Insulin: Know what your immunoassay detects. Evaluation of two new immunoassays.

BACKGROUND: Insulin is essential for glycemic regulation but diseases can cause a default or an excess of insulin secretion leading to dysregulated glycemia. Hence, measurement of insulinemia is useful to investigate hypoglycemia, determine the pathogenesis of diabetes and evaluate ß-cell function. Thus, diabetic patients need supplementation with recombinant human insulin and/or insulin analogues. Analogues have primary sequences different from native human insulin and may not be detected by some immunoassays. The objective of our study was to evaluate new insulin immunoassays by determining their ability to detect different types of human insulin or analogues. METHODS: This study compared the reactivity of two new insulin immunoassays with five well-established immunoassays on ten commercial insulins. We also measured insulin in blood samples from diabetic or pancreas transplant patients with known treatment. RESULTS: Contrary to recombinant human insulin, there were differences in the specificity to insulin analogues. We distinguished three immunoassay categories: those recognizing all types of insulin such as the non-specific BI-INS-IRMA®, Architect® and Access® immunoassays; those recognizing human insulin only (Cobas®); and those recognizing human insulin and analogues in variable proportions (Liaison XL®, iFlash® and Maglumi®). CONCLUSION: An accurate biological interpretation of insulinemia relies on knowledge of the specificity of the immunoassay used.

An integrated cell atlas of the lung in health and disease.

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.

Coral reef structural complexity provides important coastal protection from waves under rising sea levels.

Coral reefs are diverse ecosystems that support millions of people worldwide by providing coastal protection from waves. Climate change and human impacts are leading to degraded coral reefs and to rising sea levels, posing concerns for the protection of tropical coastal regions in the near future. We use a wave dissipation model calibrated with empirical wave data to calculate the future increase of back-reef wave height. We show that, in the near future, the structural complexity of coral reefs is more important than sea-level rise in determining the coastal protection provided by coral reefs from average waves. We also show that a significant increase in average wave heights could occur at present sea level if there is sustained degradation of benthic structural complexity. Our results highlight that maintaining the structural complexity of coral reefs is key to ensure coastal protection on tropical coastlines in the future.

Mapping social-ecological vulnerability to inform local decision making.

An overarching challenge of natural resource management and biodiversity conservation is that relationships between people and nature are difficult to integrate into tools that can effectively guide decision making. Social-ecological vulnerability offers a valuable framework for identifying and understanding important social-ecological linkages, and the implications of dependencies and other feedback loops in the system. Unfortunately, its implementation at local scales has hitherto been limited due at least in part to the lack of operational tools for spatial representation of social-ecological vulnerability. We developed a method to map social-ecological vulnerability based on information on human-nature dependencies and ecosystem services at local scales. We applied our method to the small-scale fishery of Moorea, French Polynesia, by combining spatially explicit indicators of exposure, sensitivity, and adaptive capacity of both the resource (i.e., vulnerability of reef fish assemblages to fishing) and resource users (i.e., vulnerability of fishing households to the loss of fishing opportunity). Our results revealed that both social and ecological vulnerabilities varied considerably through space and highlighted areas where sources of vulnerability were high for both social and ecological subsystems (i.e., social-ecological vulnerability hotspots) and thus of high priority for management intervention. Our approach can be used to inform decisions about where biodiversity conservation strategies are likely to be more effective and how social impacts from policy decisions can be minimized. It provides a new perspective on human-nature linkages that can help guide sustainability management at local scales; delivers insights distinct from those provided by emphasis on a single vulnerability component (e.g., exposure); and demonstrates the feasibility and value of operationalizing the social-ecological vulnerability framework for policy, planning, and participatory management decisions.

Combining participatory and socioeconomic approaches to map fishing effort in small-scale fisheries.

Mapping the spatial allocation of fishing effort while including key stakeholders in the decision making process is essential for effective fisheries management but is difficult to implement in complex small-scale fisheries that are diffuse, informal and multifaceted. Here we present a standardized but flexible approach that combines participatory mapping approaches (fishers' spatial preference for fishing grounds, or fishing suitability) with socioeconomic approaches (spatial extrapolation of social surrogates, or fishing capacity) to generate a comprehensive map of predicted fishing effort. Using a real world case study, in Moorea, French Polynesia, we showed that high predicted fishing effort is not simply located in front of, or close to, main fishing villages with high dependence on marine resources; it also occurs where resource dependency is moderate and generally in near-shore areas and reef passages. The integrated approach we developed can contribute to addressing the recurrent lack of fishing effort spatial data through key stakeholders' (i.e., resource users) participation. It can be tailored to a wide range of social, ecological and data availability contexts, and should help improve place-based management of natural resources.

Simulating social-ecological systems: the Island Digital Ecosystem Avatars (IDEA) consortium.

Systems biology promises to revolutionize medicine, yet human wellbeing is also inherently linked to healthy societies and environments (sustainability). The IDEA Consortium is a systems ecology open science initiative to conduct the basic scientific research needed to build use-oriented simulations (avatars) of entire social-ecological systems. Islands are the most scientifically tractable places for these studies and we begin with one of the best known: Moorea, French Polynesia. The Moorea IDEA will be a sustainability simulator modeling links and feedbacks between climate, environment, biodiversity, and human activities across a coupled marine-terrestrial landscape. As a model system, the resulting knowledge and tools will improve our ability to predict human and natural change on Moorea and elsewhere at scales relevant to management/conservation actions.

Predicting species diversity of benthic communities within turbid nearshore using full-waveform bathymetric LiDAR and machine learners.

Epi-macrobenthic species richness, abundance and composition are linked with type, assemblage and structural complexity of seabed habitat within coastal ecosystems. However, the evaluation of these habitats is highly hindered by limitations related to both waterborne surveys (slow acquisition, shallow water and low reactivity) and water clarity (turbid for most coastal areas). Substratum type/diversity and bathymetric features were elucidated using a supervised method applied to airborne bathymetric LiDAR waveforms over Saint-Siméon-Bonaventure's nearshore area (Gulf of Saint-Lawrence, Québec, Canada). High-resolution underwater photographs were taken at three hundred stations across an 8-km(2) study area. Seven models based upon state-of-the-art machine learning techniques such as Naïve Bayes, Regression Tree, Classification Tree, C 4.5, Random Forest, Support Vector Machine, and CN2 learners were tested for predicting eight epi-macrobenthic species diversity metrics as a function of the class number. The Random Forest outperformed other models with a three-discretized Simpson index applied to epi-macrobenthic communities, explaining 69% (Classification Accuracy) of its variability by mean bathymetry, time range and skewness derived from the LiDAR waveform. Corroborating marine ecological theory, areas with low Simpson epi-macrobenthic diversity responded to low water depths, high skewness and time range, whereas higher Simpson diversity relied upon deeper bottoms (correlated with stronger hydrodynamics) and low skewness and time range. The degree of species heterogeneity was therefore positively linked with the degree of the structural complexity of the benthic cover. This work underpins that fully exploited bathymetric LiDAR (not only bathymetrically derived by-products), coupled with proficient machine learner, is able to rapidly predict habitat characteristics at a spatial resolution relevant to epi-macrobenthos diversity, ranging from clear to turbid waters. This method might serve both to nurture marine ecological theory and to manage areas with high species heterogeneity where navigation is hazardous and water clarity opaque to passive optical sensors.