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Copeptin is a reliable biomarker of vasopressin and is associated with urine osmolality in patients on peritoneal dialysis
No full textInternational audienceBackground Vasopressin, a hormone regulating water metabolism, has been poorly studied in patients on peritoneal dialysis (PD). Vasopressin measurement is challenging and not routinely available in clinical practice. This study aimed to evaluate whether copeptin, a stable surrogate marker of vasopressin, could be used to assess vasopressin levels in patients on PD and to determine if vasopressin maintains its antidiuretic effect in this population. Methods We included 34 PD patients from three French nephrology centers. Plasma vasopressin was measured using radioimmunoassay, while copeptin was quantified with a non-competitive immunofluorescence assay. Urine osmolality and 24-hour urine output were assessed, and peritoneal adequacy tests were performed. Associations between copeptin, vasopressin, and clinical parameters were analyzed using Spearman correlations and mixed-effect models. Healthy controls were included for comparison. Results Copeptin levels were strongly correlated with vasopressin levels (Spearman's rho = 0.62, P < 0.001), confirming its reliability as a biomarker of vasopreassin. Higher copeptin levels were associated with increased urine osmolality (β = 3.63, P = 0.008) and decreased 24-hour urine output (β = −0.53, P = 0.008), indicating that vasopressin retains its antidiuretic activity in PD patients. Compared to healthy controls, PD patients had lower urine osmolality and required higher copeptin levels to achieve similar urine concentration, suggesting vasopressin resistance. Copeptin levels were also associated with lower residual kidney function and higher brain natriuretic peptide levels but were not influenced by blood pressure, plasma sodium, or PD characteristics. Conclusions This study provides evidence that vasopressin maintains an antidiuretic effect in PD patients, and supports the use of copeptin as a robust biomarker for vasopressin in this population
Inter-Satellite LED-Based Optical Wireless Communications for CubeSats
No full textInternational audienceThis study presents a detailed performance analysis of light-emitting diode (LED)–based optical wireless communication (OWC) systems for inter-satellite communication (ISC) in CubeSat constellations. Motivated by the stringent size, mass, and power constraints of small satellites, we investigate realistic system limitations, including LED modulation bandwidth, optical beam divergence, and environmental background noise from solar, lunar, and terrestrial sources. Analytical and simulation-based bit error rate models are developed for multiple modulation schemes, namely on-off keying (OOK), M-ary pulse position modulation (M-PPM), multiband carrierless amplitude and phase (m-CAP) modulation, and direct current-biased optical orthogonal frequency-division multiplexing (DCO-OFDM). The proposed models consistently embed LED bandwidth limitations, inter-symbol interference, and realistic background-induced shot noise into all four modulation formats, and are validated against independent waveform-level Monte Carlo simulations. The results identify optimal operating regimes for each scheme based on data rate and power constraints, highlighting m-CAP as a strong candidate for high-throughput links and M-PPM for ultra-low-power telemetry. In particular, we quantify non-trivial design boundaries in terms of required Eb/N0 andtransmit power versus data rate, range, and background conditions, and show that, even under an intentionally optimistic avalanche photodiode model, PIN receivers remain more power efficient for the considered CubeSat LED links. Additionally, we quantify the influence of beamwidth and receiver field-of-view on alignment sensitivity and noise resilience. Our findings demonstrate that LED-based OWC can provide scalable, energy-efficient and spectrally agile ISC links, enabling robust communication within next-generation CubeSat mission
A contemporary reappraisal of a 1st c. AD. polytrauma on a 65-year-old male from Qumran (Near-East): from archaeology to clinical practice
No full textInternational audienc
Use of illicit substances among patients seeking treatment for alcohol use disorder in France: unveiling the mixed associations with age of onset and gender
No full textInternational audienceBackground and aims Early alcohol onset and its association with current use of illicit substances remains understudied in France, and specific information by gender is lacking. To address this question, this study examined the effects of age of alcohol onset and gender on past month use of illicit substances among patients treated for alcohol use disorder (AUD) in France. Method The data come from the RECAP study, a national database containing detailed information on patients seeking treatment for substance use disorders collected between 2012 and 2022. The sample comprised 643 942 patients with AUD (21% females). We conducted multivariable modified Poisson regressions to identify factors associated with current use of cannabis, opioids and stimulants. Current use of illicit substances was expressed as incidence rate ratios (IRR). Results There has been a decreasing trend of age of alcohol onset over time among patients, particularly notable among women. Women in treatment for AUD were less likely to engage in illicit substance use relative to men. However, an interaction revealed a complementary mechanism: the earlier the age of alcohol onset, the more women with AUD used current opioids or stimulants relative to men. Conclusion Early age of onset remains a key feature in the development of polysubstance use among patients treated for AUD, especially among women. Age of onset should be routinely incorporated into assessment protocols as it may help identify patients with high risks of polysubstance use, which is likely to disrupt their recovery process
A robust computational framework for the mixture-energy-consistent six-equation two-phase model with instantaneous mechanical relaxation terms
No full textWe present a robust computational framework for the numerical solution of a hyperbolic 6-equation single-velocity two-phase system. The system's main interest is that, when combined with instantaneous mechanical relaxation, it recovers the solution of the 5-equation model of Kapila. Several numerical methods based on this strategy have been developed over the years. However, neither the 5- nor 6-equation model admits a complete set of jump conditions because they involve non-conservative products. Different discretizations of these terms in the 6-equation model exist. The precise impact of these discretizations on the numerical solutions of the 5-equation model, in particular for shocks, is still an open question to which this work provides new insights. We consider the phasic total energies as prognostic variables to naturally enforce discrete conservation of total energy and compare the accuracy and robustness of different discretizations for the hyperbolic operator. Namely, we discuss the construction of an HLLC approximate Riemann solver in relation to jump conditions. We then compare an HLLC wave-propagation scheme which includes the non-conservative terms, with Rusanov and HLLC solvers for the conservative part in combination with suitable approaches for the non-conservative terms. We show that some approaches for the discretization of non-conservative terms fit within the framework of path-conservative schemes for hyperbolic problems. We then analyze the use of various numerical strategies on several relevant test cases, showing both the impact of the theoretical shortcomings of the models as well as the importance of the choice of a robust framework for the global numerical strategy
Study of yrast and yrare low-lying excited states using machine learning approaches
No full textInternational audienceThe low-lying excitation energies of the states in even-even nuclei are studied using two modern machine learning algorithms: the Light Gradient Boosting Machine (LightGBM) and Sparse Variational Gaussian Process (SVGP). The obtained results demonstrate that both LightGBM and SVGP perform well on the training and validation datasets when informed by a physics-based feature space. A detailed comparison of the results obtained for and states using the Hartree-Fock-Bogoliubov theory extended by the generator coordinate method and mapped onto a five-dimensional collective quadrupole Hamiltonian shows that both ML algorithms outperform this model in terms of accuracy. The extrapolation capabilities of these algorithms were further validated using newly measured 12 data points of and states, which were not included in the training set. In addition, the partial dependence plot method and the Shapley additive explanations method are used as interpretability tools to analyze the relationship between the input features and model predictions. These tools provide in-depth insights into how the input features influence the prediction of low-lying excitation energies and help identify the most important features that drive the prediction, which are valuable for understanding the low-lying excitation energies
First observation of multi-phonon -vibrations in an odd-odd nuclear system
No full textInternational audienceThe identification of the first multi-phonon -vibrational bands in an odd-odd neutron-rich nucleus of the nuclear chart is presented. These high spin structures of hard to access Nb, produced in fission, were studied by combining a spectrometer with isotopic resolution coupled to a -ray tracking array and independently high-fold coincidence measurements. Triaxial Projected Shell Model calculations for the high-spin states are in good agreement with the measured observables for the yrast, one-phonon and two-phonon bands. The possibility of an oblate shape of an isomeric state and coexistence of triaxial and oblate configurations are investigated from the decay of the 141 keV isomer. The present work illustrates the robustness of vibration excitations in the presence of odd valence proton and neutron as well as the possibly coexisting shapes beyond the transitional region
A short derivation of Boltzmann distribution and Gibbs entropy formula from the fundamental postulate
No full textIntroducing the Boltzmann distribution very early in a statistical thermodynamics course (in the spirit of Feynmann) has many didactic advantages, in particular that of easily deriving the Gibbs entropy formula. In this note, a short derivation is proposed from the fundamental postulate of statistical mechanics and basics calculations accessible to undergraduate students
Living bacterial reservoir computers for information processing and sensing
No full textWe introduce a systems-level approach to sensing and computing in which Escherichia coli acts as a living reservoir computer, performing complex information processing through its native growth responses without requiring genetic modification or specialized instrumentation. We validate this framework by accurately classifying early-stage COVID-19 plasma samples (mild vs . severe) using only bacterial growth data, highlighting a diagnostic potential without infrastructure-dependent methods. By controlling nutrient media compositions, we also demonstrate that E. coli growth encodes nonlinear transformations that outperform linear regression, support vector machines, and multilayer perceptrons across diverse regression and classification tasks. Using simulations across genome-scale metabolic models from multiple bacterial species, we establish a strong link between phenotypic diversity and computational capacity, showing that learning capacities scale with the diversity of metabolic phenotypes. These findings position biological reservoir computing as a robust, scalable, and low-cost platform for intelligent biosensing, diagnostics, and hybrid bio-digital computation, while providing new mechanistic insights into the computational capabilities of living systems