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Process-based forest models combine biological, physical, and chemical process understanding to simulate forest dynamics as an emergent property of the system. As such, they are valuable tools to investigate the effects of climate change on forest ecosystems. Specifically, they allow testing of hypotheses regarding long-term ecosystem dynamics and provide means to assess the impacts of climate scenarios on future forest development. As a consequence, numerous local-scale simulation studies have been conducted over the past decades to assess the impacts of climate change on forests. These studies apply the best available models tailored to local conditions, parameterized and evaluated by local experts. However, this treasure trove of knowledge on climate change responses remains underexplored to date, as a consistent and harmonized dataset of local model simulations is missing.Here, our objectives were (i) to compile existing local simulations on forest development under climate change in Europe in a common database, (ii) to harmonize them to a common suite of output variables, and (iii) to provide a standardized vector of auxiliary environmental variables for each simulated location to aid subsequent investigations. Our dataset of European stand- and landscape-level forest simulations contains over 1.1 million simulation runs representing 135 million simulation years for more than 13,000 unique locations spread across Europe. The data were harmonized to consistently describe forest development in terms of stand structure (dominant height), composition (dominant species, admixed species), and functioning (leaf area index). Auxiliary variables provided include consistent daily climate information (temperature, precipitation, radiation, vapor pressure deficit) as well as information on local site conditions (soil depth, soil physical properties, soil water holding capacity, plant-available nitrogen). The present dataset facilitates analyses across models and locations, with the aim to better harness the valuable information contained in local simulations for large-scale policy support, and for fostering a deeper understanding of the effects of climate change on forest ecosystems in Europe.

We describe a rare complication of intraprocedural spontaneous thrombus formation on a transcatheter edge-to-edge repair (MitraClip; Abbott Laboratories) device in a hypercoagulable yet adequately anticoagulated patient. We also outline the novel use of a vacuum (Penumbra) aspiration system, which resulted in rapid and effective thrombus elimination.

Unraveling the mechanisms of packing of DNA inside viral capsids is of fundamental importance to understanding the spread of viruses. It could also help develop new applications to targeted drug delivery devices for a large range of therapies. In this article, we present a robust, predictive mathematical model and its numerical implementation to aid the study and design of bacteriophage viruses for application purposes. Exploiting the analogies between the columnar hexagonal chromonic phases of encapsidated viral DNA and chromonic aggregates formed by plank-shaped molecular compounds, we develop a first-principles effective mechanical model of DNA packing in a viral capsid. The proposed expression of the packing energy, which combines relevant aspects of the liquid crystal theory, is developed from the model of hexagonal columnar phases, together with that describing configurations of polymeric liquid crystals. The method also outlines a parameter selection strategy that uses available data for a collection of viruses, aimed at applications to viral design. The outcome of the work is a mathematical model and its numerical algorithm, based on the method of finite elements, and computer simulations to identify and label the ordered and disordered regions of the capsid and calculate the inner pressure. It also presents the tools for the local reconstruction of the DNA "scaffolding" and the center curve of the filament within the capsid.

Protection forests play a key role in protecting settlements, people, and infrastructures from gravitational hazards such as rockfalls and avalanches in mountain areas. Rapid climate change is challenging the role of protection forests by altering their dynamics, structure, and composition. Information on local- and regional-scale impacts of climate change on protection forests is critical for planning adaptations in forest management. We used a model of forest dynamics (ForClim) to assess the succession of mountain forests in the Eastern Alps and their protective effects under future climate change scenarios. We investigated eleven representative forest sites along an elevational gradient across multiple locations within an administrative region, covering wide differences in tree species structure, composition, altitude, and exposition. We evaluated protective performance against rockfall and avalanches using numerical indices (i.e., linker functions) quantifying the degree of protection from metrics of simulated forest structure and composition. Our findings reveal that climate warming has a contrasting impact on protective effects in mountain forests of the Eastern Alps. Climate change is likely to not affect negatively all protection forest stands but its impact depends on site and stand conditions. Impacts were highly contingent to the magnitude of climate warming, with increasing criticality under the most severe climate projections. Forests in lower-montane elevations and those located in dry continental valleys showed drastic changes in forest structure and composition due to drought-induced mortality while subalpine forests mostly profited from rising temperatures and a longer vegetation period. Overall, avalanche protection will likely be negatively affected by climate change, while the ability of forests to maintain rockfall protection depends on the severity of expected climate change and their vulnerability due to elevation and topography, with most subalpine forests less prone to loosing protective effects. Proactive measures in management should be taken in the near future to avoid losses of protective effects in the case of severe climate change in the Alps. Given the heterogeneous impact of climate warming, such adaptations can be aided by model-based projections and high local resolution studies to identify forest stand types that might require management priority for maintaining protective effects in the future.

Disturbances, such as extreme weather events, fires, floods, and biotic agents, can have strong impacts on the dynamics and structures of tropical forests. In the future, the intensity of disturbances will likely further increase, which may have more serious consequences for tropical forests than those we have already observed. Thus, quantifying aboveground biomass loss of forest stands due to stem mortality (hereafter biomass loss rate) is important for the estimation of the role of tropical forests in the global carbon cycle. So far, the long-term impacts of altered stem mortality on rates of biomass loss have not been adequately described. This study aims to analyse the consequences of long-term elevated stem mortality rates on forest dynamics and biomass loss rate. We applied an individual-based forest model and investigated the impacts of permanently increased stem mortality rates on the growth dynamics of humid, terra firme forests in French Guiana. Here, we focused on biomass, leaf area index (LAI), forest height, productivity, forest age, quadratic mean stem diameter, and biomass loss rate. Based on the simulation data, we developed a multiple linear regression model to estimate biomass loss rates of forests in different successional states from the various forest attributes. The findings of our simulation study indicated that increased stem mortality altered the succession patterns of forests in favour of fast-growing species, which increased the old-growth forests' gross primary production, though net primary production remained stable. The stem mortality rate had a strong influence on the functional species composition and tree size distribution, which led to lower values in LAI, biomass, and forest height at the ecosystem level. We observed a strong influence of a change in stem mortality on biomass loss rate. Assuming a doubling of stem mortality rate, the biomass loss rate increased from 3.2 % yr−1 to 4.5 % yr−1 at equilibrium. We also obtained a multidimensional relationship that allowed for the estimation of biomass loss rates from forest height and LAI. Via an example, we applied this relationship to remote sensing data on LAI and forest height to map biomass loss rates for French Guiana. We estimated a countrywide mean biomass loss rate of 3.0 % yr−1. The approach described here provides a novel methodology for quantifying biomass loss rates, taking the successional state of tropical forests into account. Quantifying biomass loss rates may help to reduce uncertainties in the analysis of the global carbon cycle.

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Abstract Background The Mayer-Rokitansky-Küster-Hauser syndrome, MRKHS, is a rare (orphan) disease characterized by the aplasia or hypoplasia of the uterus and the vagina. In women's health research, little is known as to how much care provision for patients with MRKHS takes into account their socio-demographic together with their clinical characteristics. This work examines the patients’ socio-demographic characteristics, highlighting issues of inappropriate and deficient provision of care. Methods The study was carried out as part of the larger TransCareO project and included a group of N=129 MRKHS patients who underwent surgery between 2008 and 2012. Using a specially developed questionnaire, we analyzed MRKHS patients’ data found both in the clinical documentation of the Department for Women's Health, University Hospital of Tübingen and the patient surveys of the Center for Rare Genital Malformations (CRGM/ ZSGF). Patients who took part in interviews were compared with non-respondents. Results Patient respondents and non-respondents did not differ as to the parameters of interest. In most cases, primary amenorrhea was reported as an admission reason. In 24% of patients, a medical intervention (hymenal incision or hormone treatment) already occurred before admission to the Center in Tübingen and proper diagnosis of MRKHS. About one third received in advance inappropriate treatment. During the therapy, more than half of the patients were in a solid partnership. 10% of the family anamneses documented the occurrence of urogenital malformations. Conclusions Care provision for MRKHS patients is largely characterized by delayed proper diagnosis and in part, by inappropriate treatment attempts; there are also indications of regional differences. Anamnestic clues such as an asymptomatic amenorrhea or renal abnormalities of unclear origin still fail to result early enough in referral to a center on the basis of suspected MRKHS diagnosis. Urogenital malformations in the family are more common in patients than in the general population. For patients, a wide range of burdens are associated with the diagnosis. Abnormalities compared to their female peers occur, for instance, in the partnership status: MRKHS patients have more rarely a partner.

Millions of workers experienced job instability during the COVID-19 (coronavirus disease 2019) pandemic. A prevailing assumption is that such experiences of instability intensify economic rationality in workers’ career decision making as a matter of course. In contrast, the authors argue that pandemic-related employment instability may have “unsettled” workers’ lives in ways that elevated nonfinancial priorities such as meaningful work. Using proportionally representative survey data ( n = 1,628), the authors compare the priorities of U.S. college-educated workers who were laid off or furloughed during the pandemic with those of workers whose jobs remained stable. Counter to expectations of heightened economic rationality, job-unstable workers were not more likely than job-stable workers to emphasize job security or salary in beliefs about good work. But they were more likely to prioritize passion for work. These findings challenge common assumptions about job prioritization in the wake of crisis-related job instability and have implications for how scholars and policy makers interpret labor force trends.

Introduction: Human and veterinary medicine are practiced separately, but literature databases such as Pubmed include articles from both fields. This impedes supporting clinical decisions with automated information retrieval, because treatment considerations would not ignore the discipline of mixed sources. Here we investigate data-driven methods from computational linguistics for automatically distinguishing between human and veterinary medical texts. Methods: For our experiments, we selected language models after a literature review of benchmark datasets and reported performances. We generated a dataset of around 48,000 samples for binary text classification, specifically designed to differentiate between human medical and veterinary subjects. Using this dataset, we trained and fine-tuned classifiers based on selected transformer-based models as well as support vector machines (SVM). Results: All trained classifiers achieved more than 99% accuracy, even though the transformer-based classifiers moderately outperformed the SVMbased one. Discussion: Such classifiers could be applicable in clinical decision support functions that build on automated information retrieval. [ABSTRACT FROM AUTHOR]