Toward a global baseline of human-wildlife conflict: Revealing geographical and taxonomic disparities in data collection efforts through a scoping literature review - Tanner Corless Smith; Nicolas Ray; Naomi Kodama; Takuya Iwamura (Biological Conservation, 2026)

Environmental life cycle assessment of surgical versus conservative care pathways for an anterior cruciate ligament injury - Polina Boiko; Anne-Violette Bruyneel; Nicolas Ray; Philippe M. Tscholl; Martin K. Patel (Journal of Cleaner Production, 2025)

2.5D Hexahedral Meshing for Reservoir Simulations - David Lopez; Yoann Coudert-Osmont; David Desobry; Alexandre Benedicto; Wan-Chiu Li; Cédric Borgese; Nicolas Ray; Dmitry Sokolov; Jeanne Pellerin (Mathematical Geosciences, 2024)

The Ontology of Biological Attributes (OBA)—computational traits for the life sciences - Ray Stefancsik; James P. Balhoff; Meghan A. Balk; Robyn L. Ball; Susan M. Bello; Anita R. Caron; Elissa J. Chesler; Vinicius de Souza; Sarah Gehrke; Melissa Haendel; Laura W. Harris; Nomi L. Harris; et al. (Mammalian Genome, 2023)
Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

How do heat demand and energy consumption change when households transition from gas boilers to heat pumps in the UK - Nicola Terry; Ray Galvin (Energy and Buildings, 2023)

Impact of anoxia and oyster mortality on nutrient and microbial planktonic components: A mesocosm study - Julie Le Ray; Béatrice Bec; Annie Fiandrino; Franck Lagarde; Nicolas Cimiterra; Patrick Raimbault; Cécile Roques; Sylvain Rigaud; Julie Régis; Behzad Mostajir; Sébastien Mas; Marion Richard (Aquaculture, 2023)

Cholinergic basal forebrain and hippocampal structure influence visuospatial memory in Parkinson’s disease - Rok Berlot; Zvezdan Pirtošek; Simon Brezovar; Blaž Koritnik; Stefan J. Teipel; Michel J. Grothe; Nicola J. Ray (Brain Imaging and Behavior, 2022)

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity - Chiara Fallerini; Nicola Picchiotti; Margherita Baldassarri; Kristina Zguro; Sergio Daga; Francesca Fava; Elisa Benetti; Sara Amitrano; Mirella Bruttini; Maria Palmieri; Susanna Croci; Mirjam Lista; et al. (Human Genetics, 2022)
The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management.

Bi-allelic truncating variants in CFAP206 cause male infertility in human and mouse - Qunshan Shen; Guillaume Martinez; Hongbin Liu; Julie Beurois; Huan Wu; Amir Amiri-Yekta; Dan Liang; Zine-Eddine Kherraf; Marie Bidart; Caroline Cazin; Tristan Celse; Véronique Satre; et al. (Human Genetics, 2021)

Correction to: A missense mutation in IFT74, encoding for an essential component for intraflagellar transport of Tubulin, causes asthenozoospermia and male infertility without clinical signs of Bardet–Biedl syndrome - Patrick Lorès; Zine-Eddine Kherraf; Amir Amiri-Yekta; Marjorie Whitfield; Abbas Daneshipour; Laurence Stouvenel; Caroline Cazin; Emma Cavarocchi; Charles Coutton; Marie-Astrid Llabador; Christophe Arnoult; Nicolas Thierry-Mieg; et al. (Human Genetics, 2021)

A missense mutation in IFT74, encoding for an essential component for intraflagellar transport of Tubulin, causes asthenozoospermia and male infertility without clinical signs of Bardet–Biedl syndrome - Patrick Lorès; Zine-Eddine Kherraf; Amir Amiri-Yekta; Marjorie Whitfield; Abbas Daneshipour; Laurence Stouvenel; Caroline Cazin; Emma Cavarocchi; Charles Coutton; Marie-Astrid Llabador; Christophe Arnoult; Nicolas Thierry-Mieg; et al. (Human Genetics, 2021)

Accuracy assessment of global internal-tide models using satellite altimetry - Loren Carrere; Brian K. Arbic; Brian Dushaw; Gary Egbert; Svetlana Erofeeva; Florent Lyard; Richard D. Ray; Clément Ubelmann; Edward Zaron; Zhongxiang Zhao; Jay F. Shriver; Maarten Cornelis Buijsman; et al. (Ocean Science, 2021)
Altimeter measurements are corrected for several geophysical parameters in order to access ocean signals of interest, like mesoscale or sub-mesoscale variability. The ocean tide is one of the most critical corrections due to the amplitude of the tidal elevations and to the aliasing phenomena of high-frequency signals into the lower-frequency band, but the internal-tide signatures at the ocean surface are not yet corrected globally. Internal tides can have a signature of several centimeters at the surface with wavelengths of about 50–250 km for the first mode and even smaller scales for higher-order modes. The goals of the upcoming Surface Water Ocean Topography (SWOT) mission and other high-resolution ocean measurements make the correction of these small-scale signals a challenge, as the correction of all tidal variability becomes mandatory to access accurate measurements of other oceanic signals. In this context, several scientific teams are working on the development of new internal-tide models, taking advantage of the very long altimeter time series now available, which represent an unprecedented and valuable global ocean database. The internal-tide models presented here focus on the coherent internal-tide signal and they are of three types: empirical models based upon analysis of existing altimeter missions, an assimilative model and a three-dimensional hydrodynamic model. A detailed comparison and validation of these internal-tide models is proposed using existing satellite altimeter databases. The analysis focuses on the four main tidal constituents: M2, K1, O1 and S2. The validation process is based on a statistical analysis of multi-mission altimetry including Jason-2 and Cryosphere Satellite-2 data. The results show a significant altimeter variance reduction when using internal-tide corrections in all ocean regions where internal tides are generating or propagating. A complementary spectral analysis also gives some estimation of the performance of each model as a function of wavelength and some insight into the residual non-stationary part of internal tides in the different regions of interest. This work led to the implementation of a new internal-tide correction (ZARON'one) in the next geophysical data records version-F (GDR-F) standards.

Genetic analyses of a large cohort of infertile patients with globozoospermia, DPY19L2 still the main actor, GGN confirmed as a guest player - Tristan Celse; Caroline Cazin; Flore Mietton; Guillaume Martinez; Delphine Martinez; Nicolas Thierry-Mieg; Amandine Septier; Catherine Guillemain; Julie Beurois; Antoine Clergeau; Selima Fourati Ben Mustapha; Mahmoud Kharouf; et al. (Human Genetics, 2021)

Influence of Paleolithic range contraction, admixture and long‐distance dispersal on genetic gradients of modern humans in Asia - Catarina Branco; Nicolas Ray; Mathias Currat; Miguel Arenas (Molecular Ecology, 2020)
Cavalli‐Sforza and coauthors originally explored the genetic variation of modern humans throughout the world and observed an overall east‐west genetic gradient in Asia. However, the specific environmental and population genetics processes causing this gradient were not formally investigated and promoted discussion in recent studies. Here we studied the influence of diverse environmental and population genetics processes on Asian genetic gradients and identified which could have produced the observed gradient. To do so, we performed extensive spatially‐explicit computer simulations of genetic data under the following scenarios: (a) variable levels of admixture between Paleolithic and Neolithic populations, (b) migration through long‐distance dispersal (LDD), (c) Paleolithic range contraction induced by the last glacial maximum (LGM), and (d) Neolithic range expansions from one or two geographic origins (the Fertile Crescent and the Yangzi and Yellow River Basins). Next, we estimated genetic gradients from the simulated data and we found that they were sensible to the analysed processes, especially to the range contraction induced by LGM and to the number of Neolithic expansions. Some scenarios were compatible with the observed east‐west genetic gradient, such as the Paleolithic expansion with a range contraction induced by the LGM or two Neolithic range expansions from both the east and the west. In general, LDD increased the variance of genetic gradients among simulations. We interpreted the obtained gradients as a consequence of both allele surfing caused by range expansions and isolation by distance along the vast east‐west geographic axis of this continent.

Correction to: Documenting lemming population change in the Arctic: Can we detect trends? - Dorothée Ehrich; Niels M. Schmidt; Gilles Gauthier; Ray Alisauskas; Anders Angerbjörn; Karin Clark; Frauke Ecke; Nina E. Eide; Erik Framstad; Jay Frandsen; Alastair Franke; Olivier Gilg; et al. (Ambio, 2020)

Documenting lemming population change in the Arctic: Can we detect trends? - Dorothée Ehrich; Niels M. Schmidt; Gilles Gauthier; Ray Alisauskas; Anders Angerbjörn; Karin Clark; Frauke Ecke; Nina E. Eide; Erik Framstad; Jay Frandsen; Alastair Franke; Olivier Gilg; et al. (Ambio, 2020)

TRY plant trait database – enhanced coverage and open access - Jens Kattge; Gerhard Bönisch; Sandra Díaz; Sandra Lavorel; Iain Colin Prentice; Paul Leadley; Susanne Tautenhahn; Gijsbert D. A. Werner; Tuomas Aakala; Mehdi Abedi; Alicia T. R. Acosta; George C. Adamidis; et al. (Global Change Biology, 2020)
Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

Towards Comprehensive Observing and Modeling Systems for Monitoring and Predicting Regional to Coastal Sea Level - Rui M. Ponte; Mark Carson; Mauro Cirano; Catia M. Domingues; Svetlana Jevrejeva; Marta Marcos; Gary Mitchum; R. S. W. van de Wal; Philip L. Woodworth; Michaël Ablain; Fabrice Ardhuin; Valérie Ballu; et al. (Frontiers in Marine Science, 2019)

Integration of data and computing infrastructures for earth science: an image mosaicking use-case - Paolo Mazzetti; Roberto Roncella; Danut Mihon; Victor Bacu; Pierre Lacroix; Yaniss Guigoz; Nicolas Ray; Gregory Giuliani; Dorian Gorgan; Stefano Nativi (Earth Science Informatics, 2016)

Association analysis of 9,560 prostate cancer cases from the International Consortium of Prostate Cancer Genetics confirms the role of reported prostate cancer associated SNPs for familial disease - Craig C. Teerlink; Stephen N. Thibodeau; Shannon K. McDonnell; Daniel J. Schaid; Antje Rinckleb; Christiane Maier; Walther Vogel; Geraldine Cancel-Tassin; Christophe Egrot; Olivier Cussenot; William D. Foulkes; Graham G. Giles; et al. (Human Genetics, 2014)

Unravelling landscape variables with multiple approaches to overcome scarce species knowledge: a landscape genetic study of the slow worm - Céline Geiser; Nicolas Ray; Anthony Lehmann; Sylvain Ursenbacher (Conservation Genetics, 2013)

Morphometric Correlation of Impulsivity in Medial Prefrontal Cortex - Sang Soo Cho; Giovanna Pellecchia; Kelly Aminian; Nicola Ray; Barbara Segura; Ignacio Obeso; Antonio P. Strafella (Brain Topography, 2013)

Validation of prostate cancer risk-related loci identified from genome-wide association studies using family-based association analysis: evidence from the International Consortium for Prostate Cancer Genetics (ICPCG) - Guangfu Jin; Lingyi Lu; Kathleen A. Cooney; Anna M. Ray; Kimberly A. Zuhlke; Ethan M. Lange; Lisa A. Cannon-Albright; Nicola J. Camp; Craig C. Teerlink; Liesel M. FitzGerald; Janet L. Stanford; Kathleen E. Wiley; et al. (Human Genetics, 2012)

Colonization history of the Swiss Rhine basin by the bullhead ( Cottus gobio ): inference under a Bayesian spatially explicit framework - SAMUEL NEUENSCHWANDER; CARLO R. LARGIADÈR; NICOLAS RAY; MATHIAS CURRAT; PASCAL VONLANTHEN; LAURENT EXCOFFIER (Molecular Ecology, 2008)
The present distribution of freshwater fish in the Alpine region has been strongly affected by colonization events occurring after the last glacial maximum (LGM), some 20 000 years ago. We use here a spatially explicit simulation framework to model and better understand their colonization dynamics in the Swiss Rhine basin. This approach is applied to the European bullhead ( Cottus gobio ), which is an ideal model organism to study fish past demographic processes since it has not been managed by humans. The molecular diversity of eight sampled populations is simulated and compared to observed data at six microsatellite loci under an approximate Bayesian computation framework to estimate the parameters of the colonization process. Our demographic estimates fit well with current knowledge about the biology of this species, but they suggest that the Swiss Rhine basin was colonized very recently, after the Younger Dryas some 6600 years ago. We discuss the implication of this result, as well as the strengths and limits of the spatially explicit approach coupled to the approximate Bayesian computation framework.

Modeling spatial distribution of amphibian populations: a GIS approach based on habitat matrix permeability - Nicolas Ray; Anthony Lehmann; Pierre Joly (Biodiversity and Conservation, 2002)

Contributory presentations/posters - N. Manoj; V. R. Srinivas; A. Surolia; M. Vijayan; K. Suguna; R. Ravishankar; K. Suguna; A. Surolia; M. Vijayan; R. Schwarzenbacher; K. Zeth; Diederichs; et al. (Journal of Biosciences, 1999)