Intracranial Aneurysm Classifier Using Phenotypic Factors: An International Pooled Analysis

Intracranial aneurysms (IAs) are usually asymptomatic with a low risk of rupture, but consequences of aneurysmal subarachnoid hemorrhage (aSAH) are severe. Identifying IAs at risk of rupture has important clinical and socio-economic consequences. The goal of this study was to assess the effect of patient and IA characteristics on the likelihood of IA being diagnosed incidentally versus ruptured. Patients were recruited at 21 international centers. Seven phenotypic patient characteristics and three IA characteristics were recorded. The analyzed cohort included 7992 patients. Multivariate analysis demonstrated that: (1) IA location is the strongest factor associated with IA rupture status at diagnosis; (2) Risk factor awareness (hypertension, smoking) increases the likelihood of being diagnosed with unruptured IA; (3) Patients with ruptured IAs in high-risk locations tend to be older, and their IAs are smaller; (4) Smokers with ruptured IAs tend to be younger, and their IAs are larger; (5) Female patients with ruptured IAs tend to be older, and their IAs are smaller; (6) IA size and age at rupture correlate. The assessment of associations regarding patient and IA characteristics with IA rupture allows us to refine IA disease models and provide data to develop risk instruments for clinicians to support personalized decision-making

Global Patterns and Controls of Nutrient Immobilization On Decomposing Cellulose In Riverine Ecosystems

Microbes play a critical role in plant litter decomposition and influence the fate of carbon in rivers and riparian zones. When decomposing low-nutrient plant litter, microbes acquire nitrogen (N) and phosphorus (P) from the environment (i.e., nutrient immobilization), and this process is potentially sensitive to nutrient loading and changing climate. Nonetheless, environmental controls on immobilization are poorly understood because rates are also influenced by plant litter chemistry, which is coupled to the same environmental factors. Here we used a standardized, low-nutrient organic matter substrate (cotton strips) to quantify nutrient immobilization at 100 paired stream and riparian sites representing 11 biomes worldwide. Immobilization rates varied by three orders of magnitude, were greater in rivers than riparian zones, and were strongly correlated to decomposition rates. In rivers, P immobilization rates were controlled by surface water phosphate concentrations, but N immobilization rates were not related to inorganic N. The N:P of immobilized nutrients was tightly constrained to a molar ratio of 10:1 despite wide variation in surface water N:P. Immobilization rates were temperature-dependent in riparian zones but not related to temperature in rivers. However, in rivers nutrient supply ultimately controlled whether microbes could achieve the maximum expected decomposition rate at a given temperature

Multi-decadal improvements in the ecological quality of European rivers are not consistently reflected in biodiversity metrics

Humans impact terrestrial, marine and freshwater ecosystems, yet many broad-scale studies have found no systematic, negative biodiversity changes (for example, decreasing abundance or taxon richness). Here we show that mixed biodiversity responses may arise because community metrics show variable responses to anthropogenic impacts across broad spatial scales. We first quantified temporal trends in anthropogenic impacts for 1,365 riverine invertebrate communities from 23 European countries, based on similarity to least-impacted reference communities. Reference comparisons provide necessary, but often missing, baselines for evaluating whether communities are negatively impacted or have improved (less or more similar, respectively). We then determined whether changing impacts were consistently reflected in metrics of community abundance, taxon richness, evenness and composition. Invertebrate communities improved, that is, became more similar to reference conditions, from 1992 until the 2010s, after which improvements plateaued. Improvements were generally reflected by higher taxon richness, providing evidence that certain community metrics can broadly indicate anthropogenic impacts. However, richness responses were highly variable among sites, and we found no consistent responses in community abundance, evenness or composition. These findings suggest that, without sufficient data and careful metric selection, many common community metrics cannot reliably reflect anthropogenic impacts, helping explain the prevalence of mixed biodiversity trends.We thank J. England for assistance with calculating ecological quality and the biomonitoring indices in the UK. Funding for authors, data collection and processing was provided by the European Union Horizon 2020 project eLTER PLUS (grant number 871128). F.A. was supported by the Swiss National Science Foundation (grant numbers 310030_197410 and 31003A_173074) and the University of Zurich Research Priority Program Global Change and Biodiversity. J.B. and M.A.-C. were funded by the European Commission, under the L‘Instrument Financier pour l’Environnement (LIFE) Nature and Biodiversity program, as part of the project LIFE-DIVAQUA (LIFE18 NAT/ES/000121) and also by the project ‘WATERLANDS’ (PID2019-107085RB-I00) funded by the Ministerio de Ciencia, Innovación y Universidades (MCIN) and Agencia Estatal de Investigación (AEI; MCIN/AEI/10.13039/501100011033/ and by the European Regional Development Fund (ERDF) ‘A way of making Europe’. N.J.B. and V.P. were supported by the Lithuanian Environmental Protection Agency (https://aaa.lrv.lt/) who collected the data and were funded by the Lithuanian Research Council (project number S-PD-22-72). J.H. was supported by the Academy of Finland (grant number 331957). S.C.J. acknowledges funding by the Leibniz Competition project Freshwater Megafauna Futures and the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung or BMBF; 033W034A). A.L. acknowledges funding by the Spanish Ministry of Science and Innovation (PID2020-115830GB-100). P.P., M.P. and M.S. were supported by the Czech Science Foundation (GA23-05268S and P505-20-17305S) and thank the Czech Hydrometeorological Institute and the state enterprises Povodí for the data used to calculate ecological quality metrics from the Czech surface water monitoring program. H.T. was supported by the Estonian Research Council (number PRG1266) and by the Estonian national program ‘Humanitarian and natural science collections’. M.J.F. acknowledges the support of Fundação para a Ciência e Tecnologia, Portugal, through the projects UIDB/04292/2020 and UIDP/04292/2020 granted to the Marine and Environmental Sciences Centre, LA/P/0069/2020 granted to the Associate Laboratory Aquatic Research Network (ARNET), and a Call Estímulo ao Emprego Científico (CEEC) contract.Peer reviewe

The recovery of European freshwater biodiversity has come to a halt

Owing to a long history of anthropogenic pressures, freshwater ecosystems are among the most vulnerable to biodiversity loss1. Mitigation measures, including wastewater treatment and hydromorphological restoration, have aimed to improve environmental quality and foster the recovery of freshwater biodiversity2. Here, using 1,816 time series of freshwater invertebrate communities collected across 22 European countries between 1968 and 2020, we quantified temporal trends in taxonomic and functional diversity and their responses to environmental pressures and gradients. We observed overall increases in taxon richness (0.73% per year), functional richness (2.4% per year) and abundance (1.17% per year). However, these increases primarily occurred before the 2010s, and have since plateaued. Freshwater communities downstream of dams, urban areas and cropland were less likely to experience recovery. Communities at sites with faster rates of warming had fewer gains in taxon richness, functional richness and abundance. Although biodiversity gains in the 1990s and 2000s probably reflect the effectiveness of water-quality improvements and restoration projects, the decelerating trajectory in the 2010s suggests that the current measures offer diminishing returns. Given new and persistent pressures on freshwater ecosystems, including emerging pollutants, climate change and the spread of invasive species, we call for additional mitigation to revive the recovery of freshwater biodiversity.N. Kaffenberger helped with initial data compilation. Funding for authors and data collection and processing was provided by the EU Horizon 2020 project eLTER PLUS (grant agreement no. 871128); the German Federal Ministry of Education and Research (BMBF; 033W034A); the German Research Foundation (DFG FZT 118, 202548816); Czech Republic project no. P505-20-17305S; the Leibniz Competition (J45/2018, P74/2018); the Spanish Ministerio de Economía, Industria y Competitividad—Agencia Estatal de Investigación and the European Regional Development Fund (MECODISPER project CTM 2017-89295-P); Ramón y Cajal contracts and the project funded by the Spanish Ministry of Science and Innovation (RYC2019-027446-I, RYC2020-029829-I, PID2020-115830GB-100); the Danish Environment Agency; the Norwegian Environment Agency; SOMINCOR—Lundin mining & FCT—Fundação para a Ciência e Tecnologia, Portugal; the Swedish University of Agricultural Sciences; the Swiss National Science Foundation (grant PP00P3_179089); the EU LIFE programme (DIVAQUA project, LIFE18 NAT/ES/000121); the UK Natural Environment Research Council (GLiTRS project NE/V006886/1 and NE/R016429/1 as part of the UK-SCAPE programme); the Autonomous Province of Bolzano (Italy); and the Estonian Research Council (grant no. PRG1266), Estonian National Program ‘Humanitarian and natural science collections’. The Environment Agency of England, the Scottish Environmental Protection Agency and Natural Resources Wales provided publicly available data. We acknowledge the members of the Flanders Environment Agency for providing data. This article is a contribution of the Alliance for Freshwater Life (www.allianceforfreshwaterlife.org).Peer reviewe

Estimates of benthic invertebrate community variability and its environmental determinants differ between snapshot and trajectory designs

Abstract Long-term data sets are essential for biodiversity research and monitoring. Researchers use 2 major approaches in the study of temporal variability of biological communities: 1) the trajectory approach (monitoring sites across several consecutive years) and 2) the snapshot approach (comparing sites among few sampling events several years apart). We used data on benthic macroinvertebrate communities in 23 near-pristine forested streams to compare these 2 approaches for different study periods ranging from 3 to 14 y. We asked whether the level of temporal turnover and the identity of the best explanatory variables underlying it were comparable across studies based on differing approaches, study periods, or total duration. The 2 approaches yielded partly different stories about the level of community variability and its environmental correlates. With the snapshot approach, variation in community similarity and factors explaining it reflected short-term (e.g., year-specific) conditions, which could be misinterpreted as long-term trends, the difference being most evident for periods that began or ended in an extreme drought year. Our results imply that snapshot studies may lead to ambiguous conclusions, whereas the trajectory approach yielded more consistent results. Trajectory data of differing length showed minor differences, apart from studies with the shortest durations. Overall, our results suggest that time sequences of ∼6 y of trajectory data (i.e., 6 generations for most benthic invertebrates in boreal streams) may be needed for the among-year similarity of macroinvertebrate communities in near-pristine streams to stabilize. If temporal replication is limited (snapshots/very short time sequences) the outcome depends strongly on the particular years included in a comparison. Based on our results, we advise caution when basing conclusions on a comparison of a few (e.g., just 2) occasions several years apart or on very short time sequences

Towards online adaptation of digital twins

Abstract Digital twins have gained a lot of attention in modern day industry, but practical challenges arise from the requirement of continuous and real-time data integration. The actual physical systems are also exposed to disturbances unknown to the real-time simulation. Therefore, adaptation is required to ensure reliable performance and to improve the usability of digital twins in monitoring and diagnostics. This study proposes a general approach to the real-time adaptation of digital twins based on a mechanism guided by evolutionary optimization. The mechanism evaluates the deviation between the measured state of the real system and the estimated state provided by the model under adaptation. The deviation is minimized by adapting the model input based on the differential evolution algorithm. To test the mechanism, the measured data were generated via simulations based on a physical model of the real system. The estimated data were generated by a surrogate model, namely a simplified version of the physical model. A case study is presented where the adaptation mechanism is applied on the digital twin of a marine thruster. Satisfactory accuracy was achieved in the optimization during continuous adaptation. However, further research is required on the algorithms and hardware to reach the real-time computation requirement

Partitioning of benthic biodiversity in boreal streams:contributions of spatial, inter-annual, and seasonal variability

Abstract 1. Studies on biodiversity patterns should optimally relate different scales of temporal community variability to spatial variability. Although temporal biodiversity variability is often negligible compared to spatial variation, it may still constitute a substantial source of overall community variability in stream ecosystems. Boreal streams exhibit seasonally recurring environmental periodicity, which can be expected to induce synchronous dynamics of abiotic variables among sites, and consequently, to produce spatial synchrony of deterministically controlled biological communities with higher intra- than inter-annual community variability. 2. We sampled benthic macroinvertebrates in 10 near-pristine boreal streams on three different seasons (spring, summer, autumn) across 4 consecutive years in northern Finland. We aimed to identify the relative contributions of spatial, inter-annual, and seasonal variability to overall benthic biodiversity; and relate variation in benthic invertebrate communities to key environmental factors, particularly in-stream habitat diversity. 3. Among-site spatial variability was clearly the most important source of variation for both species richness and community dissimilarity. Of the two temporal scales, inter-annual variability contributed more to variation in taxonomic richness and seasonal variability slightly more to variability in community composition. 4. Only inter-annual variation differed systematically from random expectation, indicating strong stability (low variability) of stream macroinvertebrate communities across years, with less variation at sites with higher substrate heterogeneity. Considering the distinct seasonality of the boreal stream environment, seasonal variability accounted for an unexpectedly low amount of total community variability. 5. Although differences between seasons were small, autumnal sampling is likely to be the least susceptible to climatic vagaries, thus providing the most consistent and predictable conditions for benthic sampling in boreal streams, particularly for bioassessment purposes. Exceptional climatic conditions are becoming more frequent in northern Europe, probably causing substantial and largely unpredictable changes in benthic community composition. As a result, the importance of temporal (relative to spatial) community variability may increase

Seasonal and spatial variation of stream macroinvertebrate taxonomic and functional diversity across three boreal regions

1. The exploration of biodiversity has predominantly been based on taxonomicmeasures, whereas functional diversity, a key component of biodiversity, is compar-atively understudied. Therefore, studies simultaneously investigating patterns oftaxonomic and functional diversity change in biological communities are of increas-ing interest. 2. We collated high-resolution macroinvertebrate and environmental data from 70 boreal headwater stream sites across three European countries (Germany,Finland, Sweden) to (1) investigate seasonal variation in taxonomic diversity, func-tional diversity, and redundancy, and (2) identify their potential drivers of spatialand seasonal variation. 3. Seasonal changes in boreal macroinvertebrate taxonomic diversity were decoupledfrom changes in functional diversity. Seasonal shifts in environmental conditions,including acidity and nutrient variability, drove fluctuations in taxonomic diversitywhich were far more pronounced than those of functional diversity. 4. Seasonal shifts in environmental conditions including variation in the quantity, quality, and state of organic carbon (dissolved vs particulate) facilitate an exchange of taxa, leading to taxonomically unique communities that exploit the pool of available seasonal resources. Thus, similar levels of functional diversity across seasons—evenas taxonomic diversity changes—suggest limited differences in interspecific changesin community function, potentially indicating functional resistance rooted inredundancy. 5. We highlight the spatial and seasonal discrepancies of freshwater communities,emphasising the need for both taxonomic and functional diversity patterns to beassessed in future biodiversity monitoring programmes. biological traits, community ecology, environmental drivers, freshwater ecosystems, functionalredundancy, inter-regional analysis, multivariate statistics, organic carbonSeasonal and spatial variation of stream macroinvertebrate taxonomic and functional diversity across three boreal regionspublishedVersio

Code and data: Understanding temporal variability across trophic levels and spatial scales in freshwater ecosystems

<p>Code and data to reproduce the results in Siqueira et al. (submitted) published as a Preprint (https://doi.org/10.32942/osf.io/mpf5x)</p> <p>The full set of results, including those made available as supplementary material, can be reproduced by running five scripts in the <strong>R_codes</strong> folder following this sequence:</p> <ul> <li>01_Dataprep_stability_metrics.R</li> <li>02_SEM_analyses.R</li> <li>03_Stab_figs.R</li> <li>04_Stab_supp_m.R</li> <li>05_Sensit_analysis.R</li> </ul> <p>and using the data available in the <strong>Input_data</strong> folder.</p> <p>The original raw data made available include the abundance (individual counts, biomass, coverage area) of a given taxon, at a given site, in a given year. See details here https://doi.org/10.32942/osf.io/mpf5x</p> <p>However, this is a collaborative effort and not all authors are allowed to share their raw data. One data set (LEPAS), out of 30, was not made available due to data sharing policies of The Ohio Division of Wildlife (ODOW). So, in code "01_Dataprep_stability_metrics.R" all data made available are imported, except the LEPAS data set. For this specific data set, code "01_Dataprep_stability_metrics.R" imports variability and synchrony components estimated using the methods described in Wang et al. (2019 Ecography; doi/10.1111/ecog.04290), diversity metrics (alpha and gamma diversity), and some variables describing the data set.</p> <p>A protocol for requesting access to the LEPAS data sets can be found here:<br> https://ael.osu.edu/researchprojects/lake-erie-plankton-abundance-study-lepas</p> <p>Dataset owner: Ohio Department of Natural Resources – Division of Wildlife, managed by Jim Hood, Dept. of Evolution, Ecology, and Organismal Biology, The Ohio State University. Email: [email protected]</p> <p>Anyone who wants to reproduce the results described in the preprint can just download the whole R project (that includes code and data) and run codes from 01 to 05.</p> <p>I am making the whole R project folder (with everything needed to reproduce the results) available as a compressed file.</p>Acknowledgments. T.S. was supported by grants #19/04033-7 and #21/00619-7, São Paulo Research Foundation (FAPESP), and by grant #309496/2021-7, Brazilian National Council for Scientific and Technological Development (CNPq). Participation by CPH was supported, in part, by US National Science Foundation grant IOS-1754838. CPH thanks the PacFish/InFish Biological Opinion Monitoring Program (administered by the US Forest Service) for use of their long-term macroinvertebrate monitoring data. JDT is supported by a Rutherford Discovery Fellowship administered by the Royal Society Te Apārangi (RDF-18-UOC-007), and Bioprotection Aotearoa and Te Pūnaha Matatini, both Centres of Research Excellence funded by the Tertiary Education Commission, New Zealand. VS was supported by a FAPESP grant #2019/06291-3 during the writing of this manuscript. The FEHM (Freshwater Ecology, Hydrology and Management) research group is funded by the "Agència de Gestió d'Ajuts Universitaris i de Recerca" (AGAUR) at the "Generalitat de Catalunya" (2017SGR1643). CCB thanks PELD-PIAP/CNPq for support. M.C. was supported by a Ramón y Cajal Fellowship (RYC2020-029829-I) and the Serra Hunter programme (Generalitat de Catalunya). GAG was supported by #DEB-2025982, NTL LTER. PH received financial support from the eLTER PLUS project (Grant Agreement #871128). JMH was supported by the Federal Aid in Sport Fish Restoration Program (F-69-P, Fish Management in Ohio), administered jointly by the United States Fish and Wildlife Service and the Division of Wildlife, Ohio Department of Natural Resources (projects FADR65, FADX09, and FADB02). KLH and RP thank the Oulanka Research Station. MBF thanks over 300 students, staff, and faculty that have participated in the Kentucky Lake Long-Term Monitoring Program at Hancock Biological Station, Murray State University, Murray, KY. MJJ thanks the Northumberland Wildlife Trust for site access. IISD-ELA zooplankton samples were counted and identified primarily by Willy Findlay and Alex Salki. Field collections within IISD-ELA were overseen by Mark Lyng and Ken Sandilands. Funding for most of the IISD-ELA data was provided by Fisheries and Oceans Canada. PP and MS were supported by the Czech Science Foundation (P505-20-17305S). LCR is grateful to the Núcleo de Pesquisas em Limnologia, Ictiologia e Aquicultura (Nupélia) at Universidade Estadual de Maringá for logistic support; CNPq/ PELD for financial support and CNPq for a scholarship. AR was supported by NSF CAREER #2047324 and by UC Berkeley new faculty funds. We thank countless colleagues at all partner institutes for their help with collecting the time series data