formulated nutrient composition and feeding management practices with milk production and milking behavior. Milk yield (mean ± SD = 37.0 ± 0.3 kg/d) was positively associated with the PMR ether extract (EE) concentration (+0.97 kg/d per percentage point [p.p.] increase) and with farms that fed barley silage as their major forage source (n = 16; +2.18 kg/d) as compared with haylage (n = 42), whereas farms that fed corn silage (n = 96; +1.23 kg/d) tended to produce more milk than farms that fed haylage. Greater milk fat content (4.09 ± 0.28%) was associated with a greater PMR-to-AMS concentrate ratio (+0.02 p.p. per unit increase) and total diet net energy for lactation (+0.046 p.p. per 0.1 Mcal/kg increase), but a lesser percentage of NFC of the PMR (−0.016 p.p. per p.p. increase of NFC percentage). Milk protein content (3.38 ± 0.14%) was positively associated with the forage percentage of the PMR (+0.003 p.p. per p.p. increase of forage percentage) and the total diet starch percentage (+0.009 p.p. per p.p. increase of starch percentage), but was negatively associated with farms feeding corn silage (−0.1 p.p. compared with haylage) as their major forage. Greater milking frequency (2.77 ± 0.40 milkings/d) was observed on farms with free-flow cow traffic systems (+0.62 milkings/d) and was positively associated with feed push-up frequency (+0.013 milkings/d per additional feed push-up), but negatively associated with PMR NFC content and forage percentage of the total ration (−0.017 milkings/d per p.p. increase of forage percentage). Lastly, greater milking refusal frequency (1.49 ± 0.82 refusals/d) was observed on farms with free-flow cow traffic systems (+0.84 refusals/d) and farms feeding barley silage (+0.58 refusals/d) than with guided flow and farms feeding either corn silage or haylage, respectively. These data give insight into the ingredients, nutrient formulations and type of diets fed on AMS dairy farms across Canada and the association of those factors with milk production and milking behaviors.

Abstract Background We sought to determine whether the Good School Toolkit-Primary violence prevention intervention was associated with reduced victimisation and perpetration of peer and intimate partner violence four years later, and if any associations were moderated by sex and early adolescent: family connectedness, socio-economic status, and experience of violence outside of school. Methods Drawing on schools involved in a randomised controlled trial of the intervention, we used a quasi-experimental design to compare violence outcomes between those who received the intervention during our trial (n = 1388), and those who did not receive the intervention during or after the trial (n = 522). Data were collected in 2014 (mean age 13.4, SD 1.5 years) from participants in 42 schools in Luwero District, Uganda, and 2018/19 from the same participants both in and out of school (mean age 18, SD: 1.77 years). We compared children who received the Good School Toolkit-Primary, a whole school violence prevention intervention, during a randomised controlled trial, to those who did not receive the intervention during or after the trial. Outcomes were measured using items adapted from the International Society for the Prevention of Child Abuse and Neglect Child Abuse Screening Tool-Child Institutional. We used mixed-effect multivariable logistic regression, with school fitted as a random-effect to account for clustering. Results 1910 adolescents aged about 16–19 years old were included in our analysis. We found no evidence of an average long-term intervention effect on our primary outcome, peer violence victimization at follow-up (aOR = 0.81, 95%CI = 0.59–1.11); or for any secondary outcome. However, exposure to the intervention was associated with: later reductions in peer violence, for adolescents with high family connectedness (aOR = 0.70, 95% CI 0.49 to 0.99), but not for those with low family connectedness (aOR = 1.07, 95% CI 0.69 to 1.6; p-interaction = 0.06); and reduced later intimate partner violence perpetration among males with high socio-economic status (aOR = 0.32, 95%CI 0.11 to 0.90), but not low socio-economic status (aOR = 1.01 95%CI 0.37 to 2.76, p-interaction = 0.05). Conclusions Young adolescents in connected families and with higher socio-economic status may be better equipped to transfer violence prevention skills from primary school to new relationships as they get older. Trial registration Clinicaltrials.gov, NCT01678846, registration date 24 August 2012. Protocol for this paper: https://www.researchprotocols.org/2020/12/e20940 .

We present the case of a 66-year-old man who presented with new incidentally found hyperdense pulmonary nodules. Further workup with a PET/CT revealed that the nodules were FDG-avid and that there was associated hypermetabolic lymphadenopathy. Due to his history of aluminum toxicity from welding, aluminosis pneumoconiosis was suspected. Biopsy of one of the nodules was done which reinforced this diagnosis. Aluminosis pneumoconiosis is a rare occupational lung disease mostly associated with industrial workers with prolonged unprotected exposure to fine aluminum dust. Prognosis depends on the duration and intensity of exposure, and there is no definitive treatment other than eliminating further exposure.

BACKGROUND: Androgen deprivation therapy (ADT) with radiotherapy can benefit patients with localized prostate cancer. However, ADT can negatively impact quality of life, and there remain no validated predictive models to guide its use. METHODS: We used digital pathology images from pretreatment prostate tissue and clinical data from 5727 patients enrolled in five phase 3 randomized trials, in which treatment was radiotherapy with or without ADT, as our data source to develop and validate an artificial intelligence (AI)–derived predictive patient-specific model that would determine which patients would develop the primary end point of distant metastasis. The model used baseline data to provide a binary output that a given patient will likely benefit from ADT or not. After the model was locked, validation was performed using data from NRG Oncology/Radiation Therapy Oncology Group (RTOG) 9408 (n=1594), a trial that randomly assigned men to radiotherapy plus or minus 4 months of ADT. Fine–Gray regression and restricted mean survival times were used to assess the interaction between treatment and the predictive model and within predictive model–positive, i.e., benefited from ADT, and –negative subgroup treatment effects. RESULTS: Overall, in the NRG/RTOG 9408 validation cohort (14.9 years of median follow-up), ADT significantly improved time to distant metastasis. Of these enrolled patients, 543 (34%) were model positive, and ADT significantly reduced the risk of distant metastasis compared with radiotherapy alone. Of 1051 patients who were model negative, ADT did not provide benefit. CONCLUSIONS: Our AI-based predictive model was able to identify patients with a predominantly intermediate risk for prostate cancer likely to benefit from short-term ADT. (Supported by a grant [U10CA180822] from NRG Oncology Statistical and Data Management Center, a grant [UG1CA189867] from NCI Community Oncology Research Program, a grant [U10CA180868] from NRG Oncology Operations, and a grant [U24CA196067] from NRG Specimen Bank from the National Cancer Institute and by Artera, Inc. ClinicalTrials.gov numbers NCT00767286, NCT00002597, NCT00769548, NCT00005044, and NCT00033631.)

Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species1,2. Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies3,4. Here, leveraging global tree databases5-7, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions.

1. Biodiversity is an important component of natural ecosystems, with higher species richness often correlating with an increase in ecosystem productivity. Yet, this relationship varies substantially across environments, typically becoming less pronounced at high levels of species richness. However, species richness alone cannot reflect all important properties of a community, including community evenness, which may mediate the relationship between biodiversity and productivity. If the evenness of a community correlates negatively with richness across forests globally, then a greater number of species may not always increase overall diversity and productivity of the system. Theoretical work and local empirical studies have shown that the effect of evenness on ecosystem functioning may be especially strong at high richness levels, yet the consistency of this remains untested at a global scale. 2. Here, we used a dataset of forests from across the globe, which includes composition, biomass accumulation and net primary productivity, to explore whether productivity correlates with community evenness and richness in a way that evenness appears to buffer the effect of richness. Specifically, we evaluated whether low levels of evenness in speciose communities correlate with the attenuation of the richness–productivity relationship. 3. We found that tree species richness and evenness are negatively correlated across forests globally, with highly speciose forests typically comprising a few dominant and many rare species. Furthermore, we found that the correlation between diversity and productivity changes with evenness: at low richness, uneven communities are more productive, while at high richness, even communities are more productive. 4. Synthesis. Collectively, these results demonstrate that evenness is an integral component of the relationship between biodiversity and productivity, and that the attenuating effect of richness on forest productivity might be partly explained by low evenness in speciose communities. Productivity generally increases with species richness, until reduced evenness limits the overall increases in community diversity. Our research suggests that evenness is a fundamental component of biodiversity–ecosystem function relationships, and is of critical importance for guiding conservation and sustainable ecosystem management decisions.

Abstract Mitotic count (MC) is the most common measure to assess tumor proliferation in breast cancer patients and is highly predictive of patient outcomes. It is, however, subject to inter‐ and intraobserver variation and reproducibility challenges that may hamper its clinical utility. In past studies, artificial intelligence (AI)‐supported MC has been shown to correlate well with traditional MC on glass slides. Considering the potential of AI to improve reproducibility of MC between pathologists, we undertook the next validation step by evaluating the prognostic value of a fully automatic method to detect and count mitoses on whole slide images using a deep learning model. The model was developed in the context of the Mitosis Domain Generalization Challenge 2021 (MIDOG21) grand challenge and was expanded by a novel automatic area selector method to find the optimal mitotic hotspot and calculate the MC per 2 mm2. We employed this method on a breast cancer cohort with long‐term follow‐up from the University Medical Centre Utrecht (N = 912) and compared predictive values for overall survival of AI‐based MC and light‐microscopic MC, previously assessed during routine diagnostics. The MIDOG21 model was prognostically comparable to the original MC from the pathology report in uni‐ and multivariate survival analysis. In conclusion, a fully automated MC AI algorithm was validated in a large cohort of breast cancer with regard to retained prognostic value compared with traditional light‐microscopic MC.