Weizmann Institute of Science

The coastal ocean links land and sea through rivers and submarine groundwater discharge, which contribute to the coastal carbon budget. Groundwater discharge, including fresh groundwater and recirculated seawater, remains poorly constrained globally. Here, we compile a global dataset of coastal groundwater chemistry and estimate fluxes of dissolved inorganic carbon and total alkalinity. Using conceptual reaction models, we analyze the alkalinity-carbon relationship to identify dominant processes. These patterns reflect carbonate dissolution and precipitation, and organic matter remineralization under oxic and anoxic conditions, indicating that coastal aquifers function as geochemical reactors. Recirculated seawater sampled inland is more enriched than nearshore groundwater, consistent with longer residence time and enhanced water-rock interaction. Groundwater contributes 3–7 percent of riverine dissolved inorganic carbon flux, equivalent to 2.7–2.9 and 2.2-2.4 trillion moles per year of dissolved inorganic carbon and alkalinity, depending on lithology and redox conditions. This emphasizes the importance of incorporating groundwater fluxes into Earth system models. Submarine groundwater discharge delivers non-negligible fluxes of dissolved inorganic carbon and total alkalinity to the coastal ocean, indicating that subsurface processes may influence marine carbon cycling, based on global groundwater chemistry data.

Short tandem repeats (STRs) are low-entropy regions in the genome, consisting of a short (1-6 bp) unit that is consecutively repeated multiple times. They are known for high mutational instability, due to so-called stutter-mutations, in which the number of units in the run increases or descreases. In particular, STRs with repeat unit length of 1-2 bp are prone to mutate even within several cell divisions. The extremely rapid accumulation of variation makes them interesting phylogenetic markers for retrospective single-cell lineage reconstruction. Here we model their mutational dynamics at the level of individual repeat unit type and then aggregate length variations over many STR loci with the aim of obtaining a very fast ``molecular clock''. We calibrate our model based on several datasets with known lineage structure prepared from cultured cells. We find that the mutational dynamics of STRs are reasonably consistent for a given cell line, but vary among different ones. This suggests that the dynamics are not entirely explained by mutations in caretaker genes, rather, various other factors play a role -- possibly tissue origin and differentiation state. Further data and research is necessary to asses their relative effects.

Abstract Köhler theory predicts the supersaturation required for an aerosol particle to activate into a cloud droplet by considering curvature and solute effects. However, in a saturated environment containing numerous aerosols, the competition for water vapor and the subsequent droplet activation process become highly complex. This complexity is further amplified by the presence of turbulence, which introduces significant variability in the local supersaturation experienced by each particle. This study employs Brownian models to investigate the activation of polydisperse aerosols in a turbulent environment. Turbulence is represented by temporally correlated stochastic disturbances, resulting in diverse supersaturation histories for individual particles. Our findings demonstrate that turbulence can enhance aerosol activation, but this effect exhibits nonlinear dependence on aerosol number concentration and size distribution breadth. Furthermore, our simulations reveal a counterintuitive phenomenon: smaller aerosols can activate at the expense of larger ones due to the influence of turbulent fluctuations. This effect becomes more pronounced with increasing turbulence intensity.

Abstract Alzheimer’s disease (AD) is characterized by a prolonged asymptomatic phase before cognitive decline emerges, yet the mechanisms driving symptom onset remain unclear. Here, we hypothesized that the transition from asymptomatic to symptomatic disease is linked to dysfunction of brain–immune communication. Retrograde neuronal tracing in the 5xFAD mouse model of amyloidosis revealed reduced brain–spleen connectivity at advanced disease stages. To probe the functional role of the brain–spleen axis in coping with disease, we denervated the splenic nerve at an early presymptomatic stage. This intervention accelerated cognitive decline, impaired splenic hematopoiesis, diminished monocyte recruitment to the brain, disrupted monocyte–microglia signaling networks, and reduced the transition of microglia from a homeostatic to the disease-associated (DAM) state. Conversely, enhancing splenic noradrenergic input increased hematopoiesis, restored monocyte homing to the brain, and delayed cognitive impairment. The protective role of splenic monocytes was independently validated in a retinal cytotoxic injury model, in which splenic denervation impaired post-insult retinal ganglion cell survival. Together, these findings identify an active brain–spleen circuit in regulating monocyte recruitment and establish peripheral monocytes as key drivers of microglial state transitions and disease progression.

Within the payloads of JUICE and Europa Clipper, there are instruments suitable for the search of specific biosignatures that can diagnose life tracks in two ways. The payloads include mass spectrometers capable of measuring isotopic abundances for identifying life, and chromatography instruments testing whether ocean worlds harbor amphiphile mixtures, which would lead to a lipid-first origin of life. In this paper we describe how the two missions may begin to test whether there may be large detectable excursions of stable isotopes of chemical elements on the icy surfaces of the Jovian icy moons that are substantially shifted from their expected isotopic distributions. The detection of an unambiguous signal would suggest a biogenic origin, provided care is taken to exclude abiotic thermal isotopic fractionation. Our suggested tests should be confirmed independently with other techniques. Stable isotope geochemistry on the icy Jovian moons has not yet been thoroughly discussed in the literature. In addition, we enquire whether insights into life's origin could be retrieved from Europa's ocean and surface, including the question of the first steps in the evolution of life. Special emphasis has been put on an approach to seek on the surface of ocean worlds chemical phenomena that are rather primitive, such as reproducing lipid micelles as roots of protocells, but nevertheless can predict a path towards life with published models.

Summary Normal and cancer cells accumulate lipid droplets (LDs) under stress to buffer lipotoxicity, but their role in regulated cell death (RCD) remains incompletely understood. Here, we explored LD accumulation across diverse apoptotic and non-apoptotic RCD modalities in human cancer cells and Drosophila germ cells. We found that LD accumulation arises from de novo LD biogenesis, whereas LD lipolysis remains active—or even enhanced—in dying germ cells and cancer cells, respectively. In Drosophila, LD accumulation in the Brummer lipase mutant inhibited germ cell death, indicating a protective function. Proteomic and imaging analyses revealed a broad redistribution of LD-associated proteins, encompassing lipid metabolism and stress response factors, as well as the pro-apoptotic effector Bax in human cancer cells. Enhanced LD–mitochondria contacts promoted active Bax translocation from mitochondria to LDs, thereby delaying apoptosis execution. Conversely, depletion of LDs sensitized cells to Bax- or truncated Bid-induced apoptosis. Collectively, these findings define LD accumulation during cell death as a delaying mechanism in which LDs sequester mitochondrial cell death regulators, attenuating their pro-death activity and revealing potential therapeutic implications for apoptosis-resistant cancers.

Tropospheric ozone is an important atmospheric trace gas that affects air quality, human health, and climate. However, its accurate retrieval from satellite observations remains challenging while in situ vertical profile measurements are sparse. The retrieval of tropospheric ozone is impeded by its weak signal compared to the dominant stratospheric ozone column, and current full physics retrievals often suffer from limited vertical resolution and show insufficient agreement to in situ observations. Recent advances in satellite instrumentation and machine learning provide an opportunity to overcome these limitations. In particular, the Tropospheric Monitoring Instrument (TROPOMI) offers high spatial resolution and signal-to-noise ratio, enabling more detailed daily observations of atmospheric ozone variability with global coverage.We explore the feasibility of a deep-learning-based technique for retrieving high-resolution tropospheric ozone profiles from TROPOMI spectral measurements combined with auxiliary meteorological information, while avoiding some of the simplifying assumptions made in existing full physics retrieval approaches. Artificial neural networks are well-suited for this task, as they can learn complex, nonlinear relationships between ozone absorption features, surface and cloud properties, observation geometry, and atmospheric state variables.The proposed methodology integrates TROPOMI spectral radiance/irradiance data (L1B) and the satellite position with meteorological information from ERA5 reanalysis data. The meteorological data includes boundary layer height and dissipation and surface pressure alongside temperature, humidity and wind speed profiles. The ground truth for the supervised training is comprised of co-located ozone profile measurements from ozone sondes (TOAR), aircraft measurements (IAGOS), lidar observations (TOLNet), and satellite microwave limb sounder (MLS). Initial retrieval model is based on feed-forward fully connected neural network (multilayer perceptron), with planned extensions to convolutional architectures and dimensionality-reduction techniques. Using data from 2021 alone (approximately 1.2×10⁴ independent ozone profiles corresponding to ~1.5×10⁷ concentration measurements) our preliminary results demonstrate strong performance. The model’s ozone profile predictions are evaluated against the ground truth observations on an independent test set (including unseen time periods and locations). In this preliminary evaluation, the model achieves a coefficient of determination (R²) of 0.841 between retrieved and observed ozone concentrations, indicating the model’s ability to capture both vertical ozone structure and spatial ozone variability.

Cloud–aerosol interactions are central to understanding the coupling between microphysical and dynamical cloud processes and precipitation. Our study focuses on shallow cumulus clouds, employing a high-resolution (10 m) large-eddy simulation using the System for Atmospheric Modeling (SAM) coupled with a Spectral Bin Microphysics (SBM) scheme. The model explicitly tracks aerosol evolution both in the air and within droplets, including activation, transport, growth through coalescence, and release back to the atmosphere via droplet evaporation, which is called the aerosol regeneration process.Simulations of single clouds, under clean and polluted background conditions, show that droplet evaporation efficiently returns large CCN to the atmosphere, demonstrating that shallow convective clouds are an efficient source of these particles in the lower and middle atmosphere. Regeneration significantly modifies the aerosol size distribution and its vertical profile in the atmosphere, and also alters droplet number and size distributions, particularly in diluted cloud regions. Under clean conditions, including the regeneration process reduces surface precipitation by approximately 50%, highlighting a strong microphysical effect.These findings underscore the importance of accurately representing aerosol regeneration in models to better quantify aerosol–cloud–precipitation interactions and their influence on the Earth’s radiation and water budgets.