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Accurate simulation of the quasi‐biennial oscillation (QBO) is challenging due to uncertainties in representing convectively generated gravity waves. We develop an end‐to‐end uncertainty quantification workflow that calibrates these gravity wave processes in E3SM for a realistic QBO. Central to our approach is a domain knowledge‐informed, compressed representation of high‐dimensional spatio‐temporal wind fields. By employing a parsimonious statistical model that learns the fundamental frequency from complex observations, we extract interpretable and physically meaningful quantities capturing key attributes. Building on this, we train a probabilistic surrogate model that approximates the fundamental characteristics of the QBO as functions of critical physics parameters governing gravity wave generation. Leveraging the Karhunen–Loève decomposition, our surrogate efficiently represents these characteristics as a set of orthogonal features, capturing cross‐correlations among multiple physics quantities evaluated at different pressure levels and enabling rapid surrogate‐based inference at a fraction of the computational cost of full‐scale simulations. Finally, we analyze the inverse problem using a multi‐objective approach. Our study reveals a tension between amplitude and period that constrains the QBO representation, precluding a single optimal solution. To navigate this, we quantify the bi‐criteria trade‐off and generate a set of Pareto optimal parameter values that balance the conflicting objectives. This integrated workflow improves the fidelity of QBO simulations and offers a versatile template for uncertainty quantification in complex geophysical models. Plain Language Summary Simulating the quasi‐biennial oscillation (QBO), a regular pattern of alternating winds high in the atmosphere, remains a major challenge for climate models. We developed an end‐to‐end workflow to calibrate gravity wave processes in the Energy Exascale Earth System Model, leading to more realistic simulations. We began by compressing complex spatio‐temporal data into a few key, physically meaningful quantities, such as the oscillation's amplitude and period. This data reduction allowed us to isolate the QBO signal from noise and other atmospheric phenomena. Next, we built a fast statistical model that predicts QBO behavior based on critical physics parameters. This surrogate efficiently captures relationships among various atmospheric features, reducing the need for computationally expensive full‐scale simulations. Our analysis revealed a trade‐off between QBO amplitude and period, meaning that improving one aspect often worsened the other. Rather than finding a single perfect solution, we identified a range of balanced settings that offer the best compromise. This integrated approach not only leads to more realistic QBO simulation but also provides a practical framework for tuning other complex atmospheric phenomena. Key Points We developed an end‐to‐end workflow that calibrates gravity wave generation in E3SMv3, improving quasi‐biennial oscillation (QBO) realism The fundamental frequency model compressed wind field data into physically interpretable quantities, isolated the QBO signal, and reduced dimensionality while retaining key QBO variability Our workflow reveals no single optimal configuration for QBO realism, but a frontier of best‐compromise solutions

Process-based agroecosystem models are powerful tools to assess performance of managed landscapes, but their ability to accurately represent reality is limited by the types of input data they can use. Ensuring these models can represent cropping field heterogeneity and environmental impact is important, especially given the growing interest in using agroecosystem models to quantify ecosystem services from best management practices and land use change. We posited that augmenting process-based agroecosystem models with additional field-specific information such as topography, hydrologic processes, or independent indicators of yield could help limit simulation artifacts that obscure mechanisms driving observed variations. To test this, we augmented the agroecosystem model Agricultural Production Systems Simulator (APSIM) with field-specific topography and satellite imagery in a simulation framework we call Foresite. We used Foresite to optimize APSIM yield predictions to match those created from a machine learning model built on remotely sensed indicators of hydrology and plant productivity. Using these improved subfield yield predictions to guide APSIM optimization, total N O 3 − N loss estimates increased by 39% in maize and 20% in soybeans when summed across all years. In addition, we found a disproportionate total amount of leaching in the lowest yielding field areas vs the highest yielding areas in maize (42% vs 15%) and a similar effect in soybeans (31% vs 20%). Overall, we found that augmenting process-based models with now-common subfield remotely sensed data significantly increased values of predicted nutrient loss from fields, indicating opportunities to improve field-scale agroecosystem simulations, particularly if used to calculate nutrient credits in ecosystem service markets.

We introduce the novel automatic dynamic relevance determination (ADRD) framework for Gaussian process regression with functional inputs, an adaptation of automatic relevance determination (ARD) priors for vector inputs. In this framework, relevance varies smoothly over the input index space resulting in smooth and parsimonious relevance profiles learned from data whose posterior can be inspected for scientific interpretation and used in downstream analyses. An ADRD model requires us to specify a weight function form that is appropriate for a given application. We explore two strategies to design the weights, namely setting up a parametric form and generating them via a basis expansion. First, we introduce the asymmetric double and squared exponential weight functions for unimodal, smoothly decaying predictive relevance profiles. Second, we present a general form for the basis expansion of the weights and explore, specifically, the Fourier, B-spline, and adaptive spline expansions. We establish an equivalence between the ADRD and ARD weights and propose an adaptation to permutation feature importance. Both motivate different exploratory tools to elicit a weight function form from data. We also discuss a fully Bayesian estimation framework via MCMC, including a set of weakly informative priors for the model parameters, as well as statistics for model validation. In two simulation studies, we show that a well specified model is able to recover the true weight function. Moreover, we present two applications to scientific data generated by an atmospheric radiative transfer computer model and a soil erosion computer model. We show empirically that, compared to ARD, ADRD generates smoother weight patterns and produces information useful for scientific interpretation and downstream analyses with a drastic reduction in the number of model parameters without compromising on prediction accuracy.

Yield monitor datasets are known to contain a high percentage of unreliable records. The current tool set is mostly limited to observation cleaning procedures based on heuristic or empirically-motivated statistical rules for extreme value identification and removal. We propose a constructive algorithm for handling well-documented yield monitor data artifacts without resorting to data deletion. The four-step Rectangle creation, Intersection assignment and Tessellation, Apportioning, and Smoothing (RITAS) algorithm models sample observations as overlapping, unequally-shaped, irregularly-sized, time-ordered, areal spatial units to better replicate the nature of the destructive sampling process. Positional data is used to create rectangular areal spatial units. Time-ordered intersecting area tessellation and harvested mass apportioning generate regularly-shaped and -sized polygons partitioning the entire harvested area. Finally, smoothing via a Gaussian process is used to provide map users with spatial-trend visualization. The intermediate steps as well as the algorithm output are illustrated in maize and soybean grain yield maps for five years of yield monitor data collected at a research agricultural site located in the US Fish and Wildlife Service Neal Smith National Wildlife Refuge.

In the context of Gaussian process regression with functional inputs, it is common to treat the input as a vector. The parameter space becomes prohibitively complex as the number of functional points increases, effectively becoming a hindrance for automatic relevance determination in high-dimensional problems. Generalizing a framework for time-varying inputs, we introduce the asymmetric Laplace functional weight (ALF): a flexible, parametric function that drives predictive relevance over the index space. Automatic dynamic relevance determination (ADRD) is achieved with three unknowns per input variable and enforces smoothness over the index space. Additionally, we discuss a screening technique to assess under complete absence of prior and model information whether ADRD is reasonably consistent with the data. Such tool may serve for exploratory analyses and model diagnostics. ADRD is applied to remote sensing data and predictions are generated in response to atmospheric functional inputs. Fully Bayesian estimation is carried out to identify relevant regions of the functional input space. Validation is performed to benchmark against traditional vector-input model specifications. We find that ADRD outperforms models with input dimension reduction via functional principal component analysis. Furthermore, the predictive power is comparable to high-dimensional models, in terms of both mean prediction and uncertainty, with 10 times fewer tuning parameters. Enforcing smoothness on the predictive relevance profile rules out erratic patterns associated with vector-input models.

O presente trabalho analisa o turismo em espaço rural na região do Baixo Alentejo, focalizando o estudo da oferta de alojamento, assim como os meios de distribuição e de comunicação das unidades de alojamento e nas parcerias existentes.A primeira parte do estudo consiste numa abordagem teórica, apoiada por uma revisão bibliográfica, sobre turismo em áreas rurais e o seu potencial enquanto fator de desenvolvimento. No estudo aborda-se também a especificidade do marketing de produtos turísticos, dando especial ênfase aos meios de distribuição e de comunicação, salientando a importância das novas tecnologias e das parcerias existentes.Este trabalho apoia-se também num estudo empírico, baseado num questionário, dirigido aos agentes da oferta, responsáveis pelos empreendimentos de turismo de habitação e de turismo no espaço rural da região do Baixo Alentejo, que visa caracterizar a oferta existente, assim como o contexto de utilização dos meios de distribuição e de comunicação por parte destas unidades de alojamento. Para este efeito foram estudadas dezasseis unidades de alojamento.Os resultados mostraram que estas unidades recorrem essencialmente ao marketing direto e possuem um baixo nível de associativismo.

Particulate matter with aerodynamic diameters lower than 10 µm, (PM10) aerosol samples were collected during summer 2013 within the framework of the Chemistry and Aerosol Mediterranean Experiment (ChArMEx) at two sites located north (Capo Granitola) and south (Lampedusa Island), respectively, of the main Mediterranean shipping route in the Straight of Sicily. The PM10 samples were collected with 12 h time resolutions at both sites. Selected metals, main anions, cations and elemental and organic carbon were determined. The evolution of soluble V and Ni concentrations (typical markers of heavy fuel oil combustion) was related to meteorology and ship traffic intensity in the Straight of Sicily, using a high-resolution regional model for calculation of back trajectories. Elevated concentration of V and Ni at Capo Granitola and Lampedusa are found to correspond with air masses from the Straight of Sicily and coincidences between trajectories and positions of large ships; the vertical structure of the planetary boundary layer also appears to play a role, with high V values associated with strong inversions and a stable boundary layer. The V concentration was generally lower at Lampedusa than at Capo Granitola V, where it reached a peak value of 40 ng m−3. Concentrations of rare earth elements (REEs), La and Ce in particular, were used to identify possible contributions from refineries, whose emissions are also characterized by elevated V and Ni amounts; refinery emissions are expected to display high La ∕ Ce and La ∕ V ratios due to the use of La in the fluid catalytic converter systems. In general, low La ∕ Ce and La ∕ V ratios were observed in the PM samples. The combination of the analyses based on chemical markers, air mass trajectories and ship routes allows us to unambiguously identify the large role of the ship source in the Straight of Sicily. Based on the sampled aerosols, ratios of the main aerosol species arising from ship emission with respect to V were estimated with the aim of deriving a lower limit for the total ship contribution to PM10. The estimated minimum ship emission contributions to PM10 were 2.0 µg m−3 at Lampedusa and 3.0 µg m−3 at Capo Granitola, corresponding with 11 and 8.6 % of PM10, respectively.

The coronavirus disease COVID-19 constitutes the most severe pandemic of the last decades having caused more than 1 million deaths worldwide. The SARS-CoV-2 virus recognizes the angiotensin converting enzyme 2 (ACE2) on the surface of human cells through its spike protein. It has been reported that the coronavirus can mildly infect cats, and ferrets, and perhaps dogs while not pigs, mice, chicken and ducks. Differences in viral infectivity among different species or individuals could be due to amino acid differences at key positions of the host proteins that interact with the virus, the immune response, expression levels of host proteins and translation efficiency of the viral proteins among other factors. Here, first we have addressed the importance that sequence variants of different animal species, human individuals and virus isolates have on the interaction between the RBD domain of the SARS-CoV-2 spike S protein and human angiotensin converting enzyme 2 (ACE2). Second, we have looked at viral translation efficiency by using the tRNA adaptation index. We find that integration of both interaction energy with ACE2 and translational efficiency explains animal infectivity. Humans are the top species in which SARS-CoV-2 is both efficiently translated as well as optimally interacting with ACE2. We have found some viral mutations that increase affinity for hACE and some hACE2 variants affecting ACE2 stability and virus binding. These variants suggest that different sensitivities to coronavirus infection in humans could arise in some cases from allelic variability affecting ACE2 stability and virus binding.

BackgroundAcute kidney injury (AKI) often complicates percutaneous coronary intervention (PCI) and coronary artery bypass graft (CABG). However, evidence on the incidence and prognostic impact of AKI after revascularisation for left main coronary artery disease (LMCAD) is scant, especially in terms of subsequent risk of persistent renal dysfunction (RD).MethodsAll consecutive patients undergoing PCI or CABG for LMCAD in two European institutions from 2015 to 2022 were enrolled. The coprimary endpoints were AKI, defined as an increase in serum creatinine (sCr) levels ≥0.3 mg/dL or increase by >50% as compared with baseline levels, and persistent RD, defined as a persistent increase of sCr at 1 year. The secondary endpoint was all-cause mortality. The risk of AKI with PCI versus CABG was assessed with multivariable logistic regression and inverse probability of treatment weighting (IPTW). The prognostic impact of transient and persistent RD at 1 year was evaluated with Cox regression analysis.Results1047 patients were included (PCI: 617, CABG: 430). Patients undergoing PCI were older, more often male and affected by chronic kidney disease. AKI occurred in 17% and 28% of patients after PCI and CABG, respectively (adjusted OR 2.82; 95% CI 1.89 to 4.21). Consistent findings were observed after IPTW. AKI was associated with increased 1-year risk of all-cause death, irrespective of revascularisation strategy, but only persistent RD (HR 9.56; 95% CI 4.06 to 22.53) worsened patients’ prognosis, unlike AKI with only transient RD (HR 0.65; 95% CI 0.08 to 5.04).ConclusionsAKI is common after LMCAD revascularisation and occurred more frequently following CABG than PCI. AKI has a substantial prognostic impact irrespective of revascularisation modality, but only when resulting in persistent RD.

Proteins are often referred to as the workhorses of cells, and their interactions are necessary to facilitate specific cellular functions. Despite the recognition that protein-protein interactions, and thus protein functions, are determined by proteoform states, such as mutations and post-translational modifications (PTMs), methods for determining the differential abundance of proteoforms across conditions are very limited. Classically, immunoprecipitation coupled with mass spectrometry (IP-MS) has been used to understand how the interactome (preys) of a given protein (bait) changes between conditions to elicit specific cellular functions. Reversing this concept, we present here a new workflow for IP-MS data analysis that focuses on identifying the differential peptidoforms of the bait protein between conditions. This method can provide detailed information about specific bait proteoforms, potentially revealing pathogenic protein states that can be exploited for the development of targeted therapies.