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Introduction & Purpose The restorative effect of physical activity in alpine environments on mental and physical health is well recognized. However, a risk of accidents and post-accident mental health problems is inherent to every sport. We aimed to characterize mental health in individuals following mountain sport accidents requiring professional medical management. Here we summarize the most important results of our study which has already been published (Salvotti et al., 2024). Methods Adult victims of mountain sport accidents treated at the hospital of the Medical University of Innsbruck (Austria) between 2018 and 2020 completed a cross-sectional survey at least six months following the admission (median 44 months, n = 307). Symptoms of post-traumatic stress disorder (PTSD, PCL-5), anxiety, depression, and somatization (PHQ), resilience (RS-13), sense of coherence (SOC-9L), post-traumatic growth (PTGI) and quality of life (EUROHIS-QOL), as well as sociodemographic and clinical information were obtained from an online survey and extracted from electronic health records. Mental health outcome patterns were investigated by semi-supervised medoid clustering and modeled by machine learning. Results Symptoms of PTSD were observed in 19% of participants. Three comparably sized subsets of participants were identified: a (1) neutral, (2) post-traumatic growth and (3) post-traumatic stress cluster. The post-traumatic stress cluster was characterized by high prevalence of symptoms of mental disorders, low resilience, low sense of coherence and low quality of life as well as by younger age, the highest frequency of pre-existing mental disorders and persisting physical health consequences of the accident. Individuals in this cluster self-reported a need for psychological or psychiatric support following the accident and more cautious behavior during mountain sports since the accident. Reliability of machine learning-based prediction of the cluster assignment based on 40 variables available during acute medical treatment of accident victims was limited. Discussion The findings demonstrate that symptoms of PTSD can occur following mountain sport accidents, which is significant considering that mountain sport athletes are typically perceived as physically and mentally exceptionally healthy individuals. The cross-sectional design of the study limits the ability to assess peri-traumatic mental health, which has been shown to be critical for identifying vulnerable patients in other populations (Schultebraucks et al., 2020). Our findings align with recent initiatives to implement peri-traumatic screening tools and enhance mental health support in trauma care (American College of Surgeons, 2022). Conclusion A subset of individuals show symptoms of mental health disorders including symptoms of PTSD when assessed at least 6 months after mountain sport accident. Since early identification of these vulnerable patients remains challenging, psychoeducational measures for all patients and low-threshold access to mental health support are key until the robust screening procedures for at risk individuals are available. References American College of Surgeons. (2022). Best practices guidelines: Screening and intervention for mental health disorders and substance use and misuse in the acute trauma patient. https://www.facs.org/media/nrcj31ku/mental-health-guidelines.pdf Salvotti, H. V., Tymoszuk, P., Ströhle, M., Paal, P., Brugger, H., Faulhaber, M., Kugler, N., Beck, T., Sperner-Unterweger, B., & Hüfner, K. (2024). Three distinct patterns of mental health response following accidents in mountain sports: A follow-up study of individuals treated at a tertiary trauma center. European Archives of Psychiatry and Clinical Neuroscience, 274, 1289-1310. https://doi.org/10.1007/s00406-024-01807-x Schultebraucks, K., Shalev, A. Y., Michopoulos, V., Grudzen, C. R., Shin, S. M., Stevens, J. S., Maples-Keller, J. L., Jovanovic, T., Bonanno, G. A., Rothbaum, B. O., Marmar, C. R., Nemeroff, C. B., Ressler, K. J., & Galatzer-Levy, I. R. (2020). A validated predictive algorithm of post-traumatic stress course following emergency department admission after a traumatic stressor. Nature medicine, 26(7), 1084–1088. https://doi.org/10.1038/s41591-020-0951-z

Data-based predictions of individual Cognitive Behavioral Therapy (CBT) treatment response are a fundamental step towards precision medicine. Past studies demonstrated only moderate prediction accuracy (i.e. ability to discriminate between responders and non-responders of a given treatment) when using clinical routine data such as demographic and questionnaire data, while neuroimaging data achieved superior prediction accuracy. However, these studies may be considerably biased due to very limited sample sizes and bias-prone methodology. Adequately powered and cross-validated samples are a prerequisite to evaluate predictive performance and to identify the most promising predictors. We therefore analyzed resting state functional magnet resonance imaging (rs-fMRI) data from two large clinical trials to test whether functional neuroimaging data continues to provide good prediction accuracy in much larger samples. Data came from two distinct German multicenter studies on exposure-based CBT for anxiety disorders, the Protect-AD and SpiderVR studies. We separately and independently preprocessed baseline rs-fMRI data from n = 220 patients (Protect-AD) and n = 190 patients (SpiderVR) and extracted a variety of features, including ROI-to-ROI and edge-functional connectivity, sliding-windows, and graph measures. Including these features in sophisticated machine learning pipelines, we found that predictions of individual outcomes never significantly differed from chance level, even when conducting a range of exploratory post-hoc analyses. Moreover, resting state data never provided prediction accuracy beyond the sociodemographic and clinical data. The analyses were independent of each other in terms of selecting methods to process resting state data for prediction input as well as in the used parameters of the machine learning pipelines, corroborating the external validity of the results. These similar findings in two independent studies, analyzed separately, urge caution regarding the interpretation of promising prediction results based on neuroimaging data from small samples and emphasizes that some of the prediction accuracies from previous studies may result from overestimation due to homogeneous data and weak cross-validation schemes. The promise of resting-state neuroimaging data to play an important role in the prediction of CBT treatment outcomes in patients with anxiety disorders remains yet to be delivered.

BackgroundIndividuals with anxiety disorders (ADs) often display hypervigilance to threat information, although this response may be less pronounced following psychotherapy. This study aims to investigate the unconscious recognition performance of facial expressions in patients with panic disorder (PD) post-treatment, shedding light on alterations in their emotional processing biases.MethodsPatients with PD (n=34) after (exposure-based) cognitive behavior therapy and healthy controls (n=43) performed a subliminal affective recognition task. Emotional facial expressions (fearful, happy, or mirrored) were displayed for 33 ms and backwardly masked by a neutral face. Participants completed a forced choice task to discriminate the briefly presented facial stimulus and an uncovered condition where only the neutral mask was shown. We conducted a secondary analysis to compare groups based on their four possible response types under the four stimulus conditions and examined the correlation of the false alarm rate for fear responses to non-fearful (happy, mirrored, and uncovered) stimuli with clinical anxiety symptoms.ResultsThe patient group showed a unique selection pattern in response to happy expressions, with significantly more correct “happy” responses compared to controls. Additionally, lower severity of anxiety symptoms after psychotherapy was associated with a decreased false fear response rate with non-threat presentations.ConclusionThese data suggest that patients with PD exhibited a “happy-face recognition advantage” after psychotherapy. Less symptoms after treatment were related to a reduced fear bias. Thus, a differential facial emotion detection task could be a suitable tool to monitor response patterns and biases in individuals with ADs in the context of psychotherapy.

The quest to decarbonize the lime and cement industry is challenging because of the amount and the nature of the CO2 emissions. The process emissions from calcination are unavoidable unless carbon capture is deployed. Nevertheless, the majority of the available carbon capture technologies are expensive and energy inefficient. The indirectly heated carbonate looping (IHCaL) process is a promising technology to capture CO2 from the lime and cement production, featuring low penalties in terms of economics and energy utilization. Previous works have highlighted the potential of the IHCaL, but the optimization of the process has not been discussed in enough detail and techno-economic implications are not yet fully understood. Within this work, ten scenarios using IHCaL technology to capture CO2 from a lime plant were simulated. Hereby, different process configurations, heat recovery strategies and fueling options were computed. The calculations for the capture facilities were performed with Aspen Plus® software and EBSILON®Professional was used to simulate the steam cycles. A techno-economic assessment was included as well, aided by the ECLIPSE software.The results demonstrate that the selection of the fuel for the combustor not only affects the CO2 balance and energy performance but is also an important cost driver —there were considerable economic advantages for the computed cases with middle-caloric solid recovered fuel (SRF). The analysis shows how the heat recovery strategy can be optimized to achieve tailored outcomes, such as reduced fuel requirement or increased power production. The specific primary energy consumption (from −0.3 to +2.5 MJLHV/tCO2,av) and cost for CO2 avoided (from −11 to +25 €/tCO2,av) using SRF are considerably low, compared with other technologies for the same application. The sensitivity study revealed that the main parameters that impact the economics are the discount rate and the project life. The capture plants are more sensitive to parameter changes than the reference plant, and the plants using SRF are more sensitive than the lignite-fueled plants. The conclusions from this work open a new pathway of experimental research to validate key assumptions and enable the industrial deployment of IHCaL technology before 2030.

Abstract Background Depression is a leading cause of disability worldwide and a significant contributor to the global burden of disease. Altered leptin levels are known to be associated with depressive symptoms, however discrepancies in the results of increased or decreased levels exist. Due to various limitations associated with commonly used antidepressant drugs, alternatives such as exercise therapy are gaining more importance. Therefore, the current study investigates whether depressed patients have higher leptin levels compared to healthy controls and if exercise is efficient to reduce these levels. Methods Leptin levels of 105 participants with major depressive disorder (MDD; 45.7% female, age mean ± SEM: 39.1 ± 1.0) and 34 healthy controls (HC; 61.8% female, age mean ± SEM: 36.0 ± 2.0) were measured before and after a bicycle ergometer test. Additionally, the MDD group was separated into three groups: two endurance exercise intervention groups (EX) differing in their intensities, and a waiting list control group (WL). Leptin levels were measured pre and post a 12-week exercise intervention or the waiting period. Results Baseline data showed no significant differences in leptin levels between the MDD and HC groups. As expected, correlation analyses displayed significant relations between leptin levels and body weight (HC: r = 0.474, p = 0.005; MDD: r = 0.198, p = 0.043) and even more with body fat content (HC: r = 0.755, p

Power-to-Ammonia (P2A) is a promising technology that can provide a low-emission energy carrier for long-term storage. This study presents an optimization approach to a novel small-scale containerized P2A concept commissioned in 2024. A dynamic nonlinear optimization problem of the P2A concept is set up, employing the non-commercial MOSAIC® software V3.0.1 in combination with the NEOS® server. In total, seven optimization solvers, ANTIGONE®, CONOPT®, IPOPT®, KNITRO®, MINOS®, PATHNLP®, and SNOPT®, are used. The first and main part of this work optimizes several disturbance scenarios of the concept and aims to determine the optimal reactor temperature profile to counter the disturbances. The optimization results suggest, for example, lowering the reactor temperature profile if the hydrogen and nitrogen inlet streams into the system decrease. The second part of this work presents a crude dynamic optimal scheduling model. This part aims to determine the amount of ammonia to be produced and sold given a randomized price of electricity for three consecutive points in time. The optimization results recommend decreasing production when the price of electricity is high and vice versa. However, the dynamic model must be improved to include fluctuations in the price of ammonia. Then, it can be used as a real-time optimization tool.

Oxyfuel is a combustion technology where the oxidant consists mainly of oxygen and carbon dioxide instead of oxygen and nitrogen. Since carbon dioxide has strongly absorbing bands in the thermal spectrum, the radiation properties of the flame change in an oxyfuel atmosphere compared to conventional combustion. When retrofitting an existing air-fired combustion system to an oxyfuel process, the oxygen content in the oxidant must be adjusted so that similar values for heat transfer by radiation are achieved. This measure allows the system to be operated with otherwise unchanged parameters. In this work, the thermal radiation of natural gas, pulverised walnut shells and lignite under an air and oxyfuel atmosphere is investigated in a semi-industrial combustion chamber with water-cooled membrane walls, at different oxygen concentrations and combustion parameters. While the radiative heat fluxes for natural gas with an oxygen content of 28 vol% in the oxidant are significantly higher than those for firing with air, the values for lignite are still below the air-firing, even with an oxygen content of 30 vol%. For walnut shells, the oxyfuel results are close to the air case for all oxygen concentrations between 27 and 33 vol%. The walnut shells show higher radiative emissions than the lignite at the same thermal output. For non-swirled flames, the radiative heat flux is lower than for swirled flames.

Chemical looping gasification (CLG) is a novel gasification technology, allowing for the efficient conversion of different solid feedstocks (e.g. biogenic residues) into a high-calorific syngas. As in any chemical looping technology, the oxygen carrier (OC), transporting heat and oxygen from the air to the fuel reactor, is crucial in attaining high process efficiencies. To investigate the fate of the OC during CLG in an industrial environment, ilmenite samples, collected during >400 hours of chemical operation in 1 MWth scale using three different biomass feedstocks, were analyzed using different lab techniques. In doing so, changes in OC particle morphology and composition induced by CLG operation were determined. Moreover, the most important physical and chemical characteristics of the utilized OC were measured. The ensuing dataset allowed for an in-depth evaluation of the CLG technology in semi-industrial scale in terms of OC lifetime and durability. It was found that in the absence of agglomeration, the cycled OC exhibits an oxygen transport capacity of 2.6 wt.-%, a particle density of 3400 kg/m3 and particle diameters between 60 and 250 µm in steady-state conditions. Moreover, it was found that OC loss via particle attrition determines the lifetime of the OC inside the 1 MWth CLG system. On the other hand, feedstock-related agglomeration, observed during CLG operation with wheat straw, was shown to impede OC circulation between AR and FR and thus prevent efficient CLG operation. In summary, the present study thus not only highlights that generally long-term CLG operation in industry-like conditions is feasible, but also provides important insights into measures to improve OC lifetime and durability inside an industrial chemical looping system, such as an optimization of cyclone efficiency or tailored pre-treatment of the utilized feedstock.

Chemical looping gasification is a novel dual fluidized bed technology for the conversion of solid feedstock to a nitrogen-free syngas without the need of pure oxygen. While multiple electrically heated lab-scale experiments have been performed, data on gas quality including tars formed during operation have not been reported yet for autothermal process operation. In this study we present autothermal operation of a chemical looping gasifier with a thermal input in the 1MWth range, utilizing two circulating fluidized bed reactors with foresting residue and industrial wood pellets as feedstock and Norwegian ilmenite as bed material. A cold gas efficiency of around 50% was achieved in the non optimized pilot plant, indicating that higher values can be reached in a commercial unit when minimizing heat losses. The carbon conversion was around 90%, and this value is expected to increase to almost 100% when raising the temperature, residence time, and cyclone efficiency in a commercial unit. The syngas has a very high quality with methane concentrations in the range of 7vol.-% to 10vol.-% and gravimetric tar content below 1g/Nm3 measured via tar protocol.

One-dimensional quantum optical models usually rest on the intuition of large-scale separation or frozen dynamics associated with the different spatial dimensions, for example when studying quasi one-dimensional atomic dynamics, potentially resulting in the violation of (3+1)-dimensional Maxwell's theory. Here, we provide a rigorous foundation for this approximation by means of the light-matter interaction. We show how the quantized electromagnetic field can be decomposed—exactly—into an infinite number of subfields living on a lower-dimensional subspace and containing the entirety of the spectrum when studying axially symmetric setups, such as with an optical fiber, a laser beam, or a waveguide. The dimensional reduction approximation then corresponds to a truncation in the number of such subfields that in turn, when considering the interaction with for instance an atom, corresponds to a modification to the atomic spatial profile. We explore under what conditions the standard approach is justified and when corrections are necessary in order to account for the dynamics due to the neglected spatial dimensions. In particular we examine what role vacuum fluctuations and structured laser modes play in the validity of the approximation.

N-chlorotaurine (NCT) a long-lived oxidant generated by leukocytes, can be synthesized chemically and applied topically as an anti-infective to different body sites, including the lung via inhalation. Here, we demonstrate the activity of NCT against viruses causing acute respiratory tract infections, namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza viruses, and respiratory syncytial virus (RSV). Virucidal activity of NCT was tested in plaque assays, confirmed by RT-qPCR assays. Attack on virus proteins was investigated by mass spectrometry. NCT revealed broad virucidal activity against all viruses tested at 37°C and pH 7. A significant reduction in infectious particles of SARS-CoV-2 isolates from early 2020 by 1 log10 was detected after 15 min of incubation in 1% NCT. Proteinaceous material simulating body fluids enhanced this activity by transchlorination mechanisms (1 −2 log10 reduction within 1–10 min). Tested SARS-CoV-2 variants B.1.1.7 (Alpha) und B.1.351 (Beta) showed a similar susceptibility. Influenza virus infectious particles were reduced by 3 log10 (H3N2) to 5 log10 (H1N1pdm), RSV by 4 log10 within a few min. Mass spectrometry of NCT-treated SARS-CoV-2 spike protein and 3C-like protease, influenza virus haemagglutinin and neuraminidase, and RSV fusion glycoprotein disclosed multiple sites of chlorination and oxidation as the molecular mechanism of action. Application of 1.0% NCT as a prophylactic and therapeutic strategy against acute viral respiratory tract infections deserves comprehensive clinical investigation.

Abstract Desflurane, isoflurane and sevoflurane, three halogenated ethers, are commonly used inhaled anesthetics, both in the operating room and in the intensive care unit (ICU). The potency and dosage of these drugs is expressed by the MAC value (minimum alveolar concentration). Their interaction with hemoglobin and its affinity for oxygen, best described by the oxygen dissociation curve (ODC), has already been investigated, with conflicting results. Altered by many factors, the ODC can be shifted to the left or to the right, therefore increasing or decreasing hemoglobin oxygen (Hb-O2) affinity. In venous blood samples of 22 healthy participants (11 female, 11 male) ODC were measured with a high-throughput method in vitro. Blood samples were either exposed to control or to three different concentrations of desflurane, isoflurane or sevoflurane prior to and during measurements (low, medium and high corresponding to MAC 0.5, MAC 1.0 and MAC 2.0). With increasing concentrations from control to medium, desflurane and isoflurane significantly decreased Hb-O2 affinity by shifting the ODC to the right (p = 0.016 and p

Power-to-Ammonia (P2A) is a promising technology that can provide a low-emission energy carrier for long-term storage. This study presents an optimization approach to a novel small-scale containerized P2A concept commissioned in 2024. A dynamic nonlinear optimization problem of the P2A concept is set up, employing the non-commercial MOSAIC® software V3.0.1 in combination with the NEOS® server. In total, seven optimization solvers, ANTIGONE®, CONOPT®, IPOPT®, KNITRO®, MINOS®, PATHNLP®, and SNOPT®, are used. The first and main part of this work optimizes several disturbance scenarios of the concept and aims to determine the optimal reactor temperature profile to counter the disturbances. The optimization results suggest, for example, lowering the reactor temperature profile if the hydrogen and nitrogen inlet streams into the system decrease. The second part of this work presents a crude dynamic optimal scheduling model. This part aims to determine the amount of ammonia to be produced and sold given a randomized price of electricity for three consecutive points in time. The optimization results recommend decreasing production when the price of electricity is high and vice versa. However, the dynamic model must be improved to include fluctuations in the price of ammonia. Then, it can be used as a real-time optimization tool. [ABSTRACT FROM AUTHOR]

Processing large and heterogeneous numbers of ideas submitted to crowdsourcing contests is a regular challenge for idea evaluators. The aim of this study is to investigate a potential use case for AI-based innovation management and to extend the knowledge of using automated novelty detection in idea evaluation processes. AI-based language models can automatically allocate short texts according to their semantic similarity in an embedded space. We represent the semantic content of crowdsourced ideas with the three contemporary text embeddings – Doc2Vec, SBERT, and GPT-3-based Ada Similarity – and compute their semantic distance to different reference sets using different novelty detection algorithms. We then compare the algorithm-generated scores with human novelty assessments to validate them. While selected novelty scores based on text embeddings correlate with humans, our results show that scores based on SBERT embeddings best match human novelty assessments. We also find that AI-based novelty detection approaches perform better for ideas below the median word count and when compared to a set of existing solutions, suggesting that the chosen language model is not the only factor influencing the applicability of the proposed approach. Furthermore, the study highlights important features and limitations of automatically generated novelty scores that need to be considered when complementing evaluators searching for new ideas in crowdsourcing contests and beyond. [ABSTRACT FROM AUTHOR]