Reconciling the EU forest, biodiversity, and climate strategies.

Forests provide important ecosystem services (ESs), including climate change mitigation, local climate regulation, habitat for biodiversity, wood and non-wood products, energy, and recreation. Simultaneously, forests are increasingly affected by climate change and need to be adapted to future environmental conditions. Current legislation, including the European Union (EU) Biodiversity Strategy, EU Forest Strategy, and national laws, aims to protect forest landscapes, enhance ESs, adapt forests to climate change, and leverage forest products for climate change mitigation and the bioeconomy. However, reconciling all these competing demands poses a tremendous task for policymakers, forest managers, conservation agencies, and other stakeholders, especially given the uncertainty associated with future climate impacts. Here, we used process-based ecosystem modeling and robust multi-criteria optimization to develop forest management portfolios that provide multiple ESs across a wide range of climate scenarios. We included constraints to strictly protect 10% of Europe's land area and to provide stable harvest levels under every climate scenario. The optimization showed only limited options to improve ES provision within these constraints. Consequently, management portfolios suffered from low diversity, which contradicts the goal of multi-functionality and exposes regions to significant risk due to a lack of risk diversification. Additionally, certain regions, especially those in the north, would need to prioritize timber provision to compensate for reduced harvests elsewhere. This conflicts with EU LULUCF targets for increased forest carbon sinks in all member states and prevents an equal distribution of strictly protected areas, introducing a bias as to which forest ecosystems are more protected than others. Thus, coordinated strategies at the European level are imperative to address these challenges effectively. We suggest that the implementation of the EU Biodiversity Strategy, EU Forest Strategy, and targets for forest carbon sinks require complementary measures to alleviate the conflicting demands on forests.

3DReact: Geometric Deep Learning for Chemical Reactions.

Geometric deep learning models, which incorporate the relevant molecular symmetries within the neural network architecture, have considerably improved the accuracy and data efficiency of predictions of molecular properties. Building on this success, we introduce 3DReact, a geometric deep learning model to predict reaction properties from three-dimensional structures of reactants and products. We demonstrate that the invariant version of the model is sufficient for existing reaction data sets. We illustrate its competitive performance on the prediction of activation barriers on the GDB7-22-TS, Cyclo-23-TS, and Proparg-21-TS data sets in different atom-mapping regimes. We show that, compared to existing models for reaction property prediction, 3DReact offers a flexible framework that exploits atom-mapping information, if available, as well as geometries of reactants and products (in an invariant or equivariant fashion). Accordingly, it performs systematically well across different data sets, atom-mapping regimes, as well as both interpolation and extrapolation tasks.

No difference in clinical parameters and drug retention in PsA patients receiving b/tsDMARD monotherapy versus combination with methotrexate: data from the RABBIT-SpA registry.

BACKGROUND: The potential benefit of methotrexate (MTX) in combination with biologic (b) and targeted synthetic (ts) disease modifying anti-rheumatic drugs (DMARDs) in psoriatic arthritis (PsA) is still a matter of debate. OBJECTIVES: To compare clinical and patient reported characteristics as well as drug retention rates in PsA patients receiving b/tsDMARD monotherapy or in combination with MTX. METHODS: RABBIT-SpA is a prospective longitudinal cohort study including axSpA and PsA patients. In this analysis, PsA patients were stratified into two groups: starting b/tsDMARD as monotherapy or in combination with MTX. Treatment retention was compared by drug survival analysis. RESULTS: 69% of the patients (n=900) started b/tsDMARD as monotherapy while 31% were treated in combination with MTX (n=405). At baseline, clinical domains like skin, nail and joint affection, dactylitis, enthesitis and axial involvement were similar between the groups. Only the patients' satisfaction concerning tolerability of the previous treatment was significantly better in the combination group at treatment start. Drug retention rates did not differ between the groups (p=0.4). At 6/12 months, 66%/48% of patients in monotherapy and 67%/48% in the combination group were still on their original treatment. CONCLUSIONS: We did not identify any clinical parameters with notable influence on the choice of b/tsDMARD mono or MTX-combination therapy in PsA. Drug retention rates are similar between mono and combination therapy. It seems that the decision to continue MTX at initiation of b/tsDMARDs is mostly based on the subjective tolerability of MTX treatment.

Enhanced Sequence-Activity Mapping and Evolution of Artificial Metalloenzymes by Active Learning.

Tailored enzymes are crucial for the transition to a sustainable bioeconomy. However, enzyme engineering is laborious and failure-prone due to its reliance on serendipity. The efficiency and success rates of engineering campaigns may be improved by applying machine learning to map the sequence-activity landscape based on small experimental data sets. Yet, it often proves challenging to reliably model large sequence spaces while keeping the experimental effort tractable. To address this challenge, we present an integrated pipeline combining large-scale screening with active machine learning, which we applied to engineer an artificial metalloenzyme (ArM) catalyzing a new-to-nature hydroamination reaction. Combining lab automation and next-generation sequencing, we acquired sequence-activity data for several thousand ArM variants. We then used Gaussian process regression to model the activity landscape and guide further screening rounds. Critical characteristics of our pipeline include the cost-effective generation of information-rich data sets, the integration of an explorative round to improve the model's performance, and the inclusion of experimental noise. Our approach led to an order-of-magnitude boost in the hit rate while making efficient use of experimental resources. Search strategies like this should find broad utility in enzyme engineering and accelerate the development of novel biocatalysts.

[Current data on rheumatological care: annual report from the National database (NDB) of the regional collaborative arthritis centres in Germany]. / Aktuelle Zahlen zur rheumatologischen Versorgung ­ Jahresbericht aus der Kerndokumentation der regionalen kooperativen Rheumazentren.

In the National database (NDB) of the German regional collaborative arthritis centres, annual data on the rheumatological care of patients with inflammatory rheumatic diseases have been collected since 1993. This first annual report presents current cross-sectional data on medication and patient-reported outcomes gathered in 2022.

Impact of DMARD treatment and systemic inflammation on all-cause mortality in patients with rheumatoid arthritis and interstitial lung disease: a cohort study from the German RABBIT register.

OBJECTIVES: To investigate the impact of disease activity and treatment with disease-modifying antirheumatic drugs (DMARDs) on all-cause mortality in patients with rheumatoid arthritis and prevalent interstitial lung disease (RA-ILD). METHODS: Patients with RA-ILD were selected from the biologics register Rheumatoid Arthritis: Observation of Biologic Therapy (RABBIT). Using time-varying Cox regression, the association between clinical measures and mortality was investigated. The impact of DMARDs was analysed by (1) Cox regression considering cumulative exposure (ie, treatment months divided by total months) and (2) time-varying Cox regression as main approach (treatment exposures at monthly level). RESULTS: Out of 15 566 participants, 381 were identified as RA-ILD cases with 1258 person-years of observation and 2.6 years median length of follow-up. Ninety-seven patients (25.5%) died and 34 (35.1%) of these were not receiving DMARD therapy at the time of death. Higher inflammatory biomarkers but not swollen and tender joint count were significantly associated with mortality. Compared with tumour necrosis factor inhibitors (TNFi), non-TNFi biologic DMARDs (bDMARDs) exhibited adjusted HRs (aHRs) for mortality below 1, lacking statistical significance. This finding was stable in various sensitivity analyses. Joint aHR for non-TNFi biologics and JAKi versus TNFi was 0.56 (95% CI 0.33 to 0.97). Receiving no DMARD treatment was associated with a twofold higher mortality risk compared with receiving any DMARD treatment, aHR 2.03 (95% CI 1.23 to 3.35). CONCLUSIONS: Inflammatory biomarkers and absence of DMARD treatment were associated with increased risk of mortality in patients with RA-ILD. Non-TNFi bDMARDs may confer enhanced therapeutic benefits in patients with RA-ILD.

Global trends and scenarios for terrestrial biodiversity and ecosystem services from 1900 to 2050.

Based on an extensive model intercomparison, we assessed trends in biodiversity and ecosystem services from historical reconstructions and future scenarios of land-use and climate change. During the 20th century, biodiversity declined globally by 2 to 11%, as estimated by a range of indicators. Provisioning ecosystem services increased several fold, and regulating services decreased moderately. Going forward, policies toward sustainability have the potential to slow biodiversity loss resulting from land-use change and the demand for provisioning services while reducing or reversing declines in regulating services. However, negative impacts on biodiversity due to climate change appear poised to increase, particularly in the higher-emissions scenarios. Our assessment identifies remaining modeling uncertainties but also robustly shows that renewed policy efforts are needed to meet the goals of the Convention on Biological Diversity.

Quantifying the impact of key factors on the carbon mitigation potential of managed temperate forests.

BACKGROUND: Forests mitigate climate change by reducing atmospheric CO 2 -concentrations through the carbon sink in the forest and in wood products, and substitution effects when wood products replace carbon-intensive materials and fuels. Quantifying the carbon mitigation potential of forests is highly challenging due to the influence of multiple important factors such as forest age and type, climate change and associated natural disturbances, harvest intensities, wood usage patterns, salvage logging practices, and the carbon-intensity of substituted products. Here, we developed a framework to quantify the impact of these factors through factorial simulation experiments with an ecosystem model at the example of central European (Bavarian) forests. RESULTS: Our simulations showed higher mitigation potentials of young forests compared to mature forests, and similar ones in broad-leaved and needle-leaved forests. Long-lived wood products significantly contributed to mitigation, particularly in needle-leaved forests due to their wood product portfolio, and increased material usage of wood showed considerable climate benefits. Consequently, the ongoing conversion of needle-leaved to more broad-leaved forests should be accompanied by the promotion of long-lived products from broad-leaved species to maintain the product sink. Climate change (especially increasing disturbances) and decarbonization were among the most critical factors influencing mitigation potentials and introduced substantial uncertainty. Nevertheless, until 2050 this uncertainty was narrow enough to derive robust findings. For instance, reducing harvest intensities enhanced the carbon sink in our simulations, but diminished substitution effects, leading to a decreased total mitigation potential until 2050. However, when considering longer time horizons (i.e. until 2100), substitution effects became low enough in our simulations due to expected decarbonization such that decreasing harvests often seemed the more favorable solution. CONCLUSION: Our results underscore the need to tailor mitigation strategies to the specific conditions of different forest sites. Furthermore, considering substitution effects, and thoroughly assessing the amount of avoided emissions by using wood products, is critical to determine mitigation potentials. While short-term recommendations are possible, we suggest risk diversification and methodologies like robust optimization to address increasing uncertainties from climate change and decarbonization paces past 2050. Finally, curbing emissions reduces the threat of climate change on forests, safeguarding their carbon sink and ecosystem services.

The protective effect of tumor necrosis factor-alpha inhibitors in COVID-19 in patients with inflammatory rheumatic diseases compared to the general population-A comparison of two German registries.

Objectives: To investigate, whether inflammatory rheumatic diseases (IRD) inpatients are at higher risk to develop a severe course of SARS-CoV-2 infections compared to the general population, data from the German COVID-19 registry for IRD patients and data from the Lean European Survey on SARS-CoV-2 (LEOSS) infected patients covering inpatients from the general population with SARS-CoV-2 infections were compared. Methods: 4310 (LEOSS registry) and 1139 cases (IRD registry) were collected in general. Data were matched for age and gender. From both registries, 732 matched inpatients (LEOSS registry: n = 366 and IRD registry: n = 366) were included for analyses in total. Results: Regarding the COVID-19 associated lethality, no significant difference between both registries was observed. Age > 65°years, chronic obstructive pulmonary disease, diabetes mellitus, rheumatoid arthritis, spondyloarthritis and the use of rituximab were associated with more severe courses of COVID-19. Female gender and the use of tumor necrosis factor-alpha inhibitors (TNF-I) were associated with a better outcome of COVID-19. Conclusion: Inflammatory rheumatic diseases (IRD) patients have the same risk factors for severe COVID-19 regarding comorbidities compared to the general population without any immune-mediated disease or immunomodulation. The use of rituximab was associated with an increased risk for severe COVID-19. On the other hand, the use of TNF-I was associated with less severe COVID-19 compared to the general population, which might indicate a protective effect of TNF-I against severe COVID-19 disease.

Population pharmacokinetics of the dual endothelin receptor antagonist aprocitentan in subjects with or without essential or resistant hypertension.

Aprocitentan is a novel, potent, dual endothelin receptor antagonist that recently demonstrated efficacy in the treatment of difficult-to-treat (resistant) hypertension. The aim of this study was to develop a population pharmacokinetic (PK) model describing aprocitentan plasma concentration over time, to investigate relationships between subject-specific factors (covariates) and model parameters, and to quantify the influence of the identified covariates on the exposure to aprocitentan via model-based simulations, enabling judgment about the clinical relevance of the covariates.PK data from 902 subjects in ten Phase 1, one Phase 2, and one Phase 3 study were pooled to develop a joint population PK model. The concentration-time course of aprocitentan was described by a two-compartment model with absorption lag time, first-order absorption and elimination, and reduced relative bioavailability following very high doses of 300 and 600 mg.The population PK model described the observed data well. Volume and clearance parameters were associated with body weight. Renal function as reflected by estimated glomerular filtration rate (eGFR), hepatic impairment, and sex were identified as relevant covariates on clearance.The subject-specific characteristics of body weight, eGFR, hepatic impairment, and sex were shown to influence exposure parameters area under the concentration-time curve and maximum concentration in steady state to a limited extent, i.e., not more than 25% different from a reference subject, and therefore do not warrant dose adjustments.

A Call for Gene Expression Analysis in Whole Blood of Patients With Rheumatoid Arthritis (RA) as a Biomarker for RA-Associated Interstitial Lung Disease.

OBJECTIVE: Rheumatoid arthritis (RA)-associated interstitial lung disease (ILD) is one of the most common and prognostic organ manifestations of RA. Therefore, to allow effective treatment, it is of crucial importance to diagnose RA-ILD at the earliest possible stage. So far, the gold standard of early detection has been high-resolution computed tomography (HRCT) of the lungs. This procedure involves considerable radiation exposure for the patient and is therefore unsuitable as a routine screening measure for ethical reasons. Here, we propose the analysis of characteristic gene expression patterns as a biomarker to aid in the early detection and initiation of appropriate, possibly antifibrotic, therapy. METHODS: To investigate unique molecular patterns of RA-ILD, whole blood samples were taken from 12 female patients with RA-ILD (n = 7) or RA (n = 5). The RNA was extracted, sequenced by RNA-Seq, and analyzed for characteristic differences in the gene expression patterns between patients with RA-ILD and those with RA without ILD. RESULTS: The differential gene expression analysis revealed 9 significantly upregulated genes in RA-ILD compared to RA without ILD: arginase 1 (ARG1), thymidylate synthetase (TYMS), sortilin 1 (SORT1), marker of proliferation Ki-67 (MKI67), olfactomedin 4 (OLFM4), baculoviral inhibitor of apoptosis repeat containing 5 (BIRC5), membrane spanning 4-domains A4A (MS4A4A), C-type lectin domain family 12 member A (CLEC12A), and the long intergenic nonprotein coding RNA (LINC02967). CONCLUSION: All gene products of these genes (except for LINC02967) are known from the literature to be involved in the pathogenesis of fibrosis. Further, for some, a contribution to the development of pulmonary fibrosis has even been demonstrated in experimental studies. Therefore, the results presented here provide an encouraging perspective for using specific gene expression patterns as biomarkers for the early detection and differential diagnosis of RA-ILD as a routine screening test.

Survey on the working, training, and research conditions of resident physicians in internistic and rheumatological continuing education-BEWUSST.

BACKGROUND: Data on the training and continuing education situation of residents in the field of internal medicine and rheumatology are not available for Germany. For this reason, the Commission for Education and Training of the German Society of Rheumatology (DGRh) initiated the BEWUSST survey on the working, training and research conditions of residents in rheumatology. METHODS: A total of 102 questions on the topics of working conditions in everyday professional life, continuing medical education and training, compatibility of career and family, compatibility of work and research, perspectives as a rheumatologist and practical activities were included in an online questionnaire. RESULTS: A total of 102 participants took part in the survey. Of the respondents 48.1% were satisfied with their professional situation, 40.2% of the participants were supervised by a specialist mentor and 54.9% were working as scientists during their work as a physician. A compatibility of family and career was possible for 34.7%. After completion of the residency 52.9% of the respondents aspired to a combined clinical and outpatient activity. CONCLUSION: Half of the trainee rheumatologists are satisfied with their professional activities, although mentoring of the assistants in training should be further improved. With respect to the desired combined clinical and outpatient activity, the existing options should be expanded or new professional fields of activity should be established, so that the specialty remains attractive for the upcoming generations.

Training the next generation of pharmacometric modelers: a multisector perspective.

The current demand for pharmacometricians outmatches the supply provided by academic institutions and considerable investments are made to develop the competencies of these scientists on-the-job. Even with the observed increase in academic programs related to pharmacometrics, this need is unlikely to change in the foreseeable future, as the demand and scope of pharmacometrics applications keep expanding. Further, the field of pharmacometrics is changing. The field largely started when Lewis Sheiner and Stuart Beal published their seminal papers on population pharmacokinetics in the late 1970's and early 1980's and has continued to grow in impact and use since its inception. Physiological-based pharmacokinetics and systems pharmacology have grown rapidly in scope and impact in the last decade and machine learning is just on the horizon. While all these methodologies are categorized as pharmacometrics, no one person can be an expert in everything. So how do you train future pharmacometricians? Leading experts in academia, industry, contract research organizations, clinical medicine, and regulatory gave their opinions on how to best train future pharmacometricians. Their opinions were collected and synthesized to create some general recommendations.

ChromaX: a fast and scalable breeding program simulator.

SUMMARY: ChromaX is a Python library that enables the simulation of genetic recombination, genomic estimated breeding value calculations, and selection processes. By utilizing GPU processing, it can perform these simulations up to two orders of magnitude faster than existing tools with standard hardware. This offers breeders and scientists new opportunities to simulate genetic gain and optimize breeding schemes. AVAILABILITY AND IMPLEMENTATION: The documentation is available at https://chromax.readthedocs.io. The code is available at https://github.com/kora-labs/chromax.

Mind the Gap: Model-Based Switching from Selatogrel to Maintenance Therapy with Oral P2Y12 Receptor Antagonists.

Background: The P2Y12 receptor antagonist selatogrel is being developed for subcutaneous self-administration with a ready-to-use autoinjector at the onset of acute myocardial infarction (AMI) symptoms. The unique pharmacological profile of selatogrel (fast, potent, and short-acting) can bridge the time gap between the onset of AMI and first medical care. A clinical Phase 1 study showed a time-dependent pharmacodynamic interaction between selatogrel and loading doses of clopidogrel and prasugrel. As treatment switching is a common clinical practice, the assessment of subsequent switching from a clopidogrel loading dose to the first maintenance dose of oral P2Y12 receptor antagonists is highly relevant. Objectives: Model-based predictions of inhibition of platelet aggregation (IPA) for the drugs triggering pharmacodynamic interactions were to be derived to support clinical guidance on the transition from selatogrel to oral P2Y12 receptor antagonists. Methods: Scenarios with selatogrel 16 mg administration or placebo followed by a clopidogrel loading dose and, in turn, prasugrel or ticagrelor maintenance doses at different times of administration were studied. Population pharmacokinetic/pharmacodynamic modeling and simulations of different treatment scenarios were used to derive quantitative estimates for IPA over time. Results: Following selatogrel/placebo and a clopidogrel loading dose, maintenance treatment with ticagrelor or a prasugrel loading dose followed by maintenance treatment quickly achieved sustained IPA levels above 80%. Prior to maintenance treatment, a short time span from 18 to 24 h was identified where IPA levels were predicted to be lower with selatogrel than with placebo if clopidogrel was administered 12 h after selatogrel or placebo. Predicted IPA levels reached with placebo alone and a clopidogrel loading dose at 4 h were consistently lower than with selatogrel administration, followed by a clopidogrel loading dose at 12 h. If a clopidogrel loading dose is administered at 12 h, selatogrel maintains higher IPA levels up to 16 h. IPA levels are subsequently lower than on the placebo until the administration of the first maintenance dose. Conclusions: Model-based predictions informed the transition from selatogrel subcutaneous administration to oral P2Y12 therapy. The application of modeling techniques illustrates the value of employing pharmacokinetic and pharmacodynamic modeling for the simulation of various clinical scenarios of switching therapies.

Learning single-cell perturbation responses using neural optimal transport.

Understanding and predicting molecular responses in single cells upon chemical, genetic or mechanical perturbations is a core question in biology. Obtaining single-cell measurements typically requires the cells to be destroyed. This makes learning heterogeneous perturbation responses challenging as we only observe unpaired distributions of perturbed or non-perturbed cells. Here we leverage the theory of optimal transport and the recent advent of input convex neural architectures to present CellOT, a framework for learning the response of individual cells to a given perturbation by mapping these unpaired distributions. CellOT outperforms current methods at predicting single-cell drug responses, as profiled by scRNA-seq and a multiplexed protein-imaging technology. Further, we illustrate that CellOT generalizes well on unseen settings by (1) predicting the scRNA-seq responses of holdout patients with lupus exposed to interferon-ß and patients with glioblastoma to panobinostat; (2) inferring lipopolysaccharide responses across different species; and (3) modeling the hematopoietic developmental trajectories of different subpopulations.

Bayesian optimization with safety constraints: safe and automatic parameter tuning in robotics.

Selecting the right tuning parameters for algorithms is a pravelent problem in machine learning that can significantly affect the performance of algorithms. Data-efficient optimization algorithms, such as Bayesian optimization, have been used to automate this process. During experiments on real-world systems such as robotic platforms these methods can evaluate unsafe parameters that lead to safety-critical system failures and can destroy the system. Recently, a safe Bayesian optimization algorithm, called SafeOpt, has been developed, which guarantees that the performance of the system never falls below a critical value; that is, safety is defined based on the performance function. However, coupling performance and safety is often not desirable in practice, since they are often opposing objectives. In this paper, we present a generalized algorithm that allows for multiple safety constraints separate from the objective. Given an initial set of safe parameters, the algorithm maximizes performance but only evaluates parameters that satisfy safety for all constraints with high probability. To this end, it carefully explores the parameter space by exploiting regularity assumptions in terms of a Gaussian process prior. Moreover, we show how context variables can be used to safely transfer knowledge to new situations and tasks. We provide a theoretical analysis and demonstrate that the proposed algorithm enables fast, automatic, and safe optimization of tuning parameters in experiments on a quadrotor vehicle.

[Survey on the working, training, and research conditions of resident physicians in internistic and rheumatological continuing education-BEWUSST]. / Befragung zu den Arbeits-, Weiterbildungs- und Forschungsbedingungen von Assistenzärztinnen und -ärzten in der internistisch-rheumatologischen Weiterbildung ­ BEWUSST.

BACKGROUND: Data on the training and continuing education situation of residents in the field of internal medicine and rheumatology are not available for Germany. For this reason, the Commission for Education and Training of the German Society of Rheumatology (DGRh) initiated the BEWUSST survey on the working, training and research conditions of residents in rheumatology. METHODS: A total of 102 questions on the topics of working conditions in everyday professional life, continuing medical education and training, compatibility of career and family, compatibility of work and research, perspectives as a rheumatologist and practical activities were included in an online questionnaire. RESULTS: A total of 102 participants took part in the survey. Of the respondents 48.1% were satisfied with their professional situation, 40.2% of the participants were supervised by a specialist mentor and 54.9% were working as scientists during their work as a physician. A compatibility of family and career was possible for 34.7%. After completion of the residency 52.9% of the respondents aspired to a combined clinical and outpatient activity. CONCLUSION: Half of the trainee rheumatologists are satisfied with their professional activities, although mentoring of the assistants in training should be further improved. With respect to the desired combined clinical and outpatient activity, the existing options should be expanded or new professional fields of activity should be established, so that the specialty remains attractive for the upcoming generations.