dataesr/scientific-paragraphs-categorization huggingface dataset:
Meteorological data To contextualize the observations from the time lapse cameras, meteorological data from the SILA weather stations (CEN) were used. The VDTSILA station is located in the valley and UMIROCA station is located near the Umiujaq airport  (CEN 2018). Detailed hourly air temperature, wind speed and direction, and precipitation (from the VDTSILA station only) data were examined, providing valuable information about the prevailing conditions occurring at specific moments or periods that were identified as being significant according to the results from the analysis of images. The 4-11 m/s interval suggested by  Li and Pomeroy (1997)  was used to contextualize snow transport with the wind speed. Due to the malfunction of the VDTSILA station between late December and early February, precipitation data could not be recorded; data from the Kuujjuarapik station (160 km south of Umiujaq) were used instead.
Fieldwork Photographs taken in situ during the summer and images taken by the time lapse cameras during the winter also helped to estimate snow depth, based on well-known scales (e. g. , human scale, length and thickness of large boulders, height of vegetation, and height of the camera from the ground). Examination of snow avalanche deposits took place from 11 June to 16 June 2018. On-site characterization included measurement of the deposits and description of the shape of the track, the appearance (e. g. , clean or dirty snow) and roughness (e. g. , smooth or coarse snow surface). The dimensions of the deposits were determined using a handheld Garmin (Schaffhausen, Switzerland) Global Positioning System in the field and high-resolution satellite imagery and elevation data (from Québec's Ministère des Ressources Naturelles, de la Faune et des Parcs) in ArcGIS (ESRI, Redlands, California, USA).
Moreover, we witnessed about 20 snow avalanches during fieldwork, allowing us to observe their flowing processes. Informal discussions with local residents helped us to better understand the occurrence of avalanches in the area and the prevailing meteorological conditions. This fieldwork complemented the data obtained from the cameras, by offering a visual corroboration of the snow avalanche dynamics at the scale of the valley, although only for a short period of time. Formal interviews will be conducted in a follow-up study.
Snow cornice development Images from the camera TAS1, installed at the edge of the plateau, provided evidence of the formation of a snow cornice over the winter of 2017-2018. We were able to observe the initial accretion of the cornice from 1 November to 20 November, a period characterized by frequent blizzard conditions (Fig. 2 ), the first of the season. Snowfall totaled 118 cm during this period, whereas wind speed averaged 6. 2 m/s, with maximum speed reaching up to 14 m/s. Data from the UMIROCA and VDTSILA stations indicated a predominantly southwesterly wind on snowy/blizzard days. The direction of these blizzard winds, almost perpendicular to the slope orientation (∼212°) was, therefore, optimal for the formation and growth of a snow cornice. In addition, the wind enabled efficient snow transport toward the ridgeline due to the southwest orientation of the gently-inclined cuesta plateau and the virtual lack of obstacles to the wind.
After 20 November 2017, the cornice continued to expand gradually. Frequent drifting and blowing snow conditions occurred from November 2017 to May 2018 and evidence of snow displacement on the upwind cuesta plateau was observed, such as sastrugi formation. Oriented with the southwesterly prevailing winds, the irregular dune-shaped features formed parallel ripples facing northeast. Wind data from the UMIROCA station showed that from 1 November 2017 to 1 June 2018, 67. 9% of total days had an average wind speed between 4 and 11 m/s, and 81. 6% of total days had a maximum wind speed within this interval. This Pagination not final (cite DOI) / Pagination provisoire (citer le DOI) denotes the efficient wind speed for dry snow transport in Arctic regions, which is favourable for the formation and growth of snow cornices  (Li and Pomeroy 1997; McClung and Schaerer 2006).
In addition, the cameras TAS2 and TAS3 only have a ∼200 m wide range of vision, representing a limited sample of the valley's 5 km long southwest side. The conditions could have been Pagination not final (cite DOI) / Pagination provisoire (citer le DOI) locally favourable (i. e. , numerous events observed on camera TAS2) or unfavourable (i. e. , very few events observed on camera TAS3) for cornice failures and snow avalanches, although it is impossible to extrapolate these events (or non-events) at the valley scale. Data collected and analyzed at this time also only covers one winter and, thus, it is impossible to obtain an interannual pattern at this stage. The final consideration concerns the meteorological data from the UMIROCA and VDTSILA stations. As the first is located at Umiujaq airport and the second is located on the valley floor, both stations do not reflect the actual conditions occurring near the ridgeline.
Acknowledgements Funding for this project was provided by the  Natural Sciences and Engineering Research Council of Canada (NSERC) ,  IPEV DeSiGN, LabEx DRIIHM  ( French program  " Investissements d'Avenir" -ANR-11-LABX -0010managed by the  French National Research Agency, ANR)  and  OHMi NUNAVIK-TUKISIG. Thanks are extended to the community of  Umiujaq  for its ongoing support. The authors also want to thank  Félix Faucher ,  Julien Lebrun , and  Þorsteinn Saemundsson  for their valuable help in the field, and the  Centre d'études nordiques (CEN)  for logistical support. Arctic Science Vol. 00, 2021 Published by  NRC   Research Press Arctic Science Downloaded from cdnsciencepub. com by BIBLIOTHEQUE UNIV SECTION SCIENCES on   10/27/21  For personal use only.
This work was supported in part by the  U. K. Engineering and Physical Sciences Research Council (EPSRC)  under Grant  EP/P010164/1 , in part by the  European Commission  through the European Infrastructure powering the internet of things (EnABLES) Project by  H2020-EU. 1. 4. 1. 2under  Grant  730957 , and in part by the  WEARable multiPLEXed biomedical electrodes (WEARPLEX)  Project through  H2020-EU. 2. 1. 1  under Grant  825339. The work of  Steve Beeby  was supported by the  Royal Academy of Engineering  through the chairs in emerging technologies scheme.
• Data collection and structure determination The X-ray diffraction data were collected at 100K on the PROXIMA-1 beamline (Synchrotron SOLEIL, St Aubin, France) using a Dectris pixel-array PILATUS 6M detector at a wavelength of 1. 07114 Å to a resolution of 2. 80 Å. The diffraction images were integrated with the program XDS  [38]. The KirBac3. 1 W46R crystals belong to the P2 1 2 1 2 space group with cell dimensions of 106. 77 Å 113. 98 Å 89. 18 Å. Molecular replacement was carried out with Phaser (CCP4 suite), using a search model derived from the closed structure of KirBac3. 1 closed state at 2. 85 Å (PDB 2WLJ). Five percent of the reflections were set aside in the free R set. The asymmetric unit has a solvent content of 69. 4% and contains two polypeptide molecules. The model was refined in real space interactively using Coot  [39]  and refined using BUSTER-TNT [40], which included a final round of translation, vibration, and screw-rotation (TLS) anisotropic refinement, as implemented in BUSTER-TNT.
An additional restrained refinement was carried out using REFMACS  [41]. The final model contains two polypeptide chains with 276 and 265 residues out of 301, respectively. The model also contains 23 solvent molecules and four ions (three K + and one Mg 2+ ). The final model having 99. 3% of all residues in the favored or allowed regions of the Ramachandran plot was validated using MolProbity  [42]. The crystals' parameters, data statistics, and refinement parameters are shown in Table  1. All structural figures were generated with PyMOL (version 1. 3r1). The PDB accession code of this structure is 7ADI. 4. 1. 3. Pepsin Digestion, Hydrogen/Deuterium Exchange Approach Coupled to Mass Spectrometry (HDX-MS) and HPLC Peptide separation of this mutant channel was performed as outlined before  [18].
Each deuteration experiment was performed in triplicate. Data were processed with MassHunter software (Agilent Technologies). The qualitative analysis, the deconvolution, and calculation of the average masses were carried out in Magtran  [43]. 4. 1. 4. Functional Studies of the KirBac W46R Protein in Black Lipid Membrane Purified KirBac3. 1 W46R (130 µg/mL) in DM (decylmaltoside) detergent (1. 5 mM) was added to the upper chamber (150 µL) to a preformed bilayer. The Orbit mini apparatus was used (Nanion, Germany, horizontal planar lipid bilayer system), with two chambers separated by a partition with a 100-µm hole where the lipid bilayer is formed by 1,2-diphytanoyl-sn-glycero-3-phosphocholine (DPhPC,10-30 pF). The lower chamber contained 150 mM KCl, 10 mM MOPS, pH 7. 4. After membrane bilayer formation, the upper chamber solution was changed to 150 mM KCl, 10 mM MOPS pH 8.
Theoretical Data 4. 2. 1. Modeling and Dynamics The X-ray structure of KirBac3. 1 with an atomic resolution of 2. 6 Å (PDB 2WLJ)  [14]  was used to model the complete WT structure. As the first 34 residues were missing in the X-ray structure, this segment was excluded from our model, and the missing atoms were constructed using the CHARMM program  [44]. Residues 1 to 34 of the protein are not likely to be involved in the gating process. The tetrameric complex was built by applying a twofold symmetry axis for this structure using the Protein Interface Surface and Assemblies software through Pisa  [45]  at EBI. The W46R mutation was achieved with Chimera  [46] , considering the modeled WT structure. The ionization states of residues in both structures were established using PROPKA3  [47, 48].
A complete system comprising a DOPC membrane, TIP3P type water molecules  [49] , and KCl ions at 150 mM concentration within a box of 116. 243 Å × 116. 243 Å × 133. 255 Å was built using the membrane-builder module of Charmm-Gui webserver  [50, 51]. This web server uses the Orientations of Proteins in Membranes (OPM)  [52]  software to imbed the protein into the membrane. We used the charmm36 force field  [53]. The WT and mutant structures were first energy minimized, then further relaxed by carrying out short molecular dynamics simulations for 600 ps at a constant temperature of 300 K using the Nose-Hoover thermostat and constant pressure of 1 atm using the Langevin piston. Such a structure constituted the starting point for the normal mode calculations and MDeNM simulations. The Leapfrog Verlet algorithm was used for propagating the movement.
1 W46R, which allowed for an extensive conformational sampling (total simulation time = 0. 4 ns × 130 replicas = 52 ns). A filtering procedure was used to determine the mode combinations allowing an isotropic distribution of the vectors  [18]. We considered for each replica a series of 7 kinetic energy excitations, each lasting 1. 5 ps, and corresponding to an 8 K temperature increase of the protein. These simulations were carried out with the CHARMM program using a dedicated script (available upon request).
| Data recordings An acute patient physiologic and laboratory evaluation (APPLE fast ) score was used for each dog at inclusion to stratify illness severity by mortality risk as previously described. 22 terial blood gases (VetStat, IDEXX Laboratories Inc. , Westbrook, Maine) were performed at inclusion, q24h during the 1st 48 hours of hospitalization and at discharge. If the dog died before discharge, the last arterial blood gas performed during hospitalization was considered as the arterial blood gas of discharge. Arterial blood samples were collected from the dorsal pedal or femoral artery, depending on the dog's compliance, comfort and body weight. Ultrasound guidance was used to facilitate arterial sampling. A 1-mL polypropylene syringe was heparinized by aspirating 1 mL of liquid sodium heparin (Héparine Choay 25000UI/5 mL; Sanofi-Aventis, Frankfurt, Germany) into the body of the syringe and then completely expelling it from the syringe, followed by 5 repetitions of 1 mL of air being drawn into and expelled from the syringe.
Results of the Cox model were presented with the values of the relative risk (risk ratio [RR]) and their 95% CI. Survival was assessed by Kaplan-Meier product limit estimates. The influence of missing data was evaluated by a robustness analysis of the results by comparing them to those obtained after imputation of missing data. Missing data were imputed according to a method based on the fully conditional specification. The method chosen was predictive mean matching. Statistical analyses were performed by 1 author (M. M. ) using R 3. 5. 2 software (R Foundation for Statistical Computing, Vienna, Austria). The packages ggplot2, Lme4, Lmertest, gridExtra, survival, and MICE were used. A P value <. 05 was considered significant.
This allows one to test several possible divisions to determine the optimal model with clear separation of the identified groups. To achieve the first part of the fourth objective of determining the age at which cognitive decline can be identified from test performance, we utilized a ML approach. We used an exploratory analysis by assessing the separability of datasets using unsupervised ML algorithms. We used clustering methods, such as Simple K-means  (Arthur and Vassilvitskii, 2006), canopy (McCallum et al. , 2000) , expectation-maximization  (Dempster et al. , 1977) , and GenClus++  (Islam et al. , 2018). Testing different numbers of clusters based on performance allowed us to determine the possible onset of cognitive decline. Then we built pairwise distributions of each attribute by age.
The battery of PT that we used resulted in a large number of dependent variables (e. g. , time estimates and accuracy metrics). For the analysis, we employed the major tests results explained in section 3. 2. 1. For the second part of objective four, we studied the informative value of the tests for detecting cognitive changes in the elderly. To enhance the performance of the clustering algorithms, we used feature-selection methods, which are designed to minimize overfitting and reduce the time needed for training, while increasing model performance metrics by eliminating less informative features from the dataset. We employed the genetic algorithm  (Hall, 1998)  and information gain attribute evaluation  (Kononenko and Hong, 1997). The genetic algorithm retrieves the most relevant features, whereas information gain attribute evaluation-based ranker lists the attributes in descending order based on their informative value for the final prediction.
Because of the relatively small size of the datasets, we used a stratified five-fold crossvalidation technique to have confidence that the predictions will generalize to unseen data. To evaluate the performance of the predictive models, we generated a receiver operating characteristic (ROC) curve averaged over five folds. We also calculated mean sensitivity, specificity, balanced accuracy (BAC), and area under the curve (AUC) values with respect to class. These performance measures were suitable as the datasets were balanced across the age attribute. Finally, we determined the cases that were misclassified by the best predictive model. We used the confusion matrix and calculated false-positive (FP) and false-negative values (FN).
POBA Dataset We used the dataset called Psychophysiological outcomes of brain atrophy (POBA; see section Acknowledgments). The methodology of the neurophysiological tests used for the dataset is well-defined and relevant to research on age-related functional changes. The accurate computerized assessment of PS was strongly aligned with the purpose of the study. The POBA dataset does not contain any complicated tests and comprises simple tasks that are suitable for those with different intelligence levels. The dataset consisted of 231 cases which included MRI examinations and psychophysiological testing results of people aged 4-84 years. Written patient or parental consent for minors for participation was obtained from each case. All participants were either patients who suffered from periodic headaches or were anxious about having organic brain pathology, or healthy participants who were examined at the beginning of their professional sports career.
The exclusion criteria were as follows: organic brain pathology, mental disorder, or head injury. The dataset is available on demand (see section 7). A thorough description of the dataset has been previously published  (Statsenko et al. , 2020). We have highlighted only the features used in this study to determine PT dynamics across the lifespan and for ML analysis. We used the following PTs:   (Graveson et al. , 2016). We analyzed the SD for each task as a separate dependent variable (SVMR_variance, CVMR_variance, AST_variance, IRT_variance, RMO_variance). 9. We used wrist dynamometry to measure the maximum muscular strength of the right (WDR_MMS) and left hand (WDL_MMS). 10. Asymmetry coefficient (AC) is calculated as the ratio of the maximum muscular strength of the wrists (see Formula 3).
Stroop Switching Card Test Dataset We used the SSCT dataset available on demand (see Data Availability section). A sample of 103 participants aged 15-75 years volunteered for the experiment. The battery consisted of standardized neuropsychological tests evaluating cognitive flexibility (TMT), inhibition (Stroop color and word test [SCWT];  Golden and Freshwater, 1978) , the SSCT  (Belghali and Decker, 2019) , updating (forward and backward digit span test;  Wechsler, 1955) , and information speed processing (digit symbol substitution test [DSST];  Wechsler et al. , 1997). A single testing session lasted for approximately 1 h, and each participant was tested individually. The dataset and the methodology of the study is described in  Belghali et al. (2020). Below is a brief description of the dataset.
Cognitive Flexibility Cognitive flexibility is the mental ability to switch between thinking about multiple concepts simultaneously. It is based on executive functions that involve conscious changes in attention (cognitive shifting) and unconscious shifts of attention between tasks (task switching). The TMT was used to assess flexibility. It is a neuropsychological test of visual attention and task switching. The subject connects 25 consecutive targets in a sequential order. TMT consists of two parts (A and B). In the first part, the targets are presented as numbers and the participant is required to connect them. In the second part, the participant is required to alternate between numbers and letters (i. e. , 1-A-2-B-3-C, etc. ). The time of completion for each part is recorded. The SSCT dataset contains the final outcome of the TMT test, which is measured as the Switch Score (SS) or TMT_BA_Time, which is the time delay between switching attention between numbers and letters (see Formula 5).
Preprocessing of Data The POBA dataset consists of a list of deidentified subject records, with one patient per row, which are stored in a commaseparated value format file. To convert data into a format suitable for ML applications, several preprocessing steps are performed. We cleaned the data by removing missing, unknown, or inappropriate values. In 26% of cases, values for wrist dynamometry attributes (WDL_MMS, WDR_MMS, and AC) were missing. We generated the values of missing attributes by using a linear regression model, which was trained on the available data as predictors and missing attributes as outcome variables. Then, the value of the AC feature was calculated using Formula 3. Then the numerical variables were normalized by subtracting the mean value and scaling to the attribute variance.
Performance Evaluation Metrics We used several objective measures to evaluate the performance of the clustering and classification methods. Confusion and error matrices were built for each predictive model to show how they distinguished between the younger and older classes. The ROC curve and AUC were used to evaluate the performance of the classifiers and summarize the trade-off between the truepositive (TPR) and false-positive rates (FPR), using different probability thresholds. The medical decision-making community has extensively published on the use of ROC graphs for the diagnostic testing  (Fawcett, 2004)  of balanced data  (Saito and Rehmsmeier, 2015). Thus, we found that this metric was appropriate for our needs. We used: Here we use: TPR(sensitivity) = TP TP + FN (8) TNR(specificity) = TN TN + FP (9) FPR = FP FP + TN = 1 -specificity (10) BAC(balanced accuracy) = Sensitivity + Specificity 2 (11) The overall accuracy of the model was defined as follows: Accuracy = TP + TN TP + TN + FP + FN (12) where TP, TN, FP, and FN are true-positive, true-negative, falsepositive, and false-negative values, respectively, representing the confusion matrix of the classification model.
Hardware and Software All experiments were conducted using a Linux Ubuntu 18. 04 workstation with 24 CPU cores and two NVIDIA GeForce GTX 1080 Ti GPU with 11 GB GDDR5X memory each, using the Python programming language and its libraries for data processing, ML, and data visualization, such as scikit-learn, NumPy, Pandas, Matplotlib, Seaborn, and Plotly. For the POBA dataset collection, we used NS-Psychotest by Neurosoft.
Association Between Test Performance and Age Figure  2A  describes the association between age and performance of participants for the PTs (i. e. , the POBA dataset). Figure  2B  shows the relationship between age and cognitive test performance (i. e. , the SSCT dataset). The color intensity and size of the ellipses are proportional to the correlation coefficients. The analysis of the PTs showed a positive correlation between age and all features except AC, which was negatively associated with age. Age was significantly   For the analysis of cognitive tests, associations between test performance and age were significant and stronger compared with those between PT performance and age. Test output values increased with age because they reflected either the time taken to complete the task or the number of errors (inaccuracy).
Prediction of the Age Group Using Machine Learning To estimate the onset of cognitive decline, we used cluster analysis. After assessing the outcome metrics of clustering into several groups, we obtained the best performance using two clusters when the cutoff value was set to 40 years of age (see Table  6 ). We achieved the best performance using the GenClus++ method (a combination of K-Means and the genetic algorithm). The misclassification of young participants was less frequent than that of older adults. This may account for the cumulative effect of individual lifestyle on cognitive status. Neurodevelopment in youth appears to be a more standardized process than brain aging of diverse origin, pace, and extent. Initially, the clustering generated low prediction accuracy (68. 4%). To improve the performance of clustering of the POBA dataset, we resorted to using the feature-selection method.
The genetic algorithm returned the following list of features that maximized prediction accuracy: AST_mean, IRT_mean, SVMR_mean, and CVMR_mean (see Table  7 ). When we ran the information gain-based ranker for the SSCT dataset, we retrieved the following informative features: SSCT_TIME, SSCT_IES, Processing_speed, TMT_BA_TIME, and SSCT_ERROR. When we fed the unsupervised ML clustering models with the aforementioned features, the separability of the subjects by age group improved considerably. To estimate the utility of a novel battery of tests for diagnosing age-related cognitive changes, we built an ML classification model, which identified the age group of participants as either below or above 40 years of age. If the prediction is reliable, it may reflect a subtle biomarker for accelerated aging (neurodegeneration) in those misclassified by the algorithm.