dataesr/scientific-paragraphs-categorization huggingface dataset:
Removing the duplicates, we managed to get six more new primary studies that have not been found from the database search process. There were some other relevant papers from snowballing, but they finally did not pass our selection criteria. These few studies may have fulfilled our criteria but may have failed to detail what they did or did not detail enough to include them according to our criteria confidently. We ended our search and selection process in the beginning of December 2020, which means that our review does not completely cover all the publications in 2020, but a major part of them. The primary studies that passed our selection criteria could still have limitations that make their contributions unreliable or flawed. Because many of the contributions do not have test cases or examples, it can be hard to know if the patterns and architectures do what they are supposed to.
Similarly, we have not seen architectural patterns or IoT security reference architectures referring to any design pattern they would be composed of. This includes patterns at the IoT "weak links": the network and IoT devices levels. Most of the primary studies do not work in all the seven layers of the IoT World Forum Reference Model for IoT architecture. They mainly operate in the Physical Devices and Controller (L1), Connectivity (L2), and Application (L6) layers. There are four layers that have little coverage in terms of patterns and architectures for addressing IoT security challenges: Edge Computing (L3), Data Accumulation (L4), Data Abstraction (L5), Collaboration and Processes (L7). We also accumulated how the research contributions in the primary studies handle the different issues presented by the OWASP IoT top ten vulnerabilities list.
Neocortical and subcortical neurons form crossstructural assemblies 6] [37] [38] [39]  To test for coordinated activity between PFC and STR, we first performed a combined principal-and independent-component analysis (PCA-ICA) from data recorded during awake behavior. This identified numerous stereotyped spiking patterns spanning both structures at a timescale consistent with cross-structural communication (Figures 1A and 1B; Table  S2 ). To quantify this timescale, we systematically varied the time windows analyzed. Unexpectedly, most (70%) of these motifs persisted for time windows as brief as 10 ms (Figures  S1B-S1F ), indicating that they formed cell assemblies. 3 f the 74 detected assemblies (containing 2-16 members; mean = 6. 8 ± 3. 1 cells; Table  S2 ), almost half were neocorticalsubcortical, spanning both STR and PFC (''STR+PFC''; n = 32; e.
Distributed assemblies coordinate functionally distinct sub-regions at a precise timescale Previous studies have documented cell assemblies confined to a single structure (e. g. , PFC, 5,45 STR,  8  and also for STR cell pairs),  46, 47  and assemblies detected independently in PFC and STR activate sequentially. 7 Synchronous cortico-subcortical (C) Behavioral correlates (same abbreviations as Figure  4J ). (D) Support vector machine models trained on STR population activity successfully classify task conditions. The SVM prediction accuracies (color-coded boxplots extend from 25% to 75% quartiles) are significantly higher than those for randomized datasets (white boxplots to the left; Wilcoxon signed-rank test: VD1 versus SD, p = 0. 0027; VD2 versus SD, p = 0. 02; VD1 versus VD2, p = 0. 0008; n = 16 sessions).
Sleep detection Each behavioral recording was preceded and followed by a rest/sleep recording session in a terra cotta flowerpot lined with a towel. These sessions lasted at least 10 min (2 of the 6 rats) or 1 hour (the other 4 rats). Sleep data were recorded in 10 of the sessions, when 58 of the assemblies were recorded. Sleep was detected by low movement speed. Headstage LED signals were first smoothed and periods when the speed did not exceed 0. 05 m/s for a duration of at least 120 s (with a tolerance of 2 s when this velocity could be exceeded).
However PCs are, by definition, orthogonal, a constraint that is not necessarily respected in the brain. To address this issue we carried out ICA on the major PCs 93 using the fast independent component analysis (fastICA) algorithm for MATLAB. This ICA returned assemblies as vectors of N weights, corresponding to the respective cell's contributions. Since the signs of the output weights are arbitrary, weights were inverted as necessary to render the highest absolute weight positive. Components were normalized such that a component with equal contribution from all N neurons recorded in a session would be composed of N equal weights each with absolute value 1/ON. Thus, neurons with weights exceeding 1/ON are referred to as ''assembly members''. The activation strength of each assembly within a given time bin is computed by projecting the matrix Z (constructed with 30 ms bins and a 10 ms sliding window) onto a template matrix obtained by the matrix product of the component and its transpose.
The 95% quantile of this distribution was considered as an alternative threshold to Mar cenko-Pastur. No significant differences were found when comparing data from the Mar cenko-Pastur threshold to the present one derived from surrogate data (p = 0. 77; Wilcoxon paired rank test). To control for whether the detected assemblies may have appeared by chance, we simulated a Poisson spike train dataset, where, in each bin, each neuron's spikes were replaced by generating Poisson-distributed numbers of spikes for each cell, according to its firing rate. 95 Since cell synchrony could appear by chance due to similar phase preferences to rhythmic activity or to similar spatial/ behavioral correlates, the replacements were made taken into account the respective firing rates at four phases of 4 Hz or theta rhythms (0 to pi/2, etc.
Matching assemblies calculated with different bin widths The PCA-ICA analyses of the dataset were performed with bin widths ranging from 10 to 150 ms. Assemblies detected at different timescales were iteratively matched by selecting pairs with maximum correlations (Spearman coefficient). First, we computed correlations between each possible pair of assemblies computed with two different bin widths. The pair with the highest coefficient was set aside and the procedure was repeated with the remaining assemblies until all assemblies were paired. When the number of assemblies was different from one timescale to another, the remaining assemblies were left unmatched. Matched pairs with significant Spearman coefficients were considered as ''highly correlated. ''.
However, no major difference was observed compared to raw signals, so the latter are reported. Results were similar with only one selected channel by area or with the average of all recorded channels, only the former were used. Coherence calculations were based on a multi-taper Fourier analysis and performed with custom-written, MATLAB-based programs. We used the Chronux toolbox cohgramc() function with a time window of 2 s, a step size of 0. 1 s and a bandwidth product of 3 with 5 tapers.
Spike-LFP analyses Since STR and PFC oscillation profiles were quite similar and the nature of STR oscillations is controversial, all analysis presented refer to PFC LFPs. To examine modulation of spiking activity by LFP oscillations, first instantaneous signal power and phase were derived from the Hilbert transform of the bandpass-filtered signal. We quantified phase consistency of spikes relative to the LFP band with both the Rayleigh test of circular uniformity and the unbiased pairwise phase consistency (PPC 100 ). For each unit and assembly, we first tested for significant entrainment to the LFP at low frequencies: 4 Hz (from 2 to 6 Hz) and theta (from 6 to 12 Hz). Unlike humans, theta in behaving rats is in this range, typically peaking at 8 Hz,  101  while the range for 4 Hz was selected on the basis of the peak in the data (Figures  2A  and 2B ; also cf.
Fujisawa et al. 102  ). To characterize phase-locking in neurons, we used two methods. With the Rayleigh test, a neuron or an assembly was considered as phase-locked if p < 0. 05 and kappa > 0. 1. This criterion was chosen on the basis of k-means separation and confirmed by eye. Moreover, to take into account the non-uniformity of the signal (cycle asymmetry) we use the correction of Siapas et al. 103  Even though the main results did not change, we report the corrected data. In the second method (used for neurons only), PPC threshold was determined from a jitter analysis: in data from each single unit, spikes were randomly jittered within a time window equivalent to a single cycle of the band under study. The actual values were considered significant if they exceeded the 95th percentile of this distribution.
Gamma burst detection We detected transient gamma burst events in both the STR and PFC LFPs. LFPs were first filtered in the high-gamma band (70-110 Hz) using the MATLAB filtfilt function (4th order Chebyshev filter). Instantaneous signal amplitude was obtained by taking the modulus of the Hilbert-transformed signal. Gamma events were defined as when the amplitude envelope exceeded the 95th percentile of the amplitude distribution and contained at least three gamma cycles. Events separated by less than 1/2 cycle were merged.
For each neuron, the average firing rate F was calculated at each bin x for each condition and the difference was taken: D(x) = F SD (x)-F VD1 (x). To test the statistical significance of the rate differences D(x) with the bootstrap procedure, the distribution of possible rate differences Dr(x) is estimated by randomly permuting the identity of each trial into proxy groups F' SD (x) and F' VD1 (x). This process was repeated 5000 times to obtain the distribution from the resampled data, Dr1(x),. , Drn(x). With this shuffled dataset, the pointwise confidence limits (demarcating the upper and lower 2. 5% of the distributions) were computed for each bin x. To deal with multiple comparison issues, the method also computes the global 5% bands. A maze zone (i. e. , contiguous series of spatial bins) is considered to have significant different firing only when D(x) crosses both the global and pointwise bands, and the zone extent is determined only by the points where D(x) lies beyond the pointwise band.
Support Vector Machine analysis With the libSVM library (https://www. csie. ntu. edu. tw/$cjlin/libsvm), support vector machines (SVMs) with RBF (radial basis function) kernel quantified how well STR or PFC population activity distinguished between trials in different pairs of trial conditions, namely VD1 versus SD, VD1 versus VD2, and VD2 versus SD. Each neuron's firing rate was averaged over each trial in a given task condition to obtain a population vector of length N, with N corresponding to the number of cells recorded during the session. Population vectors were z-scored in order to prevent neurons with high firing rates from having excessive influence on classification. Population vectors were then normalized by setting their euclidean norm to 1. First, data were split in two balanced sets (e.
g. , 4 VD1 trials and 4 VD2 trials in each set). The first set was used to find optimal SVM hyperparameters g and C, with g as the exponent of a radial basis function kernel and C, the penalty parameter of the error term. We searched in a grid of values between 0. 01 and 30 to find (C, g) values that optimized a leave-one-out cross-validation accuracy. The accuracy of the SVM, i. e. , its ability to correctly predict the epoch of the single left-out trial, was quantified. This procedure was iterated on each possible permutation (n = 8) of the dataset and for each pair (C, g). The parameters with the best average cross-validation accuracy in each session were retained for subsequent analyses. With the second dataset, SVM were fit similarly using a leave-one-out cross validation procedure but with fixed hyperparameters (C, g).
To build a null distribution of prediction accuracy, the population activity vectors for each trial in each task condition were randomly assigned to one of the task conditions being compared. SVMs were then re-computed from these randomized datasets using again the first half of the dataset for setting hyper-parameters and the second half to compute SVM accuracies. We repeated this procedure 500 times. The actual prediction accuracy distributions (from second set) were then compared to the null distributions with the Wilcoxon rank-sum test.
The SVM prediction accuracies (color-coded boxplots extend from 25% to 75% quartiles) are significantly higher than those for randomized datasets (white boxplots to the left; Wilcoxon signed-rank test: VD1 versus SD, p = 0. 0027; VD2 versus SD, p = 0. 02; VD1 versus VD2, p = 0. 0008; n = 16 sessions). Each point represents one session. See FiguresS3-S5.
ACKNOWLEDGMENTS Support came from  Agence Nationale de la Recherche   ANR-2010-BLAN-0217-01 Neurobot ,  French Ministry of Research, MEMOLIFE Laboratory of Excellence , and  Fondation Bettencourt Schueller. Thanks to  France Maloumian  for help preparing figures,  Marie-Annick Thomas  and  Suzette Doutremer  for training and advice for histology,  Yves Dupraz  for mechanical engineering,  Nicole Quenech 'du and  J er emie Teillon  for advice and training for image processing, and  Drs. M. Khamassi ,  C. Lansink ,  J. Matsumoto , and  C. Lena  for helpful suggestions.
Bias-Adjustment Method Simulations from high-resolution RCMs can still have important biases  [43, 55, 56]. This precludes their direct usage in most climate impact studies. A common solution is to apply a suitable bias-adjustment method to reduce biases. Herein, we applied a stateof-the-art parametric quantile mapping method, namely the ISIMIP3BASD method  [43]. ISIMIP3BASD was specifically designed to reduce bias over the tail of the distribution, allowing for a robust adjustment of bias at extreme values  [43]  (Table  2 ). The method is also trend-preserving across all quantiles, i. e. , not adding an artificial trend during bias adjustment  [43, 57]. The reference dataset used for bias adjustment was provided by the German Meteorological Service.
Description of STICS-Grapevine Modules STICS model followed a modular structure, with each module corresponding to a process: phenology development, LAI and root growth, radiation interception, biomass conversion, yield formation and water and nitrogen balance, among others. Herein, we only briefly described STICS (v9. 1) modules concerning grapevine phenology and soil-crop water balance that were most relevant for this study. A more detailed presentation can be found in Brisson et al. [35, [58] [59] [60] , Brisson and Perrier  [61] , García de Cortázar-Atauri  [37]  and García de Cortázar-Atauri et al. [62].
Incorporation of Soil Data Besides meteorological data, soil information is also critical for the model-based impact assessment. Soil data were grouped into two categories: soil physical characteristics and soil hydraulic properties. The former mainly consisted of soil texture, particle size distribution, surface dry albedo and bulk density, which were all derived from the Harmonized World Soil Database (HWSD, v1. 2) at about 1 km resolution  [66]. Soil surface slope was also considered to account for the complex terrain conditions in Germany, and was estimated from the European Digital Elevation Model (EU-DEM, v1. 1) at 25 m spatial resolution (source: Copernicus service). Soil hydraulic properties, including soil field capacity and wilting point, were obtained from the EU-SoilHydroGrids 1 km (v1.
0), which provided recent EU-wide estimations for soil depth up to 2 m  [44]. To harmonize the input datasets with different spatial resolutions, the simulation grid was interpolated (nearest-neighbor) to a resolution consistent with that of RCM output (~12. 5 km). Computation workload was reduced by considering only the dominant soil type within each grid, thereby neglecting potentially larger spatial variability attributed to soil variability within each grid box. Note that there were empty grids (in white color) in the results, which were either identified as non-land areas or areas without soil information according to HWSD data. More detailed information on how raw soil information was extracted and converted into model-ready inputs can be found in Yang et al. [67] [68] [69] [70].
Model Applications 2. 5. 1. Model Calibrations for Müller-Thurgau and Riesling For calibration, a 64-year-long time series (1956-2019) of observed data (BBCH09,65,81) was collected for Müller-Thurgau and Riesling at Eltviller Sonnenberg (lon 8. 126 • , lat 50. 038 • , elevation 165 m), Rheingau, Germany. The model was calibrated to simulate the budburst (BBCH09), flowering (BBCH65) and veraison (BBCH81) stage of the two studied white wine varieties. However, the model only simulated BBCH85 instead of BBCH81. Therefore, BBCH81 was estimated by linear interpolations between BBCH65 and BBCH85 (Section 2. 3. 1) using obtained GDD values at these two stages. The calibration attempted to minimize the mean squared error (MSE)  [71] [72] [73]  between observations and simulations for the three successive stages using the grid-search method.
Software Environment A Python-based (v3. 7) simulation interface was established at the High-Performance Cluster at PIK (HPC@PIK) for grid-based crop model simulations. The interface automated dataset preprocessing (including any arbitrary soil and climate datasets) and model setup and read and wrote the simulation outputs for each target grid point. Furthermore, it used the Python message-passing interface (MPI) to parallelize the gridded simulations, thus considerably facilitating large-scale crop model applications, especially when multiple climate model projections were utilized. For data processing and result analysis, the involved python libraries were mainly numpy (v1. 21. 2), panda (v1. 3. 4) and xarray (0. 19. 0). To plot the results, matplotlib (v3. 4. 3) and cartopy (v0. 20. 1) were used.
Funding: This research was funded by the  Clim4Vitis  project, " Climate change impact  mitigation for European viticulture: knowledge transfer for an integrated approach", funded by the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement no. 810176. This research was also supported by the  Portuguese Foundation for Science and Technology (FCT)  under project  UIDB/04033/2020. This work also received funding under the framework of the  CHAPEL  project, as funded by the  Ministère de l'Environnement, du Climat et du Développement of Luxembourg. We also thank the  CoaClimateRisk  project ( COA/CAC/0030/2019 ) of the FCT.
Data Availability Statement: Different regional climate model (RCM) simulations produced within the EURO-CORDEX initiative can be accessed at https://www. euro-cordex. net/ (accessed on 19 March 2022). The freely available software STICS can be accessed and downloaded at https:// www6. paca. inrae. fr/stics_eng/Download (accessed on 19 March 2022). The FAO Harmonized World Soil Database (HWSD) can be found at https://www. fao. org/soils-portal/data-hub/soil-mapsand-databases/harmonized-world-soil-database-v12/en/ (accessed on 19 March 2022). The EU-SoilHydroGrids dataset can be found at https://esdac. jrc. ec. europa. eu/content/3d-soil-hydraulicdatabase-europe-1-km-and-250-m-resolution (accessed on 19 March 2022). The European Digital Elevation Model dataset can be found at https://land. copernicus.
Ethical and regulatory aspects The study was approved by the Research Ethics Committee (Comité de Protection des Personnes Sud-Est VI, Clermont-Ferrand, France) on 21 July 2016 (ref: AU1263), the French personal data protection authority (Comité Consultatif sur le Traitement de l'Information en matière de Recherche dans le domaine de la  Santé,  and registered on Clinicaltrials. gov (NCT03195374). The research was conducted in accordance with the World Medical Association Declaration of Helsinki  (Tokyo 2004; revised). Study data were managed using REDCap  (Harris et al. , 2009 ) electronic data capture tools hosted by the sponsor of the study (University Hospital of Clermont-Ferrand, France). The REDCap web platform (http://project-redcap. org/) possesses the authorization of the French personal data protection authority.
To address our secondary objectives, a home-made questionnaire (not validated, but including several items from validated questionnaires, such as Brief Pain Inventory and DSM-5) describing all analgesic treatments (including opioids), patient sociodemographic data, clinical data [pain type, intensity (11point numerical rating scale) and duration, and associated comorbidities], misuse and associated behavior (withdrawal syndrome, craving and opioid use for non-analgesic effects) was completed. A withdrawal syndrome was confirmed when the patient answered "YES" to at least one of the possible answers to the following question: Have you ever experienced one or more of these symptoms when your opioid medication(s) was/were delayed or in the hours before usual intake or when the dose was decreased or when it was stopped suddenly? (multiple responses possible: Yawning; Sweating; Weeping eyes; Runny nose; Hot flashes; Joint/muscle pain; Restlessness/Anxiety; Cramps in muscles; Feeling sick/Nausea; Stomach ache; Goose bumps; Shaking chills).
MCA can be used to perform cross tabulations and can be considered as a useful tool for revealing the relationships among categorical variables. The significance of association as tested by Chi-squared or Fisher's exact tests provides no information regarding significant individual associations between row-column pairs. On the contrary, factorial analyses such as MCA show how the variables are related, and not just if a relationship exists. This approach enables handling a large amount of data in order to unfold hidden patterns. For these analyses, the variables were chosen according to univariate results, clinical relevance and statistical distribution (parameters always present or always absent were not considered). Only individuals without missing data were included in this analysis. Statistical analyses were performed using Stata software (version 15, StataCorp, College Station, Texas, United States) and R 3.
3%) who had no missing data for the variables included in the analysis, and 45 were removed. These two samples were similar in all assessed criteria (data not shown). The MCA showed the presence of three clusters of patients. The clusters differed mainly in age, type of pain, pain intensity, and type of opioid (weak or strong). Clusters 1 and 3, which were quite similar (sex, age, profession), differed from each other by the type of pain and their opioid treatment. Cluster 1 included mostly patients with nociplastic pain and weak opioids, whereas cluster 3 included mostly patients with different types of chronic pain and treated with strong opioids. We also noticed that cluster 3 tended to have more depressive disorders and co-prescription of benzodiazepines (marginal difference, p = 0. 09).
| Evaluation of repeat and gene expression RNA-seq data were retrieved from a recent study  (Séité et al. , 2022) , in which, beside other analyses, gene expression was quantified for 25 samples of M. natalensis, including four female (minor) workers (FW), four virgin queens (Q0m), four 3-month-old queens (Q3m), three 9-month-old queens (Q9m), three 31-month-old queens (Q31m), four more than 20-year-old queens (Q20y) and three more than 20-year-old kings (K20y). The libraries were sequenced on a HiSeq 4000 Illumina sequencer, using 150 bp paired-end chemistry. We refer to the original study for further details on mRNA extraction methods. These RNA-seq data were mapped against the genome sequence of M. natalensis  (Poulsen et al. , 2014)  (retrieved from http://gigadb. org/datas et/100057; accessed March 2019) with hisat2 v2.