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We propose denoising diffusion models for data-driven representation learning of dynamical systems. In this type of generative deep learning, a neural network is trained to denoise and reverse a diffusion process, where Gaussian noise is added to states from the attractor of a dynamical system. Iteratively applied, the neural network can then map samples from isotropic Gaussian noise to the state distribution. We showcase the potential of such neural networks in proof-of-concept experiments with the Lorenz 1963 system. Trained for state generation, the neural network can produce samples that are almost indistinguishable from those on the attractor. The model has thereby learned an internal representation of the system, applicable for different tasks other than state generation. As a first task, we fine-tune the pre-trained neural network for surrogate modelling by retraining its last layer and keeping the remaining network as a fixed feature extractor. In these low-dimensional settings, such fine-tuned models perform similarly to deep neural networks trained from scratch. As a second task, we apply the pre-trained model to generate an ensemble out of a deterministic run. Diffusing the run, and then iteratively applying the neural network, conditions the state generation, which allows us to sample from the attractor in the run's neighbouring region. To control the resulting ensemble spread and Gaussianity, we tune the diffusion time and, thus, the sampled portion of the attractor. While easier to tune, this proposed ensemble sampler can outperform tuned static covariances in ensemble optimal interpolation. Therefore, these two applications show that denoising diffusion models are a promising way towards representation learning for dynamical systems.

In this introduction to the special issue of M@n@gement for the 30th anniversary of the AIMS, dedicated to complex societal problems, we examine issues of scale and non-scalability, inspired by Tsing’s work and recent calls for an ethics of care and responsiveness, thus highlighting the need to develop more care and relationality in the study of complex problems and grand challenges. This means questioning scale, recognizing the agency and value of human and nonhuman beings, prioritizing local knowledge, mutual respect, solidarity and coexistence.

Abstract Although friction of liquids on solid surfaces is traditionally linked to wettability, recent works have unveiled the role of the solid’s internal excitations on interfacial dissipation. In order to directly evidence such couplings, we take advantage of the considerable variation of the molecular timescales of supercooled glycerol under mild change of temperature to explore how friction depends on the liquid’s molecular dynamics. Using a dedicated tuning-fork AFM, we measure the slippage of glycerol on mica. We report a 100 fold increase of slip length upon cooling, while liquid-solid friction exhibits a linear scaling with molecular relaxation rate at high temperature. This scaling can be explained by a contribution of mica’s phonons which resonate with density fluctuations in the liquid, allowing efficient momentum transfer to mica. These results suggest that engineering phononic spectra of materials could enhance flow performance in nanofluidic channels and industrially relevant membranes.

Because optimal transport (OT) acts as displacement interpolation in physical space rather than as interpolation in value space, it can avoid double-penalty errors generated by mislocations of geophysical fields. As such, it provides a very attractive metric for non-negative, sharp field comparison – the Wasserstein distance – which could further be used in data assimilation (DA) for the geosciences. However, the algorithmic and numerical implementations of such a distance are not straightforward. Moreover, its theoretical formulation within typical DA problems faces conceptual challenges, resulting in scarce contributions on the topic in the literature. We formulate the problem in a way that offers a unified view with respect to both classical DA and OT. The resulting OTDA framework accounts for both the classical source of prior errors, background and observation, and a Wasserstein barycentre in between states which are pre-images of the background state and observation vector. We show that the hybrid OTDA analysis can be decomposed as a simpler OTDA problem involving a single Wasserstein distance, followed by a Wasserstein barycentre problem that ignores the prior errors and can be seen as a McCann interpolant. We also propose a less enlightening but straightforward solution to the full OTDA problem, which includes the derivation of its analysis error covariance matrix. Thanks to these theoretical developments, we are able to extend the classical 3D-Var/BLUE (best linear unbiased estimator) paradigm at the core of most classical DA schemes. The resulting formalism is very flexible and can account for sparse, noisy observations and non-Gaussian error statistics. It is illustrated by simple one- and two-dimensional examples that show the richness of the new types of analysis offered by this unification.

Patients suffering from von Willebrand disease (VWD) have reduced quality-of-life despite current treatment options. Moreover, innovation in VWD therapeutic strategies has essentially stalled and available treatments have remained unchanged for decades. Therefore, there is an unmet need to develop new therapeutic strategies for VWD-patients, especially for the large portion of those with VWD-type 1. Due to species differences, the available VWD murine models are not suitable for preclinical studies, making it difficult to test new therapeutic approaches in vivo. With this in mind, we generated mice selectively expressing human von Willebrand factor (VWF) and human GPIbα. Because this fully humanized model was found to express low VWF (12%) and FVIII (40%) levels with normal multimer profile and activity/antigen ratio, we repositioned it as a VWD-type 1 model (hVWD1 mice). In depth characterization of this model confirmed VWD-type 1 features with a decrease in platelet adhesion and thrombus formation in vitro. In vivo, a moderate bleeding phenotype was observed which was corrected upon the administration of recombinant-VWF or upon histamine-induced release of endothelial VWF. In search for new therapeutic options for VWD, we designed a bispecific single-domain antibody that bridges VWF to albumin (KB-V13A12). Remarkably, a single subcutaneous administration of KB-V13A12 coincided with a sustained 2-fold increase in VWF antigen levels for up to 10 days and normalized haemostasis in a tail-clip model in hVWD1 mice. We have developed a unique humanized mouse model for VWD-type 1 and a promising new therapeutic that corrected haemostasis in these mice.

Abstract We introduce deep generative diffusion for multivariate and regional surrogate modeling learned from sea‐ice simulations. Given initial conditions and atmospheric forcings, the model is trained to generate forecasts for a 12‐hr lead time from simulations by the state‐of‐the‐art sea‐ice model neXtSIM. For our regional model setup, the diffusion model outperforms as ensemble forecast all other tested models, including a free‐drift model and a stochastic extension of a deterministic data‐driven surrogate model. The diffusion model additionally retains information at all scales, resolving smoothing issues of deterministic models. Furthermore, by generating physically consistent forecasts, previously unseen for such kind of completely data‐driven surrogates, the model can almost match the scaling properties of neXtSIM, as similarly deduced from sea‐ice observations. With these results, we provide a strong indication that diffusion models can achieve similar results as traditional geophysical models with the significant advantage of being orders of magnitude faster and solely learned from data.

In controlled phase II trials, major prognostic factors need to be well balanced between arms. The main procedures used are SPBR (Stratified Permuted Block Randomization) and minimization. First, we provide a systematic review of the treatment allocation procedure used in gastrointestinal oncology controlled phase II trials published in 2019. Second, we performed simulations using data from six phase II studies to measure the impacts of imbalances and bias on the efficacy estimations. From the 40 articles analyzed, all mentioned randomization in both the title and abstract, the median number of patients included was 109, and 77.5% were multicenter. Of the 27 studies that reported at least one stratification variable, 10 included the center as a stratification variable, 10 used minimization, 9 used SBR, and 8 were unspecified. In real data studies, the imbalance increased with the number of centers. The total and marginal imbalances were higher with SBR than with minimization, and the difference increased with the number of centers. The efficiency estimates per arm were close to the original trial estimate in both procedures. Minimization is often used in cases of numerous centers and guarantees better similarity between arms for stratification variables for total and marginal imbalances in phase II trials.

Abstract Resistive Random Access Memories (ReRAM) arrays provides a promising basement to deploy neural network accelerators based on near or in memory computing. However most popular accelerators rely on Ohm’s and Kirchhoff’s laws to achieve multiply and accumulate, and thus are prone to ReRAM variability and voltage drop in the memory array, and thus need sophisticated readout circuits. Here we propose a robust binary neural network, based on fully differential capacitive neurons and ReRAM synapses, used in a resistive bridge fashion. We fabricated a network layer with up to 23 inputs that we extrapolated to large numbers of inputs through simulation. Defining proper programming and reading conditions, we demonstrate the high resilience of this solution with a minimal accuracy drop, compared to a software baseline, on image classification tasks. Moreover, our solution can achieve a peak energy efficiency, comparable with the state of the art, when projected to a 22 nanometer technology.

In this study, we investigate the fully multivariate state and parameter estimation through idealised simulations of a dynamics-only model that uses the novel Maxwell elasto-brittle (MEB) sea-ice rheology and in which we estimate not only the sea-ice concentration, thickness and velocity, but also its level of damage, internal stress and cohesion. Specifically, we estimate the air drag coefficient and the so-called damage parameter of the MEB model. Mimicking the realistic observation network with different combinations of observations, we demonstrate that various issues can potentially arise in a complex sea-ice model, especially in instances for which the external forcing dominates the model forecast error growth. Even though further investigation will be needed using an operational (a coupled dynamics–thermodynamics) sea-ice model, we show that, with the current observation network, it is possible to improve both the observed and the unobserved model state forecast and parameter accuracy.

A novel generation of sea-ice models with elasto-brittle rheologies, such as neXtSIM, can represent sea-ice processes with an unprecedented accuracy at the mesoscale for resolutions of around 10 km. As these models are computationally expensive, we introduce supervised deep learning techniques for surrogate modeling of the sea-ice thickness from neXtSIM simulations. We adapt a convolutional U-Net architecture to an Arctic-wide setup by taking the land–sea mask with partial convolutions into account. Trained to emulate the sea-ice thickness at a lead time of 12 h, the neural network can be iteratively applied to predictions for up to 1 year. The improvements of the surrogate model over a persistence forecast persist from 12 h to roughly 1 year, with improvements of up to 50 % in the forecast error. Moreover, the predictability gain for the sea-ice thickness measured against the daily climatology extends to over 6 months. By using atmospheric forcings as additional input, the surrogate model can represent advective and thermodynamical processes which influence the sea-ice thickness and the growth and melting therein. While iterating, the surrogate model experiences diffusive processes which result in a loss of fine-scale structures. However, this smoothing increases the coherence of large-scale features and thereby the stability of the model. Therefore, based on these results, we see huge potential for surrogate modeling of state-of-the-art sea-ice models with neural networks.

Abstract The NA48/2 experiment at CERN reports the first observation of the K ± → π 0 π 0 μ ± ν decay based on a sample of 2437 candidates with 15% background contamination collected in 2003–2004. The decay branching ratio in the kinematic region of the squared dilepton mass above 0.03 GeV2/c 4 is measured to be (0.65 ± 0.03) × 10 −6. The extrapolation to the full kinematic space, using a specific model, is found to be (3.45 ± 0.16) × 10 −6, in agreement with chiral perturbation theory predictions.

Abstract Memristor-based neural networks provide an exceptional energy-efficient platform for artificial intelligence (AI), presenting the possibility of self-powered operation when paired with energy harvesters. However, most memristor-based networks rely on analog in-memory computing, necessitating a stable and precise power supply, which is incompatible with the inherently unstable and unreliable energy harvesters. In this work, we fabricated a robust binarized neural network comprising 32,768 memristors, powered by a miniature wide-bandgap solar cell optimized for edge applications. Our circuit employs a resilient digital near-memory computing approach, featuring complementarily programmed memristors and logic-in-sense-amplifier. This design eliminates the need for compensation or calibration, operating effectively under diverse conditions. Under high illumination, the circuit achieves inference performance comparable to that of a lab bench power supply. In low illumination scenarios, it remains functional with slightly reduced accuracy, seamlessly transitioning to an approximate computing mode. Through image classification neural network simulations, we demonstrate that misclassified images under low illumination are primarily difficult-to-classify cases. Our approach lays the groundwork for self-powered AI and the creation of intelligent sensors for various applications in health, safety, and environment monitoring.

The nutritional status of a honey bee colony is recognized as a key factor in ensuring a healthy hive. A deficient flow of nectar and pollen in the honey bee colony immediately affects its development, making room for pathogen proliferation and, consequently, for a reduction in the activities and strength of the colony. It is, therefore, urgent for the beekeepers to use more food supplements and/or substitutes in apiary management, allowing them to address colony nutritional imbalances according to the beekeeper’s desired results. In this context, the commercial market for beekeeping products is growing rapidly due to low regulation of animal food products and the beekeeper’s willingness to guarantee healthy colonies. There are numerous products (bee food additives) currently available on the worldwide market, with a highly variable and sometimes even undefined composition, claiming a set of actions at the level of brood stimulation, energy supplementation, queen rearing support, reduction of Varroa reproduction levels, improvement of the intestinal microflora of bees, Nosema prevention, and improvement of the health of honey bee colonies infested by American foulbrood, among others. To address this issue, the members of the COLOSS (Honey Bee Research Association) NUTRITION Task Force are proposing, for the first time, action on honey bee feed control and monitoring. In our common study, we focused on candy board composition and quality parameters. For that, a selected number of commercial candy boards usually found in Europe were analyzed in terms of water and ash content, pH, acidity, 5-hydroxymethylfurfural, sugars, C3-C4 sugar origin, and texture. Results revealed differences between the values found and the ones displayed on the label, demonstrating the need for regulation of the quality of these products.

Abstract Single Atom Catalysis (SAC) is an expanding field of heterogeneous catalysis in which single metallic atoms embedded in different materials catalyze a chemical reaction, but these new catalytic materials still lack fundamental understanding when used in electrochemical environments. Recent characterizations of non-noble metals like Fe deposited on N-doped graphitic materials have evidenced two types of Fe-N4 fourfold coordination, either of pyridine type or of porphyrin type. Here, we study these defects embedded in a graphene sheet and immersed in an explicit aqueous medium at the quantum level. While the Fe-pyridine SAC model is clear cut and widely studied, it is not the case for the Fe-porphyrin SAC that remains ill-defined, because of the necessary embedding of odd-membered rings in graphene. We first propose an atomistic model for the Fe-porphyrin SAC. Using spin-polarized ab initio molecular dynamics, we show that both Fe SACs spontaneously adsorb two interfacial water molecules from the solvent on opposite sides. Interestingly, we unveil a different catalytic reactivity of the two hydrated SAC motives: while the Fe-porphyrin defect eventually dissociates an adsorbed water molecule under a moderate external electric field, the Fe-pyridine defect does not convey water dissociation.

Richard R Gammon,1,* Rounak Dubey,2,* Gaurav K Gupta,3,* Colleen Hinrichsen,4,* Aikaj Jindal,5,* Divjot Singh Lamba,6,* Sadhana Mangwana,7,* Amita Radhakrishnan Nair,8,* Shaughn Nalezinski,9,* Christopher Bocquet10 1Scientific, Medical and Technical Department, OneBlood, Orlando, FL, USA; 2Department of Transfusion Medicine, All India Institute of Medical Sciences, Nagpur, India; 3Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA; 4Department of Transfusion Medicine, Princeton Medical Center at Penn Medicine, Plainsboro, NJ, USA; 5Department of Transfusion Medicine, Mohandas Oswal Hospital, Ludhiana, India; 6Department of Transfusion Medicine, Post Graduate Institute of Medical Education and Research, Chandigarh, India; 7Department of Transfusion Medicine and Immunohematology, Sri Balaji Action Medical Institute, New Delhi, India; 8Department of Transfusion Medicine Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvanathapuram, Kerala, India; 9Department of Laboratory Medicine - Transfusion Services, Concord Hospital, Concord, NH, USA; 10Association for the Advancement of Blood and Biotherapies, Bethesda, MD, USA*These authors contributed equally to this workCorrespondence: Richard R Gammon, OneBlood, 8669 Commodity Circle, Orlando, FL, 32819, USA, Tel +1407 947 7963, Fax +1 407 264 8265, Email richard.gammon@oneblood.orgAbstract: Blood donors and voluntary blood donations are essential for ensuring the blood supply that can be maintained by good patient blood management (PBM) practices. This review article explores the role of blood donation in PBM and highlights the importance of donor screening and selection processes in different regions worldwide. The donor health questionnaires and the focused physical examination guidelines have changed in the last decade to increase donor and recipient safety. This article also discusses the status of transfusion practices, including the challenges of ensuring a safe blood supply. Significant among these are the effects of the COVID-19 pandemic on the blood supply chain and the impact of an aging donor population, especially. Promoting autologous donations and other blood conservation strategies are suggested to mitigate these issues. The role of replacement donors and the upper age limit for voluntary blood donation may be decided based on the demography and donor pool. The involvement of C-suite executives is also critical in implementing and running a successful PBM program. The review highlights how these different aspects of blood donation are integral to a successful PBM program and the safety of patients who receive blood transfusions.Keywords: donors, blood centers, transfusion service

Abstract The combination of ongoing urban expansion and electrification of uses challenges the power grid. In such a context, information regarding customers’ consumption is vital to assess the expected load at strategic nodes over time, and to guide power system planning strategies. Comprehensive household consumption databases are widely available today thanks to the roll-out of smart meters, while the consumption of tertiary premises is seldom shared mainly due to privacy concerns. To fill this gap, the French main distribution system operator, Enedis, commissioned Mines Paris to derive load profiles of industrial and tertiary sectors for its prospective tools. The ELMAS dataset is an open dataset of 18 electricity load profiles derived from hourly consumption time series collected continuously over one year from a total of 55,730 customers. These customers are divided into 424 fields of activity, and three levels of capacity subscription. A clustering approach is employed to gather activities sharing similar temporal patterns, before averaging the associated time series to ensure anonymity.

Sea ice volume's significant interannual variability requires long-term series of observations to identify trends in its evolution. Despite improvements in sea ice thickness estimations from altimetry during the past few years thanks to CryoSat-2 and ICESat-2, former ESA radar altimetry missions such as the Environmental Satellite (Envisat) and especially the European Remote-Sensing Satellite (ERS-1 and ERS-2) have remained under-exploited so far. Although solutions have already been proposed to ensure continuity of measurements between CryoSat-2 and Envisat, there is no time series integrating ERS. The purpose of this study is to extend the Arctic radar freeboard time series back to 1995. The difficulty in handling ERS measurements comes from a technical issue known as the pulse blurring effect, altering the radar echoes over sea ice and the resulting surface height estimates. Here we present and apply a correction for this pulse blurring effect. To ensure consistency of the CryoSat-2, Envisat and ERS-2 time series, a multiparameter neural-network-based method to calibrate Envisat against CryoSat-2 and ERS-2 against Envisat is presented. The calibration is trained on the discrepancies observed between the altimeter measurements during the mission-overlap periods and a set of parameters characterizing the sea ice state. Monthly radar freeboards are provided with uncertainty estimations based on a Monte Carlo approach to propagate the uncertainties all along the processing chain, including the neural network. Comparisons of corrected radar freeboards during overlap periods reveal good agreement between the missions, with a mean bias of 0.30 cm and a standard deviation of 9.7 cm for Envisat and CryoSat-2 and a 0.20 cm bias and a standard deviation of 3.8 cm for ERS-2 and Envisat. The monthly corrected radar freeboards obtained from Envisat and ERS-2 are then validated by comparison with several independent datasets such as airborne, mooring, direct-measurement and other altimeter products. Except for two datasets, comparisons lead to correlations ranging from 0.41 to 0.94 for Envisat and from 0.60 to 0.74 for ERS-2. The study finally provides radar freeboard estimation for winters from 1995 to 2021 (from the ERS-2 mission to CryoSat-2).

We introduce a proof of concept to parametrise the unresolved subgrid scale of sea-ice dynamics with deep learning techniques. Instead of parametrising single processes, a single neural network is trained to correct all model variables at the same time. This data-driven approach is applied to a regional sea-ice model that accounts exclusively for dynamical processes with a Maxwell elasto-brittle rheology. Driven by an external wind forcing in a 40 km×200 km domain, the model generates examples of sharp transitions between unfractured and fully fractured sea ice. To correct such examples, we propose a convolutional U-Net architecture which extracts features at multiple scales. We test this approach in twin experiments: the neural network learns to correct forecasts from low-resolution simulations towards high-resolution simulations for a lead time of about 10 min. At this lead time, our approach reduces the forecast errors by more than 75 %, averaged over all model variables. As the most important predictors, we identify the dynamics of the model variables. Furthermore, the neural network extracts localised and directional-dependent features, which point towards the shortcomings of the low-resolution simulations. Applied to correct the forecasts every 10 min, the neural network is run together with the sea-ice model. This improves the short-term forecasts up to an hour. These results consequently show that neural networks can correct model errors from the subgrid scale for sea-ice dynamics. We therefore see this study as an important first step towards hybrid modelling to forecast sea-ice dynamics on an hourly to daily timescale.

Under the Copernicus programme, an operational CO2 Monitoring Verification and Support system (CO2MVS) is being developed and will exploit data from future satellites monitoring the distribution of CO2 within the atmosphere. Methods for estimating CO2 emissions from significant local emitters (hotspots; i.e. cities or power plants) can greatly benefit from the availability of such satellite images that display the atmospheric plumes of CO2. Indeed, local emissions are strongly correlated to the size, shape, and concentration distribution of the corresponding plume, which is a visible consequence of the emission. The estimation of emissions from a given source can therefore directly benefit from the detection of its associated plumes in the satellite image. In this study, we address the problem of plume segmentation (i.e. the problem of finding all pixels in an image that constitute a city or power plant plume). This represents a significant challenge, as the signal from CO2 plumes induced by emissions from cities or power plants is inherently difficult to detect, since it rarely exceeds values of a few parts per million (ppm) and is perturbed by variable regional CO2 background signals and observation errors. To address this key issue, we investigate the potential of deep learning methods and in particular convolutional neural networks to learn to distinguish plume-specific spatial features from background or instrument features. Specifically, a U-Net algorithm, an image-to-image convolutional neural network with a state-of-the-art encoder, is used to transform an XCO2 field into an image representing the positions of the targeted plume. Our models are trained on hourly 1 km simulated XCO2 fields in the regions of Paris, Berlin, and several power plants in Germany. Each field represents the plume of the hotspot, with the background consisting of the signal of anthropogenic and biogenic CO2 surface fluxes near to or far from the targeted source and the simulated satellite observation errors. The performance of the deep learning method is thereafter evaluated and compared with a plume segmentation technique based on thresholding in two contexts, namely (1) where the model is trained and tested on data from the same region and (2) where the model is trained and tested in two different regions. In both contexts, our method outperforms the usual segmentation technique based on thresholding and demonstrates its ability to generalise in various cases, with respect to city plumes, power plant plumes, and areas with multiple plumes. Although less accurate than in the first context, the ability of the algorithm to extrapolate on new geographical data is conclusive, paving the way to a promising universal segmentation model trained on a well-chosen sample of power plants and cities and able to detect the majority of the plumes from all of them. Finally, the highly accurate results for segmentation suggest the significant potential of convolutional neural networks for estimating local emissions from spaceborne imagery.

The hydrogenation step contributing to the high efficiencies (>25%) reached with poly-Si/SiOx passivated contacts solar cells is still poorly understood. In this study, Fourier transform infrared spectroscopy (FTIR) is used to follow the different bonding configurations of H during the fabrication process. The carrier lifetime degradation upon annealing is correlated to an important loss of Si–H bonds, from both the a­‑Si:H film and the SiOx interfaces. The subsequent hydrogenation step results in the formation of a small number of Si–H bonds near the crystalline silicon c-Si/SiOx interface, associated with the low stretching mode (LSM) and correlated to a significant lifetime improvement. These bonds feature a preferential orientation, as shown by polarized measurements.

We present cosmological constraints from the sample of Type Ia supernovae (SNe Ia) discovered and measured during the full 5 yr of the Dark Energy Survey (DES) SN program. In contrast to most previous cosmological samples, in which SNe are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being an SN Ia, we find 1635 DES SNe in the redshift range 0.10 < z < 1.13 that pass quality selection criteria sufficient to constrain cosmological parameters. This quintuples the number of high-quality z > 0.5 SNe compared to the previous leading compilation of Pantheon+ and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints, we combine the DES SN data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning 0.025 < z < 0.10. Using SN data alone and including systematic uncertainties, we find Ω _M = 0.352 ± 0.017 in flat ΛCDM. SN data alone now require acceleration ( q _0 < 0 in ΛCDM) with over 5 σ confidence. We find $({{\rm{\Omega }}}_{{\rm{M}}},w)=({0.264}_{-0.096}^{+0.074},-{0.80}_{-0.16}^{+0.14})$ in flat w CDM. For flat w _0 w _a CDM, we find $({{\rm{\Omega }}}_{{\rm{M}}},{w}_{0},{w}_{a})=({0.495}_{-0.043}^{+0.033},-{0.36}_{-0.30}^{+0.36},-{8.8}_{-4.5}^{+3.7})$ , consistent with a constant equation of state to within ∼2 σ . Including Planck cosmic microwave background, Sloan Digital Sky Survey baryon acoustic oscillation, and DES 3 × 2pt data gives (Ω _M , w ) = (0.321 ± 0.007, −0.941 ± 0.026). In all cases, dark energy is consistent with a cosmological constant to within ∼2 σ . Systematic errors on cosmological parameters are subdominant compared to statistical errors; these results thus pave the way for future photometrically classified SN analyses.

Retinitis pigmentosa (RP) is the most common inherited retinal disease (IRD) and is characterized by photoreceptor degeneration and progressive vision loss. We report 4 patients presenting with RP from 3 unrelated families with variants in TBC1D32, which to date has never been associated with an IRD. To validate TBC1D32 as a putative RP causative gene, we combined Xenopus in vivo approaches and human induced pluripotent stem cell–derived (iPSC-derived) retinal models. Our data showed that TBC1D32 was expressed during retinal development and that it played an important role in retinal pigment epithelium (RPE) differentiation. Furthermore, we identified a role for TBC1D32 in ciliogenesis of the RPE. We demonstrated elongated ciliary defects that resulted in disrupted apical tight junctions, loss of functionality (delayed retinoid cycling and altered secretion balance), and the onset of an epithelial-mesenchymal transition–like phenotype. Last, our results suggested photoreceptor differentiation defects, including connecting cilium anomalies, that resulted in impaired trafficking to the outer segment in cones and rods in TBC1D32 iPSC-derived retinal organoids. Overall, our data highlight a critical role for TBC1D32 in the retina and demonstrate that TBC1D32 mutations lead to RP. We thus identify TBC1D32 as an IRD-causative gene.

We present a joint cosmic shear analysis of the Dark Energy Survey (DES Y3) and the Kilo-Degree Survey (KiDS-1000) in a collaborative effort between the two survey teams. We find consistent cosmological parameter constraints between DES Y3 and KiDS-1000 which, when combined in a joint-survey analysis, constrain the parameter $S_8 = \sigma_8 \sqrt{\Omega_{\rm m}/0.3}$ with a mean value of $0.790^{+0.018}_{-0.014}$. The mean marginal is lower than the maximum a posteriori estimate, $S_8=0.801$, owing to skewness in the marginal distribution and projection effects in the multi-dimensional parameter space. Our results are consistent with $S_8$ constraints from observations of the cosmic microwave background by Planck, with agreement at the $1.7\sigma$ level. We use a Hybrid analysis pipeline, defined from a mock survey study quantifying the impact of the different analysis choices originally adopted by each survey team. We review intrinsic alignment models, baryon feedback mitigation strategies, priors, samplers and models of the non-linear matter power spectrum. Supplementary information: you can download the chains here or create your own chains with CosmoSIS here.

In the next few years, numerous satellites with high-resolution instruments dedicated to the imaging of atmospheric gaseous compounds will be launched, to finely monitor emissions of greenhouse gases and pollutants. Processing the resulting images of plumes from cities and industrial plants to infer the emissions of these sources can be challenging. In particular traditional atmospheric inversion techniques, relying on objective comparisons to simulations with atmospheric chemistry transport models, may poorly fit the observed plume due to modelling errors rather than due to uncertainties in the emissions. The present article discusses how these images can be adequately compared to simulated concentrations to limit the weight of modelling errors due to the meteorology used to analyse the images. For such comparisons, the usual pixel-wise ℒ2 norm may not be suitable, since it does not linearly penalise a displacement between two identical plumes. By definition, such a metric considers a displacement as an accumulation of significant local amplitude discrepancies. This is the so-called double penalty issue. To avoid this issue, we propose three solutions: (i) compensate for position error, due to a displacement, before the local comparison; (ii) use non-local metrics of density distribution comparison; and (iii) use a combination of the first two solutions. All the metrics are evaluated using first a catalogue of analytical plumes and then more realistic plumes simulated with a mesoscale Eulerian atmospheric transport model, with an emphasis on the sensitivity of the metrics to position error and the concentration values within the plumes. As expected, the metrics with the upstream correction are found to be less sensitive to position error in both analytical and realistic conditions. Furthermore, in realistic cases, we evaluate the weight of changes in the norm and the direction of the four-dimensional wind fields in our metric values. This comparison highlights the link between differences in the synoptic-scale winds direction and position error. Hence the contribution of the latter to our new metrics is reduced, thus limiting misinterpretation. Furthermore, the new metrics also avoid the double penalty issue.

The accident at the Fukushima Daiichi nuclear power plant (NPP) yielded massive and rapidly varying atmospheric radionuclide releases. The assessment of these releases and of the corresponding uncertainties can be performed using inverse modelling methods that combine an atmospheric transport model with a set of observations and have proven to be very effective for this type of problem. In the case of the Fukushima Daiichi NPP, a Bayesian inversion is particularly suitable because it allows errors to be modelled rigorously and a large number of observations of different natures to be assimilated at the same time. More specifically, one of the major sources of uncertainty in the source assessment of the Fukushima Daiichi NPP releases stems from the temporal representation of the source. To obtain a well-time-resolved estimate, we implement a sampling algorithm within a Bayesian framework – the reversible-jump Markov chain Monte Carlo – in order to retrieve the distributions of the magnitude of the Fukushima Daiichi NPP caesium 137 (137Cs) source as well as its temporal discretization. In addition, we develop Bayesian methods that allow us to combine air concentration and deposition measurements as well as to assess the spatio-temporal information of the air concentration observations in the definition of the observation error matrix. These methods are applied to the reconstruction of the posterior distributions of the magnitude and temporal evolution of the 137Cs release. They yield a source estimate between 11 and 24 March as well as an assessment of the uncertainties associated with the observations, the model, and the source estimate. The total reconstructed release activity is estimated to be between 10 and 20 PBq, although it increases when the deposition measurements are taken into account. Finally, the variable discretization of the source term yields an almost hourly profile over certain intervals of high temporal variability, signalling identifiable portions of the source term.

This article presents a pulse radio transmission system operating in the millimeter-wave regime for simultaneous binary information transmission and location in an intrabuilding environment. In particular, the location solution mutualizes the hardware architectures of three sensors of known position to extract the location information of a sensor entering their vicinity and, thus, uses the radio communication signals to extract this location information. Experimental validation considering the 60-GHz transceiver designed and realized on pseudomorphic high electron mobility transistor (pHEMT) technology is exemplary shown with localization error quantified to 16 cm.

Ensemble variational methods form the basis of the state of the art for nonlinear, scalable data assimilation, yet current designs may not be cost-effective for real-time, short-range forecast systems. We propose a novel estimator in this formalism that is designed for applications in which forecast error dynamics is weakly nonlinear, such as synoptic-scale meteorology. Our method combines the 3D sequential filter analysis and retrospective reanalysis of the classic ensemble Kalman smoother with an iterative ensemble simulation of 4D smoothers. To rigorously derive and contextualize our method, we review related ensemble smoothers in a Bayesian maximum a posteriori narrative. We then develop and intercompare these schemes in the open-source Julia package DataAssimilationBenchmarks.jl, with pseudo-code provided for their implementations. This numerical framework, supporting our mathematical results, produces extensive benchmarks demonstrating the significant performance advantages of our proposed technique. Particularly, our single-iteration ensemble Kalman smoother (SIEnKS) is shown to improve prediction/analysis accuracy and to simultaneously reduce the leading-order computational cost of iterative smoothing in a variety of test cases relevant for short-range forecasting. This long work presents our novel SIEnKS and provides a theoretical and computational framework for the further development of ensemble variational Kalman filters and smoothers.

Abstract Recent studies have demonstrated that it is possible to combine machine learning with data assimilation to reconstruct the dynamics of a physical model partially and imperfectly observed. The surrogate model can be defined as an hybrid combination where a physical model based on prior knowledge is enhanced with a statistical model estimated by a neural network (NN). The training of the NN is typically done offline, once a large enough data set of model state estimates is available. By contrast, with online approaches the surrogate model is improved each time a new system state estimate is computed. Online approaches naturally fit the sequential framework encountered in geosciences where new observations become available with time. In a recent methodology paper, we have developed a new weak‐constraint 4D‐Var formulation which can be used to train a NN for online model error correction. In the present article, we develop a simplified version of that method, in the incremental 4D‐Var framework adopted by most operational weather centers. The simplified method is implemented in the European Center for Medium‐Range Weather Forecasts (ECMWF) Object‐Oriented Prediction System, with the help of a newly developed Fortran NN library, and tested with a two‐layer two‐dimensional quasi geostrophic model. The results confirm that online learning is effective and yields a more accurate model error correction than offline learning. Finally, the simplified method is compatible with future applications to state‐of‐the‐art models such as the ECMWF Integrated Forecasting System.

Abstract The synthesis of new graphene‐based quantum materials by intercalation is an auspicious approach. However, an accompanying proximity coupling depends crucially on the structural details of the new heterostructure. It is studied in detail the Pb monolayer structure after intercalation into the graphene buffer layer on the SiC(0001) interface by means of photoelectron spectroscopy, x‐ray standing waves, and scanning tunneling microscopy. A coherent fraction close to unity proves the formation of a flat Pb monolayer on the SiC surface. An interlayer distance of 3.67 Å to the suspended graphene underlines the formation of a truly van der Waals heterostructure. The 2D Pb layer reveals a quasi ten‐fold periodicity due to the formation of a grain boundary network, ensuring the saturation of the Si surface bonds. Moreover, the densely‐packed Pb layer also efficiently minimizes the doping influence by the SiC substrate, both from the surface dangling bonds and the SiC surface polarization, giving rise to charge‐neutral monolayer graphene. The observation of a long‐ranged (3×3) reconstruction on the graphene lattice at tunneling conditions close to Fermi energy is most likely a result of a nesting condition to be perfectly fulfilled.

IntroductionChildhood chronic diseases affect family functioning and well-being. The aim of this study was to measure the impact of caring for a child with PUV, and the factors that most impact the burden of care.Patients and methodWe gave a questionnaire on the familial impact of having a child with posterior urethral valves to all parents of a child included in the CIRCUP trial from 2015 onwards. The questionnaire included questions about the parents' demographics, health, professional, financial and marital status and how these evolved since the child's birth as well as the “impact on family scale” (IOFS), which gives a total score ranging from 15 (no impact) to 60 (maximum impact). We then analyzed both the results of the specific demographic questions as well as the factors which influenced the IOFS score.ResultsWe retrieved answers for 38/51 families (74.5% response rate). The average IOFS score was 23.7 (15–51). We observed that the child's creatinine level had an effect on the IOFS score (p = 0.02), as did the parent's gender (p = 0.008), health status (p = 0.015), being limited in activity since the birth of the child (p = 0.020), being penalized in one's job (p = 0.009), being supported in one's job (p = 0.002), and decreased income (p = 0.004). Out of 38 mother/father binomials, 8/33 (24.2%) declared that they were no longer in the same relationship afterwards.ConclusionIn conclusion, having a boy with PUV significantly impacts families. The risk of parental separation and decrease in revenue is significant. Strategies aiming to decrease these factors should be put in place as soon as possible.

The intercalation of graphene with suitable atomic species is one of the most frequently applied methods to decouple the graphene layer from the substrate in order to establish the classical electronic properties of graphene. In this context, we studied the bismuth (Bi) intercalation of the $\left(6\sqrt{3}\times6\sqrt{3}\right)R30^\circ$ reconstructed so-called ‘zeroth layer graphene’ on SiC $\left(0001\right)$ . As reported earlier by Sohn et al (2021 J. Korean Phys. Soc. 78 157) two phases are formed depending on the amount of intercalated Bi, which in turn is controlled by the annealing temperature: The α phase, showing a $(1\times1)$ periodicity with respect to the substrate, and, at higher temperatures, the $(\sqrt{3}\times\sqrt{3})$ reconstructed β phase. We characterise both phases and the transformation from the α to the β phase by photoelectron spectroscopy, normal incidence x-ray standing waves, electron diffraction and electron microscopy. We clearly see an almost complete intercalation of the graphene layers in both phases, with strong (covalent) interaction between the topmost Si atoms of the substrate and the Bi intercalant, but only weak (van der Waals) interaction between Bi and the graphene layer. The n-doping of the graphene found for the α phase decreases continuously during the phase transformation, in agreement with a reduced density of the Bi intercalating layer. Missing core level shifts of the surface species as well as the normal incidence x-ray standing waves results indicate that all surface Si atoms remain saturated during the transition and no dangling bonds are formed. Low energy electron microscopy and diffraction reveal the coexistance of both phases after annealing to intermediate temperatures and allow a quantitative analysis of island sizes and numbers.

The honey bee Apis mellifera is exposed to a variety of biotic and abiotic stressors, such as the highly prevalent microsporidian parasite Nosema (Vairimorpha) ceranae and neonicotinoid insecticides. Both can affect honey bee physiology and microbial gut communities, eventually reducing its lifespan. They can also have a combined effect on the insect’s survival. The use of bacterial probiotics has been proposed to improve honey bee health, but their beneficial effect remains an open question. In the present study, western honey bees were experimentally infected with N. ceranae spores, chronically exposed to the neonicotinoid thiamethoxam, and/or supplied daily with the homofermentative bacterium Pediococcus acidilactici MA18/5M thought to improve the honey bees’ tolerance to the parasite. Deep shotgun metagenomic sequencing allowed the response of the gut microbiota to be investigated with a taxonomic resolution at the species level. All treatments induced significant changes in honey bee gut bacterial communities. Nosema ceranae infection increased the abundance of Proteus mirabilis, Frischella perrara, and Gilliamella apicola and reduced the abundance of Bifidobacterium asteroides, Fructobacillus fructosus, and Lactobacillus spp. Supplementation with P. acidilactici overturned some of these alterations, bringing back the abundance of some altered species close to the relative abundance found in the controls. Surprisingly, the exposure to thiamethoxam also restored the relative abundance of some species modulated by N. ceranae. This study shows that stressors and probiotics may have an antagonistic impact on honey bee gut bacterial communities and that P. acidilactici may have a protective effect against the dysbiosis induced by an infection with N. ceranae.

We address the problem of optimally identifying all kilonovae detected via gravitational-wave emission in the upcoming LIGO/Virgo/KAGRA observing run, O4, which is expected to be sensitive to a factor of ∼7 more binary neutron star (BNS) alerts than previously. Electromagnetic follow-up of all but the brightest of these new events will require >1 m telescopes, for which limited time is available. We present an optimized observing strategy for the DECam during O4. We base our study on simulations of gravitational-wave events expected for O4 and wide-prior kilonova simulations. We derive the detectabilities of events for realistic observing conditions. We optimize our strategy for confirming a kilonova while minimizing telescope time. For a wide range of kilonova parameters, corresponding to a fainter kilonova compared to GW170817/AT 2017gfo, we find that, with this optimal strategy, the discovery probability for electromagnetic counterparts with the DECam is ∼80% at the nominal BNS gravitational-wave detection limit for O4 (190 Mpc), which corresponds to an ∼30% improvement compared to the strategy adopted during the previous observing run. For more distant events (∼330 Mpc), we reach an ∼60% probability of detection, a factor of ∼2 increase. For a brighter kilonova model dominated by the blue component that reproduces the observations of GW170817/AT 2017gfo, we find that we can reach ∼90% probability of detection out to 330 Mpc, representing an increase of ∼20%, while also reducing the total telescope time required to follow up events by ∼20%.