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Monitoring vegetation health and its response to climate conditions is critical for assessing the impact of climate change on urban environments. While many studies simulate and map the health of vegetation, there seems to be a lack of high-resolution, low-scale data and easy-to-use tools for managers in the municipal administration that they can make use of for decision-making. Data related to climate and vegetation indicators, such as those provided by the C3S Copernicus Data Store (CDS), are mostly available with a coarse resolution but readily available as freely available and open data. This study aims to develop a systematic approach and workflow to provide a simple tool for monitoring vegetation changes and health. We built a toolbox to streamline the geoprocessing workflow. The data derived from CDS included bioclimate indicators such as the annual moisture index and the minimum temperature of the coldest month (BIO06). The biophysical parameters used are leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR). We used a linear regression model to derive equations for downscaled biophysical parameters, applying vegetation indices derived from Sentinel-2, to identify the vegetation health status. We also downscaled the bioclimatic indicators using the digital elevation model (DEM) and Landsat surface temperature derived from Landsat 8 through Bayesian kriging regression. The downscaled indicators serve as a critical input for forest-based classification regression to model climate envelopes to address suitable climate conditions for vegetation growth. The results derived contribute to the overall development of a workflow and tool for and within the CoKLIMAx project to gain and deliver new insights that capture vegetation health by explicitly using data from the CDS with a focus on the City of Constance at Lake Constance in southern Germany. The results shall help gain new insights and improve urban resilient, climate-adaptive planning by providing an intuitive tool for monitoring vegetation health and its response to climate conditions.

Specific climate adaptation and resilience measures can be efficiently designed and implemented at regional and local levels. Climate and environmental databases are critical for achieving the sustainable development goals (SDGs) and for efficiently planning and implementing appropriate adaptation measures. Available federated and distributed databases can serve as necessary starting points for municipalities to identify needs, prioritize resources, and allocate investments, taking into account often tight budget constraints. High-quality geospatial, climate, and environmental data are now broadly available and remote sensing data, e.g., Copernicus services, will be critical. There are forward-looking approaches to use these datasets to derive forecasts for optimizing urban planning processes for local governments. On the municipal level, however, the existing data have only been used to a limited extent. There are no adequate tools for urban planning with which remote sensing data can be merged and meaningfully combined with local data and further processed and applied in municipal planning and decision-making. Therefore, our project CoKLIMAx aims at the development of new digital products, advanced urban services, and procedures, such as the development of practical technical tools that capture different remote sensing and in-situ data sets for validation and further processing. CoKLIMAx will be used to develop a scalable toolbox for urban planning to increase climate resilience. Focus areas of the project will be water (e.g., soil sealing, stormwater drainage, retention, and flood protection), urban (micro)climate (e.g., heat islands and air flows), and vegetation (e.g., greening strategy, vegetation monitoring/vitality). To this end, new digital process structures will be embedded in local government to enable better policy decisions for the future.

Myostatin and activin A are structurally related secreted proteins that act to limit skeletal muscle growth. The cellular targets for myostatin and activin A in muscle and the role of satellite cells in mediating muscle hypertrophy induced by inhibition of this signaling pathway have not been fully elucidated. Here we show that myostatin/activin A inhibition can cause muscle hypertrophy in mice lacking either syndecan4 or Pax7, both of which are important for satellite cell function and development. Moreover, we show that muscle hypertrophy after pharmacological blockade of this pathway occurs without significant satellite cell proliferation and fusion to myofibers and without an increase in the number of myonuclei per myofiber. Finally, we show that genetic ablation of Acvr2b , which encodes a high-affinity receptor for myostatin and activin A specifically in myofibers is sufficient to induce muscle hypertrophy. All of these findings are consistent with satellite cells playing little or no role in myostatin/activin A signaling in vivo and render support that inhibition of this signaling pathway can be an effective therapeutic approach for increasing muscle growth even in disease settings characterized by satellite cell dysfunction.

Background: Surgical treatment for erosive pyogenic spondylodiscitis of the lumbar spine is challenging as, following debridement of the intervertebral and bony abscess, a large and irregular defect is created. Sufficient defect reconstruction with conventional implants using a posterior approach is often impossible. Therefore, we developed the “Cement-PLIF”, a single-stage posterior lumbar procedure, combining posterior lumbar interbody fusion (PLIF) with defect-filling using antibiotic-loaded polymethylmethacrylate (PMMA). This study first describes and evaluates the procedure’s efficacy, safety, and infection eradication rate. Radiological implant stability, bone-regeneration, sagittal profile reconstruction, procedure-related complications, and pre-existing comorbidities were further analyzed. Methods: A retrospective cohort study analyzing 73 consecutive patients with a minimum of a one-year follow-up from 2000–2017. Patient-reported pain levels and improvement in infectious serological parameters evaluated the clinical outcome. Sagittal profile reconstruction, anterior bone-regeneration, and posterior fusion were analyzed in a.p. and lateral radiographs. A Kaplan–Meier analysis was used to determine the impact of pre-existing comorbidities on mortality. Pre-existing comorbidities were quantified using the Charlson-Comorbidity Index (CCI). Results: Mean follow-up was 3.3 (range: 1–16; ±3.2) years. There was no evidence of infection persistence in all patients at the one-year follow-up. One patient underwent revision surgery for early local infection recurrence (1.4%). Five (6.9%) patients required an early secondary intervention at the same level due to minor complications. Radiological follow-up revealed implant stability in 70/73 (95.9%) cases. Successful sagittal reconstruction was demonstrated in all patients (p < 0.001). There was a significant correlation between Kaplan–Meier survival and the number of pre-existing comorbidities (24-months-survival: CCI ≤ 3: 100%; CCI ≥ 3: 84.6%; p = 0.005). Conclusions: The Cement-PLIF procedure for pyogenic erosive spondylodiscitis is an effective and safe treatment as evaluated by infection elimination, clinical outcome, restoration, and maintenance of stability and sagittal alignment.

Background: We investigated whether supervised machine learning (ML) algorithms can be effectively applied to segmentation results of an FDA-approved deep learning-based brain morphometry algorithm in order to aid objective empirical neuroradiological scoring and comprehensive dementia diagnosis. Methods: A single-center retrospective cohort of 275 patients (157F, 57.1%; median age: 68 years, range: 17-95 years) with suspected neurocognitive disorders was retrieved from local RIS/PACS between 01/2012 and 08/2020. Brain volumetric segmentation (BVS) of 47 anatomical regions and structures was performed using the AI-Rad Companion MR Brain (Siemens Healthineers) software based on 3 T (Siemens, Trio) isotropic (1 mm) sagittal T1-weigthed MPRAGE (3D-MPRAGE) images. Three neuroradiologists generated consensus values of well-established empirical scoring systems including global cortical atrophy (GCA), medial temporal lobe atrophy (MTA), Koedam for parietal atrophy and Fazekas scale for white matter lesions (overall 12 features). Supervised ML algorithms such as dense neural networks (DNN), tree-based algorithms were trained on BVS values within 5x-fold nested cross validation setup [1] to suggest these empirical scores as target labels in a standardized manner [2]. Results: BVS values of key anatomical structures could be matched to the respective empirical scores based on correlation metrics. In contrast to automated segmented volumes (rsp = 0.68, p< 0.001), empirical scores demonstrated large variances and low correlation with age (rsp = 0.4, p < 0.001). DNNs achieved the highest classification accuracies (70-88%) for all scores but suffered from overfitting and poor calibration. Feature importance metrics of tree-based algorithms selected the most sensible anatomical BVS features. Discussion: Supervised ML algorithms on top of deep learning-based volumetric morphometry results can objectively aid radiological and clinical diagnosis of neurocognitive disorders while providing robust and reproducible measures for follow-up evaluation. Conclusion: Multilayer ML setup is feasible to improve report quality and diagnostic accuracy of neurodegenerative disorders. Due to its anonymized nature, our study cohort could serve as benchmark data set for comparing algorithms of various vendors.

Objective The purpose of this study was to demonstrate the feasibility of a combined cooling strategy started out of hospital as an adjunctive to percutaneous coronary intervention (PCI) in the treatment of ST-elevation acute coronary syndrome (STE-ACS). Design Non-randomised, single-centre feasibility trial. Setting Department of emergency medicine of a tertiary-care facility, Medical University of Vienna, Vienna, Austria. In cooperation with the Municipal ambulance service of the city of Vienna. Patients Consecutive patients with STE-ACS presenting to the emergency medical service within 6 h after symptom onset. Interventions Cooling was initiated with surface cooling pads in the out-of-hospital setting, followed by the administration of 1000–2000 mL of cold saline at hospital arrival and completed by endovascular cooling in the catheterisation laboratory. Main outcome measures Feasibility of lowering core temperature below 35.0°C prior to immediately performed revascularisation. Safety and tolerability of the cooling procedure. Results In enrolled 19 patients (one woman, median age 51 years (IQR 45–59)), symptom onset to first medical contact (FMC) was 45 min (IQR 31–85). A core temperature below 35.0°C at reperfusion of the culprit lesion was achieved in 11 patients (78%) within 100 min (IQR 90–111) after FMC without any cooling-related serious adverse event. Temperature could be lowered from baseline 36.4°C (IQR 36.2–36.5°C) to 34.4°C (IQR 34.1–35.0°C) at the time of reperfusion. Conclusions With limitations an immediate out-of-hospital therapeutic hypothermia strategy was feasible and safe in patients with STE-ACS undergoing primary PCI. Clinical trial registration http://www.clinicaltrials.gov/ct2/show/NCT01864343; clinical trials unique identifier: NCT01864343

Background Alterations of the binding epitopes of bone morphogenetic protein-2 (BMP-2) lead to a modified interaction with the ectodomains of BMP receptors. In the present study the biological effect of a BMP-2 double mutant with antagonistic activity was evaluated in vivo. Methods Equine-derived collagenous carriers were loaded with recombinant human BMP-2 (rhBMP-2) in a well-known dose to provide an osteoinductive stimulus. The study was performed in a split animal design: carriers only coupled with rhBMP-2 (control) were implanted into prepared cavities of lower limb muscle of rats, specimens coupled with rhBMP-2 as well as BMP-2 double mutant were placed into the opposite limb in the same way. After 28 days the carriers were explanted, measured radiographically and characterized histologically. Results As expected, the BMP-2 loaded implants showed a typical heterotopic bone formation. The specimens coupled with both proteins showed a significant decreased bone formation in a dose dependent manner. Conclusion The antagonistic effect of a specific BMP-2 double mutant could be demonstrated in vivo. The dose dependent influence on heterotopic bone formation by preventing rhBMP-2 induced osteoinduction suggests a competitive receptor antagonism.