Tree species influence below-ground soil chemistry and microbial communities, both of which are key drivers of soil formation. The study compared soils under native European beech and first-generation non-native Norway spruce growing at the same site. Soil under beech was classified as Dystric Cambisol, whereas soil under spruce had developed into Entic Podzol. The objective was to link soil chemical processes with microbial community composition and the resulting quantity and quality of soil organic carbon (SOC) across soil horizons. Soil pH and concentrations of available cations and anions were measured together with dissolved organic carbon (DOC), represented by low-molecular-weight organic acids (LMMOA; ion-exchange chromatography). SOC quantity and functional group composition were characterized using Fourier-transform infrared spectroscopy. Microbial abundance and community composition were assessed by 16S/18S rRNA gene amplicon sequencing and droplet digital PCR. Total carbon contents did not differ between soils, but DOC showed horizon-specific differences, with quinate strongly enriched under spruce. More pronounced differences were observed in carbon quality and its vertical distribution. Elevated concentrations and specific forms of Si, Al, P, and S under spruce indicated progressing podzolization, a process absent under beech. Distinct soil conditions and carbon sources supported contrasting microbial communities. Higher pH and labile carbon availability under beech promoted Pseudomonadota and Bacteroidota, distinguished particularly in the L horizon. In contrast, spruce soils, especially the H horizon were enriched in fungi and metabolically versatile Actinomycetota. Increased abundance of erm resistance genes under spruce also suggested a more competitive microbial environment. Tree species effects on soil properties were detectable throughout the soil profile but weakened with depth. Overall, differences in soil chemistry, microbial communities, and enzymatic activities reflect contrasting decomposition and carbon sequestration pathways, with implications for ecosystem resilience and microbial diversity.
Artificial intelligence (AI) is transforming organic materials discovery by enabling the rapid exploration of chemical space. This review examines machine learning techniques being used to accelerate the identification of novel compounds for organic semiconductors through computational approaches linking molecular structure to properties. Key methodologies include graph neural networks, generative approaches, chemical representations, Δ $\Delta$ -learning frameworks, machine learning force fields, active learning, transfer learning, and generative models. These methods address fundamental challenges in organic materials discovery, from property prediction and inverse design to high-throughput screening and molecular generation. An example of applications to the topic of organic photovoltaics demonstrates practical impact in predicting energy levels, morphology, charge transport, exciton dynamics, and power conversion efficiency. Rather than replacing human scientists, we envision AI as a tool that amplifies their capacity to explore unconventional regions of chemical space. Advantages, drawbacks and bottlenecks of AI use in chemistry are discussed together with future research directions, such as the adoption of human-centered AI practices, the construction of materials-science-oriented benchmarking databases and protocols, the integration of green chemistry constraints into generative pipelines, and the further exploration of end-to-end in-silico-to-technical validation workflows, all tailored to the needs of the materials science community.
Establishing and maintaining laboratory colonies of the malaria vector, Anopheles funestus using newly collected material has proven challenging, in part because of their low propensity to mate in captivity. In this study we assessed how cage conditions influence the mating success of two An. funestus strains originating from different geographic areas, Angola (FANG) and Mozambique (FUMOZ). The visual environment in adult mosquito-rearing cages was manipulated either by covering the cages in different planes with black opaque cloth (referred to as black horizons) or by placing black visual markers at various positions inside the cages. Mating success was assessed by dissecting the spermathecae capsule of the females after the standard 10-day mating period. Insemination rates were consistently higher in the An. funestus FANG strain than in the FUMOZ strain in both the black horizon (odds ratio [OR] 0.31, 95% confidence interval [CI] 0.21-0.44, p < 0.001) and visual marker experiments (OR 0.12, 95% CI 0.08-0.19, p < 0.001). The inclusion of black horizons and visual markers significantly increased insemination in both strains (p < 0.001). However, strain-specific responses were evident: FANG showed significantly greater insemination rates in the side-covered cages (OR 2.06, 95% CI 1.41-3.01, p < 0.001), whereas FUMOZ insemination rates declined under the same condition (OR 0.41, 95% CI 0.24-0.70, p < 0.001). The insemination rate of the FUMOZ strain was significantly higher in top-covered cages (OR 0.59, 95% CI 0.38-0.95, p = 0.03) and when a visual marker was placed at the bottom of cage (OR 2.18, 95% CI 1.25-3.81, p = 0.006), while FANG insemination rates were unaffected by marker position. This study demonstrates that manipulating the visual environment within adult mosquito-rearing cages can significantly enhance mating success in An. funestus, although the effectiveness of specific visual cues varies between strains. While both FANG and FUMOZ responded positively to visual enhancements, their differing responses to the same conditions underscore the importance of tailoring rearing protocols as mating stimuli at their original location may be associated with specific environmental features. These findings offer preliminary guidance for improving the colonisation and maintenance of An. funestus in laboratory settings, while highlighting the need for further research to improve mating success for this species.
Ambient documentation tools (ADTs) are an emerging technology designed to help clinicians complete documentation more effectively with less time and effort. This study aimed to understand the impact of ADT on the pharmacist care experience. Data from Epic Signal, surveys, and interviews were collected between February 2024 and October 2025 for 41 medication therapy disease management (MTDM) pharmacists given ADT licenses across 33 primary care and subspecialty clinics at a large integrated health system. Study variables included the pharmacist ADT utilization rate and changes from before to after ADT implementation in the time spent in notes per encounter, as well as pharmacists' perceptions of documentation burden, patient access, undivided attention for patients, afterhours documentation, and burnout. Quantitative data were analyzed using descriptive statistics, regression models, and comparative tests of pre- vs postimplementation statistical significance and effect size. Qualitative data were mined for exemplary excerpts to deepen understanding. Thirty pharmacists from 28 clinics utilized ADT and provided usable responses. ADT was utilized for 65% of eligible encounters, and the average time in notes per encounter fell by 86 seconds after ADT implementation (P < 0.001). Pharmacist perceptions of documentation burden (P < 0.0001), undivided attention ability (P < 0.0001), and afterhours documentation (P = 0.003) improved after ADT implementation. Interview responses were largely positive for most variables and revealed multiple explanatory mechanisms. ADT meaningfully improved several care experience aspects for MTDM pharmacists over a short period of time (in 2 to 7 months). Future research with larger samples and longer time horizons across multiple health systems is needed to investigate the full and sustained impact of ADT on the care experience.
New conceptual and technological developments bring neuroscientists closer to other disciplines and other fields in neuroscience with different traditions. Although some neuroscientists may underrate the potential benefits of successful interdisciplinary collaborations, others may be unaware of the typical difficulties of such collaborations or are not trained in skills that render them fruitful. Here, we argue that interdisciplinary interactions have long been part of neuroscience, although they are often challenging, because neuroscientists may be confronted with concepts, assumptions, and interpretative horizons that differ from their own. This can lead to misunderstandings and little mutual appreciation. Using the historical development of brain imaging techniques, we distinguish between different types of interdisciplinary interactions and illustrate some of their benefits. In addition, we present various challenges for collaborations at the interface between traditional laboratory-type approaches and those of clinical or computational neuroscience or of ecological field approaches. To address these challenges, we invite neuroscientists to consider philosophers as collaboration partners with complementary expertise, which includes special consideration of language use, underlying assumptions and proficiency in conceptual analysis. This expertise can be used by neuroscientists to increase their understanding and address some difficulties in interdisciplinary interactions more effectively. The benefits of these interactions can be expected to outweigh challenges in the dialogue with philosophers. Importantly, neuroscientists can choose between reading philosophical literature, participating in joint events with philosophers, and integrating philosophers into neuroscience projects. This may allow neuroscientists to explore unforeseen possibilities to improve or initiate collaborations with scientists from other fields and disciplines.
Diabetic foot complications (DFCs) are common diabetes complications. Existing tools for predicting incident DFCs remain insufficient. This study aimed to develop and validate a novel machine learning-based model for incident DFC prediction. Using UK Biobank data, we built a longitudinal incident DFC cohort, with DFCs identified using International Classification of Diseases codes. Clinical features were screened by Cox models, and a machine learning model (DFC-Clin) was developed using fivefold cross-validation and leave-one-center-out validation. Performance was compared with diabetic foot risk stratification tools using the DeLong test. A web-based tool and risk stratification system were also developed. Among 502,175 participants, 29,766 individuals formed the incident cohort, with 1,252 incident DFC events. Demographics, blood markers, lifestyle factors, and comorbidities were selected to construct DFC-Clin, with glycated hemoglobin and body mass index emerging as the most predictive features. The model showed improved discrimination compared with existing risk stratification tools, achieving area under the receiver operating characteristic curves of 0.782 ± 0.042, 0.766 ± 0.042, and 0.747 ± 0.021 for 5-year, 10-year, and overall incident DFC prediction, respectively. DFC-Clin is a machine learning model for incident DFC prediction that uses accessible clinical features from a large population-based cohort and is coupled with a web-based application and risk stratification system. DFC-Clin estimates the risk of incident DFC across multiple time horizons and demonstrates improved discrimination compared with existing approaches. The web-based application and stratification framework are intended to support risk identification and preventive decision-making. Further studies are required for clinical deployment and evaluation on more clinical outcomes, including amputations, recurrence, and health care costs.[Figure: see text][Figure: see text][Figure: see text].
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High-risk plasma p-tau217 levels predict amyloid-β pathology in Alzheimer's disease, but little is known about the prevalence and temporal dynamics in the general population. Participants from the Gothenburg H70 Birth Cohort Study in Sweden were examined (n = 1157) and re-examined after 5-7 years (n = 771). Prevalence and 5-7 year transition of high-risk plasma p-tau217 status was determined among community-dwelling 70-year-olds. High-risk plasma p-tau217 prevalence was 3.6% at age 70 and 7.0% at age 75-77 years. Eighty-nine percent remained low-risk and 4% converted to high-risk at follow-up. Prevalence of dementia at follow-up was 1.7% if remaining in the low-risk group and 21.4% if transitioning to the high-risk group. Prevalence of dementia among participants staying in the high-risk group was 16.7% at follow-up. Individuals with low-risk plasma p-tau217 were unlikely to transition over 5-7 years. However, transitioning to a higher risk-category was associated with a higher prevalence of cognitive disability.
Retinal layer thinning is associated with disability progression and treatment failure in relapsing multiple sclerosis (RMS). However, the systematic integration of optical coherence tomography (OCT)-derived metrics into a composite measure of treatment response has not yet been evaluated. We analyzed two observational cohorts of patients with RMS who newly initiated DMT, received an MRI and OCT at baseline and 12 months, and had ≥ 24 months of clinical follow-up. No evidence of disease activity (NEDA-3/NEDA-3 + OCT) status was assigned at 12 months after DMT. Retinal thinning was defined as a reduction of ≥ 1.0 µm/year peripapillary retinal nerve fiber layer or ≥ 0.5 µm/year ganglion-cell/inner plexiform layer. The primary endpoint was confirmed disability progression occurring after the 12 month NEDA assessment. Both low-efficacy DMT and high-efficacy were included and analyzed jointly, with treatment class entered as a covariate in all models. Overall, 124 individuals (72% female, mean age 33.1 [SD ± 7.7] years, median EDSS of 2.0 [IQR 0.0-2.5]) were included. Over a median follow-up period of 3.4 years, disability progression was observed in 28 (23%) individuals. Time to and risk of disability progression did not significantly differ between EDA-3 and NEDA-3 (restricted mean survival time [RMST]: 43.4 [SE ± 2.8] vs. 48.1 [SE ± 1.3] months, p = 0.067; adjusted hazard ratio [aHR] 1.52, 95% LL-CI 0.64, p = 0.173). When retinal layer thinning was incorporated, EDA-3 + OCT was associated with a higher risk of future progression (aHR 6.59, 95% LL-CI 2.38, p = 0.005) and shorter time to progression (RMST: 41.9 [SE ± 2.1] vs. 51.9 [SE ± 0.9] months, p < 0.001). Incorporating retinal layer thinning into the NEDA-3 framework substantially improves prediction of subsequent disability progression compared with conventional NEDA-3 alone, identifying a subgroup of patients in whom ongoing neurodegeneration appears to drive disability accumulation despite suppressed inflammatory activity.
APL presenting as an extramedullary mass is exceedingly rare. Here, we describe extramedullary APL causing cauda equina syndrome, underscoring the diagnostic challenges associated with an atypical presentation, highlighting the complexity of distinguishing extramedullary APL from other conditions, and discussing the stepwise approach to diagnosis.
Adult-onset diabetes comprises subgroups differing in pathophysiology, clinical presentation, and risk of comorbidities. We investigated early phenotypic differences between individuals who later developed diabetes, stratified by subgroup at diabetes diagnosis. We conducted a pooled analysis of nine prospective European cohorts with 3309 individuals developing incident diabetes and 13,963 age- and sex-matched controls without diabetes. Cases were assigned to previously defined cluster-based subgroups: severe autoimmune (SAID), insulin-deficient (SIDD), or insulin-resistant diabetes (SIRD), and moderate obesity- (MOD) or age-related diabetes (MARD). Clinical and metabolic characteristics were retroactively assessed for three time periods (>12, 6-12, 1-6 years) before diagnosis. Despite similarly high body mass index (BMI) in MOD and SIRD at diagnosis, MOD differed from controls already >12 years before diagnosis (31% higher than controls), while BMI increased progressively in SIRD (from 14% to 25% higher than controls). Compared to controls in period 1-6 years, age-, sex-, and BMI-adjusted insulin-glucose ratio was higher in SIRD, MOD and MARD at fasting (88%, 45% and 14%, respectively) and 120 min (110%, 70%, 26%) during an oral glucose tolerance test (p < 0.0001 for all), and the first-phase insulin-glucose ratio was higher in SIRD (23% [6; 43] p = 0.0072) but lower in SIDD (-30% [-37; -22], p < 0.0001) and MARD (-29% [-34; -24], p < 0.0001). The autoimmune subgroup SAID also exhibited features of metabolic syndrome. Despite differences in HOMA2-B and HbA1c at diagnosis, insulin and glucose levels did not differ significantly between the SIDD and MARD subgroups 1-6 years earlier suggesting a rapid deterioration in glycemic control in SIDD around diagnosis. Subgroups of diabetes display different trajectories of insulin resistance, insulin deficiency, and features of the metabolic syndrome before diagnosis. ERC, local governments, private foundations, University of Helsinki, and Research councils of Finland and Sweden.
Transwell co-culture systems are models for examining cellular crosstalk, but the impact of porous membrane material on myogenic outcomes remains uncharacterized. We investigated the influence of transwell membrane material on myoblast differentiation in co-culture with human primary adipocytes and in varying concentrations of insulin-transferrin-selenium (ITS). Primary human myoblasts were differentiated on 0.4 μm polycarbonate or polyester membranes, either alone (Myo-Only) or with adipocytes (Myo + Adipo) in two different ITS concentrations ([ITS]; 0.5× or 1×). Myogenesis was evaluated by immunofluorescence, using fusion index (%) and myotube thickness (μm), and adipokine levels were measured in each co-culture compartment. Polycarbonate membranes supported a greater fusion index and myotube thickness than polyester at both 1× (p < 0.001) and 0.5× ITS (p = 0.002). Across [ITS] (1× p = 0.035; 0.5× p = 0.006), the fusion index was higher in Myo + Adipo co-cultures than in Myo-Only conditions. Leptin diffusion was reduced across polyester compared to polycarbonate membranes (0.5× p = 0.036; 1× p = 0.091), whereas adiponectin diffusion was similar between materials. These findings underscore that membrane material and medium composition are critical variables in transwell adipocyte-myoblast co-culture systems. This study highlights the importance in the selection and reporting of these parameters to improve reproducibility across future studies.
Schools offer an influential setting for delivering health promotion interventions. However, the success of these efforts largely depends on how well the implementation context is understood and addressed. While "context" is recognized as a key concept in implementation science, it remains inconsistently defined and applied. Therefore, the aim of this scoping review was to critically examine theoretical approaches used in implementation of school health intervention programs, and to explore how these approaches conceptualize and address context. Ultimately, the goal is to develop a conceptual framework that can support researchers, practitioners, and decision-makers in better understanding and addressing context in school health promotion. This scoping review was conducted following the Joanna Briggs Institute (JBI) guidelines. A search was conducted in Medline and ASSIA on June 18th, 2024. Articles were screened independently by two authors. After data extraction, a reflexive thematic analysis approach was used to analyze data. Our findings reveal that various theoretical approaches including implementation, adaptation, and system frameworks, as well as organizational and actor-network theory, have been utilized to conceptualize and address context in school health promotion. These approaches were applied across different stages of implementation: planning, delivery, and evaluation. However, our results confirm considerable variation in how these theoretical approaches address and define context. These different ways of defining context not only highlight a lack of consensus around its definition but also have significant implications leading to the prioritization of certain aspects of context over others, resulting in a fragmented, incomplete understanding of the social, cultural, economic, and political setting within which interventions are implemented. Using reflexive thematic analysis, we identified two overarching themes. The first theme, dimensions of context, comprises four contextual dimensions: organizational, socio-cultural, economic, and political. The second theme, features of context, captures the dynamic, interconnected, non-linear nature of context, and its spatial and temporal aspects. Together, these dimensions and features are integrated into a visual framework, the Contextual Framework for School Implementation (CFSI). The framework is further supported by practical guiding questions spanning all implementation phases: planning, delivery, and evaluation, emphasizing the importance of considering implementation context from the outset of program development. This review examined how theoretical approaches have been used to understand and address context in school health promotion. It shows that while context is often recognized as important, current ways of describing and applying it are fragmented and incomplete. By integrating the key dimensions and features of context, the proposed framework provides phase-specific questions to guide planning, delivery, and evaluation for understanding how context shapes implementation. The framework may also help explain certain implementation challenges, supporting both researchers and health practitioners in navigating contextual complexity throughout the implementation process, thereby informing context-sensitive health policy and practice. This study was based on a registered protocol (https://osf.io/azdv4/). It is part of the Changemaker project funded by the European Union (GA No 101137359).
Two recombinant bone morphogenetic proteins (BMP-2 and BMP-7) have received FDA approval for bone-related therapies. However, their clinical performance is limited by high costs, the need for supraphysiological doses, and adverse side effects. Here, we describe a chemically modified mRNA (cmRNA) encoding BMP-7 that promotes osteogenesis and functional ossification. The BMP-7 cmRNA is delivered using optimized lipid vectors and a composite fibrin-calcium phosphate scaffold. Among several lipids evaluated, two previously unexplored lipids efficiently condense mRNA and mediate its in vivo delivery. Transfer of BMP-7 cmRNA lipoplexes to human mesenchymal stromal cells activates intracellular vesicle transport and cytoskeletal remodeling, and regulates extracellular matrix production and calcium-associated processes. These responses were accompanied by robust mineralization and activation of key osteogenic pathways. In vivo, BMP-7 mRNA-activated scaffolds promote the formation of ossified tissue, with the highest dose yielding the largest ectopic bony growth. We further observe concurrent angiogenesis and neurogenesis, demonstrating coordinated tissue regeneration. This platform enables effective in vivo mRNA delivery for bone healing and can be applied to other tissues, facilitating the development of mRNA therapeutics in regenerative medicine.
To describe the short-term and long-term mortality of pulmonary embolism patients admitted to the ICU. Retrospective cohort study of data from the Netherlands Intensive Care Evaluation registry. All ICUs in the Netherlands. All adult critically ill patients (≥ 18 yr) with pulmonary embolism as ICU admission diagnosis between 2013 and 2023 were included in the study. None. The primary outcome is hospital mortality for patients with pulmonary embolism admitted to the ICU, as this represents short-term outcomes of pulmonary embolism and its treatment. The secondary outcome was 1-year mortality as a long-term outcome. Next, we compared patient characteristics and outcomes for survivors and nonsurvivors of the hospital admission. Of 10,210 eligible patients, 1,506 patients died (14.6%) during admission. This hospital mortality rate was higher in high-risk pulmonary embolism patients (n = 1372, 25.4%) than in non-high-risk pulmonary embolism patients (n = 134, 2.8%). Multivariable analysis also shows a higher 1-year mortality rate in high-risk pulmonary embolism patients than in non-high-risk pulmonary embolism patients (hazard ratio [HR], 3.98; CI, 3.59-4.40). The 1-year mortality of hospital survivors after hospital discharge is also higher in high-risk pulmonary embolism patients than in non-high-risk pulmonary embolism patients (HR, 1.70; CI, 1.49-1.96). This nationwide registry study confirmed that high-risk pulmonary embolism patients have a higher mortality than patients with non-high-risk pulmonary embolism admitted to the ICU. The unfavorable difference in mortality risk persists in the first year after hospital discharge. These numbers should be considered when making management decisions in patients with pulmonary embolism.
In recent years, millions of adolescents have joined school strikes to demand climate action from governments and industries, standing in solidarity with young people from future generations and from vulnerable geographical regions (i.e., the Global South). The goal of the present study is to explore adolescents' climate activism from a developmental science perspective, analyzing how climate activism may be rooted in adolescents' developing identity and developmentally salient motives. Eleven 14-18-year-old Dutch adolescent climate activists (six female, two male, one non-binary, two not disclosed) participated in an online semi-structured interview between September 2022 and 2023. Data were analyzed in NVivo through theoretical reflexive thematic analysis, exploring patterns of meaning across the dataset while embracing researchers' active, subjective, and reflective role in data analysis. We constructed three themes: "Activism is motivated by the desire to make contributions to a just world;" "Activism is an autonomous choice that helps explore and express who I am;" and "Activism makes me feel connected to (some but not all) others." Taken together, the present analysis suggests that adolescents' climate activism-and pro-environmental engagement more generally-is driven by and satisfies their developmentally salient motives to contribute to a socially just world, to make autonomous choices, to explore and express their identity, and to feel connected to others. As such, our work sheds light on how we may promote and support adolescents' engagement in acts of solidarity to contribute to today's societal challenges, and suggests avenues for further research.
Current systematic review analysed the content of generic patient-reported sleep measures (PRSMs) using (1) the International Classification of Functioning, Disability and Health (ICF) and (2) semantic analysis. A literature search identified 27 PRSMs applicable across multiple sleep disorders. Meaningful concepts within items of these PRSMs were linked to ICF categories using standardised linking rules. Additionally, semantic relations (contextual, causal, temporal or state descriptive) among these concepts were explored. An overview of the ICF content covered within and across questionnaires was compiled. Three main findings emerged. First, the majority of concepts were linked to the ICF component body functions (71.5%), indicating a focus on physiological and psychological symptoms, while activities and participation (14.7%), environmental factors (3.5%), and personal factors (2.2%) were less represented. Second, semantic analysis revealed that multidimensional content often framed symptoms within activities rather than measuring the functional or environmental impact of sleep disorders. Third, the operationalization of similar constructs varied across PRSMs, and their questionnaire labels could diverge from actual item content. This study provides a standardised database source for content-based evaluation, comparison and selection of PRSMs. Clinicians and researchers should be aware of the conceptual scope of PRSMs to ensure alignment with their specific clinical or research objectives. Recommendations are offered to guide sleep medicine measurement towards a more comprehensive approach integrating biological, psychological, social, environmental and personal aspects.
Early detection of sepsis in intensive care units remains a major clinical challenge. Artificial intelligence based predictive models have emerged as promising tools to support early identification of sepsis, yet their clinical readiness and methodological robustness remain heterogeneous. To map and critically synthesize the available evidence on artificial intelligence based predictive models for early sepsis detection in intensive care units. A scoping review was conducted following the PRISMA ScR framework. Multiple databases were systematically searched to identify studies developing or validating artificial intelligence based models for early sepsis detection in adult intensive care settings. Data were extracted on study design, data sources, model type, prediction horizon, validation strategies, and reported performance. Thirty seven studies were included. Most models were developed using retrospective electronic health record data and relied on machine learning techniques, with limited external validation. Reported performance varied widely, and few studies addressed clinical implementation, interpretability, or integration into real time workflows. Although artificial intelligence based models show potential for early sepsis detection, substantial gaps remain regarding external validation, clinical integration, and real world applicability. Future research should prioritize methodological transparency and implementation focused evaluation.
The zebrafish heart regenerates upon injury. During injury response, fibroblasts and endothelial cells accumulate at the site of damage, and cardiomyocyte cell cycle reentry allows cardiac muscle regrowth. It is relevant to understand how the different cell types communicate with each other to coordinate regeneration. We present an in silico meta-analysis of ligand-receptor (LR) interactions among periostin b+ fibroblasts, kdlr+ endothelial cells, sox10-derived and rest of ventricular cardiomyocytes, at 7 days post-injury. Using bulk RNA-seq data sets from fluorophore-activated cell-sorted populations, we selected for differentially expressed genes encoding LR pairs. Human-centric interaction data from the OmniPath database were adapted to zebrafish data through ortholog mapping to reconstruct a comprehensive interactome. We observed that fibroblasts and, to a lesser extent, endothelial cells emerged as signaling hubs, while cardiomyocytes primarily acted as signal recipients. Network analysis, combining PageRank, expression change, and literature-based novelty, revealed both known and novel candidate genes in regeneration, and allowed pathway enrichment analysis. An interactive web tool enables exploration of the ranked interaction data set, providing a systematic resource to guide future functional studies. This study provides a systematic and unbiased map of regenerative signaling in the zebrafish heart, establishing a resource to guide functional investigations.
Variation in the expression of behavior is a critical measure for understanding how socio-ecological factors shape cognitive and behavioral evolution and adaptability. Detailed descriptions of behavioral repertoires and how they are combined and structured into programs of actions is an essential foundation for this work. However, comparisons within and across species are made challenging where there is substantial variation in the level of detail at which behaviors are described. Here, we use a systematic, multi-level framework to describe a recently reported chimpanzee tool use behavior-algae fishing-at three levels of granularity: Functional Behavioral Categories, Behaviors, and Behavioral Elements. We then describe how these units are combined into structured programs of action. Despite variation in the detail at which tool use behaviors are described in the literature, we suggest that chimpanzees' algae fishing repertoire is relatively large, as compared to other forms of chimpanzee tool using, and flexibly deployed at each level of description. The varied use of techniques by adults suggests that there is no single optimal solution for algae fishing, and that chimpanzees benefit from maintaining multiple strategies for this dynamic foraging challenge. We provide an example of a structured framework that can be applied to describe different levels of detail and used to show within- and between-task variation. Systematic frameworks that can be consistently applied across species and contexts are critical for providing the like-with-like comparisons necessary for robust investigations of species-level cognition and behavior.