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This compressed dataset includes the queried CVS files from 16 GCAM data bases generated for the study titled "Trade-Offs in Land-Based Carbon Removal Measures under 1.5°C and 2°C Futures". The data sets provided here came from the GCAM model output. Please find the model and code information at the GitHub repo: realxinzhao/paper-LandBasedCDR-GCAM. In addition, the data were used for generating results used in the paper. See more information at realxinzhao/paper-LandBasedCDR-DisplayItems.

Bioassays of effect of botanical extracts against sucking pests of Horticultural crops

Disclaimer: These datasets were generated during the course of academic research conducted at the Faculty of Medicine, The Chinese University of Hong Kong, which received ethics approval by The Joint Chinese University of Hong Kong ��� New Territories East Cluster Clinical Research Ethics Committee and/or the Institutional Review Board of the University of Hong Kong/Hospital Authority Hong Kong West Cluster of the Hospital Authority. They are published for further advancement of medical research in full compliance with University Regulations and Policy on Dataset Deposit and Sharing. For additional information: https://libguides.lib.cuhk.edu.hk/RDM/dataset_deposit The use of these datasets should provide acknowledgements of such efforts by citing this DOI. Data set on heart failure due to different causes

Sturla F, Caimi A, Romarowski RM, Nano G, Glauber M, Redaelli A, Votta E, Marrocco-Trischitta MM. Fast Approximate Quantification of Endovascular Stent Graft Displacement Forces in the Bovine Aortic Arch Variant. J Endovasc Ther. 2022 May 19:15266028221095403. doi: 10.1177/15266028221095403. Epub ahead of print. PMID: 35588222. Abstract Purpose: Displacement forces (DFs) identify hostile landing zones for stent graft deployment in thoracic endovascular aortic repair (TEVAR). However, their use in TEVAR planning is hampered by the need for time-expensive computational fluid dynamics (CFD). We propose a novel fast-approximate computation of DFs merely exploiting aortic arch anatomy, as derived from the computed tomography (CT) and a measure of central aortic pressure. Materials and methods: We tested the fast-approximate approach against CFD gold-standard in 34 subjects with the "bovine" aortic arch variant. For each dataset, a 3-dimensional (3D) model of the aortic arch lumen was reconstructed from computed tomography angiography and CFD then employed to compute DFs within the aortic proximal landing zones. To quantify fast-approximate DFs, the wall shear stress contribution to the DF was neglected and blood pressure space-distribution was averaged on the entire aortic wall to reliably approximate the patient-specific central blood pressure. Also, DF values were normalized on the corresponding proximal landing zone area to obtain the equivalent surface traction (EST). Results: Fast-approximate approach consistently reflected (r2=0.99, p<0.0001) the DF pattern obtained by CFD, with a -1.1% and 0.7° bias in DFs magnitude and orientation, respectively. The normalized EST progressively increased (p<0.0001) from zone 0 to zone 3 regardless of the type of arch, with proximal landing zone 3 showing significantly greater forces than zone 2 (p<0.0001). Upon DF normalization to the corresponding aortic surface, fast-approximate EST was decoupled in blood pressure and a dimensionless shape vector (S) reflecting aortic arch morphology. S showed a zone-specific pattern of orientation and proved a valid biomechanical blueprint of DF impact on the thoracic aortic wall. Conclusion: Requiring only a few seconds and quantifying clinically relevant biomechanical parameters of proximal landing zones for arch TEVAR, our method suits the real preoperative decision-making process. It paves the way toward analyzing large population of patients and hence to define threshold values for a future patient-specific preoperative TEVAR planning.

Dataset and R code to run the thermal model for 1985-2019.

Data set from Moons P, Luyckx K, Kovacs AH, Holbein CE, Thomet C, Budts W, Enomoto J, Sluman MA, Yang HL, Jackson JL, Khairy P, Cook SC, Chidambarathanu S, Alday L, Eriksen K, Dellborg M, Berghammer M, Johansson B, Mackie AS, Menahem S, Caruana M, Veldtman G, Soufi A, Fernandes SM, White K, Callus E, Kutty S, Apers S; APPROACH-IS Consortium and the International Society for Adult Congenital Heart Disease (ISACHD). Prevalence and Effects of Cigarette Smoking, Cannabis Consumption, and Co-use in Adults From 15 Countries With Congenital Heart Disease. Can J Cardiol. 2019 Dec;35(12):1842-1850. doi: 10.1016/j.cjca.2019.07.635. Epub 2019 Aug 14. PMID: 31813510. This is the abstract: Background: The prevalence and effects of cigarette smoking and cannabis use in persons with congenital heart disease (CHD) are poorly understood. We (1) described the prevalence of cigarette smoking, cannabis consumption, and co-use in adults with CHD; (2) investigated intercountry differences; (3) tested the relative effects on physical functioning, mental health, and quality of life (QOL); and (4) quantified the differential effect of cigarette smoking, cannabis use, or co-use on those outcomes. Methods: APPROACH-IS was a cross-sectional study, including 4028 adults with CHD from 15 countries. Patients completed questionnaires to measure physical functioning, mental health, and QOL. Smoking status and cannabis use were assessed by means of the Health Behaviour Scale-Congenital Heart Disease. Linear models with doubly robust estimations were computed after groups were balanced with the use of propensity weighting. Results: Overall, 14% of men and 11% of women smoked cigarettes only; 8% of men and 4% of women consumed cannabis only; and 4% of men and 1% of women used both substances. Large intercountry variations were observed, with Switzerland having the highest prevalence for smoking cigarettes (24% of men, 19% of women) and Canada the highest for cannabis use (19% of men, 4% of women). Cigarette smoking had a small negative effect on patient-reported outcomes, and the effect of cannabis was negligible. The effect of co-use was more prominent, with a moderate negative effect on mental health. Conclusions: We found significant intercountry variability in cigarette and cannabis use in adults with CHD. Co-use has the most detrimental effects on patient-reported outcomes.

In-session dropout prediction model This project describes an in-session prediction model that predicts student early dropout from online learning exercises. Dropout prediction models for Massive Open Online Courses (MOOCs) have shown high accuracy rates in the past and make personalized interventions possible. While MOOCs have traditionally high dropout rates, school homework and assignments are supposed to be completed by all learners. In the pandemic, online learning platforms were used to support school teaching. In this setting, dropout predictions have to be designed differently as a simple dropout from the (mandatory) class is not possible. The aim of our work is to transfer traditional temporal dropout prediction models to in-session dropout prediction for school-supporting learning platforms. For this purpose, we used data from more than 164,000 sessions by 52,000 users of the online language learning platform orthografietrainer.net. We calculated time-progressive machine learning models that predict dropout after each step (completed sentence) in the assignment using learning process data. The multilayer perceptron is outperforming the baseline algorithms with up to 87% accuracy. By extending the binary prediction with dropout probabilities, we were able to design a personalized intervention strategy that distinguishes between motivational and subject-specific interventions. A random state is not set, thus, results might differ marginally. Whole project described in: N. Rzepka, K. Simbeck, H.-G. Müller, and N. Pinkwart Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU, SciTePress, 2022, ISBN 978-989-758-562-3 {"references": ["N. Rzepka, K. Simbeck, H.-G. M\u00fcller, and N. Pinkwart Keep It Up: In-session Dropout Prediction to Support Blended Classroom Scenarios Proceedings of the 14th International Conference on Computer Supported Education - Volume 2: CSEDU, SciTePress, 2022, ISBN 978-989-758-562-3"]}

This repository includes the data used to compile the results presented in the scientific publication: "Non-exponential transverse relaxation decay in subcortical grey matter". Rita Oliveira, Antoine LuttiLaboratory for Research in Neuroimaging (LREN)Department of Clinical Neuroscience, Lausanne University Hospital and University of LausanneMont-Paisible 16, CH-1011 Lausanne, SwitzerlandClassically, the MRI transverse relaxation decay is analyzed by fitting the signal decay over echo time voxel-wise with a monoexponential function (Exp), for which a decay rate R2∗ is estimated. However, the presence of magnetic material within the tissue, such as iron-loaded cells, myelin, or blood vessels, introduces variations in the magnetic field, which can modify the exponential behaviour of the decay (1,2). In such inhomogeneous magnetic fields, the theory predicts a transient regime starting with a Gaussian behaviour at short echo times and approaching a monoexponential relaxation at long echo times (1,3–6).We highlight three different analytical descriptions of the signal decay that account for the transient regime of the transverse relaxation decay: the Anderson and Weiss, 1953 model (AW), the Sukstanskii and Yablonskiy, 2003 model (SY), and following a Padé approximation (Padé) of the transition from Gaussian to exponential decay.This repository includes transverse relaxation decay data that enables the observation of the non-exponential MRI transverse relaxation. The data was acquired from 5 healthy volunteers at 3T. AW, SY, Padé, and Exp are the different methods that we used to fit the data with. Here we focus on the analysis of subcortical brain regions: Substantia Nigra, Pallidum, Putamen, Caudate, and Thalamus. Data DescriptionThe necessary files to compile the results presented in the scientific publication can be found in the ‘multiecho’ folder. There are three different folders corresponding to three repetitions of the acquisition (‘rep1’ to ‘rep3’). The data consists of:• resc_den_ subject_name_N.nii: magnitude image file corresponding to echo N. These files were previously denoised and rescaled (resc_den). The description field of the header of the images contains the corresponding TE at which the image was acquired, which will be needed in the fitting routine. Since we focus on the analysis of subcortical brain regions (Substantia Nigra, Pallidum, Putamen, Caudate, and Thalamus), the multi-echo data is masked within this region.• nf: value of the noise floor level. Corresponds to the noncentrality parameter of a Rician distribution fitted to the background signal. In the ‘anat’ folder the user has access to:• MT: Magnetization Transfer map (MTsat) that serves as a reference anatomical image.• ROI folder: contains masks of each of the 5 regions of interest analyzed in the scientific paper: Substantia Nigra, Pallidum, Putamen, Caudate, and Thalamus. The ‘modelfits’ folder contains pre-computed results for each subject analyzed. If the user uses the analysis code that comes along with this dataset (https://github.com/LREN-physics/TransverseRelaxation), this folder will be overwritten with the new results. For each method (‘AW’, ‘SY’, ‘Pade’, ‘Exp’) there is a folder containing the corresponding resulting maps. These maps are:• R2s.nii: map of R2,micro∗ [ms-1] for ‘AW’, ‘SY’, and ‘Pade’ options. Map of R2∗ [ms-1] for ‘Exp’ fit.• OmegaSq.nii: map of 〈\(\Omega^2\)〉 [ms-2]. Not available for ‘Exp’ fit.• TE0signal.nii: map of the initial signal amplitude S0.• T2mol.nii: map of the inverse of effective transverse relaxation rate resulting from processes on the nanoscale [ms]• AIC.nii: map of Akaike information criterion regarding the fitting procedure.• MSE.nii: maps of the mean square error of the fitting procedure.• DataMatrix.mat: matrix containing the data used for the fitting procedure.• VoxelIndices.mat: vector containing the indices of the voxels corresponding to the analyzed data, which is restricted to the subcortical regions.• Params.mat: structure containing the parameters used for the analysis.Inside ‘modelfits’ there are also two folders corresponding to two different regimes that can describe the transverse relaxation decay: static dephasing regime (‘SDR’) or diffusion narrowing regime (‘DNR’). Under the assumption of SDR, we computed:• ki_ppm.nii: maps of 𝛥𝜒, which is the difference in susceptibility of the magnetic inclusions to the surrounding tissue [addimentional, in ppm and in SI units]• zeta.nii: maps of 𝜁, which is the volume fraction of the magnetic inclusions [addimentional]Under the assumption of DNR, we computed:• alpha.nii: 𝛼=𝜏〈\(\sqrt{\Omega^2}\)〉 [addimentional]• tau_ms.nii: maps of 𝜏, which is the time scale for water molecules to diffuse away from magnetic inclusions [ms]Please refer to the corresponding article for a complete description of the methods and corresponding estimated parameters.

Biochemical analysis of similarities between human and SARS-CoV-2 proteins