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Background and Aims: This study aims at evaluating the ease of use of the new calorimeter for the measurement of energy expenditure (EE) in intensive care unit (ICU) patients. EE in ICU patients is highly variable depending on the severity of the disease and treatments. Clinicians need to measure EE by indirect calorimetry (IC) to optimize nutritional support for the better clinical outcome. However, indirect calorimeters available on the market have insufficient accuracy for clinical and research use. Difficulties of handling and interpretation of results often limit IC in ICU patients. An accurate, easy-to-use calorimeter has been developed to meet these needs. The Study Device: The new calorimeter (Quark RMR 2.0, COSMED) is capable of IC measurements in mechanically ventilated patients without warm-up and limited calibration. The disposable in-line pneumotach flow meter and direct sampling of respiratory gas from the ventilator circuit enables the accurate measurement of oxygen consumption volume (VO2) and CO2 production volume (VCO2) to derive the energy expenditure. The software interface to manage the device and the collected data provides easy-to-use, user-friendly interface. This calorimeter bears an European Commission (EC) Conformity Mark, and will be used in the way it is intended to be used as described in the instruction manual. Currently used indirect calorimeters at each study center will be used as the comparator. This study will evaluate the ease of use of the new calorimeter (Quark RMR 2.0 (COSMED, Italy)) in intensive care unit (ICU) patients compared to currently used calorimeters (i.e. Quark RMR 1.0(COSMED, Italy) or Deltatrac Metabolic Monitor (Datex, Finland)), as well as the stability and the feasibility of the measurements in various clinically relevant situations. Time needed to prepare and start indirect calorimetry (IC) measurement will be compared as the measure of the ease of use of the calorimeter.

The prevalence of type 1 diabetes has been steadily increasing for about 20 years. Despite therapeutic progress, between 20 and 65% of people with diabetes develop diabetic neuropathy, resulting in increased morbidity and mortality. Diabetic neuropathy is not limited to sensitive pain in the lower limbs. It also affects the fibres of the autonomic nervous system (ANS), which results in systemic complications, often disabling (erectile dysfunction, dysidrosis, gastroparesis, orthostatism, etc.) and a probable alteration in the body's thermogenic capacities, although this possibility has not been studied in humans. In rodents, it is possible to activate induced thermogenesis via central stimulation of the ANS or to inactivate it, which promotes the development of obesity and greater insulin resistance. This knowledge is based on cellular and animal models that have identified the bio-molecular mechanisms that give brown adipose tissue (BAT) the ability to dissipate energy in the form of heat. Induced thermogenesis is mediated by decoupling proteins 1 (DCS-1) located on the mitochondrial inner membrane of the TAB. DCS-1 decouple oxidative phosphorylation from ATP production, dissipating the proton gradient. The activation of UCP1 is particularly influenced by the sympathetic system and more particularly by catecholamines which will bind to ß3 adrenergic receptors (Rß3). In humans, the persistence of active areas of TAB has recently been demonstrated by positron emission tomography (PET) imaging using a glucose analogue radiotracer, 18F-Fluoro-Deoxy-Glucose (18F-FDG), coupled with the scanner (CT). Recently, it has been shown that the use of 18F-FDG PET coupled with magnetic resonance (MRI) is equally effective in differentiating TAB from white fat tissue with less patient irradiation. The activity of the TAB is estimated using the measurement of SUV (standard uptake value) which represents the total glycolytic activity of the tissue and is also commonly referred to as the total metabolic volume. It has been shown in humans that TAB activity is inversely correlated with body mass index and age and positively correlated with exposure to cold and stress levels[6]. Among diabetics, the data are disparate but the spontaneous prevalence of TAB appears to be reduced compared to the general population (1.1% vs 7.5%). To date, no studies have investigated a possible link between the decrease in TAB activity observed in diabetics and the presence of autonomic neuropathy, which is a common and often under-diagnosed complication of diabetes. The main purpose of this study is to evaluate whether the activity and distribution of TAB in patients with diabetes is influenced by the presence of diabetic neuropathy. On the other hand, if the existence of diabetic neuropathy influences energy expenditure in the event of exposure to cold. Finally, whether any differences in the activity and distribution of TAB could be related to changes in the central nervous system. The investigators plan to include a total of 24 patients with type 1 diabetes and separate them into 2 groups: group A; no neuropathic complications and group B; presence of neuropathy. All patients will be characterized in terms of clinical, metabolic and energy expenditure. The activity of the TAB will be evaluated through the use of 18F-FDG PET/IRM imaging, after a cold stimulation protocol (refrigerated jacket) in order to activate the TAB in a homogeneous manner among the participants. Influence of diabetic neuropathy on cold induced brown adipose tissue in type 1 diabetic patients.

This readme file provides all data and R codes used to perform the analyses presented in Figs. 2-4 of the main text and Supplementary Information Figures S1-S2-S3. FIGURE 2 - Seasonally_dated_GDs.txt: Contains information on the timing (Season) of rockfall (GD) in a given tree (Id) and a given year (yr) over the past 100 years. Inv refers to the operators which analyzed growth disturbances in the tree-ring series. Lat / Long refers to the position of the tree in CH1903/ Swiss Grid projection. Intensity (1-4) refers to (1), intermediate (2) and strong (3) GD. Intensity 4 was attributed to injuries (I). Only the 408 GD rated 3 (strong TRD) and 4 (injuries) were used in Fig. 2. Acronyms used for Response_type read as follows: TRD: Tangential rows of traumatic resin ducts; I: Injuries. Acronyms used for Season refer to Dormancy (1_D), early (2_EE), middle (3_ME) and late (4_LE) earlywood, whereas a GD found in the latewood was attributed to either the early (5_EL) or late (6_LL) latewood. - Trends_in_seasonality_R1.R: The data contained in "Seasonally_dated_GDs" were processed with the R script "Trends_in_Seasonality.R". This seasonal trend analysis code is inspired by work published by Schlögl et al. (2021; https://doi.org/10.1016/j.crm.2021.100294) and Heiser et al. (2022; https://doi.org/10.1029/2011JF002262). FIGURE 3-4-S1 - Tasch_GD.txt: Contains the raw data on rockfall impacts (GD) in a given year (yr) as found in all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - Rockfall_frequency_climate.R: The data contained in "Tasch_GD.txt" were processed with the R script "Rockfall_frequency_climate.R". - The temperature (Imfeld23_tmp.txt) and precipitation (Imfeld23_prc.txt) data used in Fig. 3 are from the Imfeld et al. 2023 (10.5194/cp-19-703-2023) gridded dataset (1x1 km lat/long) and were extracted at the grid point centered on the Täschgufer site. - The script set with temperature series enables to compute Fig. 4 (l.149:216) and Fig. 3 (l. 216:330); the script set with precipitation series enables to compute Fig. S1 FIGURE S2 - Tasch_GD.txt: Contains the raw data on rockfall impacts (GD) at the Täschgufer site in a given year (yr) as found in all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - Rockfall_frequency_borehole.R: is adapted from "Rockfall_frequency_climate.R" to work with the borehole dates. - Corvatsch0_6R1: Contains the Corvatsch borehole temperature series (2000-2020, 0.6m depth) (Hoelzle, M. et al. https://doi.org/10.5194/essd-14-1531-2022, 2022). FIGURE S3 - Plattje_GD.txt: Contains the raw data on rockfall impacts (GD) at the Plattje site in a given year (yr) as found all trees available in that same year (Sample_depth) as well as the cumulated diameter at breast height (cumulated_DBH) of all trees present in that same year. - - Rockfall_frequency_climate_Plattje.R: The data contained in "Plattje_GD.txt" were processed with the R script "Rockfall_frequency_climate_Plattje.R". - The temperature (Imfeld23_tmp_Plattje.txt) and precipitation (Imfeld23_prc_Plattje.txt) data used in Fig. 3 are from Imfeld et al. 2023 (10.5194/cp-19-703-2023) gridded dataset (1x1 km lat/long) and were extracted at the grid point centered on the Plattje site.

Data from: 30 Years Brings Changes to the Arthropod Community of Kibale National Park, Uganda by Opito, E.A., T. Alanko, U. Kalbitzer, M. Nummelin, P. Omeja, A. Valtonen, and Colin A. Chapman. 2023, Biotropica, Article DOI: 10.1111/btp.13206

Data and GrADS scripts needed to reproduce the figures in the article "Probabilistic forecasts of near-term climate change: verification for temperature and precipitation changes from years 1971-2000 to 2011-2020", submitted for publication in Climate Dynamics. Please see the file README for further details.

Dataset of process simulations results of the natural gas sweetening and flue gas treatment (first and second sheet, respectively as indicated by the sheet name in the .xlsx file). The dataset refers to the publication Bayesian Symbolic Learning to Build Analytical Correlations from Rigorous Process Simulations: Application to CO2 Capture Technologies by V. Negri, Vàzquey D., Sales-Pardo, Marta, Guimerà, R. and Guillén-Gosàlbez, G. The training and testing dataset are used to generate the figures in the main manuscript and supplementary information.

Dataset related to the article: Virtanen, E.A., Lappalainen, J., Nurmi, M., Viitasalo, M., Tikanmäki, M., Heinonen, J., Atlaskin, E., Kallasvuo, M., Tikkanen, H., Moilanen, A. (2022) Balancing profitability of energy production, societal impacts and biodiversity in offshore wind farm design. Renewable and Sustainable Energy Reviews 158, 112087. Dataset includes suitability maps for offshore windfarms, where priority values are scaled between 0-1 (note the reversed value scale): analysis solution (A) economy, (B) society, (C) biodiversity, (D) restrictions, (E) A+B+C without restrictions and (F) A+B+C with restrictions. Dataset includes also the conflict map (and R script), where each three main solutions (A, B, C) are mapped onto an RGB color composite map. Additional details can be found from the published article: https://doi.org/10.1016/j.rser.2022.112087

Aim: Climate warming and biodiversity loss both alter plant productivity, yet we lack an understanding of how biodiversity regulates the responses of ecosystems to warming. In this study, we examine how plant diversity regulates the responses of grassland productivity to experimental warming using meta-analytic techniques. Location: Global Major taxa studied: Grassland ecosystems Methods: Our meta-analysis is based on warming responses of 40 different plant communities obtained from 20 independent studies on grasslands across five continents. Results: Our results show that plant diversity and its responses to warming were the most important factors regulating the warming effects on plant productivity, among all the factors considered (plant diversity, climate and experimental settings). Specifically, warming increased plant productivity when plant diversity (indicated by effective number of species) in grasslands was lesser than 10, whereas warming decreased plant productivity when plant diversity was greater than 10. Moreover, the structural equation modelling showed that the magnitude of warming enhanced plant productivity by increasing the performance of dominant plant species in grasslands of diversity lesser than 10. The negative effects of warming on productivity in grasslands with plant diversity greater than 10 were partly explained by diversity-induced decline in plant dominance. Main Conclusions: Our findings suggest that the positive or negative effect of warming on grassland productivity depends on how biodiverse a grassland is. This could mainly owe to differences in how warming may affect plant dominance and subsequent shifts in interspecific interactions in grasslands of different plant diversity levels.