Age differences in the change in cognition after stroke.

OBJECTIVE: To compare changes in cognitive trajectories after stroke between younger (18-64) and older (65+) adults, accounting for pre-stroke cognitive trajectories. MATERIALS AND METHODS: Pooled cohort study using individual participant data from 3 US cohorts (1971-2019), the Atherosclerosis Risk In Communities Study (ARIC), Framingham Offspring Study (FOS), and REasons for Geographic And Racial Differences in Stroke Study (REGARDS). Linear mixed effect models evaluated the association between age and the initial change (intercept) and rate of change (slope) in cognition after compared to before stroke. Outcomes were global cognition (primary), memory and executive function. RESULTS: We included 1,292 participants with stroke; 197 younger (47.2 % female, 32.5 % Black race) and 1,095 older (50.2 % female, 46.4 % Black race). Median (IQR) age at stroke was 59.7 (56.6-61.7) (younger group) and 75.2 (70.5-80.2) years (older group). Compared to the young, older participants had greater declines in global cognition (-1.69 point [95 % CI, -2.82 to -0.55] greater), memory (-1.05 point [95 % CI, -1.92 to -0.17] greater), and executive function (-3.72 point [95 % CI, -5.23 to -2.21] greater) initially after stroke. Older age was associated with faster declines in global cognition (-0.18 points per year [95 % CI, -0.36 to -0.01] faster) and executive function (-0.16 [95 % CI, -0.26 to -0.06] points per year for every 10 years of higher age), but not memory (-0.006 [95 % CI, -0.15 to 0.14]), after compared to before stroke. CONCLUSION: Older age was associated with greater post-stroke cognitive declines, accounting for differences in pre-stroke cognitive trajectories between the old and the young.

Resolution of SLC6A1 variable expressivity in a multi-generational family using deep clinical phenotyping and Drosophila models.

Purpose: Variants in SLC6A1 result in a rare neurodevelopmental disorder characterized by a variable clinical presentation of symptoms including developmental delay, epilepsy, motor dysfunction, and autism spectrum disorder. SLC6A1 haploinsufficiency has been confirmed as the predominant pathway of SLC6A1-related neurodevelopmental disorders (NDDs), however, the molecular mechanism underlying the variable clinical presentation remains unclear. Methods: Here, through work of the Undiagnosed Diseases Network, we identify an undiagnosed individual with an inherited p.(A334S) variant of uncertain significance. To resolve this case and better understand the variable expressivity with SLC6A1, we assess the phenotypes of the proband with a cohort of cases diagnosed with SLC6A1-related NDDs. We then create an allelic series in the Drosophila melanogaster to functionally characterize case variants. Results: We identify significant clinical overlap between the unsolved case and confirmed cases of SLC6A1-related NDDs and find a mild to severe clinical presentation associated with missense variants. We confirm phenotypes in flies expressing SLC6A1 variants consistent with a partial loss-of-function mechanism. Conclusion: We conclude that the p.(A334S) variant is a hypomorphic allele and begin to elucidate the underlying variability in SLC6A1-related NDDs. These insights will inform clinical diagnosis, prognosis, treatment and inform therapeutic design for those living with SLC6A1-related NDDs.

Efficacy and safety in early-phase clinical trials for refractory colorectal cancer: A meta-analysis.

BACKGROUND: Despite recent metastatic colorectal cancer (mCRC) therapeutic innovations a comprehensive synthesis of patient outcome and risk-benefit assessment of phase 1/2 trials is missing. The aim of this meta-analysis is to assess efficacy, safety, and trends over time for phase 1 and 2 mCRC trials by examining clinical benefit rate (CBR), overall response rate (ORR), grade 3 or higher adverse events (AE), and discontinuation due to AE. METHODS: The PRISMA guidelines were followed. We searched PubMed and Embase for publications of phase 1/2 trials between 2010-2021. Trials reporting on new therapies for treatment-refractory mCRC were included. RESULTS: The search strategy yielded 4175 unique reports, of which 258 publications were eligible. These publications report data of 277 unique treatment arms. Overall ORR was 6 %, CBR was 27 % in phase 1 % and 36 % in phase 2 trials. CBR increased from 23 % in 2010-2012 to 42 % in 2019-2021. Compared to 2010-2012, trials in 2019-2021 more often tested immunomodulators (4 % vs 23 %), included molecularly preselected populations (4 % vs 38 %) and younger patients (median age<60 44 % vs 66 %). Grade 3 + AE occurred in 35 % of patients, most frequently in trials investigating targeted treatments. CONCLUSIONS: Treatment efficacy in phase 1/2 trials is modest but improved from 2010 to 2021. This improvement is accompanied by a shift towards testing in a younger, fitter, and more strictly molecularly preselected population, as well as an increased focus on targeted and immunotherapies.

An ICT-enabled community oriented primary care intervention in mining communities during COVID-19 (2019-2022): perceived changes in the role and place of community health workers.

BACKGROUND: This is a study of service provider perceptions of the place, role and practices of CHWs in a four-year, large-scale private sector funded, public service ICT-enabled COPC intervention with rural and remote mining communities. Like all South African communities, apart from large mining house employees and some contractors, most people use available public healthcare services and private traditional as well as limited allopathic private sector providers. In addition to the limitations of facility centred primary healthcare and a fragmented health care system, the many negative health effects of mining on the communities, go unattended. METHODS: This is a rapid, qualitative pragmatic study. Using site and participation convenience sampling, 37 semi-structured individual or group interviews were conducted with 57 stakeholders from 38 of the 135 intervention PHC facilities. Using a data driven, inductive approach, the results were analysed thematically in terms of perceived changes in the role and place of CHWs. RESULTS: CHWs registered 42 490 households and captured the demographic and social profiles as well as the health status of over 154 910 individuals using AitaHealth™. These data provided healthcare professionals and managers with knowledge about community demographics, at-risk groups and vulnerable individuals. The intervention changed the locational focus of CHW practice and expanded their scope of work and competencies in household comprehensive health education, advice and care. It led to a growth in community and professional confidence in CHWs as trusted members of mining community PHC teams and to more focused and efficient clinic work. CONCLUSION: This ICT-enabled COPC intervention adopted a comprehensive approach to healthcare delivery that started by including CHWs in PHC teams and locating them in communities. Inclusive and systematic continuous learning, clinically-led CHW service support and ICT-enabled information technology engendered trust in CHWs as competent PHC members, and grew community confidence in them and the PHC system as a whole. Although health, care and other professionals and workers valued the changes the intervention brought to their work as well as people's lives in underserved and vulnerable mining communities, its sustainability is contingent on the vagaries of political will and financial commitment.

Empirical evidence and theoretical understanding of ecosystem carbon and nitrogen cycle interactions.

Interactions between carbon (C) and nitrogen (N) cycles in terrestrial ecosystems are simulated in advanced vegetation models, yet methodologies vary widely, leading to divergent simulations of past land C balance trends. This underscores the need to reassess our understanding of ecosystem processes, given recent theoretical advancements and empirical data. We review current knowledge, emphasising evidence from experiments and trait data compilations for vegetation responses to CO2 and N input, alongside theoretical and ecological principles for modelling. N fertilisation increases leaf N content but inconsistently enhances leaf-level photosynthetic capacity. Whole-plant responses include increased leaf area and biomass, with reduced root allocation and increased aboveground biomass. Elevated atmospheric CO2 also boosts leaf area and biomass but intensifies belowground allocation, depleting soil N and likely reducing N losses. Global leaf traits data confirm these findings, indicating that soil N availability influences leaf N content more than photosynthetic capacity. A demonstration model based on the functional balance hypothesis accurately predicts responses to N and CO2 fertilisation on tissue allocation, growth and biomass, offering a path to reduce uncertainty in global C cycle projections.

Delta radiomics to track radiation response in lung tumors receiving stereotactic magnetic resonance-guided radiotherapy.

Background and purpose: Lung cancer is a leading cause of cancer-related mortality, and stereotactic body radiotherapy (SBRT) has become a standard treatment for early-stage lung cancer. However, the heterogeneous response to radiation at the tumor level poses challenges. Currently, standardized dosage regimens lack adaptation based on individual patient or tumor characteristics. Thus, we explore the potential of delta radiomics from on-treatment magnetic resonance (MR) imaging to track radiation dose response, inform personalized radiotherapy dosing, and predict outcomes. Materials and methods: A retrospective study of 47 MR-guided lung SBRT treatments for 39 patients was conducted. Radiomic features were extracted using Pyradiomics, and stability was evaluated temporally and spatially. Delta radiomics were correlated with radiation dose delivery and assessed for associations with tumor control and survival with Cox regressions. Results: Among 107 features, 49 demonstrated temporal stability, and 57 showed spatial stability. Fifteen stable and non-collinear features were analyzed. Median Skewness and surface to volume ratio decreased with radiation dose fraction delivery, while coarseness and 90th percentile values increased. Skewness had the largest relative median absolute changes (22 %-45 %) per fraction from baseline and was associated with locoregional failure (p = 0.012) by analysis of covariance. Skewness, Elongation, and Flatness were significantly associated with local recurrence-free survival, while tumor diameter and volume were not. Conclusions: Our study establishes the feasibility and stability of delta radiomics analysis for MR-guided lung SBRT. Findings suggest that MR delta radiomics can capture short-term radiographic manifestations of the intra-tumoral radiation effect.

Alzheimer's disease protective allele of Clusterin modulates neuronal excitability through lipid-droplet-mediated neuron-glia communication.

Genome-wide association studies (GWAS) of Alzheimer's disease (AD) have identified a plethora of risk loci. However, the disease variants/genes and the underlying mechanisms remain largely unknown. For a strong AD-associated locus near Clusterin (CLU), we tied an AD protective allele to a role of neuronal CLU in promoting neuron excitability through lipid-mediated neuron-glia communication. We identified a putative causal SNP of CLU that impacts neuron-specific chromatin accessibility to transcription-factor(s), with the AD protective allele upregulating neuronal CLU and promoting neuron excitability. Transcriptomic analysis and functional studies in induced pluripotent stem cell (iPSC)-derived neurons co-cultured with mouse astrocytes show that neuronal CLU facilitates neuron-to-glia lipid transfer and astrocytic lipid droplet formation coupled with reactive oxygen species (ROS) accumulation. These changes cause astrocytes to uptake less glutamate thereby altering neuron excitability. Our study provides insights into how CLU confers resilience to AD through neuron-glia interactions.

Modelling the impacts of climate change on agrochemical fate and transport by water on a catchment scale.

The export of agrochemicals and their transformation products (TPs) following their application in the agricultural fields poses a threat to water quality. Future changes in climatic conditions (e.g. extreme weather events such as heavy rainfall or extended dry periods) could alter the degradation and mobility of agrochemicals. In this research, we use an integrated modelling framework to understand the impact of extreme climate events on the fate and transport of the agrochemical S-Metolachlor and two of its TPs (M-OXA, Metolachlor Oxanilic Acid and M-ESA, Metolachlor Ethyl Sulfonic Acid). This is done by coupling climate model outputs to the Zin-AgriTra agrochemical reactive transport model in four simulation scenarios. 1) Reference (2015-2018), 2) Very dry (2038-2041), 3) Very wet (2054-2057) and 4) High temperature (2096-2099) conditions of a selected RCP8.5 based regional climate scenario. The modelling framework is tested on an agricultural catchment, Wulka, in Burgenland, Austria. The model results indicate that 13-14 % of applied S-Metolachlor is retained in the soil, and around 85 % is degraded into TPs in the different scenarios. In very dry and high-temperature scenarios, degradation is higher, and hence, there is less S-Metolachlor in the soil. However, a large share of formed M-OXA and M-ESA are retained in the soil, which is transported via overland and groundwater flow, leading to a build-up effect in M-OXA and M-ESA river concentrations over the years. Though a small share of S-Metolachlor and TPs are transported to rivers, their river export is affected by the intensity and amount of rainfall. The very wet and high-temperature scenarios show higher S-Metolachlor and TP concentrations at the catchment outlet due to higher river discharge. The reference scenario shows higher river peak concentrations associated with higher overland flow caused by measured hourly rainfall compared to disaggregated daily precipitation data in the other scenarios.

Multimodal Deep Learning Improves Recurrence Risk Prediction in Pediatric Low-Grade Gliomas.

BACKGROUND: Postoperative recurrence risk for pediatric low-grade gliomas (pLGGs) is challenging to predict by conventional clinical, radiographic, and genomic factors. We investigated if deep learning of MRI tumor features could improve postoperative pLGG risk stratification. METHODS: We used pre-trained deep learning (DL) tool designed for pLGG segmentation to extract pLGG imaging features from preoperative T2-weighted MRI from patients who underwent surgery (DL-MRI features). Patients were pooled from two institutions: Dana Farber/Boston Children's Hospital (DF/BCH) and the Children's Brain Tumor Network (CBTN). We trained three DL logistic hazard models to predict postoperative event-free survival (EFS) probabilities with 1) clinical features, 2) DL-MRI features, and 3) multimodal (clinical and DL-MRI features). We evaluated the models with a time-dependent Concordance Index (Ctd) and risk group stratification with Kaplan Meier plots and log-rank tests. We developed an automated pipeline integrating pLGG segmentation and EFS prediction with the best model. RESULTS: Of the 396 patients analyzed (median follow-up: 85 months, range: 1.5-329 months), 214 (54%) underwent gross total resection and 110 (28%) recurred. The multimodal model improved EFS prediction compared to the DL-MRI and clinical models (Ctd: 0.85 (95% CI: 0.81-0.93), 0.79 (95% CI: 0.70-0.88), and 0.72 (95% CI: 0.57-0.77), respectively). The multimodal model improved risk-group stratification (3-year EFS for predicted high-risk: 31% versus low-risk: 92%, p<0.0001). CONCLUSIONS: DL extracts imaging features that can inform postoperative recurrence prediction for pLGG. Multimodal DL improves postoperative risk stratification for pLGG and may guide postoperative decision-making. Larger, multicenter training data may be needed to improve model generalizability.

End-to-end reproducible AI pipelines in radiology using the cloud.

Artificial intelligence (AI) algorithms hold the potential to revolutionize radiology. However, a significant portion of the published literature lacks transparency and reproducibility, which hampers sustained progress toward clinical translation. Although several reporting guidelines have been proposed, identifying practical means to address these issues remains challenging. Here, we show the potential of cloud-based infrastructure for implementing and sharing transparent and reproducible AI-based radiology pipelines. We demonstrate end-to-end reproducibility from retrieving cloud-hosted data, through data pre-processing, deep learning inference, and post-processing, to the analysis and reporting of the final results. We successfully implement two distinct use cases, starting from recent literature on AI-based biomarkers for cancer imaging. Using cloud-hosted data and computing, we confirm the findings of these studies and extend the validation to previously unseen data for one of the use cases. Furthermore, we provide the community with transparent and easy-to-extend examples of pipelines impactful for the broader oncology field. Our approach demonstrates the potential of cloud resources for implementing, sharing, and using reproducible and transparent AI pipelines, which can accelerate the translation into clinical solutions.

Tau is required for glial lipid droplet formation and resistance to neuronal oxidative stress.

The accumulation of reactive oxygen species (ROS) is a common feature of tauopathies, defined by Tau accumulations in neurons and glia. High ROS in neurons causes lipid production and the export of toxic peroxidated lipids (LPOs). Glia uptake these LPOs and incorporate them into lipid droplets (LDs) for storage and catabolism. We found that overexpressing Tau in glia disrupts LDs in flies and rat neuron-astrocyte co-cultures, sensitizing the glia to toxic, neuronal LPOs. Using a new fly tau loss-of-function allele and RNA-mediated interference, we found that endogenous Tau is required for glial LD formation and protection against neuronal LPOs. Similarly, endogenous Tau is required in rat astrocytes and human oligodendrocyte-like cells for LD formation and the breakdown of LPOs. Behaviorally, flies lacking glial Tau have decreased lifespans and motor defects that are rescuable by administering the antioxidant N-acetylcysteine amide. Overall, this work provides insights into the important role that Tau has in glia to mitigate ROS in the brain.

AI-MARRVEL - A Knowledge-Driven AI System for Diagnosing Mendelian Disorders.

BACKGROUND: Diagnosing genetic disorders requires extensive manual curation and interpretation of candidate variants, a labor-intensive task even for trained geneticists. Although artificial intelligence (AI) shows promise in aiding these diagnoses, existing AI tools have only achieved moderate success for primary diagnosis. METHODS: AI-MARRVEL (AIM) uses a random-forest machine-learning classifier trained on over 3.5 million variants from thousands of diagnosed cases. AIM additionally incorporates expert-engineered features into training to recapitulate the intricate decision-making processes in molecular diagnosis. The online version of AIM is available at https://ai.marrvel.org. To evaluate AIM, we benchmarked it with diagnosed patients from three independent cohorts. RESULTS: AIM improved the rate of accurate genetic diagnosis, doubling the number of solved cases as compared with benchmarked methods, across three distinct real-world cohorts. To better identify diagnosable cases from the unsolved pools accumulated over time, we designed a confidence metric on which AIM achieved a precision rate of 98% and identified 57% of diagnosable cases out of a collection of 871 cases. Furthermore, AIM's performance improved after being fine-tuned for targeted settings including recessive disorders and trio analysis. Finally, AIM demonstrated potential for novel disease gene discovery by correctly predicting two newly reported disease genes from the Undiagnosed Diseases Network. CONCLUSIONS: AIM achieved superior accuracy compared with existing methods for genetic diagnosis. We anticipate that this tool may aid in primary diagnosis, reanalysis of unsolved cases, and the discovery of novel disease genes. (Funded by the NIH Common Fund and others.).

Stepwise Transfer Learning for Expert-level Pediatric Brain Tumor MRI Segmentation in a Limited Data Scenario.

Purpose To develop, externally test, and evaluate clinical acceptability of a deep learning pediatric brain tumor segmentation model using stepwise transfer learning. Materials and Methods In this retrospective study, the authors leveraged two T2-weighted MRI datasets (May 2001 through December 2015) from a national brain tumor consortium (n = 184; median age, 7 years [range, 1-23 years]; 94 male patients) and a pediatric cancer center (n = 100; median age, 8 years [range, 1-19 years]; 47 male patients) to develop and evaluate deep learning neural networks for pediatric low-grade glioma segmentation using a stepwise transfer learning approach to maximize performance in a limited data scenario. The best model was externally tested on an independent test set and subjected to randomized blinded evaluation by three clinicians, wherein they assessed clinical acceptability of expert- and artificial intelligence (AI)-generated segmentations via 10-point Likert scales and Turing tests. Results The best AI model used in-domain stepwise transfer learning (median Dice score coefficient, 0.88 [IQR, 0.72-0.91] vs 0.812 [IQR, 0.56-0.89] for baseline model; P = .049). With external testing, the AI model yielded excellent accuracy using reference standards from three clinical experts (median Dice similarity coefficients: expert 1, 0.83 [IQR, 0.75-0.90]; expert 2, 0.81 [IQR, 0.70-0.89]; expert 3, 0.81 [IQR, 0.68-0.88]; mean accuracy, 0.82). For clinical benchmarking (n = 100 scans), experts rated AI-based segmentations higher on average compared with other experts (median Likert score, 9 [IQR, 7-9] vs 7 [IQR 7-9]) and rated more AI segmentations as clinically acceptable (80.2% vs 65.4%). Experts correctly predicted the origin of AI segmentations in an average of 26.0% of cases. Conclusion Stepwise transfer learning enabled expert-level automated pediatric brain tumor autosegmentation and volumetric measurement with a high level of clinical acceptability. Keywords: Stepwise Transfer Learning, Pediatric Brain Tumors, MRI Segmentation, Deep Learning Supplemental material is available for this article. © RSNA, 2024.

Homopolymer switches mediate adaptive mutability in mismatch repair-deficient colorectal cancer.

Mismatch repair (MMR)-deficient cancer evolves through the stepwise erosion of coding homopolymers in target genes. Curiously, the MMR genes MutS homolog 6 (MSH6) and MutS homolog 3 (MSH3) also contain coding homopolymers, and these are frequent mutational targets in MMR-deficient cancers. The impact of incremental MMR mutations on MMR-deficient cancer evolution is unknown. Here we show that microsatellite instability modulates DNA repair by toggling hypermutable mononucleotide homopolymer runs in MSH6 and MSH3 through stochastic frameshift switching. Spontaneous mutation and reversion modulate subclonal mutation rate, mutation bias and HLA and neoantigen diversity. Patient-derived organoids corroborate these observations and show that MMR homopolymer sequences drift back into reading frame in the absence of immune selection, suggesting a fitness cost of elevated mutation rates. Combined experimental and simulation studies demonstrate that subclonal immune selection favors incremental MMR mutations. Overall, our data demonstrate that MMR-deficient colorectal cancers fuel intratumor heterogeneity by adapting subclonal mutation rate and diversity to immune selection.

Open Access Data and Deep Learning for Cardiac Device Identification on Standard DICOM and Smartphone-based Chest Radiographs.

Purpose To develop and evaluate a publicly available deep learning model for segmenting and classifying cardiac implantable electronic devices (CIEDs) on Digital Imaging and Communications in Medicine (DICOM) and smartphone-based chest radiographs. Materials and Methods This institutional review board-approved retrospective study included patients with implantable pacemakers, cardioverter defibrillators, cardiac resynchronization therapy devices, and cardiac monitors who underwent chest radiography between January 2012 and January 2022. A U-Net model with a ResNet-50 backbone was created to classify CIEDs on DICOM and smartphone images. Using 2321 chest radiographs in 897 patients (median age, 76 years [range, 18-96 years]; 625 male, 272 female), CIEDs were categorized into four manufacturers, 27 models, and one "other" category. Five smartphones were used to acquire 11 072 images. Performance was reported using the Dice coefficient on the validation set for segmentation or balanced accuracy on the test set for manufacturer and model classification, respectively. Results The segmentation tool achieved a mean Dice coefficient of 0.936 (IQR: 0.890-0.958). The model had an accuracy of 94.36% (95% CI: 90.93%, 96.84%; 251 of 266) for CIED manufacturer classification and 84.21% (95% CI: 79.31%, 88.30%; 224 of 266) for CIED model classification. Conclusion The proposed deep learning model, trained on both traditional DICOM and smartphone images, showed high accuracy for segmentation and classification of CIEDs on chest radiographs. Keywords: Conventional Radiography, Segmentation Supplemental material is available for this article. © RSNA, 2024 See also the commentary by Júdice de Mattos Farina and Celi in this issue.

Loss-of-function in RBBP5 results in a syndromic neurodevelopmental disorder associated with microcephaly.

PURPOSE: Epigenetic dysregulation has been associated with many inherited disorders. RBBP5 (HGNC:9888) encodes a core member of the protein complex that methylates histone 3 lysine-4 and has not been implicated in human disease. METHODS: We identify 5 unrelated individuals with de novo heterozygous variants in RBBP5. Three nonsense/frameshift and 2 missense variants were identified in probands with neurodevelopmental symptoms, including global developmental delay, intellectual disability, microcephaly, and short stature. Here, we investigate the pathogenicity of the variants through protein structural analysis and transgenic Drosophila models. RESULTS: Both missense p.(T232I) and p.(E296D) variants affect evolutionarily conserved amino acids located at the interface between RBBP5 and the nucleosome. In Drosophila, overexpression analysis identifies partial loss-of-function mechanisms when the variants are expressed using the fly Rbbp5 or human RBBP5 cDNA. Loss of Rbbp5 leads to a reduction in brain size. The human reference or variant transgenes fail to rescue this loss and expression of either missense variant in an Rbbp5 null background results in a less severe microcephaly phenotype than the human reference, indicating both missense variants are partial loss-of-function alleles. CONCLUSION: Haploinsufficiency of RBBP5 observed through de novo null and hypomorphic loss-of-function variants is associated with a syndromic neurodevelopmental disorder.

Pushing the limits: expanding the temperature tolerance of a coral photosymbiont through differing selection regimes.

Coral thermal bleaching resilience can be improved by enhancing photosymbiont thermal tolerance via experimental evolution. While successful for some strains, selection under stable temperatures was ineffective at increasing the thermal threshold of an already thermo-tolerant photosymbiont (Durusdinium trenchii). Corals from environments with fluctuating temperatures tend to have comparatively high heat tolerance. Therefore, we investigated whether exposure to temperature oscillations can raise the upper thermal limit of D. trenchii. We exposed a D. trenchii strain to stable and fluctuating temperature profiles, which varied in oscillation frequency. After 2.1 yr (54-73 generations), we characterised the adaptive responses under the various experimental evolution treatments by constructing thermal performance curves of growth from 21 to 31°C for the heat-evolved and wild-type lineages. Additionally, the accumulation of extracellular reactive oxygen species, photophysiology, photosynthesis and respiration rates were assessed under increasing temperatures. Of the fluctuating temperature profiles investigated, selection under the most frequent oscillations (diurnal) induced the greatest widening of D. trenchii's thermal niche. Continuous selection under elevated temperatures induced the only increase in thermal optimum and a degree of generalism. Our findings demonstrate how differing levels of thermal homogeneity during selection drive unique adaptive responses to heat in a coral photosymbiont.

Nesting Biology of Megachile (Chrysosarus) jenseni (Hymenoptera: Megachilidae) in Two Contrasting Pampean Agroecosystems: A Potential Pollinator for Alfalfa?

Several crops depend on both managed and wild bees to produce fruits and/or seeds, and the efficiency of numerous wild bees is higher than that of some managed species. Therefore, knowing and understanding the required resources for wild bees could enabled the establishment of management practices to increase their populations. Here, we provide information about the nesting biology of Megachile (Chrysosarus) jenseni, a Faboideae-specialist bee species. Based on observations from two populations occurring in contrasting agroecosystems, this bivoltine species showed common behavioral features shared with other species of subgenus Chrysosarus, such as the use of petal pieces and mud as nesting materials and the utilization of pre-existing cavities. Both studied populations showed a bivoltine life cycle with a rapid early-summer generation and a second generation, with most individuals overwintering. Main causes of mortality were unknown diseases (or other factors), causing the death of preimaginal stages. Moreover, this species was attacked by a cleptoparasite megachilid (Coelioxys remissa), a parasitic eulophid wasp (Melittobia sp.), and a bee fly (Anthrax oedipus). Finally, we discussed the potential use of this leaf-cutter bee species for alfalfa pollination.

Forecasting the Burden of Cardiovascular Disease and Stroke in the United States Through 2050-Prevalence of Risk Factors and Disease: A Presidential Advisory From the American Heart Association.

BACKGROUND: Cardiovascular disease and stroke are common and costly, and their prevalence is rising. Forecasts on the prevalence of risk factors and clinical events are crucial. METHODS: Using the 2015 to March 2020 National Health and Nutrition Examination Survey and 2015 to 2019 Medical Expenditure Panel Survey, we estimated trends in prevalence for cardiovascular risk factors based on adverse levels of Life's Essential 8 and clinical cardiovascular disease and stroke. We projected through 2050, overall and by age and race and ethnicity, accounting for changes in disease prevalence and demographics. RESULTS: We estimate that among adults, prevalence of hypertension will increase from 51.2% in 2020 to 61.0% in 2050. Diabetes (16.3% to 26.8%) and obesity (43.1% to 60.6%) will increase, whereas hypercholesterolemia will decline (45.8% to 24.0%). The prevalences of poor diet, inadequate physical activity, and smoking are estimated to improve over time, whereas inadequate sleep will worsen. Prevalences of coronary disease (7.8% to 9.2%), heart failure (2.7% to 3.8%), stroke (3.9% to 6.4%), atrial fibrillation (1.7% to 2.4%), and total cardiovascular disease (11.3% to 15.0%) will rise. Clinical CVD will affect 45 million adults, and CVD including hypertension will affect more than 184 million adults by 2050 (>61%). Similar trends are projected in children. Most adverse trends are projected to be worse among people identifying as American Indian/Alaska Native or multiracial, Black, or Hispanic. CONCLUSIONS: The prevalence of many cardiovascular risk factors and most established diseases will increase over the next 30 years. Clinical and public health interventions are needed to effectively manage, stem, and even reverse these adverse trends.

Orchidinae-205: A new genome-wide custom bait set for studying the evolution, systematics, and trade of terrestrial orchids.

Terrestrial orchids are a group of genetically understudied, yet culturally and economically important plants. The Orchidinae tribe contains many species that produce edible tubers that are used for the production of traditional delicacies collectively called 'salep'. Overexploitation of wild orchids in the Eastern Mediterranean and Western Asia threatens to drive many of these species to extinction, but cost-effective tools for monitoring their trade are currently lacking. Here we present a custom bait kit for target enrichment and sequencing of 205 novel genetic markers that are tailored to phylogenomic applications in Orchidinae s.l. A subset of 31 markers capture genes putatively involved in the production of glucomannan, a water-soluble polysaccharide that gives salep its distinctive properties. We tested the kit on 73 taxa native to the area, demonstrating universally high locus recovery irrespective of species identity, that exceeds the total sequence length obtained with alternative kits currently available. Phylogenetic inference with concatenation and coalescent approaches was robust and showed high levels of support for most clades, including some which were previously unresolved. Resolution for hybridizing and recently radiated lineages remains difficult, but could be further improved by analysing multiple haplotypes and the non-exonic sequences captured by our kit, with the promise to shed new light on the evolution of enigmatic taxa with a complex speciation history. Offering a step-up from traditional barcoding and universal markers, the genome-wide custom loci targeted by Orchidinae-205 are a valuable new resource to study the evolution, systematics and trade of terrestrial orchids.