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In-situ fluorescence is a widely used method to estimate the chlorophyll-a (Chla) concentration, a proxy of the phytoplankton biomass. With the emergence of autonomous platforms such as BioGeoChemical-Argo (BGC-Argo) profiling floats, its use has expanded to global scale observations. However, the relationship between in-situ fluorescence and Chla may vary significantly, leading to major discrepancies between oceanic regions. This study aims to investigate the main sources of the natural variability in the in-situ fluorescence signal in the global open ocean, specifically the influence of the phytoplankton community composition. In this view, we analyzed a combination of three datasets comprising concomitant measurements of in-situ fluorescence, pigment concentrations and phytoplankton absorption spectra. Two datasets cover several contrasted bioregions of the global ocean whereas the third one consists of a regional time series in the northwestern Mediterranean Sea, which allows to examine the effect of phytoplankton community composition on the fluorescence signal on the global, seasonal and vertical scales. We studied the variability of the two major drivers of the natural variability of the fluorescence process, i.e. the light absorption and the fluorescence quantum yield of phytoplankton, in regards of the variability of the pigment composition of the communities. The community composition correlates substantially with the Chla-to-fluorescence ratio, with high fluorescence values associated with phytoplankton communities dominated by large cells. This trend may be explained by the combined effects of the community composition on the phytoplankton absorption coefficient and the fluorescence quantum yield, and is consistently observed globally, seasonally and vertically. Non-photosynthetic pigments also appear to play a critical role in oligotrophic surface waters, leading to a reduction of the quantum yield of fluorescence. The results indicate that the phytoplankton community composition plays a key role in the relationship between the in-situ fluorescence signal and Chla concentration. Therefore, we suggest that taking into account the composition of phytoplankton communities in the retrieval of the Chla concentration from current in-situ fluorometers, those mounted on BGC-Argo floats in particular, would lead to a better estimation of the phytoplankton biomass on a wide range of spatial and temporal scales.

Phytoplankton community composition is a key determinant of ocean biogeochemical cycles, yet its observation from autonomous platforms remains challenging. In this study, we assessed the potential of in situ multispectral excitation fluorescence (MXF) to predict phytoplankton community structure indices in the Northwestern Mediterranean Sea. With a view toward applications on Biogeochemical-Argo (BGC-Argo) profiling floats, we evaluated a miniaturised, three-excitation-channel fluorometer. Laboratory measurements on ten phytoplankton strains confirmed that MXF ratios at 440, 470, and 532 nm provide taxon-specific signatures, especially for picocyanobacteria and green algae. Field observations of phytoplankton pigments were clustered into four ecologically distinct phytoplankton communities across the seasonal cycle, which defined the targeted phytoplankton community structure indices. A machine learning model was then trained to classify these clusters using MXF and additional bio-optical indices. Results show that existing BGC-Argo configurations (single-wavelength fluorescence, particulate backscattering, and beam attenuation coefficients) reliably distinguish broad community structures, such as pico- versus microphytoplankton dominance, but resolving finer pigment-based differences requires the additional spectral information provided by MXF. The different excitation channels contributed unequally: 440 and 470 nm provided robust pigment sensitivity across communities, while 532 nm was particularly informative for detecting phycoerythrin-rich taxa. Overall, combining MXF with bio-optical proxies improved classification performance by integrating pigment-specific and size-structure information, demonstrating the potential of MXF to enhance autonomous monitoring of phytoplankton community dynamics and their role in ocean biogeochemical cycles.

Purpose: Little is known about the consequences of COVID-19 lockdowns on physical activity (PA), eating behavior, and mental health in post-bariatric surgery (BS) patients. We aimed to analyze the relations between changes in PA during COVID-19 lockdowns and changes in body weight and a comprehensive set of lifestyle and psychological outcomes in patients who have undergone BS.Material and Methods: In April–May 2020 (lockdown#1), we performed an online survey in a cohort of 937 adults who underwent BS and were followed-up at our university medical center for at least one year. We assessed changes in PA, eating behavior, body weight, fatigue, and depression (PHQ-9). In November–December 2020 (lockdown#2), we recorded body weight in 280 patients who had reported decreased PA during lockdown #1.Results: During lockdown #1 (N = 420 patients included, 44% response rate), decreased PA was reported by 67% patients. Compared to those who reported increased or unchanged PA, patients with decreased PA were more likely to report a ≥ 5% weight gain (OR (95% CI): 3.15 (1.46–7.65), increased fatigue (2.08 (1.36–3.23)), a worsening of eating behavior (2.29 (1.47–3.58)), and moderate-to-severe depressive symptoms (4.74 (2.14–11.76)). During lockdown #2 (N = 225 patients, 80% response rate), significant weight gain since before lockdown #1 was reported (+ 2.8 (95% CI: 1.7–3.8) kg, p < 0.001), with 36% patients reporting a ≥ 5% weight gain.Conclusions: PA may counteract detrimental effects of COVID-19 lockdown on post-BS weight trajectories and mental health outcomes. Follow-up measures are needed in this setting to assess the long-term impact of lockdown.

Context. In the search for exoplanets using radial velocities (RV), stellar activity has become one of the main limiting factors for detectability. Fortunately, activity-induced RV signals are wavelength-dependent or chromatic, unlike planetary signals. This study exploits the broad spectral coverage provided by the combined use of SOPHIE and SPIRou velocimeters to investigate the chromatic nature of the activity signal of the highly active M dwarf EV Lac. Aims. We aim to understand the origin of the strong wavelength dependence (chromaticity) observed in the RV signal of EV Lac by selecting spectral lines based on physical properties. In particular, we explore the impact of starspots by defining the contrast effect at the level of individual lines. The Zeeman effect is also considered in this study. Methods. SPIRou and SOPHIE spectra were reduced using the line-by-line (LBL) method. We performed custom RV calculations, using groups of spectral lines selected for their sensitivity to either the spot-to-photosphere contrast or the Zeeman effect. The sensi- tivity of each line to the spot is defined using a two-temperature model based on PHOENIX spectra, while Landé factors were used to quantify Zeeman sensitivity. Results. We find that the spectral lines are distributed in two distinct families of contrasts, producing anti-correlated RV signals. This leads to a partial cancellation of the total RV signal, especially at longer wavelengths and provides a natural explanation for the strong chromaticity observed in EV Lac. This sign-reversal effect is demonstrated here, for the first time, on empirical data. Building on this discovery, we propose a new approach to constraining spot temperatures and to mitigating stellar activity. This will open up promising avenues for improving activity corrections and enhancing the detection of exoplanets around active M dwarfs.

Measuring the abundances of C- and O-bearing species in exoplanet atmospheres enables us to constrain the C/O ratio, that contains indications about the planet formation history. With a wavelength coverage going from 0.95 to 2.5 microns, the high-resolution (R\\(\\)70 000) spectropolarimeter SPIRou can detect spectral lines of major bearers of C and O in exoplanets. Here we present our study of SPIRou transmission spectra of WASP-76 b acquired for the ATMOSPHERIX program. We applied the publicly available data analysis pipeline developed within the ATMOSPHERIX consortium, analysing the data using 1-D models created with the petitRADTRANS code, with and without a grey cloud deck. We report the detection of H\\(_2\\)O and CO at a Doppler shift of around -6 km.s\\(^-1\\), consistent with previous observations of the planet. Finding a deep cloud deck to be favoured, we measured in mass mixing ratio (MMR) log(H\\(_2\\)O)\\(_MMR\\) = -4.52 \\(\\) 0.77 and log(CO)\\(_MMR\\) = -3.09 \\(\\) 1.05 consistent with a sub-solar metallicity to more than 1\\(\\). We report 3\\(\\) upper limits for the abundances of C\\(_2\\)H\\(_2\\), HCN and OH. We estimated a C/O ratio of 0.94 \\(\\) 0.39 (\\(\\) 1.7 \\(\\) 0.7 x solar, with errors indicated corresponding to the 2\\(\\) values) for the limbs of WASP-76 b at the pressures probed by SPIRou. We used 1-D ATMO forward models to verify the validity of our estimation. Comparing them to our abundance estimations of H\\(_2\\)O and CO, as well as our upper limits for C\\(_2\\)H\\(_2\\), HCN and OH, we found that our results were consistent with a C/O ratio between 1 and 2 x solar, and hence with our C/O estimation. Finally, we found indications of asymmetry for both H\\(_2\\)O and CO when investigating the dynamics of their signatures, pointing to a complex scenario involving possibly both a temperature difference between limbs and clouds being behind the asymmetry this planet is best known for.

Aligning functional schematics with 2D and 3D scene acquisitions is crucial for building digital twins, especially for old industrial facilities that lack native digital models. Current manual alignment using images and LiDAR data does not scale due to tediousness and complexity of industrial sites. Inconsistencies between schematics and reality, and the scarcity of public industrial datasets, make the problem both challenging and underexplored. This paper introduces IRIS-v2, a comprehensive dataset to support further research. It includes images, point clouds, 2D annotated boxes and segmentation masks, a CAD model, 3D pipe routing information, and the P&ID (Piping and Instrumentation Diagram). The alignment is experimented on a practical case study, aiming at reducing the time required for this task by combining segmentation and graph matching.