Climate change can reduce surface-water supply by enhancing evapotranspiration in forested mountains, especially during heatwaves. We investigate this "drought paradox" for the European Alps using a 1212-station database and hyper-resolution ecohydrological simulations to quantify blue (runoff) and green (evapotranspiration) water fluxes. During the 2003 heatwave, evapotranspiration in large areas over the Alps was above average despite low precipitation, amplifying the runoff deficit by 32% in the most runoff-productive areas (1300-3000 m above sea level). A 3 °C air temperature increase could enhance annual evapotranspiration by up to 100 mm (45 mm on average), which would reduce annual runoff at a rate similar to a 3% precipitation decrease. This * Corresponding author 2 suggests that green water feedbacks-often poorly represented in large-scale model simulations-pose an additional threat to water resources, especially in dry summers. Despite uncertainty in the validation of the hyper-resolution ecohydrological modelling with observations, this approach permits more realistic predictions of mountain region water availability. Although relatively small in size, the European Alps (hereafter "Alps") contribute a disproportionally large amount of water, especially during summer, to four major European rivers 1 , in the basins of which reside more than 170 million people 2. For this reason, they are referred to as "the water towers of Europe" 3. At the same time, water scarcity and droughts in central Europe are becoming more frequent 4. The summer droughts of 2003, 2010, 2015 and 2018 have raised concerns about the vulnerability of the European water budget to climate change 2,5 as these events have affected more than 17% of the European population with an annual economic impact exceeding € 6.2 billion between 2001 and 2006 6. Temperature in the Alps is increasing at a fast pace 7 , relative humidity is generally decreasing 8 , evapotranspiration (ET) is increasing 9 , Alpine glaciers are shrinking and the distribution of snow is shifting to higher elevation 10 , while climatic extremes are becoming more frequent 11. The complex topography, the interactions between water and vegetation and the multiple processes shaping the water cycle in mountainous areas hinder the quantification of the different water budget components in traditional large-scale climate change impact assessment studies 12. For example, climate change can shift the partitioning of water fluxes in the hydrosphere and biosphere moving blue water (runoff and streamflow) into green water (ET) 13,14. Quantifying how these fluxes change with elevation, seasonally, and interannually is an important and challenging scientific question. Large uncertainties are associated with the vegetation response to water stress 15,16. Studies in different parts of the Alps have found contrasting impacts of droughts on vegetation 17 , spanning 3 from increased mortality in dry inner-Alpine valleys 18 to enhanced productivity in wet pre-Alpine hills 19. These discrepan...

Glioblastoma is a highly invasive brain tumor, whose cells infiltrate surrounding normal brain tissue beyond the lesion outlines visible in the current medical scans. These infiltrative cells are treated mainly by radiotherapy. Existing radiotherapy plans for brain tumors derive from population studies and scarcely account for patient-specific conditions. Here we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. Our method, for the first time, integrates complementary information from high resolution MRI scans and highly specific FET-PET metabolic maps to infer tumor cell density in glioblastoma patients. The Bayesian framework quantifies imaging and modeling uncertainties and predicts patient-specific tumor cell density with credible intervals. The proposed methodology relies only on data acquired at a single time point and thus is applicable to standard clinical settings. An initial clinical population study shows that the radiotherapy plans generated from the inferred tumor cell infiltration maps spare more healthy tissue thereby reducing radiation toxicity while yielding comparable accuracy with standard radiotherapy protocols. Moreover, the inferred regions of high tumor cell densities coincide with the tumor radioresistant areas, providing guidance for personalized doseescalation. The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design.

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Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains to be learned about the links between the microbiome and human health. The fusion of artificial intelligence with rich microbiome datasets can offer an improved understanding of the microbiome’s role in human health. To gain actionable insights it is essential to consider both the predictive power and the transparency of the models by providing explanations for the predictions. We combine the collection of leg skin microbiome samples from two healthy cohorts of women with the application of an explainable artificial intelligence (EAI) approach that provides accurate predictions of phenotypes with explanations. The explanations are expressed in terms of variations in the relative abundance of key microbes that drive the predictions. We predict skin hydration, subject's age, pre/post-menopausal status and smoking status from the leg skin microbiome. The changes in microbial composition linked to skin hydration can accelerate the development of personalized treatments for healthy skin, while those associated with age may offer insights into the skin aging process. The leg microbiome signatures associated with smoking and menopausal status are consistent with previous findings from oral/respiratory tract microbiomes and vaginal/gut microbiomes respectively. This suggests that easily accessible microbiome samples could be used to investigate health-related phenotypes, offering potential for non-invasive diagnosis and condition monitoring. Our EAI approach sets the stage for new work focused on understanding the complex relationships between microbial communities and phenotypes. Our approach can be applied to predict any condition from microbiome samples and has the potential to accelerate the development of microbiome-based personalized therapeutics and non-invasive diagnostics.

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A hybrid Monte Carlo transport scheme combining event-by-event and condensed-history simulation with a full account of energy-loss straggling was used to study the dosimetric characteristics of the Auger-emitting radionuclides 67Ga, 99mTc, 111In, 123I, 125I and 201Tl at the single-cell level. The influence of the intracellular localization of the Auger radionuclide upon cellular S-values, radial dose rate profiles and dose-volume-histograms (DVHs) was investigated. For the case where the radiopharmaceutical was either internalized into the cytoplasm or remained bound onto the cell surface (non-internalized), the dose to the cell nucleus was found to differ significantly from the MIRD values and other published data. In this case, the assumption of a homogeneous distribution throughout the cell is shown to significantly overestimate the nuclear dose. A dosimetric case study relevant to the radioimmunotherapy of single lymphoma B-cells with 125I and 123I is presented.

The Lennard-Jones (LJ) potential is a cornerstone of Molecular Dynamics (MD) simulations and among the most widely used computational kernels in science. The LJ potential models atomistic attraction and repulsion with century old prescribed parameters (q = 6, p = 12, respectively), originally related by a factor of two for simplicity of calculations. We propose the inference of the repulsion exponent through Hierarchical Bayesian uncertainty quantification We use experimental data of the radial distribution function and dimer interaction energies from quantum mechanics simulations. We find that the repulsion exponent p ≈ 6.5 provides an excellent fit for the experimental data of liquid argon, for a range of thermodynamic conditions, as well as for saturated argon vapour. Calibration using the quantum simulation data did not provide a good fit in these cases. However, values p ≈ 12.7 obtained by dimer quantum simulations are preferred for the argon gas while lower values are promoted by experimental data. These results show that the proposed LJ 6-p potential applies to a wider range of thermodynamic conditions, than the classical LJ 6-12 potential. We suggest that calibration of the repulsive exponent in the LJ potential widens the range of applicability and accuracy of MD simulations.

Monte Carlo transport calculations of dose point kernels (DPKs) and depth dose profiles (DDPs) in both the vapor and liquid phases of water are presented for electrons with initial energy between 10 keV and 1 MeV. The results are obtained by the MC4 code using three different implementations of the condensed-history technique for inelastic collisions, namely the continuous slowing down approximation, the mixed-simulation with delta-ray transport and the addition of straggling distributions for soft collisions derived from accurate relativistic Born cross sections. In all schemes, elastic collisions are simulated individually based on single-scattering cross sections. Electron transport below 10 keV is performed in an event-by-event mode. Differences on inelastic interactions between the vapor and liquid phase are treated explicitly using our recently developed dielectric response function which is supplemented by relativistic corrections and the transverse contribution. On the whole, the interaction coefficients used agree to better than approximately 5% with NIST/ICRU values. It is shown that condensed phase effects in both DPKs and DDPs practically vanish above 100 keV. The effect of delta-rays, although decreases with energy, is sizeable leading to more diffused distributions, especially for DPKs. The addition of straggling for soft collisions is practically inconsequential above a few hundred keV. An extensive benchmarking with other condensed-history codes is provided.