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OBJECTIVE: A subset of patients with atrial fibrillation (AF) presents without established AF risk factors and normal left ventricular (LV) systolic function, called idiopathic AF (IAF). Traditionally, echocardiography derived LV dimensions and ejection fraction (EF) are used to exclude LV dysfunction in IAF, but their sensitivity is limited. Our objective is to evaluate the presence of subtle alterations in LV function despite normal LVEF in patients with IAF compared to healthy controls, using speckle-tracking echocardiography (STE) based global longitudinal strain (GLS).METHODS: Standard transthoracic echocardiography was performed in 80 patients with IAF and 129 healthy controls. Patients with overt cardiac disease as well as known established AF risk factors were excluded. STE analysis was performed to assess GLS of the LV, and left atrial strain (LAS).RESULTS: LVEF was normal and comparable between patients with IAF and healthy controls (63 ± 4% for both groups; p = 0.801). Mean GLS was within normal limits for both groups but statistically significantly more negative in patients with IAF (-20.6 ± 2.5% vs. -19.7 ± 2.5%; p = 0.016), however not when indexed for ventricular cycle length (p = 0.784). No differences in LA volume or non-indexed LAS were seen in patients with IAF compared to healthy controls.CONCLUSIONS: In this selected group of IAF patients, STE did not detect any overt LV or LA dysfunction compared to healthy controls. Thus, IAF occurred in these patients not only in the absence of established AF risk factors but also without evidence of ventricular or atrial dysfunction.

Delta-8-tetrahydrocannabinol (Δ8-THC) has emerged as a new retail cannabinoid product in the U.S. This study queried Δ8-THC users about product use characteristics and self-reported drug effects. Participants were recruited via a large online crowdsourcing platform (Amazon Mechanical Turk). Adults (

The key features of patients with a microduplication 5q35.2q35.3 (including the NSD1 gene) are short stature, microcephaly, mild developmental delay, behavioral problems, digital anomalies and congenital anomalies of internal organs. This core phenotype can be viewed as the reversed phenotype of Sotos syndrome, which is caused by a microdeletion in the same chromosomal region or a pathogenic variant in the NSD1 gene, and includes tall stature and macrocephaly, developmental delay, and epilepsy. Here, we report on a patient and his mother, both with a 5q35.2q35.3 duplication, adding a fifth family to the recently published overview of 39 patients of Quintero-Rivera et al. Our patient had several congenital anomalies, intrauterine growth restriction with a persisting short stature, while his mother was only mildly affected with decreased growth parameters. In addition, he had hemophagogocytic lymphohistiocytosis (HLH) triggered by Haemophilus influenzae and was recently diagnosed with Ewing sarcoma. Our cases carry the smallest duplication published (ca 332 kb, arr[hg19] 5q35.2q35.3(176493106-176824785)x3) further narrowing the distal side of the critical region of the 5q35.2q35.3 duplication. Besides broadening the clinical phenotypic spectrum, our report indicates that the 5q35.2q35.3 microduplication also shows a large intra-familial variability and expression.

Successful management of opioid withdrawal improves long-term treatment outcomes and reduces opioid use-related morbidity and mortality. Mechanistically supported pharmacotherapeutic approaches are needed to effectively manage acute and protracted opioid withdrawal. Buspirone is a D2 antagonist and 5-HT1a agonist that may decrease opioid withdrawal. Individuals (

Aim The Enhanced Recovery After Surgery (ERAS® ) Society guidelines aim to standardise perioperative care in colorectal surgery via 25 principles. We aimed to assess the variation in uptake of these principles across an international network of colorectal units. Method An online survey was circulated amongst European Society of Coloproctology members in 2019/20. For each ERAS® principle, respondents were asked to score how frequently the principle was implemented in their hospital, from 1 ('rarely') to 4 ('always'). Respondents were also asked to recall whether practice had changed since 2017. Subgroup analyses based on hospital characteristics were conducted. Results Of hospitals approached, 58% responded to the survey (195/335), with 296 individual responses (multiple responses were received from some hospitals). The majority were European (163/195 [83.6%]). Overall, respondents indicated they 'most often' or 'always' adhered to most individual ERAS® principles (18/25 [72%]). Variability in uptake of principles was reported, with universal uptake of some principles (e.g., prophylactic antibiotics; early mobilisation) and inconsistency from 'rarely' to 'always' in others (e.g., no nasogastric intubation; no preoperative fasting and carbohydrate drinks). In alignment with 2018 ERAS® guideline updates, adherence to principles for prehabilitation, managing anaemia, and postoperative nutrition appears to have increased since 2017. Conclusions Uptake of ERAS® principles varied across hospitals, and not all 25 principles were equally adhered to. Whilst some principles exhibited a high level of acceptance, others had a wide variability in uptake indicative of controversy or barriers to uptake. Further research into specific principles is required to improve ERAS® implementation.

Funding Acknowledgements Type of funding sources: None. Background Heart failure with preserved ejection fraction (HFpEF) frequently coexists with atrial fibrillation (AF). The gold-standard test for HFpEF diagnosis remains invasive assessment of left ventricular end-diastolic pressures but noninvasive diagnostic scoring systems have been developed to aid accurate diagnosis. However, the diagnosis of HFpEF in patients with AF is more difficult because some diagnostic criteria for HFpEF may be associated with AF instead of HFpEF per se. Purpose To compare the guidelines and scoring systems for HFpEF diagnosis in patients with AF, and to indicate the most relevant clinical and echocardiographic parameters supporting the diagnosis of HFpEF in an AF cohort. Methods Consecutive patients with paroxysmal or persistent AF scheduled for catheter ablation were included. Clinical characteristics and transthoracic echocardiography examination was collected at baseline visit. The European Society of Cardiology (ESC) guidelines from 2016 and 2021 as well as scoring systems (HFA-PEFF and H2FPEF scores) were used to diagnose HFpEF. In the H2FPEF score, patients with AF alone, score of 3, were assessed as group with the low risk for HFpEF (instead of intermediate risk according to the H2FPEF score), as all enrolled patients had AF. Multiple logistic regression was performed to find optimal parameters predicting HFpEF, that was defined as diagnosis of HFpEF according to all following scores: ESC guidelines from 2016 and 2021 and high risk in HF2FPEF and HF-PEFF scores. Results We analysed 325 patients (median age 65 [59-72], 36% women). Considerable differences were observed in HFpEF diagnoses when stratifying patients according to ESC 2016 and ESC 2021 guideline definitions; the percentage of HFpEF patients reduced from 55% (n=180) to 28% (n=90). The agreement of HF2PEF score relative to HFA-PEFF score was 46% (n=25/55), 54% (n=115/213) and 35% (n=20/57) of patients regarding low, intermediate and high risk for HFpEF, respectively. Overall, 5.8% of patients (n=19) had HFpEF (Figure 1). These 19 patients were older (median age 70 vs 65, p Conclusions The differences in HFpEF definition in the guidelines and various scoring systems lead to considerable difference in stratification of HFpEF in AF patients. In our population, just 5.8% of AF patients were diagnosed with HFpEF regarding ESC guidelines and high risk for HFpEF in scoring systems.

Funding Acknowledgements Type of funding sources: None. Background Catheter ablation (CA) has been established as an effective therapeutic option to achieve rhythm control in patients with atrial fibrillation (AF). However, AF recurrence occurs in approximately 45% of the patients undergoing CA. The association between AF recurrence after CA and left atrial (LA) strain, which is a surrogate marker of LA mechanical dysfunction already appearing before the enlargement of the LA, remains less clear. Aim The purpose of this study was to evaluate the association between LA function assessed by strain echocardiography and AF recurrence after CA. Methods This sub-study of ISOLATION (NCT04342312), involved consecutive patients with paroxysmal or persistent AF scheduled for first cryoballoon or radiofrequency CA. Clinical characteristics and echocardiography examination were collected at baseline visit before CA. Patients were followed for 12 months. Follow-up consisted of outpatient visits and heart rhythm monitoring (48 day Holter, 12-lead electrocardiogram, 7 day photoplethysmography-based rhythm app) at 3, 6, and 12 months. Results We analyzed 312 patients (median age 65, 36% women). More than half (52%; n=162) underwent radiofrequency CA and 84 (27 %) had AF recurrence within one year follow-up. No statistically significant differences in baseline characteristic were observed between patients with and without recurrent AF, expect higher prevalence of persistent AF prior to CA in patients with recurrence of AF (75% vs. 46%, p Conclusions LA function assessed by LA strain is associated with AF recurrence after CA, even in patients with non-dilated LA. Whether integration of results derived from systematic preprocedural LA strain analysis into patients’ selection strategies may help to identify patients profiting from CA required further study.

Coastal deltas are extremely susceptible to flooding from sea, rivers, heavy rain and even more severe combinations thereof. Many coastal deltas are densely populated, and flood risk forms a serious threat that will likely increase in the future. There are two main mechanisms to reduce the devastating impacts of these floods; (1) adaptation to the increasing climate risks and (2) improved early warning and emergency response. We will present the Destination Earth digital twin on coastal compound flood inundation forecasting and climate adaptation.This digital replica of the delta will connect DE data to impact models and will generate user oriented actionable output. High-resolution meteorological forecasts and regular updated climate scenarios form the input for regional coastal and inland models that provide boundary conditions for local models. Local models are used for simulation of small-scale hydrological processes, high-resolution 2D compound flood inundation and impact on population and buildings. A flexible data post-processing routine, to be co-designed with the end-users, will be implemented to translates model results into actionable context-specific information. It will, for example, produce flood inundation and risk maps as well as other user-requested indicators. For the climate adaptation component, the system will allow users to run WhatIf scenarios to explore adaptation measures. The data products will be disseminated to the users via Web-API, WEB-mapping service or through (S)FTP, depending on the users’ preferences.We will outline and demonstrate our digital twin that serves both flood impact reduction mechanisms, i.e. climate adaptation and forecasting extremes. We will highlight end users’ needs and requirement that are involved through co-creation/design of the service.

Anthropogenic impact on groundwater storage depletion has been detected in many places from catchment to global scales, including in our study area, the Bandung groundwater basin, Indonesia. Groundwater abstraction of various magnitudes, pumped out from numerous depths of aquifers, stimulates different changes in hydraulic heads among vertical subsurface stratifications. Such circumstances generate groundwater movement, where it flows from the upper layer storage to underlying aquifers, referred to as aquifer interaction in this study. Meanwhile, remote-sensing products such as GRACE measure the integrated water storage changes over depth and space, making it difficult to capture and derive the storage internal vertical groundwater fluxes from such signals. As an alternative, environmental water tracers (EWT) have been used to investigate subsurface water movement and to gain a conceptual understanding of groundwater flow dynamics. However, quantitative measurement of the rates of fluxes is not often possible, despite being essential to ensure sustainable groundwater resources management.In this study, we utilize (a) groundwater flow modeling in conjunction with (b) EWT data to quantify the aquifer interaction driven by multi-layer groundwater abstraction in the Bandung groundwater basin, Indonesia. The available environmental water tracers data include major ion elements (Na+/K+, Ca2+, Mg2+, Cl-, So24-, HCO3-), stable isotope data (d2H and d18O), and groundwater age estimates (radiocarbon/14C content). These measurements are used to qualitatively evaluate the numerical groundwater flow model. The model, forced by recharge calculated using the hydrological model of wflow_sbm, is calibrated by minimizing the difference between the dynamic steady-state simulated and the observed groundwater levels.We evaluate the groundwater flow model and the EWT-driven analysis from multiple perspectives. The results suggest that the groundwater recharge is uniformly distributed spatially, the groundwater is flowing regionally from the basin periphery inward to the basin’s center following the topographical distribution, and vertical groundwater fluxes are identified. All three deductions, in a qualitative sense, are agreed upon by both the EWT observations and the groundwater flow model. From the groundwater flow model, we quantify that the aquifer interaction is equivalent to, on average, 0.110 m/year, which is highly significant compared to the other groundwater budgets. We also determine the unconfined aquifer storage volume decrease, calculated from the change in the groundwater table, that results in an average declining rate of 51 Mm3/year. This number shows that the upper aquifer storage is dwindling at a rate that is disproportionate to its groundwater abstraction, hugely influenced by the aquifer interaction. The storage lost from only this partition contributes up to 60.3% of the total groundwater storage lost, despite contributing to only 32.3% of the groundwater abstraction. Additionally, we also investigate and examine the correlation between the groundwater level changes and the groundwater abstraction zones. The results of our study confirm that quantification of the aquifer interaction and groundwater level change dynamics driven by multi-layer groundwater abstraction in multi-layer hydrogeological settings is possible by our proposed methods. Applying such methods will assist in deriving basin-scale groundwater policies and management strategies under the changing anthropogenic and climatic factors, thereby ensuring sustainable groundwater management.

Estuarine salt intrusion causes problems with freshwater availability in many deltas. For water managers to mitigate and adapt to salt intrusion, they require timely and accurate forecasts. Data-driven models derived with machine learning can help with this, as they can mimic complex non-linear systems and are computationally very efficient. We set up such a model for salt intrusion in the Rhine-Meuse delta. The model predicts chloride concentrations at Krimpen aan den IJssel, an important location for freshwater provision. As input features, we selected observations of water level, discharge, chloride concentration and wind speed. We then used the Boruta algorithm to select a subset of relevant features. We set up a Long Short-Term Memory network (LSTM) to make predictions of chloride concentrations one day ahead and ran the resulting model multiple times to simulate a multi-day forecast. This model predicts baseline concentrations and peak timing well, but peak height is underestimated, a problem that gets worse with increasing lead time. Because this model is reasonably successful, we aim to extend it to other locations in the delta. We also expect a similar setup can work in other deltas, especially those with a similar or simpler geometry. A more complete version of this model should finally be made suitable for use in an operational forecasting system.

The Rhine catchment's water resources are likely to continue to experience considerable pressure from growing irrigated land. Concerns regarding the current and future status of water availability to balance conflicting water allocations for maintaining ecosystem services, shipping, and biodiversity are highlighted as freshwater resources become more limited. Contrary to other sectors, agricultural water consumption primarily consists of actual evapotranspiration, resulting in only a small amount of flow returning to receiving water bodies. As a feasible alternative, actual evapotranspiration is therefore increasingly used to quantify agricultural water use. In hydrological models, agricultural water demand is typically assessed by the volume of water required to fully restore soil moisture to predefined thresholds for sustaining optimal crop growth, under the assumption that there will be enough water available during the growing season to fulfill the demand. However, the assumption of ideal crop growing circumstances (close to potential evapotranspiration) is not necessarily true in dry season, when limited water supply influences irrigation decision-making and will lead to inaccurate estimation of actual water use. This PhD research, as part of the HorizonEurope project Stars4Water, aims to produce historical spatiotemporal estimates of agricultural water use over the Rhine catchment by using satellite observations. To isolate the actual evapotranspiration due from irrigated land, the actual evapotranspiration from the hydrological model wflow_sbm without irrigation scheme will be compared against the actual evapotranspiration derived from satellite retrievals. Land surface temperature observations will be assimilated to constrain the actual evapotranspiration estimates to consider the relationship between the water and energy balance.

Real-time reservoir operations are highly dependent on decisions made by reservoir operators, which are difficult to simulate accurately with process-based hydrological models. Data-driven models, particularly those based on machine learning (ML), have been shown to be able to overcome the limitations typically encountered in process-based models. Despite a large number of ML studies in reservoir operation modelling, only few studies have focused on ML model performance in real-time reservoir operation and outflow forecasting. This study aims to investigate the capabilities of the Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Gated Recurring Unit (GRU) in simulating and reforecasting real-time (daily) reservoir operation and outflow, considering uncertainties in input data, training-testing periods and different algorithms. A major, multi-purpose reservoir, namely the Sirikit reservoir, in the upper Chao Phraya River basin Thailand was used as the case study. The main inputs for the ML operation models included the daily reservoir outflow, inflow, storage and the month of the year. We applied the distributed wflow_sbm model for inflow simulation (using MSWEP precipitation data) and inflow reforecasting (using ECMWF precipitation data). Daily reservoir storage was obtained from observations and real-time recalculation based on the reservoir water balance. The ML operation models were trained and tested with 10-fold cross-validation. Results show that RNN, LSTM and GRU can reconstruct real-time reservoir operation and provide accurate outflow when training data cover both regular and extreme conditions. For multi-day reforecasting, the model performances are appropriate for the current day up to 2-day lead times for low outflows and up to 6-7 days for high outflows. GRU is potentially the most accurate, robust and convenient model to be used in practice. We conclude that with some further improvements, the ML operation models can be effective and applicable tools to support decision-making for real-time operational water management.

Flash flood early warning requires accurate rainfall forecasts with a high spatial and temporal resolution. As the first few hours ahead are already not sufficiently well captured by the rainfall forecasts of numerical weather prediction (NWP) models, rainfall nowcasting can provide an alternative. This observation-based method, however, quickly loses skill after the first few hours of the forecast due to growth and dissipation processes that are not accounted for. In addition, providing an additional forecasting method can let users drown in the amount of available information. A promising way forward is a seamless forecasting system, which combines the aforementioned forecasting methods. By optimally combining (blending) rainfall nowcasts with NWP forecasts, we can extend the skillful lead time of short-term rainfall forecasts and provide users with more consistent, seamless forecasts.We implemented an adaptive scale-dependent ensemble blending method in the open-source pysteps library. In this implementation, the blending of the extrapolation (ensemble) nowcast, (ensemble) NWP and noise components is performed level-by-level, which means that the blending weights vary per spatial cascade level. These scale-dependent blending weights are computed from the recent skill of the forecast components, and converge to a climatological value, which is computed from a multi-day rolling window and can be adjusted to the (operational) needs of the user. To constrain the (dis)appearance of rain in the ensemble members to regions around the rainy areas, we have developed a Lagrangian blended probability matching scheme and incremental masking strategy.We evaluate the method using three heavy and extreme (July 2021) rainfall events in four Belgian and Dutch catchments, focusing on both the rainfall forecasts and the resulting discharge forecasts using the fully distributed wflow_sbm hydrological model. We benchmark the results of the 48-member blended forecasts against the deterministic Belgian NWP forecast, a 48-member nowcast and a simple 48-member linear blending approach. When focusing on the resulting rainfall forecasts, the introduced blending approach predominantly performs similarly or better than only nowcasting (in terms of event-averaged CRPS and CSI values) and adds value compared to NWP for the first hours of the forecast. This holds for both the radar domain and catchment scale, although the difference, particularly with the linear blending method, reduces when we focus on catchment-average cumulative rainfall sums instead of instantaneous rainfall rates. We find similar results for the resulting discharge forecasts, although the effect of the catchment size and corresponding lag times becomes influential and determines the added value of nowcasting over NWP. By properly combining observations and NWP forecasts, blending methods such as these are a crucial component of seamless hydrometeorological forecasting systems.

Employing a methodology reported in a recent theoretical study on male astronauts, this study estimated the effects of body size and aerobic countermeasure (CM) exercise in a four-person, all-female crew composed of individuals drawn from a stature range (1.50- to 1.90-m) representative of current space agency requirements (which exist for stature, but not for body mass) upon total energy expenditure (TEE), oxygen (O2) consumption, carbon dioxide (CO2) and metabolic heat (Hprod) production, and water requirements for hydration, during space exploration missions. Assuming geometric similarity across the stature range, estimates were derived using available female astronaut data (mean age: 40-years; BMI: 22.7-kg·m−2; resting VO2 and VO2max: 3.3- and 40.5-mL·kg−1·min−1) on 30- and 1080-day missions, without and with, ISS-like countermeasure exercise (modelled as 2 × 30-min aerobic exercise at 75% VO2max, 6-day·week−1). Where spaceflight-specific data/equations were not available, terrestrial equivalents were used. Body size alone increased 24-h TEE (+ 30%), O2 consumption (+ 60%), CO2 (+ 60%) and Hprod (+ 60%) production, and water requirements (+ 17%). With CM exercise, the increases were + 25–31%, + 29%, + 32%, + 38% and + 17–25% across the stature range. Compared to the previous study of theoretical male astronauts, the effect of body size on TEE was markedly less in females, and, at equivalent statures, all parameter estimates were lower for females, with relative differences ranging from -5% to -29%. When compared at the 50th percentile for stature for US females and males, these differences increased to − 11% to − 41% and translated to larger reductions in TEE, O2 and water requirements, and less CO2 and Hprod during 1080-day missions using CM exercise. Differences between female and male theoretical astronauts result from lower resting and exercising O2 requirements (based on available astronaut data) of female astronauts, who are lighter than male astronauts at equivalent statures and have lower relative VO2max values. These data, combined with the current move towards smaller diameter space habitat modules, point to a number of potential advantages of all-female crews during future human space exploration missions.

Flash flood early warning requires accurate rainfall forecasts with a high spatial and temporal resolution. As the first few hours ahead are already not sufficiently well captured by the rainfall forecasts of numerical weather prediction (NWP) models, radar rainfall nowcasting can provide an alternative. Because this observation-based method quickly loses skill after the first 2 hr of the forecast, it needs to be combined with NWP forecasts to extend the skillful lead time of short-term rainfall forecasts, which should increase decision-making times. We implemented an adaptive scale-dependent ensemble blending method in the open-source pysteps library, based on the Short-Term Ensemble Prediction System scheme. In this implementation, the extrapolation (ensemble) nowcast, (ensemble) NWP, and noise components are combined with skill-dependent weights that vary per spatial scale level. To constrain the (dis)appearance of rain in the ensemble members to regions around the rainy areas, we have developed a Lagrangian blended probability matching scheme and incremental masking strategy. We describe the implementation details and evaluate the method using three heavy and extreme (July 2021) rainfall events in four Belgian and Dutch catchments. We benchmark the results of the 48-member blended forecasts against the Belgian NWP forecast, a 48-member nowcast, and a simple 48-member linear blending approach. Both on the radar domain and catchment scale, the introduced blending approach predominantly performs similarly or better than only nowcasting (in terms of event-averaged continuous ranked probability score and critical success index values) and adds value compared with NWP for the first hours of the forecast, although the difference, particularly with the linear blending method, reduces when we focus on catchment-average cumulative rainfall sums instead of instantaneous rainfall rates. By properly combining observations and NWP forecasts, blending methods such as these are a crucial component of seamless prediction systems.

Cannabis is one of the most frequently used psychoactive substances in the world. The most common route of administration for cannabis and cannabinoid constituents such as Δ-9-tetrahydrocannabinol (THC) and cannabidiol (CBD) is via smoking or vapor inhalation. Preclinical vapor models have been developed, although the vaporization devices and delivery methods vary widely across laboratories. This review examines the emerging field of preclinical vapor models with a focus on cannabinoid exposure in order to (1) summarize vapor exposure parameters and other methodological details across studies; (2) discuss the pharmacological and behavioral effects produced by exposure to vaporized cannabinoids; and (3) compare behavioral effects of cannabinoid vapor administration with those of other routes of administration. This review will serve as a guide for past and current vapor delivery methods in animals, synergize findings across studies, and propose future directions for this area of research.

Purpose: In this study, we aimed to explore whether low levels of mitochondrial DNA (mtDNA) content in the primary tumor could predict better outcome for breast cancer patients receiving anthracycline-based therapies. We hypothesized that tumor cells with low mtDNA content are more susceptible to mitochondrial damage induced by anthracyclines, and thus are more susceptible to anthracycline treatment.Experimental Design: We measured mtDNA content by a qPCR approach in 295 primary breast tumor specimens originating from two well-defined cohorts: 174 lymph node–positive patients who received adjuvant chemotherapy and 121 patients with advanced disease who received chemotherapy as first-line palliative treatment. The chemotherapy regimens given were either anthracycline-based (FAC/FEC) or methotrexate-based (CMF).Results: In both the adjuvant and advanced settings, we observed increased benefit for patients with low mtDNA content in their primary tumor, but only when treated with FAC/FEC. In multivariable Cox regression analysis for respectively distant metastasis-free survival and progression-free survival, the HR for the FAC/FEC-treated mtDNA low group in the adjuvant setting was 0.46 [95% confidence interval (CI), 0.24–0.89; P = 0.020] and in the advanced setting 0.49 (95% CI, 0.27–0.90; P = 0.022) compared with the FAC/FEC-treated mtDNA high group. We did not observe these associations in the patients treated with CMF.Conclusions: In our two study cohorts, breast cancer patients with low mtDNA content in their primary tumor had better outcome from anthracycline-containing chemotherapy. The frequently observed decrease in mtDNA content in primary breast tumors may be exploited by guiding chemotherapeutic regimen decision making. Clin Cancer Res; 23(16); 4735–43. ©2017 AACR.

Supplementary Table 1: Clinicopathological variables of node-positive patients receiving adjuvant chemotherapy (FAC/FEC or CMF) (complete adjuvant cohort). Supplementary Table 2: Clinicopathological variables of the patients with recurrent disease receiving first-line chemotherapy (FAC/FEC or CMF) (complete advanced cohort). Supplementary Table 3: Patient response to first-line FAC/FEC or CMF chemotherapy after disease recurrence (advanced cohort). Supplementary Table 4: Performance of the calibration curves taken along in each plate for the used qPCR assays. Supplementary Table 5: Association between mtDNA content and chemotherapy given, either adjuvant in the adjuvant cohort, or as first-line for disease recurrence in the advanced cohort. Supplementary Table 6: Association between adjuvant chemotherapy given and clinicopathological variables of node-positive patients (adjuvant cohort). Supplementary Table 7: Association between first-line chemotherapy given and clinicopathological variables of patients with recurrent disease (advanced cohort). Supplementary Table 8: Cox regression analysis for DMFS of node-positive patients receiving FAC/FEC adjuvant chemotherapy (adjuvant cohort) limited to the 86 patients with no missing values. Supplementary Table 9: Cox regression analysis for PFS of patients receiving FAC/FEC first-line chemotherapy for their recurrent disease (advanced cohort) limited to the 70 patients with no missing values. Supplementary Table 10: Univariable Cox regression analysis of postulated markers for anthracycline sensitivity. Supplementary Table 11: Association between mtDNA content and postulated markers for anthracycline sensitivity.

L’administration publique contemporaine doit faire face à des phénomènes de plus en plus complexes et interdépendants. Dans ce contexte, le modèle conçu par l’Institut de hautes études en administration publique (modèle IDHEAP) permet de comprendre l’action des organisations publiques. Il offre également des solutions et des conseils pour concevoir l’administration publique dans une perspective systémique, dynamique et englobante. Les composantes essentielles du système de l’administration publique et de son environnement sont abordées sous un angle pratique, étayé par les dernières connaissances théoriques et empiriques (valeurs, droit, gestion des ressources humaines, transformation digitale…). Enfin, plusieurs études de cas montrent comment utiliser le modèle pour concevoir et développer concrètement l’action publique. Cet ouvrage est le fruit de l’expérience acquise par les membres de l’Institut en accompagnant et en analysant la transformation des organisations publiques.

Estuarine salt intrusion causes problems with freshwater availability in many deltas. Water managers require timely and accurate forecasts to be able to mitigate and adapt to salt intrusion. Data-driven models derived with machine learning are ideally suited for this, as they can mimic complex non-linear systems and are computationally efficient. We set up a Long Short Term Memory (LSTM) model to forecast salt intrusion in the Rhine-Meuse delta. It forecasts chloride concentrations up to 7 days ahead at Krimpen aan den IJssel, an important location for freshwater provision. The model forecasts baseline concentrations and peak timing well, but peak height is underestimated, a problem that becomes worse with increasing lead time. Between lead times of 1 and 7 days, forecast precision declines from 0.9 to 0.7 and forecast recall declines from 0.7 to 0.5 on average. Given these results, we aim to extend the model to other locations in the delta. We expect that a similar setup can work in other deltas, especially those with a similar or simpler channel network.

AIMS: Heart failure (HF) represents a clinical syndrome resulting from different aetiologies and degrees of heart diseases. Among these, a key role is played by primary heart muscle disease (cardiomyopathies), which are the combination of multifactorial environmental insults in the presence or absence of a known genetic predisposition. The aim of the Maastricht Cardiomyopathy registry (mCMP-registry; NCT04976348) is to improve (early) diagnosis, risk stratification, and management of cardiomyopathy phenotypes beyond the limits of left ventricular ejection fraction (LVEF). METHODS AND RESULTS: The mCMP-registry is an investigator-initiated prospective registry including patient characteristics, diagnostic measurements performed as part of routine clinical care, treatment information, sequential biobanking, quality of life and economic impact assessment, and regular follow-up. All subjects aged ≥16 years referred to the cardiology department of the Maastricht University Medical Center (MUMC+) for HF-like symptoms or cardiac screening for cardiomyopathies are eligible for inclusion, irrespective of phenotype or underlying causes. Informed consented subjects will be followed up for 15 years. Two central approaches will be used to answer the research questions related to the aims of this registry: (i) a data-driven approach to predict clinical outcome and response to therapy and to identify clusters of patients who share underlying pathophysiological processes; and (ii) a hypothesis-driven approach in which clinical parameters are tested for their (incremental) diagnostic, prognostic, or therapeutic value. The study allows other centres to easily join this initiative, which will further boost research within this field. CONCLUSIONS: The broad inclusion criteria, systematic routine clinical care data-collection, extensive study-related data-collection, sequential biobanking, and multi-disciplinary approach gives the mCMP-registry a unique opportunity to improve diagnosis, risk stratification, and management of HF and (early) cardiomyopathy phenotypes beyond the LVEF limits. ispartof: ESC HEART FAILURE vol:9 issue:2 pages:1463-1470 ispartof: location:England status: published

Purpose The Irritable Bowel Syndrome Quality of Life (IBS-QoL) questionnaire is a commonly used and validated IBS-specific QoL instrument. However, this questionnaire is in contrast to the EQ-5D-5L, not preference-based and as such does not allow calculation of QALYs. The objective of this study was to describe the convergent- and known-group validity of both questionnaires and to develop a mapping algorithm from EQ-5D-5L which enable IBS-QoL scores to be transformed into utility scores for use in economic evaluations. Methods We used data from two multicenter randomized clinical trials, which represented the estimation and external validation dataset. The convergent validity was investigated by examining correlations between the EQ-5D-5L and IBS-QoL and the known-group validity by calculating effect sizes. Ordinary least squares (OLS), censored least absolute deviations (CLAD), and mixture models were used in this mapping approach. Results 283 IBS patients were included (n = 189 vs. n = 84). Mean IBS-QoL score was 71.13 (SD 15.66) and mean EQ-5D-5L utility score was 0.73 (SD 0.19). The overall sensitivity of the IBS-QoL and EQ-5D-5L to discriminate between patient and disease characteristics was similar. CLAD model 4, containing the total IBS-QoL score and squared IBS-SSS (IBS severity scoring system), was chosen as the most appropriate model to transform IBS-QoL scores into EQ-5D-5L utility scores. Conclusion This study reports the development of an algorithm where the condition-specific questionnaire IBS-QoL can be used to calculate utility values for use in economic evaluations. Including a clinical measure, IBS-SSS, in the model improved the performance of the algorithm.

Optical remote sensing images are a common data sources for surface water monitoring, while they are easily contaminated by clouds, cloud shadows, terrain shadows, etc., resulting in spatial gaps in surface water images. This paper proposes a surface water gap-filling method based on Naive Bayes classification. It uses the historical cloud-free binary (water, non-water) surface water images as prior data and the uncontaminated pixels in the partially contaminated ternary (water, non-water, contaminated pixels) surface water image as evidence to identify the category of gap pixels to achieve the purpose of gap-filling. This method considers the relationship between disconnected water bodies and does not depend on terrain data. When the image is heavily covered by clouds, this method can also reconstruct the complete water extent accurately. Five study areas with different scenarios including rivers, lakes or reservoirs, are selected to evaluate the method. Results show that the average gap-filling accuracy in all five study areas is over 90 %. After gap-filling, the time series of surface water area presents a good correlation with the time series of water level (e.g., the coefficient of determinationR2 = 0.95 in the Dartmouth reservoir). The proposed method is proved effective in filling gaps caused by clouds, cloud shadows and terrain shadows in surface water image, and it would be suitable for high-frequency surface water monitoring and near real-time surface water mapping.

Contains fulltext : 291048.pdf (Publisher’s version ) (Open Access) BACKGROUND: Presence of left ventricular diastolic dysfunction (DD) is key in the pathogenesis of heart failure with preserved ejection fraction (HFpEF). However, non-invasive assessment of diastolic function is complex, cumbersome, and largely based on consensus recommendations. Novel imaging techniques may help detecting DD. Therefore, we compared left ventricular strain-volume loop (SVL) characteristics and diastolic (dys-)function in suspected HFpEF patients. METHOD AND RESULTS: 257 suspected HFpEF patients with sinus rhythm during echocardiography were prospectively included. 211 patients with quality-controlled images and strain and volume analysis were classified according to the 2016 ASE/EACVI recommendations. Patients with indeterminate diastolic function were excluded, resulting in two groups: normal diastolic function (control; n = 65) and DD (n = 91). Patients with DD were older (74.8 ± 6.9 vs. 68.5 ± 9.4 years, p

The field of automated machine learning (AutoML) introduces techniques that automate parts of the development of machine learning (ML) systems, accelerating the process and reducing barriers for novices. However, decisions derived from ML models can reproduce, amplify, or even introduce unfairness in our societies, causing harm to (groups of) individuals. In response, researchers have started to propose AutoML systems that jointly optimize fairness and predictive performance to mitigate fairness-related harm. However, fairness is a complex and inherently interdisciplinary subject, and solely posing it as an optimization problem can have adverse side effects. With this work, we aim to raise awareness among developers of AutoML systems about such limitations of fairness-aware AutoML, while also calling attention to the potential of AutoML as a tool for fairness research. We present a comprehensive overview of different ways in which fairness-related harm can arise and the ensuing implications for the design of fairness-aware AutoML. We conclude that while fairness cannot be automated, fairness-aware AutoML can play an important role in the toolbox of an ML practitioner. We highlight several open technical challenges for future work in this direction. Additionally, we advocate for the creation of more user-centered assistive systems designed to tackle challenges encountered in fairness work.

Aims Heart failure with preserved ejection fraction (HFpEF) is associated with stiffened myocardium and elevated filling pressure that may be captured by heart sound (HS). We investigated the relationship between phonocardiography (PCG) and echocardiography in symptomatic patients suspected of HFpEF. Methods and results Consecutive symptomatic patients with sinus rhythm and left ventricular ejection fraction >45% were enrolled. Echocardiography was performed to evaluate the patients’ diastolic function, accompanied by PCG measurements. Phonocardiography features including HS amplitude, frequency, and timing intervals were calculated, and their abilities to differentiate the ratio between early mitral inflow velocity and early diastolic mitral annular velocity (E/e′) were investigated. Of 45 patients, variable ratio matching was applied to obtain two groups of patients with similar characteristics but different E/e′. Patients with a higher E/e′ showed higher first and second HS frequencies and more fourth HS and longer systolic time intervals. The interval from QRS onset to first HS was the best feature for the prediction of E/e′ > 9 [area under the curve (AUC): 0.72 (0.51–0.88)] in the matched patients. In comparison, N-terminal pro-brain natriuretic peptide (NT-proBNP) showed an AUC of 0.67 (0.46–0.85), a value not better than any PCG feature (P > 0.05). Conclusion Phonocardiography features stratify E/e′ in symptomatic patients suspected of HFpEF with a diagnostic performance similar to NT-proBNP. Heart sound may serve as a simple non-invasive tool for evaluating HFpEF patients.

Concerns regarding unfairness and discrimination in the context of artificial intelligence (AI) systems have recently received increased attention from both legal and computer science scholars. Yet, the degree of overlap between notions of algorithmic bias and fairness on the one hand, and legal notions of discrimination and equality on the other, is often unclear, leading to misunderstandings between computer science and law. What types of bias and unfairness does the law address when it prohibits discrimination? What role can fairness metrics play in establishing legal compliance? In this paper, we aim to illustrate to what extent European Union (EU) non-discrimination law coincides with notions of algorithmic fairness proposed in computer science literature and where they differ. The contributions of this paper are as follows. First, we analyse seminal examples of algorithmic unfairness through the lens of EU non-discrimination law, drawing parallels with EU case law. Second, we set out the normative underpinnings of fairness metrics and technical interventions and compare these to the legal reasoning of the Court of Justice of the EU. Specifically, we show how normative assumptions often remain implicit in both disciplinary approaches and explain the ensuing limitations of current AI practice and non-discrimination law. We conclude with implications for AI practitioners and regulators.

Study region: Citarum river basin, West Java — Indonesia. Study focus: We look at the skill of Empirical Quantile Mapping corrected ECMWF SEAS5 (SEAS5 EQM bias-corrected) based streamflow forecasts in the Citarum river basin. We focus on July to October because these are agriculturally important months in Java. We use a high-resolution hydrologic model (wflow_sbm) with data for the period 1989–2009. New hydrological insights for the region: Water users and agricultural practitioners commonly need monthly to seasonal hydrological forecasts. The forecasts should be sufficiently skillful and provide information that is relevant to the decisions makers in order to have practical value to them. We assess if skilful SEAS5 EQM bias-corrected based seasonal forecasts are available with the purpose to support rice production. In this streamflow forecast calibration, we look at different aggregation days and different lead times. For the verification, we use the Continuous Ranked Probability Skill Score (CRPSS), Brier Skill Score (BSS), and Mean Average Error (MAE). We also look at the correlation, the Root Mean Square Error (RMSE), and the Receiver operating characteristic Skill (ROCS). The LT1 and LT2 forecast show higher skills than longer lead times. Meanwhile, streamflow based on the aggregated forecast at 30 to 60 days aggregation days is more skillful than larger aggregations. In Indonesia, this study is a study that initiates using a hydrological model with inputs from a seasonal rainfall forecast.

Fairlearn is an open source project to help practitioners assess and improve fairness of artificial intelligence (AI) systems. The associated Python library, also named fairlearn, supports evaluation of a model's output across affected populations and includes several algorithms for mitigating fairness issues. Grounded in the understanding that fairness is a sociotechnical challenge, the project integrates learning resources that aid practitioners in considering a system's broader societal context.

The Wisconsin Idea is a widely acclaimed late nineteenth-century vision for Wisconsin higher education that promotes democratic engagement, the search for truth, and developing knowledge of importance to Wisconsin citizens. While the Wisconsin Idea is often celebrated as an enduring feature of public higher education in Wisconsin, this paper points to a myriad of cultural, economic, and political forces that have reshaped its meaning. Among these forces, fragmenting purposes of higher education, globalization, and changes in state political culture have edited understandings of the Wisconsin Idea in ways that have eliminated its moral and democratic purposes. These shifts that occurred throughout the twentieth century and early twenty-first mirror changes in the broader American higher education landscape. Ultimately, such changes have resulted in divided lives among students, faculty, staff, and administrators that seek wholeness, connection, and deep civic purposes in their work. This paper retraces the Wisconsin Idea’s history and concludes with a discussion about ways in which its early notions might be revived to inspire new thinking about higher education’s role in society.

A relationship between the gut microbiome and alcohol use disorder has been suggested. Excessive alcohol use produces changes in the fecal microbiome and metabolome in both rodents and humans. Yet, these changes can be observed only in a subgroup of the studied populations, and reversal does not always occur after abstinence. We aimed to analyze fecal microbial composition and function in a translationally relevant baboon model of chronic heavy drinking that also meets binge criteria (drinking too much, too fast, and too often), i.e., alcohol ~1 g/kg and blood alcohol levels (BALs) ≥ 0.08 g/dL in a 2-hour period, daily, for years. We compared three groups of male baboons (Papio anubis): L = Long-term alcohol drinking group (12.1 years); S = Short-term alcohol drinking group (2.7 years); and C = Control group, drinking a non-alcoholic reinforcer (Tang®) (8.2 years). Fecal collection took place during 3 days of Drinking (D), followed by a short period (3 days) of Abstinence (A). Fecal microbial alpha- and beta-diversity were significantly lower in L vs. S and C (p’s Streptococcus genus showed a relative increase in L vs. S and C (p’s p p

Background Speckle tracking echocardiographic global longitudinal strain (GLS) predicts outcome in patients with new onset heart failure. Still, its incremental value on top of left ventricular ejection fraction (LVEF) in patients with nonischemic, nonvalvular dilated cardiomyopathy (DCM) after optimal heart failure treatment remains unknown. Methods and Results Patients with DCM were included at the outpatient clinics of 2 centers in the Netherlands and Italy. The prognostic value of 2‐dimensional speckle tracking echocardiographic global longitudinal strain was evaluated when being on optimal heart failure medication for at least 6 months. Outcome was defined as the combination of sudden or cardiac death, life‐threatening arrhythmias, and heart failure hospitalization. A total of 323 patients with DCM (66% men, age 55±14 years) were included. The mean LVEF was 42%±11% and mean GLS after optimal heart failure treatment was −15%±4%. Twenty percent (64/323) of all patients reached the primary outcome after optimal heart failure treatment (median follow‐up of 6[4–9] years). New York Heart Association class ≥3, LVEF, and GLS remained associated with the outcome in the multivariable‐adjusted model (New York Heart Association class: hazard ratio [HR], 3.43; 95% CI, 1.49–7.90, P =0.004; LVEF: HR, 2.13; 95% CI, 1.11–4.10, P =0.024; GLS: HR, 2.24; 95% CI, 1.18–4.29, P =0.015), whereas left ventricular end‐diastolic diameter index, left atrial volume index, and delta GLS were not. The addition of GLS to New York Heart Association class and LVEF improved the goodness of fit (log likelihood ratio test P Conclusions Within this bicenter study, GLS emerged as an independent and incremental predictor of adverse outcome, which exceeded LVEF in patients with optimally treated DCM. This presses the need to routinely include GLS in the echocardiographic follow‐up of DCM.

Contains fulltext : 243955.pdf (Publisher’s version ) (Open Access) PURPOSE: Pathogenic variants in SETD1B have been associated with a syndromic neurodevelopmental disorder including intellectual disability, language delay, and seizures. To date, clinical features have been described for 11 patients with (likely) pathogenic SETD1B sequence variants. This study aims to further delineate the spectrum of the SETD1B-related syndrome based on characterizing an expanded patient cohort. METHODS: We perform an in-depth clinical characterization of a cohort of 36 unpublished individuals with SETD1B sequence variants, describing their molecular and phenotypic spectrum. Selected variants were functionally tested using in vitro and genome-wide methylation assays. RESULTS: Our data present evidence for a loss-of-function mechanism of SETD1B variants, resulting in a core clinical phenotype of global developmental delay, language delay including regression, intellectual disability, autism and other behavioral issues, and variable epilepsy phenotypes. Developmental delay appeared to precede seizure onset, suggesting SETD1B dysfunction impacts physiological neurodevelopment even in the absence of epileptic activity. Males are significantly overrepresented and more severely affected, and we speculate that sex-linked traits could affect susceptibility to penetrance and the clinical spectrum of SETD1B variants. CONCLUSION: Insights from this extensive cohort will facilitate the counseling regarding the molecular and phenotypic landscape of newly diagnosed patients with the SETD1B-related syndrome.

Background Irritable Bowel Syndrome (IBS) is a prevalent, chronic gastrointestinal disorder that imposes a substantial socioeconomic burden. Peppermint oil is a frequently used treatment for IBS, but evidence about cost-effectiveness is lacking. Objective We aimed to assess cost-effectiveness of small-intestinal release peppermint oil versus placebo in IBS patients. Methods In a multicenter randomized placebo-controlled trial, cost-effectiveness was evaluated from a societal perspective. The incremental cost-effectiveness ratios (ICERs) were expressed as (1) incremental costs per Quality Adjusted Life Years (QALY), and (2) incremental costs per successfully treated patient, that is per abdominal pain responder (according to FDA definitions), both after an eight-week treatment period with placebo versus peppermint oil. Cost-utility and uncertainty were estimated using non-parametric bootstrapping. Sensitivity analyses were performed. Results The analysis comprised 126 patients (N = 64 placebo, N = 62 small-intestinal release peppermint oil). Peppermint oil was a dominant treatment compared to placebo in 46% of bootstrap replications. Peppermint oil was also more effective but at higher cost in 31% of replications. The net-benefit acceptability curve showed that peppermint oil has a 56% probability of being cost-effective at a conservative willingness-to-pay threshold of €10.000/QALY. Peppermint oil was also a dominant treatment per additional successfully treated patient according to FDA definitions, that is in 51% of replications. In this case, the acceptability curve showed an 89% probability of being cost-effective. Conclusions In patients with IBS, small-intestinal release peppermint oil appears to be a cost-effective treatment although there is uncertainty surrounding the ICER. When using abdominal pain responder as outcome measure for the ICER, peppermint oil has a high probability of being cost-effective. The use of peppermint oil, which is a low-cost treatment, can be justified by the modest QALY gains and slightly higher proportion of abdominal pain responders. More research and long-term data are necessary to confirm the cost-effectiveness of peppermint oil. NCT02716285.

Rationale The legalization of medicinal use of Cannabis sativa in most US states and the removal of hemp from the Drug Enforcement Agency (DEA) controlled substances act has resulted in a proliferation of products containing Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) for oral consumption (e.g., edibles, oils and tinctures) that are being used for recreational and medicinal purposes. Objective This study examined the effects of cannabinoids THC and CBD when administered orally on measures of pain sensitivity, body temperature, locomotor activity, and catalepsy (i.e., cannabinoid tetrad) in male and female Sprague Dawley rats. Methods Rats (N=24, 6 per sex/drug group) were administered THC (1-20 mg/kg), CBD (3-30 mg/kg), or sesame oil via oral gavage. Thermal and mechanical pain sensitivity (tail flick assay, von Frey test), rectal measurements for body temperature, locomotor activity, and the bar-test of catalepsy were completed. A separate group of rats (N=8/4 per sex) were administered morphine (5-20 mg/kg; intraperitoneal, IP) and evaluated for pain sensitivity as a positive control. Results We observed classic tetrad effects of antinociception, hypothermia, hyper- and hypolocomotion, and catalepsy after oral administration of THC that were long lasting (>7 hours). CBD modestly increased mechanical pain sensitivity and produced sex-dependent effects on body temperature and locomotor activity. Conclusions Oral THC and CBD produced long lasting effects, that differed in magnitude and time course when compared with other routes of administration. Examination of cannabinoid effects administered via different routes of administration, species, and in both males and females is critical to enhance translation.

Trompeter, K, Weerts, J, Fett, D, Firouzabadi, A, Heinrich, K, Schmidt, H, Brüggemann, GP, and Platen, P. Spinal and pelvic kinematics during prolonged rowing on an ergometer vs. indoor tank rowing. J Strength Cond Res 35(9): 2622-2628, 2021-This investigation aimed to compare spinopelvic kinematics during rowing on an ergometer vs. in a rowing tank and to evaluate changes with progressing fatigue. Spinal and pelvic kinematics of 8 competitive scull rowers (19.0 ± 2.1 years, 179.9 ± 7.6 cm, and 74.8 ± 8.1 kg) were collected during 1 hour of rowing on an ergometer and in a rowing tank using a routine training protocol. Kinematics of the upper thoracic spine, lower thoracic spine, lumbar spine, and pelvis were determined using an infrared camera system (Vicon, Oxford, United Kingdom). There was a greater lumbar range of motion (ROM) and less posterior pelvic tilt at the catch during rowing on the ergometer compared with in the rowing tank (p = 0.001-0.048), but no differences in pelvic ROM. In the rowing tank, the pelvic ROM increased over time (p = 0.002) and the ROM of the lower thoracic spine decreased (p = 0.002). In addition, there was an extended drive phase (when the rower applies pressure to the oar levering the boat forward) and an abbreviated recovery phase (setting up the rower's body for the next stroke) in the rowing tank (p = 0.032). Different rowing training methods lead to differences in spinopelvic kinematics, which may lead to substantially different spinal loading situations. Greater pelvic rotation and lesser lumbar ROM are considered ideal; therefore, the present results indicate that rowing in the rowing tank might facilitate the maintenance of this targeted spinopelvic posture, which might help protect the lower back. Rowers, coaches, and researchers should consider the differences between rowing training methods, especially when giving training recommendations.

Tweetable abstract #eHealth and #ArtificialIntelligence (AI) bring new possibilities for #HeartFailure (HF) care. We elaborate on potential benefits of #AI in #HF and highlight important bottlenecks for its implementation. #Editorial #Cardiology.

Distributed hydrological models rely on hydrography data such as flow direction, river length, slope and width. For large-scale applications, many of these models still rely on a few flow direction datasets, which are often manually derived. We propose the Iterative Hydrography Upscaling (IHU) method to upscale high-resolution flow direction data to the typically coarser resolutions of distributed hydrological models. The IHU aims to preserve the upstream–downstream relationship of river structure, including basin boundaries, river meanders and confluences, in the D8 format, which is commonly used to describe river networks in models. Additionally, it derives representative sub-grid river length and slope parameters, which are required for resolution-independent model results. We derived the multi-resolution MERIT Hydro IHU dataset at resolutions of 30 arcsec (∼ 1 km), 5 arcmin (∼ 10 km) and 15 arcmin (∼ 30 km) by applying IHU to the recently published 3 arcsec MERIT Hydro data. Results indicate improved accuracy of IHU at all resolutions studied compared to other often-applied upscaling methods. Furthermore, we show that MERIT Hydro IHU minimizes the errors made in the timing and magnitude of simulated peak discharge throughout the Rhine basin compared to simulations at the native data resolutions. As the method is open source and fully automated, it can be applied to other high-resolution hydrography datasets to increase the accuracy and enhance the uptake of new datasets in distributed hydrological models in the future.

Employing a methodology reported in a recent theoretical study on male astronauts, this study estimated the effects of body size and aerobic countermeasure (CM) exercise in a four-person, all-female crew composed of individuals drawn from a stature range (1.50- to 1.90-m) representative of current space agency requirements upon total energy expenditure (TEE), oxygen (O2) consumption, carbon dioxide (CO2) and metabolic heat (Hprod) production, and water requirements for hydration, during space exploration missions. Assuming geometric similarity across the stature range, estimates were derived using available female astronaut data (mean age: 40-y; BMI: 22.7-kg·m− 2; resting VO2 and VO2max: 3.3- and 40.5-mL·kg− 1·min− 1) on 30- and 1080-d missions, without and with, ISS-like countermeasure exercise (modelled as 2x30min aerobic exercise at 75% VO₂max, 6d·wk1). Where spaceflight-specific data/equations were not available, terrestrial equivalents were used. Body size alone increased 24-h TEE (+ 30%), O₂ consumption (+ 60%), CO₂ (+ 60%) and Hprod (+ 60%) production, and water requirements (+ 17%). With CM exercise, the increases were 25–31%, 29%, 32%, 38% and 17–25% across the stature range. Compared to the previous study of theoretical male astronauts, the effect of body size on TEE was markedly less in females, and, at equivalent statures, all parameter estimates were lower for females, with relative differences ranging from − 5% to -29%. When compared at the 50th percentile for stature for US females and males, these differences increased to -11% to -41% and translated to larger reductions in TEE, O2 and water requirements, and less CO2 and Hprod during 1080-d missions using CM exercise. Differences between female and male theoretical astronauts result from lower resting and exercising O2 requirements – based on available astronaut data of female astronauts who are lighter than male astronauts at equivalent statures, and having lower relative VO₂max values. These data, combined with the current move towards smaller diameter space habitat modules point to a number of potential advantages of all-female crews during future human space exploration missions.

Feature importance is an approach that helps to explain machine learning model predictions. It works through assigning importance scores to input features of a particular model. Different techniques exist to derive these scores, with widely varying underlying assumptions of what importance means. Little research has been done to verify whether these assumptions match the expectations of the target user, which is imperative to ensure that feature importance values are not misinterpreted. In this work, we explore data scientists’ mental models of (local) feature importance and compare these with the conceptual models of the techniques. We first identify several properties of local feature importance techniques that could potentially lead to misinterpretations. Subsequently, we explore the expectations data scientists have about local feature importance through an exploratory (qualitative and quantitative) survey of 34 data scientists in industry. We compare the identified expectations to the theory and assumptions behind the techniques and find that the two are not (always) in agreement.

Background Heart failure with preserved ejection fraction (HFpEF) is an important health care problem, responsible for a decreased quality of life, high health care costs, and a poor prognosis. Sleep disordered breathing (SDB) is suggested to be an important comorbidity in HFpEF, but its prevalence is probably underestimated due to limited SDB screening in clinical practice. Currently, no parameter reflects the full complexity and prognostic effect of SDB in HFpEF. Time spent with an oxygen saturation below 90% (T90), representing the hypoxemic burden, is an independent predictor of mortality and is superior compared to the widely accepted apnoea hypopnea index (AHI) in patients with HF with reduced ejection fraction (HFrEF). However, this association has not been explored in HFpEF patients. Purpose This study investigates the prevalence of SDB, comprising the diagnoses obstructive sleep apnoea and central sleep apnoea, in a HFpEF cohort. Moreover, the prognostic association of hypoxemic burden in SDB, expressed as T90, with more frequent HF hospitalizations or all-cause mortality is assessed in HFpEF patients. Methods Patients were prospectively included from our specialised HFpEF outpatient clinic from August 2017 until July 2021. All patients underwent a detailed diagnostic workup. An at home sleep monitoring device was used to obtain oximetry data, including T90, and information on respiratory effort, nasal flow and snoring. The patients were divided into tertiles based on T90 or AHI. In these tertiles pairwise Kaplan-Meier analysis was conducted to compare the probability of the composite endpoint of HF hospitalization or all-cause mortality. A Bonferroni correction was made with a statistical significance of p Results This analysis comprises 126 HFpEF patients, with a median age of 75 [70–80] years, 86 (68.3%) patients were female. In total 60 (47.6%) patients were diagnosed with SDB. The median T90 value was 13.7 minutes, with tertiles low (31.2 minutes). The mean follow up time was 40 months (95% CI 37 to 42) and in total 22 patients (17.5%) reached the composite endpoint. Kaplan-Meier analysis showed a gradual higher probability of HF hospitalization or death with higher T90 tertiles, with a significant difference in the highest compared to the lowest T90 tertile (χ2=7.4, p=0.006) (figure 1). Kaplan Meier analysis comparing tertiles based on the AHI (AHI 12.2) showed no different probability of the composite endpoint (χ2=3.1 p=0.208). Conclusion SDB is highly prevalent in HFpEF and the hypoxemic burden (expressed as T90) is an important prognostic factor. T90 may be a superior prognostic marker in HFpEF compared to the widely accepted AHI. Future research should evaluate whether targeting the hypoxemic burden in HFpEF treatment is effective in improving the prognosis. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Dutch Heart Foundation

Background Iron deficiency (ID) is suggested to be one of the key comorbidities contributing to heart failure with preserved ejection fraction (HFpEF) and concomitant exercise intolerance, possibly by inducing systemic inflammation and microvascular dysfunction (MVD). ID is associated with poorer prognosis in HFpEF, but its exact impact on exercise capacity in HFpEF patients remains to be further investigated. Purpose To evaluate the effect of ID on exercise capacity in HFpEF patients by assessing skeletal muscle metabolism. Additionally, to assess the association between ID and MVD in HFpEF patients. Methods This prospective study included patients diagnosed with HFpEF according to the ESC Heart Failure 2016 guidelines between January 2018 and May 2021. Patients were excluded if they were of childbearing potential, had any iron supplementation 6 months or chemotherapy 1 year prior to inclusion, were known with significant peripheral artery disease, or had any contraindication for phosphorus-magnetic resonance spectroscopy (31P-MRS). Iron status was defined as absolute ID (serum ferritin Results Twenty-four HFpEF patients without ID and 18 with ID were included, including 14 patients with absolute ID. Clinical characteristics of patients without ID and with ID were comparable: median age 74 [69–79] vs. 77 [69–81] years, 13 (54%) vs. 14 (78%) were females, and 20 (83%) vs. 16 (89%) had a history of hypertension (Table 1). Both groups showed similar halftime of PCr recovery after exercise (29.2 [22.8–33.2] vs. 27.0 [21.1–31.4] seconds, p=0.416), and similar skin hyperaemic flow increase (1142 [576–2247] vs. 1023 [574–1511] %, p=0.554). These measures of skeletal muscle metabolism and microvascular function were not correlated. In a subset of patients (11 without ID and 13 with ID), elevated high-sensitive C-reactive protein (hsCRP) was correlated with PCr recovery halftime in those with ID (R2 0.565, p=0.003). This correlation was not found in patients without ID (R2 0.119, p=0.300) in the original data (Figure 1), but was found after removal of a prominent outlier (R2 0.654, p=0.005). Conclusion HFpEF patients without ID showed comparable skeletal muscle oxidative capacity and microvascular skin hyperaemia compared to HFpEF patients with ID. Post-hoc analysis suggests that inflammation affects skeletal muscle metabolism in HFpEF patients, possibly regardless of ID. Future studies on the effects of ID and inflammation on cellular metabolism could suggest therapeutic targets in HFpEF. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): This study was funded by Vifor Pharma. The funder had no influence on the study results.