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Abstract Ligation of the B cell antigen receptor (BCR) initiates humoral immunity. However, BCR signaling without appropriate co-stimulation commits B cells to death rather than to differentiation into immune effector cells. How BCR activation depletes potentially autoreactive B cells while simultaneously primes for receiving rescue and differentiation signals from cognate T lymphocytes remains unknown. Here, we use a mass spectrometry-based proteomic approach to identify cytosolic/nuclear shuttling elements and uncover transcription factor EB (TFEB) as a central BCR-controlled rheostat that drives activation-induced apoptosis, and concurrently promotes the reception of co-stimulatory rescue signals by supporting B cell migration and antigen presentation. CD40 co-stimulation prevents TFEB-driven cell death, while enhancing and prolonging TFEB’s nuclear residency, which hallmarks antigenic experience also of memory B cells. In mice, TFEB shapes the transcriptional landscape of germinal center B cells. Within the germinal center, TFEB facilitates the dark zone entry of light-zone-residing centrocytes through regulation of chemokine receptors and, by balancing the expression of Bcl-2/BH3-only family members, integrates antigen-induced apoptosis with T cell-provided CD40 survival signals. Thus, TFEB reprograms antigen-primed germinal center B cells for cell fate decisions.

Abstract Built structures increasingly dominate the Earth’s landscapes; their surging mass is currently overtaking global biomass. We here assess built structures in the conterminous US by quantifying the mass of 14 stock-building materials in eight building types and nine types of mobility infrastructures. Our high-resolution maps reveal that built structures have become 2.6 times heavier than all plant biomass across the country and that most inhabited areas are mass-dominated by buildings or infrastructure. We analyze determinants of the material intensity and show that densely built settlements have substantially lower per-capita material stocks, while highest intensities are found in sparsely populated regions due to ubiquitous infrastructures. Out-migration aggravates already high intensities in rural areas as people leave while built structures remain – highlighting that quantifying the distribution of built-up mass at high resolution is an essential contribution to understanding the biophysical basis of societies, and to inform strategies to design more resource-efficient settlements and a sustainable circular economy.

Background: Toxicological analysis of patients with acute recreational drug poisoning can improve our understanding of substance use patterns, clinical symptoms, and improve treatment. Patient history alone may be incomplete or misleading. The objective was to assess the differences in patient history and analytical results, to describe the clinical characteristics, implications and hospital management, and to describe the drug use pattern over time. Methods: A retrospective study including all patients admitted to our toxicology unit with recreational drug toxicity and analytical testing from October 2014 to December 2022. Results: 872 patients were included. Patient history revealed a median of one ingested substance class: opiates/opioids, benzodiazepines/Z-drugs, and Pregabalin were predominant. Urine analysis revealed a median of three ingested substance classes (p < 0.001). Benzodiazepines/Z-drugs, Pregabalin, and THC were severely underreported. Agitation and aggression, anxiety, hallucinations, and psychosis were frequent, associated with cocaine, cathinone/phenethylamine, and amphetamine/MDMA detection and required sedation. Coma was also frequent, associated with opiate/opioid, benzodiazepine/Z-drug, GBL/GHB, and Pregabalin detection and required intubation, and/or application of Naloxone and/or Flumazenil. Twelve patients arrived in cardiac arrest; all were positive for opiates/opioids. Four patients died: three with Benzodiazepines/Z-drugs, Pregabalin and opiates/opioids detected, one with cathinones/phenethylamines detected. While cathinones/phenethylamines and synthetic cannabinoid receptor agonists were mainly detected between 2014–2016, detection decreased significantly between 2017–2022 after NPS legislation passed. Pregabalin detection increased. Conclusions: Patient history is inaccurate, and patients frequently underreport ingested drugs. Opiates and opioids are still the main cause of morbidity and mortality. Pregabalin is increasingly abused. NPS legislation effectively decreased cathinone/phenethylamine and synthetic cannabinoid receptor agonist overdoses.

BackgroundSepsis, a life-threatening condition caused by the dysregulated host response to infection, is a major global health concern. Understanding the impact of viral or bacterial pathogens in sepsis is crucial for improving patient outcomes. This study aimed to investigate the human cytomegalovirus (HCMV) seropositivity as a risk factor for development of sepsis in patients with COVID-19.MethodsA multicenter observational study enrolled 95 intensive care patients with COVID-19-induced sepsis and 80 post-surgery individuals as controls. HCMV serostatus was determined using an ELISA test. Comprehensive clinical data, including demographics, comorbidities, and 30-day mortality, were collected. Statistical analyses evaluated the association between HCMV seropositivity and COVID-19 induced sepsis.ResultsThe prevalence of HCMV seropositivity did not significantly differ between COVID-19-induced sepsis patients (78%) and controls (71%, p = 0.382) in the entire cohort. However, among patients aged ≤60 years, HCMV seropositivity was significantly higher in COVID-19 sepsis patients compared to controls (86% vs 61%, respectively; p = 0.030). Nevertheless, HCMV serostatus did not affect 30-day survival.DiscussionThese findings confirm the association between HCMV seropositivity and COVID-19 sepsis in non-geriatric patients. However, the lack of an independent effect on 30-day survival can be explained by the cross-reactivity of HCMV specific CD8+ T-cells towards SARS-CoV-2 peptides, which might confer some protection to HCMV seropositive patients. The inclusion of a post-surgery control group strengthens the generalizability of the findings. Further research is needed to elucidate the underlying mechanisms of this association, explore different patient populations, and identify interventions for optimizing patient management.ConclusionThis study validates the association between HCMV seropositivity and severe COVID-19-induced sepsis in non-geriatric patients, contributing to the growing body of evidence on viral pathogens in sepsis. Although HCMV serostatus did not independently influence 30-day survival, future investigations should focus on unraveling the intricate interplay between HCMV, immune responses, and COVID-19. These insights will aid in risk stratification and the development of targeted interventions for viral sepsis.

Abstract Built structures, i.e. the patterns of settlements and transport infrastructures, are known to influence per-capita energy demand and CO2 emissions at the urban level. At the national level, the role of built structures is seldom considered due to poor data availability. Instead, other potential determinants of energy demand and CO2 emissions, primarily GDP, are more frequently assessed. We present a set of national-level indicators to characterize patterns of built structures. We quantify these indicators for 113 countries and statistically analyze the results along with final energy use and territorial CO2 emissions, as well as factors commonly included in national-level analyses of determinants of energy use and emissions. We find that these indicators are about equally important for predicting energy demand and CO2 emissions as GDP and other conventional factors. The area of built-up land per capita is the most important predictor, second only to the effect of GDP.

IntroductionAn increasing amount of longitudinal health data is available on critically ill septic patients in the age of digital medicine, including daily sequential organ failure assessment (SOFA) score measurements. Thus, the assessment in sepsis focuses increasingly on the evaluation of the individual disease's trajectory. Machine learning (ML) algorithms may provide a promising approach here to improve the evaluation of daily SOFA score dynamics. We tested whether ML algorithms can outperform the conventional ΔSOFA score regarding the accuracy of 30-day mortality prediction.MethodsWe used the multicentric SepsisDataNet.NRW study cohort that prospectively enrolled 252 sepsis patients between 03/2018 and 09/2019 for training ML algorithms, i.e. support vector machine (SVM) with polynomial kernel and artificial neural network (aNN). We used the Amsterdam UMC database covering 1,790 sepsis patients for external and independent validation.ResultsBoth SVM (AUC 0.84; 95% CI: 0.71-0.96) and aNN (AUC 0.82; 95% CI: 0.69-0.95) assessing the SOFA scores of the first seven days led to a more accurate prognosis of 30-day mortality compared to the ΔSOFA score between day 1 and 7 (AUC 0.73; 95% CI: 0.65-0.80; p = 0.02 and p = 0.05, respectively). These differences were even more prominent the shorter the time interval considered. Using the SOFA scores of day 1 to 3 SVM (AUC 0.82; 95% CI: 0.68 0.95) and aNN (AUC 0.80; 95% CI: 0.660.93) led to a more accurate prognosis of 30-day mortality compared to the ΔSOFA score (AUC 0.66; 95% CI: 0.58-0.74; p < 0.01 and p < 0.01, respectively). Strikingly, all these findings could be confirmed in the independent external validation cohort.ConclusionsThe ML-based algorithms using daily SOFA scores markedly improved the accuracy of mortality compared to the conventional ΔSOFA score. Therefore, this approach could provide a promising and automated approach to assess the individual disease trajectory in sepsis. These findings reflect the potential of incorporating ML algorithms as robust and generalizable support tools on intensive care units.

IntroductionIn modern times, bacterial infections have become a growing problem in the medical community due to the emergence of antibiotic-resistant bacteria. In fact, the overuse and improper disposal of antibiotics have led to bacterial resistance and the presence of such bacteria in wastewater. Therefore, it is critical to develop effective strategies for dealing with antibiotic-resistant bacteria in wastewater. Electroporation has been found to be one of the most promising complementary techniques for bacterial inactivation because it is effective against a wide range of bacteria, is non-chemical and is highly optimizable. Many studies have demonstrated electroporation-assisted inactivation of bacteria, but rarely have clinical antibiotics or bacteria resistant to these antibiotics been used in the study. Therefore, the motivation for our study was to use a treatment regimen that combines antibiotics and electroporation to inactivate antibiotic-resistant bacteria.MethodsWe separately combined two antibiotics (tetracycline and chloramphenicol) to which the bacteria are resistant (with a different resistance mode) and electric pulses. We used three different concentrations of antibiotics (40, 80 and 150 µg/ml for tetracycline and 100, 500 and 2000 µg/ml for chloramphenicol, respectively) and four different electric field strengths (5, 10, 15 and 20 kV/cm) for electroporation.Results and discussionOur results show that electroporation effectively enhances the effect of antibiotics and inactivates antibiotic-resistant bacteria. The inactivation rate for tetracycline or chloramphenicol was found to be different and to increase with the strength of the pulsed electric field and/or the concentration of the antibiotic. In addition, we show that electroporation has a longer lasting effect (up to 24 hours), making bacteria vulnerable for a considerable time. The present work provides new insights into the use of electroporation to inactivate antibiotic-resistant bacteria in the aquatic environment.

Objectives Little is known about in-hospital-stroke (IHS) patients with large vessel occlusion and subsequent transfer to referral centres for endovascular therapy (EVT). However, this subgroup is highly relevant given the substantial amount of IHS, the ongoing trend towards greater use of EVT and lack of EVT possibilities in rural hospitals. The study objective is to explore the clinical outcomes of this vulnerable patient group, given that both IHS and interhospital transfer are associated with worse clinical outcomes due to a higher proportion of pre-existing conditions and substantial time delays during transfer.Design and setting Prospectively collected data of patients receiving EVT after interhospital transfer from 14 rural hospitals of the Telemedical Stroke Network in Southeast Bavaria (TEMPiS) between February 2018 and July 2020 was analysed.Participants 49 IHS and 274 out-of-hospital-stroke (OHS) patients were included.Outcome measures Baseline characteristics, treatment times and outcomes were compared between IHS and OHS. The primary endpoint was a 3-month modified Rankin Scale (mRS).Results In IHS patients, atrial fibrillation (55.3% vs 35.9%, p=0.012), diabetes (36.2% vs 21.1%, p=0.024) and use of oral anticoagulants (44.7% vs 20.8%, p

This dataset includes data on the embodied human appropriation of net primary production (eHANPP) associated with products derived from agriculture and forestry. The human appropriation of net primary production (HANPP) is an indicator of changes in the yearly availability of biomass energy from photosynthesis that remains available in terrestrial ecosystems after harvest, under current land use, compared to the net primary production of the potential natural vegetation. HANPP is an indicator of land-use intensity that is relevant for biodiversity and biogeochemical cycles. The eHANPP indicator allocates HANPP to products and allows tracing trade flows from origin (the country where production takes place) to consumption (the country where products are consumed), thereby underpinning research into the telecouplings in global land use. The datasets described in this article trace eHANPP associated with the bilateral trade flows between 222 countries. It covers 161 primary crops, 13 primary animal products and 4 primary forestry products, as well as the end uses of these products for the years 1986 to 2013.

A cluster of eleven research and innovation projects, funded under the same call of the EU’s H2020 programme, are developing breakthrough and game-changing renewable energy technologies that will form the backbone of the energy system by 2030 and 2050 are, at present, at an early stage of development. These projects have joined forces at a collaborative workshop, entitled ‘Low-TRL Renewable Energy Technologies’, at the 10th Sustainable Places Conference (SP2022), to share their insights, present their projects’ progress and achievements to date, and expose their approach for exploitation and market uptake of their solutions.

Abstract Over the last 60 years, the diamond anvil cell (DAC) has emerged as the tool of choice in high pressure science because materials can be studied at megabar pressures using X-ray and spectroscopic probes. In contrast, the pressure range for neutron diffraction has been limited due to low neutron flux even at the strongest sources and the resulting large sample sizes. Here, we introduce a neutron DAC that enables break-out of the previously limited pressure range. Key elements are ball-bearing guides for improved mechanical stability, gem-quality synthetic diamonds with novel anvil support and improved in-seat collimation. We demonstrate a pressure record of 1.15 Mbar and crystallographic analysis at 1 Mbar on the example of nickel. Additionally, insights into the phase behavior of graphite to 0.5 Mbar are described. These technical and analytical developments will further allow structural studies on low-Z materials that are difficult to characterize by X-rays.

Infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) in pregnancy are associated with the development of preeclampsia and fetal growth restriction (FGR). Recently, preeclampsia was linked to impaired maternal hemodynamic function. This retrospective study evaluated singleton pregnancies with COVID-19 during pregnancy and healthy pregnant controls matched for gestational age from November 2020 to March 2022. Non-invasive assessment of maternal hemodynamics by continuous wave Doppler ultrasound measurements (USCOM-1A® Monitor) and oscillometric arterial stiffness (Arteriograph) was performed. Overall, 69 pregnant women were included—23 women after COVID-19 during pregnancy and 46 healthy controls. While two women (8.7%) were admitted to the hospital due to COVID-19-related symptoms, none required intensive care unit admission or non-invasive/invasive ventilation. There were no statistically significant differences in the majority of hemodynamic parameters between the two cohorts. The prevalence of FGR was significantly higher in the COVID-19 during pregnancy group (9.5% vs. healthy controls: 0.0%; p = 0.036), especially in nulliparous women. No difference in angiogenic markers and neonatal outcomes were observed between pregnant women after COVID-19 and healthy controls. In conclusion, no significant differences in hemodynamic parameters or neonatal outcome were observed in women with COVID-19 during pregnancy. However, an increased prevalence of FGR could be described.

Organic horizons including peat deposits are important terrestrial carbon pools, and various chemical, biological, and water exchange processes take place within them. Accurate information on the spatial distribution of organic soils and their properties is important for decision-making and land management. In this study, we present a machine learning approach for mapping the distribution of organic soils and determining the thickness of the peat layer using more than 24,000 peat layer thickness measurements obtained from field data, airborne laser scanning (ALS) data and various indices obtained from therein, as well as other cartographic materials. Our objectives encompassed two primary aims. Firstly, we endeavored to develop updated cartographic materials depicting the spatial distribution of peat layers. Secondly, we aimed to predict the depth of peat layers, thereby enhancing our understanding of soil organic carbon content. Continentality, a wet area map, latitude, a depth to water map with catchment area of 10 ha, and a digital elevation model were the most important covariates for the machine learning model. As a result, we obtained a map with three peat layer thickness classes, an overall classification accuracy of 0.88, and a kappa value of 0.74. This research contributes to a better understanding of organic soil dynamics and facilitates improved assessments of soil organic carbon stocks.

IntroductionAssessment methods for physical activity and fitness are of upmost importance due to the possible beneficial effect of physical conditioning on neurodegenerative diseases. The implementation of these methods can be challenging when examining elderly or cognitively impaired participants. In the presented study, we compared three different assessment methods for physical activity from the Dementia-MOVE trial, a 6-months intervention study on physical activity in Alzheimer’s disease. The aim was to determine the comparability of physical activity assessments in elderly participants with cognitive impairment due to Alzheimer’s disease.Material or methods38 participants (mean age 70 ± 7 years) with early-stage Alzheimer’s disease (mean MoCA 18.84 ± 4.87) were assessed with (1) fitness trackers for an average of 12 (± 6) days, (2) a written diary on daily activities and (3) a questionnaire on physical activity at three intervention timepoints. For comparison purposes, we present a transformation and harmonization method of the physical assessment output parameters: Metabolic equivalent of task (MET) scores, activity intensity minutes, calorie expenditure and moderate-to-vigorous physical activity (MVPA) scores were derived from all three modalities. The resulting parameters were compared for absolute differences, correlation, and their influence by possible mediating factors such as cognitive state and markers from cerebrospinal fluid.ResultsParticipants showed high acceptance and compliance to all three assessment methods. MET scores and MVPA from fitness trackers and diaries showed high overlap, whilst results from the questionnaire suggest that participants tended to overestimate their physical activity in the long-term retrospective assessment. All activity parameters were independent of the tested Alzheimer’s disease parameters, showing that not only fitness trackers, but also diaries can be successfully applied for physical activity assessment in a sample affected by early-stage Alzheimer’s disease.DiscussionOur results show that fitness trackers and physical activity diaries have the highest robustness, leading to a highly comparable estimation of physical activity in people with Alzheimer’s disease. As assessed parameters, it is recommendable to focus on MET, MVPA and on accelerometric sensor data such as step count, and less on activity calories and different activity intensities which are dependent on different variables and point to a lower reliability.