FACETS

Invasive round goby Neogobius melanostomus have advanced eastward through the state of New York and provinces of Ontario and Quebec over the past two decades and are approaching Lake Champlain, one of the largest lakes in North America. This manuscript describes international efforts to monitor round goby populations during 2021-2025 on (a) the southern approach to Lake Champlain via the Hudson River and Champlain Canal, and (b) the northern approach to Lake Champlain via the Saint Lawrence River and Richelieu River. Monitoring utilized environmental DNA (eDNA), backpack electrofishing, beach seining, benthic trawling, and viral hemorrhagic septicemia virus (VHSV) testing. In the Champlain Canal, round goby were captured as far north as the downstream side of the C1 dam (97 kilometers [km] from Lake Champlain) while eDNA detections occurred as far north as the upstream side of the C2 dam (90 km from Lake Champlain). In the Richelieu River, round goby were captured as far south as Saint-Marc-sur-Richelieu (82 km from Lake Champlain) while the southern-most eDNA detections occurred near the Canadian side of the international border (4 km from Lake Champlain). Water temperature influenced habitat usage of round goby in the Champlain Canal, with catch rates in near-shore areas declining at < 10 {degrees}C. All VHSV test results were non-detections at the mouth of the Richelieu River, while one positive and two inconclusive results occurred along the Champlain Canal. Together, these data have informed multiple mitigation measures and have implications for management of aquatic invasive species across North America.

Waterborne horizontal transmission of viral diseases in fish relies on the release of infectious virus particles (termed shedding) into the aquatic environment. Both the rate and duration of shedding are critical for efficient viral spread, making interventions that reduce shedding valuable for disease control. While vaccines primarily aim to protect individuals from infection and severe disease, they should ideally also limit pathogen transmission by reducing shedding. In this study, we evaluated the capacity of two commercial vaccines - Clynav (DNA vaccine) and AlphaJect Micro 1-PD (inactivated whole-virus vaccine) - to reduce Salmonid alphavirus subtype 3 (SAV3) shedding following experimental infection of Atlantic salmon post-smolts. In individually housed fish, the AlphaJect Micro 1-PD vaccine significantly reduced the proportion of SAV3-shedding fish, the duration of shedding, and the cumulative shedding. The Clynav vaccine significantly reduced the shedding duration and also reduced the proportion of shedding fish. In cohort tanks with concurrent Tenacibaculum dicentrarchi co-infection, the AlphaJect Micro 1-PD vaccine significantly reduced the cumulative shedding but increased the number of shedding days. These results demonstrate the potential of vaccines to limit SAV3 transmission, while also highlighting how co-infections likely influence vaccine efficacy.

Quagga mussels (Dreissena rostriformis bugensis) are among the most impactful invaders in freshwaters of the Northern Hemisphere. As filter-feeders, they can reduce harmful algal blooms (HABs), but their effects are expected to be dependent on cyanobacteria species and water temperature. However, conclusive studies on these traits and their combination are lacking. Here, we combined laboratory experiments with an analysis of long-term data from a temperate shallow lake 10 years before and after quagga mussel invasion, respectively. We tested the hypotheses that quagga mussel filtration rates in the laboratory would 1) vary among common cyanobacteria species and 2) decrease above a critical temperature. Regarding the field data, we expected that 3) quagga mussels can reduce the summer biovolume of palatable cyanobacteria, but that 4) this effect disappears above a critical temperature. Our results support all four hypotheses. In laboratory experiments, Dolichospermum flos-aquae was classified as palatable to quagga mussels, while Aphanizomenon flos-aquae, Anabaenopsis elenkinii and Microcystis aeruginosa were less-palatable cyanobacteria. Filtration rates decreased above 28.9{degrees}C (CI: 27.6-30.2{degrees}C) with mussels dying at 32{degrees}C. Our long-term lake data show that cyanobacteria biovolumes were lower after quagga mussel invasion, but only below 27.7{degrees}C (CI: 26.9-28.4{degrees}C), confirming a critical thermal window for quagga mussel filtration. Global warming will therefore facilitate HABs by increasing the growth rates of cyanobacteria and reducing the filtration rates of quagga mussels above critical summer water temperatures, which are increasingly being reached in invaded lakes. This critical thermal window must be considered when making HAB predictions. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=93 SRC="FIGDIR/small/707163v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@175851eorg.highwire.dtl.DTLVardef@76a481org.highwire.dtl.DTLVardef@12a3965org.highwire.dtl.DTLVardef@11e3e7d_HPS_FORMAT_FIGEXP M_FIG C_FIG

The North American beaver (Castor canadensis) is an ecosystem engineer that strongly influences stream hydrology and ecosystems by constructing dams and canals. Previous research has shown that changes in the extent of beaver ponds and wetlands mapped using aerial photographs can serve as a proxy indicator of shifting regional abundance of beavers. In this study we investigated the use of freely available optical satellite data to measure changes in beaver pond surface water area on the 184 km2 Michipicoten Island in Lake Superior (Ontario, Canada) after a large decline in the beaver population that followed the arrival of grey wolves (Canis lupus). Inter-annual variability in pond extents was measured using sub-pixel mapping methods applied to the 30 m resolution Landsat (1985-2023) and 10 m Sentinel-2 (2016-2023) satellite records. After a > 90% decline in the number of surveyed beaver colonies between 2015-2018, beaver pond surface water area was reduced by 38-42% for ponds < 0.5 ha and by 48% for ponds < 0.1 ha by 2023. While these recent ponding reductions occurred during a period of above average precipitation, two previous smaller reductions were associated with low precipitation, water balance index, and Lake Superior water levels, suggesting that they were caused by drought and not beaver colony declines. While further testing is warranted, our results show that satellite-mapped changes in beaver ponds can provide a cost-effective metric for assessing large-scale population trends in the boreal zone.

If youve ever complained about a species name thats a mouthful--say, the soldier fly Parastratiosphecomyia stratiosphecomyioides or the myxobacterium Myxococcus llanfairpwllgwyngyllgogerychwyrndrobwllllantysiliogogogochensis--youre in very good company. But could the readability of binomial scientific names cause more than complaints? Could it influence how much species are studied and talked about? We examined a random sample of 3,019 species names spanning 29 phyla/divisions. We tested whether name length and reading difficulty are associated with species representation in the scientific literature (measured via literature mentions) and their visibility to the public (measured via Wikipedia pageviews). Both species name traits showed significant negative relationships with literature mentions and Wikipedia reads. Increasing name length from 10 to 30 characters is associated with a 66% decrease in expected mentions and a 65% decrease in Wikipedia reads, while shifting from the most to the least readable name in the dataset corresponds to 53% and 76% decreases. These patterns are consistent with something familiar: the fickleness of human attention, responding to features of the world that are far from rational. While creativity in naming is a cherished part of taxonomy, a touch of orthographic restraint may ultimately benefit both science and the species themselves--especially among understudied uncharismatic taxa.

Fishless lakes, critical drivers of biodiversity across freshwater landscapes, are becoming increasingly rare due to fish introductions. Although the impacts of fish introduction are well understood in high-elevation fishless lakes, their effects on fishless kettle lakes remain poorly understood. Many kettle lakes are disconnected from the surface water network and are therefore fishless. In this study, we examined how amphibian and zooplankton communities differ between fishless and fish-bearing kettle lakes by comparing 36 lakes in Quebec, Canada. Some kettle lakes are hydrologically connected to surrounding aquatic ecosystems, allowing natural colonization by fish. We therefore also evaluated how amphibian and zooplankton communities differ between connected and disconnected kettle lakes. Fish presence was associated with differences at each stage of the amphibian life cycle. Reproductive calls of adult amphibians were detected regardless of fish presence, indicating that reproduction occurred in all lake types. However, the presence of fish was associated with fewer amphibian egg masses and lower larval abundance, and the absence of salamanders at the larval stage. Small-bodied zooplankton were more abundant in fish-bearing lakes, while overall species richness was lower. In particular, Chaoborus americanus, a large top-predatory zooplankton species, was found exclusively in fishless lakes. In contrast to fish presence, hydrological connectivity had no significant effect on most communities, except for adult American toads, adult wood frogs, and mink frogs larvae, which responded positively to the interaction between fish presence and connectivity. Based on our results, we recommend avoiding fish stocking of kettle lakes to preserve essential reproductive habitats for amphibians, maintain refuges for sensitive zooplankton species, and safeguard the spatial heterogeneity that underpins landscape-scale biodiversity.

Natural language processing was applied to 3,586 Australian health practitioner tribunal decisions (1999-2026) to identify patterns in professional misconduct, outcomes, and temporal trends at a scale impractical through manual analysis. A text classification approach categorised 2,428 disciplinary decisions across seven misconduct types with acceptable accuracy for the major categories (per-class F1 0.47-0.82). Boundary violations were the most prevalent misconduct type (30.2%), followed by dishonesty/fraud (29.7%) and professional conduct breaches (28.0%). Reprimand was the most common outcome (53.0%), followed by cancellation (40.2%). Significant increasing trends were identified for boundary violations, dishonesty/fraud, professional conduct breaches, and communication failures. Boundary violations were associated with higher cancellation odds (OR = 1.36, p < 0.001). Opioid medications appeared in 67% of prescribing misconduct decisions. Significant jurisdictional variation in both misconduct types and outcomes was observed, with large effect sizes between major jurisdictions. The findings provide an empirical foundation for monitoring disciplinary trends under the National Law.

Digital health technologies are powerful-enhancing data collection, participant engagement, and personalized health interventions-yet their rapid proliferation has outpaced guidance for research participant protection. Current practice assists researchers in identifying risks but provides limited support for comprehensive risk management. To address this gap, we developed the Digital Health Checklist-Risk Management (DHC-RM) Tool, which integrates the established Digital Health Checklist with approaches from safety risk management. We conducted a study (n=40) comparing the DHC-RM Tool with current practice using a randomized experimental difference-in-differences design. Primary outcomes were the quantity, variety, and novelty of risks identified; secondary outcomes were the same constructs applied to risk control development. Compared with current practice, use of the DHC-RM Tool resulted in dramatically improved performance across all primary outcomes. Users identified on average 14.7 additional risks (compared to baseline) versus 0.26 in the control group and a higher number of risks in each of six pre-identified risk domains. Half of all distinct risks identified in the comparison phase were identified exclusively using the tool. The tool also improved risk control design, producing 9.63 additional risk control strategies per participant compared with 0.15 for current practice and yielding substantially greater novelty and variety. User feedback was also positive: 75% of participants reported they would use the tool again, citing its structured workflow, just-in-time examples, improved insight into risks, and its value for IRB communication. Suggestions for refinement focused primarily on expanding training examples and providing additional support for risk control development. The DHC-RM Tool significantly improves risk management practice in digital health research. By embedding structured, ethics-informed risk management into digital health research design, the DHC-RM Tool has the potential to improve participant protection while also streamlining ethics approval. Author SummaryDigital health research can put participants (and others) at risk in ways that dont always occur to the researchers who are designing a study. Researchers also face challenges in prioritizing risks and coming up with ideas to reduce those risks. We developed a new approach, the Digital Health Checklist - Risk Management Tool (DHC-RM Tool), to give researchers the support they need to identify, assess, and address research participant risks in this fast-moving field. Our experimental study found that use of the DHC-RM Tool led to a very large improvement in how well researchers managed the risks of digital health research studies. Using the toolkit, they were able to identify more risks than they identified using current practice-including risks they would not otherwise have considered. They were also able to come up with more changes to reduce the risks associated with digital health research studies, including changes they would not otherwise have considered. Those who used the toolkit found it beneficial and easy to use. The DHC-RM Tool fills an important gap in the science and practice of participant protection in digital health research.

Infectious disease forecasts can inform public health decision-making. Wastewater monitoring is a relatively new epidemiological data source with multiple potential applications, including forecasting. Incorporating wastewater data into epidemiological forecasting models is challenging, and relatively few studies have assessed whether this improves forecast performance. We present and evaluate a semi-mechanistic wastewater-informed forecasting model. The model forecasts COVID-19 hospital admissions at the state and territorial levels in the United States, based on incident hospital admissions data and, optionally, SARS-CoV-2 wastewater concentration data from multiple wastewater sampling sites. From February through April 2024, we produced real-time wastewater-informed COVID-19 forecasts using development versions of the model and submitted them to the United States COVID-19 Forecast Hub ("the Hub"). We then published an open-source R package, wwinference, that implements the model with or without wastewater as an input. Using proper scoring rules and measures of model calibration, we assess both our real-time submissions to the Hub and retrospective hypothetical forecasts from wwinference made with and without wastewater data. While the models performed similarly with and without the wastewater signal included, there was substantial heterogeneity for individual locations and dates where wastewater data meaningfully improved or degraded the models forecast performance. Compared to other models submitted to the Hub during the period spanned by our submissions, the real-time wastewater-informed version of our model ranked fourth of 10 models, with the hospital admissions-only version of our model ranking second out of 10 models. Across the 2023-2024 winter epidemic wave, retrospective forecasts from wwinference would have performed similarly with and without the wastewater signal included: fifth and fourth out of 10 models, respectively. To better understand the drivers of differential forecast performance with and without wastewater, we performed an exploratory analysis investigating the relationship between characteristics of the input data and improved and reduced performance in our model. Based on that analysis, we identify and discuss key areas for further model development. To our knowledge, this is the first work that conducts an evaluation of real-time and retrospective infectious disease forecasts across the United States both with and without wastewater data and compared to other forecasting models. Author SummaryWastewater-based epidemiology, in combination with clinical surveillance, has the potential to improve situational awareness and inform outbreak responses. We developed a model that uses data on the pathogen concentration in wastewater from one or more wastewater treatment plants in combination with hospital admissions to produce short-term forecasts of hospital admissions. We produced and submitted forecasts of 28-day ahead COVID-19 hospital admissions from this model to the U.S. COVID-19 Forecast Hub during the spring of 2024 and found that it performed well in comparison to other models during that limited time period. To assess the added value of incorporating wastewater data into the model and to investigate how it would have performed had we submitted it during the entire 2023-2024 winter epidemic wave, we performed a retrospective analysis in which we produced forecasts from the model with and without including wastewater data, using data that would have been available in real-time as of each forecast date. Both versions of the model would have been median overall performers had they been submitted to the Hub throughout the season. When comparing the models performance with and without wastewater data included, we found that overall forecast performance was very similar, with wastewater data slightly reducing overall average forecast performance. Within this result, there was significant heterogeneity, with clear instances of wastewater data improving and detracting from forecast performance. We used trends in the observed data to generate hypotheses as to the drivers of improved and reduced relative forecast performance within our model. We conclude by suggesting future work to improve the model and more broadly the application of wastewater-based epidemiology to forecasting.

Objectives: To assess the availability of key clinical trial registration data and compliance with legal reporting requirements for all Phase 2-4 drug trials registered on the new European Clinical Trial Information System (CTIS) registry. This study is the first ever assessment of data quality and legal compliance with reporting requirements on CTIS. Design: Cross-sectional observational study of CTIS registry data combined with manual review of results documents. Setting: Cohort of all 7,547 Phase II-IV clinical trials registered on CTIS as of November 2025. Main outcome measures: Number and proportion of missing data points in CTIS registration data. Proportion of completed clinical trials that are compliant with regulatory reporting requirements. Results: Trial registration data quality was high overall with more than 99% of expected data present. Of 234 clinical trials legally required to report results, fewer than half (49.6%) fully reported results within the required timeframe, 20 trials (8.5%) fully reported results late, and 98 trials (41.9%) failed to fully report results. Legal compliance was similar for adult trials (79/158) and paediatric trials (37/76). Conclusions: Sponsor compliance with legal reporting requirements is weak. Current efforts by European regulators to monitor and enforce compliance appear to be insufficient. New results reporting functions currently being set up by trial registries worldwide will require quality assurance processes. Trial registration: Study protocol prospectively registered on OSF: https://osf.io/sn4j2/overview

BackgroundThe strength and transparency of clinical trial evidence supporting drug approvals has become increasingly scrutinized, particularly considering the increased use of regulatory flexibility and expedited pathways. While U.S. Food and Drug Administration (FDA) standards have been extensively analyzed, evidence standards at the European Medicines Agency (EMA) remain less well-characterized. Thus, this study aims to systematically assess the design, quality, and outcomes of pivotal efficacy trials supporting EMA drug approvals between 2020 and 2023. MethodsWe conducted a cross-sectional analysis of new medicines and biosimilars receiving positive opinions from the EMAs Committee for Medicinal Products for Human Use (CHMP) and subsequent approval by the European Commission between January 2020 and December 2023. Data were extracted from European Public Assessment Reports (EPARs) and EMA medicine databases. Key variables included trial design features, primary endpoint type and achievement status, and justification for approval in cases of failed efficacy endpoints. ResultsBetween 2020 and 2023, 232 drugs were approved by the EMA for 281 indications. Of these, 205 (88.4%) were new active substances and 65 (28.0%) were granted orphan designation. Forty-six products (19.8%) were approved via a special regulatory program, most commonly Conditional Approval (26 products; 11.2%). Cancer was the leading therapeutic area, accounting for 61 approvals (26.3%). Approvals were supported by 393 pivotal clinical trials. Of these, 327 (83.2%) were randomized controlled trials (RCTs) and 218 (66.6% of RCTs) had a superiority design. A total of 232/393 trials (59.0%) relied on surrogate endpoints. Overall, 22 approvals (9.5%) were supported by at least one pivotal trial in which at least one primary endpoint was not met; in seven of these cases (31.8%), the failed trial was the sole pivotal trial. The most common rationale for approval despite null primary results was reliance on the totality of evidence, secondary endpoints, or clinical judgment (9 products; 40.9%). ConclusionsOur findings reveal substantial variability in the design and evidentiary strength of pivotal trials supporting EMA approvals between 2020 and 2023. While the majority of studies were RCTs, reliance on surrogate endpoints was common. That 10% of approvals were based on pivotal trials with null primary endpoints highlights the nuanced role of regulatory judgment in therapeutic evaluation. These findings prompt reflection on evolving evidence standards in drug regulation and underscore the need for transparency and consistent justifications.

We developed a model to predict surface water temperature across U.S. lakes using satellite remote sensing and in situ observations to enhance cyanobacterial harmful algal bloom (cyanoHAB) forecasting. The study focused on Sentinel-3 Ocean and Land Colour Instrument (OLCI) sensor resolved lakes. We developed random forest models using both Landsat-derived and in-situ-measured surface water temperature. Landsat models offered broad spatial and temporal coverage of all OLCI resolved lakes, but they were sensitive to cloud cover and required filtering to minimize error. In contrast, the in situ model represented fewer OLCI resolved lakes, but yielded lower mean absolute error and bias. The models predicted lake surface temperature across the entire calendar year, with best performance (RMSEapplied=1.11; biasapplied=0.01; MAEapplied=0.77) from the in situ model. This approach allowed for the continuous prediction of lake surface temperatures from 1.1 to 31.6 {degrees}C for unfrozen, open-water conditions critical for improving the accuracy of cyanoHAB forecasting. A key strength of this study was the use of an extensive dataset and model validation against in situ observations, which improved predictive accuracy throughout the year across all seasons. The predictive model offers a water resource tool for management, ecosystem protection, and public health.

ImportanceFunding of womens health research has been low, with a narrow focus on what is considered womens health. Understanding which lifespan stages and areas of womens health are funded is essential to determine the breadth of womens health research and identify where gaps in research are concentrated. ObjectiveTo examine which lifespan stages and areas of womens health were more likely to be funded in open Canadian grant competitions. Evidence ReviewPublicly available funded Canadian Institutes of Health Research (CIHR) Project Grant abstracts from 2009 and 2023 were coded for mention of a hormonal transition period (puberty, menstrual cycle, pregnancy/postpartum, perimenopause/menopause), exogenous hormone use (hormonal contraception, fertility treatments, menopause hormone therapy), and/or a female-specific health condition. Abstracts were also coded for Indigenous health and Two Spirit, Lesbian, Gay, Bisexual, Trans, Transgender or Trans Identified, Queer, Intersex, Asexual, Plus (S2/LGBTQIA+) populations. Remaining grant abstracts were grouped by common theme.Abstracts were analyzed for changes in research representation and funding over time and whether funding was lower than expected based on population prevalence or proportion of the lifespan spent in that stage. FindingsNearly 50% of female-specific research focused on cancers (breast, gynecologic) or pregnancy and did not significantly increase in funding or representation over time. Of the funded grant abstracts that focused on pregnancy, ~22% examined outcomes pertaining only to the fetus/offspring, not the birthing parent. Over 15 years, 2.37% of all CIHR abstracts over 15 years were devoted to pregnancy, whereas only 0.24% was devoted to other hormonal life stages (menstrual cycles, menopause). For all hormonal transition stages except pregnancy, the proportion of grants and funding devoted to that stage was lower than expected based on the proportion of the lifespan spent in that stage. Conclusions and RelevanceThese findings reflect the narrow breadth of womens health, which largely focused on cancers (breast, gynecologic) or pregnancy, rather than being distributed across key life course stages that shape womens health. To advance science for all, the heterogeneity and complexity in womens health across the lifespan must be embraced and barriers for womens health research must be removed. Key PointsO_ST_ABSQuestionC_ST_ABSWhich areas and life stages of womens health are most likely to be funded in Canadian open grant competitions, and where are funding gaps concentrated? FindingsNearly half of female-specific grants focused on cancer or pregnancy, with little change over time. Pregnancy dominated hormonal-stage research, often excluding maternal outcomes, while menstrual and menopausal stages were rarely funded. For most life stages, funding was lower than expected based on lifespan representation. MeaningWomens health research funding remains narrowly focused. Broader, life-course-inclusive investment is needed to address critical gaps and advance equitable health science.

BackgroundMississippi consistently ranks among the lowest U.S. states in health-related quality of life (HRQoL) outcomes, with disparities most severe in rural and socioeconomically disadvantaged counties, particularly the Mississippi Delta. Understanding longitudinal spatial and temporal patterns is essential for equity-focused public health strategies and chronic disease prevention. ObjectiveThis study examined geographic disparities in HRQoL across Mississippis 82 counties from 2015 to 2025 to identify persistent hotspots, assess convergence with national averages, and inform targeted interventions. MethodsCounty-level data from the County Health Rankings & Roadmaps (2015-2025 releases) were used to construct an annual principal component analysis-derived composite HRQoL score from Behavioral Risk Factor Surveillance System measures (percentage reporting poor or fair health, physically unhealthy days, and mentally unhealthy days). Spatial patterns were assessed using choropleth maps, Morans I, and Local Indicators of Spatial Association (LISA). A spatial autoregressive lag model with year fixed effects identified key drivers. ResultsMississippi showed persistent disadvantage in poor or fair health (stable gap of 0.06-0.07 percentage points above national averages) but recent convergence in physically unhealthy days and reversal in mentally unhealthy days (fewer days in disadvantaged subgroups, e.g., high child poverty and low education). LISA maps revealed enduring High-High hotspots of poor HRQoL in the Delta region, while Low-Low cold spots along the Gulf Coast contracted substantially by 2025 (many formerly advantaged counties now non-significant). The spatial lag model confirmed significant dependence ({rho} = 0.13, P < .001), with adult smoking and uninsurance as leading modifiable predictors. ConclusionsMississippis HRQoL trajectory shows symptom-based improvements alongside enduring structural disparities in self-rated health and persistent geographic inequities. Expanding smoke-free policies (particularly in the Delta), strengthening primary care access, and increasing insurance coverage represent high-impact strategies to reduce disparities, advance health equity, and support chronic disease prevention in high-burden regions of the U.S. South.

Debates about public trust in science often contrast deficit-based models, which emphasize the role of scientific knowledge, with constructivist perspectives that highlight the coexistence of multiple epistemologies. However, both approaches tend to overlook the mechanisms that link scientific knowledge, alternative epistemic orientations, and mistrust in science. To address this gap, the study applies a multilevel structural equation model within a multidimensional framework to examine conspiratorial reasoning as a key mechanism through which scientific knowledge influences science mistrust. Using cross-national survey data from Europe during the COVID-19 pandemic, the analysis also considers how this pathway is moderated by individual cognitive, motivational, and ideological traits, as well as macro-level political, cultural and economic factors. The findings reveal that conspiratorial reasoning significantly mediates the relationship between scientific knowledge and mistrust at both individual and regional levels. Moreover, the strength of these associations is conditioned by factors like informational engagement, regional value climates, and religiosity. Overall, the results suggest that scientific knowledge serves as a conditional epistemic resource, rather than a consistent buffer against mistrust in science.

O_LIEven for wildlife species that often persist in urban and suburban areas, vehicle collisions remain a common source of mortality, and roads can fragment and degrade habitat. Quantifying animal behavior near roads can help wildlife managers develop management strategies to reduce mortality from vehicles while maintaining connectivity. C_LIO_LITo determine how roads affect movement of bobcats (Lynx rufus)--a common mesopredator in urban and suburban areas of North America--we analyzed GPS tracking data from bobcats using continuous-time movement analyses. Our study focused on three questions regarding bobcat movement near roads: (1) Are roads barriers to bobcat movement? (2) How often do bobcats use wildlife crossing structures to cross roads? (3) How does bobcat movement behavior change when bobcats are closer to roads? C_LIO_LIWe found that bobcats crossed roads 11% less frequently than expected from random chance, and this effect was largely driven by interstates and major local roads. We found little evidence that bobcats selectively used culverts or underpasses to cross roads, or that bobcat movement behavior (i.e., speed or home range size) varied with road density, although daily distance traveled increased with road density. C_LIO_LISynthesis and applications: Managers attempting to reduce bobcat mortality from vehicle collisions must do more than simply building wildlife crossing structures. Fences to funnel bobcats toward crossing structures, rumble strips to scare bobcats from roads, reduced speed limits, and wildlife warning signs for drivers may be more effective tools for reducing bobcat-vehicle collisions. This study also provides a rigorous framework for considering the implications of movement behavior for lack of connectivity and mortality as distinct but not mutually exclusive threats posed by roads for wildlife. C_LI

Aquatic non-native invasive species are notoriously difficult to eradicate, particularly in pondscapes where populations can spread rapidly, persist in unmanaged refugia, and recolonise treated sites. In such contexts, high-intensity management interventions may be justified, balancing short-term collateral impacts against the prevention of permanent establishment and long-term damages. Chemical eradication methods, such as rotenone or herbicide application, can be effective but raise ethical and environmental concerns. Here, we evaluate quicklime (calcium oxide, CaO) application as a more sustainable alternative control tool for pondscape invaders compared to other chemical methods, using the African clawed frog (Xenopus laevis) invasion in Belgium as a case study. When applied to water, quicklime hydrates exothermically to calcium hydroxide (Ca(OH)), which releases OH ions upon dissolution, temporarily and rapidly increasing pH to lethal levels. In winter 2023, three ponds with breeding populations of X. laevis of low ecological value were drained, fenced, and treated with quicklime. Treatment effectiveness was assessed through pH measurements, visual surveys, and environmental DNA (eDNA) quantification. Immediately after treatment, large numbers of deceased post-metamorphic individuals were recovered, indicating treatment-induced mortality. Eight weeks post-treatment, eDNA concentrations were markedly lower in two of the three ponds (reductions of 100% and 80%) compared to those during the same period one year later. Although eDNA concentrations increased again during the following summer suggesting partial population recovery through survival and/or recolonisation, they remained lower than pre-treatment conditions. Water pH returned to near baseline levels within one month. We provide the first field-based preliminary evidence that quicklime can induce large-scale mortality in X. laevis populations in small to medium-sized ponds. We discuss practical considerations, limitations, and broader applicability, proposing quicklime as a high-intensity option within integrated management strategies for pondscape invaders.

Background and ObjectivesPatient portals have become essential infrastructure for healthcare delivery following the 21st Century Cures Act, yet adoption remains inequitable. Understanding demographic and geographic determinants of portal activation is critical for addressing digital health disparities, particularly among neurology patients who face unique access barriers. We examined the demographic, geographic, and neighborhood-level factors associated with patient portal activation among neurology patients at multiple geographic scales in the Washington, DC metropolitan area. MethodsWe conducted a retrospective cohort study of 72,417 adult neurology patients seen at two academic medical centers sharing an electronic health record in Washington, DC (February 2021-February 2026). We examined portal activation using multivariable logistic regression and geographic analysis at four nested scales: the metropolitan catchment area, DCs eight wards, individual census tracts (via geocoded patient addresses), and individual DC residents. ResultsPortal activation was 64.7% overall. Activation varied by race/ethnicity (Non-Hispanic White 76.1%, Non-Hispanic Black 57.0%, Non-Hispanic Asian 57.6%, Hispanic 55.0%) and geography (DC Ward 2: 82.0% vs. Ward 7: 48.0%). Ward-level educational attainment (r = 0.948), broadband access (r = 0.889), and income (r = 0.811) were strongly correlated with activation. Within individual wards, Non-Hispanic White patients activated at 84-91% while Non-Hispanic Black patients activated at 48-64%, demonstrating that neighborhood resources alone do not explain disparities. DiscussionPatient portal activation is shaped by demographic, socioeconomic, and geographic factors operating at multiple levels. Persistent within-ward racial disparities indicate that geographically targeted interventions must be paired with culturally tailored approaches to achieve digital health equity.

Motivation: Viral surveillance from mixed samples (e.g. wastewater) has become critical in public health efforts to track and contain pathogens. However, existing open-source bioinformatics tools for viral consensus sequence generation are optimized for individual viruses (rather than multiple potential viruses of interest). Results: MultiVirusConsensus is an accurate and efficient open-source pipeline for identification and consensus sequence generation of multiple viruses from mixed samples. It utilizes the memory-efficient ViralConsensus tool via bash process substitution to simultaneously perform consensus sequence calling on all viruses of interest (1) completely in parallel, and (2) by piping datastreams between tools without writing/reading intermediate files (thus eliminating slowdowns related to slow disk accesses). Availability: MultiVirusConsensus is freely available as an open-source software project at: https://github.com/niemasd/MultiVirusConsensus

Research using model organisms to tackle questions in life sciences and biomedical sciences has been in the spotlight of scientific literature for the better part of the twentieth century. This attention has perceptibly faded over the last twenty years, at least. We set to document this process by examining the publication trends of 48 journals encompassing a broad range of topics and impact factors for eight classic model organisms. We found that the representation of model-organism research has been in continuous decline in the last three decades, with a significant acceleration since 2010. We investigated the origin of the change, from the size of research communities to the shifts in topics and in use of model organisms. While model organism communities appear stable, model organism papers are outpaced by the rest of scientific literature. Also, among papers using model organisms, we note a progressive shift toward applied research, with differences between different model organism species. The mouse, in particular, logically remains the preferred system to study diseases, while non-mouse model organisms continue to be used predominantly to dissect mechanisms of life. We reflect on the consequences of the fading representation that we measured for the future of life sciences. Fundamentally, model organisms afford a direct access to causality in life sciences and their fading from the picture may impact life sciences as a whole. More pragmatically, it will also affect funding, and thereby jeopardizes the maintenance of model organism resources such as repositories built over decades.