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micrOMEGAs is a numerical code to compute dark matter (DM) observables in generic extensions of the Standard Model of particle physics. We present a new version of micrOMEGAs that includes a generalization of the Boltzmann equations governing the DM cosmic abundance evolution which can be solved to compute the relic density of N-component DM. The direct and indirect detection rates in such scenarios take into account the relative contribution of each component such that constraints on the combined signal of all DM components can be imposed. The co-scattering mechanism for DM production is also included, whereas the routines used to compute the relic density of feebly interacting particles have been improved in order to take into account the effect of thermal masses of t-channel particles. Finally, the tables for the DM self-annihilation - induced photon spectra have been extended down to DM masses of 110 MeV, and they now include annihilation channels into light mesons. 39 pages, 1 figure

Given the global biodiversity crisis, there is an urgent need for new tools to monitor populations of endangered marine megafauna, like sharks. To this end, Baited Remote Underwater Video Stations (BRUVS) stand as the most effective tools for estimating shark abundance, measured using the MaxN metric. However, a bottleneck exists in manually computing MaxN from extensive BRUVS video data. Although artificial intelligence methods are capable of solving this problem, their effectiveness is tested using AI metrics such as the F-measure, rather than ecologically informative metrics employed by ecologists, such as MaxN. In this study, we present both an automated and a semi-automated deep learning approach designed to produce the MaxN abundance metric for three distinct reef shark species: the grey reef shark (Carcharhinus amblyrhynchos), the blacktip reef shark (C. melanopterus), and the whitetip reef shark (Triaenodon obesus). Our approach was applied to one-hour baited underwater videos recorded in New Caledonia (South Pacific). Our fully automated model achieved F-measures of 0.85, 0.43, and 0.72 for the respective three species. It also generated MaxN abundance values that showed a high correlation with manually derived data for C. amblyrhynchos (R = 0.88). For the two other species, correlations were significant but weak (R = 0.35–0.44). Our semi-automated method significantly enhanced F-measures to 0.97, 0.86, and 0.82, resulting in high-quality MaxN abundance estimations while drastically reducing the video processing time. To our knowledge, we are the first to estimate MaxN with a deep-learning approach. In our discussion, we explore the implications of this novel tool and underscore its potential to produce innovative metrics for estimating fish abundance in videos, thereby addressing current limitations and paving the way for comprehensive ecological assessments.

The increasing availability of seabed images has created new opportunities and challenges for monitoring and better understanding the spatial distribution of fauna and substrata. To date, however, deep-sea substratum classification relies mostly on visual interpretation, which is costly, time-consuming, and prone to human bias or error. Motivated by the success of convolutional neural networks in learning semantically rich representations directly from images, this work investigates the application of state-of-the-art network architectures, originally employed in the classification of non-seabed images, for the task of hydrothermal vent substrata image classification. In assessing their potential, we conduct a study on the generalization, complementarity and human interpretability aspects of those architectures. Specifically, we independently trained deep learning models with the selected architectures using images obtained from three distinct sites within the Lucky-Strike vent field and assessed the models' performances on-site as well as off-site. To investigate complementarity, we evaluated a classification decision committee (CDC) built as an ensemble of networks in which individual predictions were fused through a majority voting scheme. The experimental results demonstrated the suitability of the deep learning models for deep-sea substratum classification, attaining accuracies reaching up to 80% in terms of F1-score. Finally, by further investigating the classification uncertainty computed from the set of individual predictions of the CDC, we describe a semiautomatic framework for human annotation, which prescribes visual inspection of only the images with high uncertainty. Overall, the results demonstrated that high accuracy values of over 90% F1-score can be obtained with the framework, with a small amount of human intervention.

We introduce BSMArt, a python program for the exploration of parameter spaces of theories Beyond the Standard Model. Especially designed for use with the SARAH family of tools, it is also sufficiently flexible to be used with a wide variety of external codes. BSMArt contains the first public release of the Active Learning scan by the same authors; but contains several additional scanning algorithms, ranging from the very simple to MultiNest and Diver. A BSMArt scan can be set up in a matter of minutes with only minimal editing of configuration files; installation scripts for all relevant tools and examples are provided. Comment: 33 pages; no figures. Code available at https://goodsell.pages.in2p3.fr/bsmart/

Paraffin waxes are widely recognized as emerging marine pollutants, even their classification by the recent monitoring programs and the knowledge of their occurrence, and sources of contamination in marine ecosystems are poorly defined and reported. Wax presence and distribution have been evaluated in different environmental compartments in the Pelagos Sanctuary (Mediterranean Sea) floating on the sea surface and stranded on beaches, focussing on their characterization, accumulation areas and pollution inputs. More than 2,500 yellow paraffin residues were detected and analysed in the study area showing a prevailing dimension smaller than 5 mm. The Genoa Canyon and the waters facing Gorgona Island resulted in the more polluted areas representing two distinct hotspots of wax accumulation potentially related to the high density of tanker vessels sailing to and from the harbour of Genova and Livorno. Higher concentrations of beached particles were found along the Tuscan coast (11 items/100 m) and on Pianosa Island (110 items/m2). This study gives valuable insights into paraffin wax pollution in the Pelagos Sanctuary, emphasizing the need for harmonized monitoring and detection methods to elucidate the potential impacts on marine organisms. Moreover, mitigating actions are crucial to prevent and curb the waxes pollution of marine ecosystems.

Natural history museums worldwide house billions of apposite specimens, offering the potential for cost-free parasitological datasets. Herein, we provide novel morphological and molecular data (28S and cox1) for the polyopisthocotylean Plectanocotyle gurnardi sensu stricto from the type-host Eutrigla gurnardus from Sweden based on newly collected specimens from the Northeast Atlantic, and specimen from T. Odhner's collections at the Swedish Museum of Natural History (Stockholm, Sweden). The newly generated 28S sequences of P. gurnardi from E. gurnardus from the Northeast Atlantic were identical to those from the Western Mediterranean, and nested in a single clade, suggesting the presence of a single species. A 28S sequences of P. gurnardi sensu stricto from Sweden and those from the U.K. (type locality for P. caudata) were identical; we confirm that P. caudata and P. gurnardi are conspecific and formally synonymize them. A single 28S sequence of Plectanocotyle sp. from Chelidonichthys lastoviza off France differed from P. gurnardi from the Northeast Atlantic by 3–4 % and from P. gurnardi from France by 3%. Plectanocotyle sp. ex C. lastoviza off France is clearly not P. gurnardi, suggesting an oioxenic specificity of P. gurnardi to E gurnardus. Careful re-examination of Plectanocotyle cf. gurnardi from C. lastoviza from the Western Mediterranean from the Helminthology collection of Muséum national d'Histoire naturelle (Paris, France) revealed that it differs from all congeners by morphometry (size of clamps, of terminal lappet and its hamuli and uncinuli, and size of atrial spines). The cox1 divergences between P. cf. gurnardi and P. major, P. lastovizae, and P. gurnardi sensu stricto were 10–11 %, 10–11 % and 8 % respectively, falling within the interspecific variations range. Plectanocotyle from the Mediterranean is described as a new species, P. jeanloujustinei n. sp. We apprise nomenclature problems in Plectanocotyle and consider P. elliptica a species inquirenda.

The Mozambique Channel, a complex domain composed of passive margins and oceanic crust affected by younger strike-slip and volcanic activity, is still poorly studied for surface heat flow. We present 33 new marine-type heat flow estimations acquired offshore in 2014-2015 during the MOZ cruises, completed with 4 new heat flow values derived from reappraised old well sites (PAMELA project). Offshore heat flow is generally low to normal and does not display strong variations. As poor data coverage persists in large parts of the area, we performed a heat flow prediction mapping using the similarity method. Based on an extrapolation of old and new data guided by 14 geological and geophysical proxies, we obtain on a 0.1 • × 0.1 • grid a predicted heat flow and its uncertainty from the statistics of all values at global grid points with similar proxies. We succeeded to obtain good predictability on 70% of pixels and an RMS error <2.3 mW/m 2. The proxies selected for the prediction include a map of genetic domains, sediment thickness, topography and bathymetry, crustal ages, Curie point depth, Free air anomaly, distances to volcanoes, tectonic structures and continent-ocean boundaries. The new Mozambique heat flow map shows a predominantly low offshore heat flow of around 50 mW/m 2 , associated with the Jurassic and early Cretaceous age of the oceanic crust. Several heat flow anomalies standoff to this background heat flow with two main orientations: low heat flow anomalies with a N-S orientation and high anomalies with a mostly NE-SW orientation, which are linked mainly to tectonic structures. International audience

The mechanisms behind the beneficial effects of dietary peptides on fish skeletal development remain unrevealed. In the present study, we evaluated the effect of 0% (C, Control), 6% (P6) and 12% (P12) levels of small peptide incorporation on European sea bass (Dicentrarchus labrax) early larval skeletal development and post-larval skeletal integrity against a swimming challenge test. Survival was not affected by the peptide diets, whereas P6 presented the lowest growth rate. Larval quality control underlined the advantageous effect of P12 on reducing the frequency of cephalic deformities (e.g., branchiostegal rays, operculum and cross-bite), pre-haemal lordosis and vertebrae bone loss. Simultaneously, individuals from P12 group exhibited an earlier mineralization of the vertebral column and were less prone to develop swimming-induced haemal lordosis (16.0 ± 0.1%) and scoliosis (3.3 ± 0.6%). Expression analysis of genes involved in digestive function, protein transport, muscle ontogeny and bone mineralization revealed a peptide-enhanced larvae development of the P12 group. An early nutritional programming of the post-larval musculoskeletal system is proposed. Limitations induced by the differential free amino-acid profiles are discussed. A potential developmental-stage-specific incorporation of peptide diets in European sea bass rearing is suggested.