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Summary Past climatic fluctuations have played a major role in shaping the current plant biodiversity. Although harbouring an exceptional biota, oceanic islands have received little attention in studies on species demographic history and past vegetation patterns. We investigated the impact of past climatic changes on the effective population size of a tree (Coffea mauritiana) that is endemic to Reunion Island, located in the south‐western Indian Ocean (SWIO). Demographic changes were inferred using summary statistics calculated from genomic data. Using ecological niche modelling and the current distribution of genetic diversity, the paleodistribution of the species was also assessed. A reduction in the effective population size of C. mauritiana during the last glaciation maximum was inferred. The distribution of the species was reduced on the western side of the island, due to low rainfall. It appeared that a major reduction in rainfall and a slight temperature decrease prevailed in the SWIO. Our findings indicated that analyses on the current patterns of intraspecific genetic variations can efficiently contribute to past climatic changes characterisation in remote islands. Identifying area with higher resilience in oceanic islands could provide guidance in forest management and conservation faced to the global climate change.

Abstract Background In the near future, the incidence of mosquito-borne diseases may expand to new sites due to changes in temperature and rainfall patterns caused by climate change. Therefore, there is a need to use recent technological advances to improve vector surveillance methodologies. Unoccupied Aerial Vehicles (UAVs), often called drones, have been used to collect high-resolution imagery to map detailed information on mosquito habitats and direct control measures to specific areas. Supervised classification approaches have been largely used to automatically detect vector habitats. However, manual data labelling for model training limits their use for rapid responses. Open-source foundation models such as the Meta AI Segment Anything Model (SAM) can facilitate the manual digitalization of high-resolution images. This pre-trained model can assist in extracting features of interest in a diverse range of images. Here, we evaluated the performance of SAM through the Samgeo package, a Python-based wrapper for geospatial data, as it has not been applied to analyse remote sensing images for epidemiological studies. Results We tested the identification of two land cover classes of interest: water bodies and human settlements, using different UAV acquired imagery across five malaria-endemic areas in Africa, South America, and Southeast Asia. We employed manually placed point prompts and text prompts associated with specific classes of interest to guide the image segmentation and assessed the performance in the different geographic contexts. An average Dice coefficient value of 0.67 was obtained for buildings segmentation and 0.73 for water bodies using point prompts. Regarding the use of text prompts, the highest Dice coefficient value reached 0.72 for buildings and 0.70 for water bodies. Nevertheless, the performance was closely dependent on each object, landscape characteristics and selected words, resulting in varying performance. Conclusions Recent models such as SAM can potentially assist manual digitalization of imagery by vector control programs, quickly identifying key features when surveying an area of interest. However, accurate segmentation still requires user-provided manual prompts and corrections to obtain precise segmentation. Further evaluations are necessary, especially for applications in rural areas.

Abstract. Regional land carbon budgets provide insights on the spatial distribution of the land uptake of atmospheric carbon dioxide, and can be used to evaluate carbon cycle models and to define baselines for land-based additional mitigation efforts. The scientific community has been involved in providing observation-based estimates of regional carbon budgets either by downscaling atmospheric CO2 observations into surface fluxes with atmospheric inversions, by using inventories of carbon stock changes in terrestrial ecosystems, by upscaling local field observations such as flux towers with gridded climate and remote sensing fields or by integrating data-driven or process-oriented terrestrial carbon cycle models. The first coordinated attempt to collect regional carbon budgets for nine regions covering the entire globe in the RECCAP-1 project has delivered estimates for the decade 2000–2009, but these budgets were not comparable between regions, due to different definitions and component fluxes reported or omitted. The recent recognition of lateral fluxes of carbon by human activities and rivers, that connect CO2 uptake in one area with its release in another also requires better definition and protocols to reach harmonized regional budgets that can be summed up to the globe and compared with the atmospheric CO2 growth rate and inversion results. In this study, for the international initiative RECCAP-2 coordinated by the Global Carbon Project, which aims as an update of regional carbon budgets over the last two decades based on observations, for 10 regions covering the globe, with a better harmonization that the precursor project, we provide recommendations for using atmospheric inversions results to match bottom-up carbon accounting and models, and we define the different component fluxes of the net land atmosphere carbon exchange that should be reported by each research group in charge of each region. Special attention is given to lateral fluxes, inland water fluxes and land use fluxes.

AbstractA new approach for the replacement of heart valves consists of obtaining an acellular matrix from animal aortic valves that performs mechanically, is nonantigenic, and is free from calcification and fibroblast proliferation. Novel biochemical treatments must be developed for this purpose. In this work, we focus on the characterization of collagen in acellular bovine cardiovascular tissues, fresh or glutaraldehyde treated, and stored in different solutions [phosphate‐buffered saline (PBS), ethanol, octanol, and glutaraldehyde], to determine whether the resulting fibrous material is structurally preserved. The preservation of the triple helical structure of collagen is checked by differential scanning calorimetry (DSC), which is a well suited technique to analyze thermal transitions in proteins, such as denaturation. To get insight into the molecular dynamics of collagen in the nanometric range, we used thermally stimulated currents, a dielectric technique running at low frequency, that measure the dipolar reorientations in proteins submitted to a static electrical field. The combined use of these two techniques allowed us to evaluate the physical structure and conformation of collagen after the different chemical treatments. We have found that the glutaraldehyde treatment followed by octanol storage preserves the triple helical conformation of the polypeptidic chains of collagen, contrary to the ethanol and PBS storage that induce drastic changes in the thermal and dielectric behavior of the protein. Moreover, this particular chemical treatment stabilizes the collagen structure (shift toward high temperature of the collagen denaturation and stiffening of the chains by a cross‐linking action) when compared to the control sample, and so could provide interesting fibrous material for the conception of bioprosthetic heart valve. © 2002 Wiley Periodicals, Inc., J Biomed Mater Res 64A: 330–338, 2003

Predicting likely species responses to an alteration of their local environment is key to decision‐making in resource management, ecosystem restoration and biodiversity conservation practice in the face of global human‐induced habitat disturbance. This is especially true for forest trees which are a dominant life form on Earth and play a central role in supporting diverse communities and structuring a wide range of ecosystems. In Europe, it is expected that most forest tree species will not be able to migrate North fast enough to follow the estimated temperature isocline shift given current predictions for rapid climate warming. In this context, a topical question for forest genetics research is to quantify the ability for tree species to adapt locally to strongly altered environmental conditions (Kremer et al. ). Identifying environmental factors driving local adaptation is, however, a major challenge for evolutionary biology and ecology in general but is particularly difficult in trees given their large individual and population size and long generation time. Empirical evaluation of local adaptation in trees has traditionally relied on fastidious long‐term common garden experiments (provenance trials) now supplemented by reference genome sequence analysis for a handful of economically valuable species. However, such resources have been lacking for most tree species despite their ecological importance in supporting whole ecosystems. In this issue of Molecular Ecology, De Kort et al. () provide original and convincing empirical evidence of local adaptation to temperature in black alder, Alnus glutinosa L. Gaertn, a surprisingly understudied keystone species supporting riparian ecosystems. Here, De Kort et al. () use an innovative empirical approach complementing state‐of‐the‐art landscape genomics analysis of A. glutinosa populations sampled in natura across a regional climate gradient with phenotypic trait assessment in a common garden experiment (Fig. ). By combining the two methods, De Kort et al. () were able to detect unequivocal association between temperature and phenotypic traits such as leaf size as well as with genetic loci putatively under divergent selection for temperature. The research by De Kort et al. () provides valuable insight into adaptive response to temperature variation for an ecologically important species and demonstrates the usefulness of an integrated approach for empirical evaluation of local adaptation in nonmodel species (Sork et al. ).

The fast decay in PEM fuel cells of a highly active, high performance, but unstable Fe/N/C catalyst like our NC_Ar + NH3 follows a chemical, not an electrochemical, demetallation mechanism for its ORR active FeN4 sites in the catalyst micropores.

A monokaryotic strain of the white-rot fungus Pycnoporus cinnabarinus was shown to produce, in a 2-L bioreactor culture, 100 mg·L-1 benzaldehyde (bitter almond aroma) from L-phenylalanine with a productivity of 33 mg·L-1·day-1. The addition of HP20 resin, a styrene divinylbenzene copolymer highly selective for benzaldehyde, enabled an eightfold increase in the production of benzaldehyde and a twofold increase in productivity. In the presence of HP20 resin, the production of 790 mg·L-1 benzaldehyde was concomitant with the synthesis of cinnamic acid derivatives of high organoleptic notes such as cinnamaldehyde, cinnamyl alcohol, and methyl cinnamate.Key words : benzaldehyde, L-phenylalanine, Pycnoporus cinnabarinus, adsorbents.