dataesr/scientific-paragraphs-categorization huggingface dataset:
Comet assay Proliferating TR146 cells were exposed to food-grade TiO 2 (E171) and the two TiO 2 test materials for the indicated times at the indicated concentrations. The comet assay was performed under alkaline conditions using a Comet SCGE assay kit (Enzo Life Sciences, Villeurbanne, France) according to the manufacturer's instructions. Brie y, 800 cells embedded in low-melting agarose were spread in each sample area of the comet slide. Electrophoresis was performed in alkaline solution (0. 3 N NaOH, 1 mM EDTA) at 4° C for 30 min at 35 V in a large electrophoresis tank (35 cm between electrodes). After staining with CYGREEN® Nucleic Acid Dye, slides were observed at 20× magni cation using a Nikon 50i uorescence microscope equipped with a Luca S camera. At least 60 cells were analysed per sample using OpenComet software.
5a ). In addition, the TEER of differentiated TR146 cells was measured after 2 h of exposure to each TiO 2 sample to assess their respective impacts on epithelial integrity. Regardless of the time point tested during the 48 h after treatment, no alterations were detected at any dose, suggesting that epithelial barrier permeability and monolayer integrity were not affected regardless of the TiO 2 product or particle size (Fig. 5b ). Taken together, these data indicate that the epithelium formed by TR146 cells is not noticeably altered by exposure to foodborne TiO 2 but that cycling cells could be sensitized.
These structures represent the major staining found in the control or after exposure to TiO 2 -NPs with a nominal size of 21 nm (Fig. 6a ). However, in the presence of DNA double-strand breaks, 53BP1 is recruited to the damaged site and forms foci. Interestingly, food-garde E171 and JRC10200a induced 53BP1 foci formation in a subset of TR146 cells, mainly colocalizing with the γH2AX signal (Fig. 6a ). This staining was observed in proliferating as well as in differentiated cells. Hence, these data showed that food-grade and JRC10200a TiO 2 but not TiO 2 -NPs activate the DNA damage biomarkers γH2AX and 53BP1 after 2 h of treatment, strongly supporting the formation of DNA double-strand breaks. To con rm this result, we performed an alkaline comet assay in proliferating cells. Under our conditions, E171 and JRC10200a signi cantly increased the amount of fragmented chromatin compared to untreated cells, as revealed by an increase in the % tail DNA, whereas no difference was observed after treatment with TiO 2 (Fig.
Funding The study was supported by  INRAE  ( Institut national de recherche pour l'agriculture , l'alimentation et l'environnement). This work has received fundings from the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement no. 720964  [npSCOPE], by the  French National Research Agency (ANR)  under grant number  ANR-16-CE34-0011-01  [PAIPITO], and by the  Luxembourg National Research Fund  via the project  INTER/DFG/19/13992454.
Enzyme-Linked Immunosorbent Assay (ELISA) to Detect Serum Cytokines Serum cytokine (IL-1β, TNF-α, IL-9, IL-23, IL-6, IL-17A, IL-21, IL-22, IL-10, and TGF-β) were assessed using a cytokine panel by Bio-Plex assay (Bio-Rad Laboratories, Richmond, CA, United States), following the manufacturer's instructions. Data were analyzed using the Bio-Plex Manager software, version 4. 1. 1 (Bio-Rad Laboratories), reported as fluorescence intensity (FI) and subsequently converted into concentration (pg/ml).
PCoA was performed to compare the overall composition of the bacterial community within samples. The taxonomy assignment of OTUs was carried out by using a Naive Bayes classifier trained on a custom 97% clustered version of the Greengenes rDNA v13_10 in agreement with the method previously reported  (14). A co-occurrence network was computed, at species level, for each group separately (Controls, ERA_T0 and ERA_T1), as previously described by Schippa et al. (28). Only bacterial species showing a mean relative abundance of 0. 1% in almost one group have been considered for analysis. A second matrix based on Spearman rank sum correlation between the relative abundance of bacterial species and the clinical variables was computed for each group. Only values less than -0. 6 and greater than 0. 6 The presence of statistically significant difference between group of data were also reported.
The Wilcoxon Signed-Ranks test was performed to assess significant differences between the different time-point considered for the ERA group. The * and ** difference is statistically significant. were considered for species-species correlations while values less than -0. 5 and 0. 5 were considered as thresholds for correlations between species and both clinical and metabolic variables. A p ≤ 0. 05 based on bootstrapping of 1000 repetitions was considered as threshold to assess the statistical significance of considered correlations. For each group, the concurrence network was integrated with the Spearman rank sum correlations and, subsequently, data were imported and visualized in Cytoscape v. 3. 7. 2. Topological parameters relative only to the co-occurrence microbial networks were calculated by using the Network Analyzer plugin included in Cytoscape.
DISCUSSION The study aimed to characterize fecal gut microbiota and fecal SCFAs concentrations in ERA patients compared to controls and to analyze their variation after 3 months (T1) of MTX plus glucocorticoids treatment in ERA patients, along with the estimation of clinical and clinimetric features. At the same time, we evaluated the relationship between circulating Th17/Treg levels, their own cytokines and gut microbiota by network analysis. Our results showed significant differences in all the ecological index considered to evaluate alpha and beta diversity comprising those integrating genetic distances among groups of microbes. Obtained data strongly evidenced that not only untreated ERA patients are characterized by a lower microbial biodiversity compared to controls, but also that, on the base of the global gut microbial composition, it is possible to determine a significant partition between the two groups, evidencing a wide involvement of microbiota structure in the modification associated with ERA.
Participants were also asked how they had completed the questionnaire (i. e. , alone, with the help of a family member or health care professional). For the first question, participants were asked to specify whether they had suffered from pain within the two weeks prior the survey completion. Only participants who had experienced pain within the last two weeks could continue with the questionnaire. Therefore, for patients who had not experienced pain, only socio-demographic data and clinical status were collected. Question 2 focused on the localization of pain. In questions 3 to 5, a visual analogue scale (VAS) ranging from 0 to 10 was used to assess pain intensity (0 = no pain, 10 = most intense pain). Questions 6 to 9 were based on the DN4 questionnaire  [26]. The DN4 is a validated and simplified questionnaire administered by clinicians to detect neuropathic pain.
For questions 23 to 27, participants were asked to indicate whether they were taking pharmacological and/or non-pharmacological treatments for their pain, and if so to list them. The different pharmacological treatments were then classified into nine categories based on the WHO classification. Level 1 painkillers denoted non-opioids, level 2 painkillers referred to weak opioids and level 3 painkillers were strong opioids  [27]. For questions 25 and 27, patients used the VAS ranging from 0 to 10 to assess the efficiency of these treatments (0 = not efficient, 10 = strongly efficient). Finally, for question 28, they were asked if they were willing to test new pharmacological or non-pharmacological treatments. Data were exported and analyzed from the Google form in. csv format. Data collected from the paper version were added to these files.
Statistical analyses were performed using R studio (version 4. 0. 2) software. For the descriptive analyses, we used subject counts and percentages to describe the categorical responses and means ± standard deviation (SD) when assessing pain intensity/influence or treatment efficiency. Regarding questions with multiple select answers, we based the calculation on the percentage of the number of participants who replied to each question. As the variables were categorical and/or ordinal variables, non-parametric statistics (i. e. , Pearson's chi-squared test, Fischer test and Wilcoxon rank sum test) were performed.
Demographic Information and Clinical Status From the 300 patients contacted by email, 59 surveys were collected, and 8 were excluded from the analysis as there were incomplete data on pain. A total of 51 participants (41 from the online version and 10 from the paper version) were included in the analysis, representing a 17% acquisition rate. Seven participants (14%) completed the survey by themselves, 10 (20%) needed the help of a family member, eight (16%) needed the help of a health care professional (i. e. , two psychologists, three occupational therapists, one nurse, one social worker and one ALIS member) and 26 (51%) did not answer to this question (i. e. , which corresponds to patients who did not experience pain). Figure  1  provides an illustrative summary of the results. Among the respondents that declared their sex, there was the same number of women as men (n = 18).
Indeed, we do not know how many participants did actually read the email; some LIS patients may not have had the tools and help to fill the questionnaire; and ALIS sends several questionnaires per year, which can be discouraging for the LIS patients because it requires time and effort. However, to our knowledge, this is the first survey that directly addresses LIS patients regarding pain management. Secondly, only patients who had pain in the two weeks prior the completion of the survey were included. Longitudinal data are required to see if there is a correlation between the presence of these painful sensations and the diagnosis of these patients (i. e. , a patient whose attention is diminished because of pain could be misdiagnosed as UWS). Thirdly, some questions lack precision, especially regarding the type of care, the influence of temperature or the dosage of pain treatments.
Elevated plus maze task Elevated plus maze (EPM) was a plus shape apparatus elevated 50 cm above the ground. It consisted of two open arms (45 cm × 10 cm long and width, respectively), two enclosed arms (45 cm × 10 cm × 30 cm, long, width and high respectively), and a middle compartment (10 cm × 10 cm). Thirty min before starting the test, rats were moved to the testing room under dim light in order to familiarization. Each rat was positioned in the middle compartment while it was facing to an open arm and permitted to freely search the maze for 5 min. Ethanol 70% was used to clean the maze after each 5 min run. Locomotion was recorded by a CCD camera (Panasonic Inc, Japan) hanging from the ceiling above the maze, digitized and saved to the computer by a video tracking software (Ethovision, versionXT7, The Netherlands).
A significant amount of emotional data with different languages has been built and, emotion-recognition studies have been conducted. Among the emotional corpus, IEMOCAP is multimodal emotional dataset in English and has been used as data for research on emotion recognition. For emotion recognition, multimodal recognition can be combined-for example, by combining speech-signal recognition with image recognition (face recognition and body-language recognition) and natural language recognition with noting exclamation words. In the case of such a combination, a better recognition efficiency will be achieved. It can be said that human interaction is marked Sensors 2022,  22 , 1414 2 of 20 by affects (attitudes and emotions)  [1]. It is known that attitudes are constructed for each language and culture and must be learned by children or second-language students  [2].
Related Work The research in  [7]  has surveyed and evaluated quite a significant number of studies on speech emotion recognition for different corpuses including IEMOCAP  [8]. IEMOCAP was a corpus collected by the Speech Analysis and Interpretation Laboratory (SAIL) at the University of Southern California (USC). IEMOCAP launched in 2008, and since then there have been many studies on emotions using this corpus. In general, for convenience of comparison, most studies performed recognition for the same four emotions even though IEMOCAP has data for nine emotions (happiness, anger, sadness, frustration, surprise, fear, excitement, other, and a neutral state). Those four emotions are anger, happiness, sadness, and neutrality. For the happiness emotion, some studies consider excitement as happiness or combine excitement and happiness into a common emotion called happiness.
For the models that were used for emotion recognition, the vast majority of IEMOCAP emotion-recognition studies have used neural-network models. The commonality of the studies listed in Table  14  is that there is no data augmentation for IEMOCAP. In  [9] , the authors used SVM to recognize four emotions from IEMOCAP with an average accuracy of 71. 9%. The studies listed in Table  14  from 2015 to now all used neural-network models. Studies using the LSTM model  [10] [11] [12] [13] [14] [15] [16]  account for a fairly large proportion of the total number of studies. Besides, studies were using CNN in combination with LSTM  [17] [18] [19] [20]. CNN, DCNN, and multi-channel CNN models were used in  [21] [22] [23] [24]. A combination of CNN and RNN models to get the CRNN model was used in  [25].
Proposed Materials and Methods In this section, we present the IEMOCAP corpus for experiments, data augmentation, feature parameters, and deep neural network (DNN) models for our research. The last part of the section is a brief description of the performance parameters used to evaluate the research results. IEMOCAP is a multimodal emotional corpus. Ten actors were recorded in dyadic sessions (five sessions with two subjects each). In total, the database contained approximately twelve hours of data. With this database, the authors hoped to be able to expand and generalize their results about the relationship and interplay between speech, facial expressions, head motion, and hand gestures during an expressive speech and conversational interactions. The distribution of the sample number for nine emotions is given in Figure  1.
After data augmentation, the sum of files for four emotions was 1075 × 4 + 1014 × 4 + 1007 × 4 + 1639 × 4 = 4735 × 4 = 18,940 files. In our experiments, the feature parameters were divided into two sets: S1 and S2. The parameter set S1 includes 128 Mel-spectral coefficients. The parameter set S2 includes set S1 plus 25 parameters as shown in Table  1. So, S2 includes 153 parameters. Librosa  [37]  was used to compute the set of feature parameters S1 and S2. The sampling frequency of IEMOCAP wav files was 16,000. With a frame width of 256 samples and a frame shift of 128 samples, the average number of frames per wav file was 372 for IEMOCAP wav files. The frame shift was changed according to the sample number of the file. The smaller the number frame in the file, the smaller the frame shift.
Chroma features consist of a twelve-element vector with each dimension representing the intensity associated with a particular semitone, regardless of the octave. The roll-off frequency denotes the approximate low-bass and high-treble limits in a frequency-response curve. Pitch reflects the bass or the treble of the perceived sound, and each sound is emitted with a fixed frequency. The pitch of the sound depends on these frequencies, and the higher the frequency of the sound, the higher the frequency of the perceived sound that will increase and vice versa. FRMS is a root-mean-square (RMS) value calculated for each frame. Our contribution compared with previous studies on speech emotion recognition to the IEMOCAP corpus is the way the data were augmented. We augmented the data in two ways: changing the voice and adding noise.
Results and Discussion The experiments were performed on the computer system with the following hardware configuration: Intel Core i7-8700@3. 2GHz × 12 threads processor, 32 GB RAM, 2 TB storage, and NVIDIA GeForce RTX 2080Ti 11GB RAM. The following are the software versions used: Ubuntu 19. 10 operating system, Python 3. 8, Keras: 2. 4. 3, Tensorflow 2. 2. 0, Tensorflow-gpu 2. 3. 0, Praat 6. 1. 16, and Librosa 0. 7. 2. The data were divided into ten folds, and cross-validation was performed. One fold was extracted from ten folds to dedicate for testing. Out of the remaining nine folds, one was extracted for validation and the remaining eight for training. Extracting one fold from eight folds was done in rotation. In our case, cross-validation was chosen because it generally results in a less-biased or less-optimistic estimate of the model skill than other methods, such as a simple train/test split  [50].
As an example (Figure  7i ), for the anger emotion, the sample number for the tp of this emotion was 415. For the false-negative ( f n) case, eight anger samples were incorrectly identified as excitement; two anger samples were incorrectly identified as sadness; and five anger samples were incorrectly identified as neutrality. For the false-positive ( f p) case, six excitement samples, zero sadness samples, and two neutrality samples were incorrectly identified as anger. For the true-negative (tn) case, (387 + 1 + 9) samples of excitement, 408 samples of sadness, and (646 + 2 + 6) samples of neutrality were correctly identified as not-anger. The cases of f n, f p, and tn were also determined in a similar way for the remaining three emotions. For comparison, we performed IEMOCAP's four-emotion recognition with the same GRU model and the same S2 parameter set but without data augmentation.
Conclusions In this article, we presented the results of speech emotion recognition with the IEMO-CAP corpus. Three deep neural-network models CNN, CRNN, and GRU were used for emotion recognition in our case, and, in general, the GRU model had a slight advantage over the CNN and CRNN models. Data augmentation including changing the voice also contributed to recognition performance. Besides the Mel-spectral coefficients, other spectral features of the speech signal also increased the average recognition accuracy. The results of our research demonstrate that the proposed method outperforms the state-of-the-art methods (see Table  14 ). For machine learning including deep learning, the more data available, the better the performance of the recognition system. If noise addition is a possible solution for different signal types, then data augmentation by changing the voice is a very specific approach to speech data.
Data collection The data we draw on comprise 62 interviews with elite chefs. Interviewees were initially from the UK and were identified using the Michelin Guide. We invited chefs to be interviewed by contacting them directly using social media, by emailing or writing to them or their media representatives. At the end of every interview, we asked for referrals to colleagues that might be willing to be interviewed. Because of the nature of the industry, many of the chefs we were referred to worked abroad. In these cases, initial contact was made by Skype. When we achieved sufficient referrals in one particular country (e. g. 3-6), we visited that country to undertake interviews. Visits were made to France, Italy, Switzerland, German, Belgium, Demark, China, Australia, Singapore and the USA.
Stage 4. During this final stage of analysis, we elected to undertake what ultimately became multiple rounds of additional data collection to probe, test and flesh out our findings. We reinterviewed approximately 25% of our original study participants, and recruited a further 15 chefs with work experiences in Europe, Asia and North America. Our aim was to develop our increasingly detailed understanding of how suffering mattered in processes of identity construction. We continued sampling, refining our codes and revising our narratives until we settled on a set of empirically and theoretically novel insights, which we now present in the sections that follow.
Thus, we argue that what we see in our data is chefs trading upon, and variously invoking, what we call the aesthetics of suffering as they worked to forge a particular understanding of who they are. To further progress research concerned with embodied identity work, and the connection between suffering and identity in particular, we suggest there are at least two areas that might be probed by future research. First, the data we present are drawn from a very particular, highly institutionalized context, this being fine dining restaurants operating more or less exclusively according to classically French culinary principles. The experiences our chefs report -and thus our theorizing -are therefore intertwined with the ideology of the 'brigade de cuisine'  (Trubek, 2000) ; this being a context where suffering and the ability to endure it was (in our data at least) a relatively universal experience.
Development of PCR-HRM Choice of target sequences and primer design Two gene sequences were targeted in this study. First, primers targeting a conserved region coding for the 7SL RNA subunit were used, as described previously  [17]. This allowed amplification of a 119-base-pair (bp) DNA fragment. The second pair of primers targeting the HSP70 gene was designed here. Thus, using Clustal X, HSP gene sequences from L. major, L. infantum, and L. tropica were aligned and primers were designed in conserved regions flanking a 280-bp polymorphic internal region containing multiple SNPs, using the Primer3Plus software program (http:// www. bioin forma tics. nl/ cgibin/ prime r3plus/ prime r3plus. cgi). Primer-BLAST (http:// www. ncbi. nlm. nih. gov/ tools/ primer-blast/) was also used to check the primers' sequence specificity.
DNA sequencing and analysis Fragments generated by conventional PCR targeting 7SL RNA and HSP70 genes from each Leishmania strain used (L. major, L. infantum, L. tropica) as well as from animal samples were purified with phosphatase and exonuclease enzymes (Thermo Fisher Scientific, USA) and sequenced in both directions using the BigDye Terminator v3. 1 Cycle Sequencing Kit (Applied Biosystems, USA). Sequencing was performed on an ABI 3500 sequencer (Applied Biosystems, USA). The generated sequences were visualized and manually corrected using BioEdit software, and then submitted to BLAST (basic local alignment search tool) analysis for homology searches using the National Center for Biotechnology Information (NCBI) server (http:// blast. ncbi. nlm. nih. gov/). Direct sequencing assays were also carried out on PCR-HRM amplified products from animal samples, in order to confirm their Leishmania species identity and eventually validate ambiguous cases.
To obtain the melting curves, the PCR program was followed by a melting program, in which a dissociation of the amplicon was performed at 95 °C for 1 min, then rapid cooling at 40 °C for 1 min, followed immediately by a fusion step at 60 °C for 1 s, with ramp rates at 4. 4, 2. 2, and 1 °C/s, respectively for each step. Finally, melting curves were obtained by reassociation at 95 °C, with a ramp rate of 0. 02 °C/s and 25 acquisitions/°C, which are sufficient to result in a resolution appropriate for HRM analysis, as recommended by the manufacturer. Results were analyzed using the LightCycler ® 480 SW 5. 1 software package, including the gene scanning module (version 1. 5. 0), provided by the manufacturer. For each experiment, PCR efficiency was evaluated using the threshold cycle (Ct) and melting curves were normalized according to obtained reaction values.