dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions As a preliminary study, it appears that the IGlove is able to assess an evolution of dexterity as the LapSim software would do. A model of curve of apprenticeship can be extracted and used to predict the learning dynamics of a trainee. The data process can be improved for more accuracy. Its design can also be reconsidered, as its ergonomics directly influences the outcome of an exercise. This work shows that the possibilities of utilizing IMU sensors for surgical skills assessment are immense, particularly in those simulators that don't have haptic parts. The good correlation between IGlove and LapSim suggests possibilities to be used as complementary tools including sophisticated processing techniques in those tasks that include fine movements. It also shows that still a lot of work can be done for discovering all the ways of assessing a surgeon gesture.
Description of Model In this work, the zero-dimensional plasma-chemical kinetics simulation code GlobalKin  [19]  is used. GlobalKin solves the particle balance equation for neutral and charged particles eq. (  1 ), and the energy balance equation for electrons eq. (  2 ). dN i dt = S V - D i N i γ i γ i Λ D + 4D i v th,i + j D j N j γ j f ij γ j Λ D + 4D j v th,j + S i (1) d dt 3 2 n e k B T e = P d - i 3 2 n e ν mi 2m e M i k B (T e -T i )+ l n e k l N l ∆ǫ l (2) where N is the number density of particle i, Λ D is the diffusion length, D is the diffusion coefficient and v th,i is the thermal velocity of species i. j denotes a species incident on the surface, which after a surface interaction returns to the gas phase as species i, γ the surface reaction coefficient, f the return fraction of species i after the surface reaction of species j.
Flow cytometry Ex vivo phenotypic analysis was performed in freshly collected whole blood using an eight-color staining protocol and a panel of monoclonal antibodies previously described  (28). Purified naive CD4 T-cells were surface stained ex vivo and upon culture, followed by intracellular staining using eBioscience FoxP3 kit (eBioscience, San Diego, CA, USA), as described  (28). At least 150,000 events were acquired for each sample on a BD LSRFortessa (BD Biosciences, San Jose, CA, USA). Data were analyzed using FlowJo software (TreeStar, Ashland, OR, USA) after doublet exclusion. Results are presented as proportion of a cell population or as mean fluorescence intensity (MFI) of a given marker within the specified population.
Tcr spectratyping analysis Total RNA was extracted from 10 5 to 10 6 cells with RNeasy kit (Qiagen, MD, USA), and first-strand cDNA synthesized from 1 to 2 µg of RNA (SuperScript III) using an equivolume mixture of random hexamers and oligo (dT). Amplification of the TCRVβ CDR3 was performed using primers specific for each TRBV family and a common TRCB reverse primer  (31) , followed by a run-off reaction that extends each different PCR product with a second TRCB FAM-labeled primer; and the third step, in which each different fluorescent TRBV-TRBC PCR fragment was separated using a capillary electrophoresis-based DNA automated sequencer. Data were collected and analyzed with GeneMapper v4. 0 (Thermo Fischer Scientific) for size and fluorescence intensity determination. 1 ; Table  S1  in Supplementary Material).
Associated microbial communities. Microbiomes, in this case associated bacteria and Archaea, were studied at day 5 and 10 for the macroalga and at day 5 and 15 for the coral in order to assess changes in both associated communities according to the type of interaction (no interaction, contactless and contact interactions) and duration. For a given type of interaction, no significant difference in composition of the microbial communities could be detected over time (PERMANOVA p = 0. 133). A total of 54,696 unique operational taxonomic units (OTUs) were detected in the entire dataset of 1,564,936 sequences. The total number of OTUs, Chao1 richness, evenness and Shannon index were 10-16% higher in A. taxiformis than in A. calycularis. Since bacterial composition did not change with time, comparisons between treatments were achieved on time-pooled samples.
An assessment of in situ interaction effects was made using the metabolomic approach combining negative (1316 ions selected) and positive (2621 ions selected) modes. Data on both species in contact interaction were compared to data obtained from specimens without any visible interaction, the latter being defined as references. Metabolomic fingerprints of both species did not show differences between references and closely interacting specimens (separate PERMANOVA per species: p = 0. 08 for macroalgae, p = 0. 45 for corals; Fig. 9 ). Macroalgal bioactivities of field material exceeded and varied more than those measured from macroalgae in the ex situ experiment (gamma = 0. 8-13. 2), likely due to the influence of a higher number of factors such as herbivory and competition with other sessile organisms in natural conditions.
In situ experiment. Samples of co-occurring A. calycularis (n = 14) and A. taxiformis (n = 14) were taken at 7-10 m depth in La Herradura (Spain) (36°43′ 25. 74″ N, 3°44′ 13. 35″ W) the 1 st May of 2015, in order to analyze their metabolomic fingerprint. Macroalgae (n = 14) and corals (n = 22) without any interspecific contact were also sampled as references. Both species were found on vertical walls exposed to currents. The macroalgal coverage was high and visually estimated up to 80%. Seawater composition and temperatures. Phosphate, nitrate and nitrite concentrations were measured on aquaria seawaters sampled every 3 days using a BioMate 3 UV/VIS Spectrophotometer (Thermo Scientific ® ) and following standardized protocols  18. Water temperature was surveyed during the experiment using a water temperature data logger TidbiT v2 UTBI-001 (HOBO ® ).
Samples were pooled together in equal proportions based on their molecular weight and DNA concentrations and purified using calibrated Ampure XP beads. DNA libraries were prepared by following Illumina TruSeq DNA library preparation protocol and paired-end (2 × 250 bp) sequencing performed at MR DNA (www. mrdnalab. com, Shallowater, TX, USA) on a MiSeq following the manufacturer's guidelines. All the diversity analyses were performed using the program QIIME 1. 8. 0: Quantitative Insights Into Microbial Ecology  59. Sequences were screened for a minimum read length of 350 bp and less than 2 or more undetermined nucleotides. The filtered dataset, containing only high quality sequences, was submitted to a conservative chimera detection filter using the ChimeraSlayer method  60. Selected high quality chimera-free sequences were clustered into Operational Taxonomic Units (OTUs) within reads using the UCLUST  61  with a pairwise identity threshold of 0.
97. Representative sequences for each OTU were picked using the "most-abundant" method and OTU sequence alignment was performed with Pynast  59. The Ribosomal Database Project (RDP)  62  classifier was used for taxonomic assignment with a 95% confidence threshold. Sequences with the best match for eukaryotes (i. e. chloroplasts and mitochondria) were excluded from the OTU table in downstream analyses as well as rare OTUs (singletons and doubletons). To assign each OTU to the closest matching described taxon, searches were performed against the Greengenes reference database (version 12_10)  63  with a maximum e-value to record an assignment of 0. 001. Singletons were removed before analysis. Metabolomic fingerprinting. Methanol (MeOH), dichloromethane (DCM) and acetonitrile of analytical quality (Chromasolv ® , gradient grade) were provided by Sigma-Aldrich.
MS parameters were set as follow for positive mode (and negative mode): nebulizer gas, N 2 at 31 psi (51 psi); dry gas, N 2 at 8 L. min -1 (12 L. min -1 ), capillary temperature at 200 °C and voltage at 2500 V (3000 V). Data were acquired from 50 to 1200 amu. The mass spectrometer was systematically calibrated with a formate/acetate solution forming clusters on the studied mass range before analysis of a full set of samples. The same calibration solution was automatically injected before each analysis for internal mass calibration. Raw analyses were automatically recalibrated using the calibrant portion, before exporting data in netCDF files (centroid mode) using Bruker Compass DataAnalysis 4. 3. Sets of converted analysis were consequently processed using XCMS software  65. Each individual ion of each analysis was finally normalized if necessary (modeling the relationship of the intensity of equivalent ions of pooled samples and injection order) according to the methodology described by van der Kloet et al.
Statistical analyses. Non-parametric permutation analysis to test treatments on coral behavior was realized using package "ez" 67 (1e 5 permutations) under R  68. Non-parametric analysis (Kruskal-Wallis test followed by Steel-Dwass-Critchlow-Fligner post-hoc test, and Friedman test followed by Nemenyi post-hoc test) were used to compare bioactivities and coral behavior for separate factors (Friedman for dates with tagged colonies, and Kruskal-Wallis for treatments) and performed in XLSTAT Version 2015. 4. 01. 20575. Principal Components Analysis (PCA) were realized using package "ade4"  69  , while Permutational Multivariate Analysis of Variance Using Distance Matrices (PERMANOVA) tests were performed with function "adonis" with package "vegan"  70. Regressions to calculate gamma were performed under Statgraphics Centurion XV Version 15.
Acknowledgements This work was supported by the  ERA-NET   Biome  project " SEAPROLIF ". Metabolomic analyses were performed on the MALLABAR platform (Funded by the  CNRS , the  Provence Alpes Côte d' Azur Region , the TOTAL Fundation). Portuguese participation in the project was funded by  Fundação para a Ciência e a Tecnologia (FCT)  through grant  Netbiome/0002/2011. A. H. E. was supported by  FCT  fellowships ( SFRH/BPD/63703/2009  and  SFRH/BPD/107878/2015 ). O. Ocaña Vicente  and  D. Rodrigues  are acknowledged for their support during field sampling of corals. M. Selva  and  R. Delespaul-Schaffner  are acknowledged for their help in aquarium set-up and coral behavioral survey, and  S. Dupouyet  for her assistance in samples preparation.
Dynamic light scattering measurements Dynamic light scattering measurements were carried out at 23˚C on a Brookhaven DLS/SLS BI-160 goniometer with digital autocorrelator BI-9000AT (Brookhaven, New York, USA). A solid-state laser (JDSU, CDPS532M-050) with output power 50 mW at λ = 532 nm was used as a light source. The studies on the solutions' properties were performed at a scattering angle θ of 90˚. The time-average intensity correlation functions g 2 (τ)-1 were obtained with an acquisition time of 300 s for each run, with the help of the Brookhaven Instruments Dynamic Light Scattering Software. The mathematical analysis was based on the fitting of Kohlrausch-Williams-Watts stretched exponential function  [30]  to the correlation functions g 2 (τ)-1. The values of relaxation times of the fast τ f and slow τ s components as well as fractional contribution of the fast process (A) and the exponent of the stretched exponential (β) were estimated with the help of Marquardt-Levenberg minimization method.
5 nm range, reaching the maximum value of 38. 8 nm for solutions with 20% water content, and subsequently decreasing to the level of 26. 3 nm for solvents with 70% water content (Table  2 ). The studies of aqueous solutions of AM, in turn, demonstrated the radius of gyration for this polysaccharide from cereal grain to be 25 nm  [2, 3]  and for potato amylose-62. 5 nm  [36]. By comparing these datasets, it could be concluded that in solutions of up to 40% H 2 O content, amylose adopts a helix conformation  [37]. For the discussed solution (1), the average R h value determined in the DLS tests equals 780 nm (Table  3 ), and indicates that the diffusion of AM chains in the solution is strongly limited by the biopolymer-solvent interactions. The comparison of the properties described by the R g value from (Table  2 ) with the results of DLS measurements suggests (S1 Fig) ,.
In this study, in order to predict treatment response and patient survival based on baseline FDG-PET images in a database of 65 patients with locally advanced esophageal cancer after CRT using 61 features extracted per patient. This method was compared to another feature selection method based on a support vector machine (SVM) as well as to a standard statistical analysis: the Mann-Whitney U test for predictive study and the univariate Kaplan-Meier analysis for prognostic study.
FDG-PET/CT imaging The FDG-PET/CT data were acquired on a Biograph 1 Sensation 16 Hi-Rez device (Siemens Medical Solutions, IL, USA). Patients were required to fast for at least 6 hours before imaging. A total of 5 MBq/kg of FDG was injected after 20 min of rest. Sixty minutes later (±10 min), 6 to 8 bed positions per patient were acquired using a whole-body protocol (3 min per bed position). The PET images were reconstructed using Fourier rebinding and attenuation-weighted ordered subset expectation maximization algorithms. The images were corrected for random coincidences, scatter, and attenuation. Finally, the FDG-PET images were smoothed with a Gaussian filter (full width at half maximum = 5 mm). The reconstructed image voxel size was 4 × 4 × 2 mm 3.
Proposed feature selection strategy based on RF The workflow of the feature selection strategy is given Fig 2. To determine predictive and prognostic features, the 61 features were pre-selected to maintain F u uncorrelated features. Second, a feature selection was performed using the RF algorithm  [28]  to maintain the most important predictive (and prognostic) features. Then, the subset of complementary features with the best predictive (and prognostic) value was found using RF again. For the pre-selection step, a study based on a Spearman's rank correlation analysis was performed with the Statistics Toolbox of MATLAB (version 2014b, MathWorks, Inc. , Natick, MA, USA) to maintain the uncorrelated features. Features were compared one by one and considered as significantly correlated if the absolute value of the Spearman's correlation coefficient (|ρ|) was higher or equal to 0.
Evaluation and comparisons with other methods To evaluate our proposed feature selection strategy, a new classification was made based on the training sample reduced to the m 2 predictive (or the n 2 prognostic) features. This evaluation was done with 2 types of classifiers, a RF and a SVM classifier. These classifications were evaluated by comparing ground truth and estimated labels leading to 2 misclassification rates (RF err and SVM err ). Because of the small number of observations in the database, a validation protocol called random permutations was used. This process randomly divides the database into 2 subsets: two-thirds of the data are used for the training sample and one-third for the test sample. This process is repeated 10 times, leading to an average and a standard deviation of performance indices.
Statistical analysis The workflow of the statistical analysis, calculated using MedCalc software for Windows (version 12. 7, MedCalc Software, Ostend, Belgium), is shown Fig 2. First, features selected using RF (m 2 or n 2 ) were combined using a RF in order to turn the subset into 1 feature. The performances of the RF methodology were studied using a receiver operation characteristic (ROC) methodology  [31]  leading to an area under the curve (AUC), a sensitivity (Se) and a specificity (Sp). Furthermore, for the prognostic study, a Kaplan-Meier analysis was performed leading to median survival, percentage of deaths in each group, and hazard ratio (HR). Our RF feature selection strategy was compared to a Mann-Whitney U test (predictive study) and to a univariate Kaplan-Meier analysis (prognostic study).
Results From our database, the mean MTV was 19. 6 ± 20. 5 cm 3 (range 2. 5-141 cm 3 ) and the mean SUV max was 12. 3 ± 4. 9 (range 3. 5-25. 6). Table  3  shows the results of the influence of the different steps of our feature selection strategy. Even if our proposed method gives the best performances for the predictive study, there was no statistically significant difference between the methods, while for the prognostic study, our proposed method was statistically different than the 3 other feature selection strategies (P < 0. 05). Results of the Spearman's analysis of the 61 initial features are given in Table  4. Concerning clinical data, the patient's usual weight and current weight were correlated (|ρ| > 0. 96), as well as the albumin level, the nutritional risk index (NRI) and malnutrition (|ρ| > 0.
88). This is due to the fact that NRI and malnutrition features were obtained from the albumin level. None of the clinical data were correlated with the other studied features. Only 8 PET image features were not correlated: V 10 , SUV 80 , COV, skewness, kurtosis, SZE, SZLGE, and GLNUz. At the end of this correlation study, 9 groups of significantly correlated features (|ρ| ! 0. 8, P < 0. 05) were identified. The patient's weight, NRI, V 10 -V 90 , energy (1 st order), MTV, SUV max , homogeneity (GLC matrix), busyness (GLD matrix), and ZLNU (GLSZ matrix) were used as leaders of their correlation groups. This step led to the pre-selection of F u /F i = 28/61 features (13 clinical and 15 from images). Then, classifications using the proposed feature selection strategy were realized based on these uncorrelated features.
039), while the clinical stage was not significant. Giganti et al. [42]  also investigated the association between preoperative texture analysis from multidetector CT and OS in gastric cancer. Given the number of features (107) and the number of patients (56), a feature selection was done based on a random survival forest. This study showed that multidetector CT texture analysis is a promising, non-invasive diagnostic tool to evaluate the aggressiveness of gastric cancer. Given the limited number of patients in our database (65), only 45 FDG-PET features were extracted, while many other features have been proposed in the literature. The chosen features correspond to those generally studied in the literature  [17]  [22]  [36]. Nevertheless, a larger patient cohort will help to integrate more FDG-PET, CT, or MRI features.
It should also be noted that our database is composed predominantly of SCC patients (88%). This ratio is of the same order of magnitude as the database used by Tan et al. [36]. Nevertheless, results can depend on the initial patient database. Several methods have been proposed in the literature to reduce the number of features by eliminating those with an insufficient repeatability and reproducibility  [26] [48] [49]. This strategy is included in the pre-selection step of our feature selection, because for each group, the representative feature was the one corresponding to the most robust with respect to image reconstruction settings  [29]. Even if pre-selection is performed, the number of uncorrelated features remains high. In general, a nonlinear relationship exists between a feature and the patient outcome.
For all statistical approaches, when using machine learning, it is better to have a large database. The principle is to perform a partition of training and evaluation data. Unfortunately, this is generally difficult to obtain from clinical studies. As a surrogate, the same database for both the training and evaluation process was used  [50]. As the number of patients in the database (65) is only 2. 32-fold F u , a random permutation of 10 iterations was used to avoid overfitting. Our proposed selection strategy consists of 2 successive selections, reducing the number of features used to build the classification model. The first is a Spearman's rank correlation analysis, which only keeps uncorrelated features for the next step (F u = 28/61). The second is based on the RF coefficient of importance.
The combination of these 2 steps improves the classification performances with respect to the use of only 1 (Table  3 ). We have already developed a two steps combination for features selection  [51]  using a genetic algorithm for the second step rather than a method based on the coefficient of importance. This method, called GARF (genetic algorithm based on random forest), was evaluated on the same database. AUCs were improved using the coefficient of importance with a smaller number of selected features, if compared to the genetic algorithm (2 in the predictive and 3 in the prognosis studies against 9 in the predictive and 8 in the prognostic studies for GARF). Furthermore, GARF results are sensitive to several parameters of the fitness function of the genetic algorithm leading to optimize these parameters.
A comparison of these 2 resampling approaches has been done (see Table C in S1 File) showing a statistically significant difference in favor of absolute resampling on our database (P = 0. 04 in the predictive study and P = 0. 01 in the prognostic study). For the construction of the RF, a value of T = 500 decision trees was chosen. Other values of T were also studied (see S2 Fig)  without  showing a significant difference using the Wilcoxon signed-rank test. However, it is known that increasing the number of trees does not reduce the classification performance but tends to converge toward good results  [28]. According to our experiments, a value of 500 is a good compromise between the performance of the classifier and the computation duration. Even if the initial database is the same, the best complementary features selected by the RF algorithm may be different from those found for individual features using standard statistics.
Conclusion Because of the large number of studied features with respect to the number of patients, a RF algorithm was used to determine the subset of complementary features with the highest predictive and prognostic values. We have shown that the RF classifier can improve the predictive and prognostic values compared to the Mann-Whitney U test and the univariate Kaplan-Meier analysis when applied to several tens of features in a limited patient database. Machine learning algorithms are promising for the prediction of treatment response and survival when using several tens of features. Their impact on medical imaging research and clinical routines still has to be evaluated.
Recent study has also demonstrated that PHA 543613 exerts neuroprotective effects on the striatal dopaminergic neurons with a reduction in microglial activation in a PD rat model  [28]. Thus, data published in literature relate that therapies targeting glial cells, and more precisely the agonists of α7nAChRs, might provide benefit in neurodegenerative disorders  [36]. However, the interactions between the neuroprotective and anti-inflammatory effects are not clearly elucidated. Regarding this question, we used a rat model of acute neuroinflammation obtained by unilateral striatal injection of quinolinic acid (QA) in which we evaluated the effects of a chronic administration of PHA 543613. QA is a strong agonist of glutamate NMDA (N-methyl-D-aspartate) receptors. Overactivation of NMDA receptors causes a massive intracellular influx of calcium that leads to neuronal death by activation of various enzymes triggering different cell components then leading to neuronal death  [37].
Using the β-vision software (Biospace Lab, Paris, France), the level of bound radioactivity was directly determined by counting the number of β-particles emitted from the delineated area. The radioligand signal in the ROIs was measured for 6 sections per brain and expressed as counts per minute per square millimeter (cpm/mm 2 ). Specific binding was determined by substracting non-specific binding from total binding. Radioactivity was quantified using an image analyzer (M3-vision™ Biospace Instruments, Paris, France). The percentage increase of TSPO binding in IL versus (vs) CL hemisphere was calculated as : IL-CL x 100.