dataesr/scientific-paragraphs-categorization huggingface dataset:
In 1991, Dr. Sbaitso was created as an AI speech synthesis program created for MS DOS Personal Computers. In this software, Dr Sbaitso was a psychologist, with very limited possibilites  3. Four years later, A. L. I. C. E (Artificial Linguistic Internet Computer Entity) included 40. 000 knowledge categories that was later awarded the Loebner Prize thrice  4. In 2001, SmarterChild was made available as a bot distributed across SMS networks and is now considered as a precursor to Apple's Siri, which was released on iPhones in 2010. Chatbots are now widely used in several forms as voice-based agents, such as Siri (Apple, Palo Alto, California, USA), Google Now (Google, Mountain View, California, USA), Alexa (Amazon, Seattle, Washington, USA) or Cortana (Microsoft, Seattle, Washington, USA). But they are not used in healthcare yet.
The participants completed Webbased versions of the 9-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder scale (GAD-7), and the Positive and Negative Affect Scale at baseline and 2-3 weeks later (T2). Participants in the Woebot group engaged with the conversational agent an average of 12. 14 (SD 2. 23) times over the study period. No significant differences existed between the groups at baseline, and 83% (58/70) of participants provided data at T2 (17% attrition). Patients in the Woebot group significantly reduced their symptoms of depression over the study period as measured by the PHQ-9 (F=6. 47; P=. 01), in an intent-totreat univariate analysis, while those in the information control group did not. Another chatbot, Koko, was designed to express empathy, which could be positively leveraged in cancer-patients  11.
In this study, Morris et al report on the design of a conversational agent that could express empathic support in ways that might approach, or even match, human capabilities. The authors also assessed how users might appraise this system. The authors used responses from an existing pool of online peer support data that were repurposed by the chatbot and presented to the user. Information retrieval techniques and word embeddings were used to select historical responses that best matched a user's concerns. Ratings from 37,169 users were collected to evaluate the system. A controlled experiment (N=1284) to test whether the alleged source of a response (human or machine) might change user perceptions was performed: the majority of responses created by the agent (2986/3770, 79. 20%) were deemed acceptable by users.
Expression of caring and understanding is valued in supportive human communications. A study recently published by Liu et al. assessed whether humans could perceive empathy from a chatbot: in the first group of people (N = 158), participants simply read a dialogue between a chatbot and a human user. In a second group, participants (N = 88) interacted with a real chatbot. Three types of empathic expression were tested: sympathy, cognitive, and affective empathy, on the individuals' perceptions of the service and the chatbot. Data revealed that expression of sympathy and empathy is favored over unemotional provision of advice, in support of the Computers are Social Actors (CASA) paradigm  13.
Ongoing trials A search on ClinicalTrials. gov returns four trials evaluating chatbots in healthcare. In the United Kingdom, a non-randomized trial is being performed by the National Health Service to compare the Babylon chatbot to the non-emergency 111 telephone number  19. Patients can interact with an automatic agent in order to describe their symptoms. Advices and information are given in return by the chatbot. Randomized studies demonstrating the superiority (or at least non-inferiority) of chatbots, compared to an intervention performed by a physician, do not exist. If chatbots are to be safely used by a large number of patients, they must be evaluated like a medical device, or even a drug.
The Buddy Study (NCT02742740) evaluates an ECA Oncology Trial Advisor for Cancer Trials that acts as an advisor to patients on chemotherapy regimens, promoting protocol adherence and retention, providing anticipatory guidance and answering questions. The chatbot also serves as a conduit to capture information about complaints or adverse events. Usability metrics will include session time, satisfaction, and error rates. Subjects will be identified from among patients on chemotherapy regimens at the Boston Medical Center  20. All subjects will be enrolled for 2 months and randomized to the chatbot group or control group. Baseline data will be collected after enrollment and before randomization and will include: sociodemographic data, mental and social health, medical diagnosis (cancer type and stage), the Short Assessment of Health Literacy and the Short Portable Mental Status Questionnaire (SPMSQ) for cognitive impairment.
The exit interview will be conducted 2 months after baseline. Data collected at the exit interview will include: mental and social health and the satisfaction with ECA Review of the subject's medical chart will be performed bi-weekly while the subject is enrolled. Chart review will be performed by a staff member who is blinded to the subject's group (intervention vs control). Primary outcome will be treatment protocol adherence, defined by the number of treatment visits attended/number of treatment visits scheduled. The secondary outcome will measure: subject satisfaction, number of adverse events as reported through the ECA and directly to clinic by patient, time to detect and resolve adverse events as reported through the ECA and directly to clinic by patient, adverse event false alarm rate, as reported through ECA and directly to clinic by patient.
A third study, the RAISE project (NCT01458002 )  21  , is designed to promote exercise and sun protection. The primary aims are to develop and assess the effectiveness of a tailored internet intervention on a national sample, to develop and assess the effectiveness of the internet intervention enhanced by a relational agent, and to determine if the intervention with the relational agent can outperform the regular tailored internet intervention. The study will include three groups (Control, Internet, Internet plus Relational Agent). A representative national sample of 1639 individuals at risk for both behaviors will be recruited. A fourth study, INCASE, a randomized, controlled, blind study will be launched in France, comparing the information given by the Vik chatbot vs a multidisciplinary group of physicians (from medical, radiation and surgical oncology) to breast cancer patients (NCT03556813).
The study aims to estimate the influence of machine scale size on the behavior of plasma with extrinsic seeded impurities in the scrape-off layer (SOL) and divertor. This is performed through the comparison of plasma boundary simulations using the SOLPS-ITER code including drifts and currents of nitrogen (N) and neon (Ne) injection in ITER and ASDEX Upgrade (AUG) geometries. Trends are examined between the two seeding species in each individual device and by a comparison of the differences between the two machines. In the modeling results, the radiated power peak is located near the X-point in the inner divertor for the AUG cases and in the vicinity of the strike points in both divertors in ITER. The simulations also find less Ne impurity ions in the divertor volume than N and more significant Ne radiation inside the separatrix for AUG, consistent with published experimental findings.
In ITER, both species radiate mostly from the divertor, in agreement with the existing SOLPS-4. 3 simulation database obtained without drifts and with a less sophisticated treatment of parallel impurity transport. Drifts are important players in determining the plasma background for AUG and are comparatively less important in ITER. In both devices, the spatial distribution of the impurity ion density is complex, with their parallel flow patterns correlating with the thermal and friction force balance. Within this isolated modeling study, the principal reasons for different behavior between N and Ne on AUG and ITER appear to be the combination of a stronger drift effect and reduced screening of recycled fuel and impurity from divertor to private flux region on AUG leading to a more extended, colder plasma than in ITER.
Key input parameters for the simulations are compared in Table  1. In particular, injected powers of P in = 100 MW and 5 MW at the inner core boundary of the numerical grid for ITER and AUG respectively, noting that the power is distributed equally between ions and electrons for ITER, but not for AUG where, as noted above, the different profiles for χ ⊥i and χ ⊥e are selected to match upstream profile measurements even though no attempt is made here to compare the code predictions in the divertor with experiment. For AUG 1. 8 MW and 3. 2 MW are carried in the ion and electron channels respectively. Naturally, given the difference in scale size, the absolute quantities of fuel (deuterium) and seeded impurities are very much larger for ITER than AUG, but the ratios of impurity to fuel input are similar for the two machines for Ne and at least comparable for N.
3 simulation database  [1, 3]. In the case of AUG, injection is through the divertor dome structure, as in experiment. A more comparable study would have been to inject impurities/fuel also into the ITER subdivertor volume, as is now likely to be the case in reality for divertor detachment control when ITER operates, but the choice of an upper lateral injection is historical since, as mentioned, this is the location which has been chosen for the vast majority of ITER simulations to date. Using the same injection point as in previous studies allows the new SOLPS-ITER drift runs presented here to be directly compared with older SOLPS-4. 3 cases without drifts. Comparative simulations with divertor injection are underway.
As discussed in  [11] , the key drivers for the observed behavior in the AUG simulations are ExB drifts; when these drifts are deactivated the strongly detached, HFSHD region in the inner divertor leg is greatly reduced and the target loading is more symmetric. The absence of strong asymmetry in the ITER cases is an indication for a weaker effect of drifts (see Section 3. 1). To zero order this is expected given the relative difference in toroidal magnetic field strength between the two devices (5. 3 T cf. 2. 5 T). Separate code runs for ITER without drifts activated (not shown here) produce similar results to those shown in Figs. 4 5 6 , confirming the relatively low drift impact. In addition, the radiation distributions found in simulations within the main ITER divertor design simulation database obtained with the SOLPS-4.
The simulations find a pronounced in-out divertor asymmetry on AUG, featuring a dense plasma region on the high field side, similar to experimental observation. In the ITER case, the divertor plasma is much more symmetric and remains hotter over a much larger region below the X-point. These differences in plasma background are to zero order driven by the much more significant private flux region drift flows found in the AUG case. The ITER drift cases give very similar results to those from the main (SOLPS-4. 3) ITER simulation database which does not feature drift capability, further confirming the lower impact of drifts on ITER solutions. Radiation in the modeling results is found to be more localized in the divertor for ITER and extends up to the X-point in AUG, largely due to the very cold and dense inner divertor which leads to most of the seeded impurity being localized there.
Earlier findings  [38]  confirmed the validity of this method for carbon aerogels. The combination of pore size distributions based on CO 2 and N 2 isotherms reveals the coexistence of mesopores, micropores and ultramicropores (d < 0. 7 nm) in the samples (Figs. 2  and 3 ). In spite of the difference of the cumulative pore size distribution functions starting in the higher pore size range (Fig. 2 ), the pore volumes in the ultramicropore range are very similar in all the three samples (Fig. 3 ). XPS analysis was used to characterise the surface chemistry of the samples. The data listed in Table  3  show that HNO 3 oxidation radically enhanced the surface oxygen content. The difference between the CA and CX sample reveals the influence of the pore structure in the pyrolytic transformation process.
Acknowledgements The authors are grateful to the  Institut Laue-Langevin  and  ISIS Neutron and Muon Source  facilities for access to the instruments. The technical support of  D. Renzy  and  S. Baudoin  is warmly acknowledged. We thank  E. Geissler  for proofreading the manuscript. This research is supported by the  Hungarian Scientific Research Fund 128410. Financial support from the  BME-Bionotechnology FIKP grant of EMMI (BME FIKP-BIO)  and  VEKOP-2. 3. 2-16-2017-00013  is acknowledged. The  VEKOP  project is supported by the  EU  and by Hungary, cofinanced by the  European Regional Development Fund. The work is also part of the  EU  project  NANOMED  ( H2020-MSCA-RISE-2016 , No. 734641 ). r e f e r e n c e s.
Uniaxial tensile tests were performed using INSTRON 5982 machine with 10 mm gauge extensometer at a strain rate of 10 -3 s -1. In situ tensile tests were performed using a scanning electron microscope (FEG-SEM Zeiss LEO1530 operating at 20 kV) and a Micromecha machine adapted to EBSD configuration. Secondary electron (SE) imaging and EBSD mapping were recorded during the traction loading at targeted strains. The in situ samples (50 mm length × 2. 5 mm width × 0. 5 mm thickness) were pre-polished to mirror-like finishing. The EBSD data were rendered by the OIM software. The phase constitution of the pre-and post-deformation samples were analysed by X-ray diffraction (XRD) using Bruker D8 ADVANCE with Cu Kα radiation operating at 40 kV and 40 mA. TEM (JEOL 2100 microscope operating at 200 kV) was used to characterize the deformed samples.
Funding: This resarch was funded by regular funding from the  Centre National de la Recherche Scientifique (CNRS)  and  Institut Curie. E. L. is supported by a doctoral research fellowship from the  Doctoral School ABIES, AgroParisTech. N. E. B. is supported by a doctoral research fellowship from the  Doctoral School SDSV, Université Paris-Saclay , and from  "La Fondation pour la Recherche Médicale ". S. T. , H. R. , V. B. , and D. M. are supported by  Institut National de la Recherche Agronomique (INRA). L. V. is supported by  Inserm.
Western blot). In iTRAQ, a correct peptide and protein quantitation needs a correct interpretation of MS/MS spectra, which depends on a trustworthy peptide and protein identification. The identification and quantitation can invariably be performed by commercial built-in algorithms integrated to companion software packages; they fully retreat data generated by the manufacturer's mass spectrometers (e. g. Paragon -ProteinPilot for ABSciex mass spectrometers). Alternatively, analyses may be split into separate stages through a composite suite of commercial or free algorithms integrated to manufacturer-independent software (e. g. Mascot in Mascot Server, jTRAQx). It is noteworthy that, although tools to compute RPA are widely available, the computation of the ratios of RSPI is not supported by any available companion software, and so, it requires an alternative suite.
In this work, we validated the RSPI methodology and we built an automated data processing algorithm called Customizable iTRAQ Ratio Calculator (CiR-C) to refine critical parameters related to peptide confidence and selection. The composite suite made up of the Mascot algorithm, for peptide and protein identification, end-to-end with the jTRAQx software for computation of RSPI at the peptide level and the CiR-C algorithm for data integration and definitive protein quantitation turned out to be a successful combination. It has provided a great improvement compared with already available solutions for iTRAQ-based high-throughput quantitative proteomics and multiplexed analysis of biological systems.
Peptides were labeled with iTRAQ 4-plex reagents and analyzed by nano-HPLC MS/MS. The 4-plex distribution rotated between independent experiments (biological replicates) to circumvent tag-related bias. the analytical column and then into the mass spectrometer by a linear gradient from 5 to 25% in 70 min, then to 95% of solvent B (98% ACN, 0. 1% FA) over 120 min at a flow rate of 200 nl/min. Data-Dependent Acquisition (DDA) was carried out by IDA (Information-Dependent Acquisition) mode of Analyst 1. 7 TF software (ABSciex). The data from MS and MS/MS were continuously recorded with a cyclic duration of 3 s. For each MS scan, up to 50 precursors were selected for fragmentation based on their intensity (greater than 20,000 cps), their charge state (2+ and 3+) and if the precursor had already been selected for fragmentation (dynamic exclusion).
Mass spectrometry data processing and relative protein identification/quantification Quantitation method based on RSPI MS and MS/MS data for five independent experiments (biological replicates) (*. wiff, 1 per fraction, 12 files per experiment) were submitted to Mascot Server 2. 2. 03 via ProteinPilot (version 5. 0, ABSciex) for protein identification, and searched against two complementary Sus scrofa databases: a Swiss-Prot database (2015 10 release) and a TrEMBL database (2015 10 release). Carbamidomethyl (C) was defined as a fixed modification. Oxidation (O), iTRAQ4plex (K), iTRAQ4plex (Y), iTRAQ4plex (N-term) were defined as variable modifications. MS/MS fragment mass tolerance was set at 0. 3 Da. Precursor mass tolerance was set at 0. 2 Da. Mascot raw data files (*. dat, 1 per experiment) were saved for further isobaric tags-based peptide and protein quantitation with the Java implementation of the Quant algorithm, jTRAQx (version 1.
Quantitation method based on RPA MS and MS/MS data for five independent experiments (biological replicates) (*. wiff, 1 per fraction, 12 files per experiment) were submitted to protein identification using the Paragon algorithm as implemented in the ProteinPilot software (version 5. 0, AB SCIEX) and searched against two complementary Sus scrofa databases: a Swiss-Prot database (2015 10 release, 1422 entries) and a TrEMBL database (2015 10 release, 47465 entries). Quantitation was conducted with or without auto bias correction, an available option implemented in the ProteinPilot software to normalize uneven protein across the multiplex samples, improving further quantitation. Mass tolerances and identification parameters were automatically set and optimized for the ABSciex 5600+ TripleTOF™-generated MS/MS data (MS/MS Fragment mass tolerance was set at 0.
Statistical analysis Western blot quantitation was compared with RSPI-and RPA-based quantitation using the Bland-Altman comparison method  [20, 21]  and Pearson correlation coefficient test (minimum significance threshold P=0. 05). iTRAQ ratios of UPS1 spiked-in proteins were compared using one-way ANOVA analysis and Dunnett's post-tests (minimum significance threshold P=0. 05). Statistical analysis was performed using available tests as implemented in the GraphPad Prism software (version 5. 04). Protein ratios were calculated as median values of peptide ratios. No further data transformation or data normalization was performed prior to statistical analysis.
Comparison between the data processing outputs: MASCOT -jTRAQx versus Paragon -ProteinPilot Multiplex analysis of CNI-exposed partial proteomes was conducted and optimized in vitro using the epithelial tubular proximal cell line LLC PK-1 serving as a model. Five series of samples were prepared and processed according to the optimized custom-made iTRAQ protocols (Scheme 1A). The samples labeling strategy is summarized in Table  1. The RPA strategy resulted in Paragon identifying 9788 + -3270 peptides related to 1100 + -132 proteins per series. After identification and quantitation by the Paragon -ProteinPilot suite, the CiR-C algorithm included 4105 peptides (114 proteins) split into 35 Swiss-Prot entries and 79 TrEMBL entries. The RSPI strategy resulted in Mascot Server identifying 14291 + -4582 trypsin-digested peptides related to 1160 + -115 proteins per series.
After identification by the Mascot -jTRAQx suite, the CiR-C algorithm included 32169 peptides (370 proteins) split into 131 Swiss-Prot and 239 TrEMBL entries. The CiR-C shell script excluded irrelevant data according to criteria summed up in Table  2 : (i) identification confidence: peptides are retained if the probability that the observed positive match is a random match is below 5% (P<0. 05, Mascot score > 30; Paragon confidence score > 95); (ii) quantification confidence: peptides are retained if all iTRAQ ratios have been successfully calculated, i. e. , peptides with 0. 0 ratios or uncalculated ratios (null ratios) are discarded; (iii) peptides related to 'Fragment'-and 'REVERSED'-annotated proteins are discarded. After irrelevant data removal, CiR-C drew up an exhaustive catalog of identified peptide sequences with their associated Swiss-Prot or TrEMBL accession IDs.
Peptides were assigned to a frequency index of positive matches (identification in {1;2;3;4;5} out of 5 independent experiments [biological replicates]) and CiR-C drew a second catalog of peptides with the highest frequency index (n=5). Protein ratios were calculated as both overall and series-specific median values of peptide ratios associated with a given accession ID and frequency index. CiR-C discarded 7597 + -3291 (75. 4% + -9. 0) peptides quantified by RPA and 4377 + -2367 (29. 4% + -5. 5) peptides quantified by the RSPI (Table  2 ).
074, P=0. 526) (Figure  2A ). Further- The MS/MS data from the five replicate experiments were submitted to identification (Mascot versus Paragon), primary quantification (jTRAQx versus ProteinPilot) and data refining, statistical analysis (CiR-C). This table sums up data inclusion criteria, identification output and data refining yield, as numbers of identified, discarded of retained peptides more, the average difference between Paragon -ProteinPilot -CiR-C and Western blot methods was -0. 275 + -0. 961 (approx. 30% of a given iTRAQ ratio) while 95% Limits of Agreement were -2. 163 and 1. 609 (Figure  2B ). Auto bias correction did improve neither accuracy nor precision as it worsened ratio compression and non-correlation to Western blot (zero-slope; Pearson correlation coefficient r = 0.
Discussion and conclusion Since its first description by Ross et al. in 2004, iTRAQ has been widely used for multiplexed analysis of proteomes. The advantages and drawbacks of iTRAQ have been widely addressed in the literature, following the many developments of technical optimizations, analysis strategies and tools to improve quantitation precision and accuracy  [24] [25] [26] [27]. However, there is still no consensual technique in sample preparation and analysis or data processing. iTRAQ results in rich and complex MS/MS datasets which require thorough processing and solid statistics to reach relevant conclusions. In this respect, the choice of a tag ratio calculation method is crucial. Two potential strategies have been proposed: either RPA or RSPI. The two methods are not equivalent since peak area measurement suffers from a major bias originated from the way reporter ion signals are processed, as described by Boehm et al.
In contrast, with the lowest bias and best correlation, the RSPI from Mascot -jTRAQx -CiR-C data processing strategy provided the most reliable set of quantitation ratios when compared with Western blot performance. As compared with the Western blot 'quality control' , the median RPA showed dramatic differences leading to a global misrepresentation of changes in protein expression. Conversely, the median RSPI for the set of proteins of interest were well correlated and showed moderate and unbiased differences compared with Western blot. Similar observations were made monitoring the protein abundance of spiked-in UPS1 proteins after differential spike-in. Western blot was chosen as a non-MS-based quality control for iTRAQ because the technique has already been successfully used to validate iTRAQ.