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Models relating phenotype space to fitness (phenotype–fitness landscapes) have seen important developments recently. They can roughly be divided into mechanistic models (e.g., metabolic networks) and more heuristic models like Fisher’s geometrical model. Each has its own drawbacks, but both yield testable predictions on how the context (genomic background or environment) affects the distribution of mutation effects on fitness and thus adaptation. Both have received some empirical validation. This article aims at bridging the gap between these approaches. A derivation of the Fisher model “from first principles” is proposed, where the basic assumptions emerge from a more general model, inspired by mechanistic networks. I start from a general phenotypic network relating unspecified phenotypic traits and fitness. A limited set of qualitative assumptions is then imposed, mostly corresponding to known features of phenotypic networks: a large set of traits is pleiotropically affected by mutations and determines a much smaller set of traits under optimizing selection. Otherwise, the model remains fairly general regarding the phenotypic processes involved or the distribution of mutation effects affecting the network. A statistical treatment and a local approximation close to a fitness optimum yield a landscape that is effectively the isotropic Fisher model or its extension with a single dominant phenotypic direction. The fit of the resulting alternative distributions is illustrated in an empirical data set. These results bear implications on the validity of Fisher’s model’s assumptions and on which features of mutation fitness effects may vary (or not) across genomic or environmental contexts.

Abstract Background Cardiac surgery-associated acute kidney injury (CSA-AKI) is frequent. While two network meta-analyses assessed the impact of pharmacological interventions to prevent CSA-AKI, none focused on non-pharmacological interventions. We aim to assess the effectiveness of non-pharmacological interventions to reduce the incidence of CSA-AKI. Methods We searched PubMed, Embase, Central and clinical trial registries from January 1, 2004 (first consensus definition of AKI) to July 1, 2023. Additionally, we conducted manual screening of abstracts of major anesthesia and intensive care conferences over the last 5 years and reference lists of relevant studies. We selected all randomized controlled trials (RCTs) assessing a non-pharmacological intervention to reduce the incidence of CSA-AKI, without language restriction. We excluded RCTs of heart transplantation or involving a pediatric population. The primary outcome variable was CSA-AKI. Two reviewers independently identified trials, extracted data and assessed risk of bias. Random-effects meta-analyses were conducted to calculate risk ratios (RRs) with 95% confidence intervals (CIs). We used the Grading of Recommendations Assessment, Development, and Evaluation to assess the quality of evidence. Results We included 86 trials (25,855 patients) evaluating 10 non-pharmacological interventions to reduce the incidence of CSA-AKI. No intervention had high-quality evidence to reduce CSA-AKI. Two interventions were associated with a significant reduction in CSA-AKI incidence, with moderate quality of evidence: goal-directed perfusion (RR, 0.55 [95% CI 0.40–0.76], I 2 = 0%; P het = 0.44) and remote ischemic preconditioning (RR, 0.86 [0.78–0.95]; I 2 = 23%; P het = 0.07). Pulsatile flow during cardiopulmonary bypass was associated with a significant reduction in CSA-AKI incidence but with very low quality of evidence (RR = 0.69 [0.48; 0.99]; I 2 = 53%; P het < 0.01). We found high quality of evidence for lack of effect of restrictive transfusion strategy (RR, 1.02 [95% CI 0.92; 1.12; P het = 0.67; I 2 = 3%) and tight glycemic control (RR, 0.86 [95% CI 0.55; 1.35]; P het = 0.25; I 2 = 26%). Conclusions Two non-pharmacological interventions are likely to reduce CSA-AKI incidence, with moderate quality of evidence: goal-directed perfusion and remote ischemic preconditioning.

Deleterious mutations tend to be recessive. Several theories, notably those of Fisher (based on selection) and Wright (based on metabolism), have been put forward to explain this pattern. Despite a long-lasting debate, the matter remains unresolved. This debate has focused on the average dominance of mutations. However, we also know very little about the distribution of dominance coefficients among mutations, and about its variation across environments. In this article we present a new approach to predicting this distribution. Our approach is based on a phenotypic fitness landscape model. First, we show that under a very broad range of conditions (and environments), the average dominance of mutation of small effects should be approximately one-quarter as long as adaptation of organisms to their environment can be well described by stabilizing selection on an arbitrary set of phenotypic traits. Second, the theory allows predicting the whole distribution of dominance coefficients among mutants. Because it provides quantitative rather than qualitative predictions, this theory can be directly compared to data. We found that its prediction on mean dominance (average dominance close to 0.25) agreed well with the data, based on a meta-analysis of dominance data for mildly deleterious mutations. However, a simple landscape model does not account for the dominance of mutations of large effects and we provide possible extension of the theory for this class of mutations. Because dominance is a central parameter for evolutionary theory, and because these predictions are quantitative, they set the stage for a wide range of applications and further empirical tests.

Sugarcane ( Saccharum spp.) is a major crop for sugar and bioenergy production. Its highly polyploid, aneuploid, heterozygous, and interspecific genome poses major challenges for producing a reference sequence. We exploited colinearity with sorghum to produce a BAC-based monoploid genome sequence of sugarcane. A minimum tiling path of 4660 sugarcane BAC that best covers the gene-rich part of the sorghum genome was selected based on whole-genome profiling, sequenced, and assembled in a 382-Mb single tiling path of a high-quality sequence. A total of 25,316 protein-coding gene models are predicted, 17% of which display no colinearity with their sorghum orthologs. We show that the two species, S. officinarum and S. spontaneum , involved in modern cultivars differ by their transposable elements and by a few large chromosomal rearrangements, explaining their distinct genome size and distinct basic chromosome numbers while also suggesting that polyploidization arose in both lineages after their divergence. Sugarcane ( Saccharum spp.) is a crop of major economic significance but has complex genome structure. Here, the authors generate a BAC-based monoploid sugarcane reference sequence.

Intensive agriculture has led to several drawbacks such as biodiversity loss, climate change, erosion, and pollution of air and water. A potential solution is to implement management practices that increase the level of provision of ecosystem services such as soil fertility and biological regulation. There is a lot of literature on the principles of agroecology. However, there is a gap of knowledge between agroecological principles and practical applications. Therefore, we review here agroecological and management sciences to identify two facts that explain the lack of practical applications: (1) the occurrence of high uncertainties about relations between agricultural practices, ecological processes, and ecosystem services, and (2) the site-specific character of agroecological practices required to deliver expected ecosystem services. We also show that an adaptive-management approach, focusing on planning and monitoring, can serve as a framework for developing and implementing learning tools tailored for biodiversity-based agriculture. Among the current learning tools developed by researchers, we identify two main types of emergent support tools likely to help design diversified farming systems and landscapes: (1) knowledge bases containing scientific supports and experiential knowledge and (2) model-based games. These tools have to be coupled with well-tailored field or management indicators that allow monitoring effects of practices on biodiversity and ecosystem services. Finally, we propose a research agenda that requires bringing together contributions from agricultural, ecological, management, and knowledge management sciences, and asserts that researchers have to take the position of “integration and implementation sciences.”

ObjectivesTarget trial emulation is an approach that is increasingly used to improve transparency in observational studies and help mitigate biases. For studies declaring that they emulated a target trial, we aimed to evaluate the specification of the target trial, examine its consistency with the observational emulation and assess the risk of bias in the observational analysis.DesignMethodological systematic review reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.Data sourcesThe database MEDLINE (Medical Literature Analysis and Retrieval System Online) was interrogated for all studies published from 1 January 2021 to 3 July 2022. We performed an additional manual search of 20 general medical and specialised journals that spanned the same period.Eligibility criteriaAll studies that declared emulating a hypothetical or real randomised trial were eligible.Data extraction and synthesisTwo independent reviewers performed the whole systematic review process (screening and selection of studies, data extraction and risk of bias assessment). The main outcomes were the definition of the key protocol components of the target trial and its emulation, consistency between the target trial and its emulation and risk of bias according to the ROBINS-I (Risk Of Bias In Non-randomised Studies - of Interventions) tool.ResultsAmong the selected sample of 100 studies, 24 (24%) did not specify the target trial. Only 40 studies (40%) provided detailed information on all components of the target trial protocol. Eligibility criteria, intervention strategies and outcomes were consistent between the target trial and its emulation in 35 studies (46% of those specifying the target trial). Overall, 28 studies (28%) exhibited serious risk of bias and 41 (41%) had misalignments in the timing of eligibility assessment, treatment assignment and the start of follow-up (time-zero). As compared with studies that did not specify the target trial, those that did specify the trial less frequently seemed to have both time-zero issues (39% vs 52%) and serious risk of bias (26% vs 33%).ConclusionsOne-quarter of studies declaring that they emulated a target trial did not specify the trial. Target trials and their emulations were particularly inconsistent for studies emulating a real randomised trial. Risk of methodological issues seemed lower in observational analyses that specified versus did not specify the target trial.

Spinal cord injury (SCI) induces a pronounced neuroinflammation driven by activation and proliferation of resident microglia as well as infiltrating peripheral monocyte-derived macrophages. Depending on the time post-lesion, positive and detrimental influences of microglia/macrophages on axonal regeneration had been reported after SCI, raising the issue whether their modulation may represent an attractive therapeutic strategy. Colony-stimulating factor 1 (CSF1) regulates microglia/macrophages proliferation, differentiation and survival thus, pharmacological treatments using CSF1 receptor (CSF1R) inhibitors had been used to ablate microglia. We analyzed the effect of chronic (10 weeks) food diet containing GW2580 (a CSF1R inhibitor) in mice that underwent lateral spinal cord hemisection (HS) at vertebral thoracic level 9. Treatment started 4 weeks prior to SCI and continued until 6 weeks post-lesion. We first demonstrate that GW2580 treatment did not modify microglial response in non-injured spinal cords. Conversely, a strong decrease in proliferating microglia was observed following SCI. Second, we showed that GW2580 treatment improved some parameters of motor recovery in injured animals through better paw placement. Using and magnetic resonance imaging (MRI), we then established that GW2580 treatment had no effect on lesion extension and volume. However, histological analyses revealed that GW2580-treated animals had reduced gliosis and microcavity formation following SCI. In conclusion, CSF1R blockade using GW2580 specifically inhibits SCI-induced microglia/macrophages proliferation, reduces gliosis and microcavity formations and improves fine motor recovery after incomplete SCI. Preventing microglial proliferation may offer therapeutic approach to limit neuroinflammation, promote tissue preservation and motor recovery following SCI.

Introduction Adverse drug reaction reports are usually manually assessed by pharmacovigilance experts to detect safety signals associated with drugs. With the recent extension of reporting to patients and the emergence of mass media-related sanitary crises, adverse drug reaction reports currently frequently overwhelm pharmacovigilance networks. Artificial intelligence could help support the work of pharmacovigilance experts during such crises, by automatically coding reports, allowing them to prioritise or accelerate their manual assessment. After a previous study showing first results, we developed and compared state-of-the-art machine learning models using a larger nationwide dataset, aiming to automatically pre-code patients’ adverse drug reaction reports. Objectives We aimed to determine the best artificial intelligence model identifying adverse drug reactions and assessing seriousness in patients reports from the French national pharmacovigilance web portal. Methods Reports coded by 27 Pharmacovigilance Centres between March 2017 and December 2020 were selected ( n = 11,633). For each report, the Portable Document Format form containing free-text information filled by the patient, and the corresponding encodings of adverse event symptoms (in Medical Dictionary for Regulatory Activities Preferred Terms) and seriousness were obtained. This encoding by experts was used as the reference to train and evaluate models, which contained input data processing and machine-learning natural language processing to learn and predict encodings. We developed and compared different approaches for data processing and classifiers. Performance was evaluated using receiver operating characteristic area under the curve (AUC), F-measure, sensitivity, specificity and positive predictive value. We used data from 26 Pharmacovigilance Centres for training and internal validation. External validation was performed using data from the remaining Pharmacovigilance Centres during the same period. Results Internal validation: for adverse drug reaction identification, Term Frequency-Inverse Document Frequency (TF-IDF) + Light Gradient Boosted Machine (LGBM) achieved an AUC of 0.97 and an F-measure of 0.80. The Cross-lingual Language Model (XLM) [transformer] obtained an AUC of 0.97 and an F-measure of 0.78. For seriousness assessment, FastText + LGBM achieved an AUC of 0.85 and an F-measure of 0.63. CamemBERT (transformer) + Light Gradient Boosted Machine obtained an AUC of 0.84 and an F-measure of 0.63. External validation for both adverse drug reaction identification and seriousness assessment tasks yielded consistent and robust results. Conclusions Our artificial intelligence models showed promising performance to automatically code patient adverse drug reaction reports, with very similar results across approaches. Our system has been deployed by national health authorities in France since January 2021 to facilitate pharmacovigilance of COVID-19 vaccines. Further studies will be needed to validate the performance of the tool in real-life settings.