dataesr/scientific-paragraphs-categorization huggingface dataset:
Background: Artificial intelligence (AI)-based chatbots can offer personalized, engaging, and on-demand health promotion interventions. Objective: The aim of this systematic review was to evaluate the feasibility, efficacy, and intervention characteristics of AI chatbots for promoting health behavior change. Methods: A comprehensive search was conducted in 7 bibliographic databases (PubMed, IEEE Xplore, ACM Digital Library, PsycINFO, Web of Science, Embase, and JMIR publications) for empirical articles published from 1980 to 2022 that evaluated the feasibility or efficacy of AI chatbots for behavior change. The screening, extraction, and analysis of the identified articles were performed by following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.
Data Extraction and Quality Assessment Several summary tables were used to extract information from the selected articles, including study characteristics (ie, author, publication year, study design, participants, age of the sample, sample size, country, and target health behaviors), chatbot-based intervention features (ie, chatbot types, chatbot components or functionality, settings, existing AI technology, input data sources, platform, theoretical foundation, and AI algorithms), and intervention outcomes (ie, health behavioral outcomes or primary outcomes, feasibility, usability, acceptability, and engagement). Feasibility, acceptability, and usability did not have a consistent definition across the studies. Therefore, for the ease of comprehension and systematic representation, the authors categorized the data on feasibility, acceptability, and usability based on their definitions.
Apart from the assessment of each risk type across studies, all studies were rated on the following scale: 1=compliant, 0=not clear, 0=not compliant, and 0=not reported or not applicable. A total score was calculated for each study. AI techniques specific to AI chatbot interventions were also appraised using the CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension guidance for AI studies  [20]. We used a checklist of four domains: (1) whether the rationale for using AI was specified through the use of AI in the context of the clinical pathway, (2) whether the inclusion and exclusion criteria at the level of the input data and the description of the approaches to handle unavailable input data were specified, (3) whether the input data acquisition processes and the specifications of human-AI interaction in the collection of input data were described, and (4) whether the output of the AI algorithm and its significance in the context of the studies' outcomes were described.
Infrastructure of the Chatbots: Input Data for Personalized Services To deliver personalized services using AI chatbots, most chatbots or studies (9/15, 60%) required input data on the users' background, goals, and behavioral performance and chatbots' usability and evidence-based content. The users' background information or baseline characteristics were collected by 4 AI chatbots. Paola  [22]  measured the baseline level of physical activity and Mediterranean diet; SFA  [24]  measured time to first cigarette and cigarettes per day; CASC  [25]  measured demographics, motivation levels for smoking cessation, number of cigarettes smoked per day, and years of smoking; SMAG  [27]  measured demographics and type of smoking dependence; and Tess  [6]  used electronic health records. Information on the users' goals, that is, who, when, where, what, and how, was collected by 3 chatbots.
HLCC  [21]  asked the users (office workers) to set realistic stair climbing goals, Paola  [22]  enabled the users to set dietary goals and daily step target every week based on the previous week's outcomes, and SFA  [24]  asked the users to set the target quit date for smoking. Real-time feedback on usability was collected by 3 chatbots. DigiQuit  [23]  collected feedback on the message content and timing, Tess  [6]  collected data on the usefulness of the message, and Vik  [26]  collected data on the relevance of the reminders. Real-time feedback on the behavioral performance of the users was collected by 5 chatbots. HLCC  [21]  collected performance content and pictures; Paola  [22]  collected data on daily steps and dietary patterns; Vik  [26]  collected data on medication adherence levels; SMAG  [27]  monitored the users' smoking levels along with information on location, alone or accompanied, ongoing activity, and mood to create smoking profiles for them; and HCAI  [28]  gathered data automatically through sensors on phones and integrated devices such as wearables and self-reported information such as on dietary consumption.
Acknowledgments XL and SQ conceived the research topics and questions. CCT and AA performed the literature search and screening. AA, CCT, and SQ performed the data extraction and analysis. AA and SQ developed the first draft. DW reviewed the paper and provided key feedback and edits. All the authors reviewed the final manuscript. The research reported in this publication was supported by the  National Institutes of Allergy and Infectious Diseases  under award  R01AI127203-5S1. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  National Institutes of Health. The authors would also like to acknowledge the generous funding support from the  University of South Carolina Big Data Health Science Center , a  University of South Carolina excellence   initiative program  (grants  BDHSC-2021-14  and  BDHSC-2021-11 ).
Furthermore, we could not detect zinc in either the P1 C64S and P1 C67S single mutants or the P1 C92S-C95S double mutant of the shorter P1_50-100 fragment (Figure  S11(A  and C )). Contrary to P1_1-100, P1_102-157 has an original fold that could not be retrieved from any A search in ZincBind, the database of zinc proteins,  36  identified only six proteins with such architecture. Among them, only the HCHC box of the Topless related protein was similarly organized, with the N-terminal ligand in a helix and the three others belonging to loops, although with an inversed Nd1/Ne2 coordination geometry (Table  2 ). Hence, P1 ZnF2 appears to be a completely new zinc finger motif. This motif stabilizes P1 C-terminal 3D structure, while EDTA-mediated zinc chelation as monitored by  1  H NMR promotes its unfolding (Figure  S13 ).
Deciphering the overall P1 structure by NMR To unravel the structure of the full-length P1 protein, we led a parallel NMR investigation on P1 and its two sub-regions P1_1-100 and P1_102-57 (Figure  3(A  and B )). 1  H and  15  N chemical shifts of residues were identical between the isolated domains and the whole protein. Titration of 15 N-1 H-labelled P1_102-157 by the unlabeled P1_1-100 fragment showed no change in P1_102-157 amide chemical shifts Figure  4(A) ). This analysis unambiguously revealed that the structures of the isolated domains were retrieved in the full-length protein, with no strong contact between them, a finding also observed in an Y2H assay (Figure  S14 ). Translation of chemical shifts into backbone dihedral angles with the TALOS software (Table  3 ) and analysis of the 1 H-1 H NOEs pattern (Figure  4B ) established that the Cterminal helix in P1_1-100 (Ala93 to Glu96 in P1_1-100, Figure  2(A) ) and the N-terminal helix in P1_102-157 (Glu104 to Glu126, Figure  2(D) ) seen in X-Ray structures were extending to a long helix from Ala93 to Glu126, covering the entire linker.
Oligomeric structure of P1 A remarkable feature of P1_1-100 crystal structure was the formation of a dimer from paired antiparallel monomer (Figure  5 (C) and Figure  S14 ). In the asymmetric unit, the dimeric interface covers 506 A 2 and involves five hydrogen bonds, 10 hydrophobic contacts and four salt bonds (Figure  5 (C and D), Figure  S15(A)  and Table  S4 ). It is classified as labile by the PISA algorithm,  41  in accordance with the faint proportion of P1 dimers detected in ESI MS analysis for full length P1. 28  Nevertheless, the probability of being Table  3  Phi and Psi angles estimated by Talos 38 from 1 Ha,  13  CO,  13  Ca,  13  Cb chemical shifts. a Diffusion tensor components were estimated by TENSOR2 39 from 15 N relaxation data recorded on full-length P1_1-157 but taking the data for each domain (P1_1-100 and P1_102-157) separately.
5 buffer. To determine the amount of Zn still bounded to proteins, we performed a PAR/PMPS assay as follows: 80 ml of PAR probe were added to 720 ml of protein to a final concentration of 100 mM. Then we used 1 mM PMPS (p-hydroxymercuriphenylsulfonic acid) which forms mercaptide bond with Cys residues and thus induces the release of the remaining Zn still bound to protein. Zn released from P1 directly reacts with PAR probe to form Zn(PAR) 2 complexes colored in far red. We used spectrophotometry absorbance analyses at A 500nm and the GraphPad Prism R software for non-linear regression and graphic representation to determine the amount of Zn released by PMPS addition. Values from two independent experiments were used for non-linear regression. We used the following equation for Zn affinity determination according to:.
Analyses in native conditions were carried out using 5 lM to 10 lM of P1 and mutants in 50 mM ammonium acetate, pH 8. 0. Optimal acceleration voltages applied on the sample cone (Vc) and a well-adapted pressure in the interface (Pi) was used to preserve weak noncovalent interactions in the gas phase. MS data were acquired and processed using MassLynx TM 4. 1 (Waters). Peak intensities of the main charge states of the different species were used to calculate ratios of each detected species. For EDTA treatment, P1 (5 mM) and sub-regions (10 mM) were incubated overnight at 4 °C with 40 to 400 equivalent EDTA and 1 mM DTT. For dynamic monitoring of zinc reversibility, EDTAtreated proteins were incubated with 60 to 100 lM zinc acetate.
After 5 weeks, diffracting crystals of P1_102-157 were obtained at 4 °C from condition 7 of the Classic kit (Qiagen) containing 0. 1 M tri-Sodium citrate pH 5,6, 20 %(v/ v) Isopropanol and 20 %(w/v) PEG 4000. Crystals were cryo-protected by flash freezing in liquid N 2. X-Ray data collection, processing and structure determination Crystal diffraction datasets were collected at the European Synchrotron Radiation Facility (ESRF, Grenoble) at beam lines ID23-1 and ID29 using a Pixel detector (Pilatus 6 M) and processed by XDS  64  and Scala 65 from the CCP4 programs suite. 66 1_1-100 crystals belong to the F4 1 32 space group and contain two molecules in the asymmetric unit. ] P1_102-157 crystals belong to the P12 1 1 space group and contain two molecules in the asymmetric unit. P1_102-157 structure was determined to 2.
10 A by the single wavelength anomalous diffraction (SAD) method using AUTOSOL from PHENIX  70  and ARP/WARP  [68] [69]  and P1_102-157 phasing data set. A higher resolution structure was determined using a higher resolution dataset (P1_102-157 at 1,98 A) and molecular replacement with PHASER. 71 he ClusPro-DC server (https://cluspro. org) was used for protein-protein docking. 42 PISA (https:// www. ebi. ac. uk/pdbe/pisa/) was used as an interactive tool for the exploration of macromolecular interfaces. 41 Area surfaces were determined at http:// www. ebi. ac. uk/msd-srv/prot_int/pistart. html. After model building using Coot  72  and refinement by REFINE from PHENIX, final structures exhibited an R(%) / R(%) free of 0,185 / 0,214 for P1_1-100 and 0,193 / 0,223 for P1_102-157. Final refinement statistics for the structures are listed in Table The coordinates and structure factors for Nterminal P1_1-100, C-terminal P1_102-157 and P1_1-157 proteins have been deposited in the Protein Data Bank (PDB) under ID codes 6TY0, 6TY2 and 6XV2, respectively.
Nuclear magnetic resonance analyses and structure calculations Production of proteins for NMR studies followed the protocol detailed in  73  and in the Table  S2. Briefly,  15  N-edited HSQC-TOCSY and -NOESY experiments were used to assign resonances of the short constructs. The assignment of full P1_1-157 was deduced from these experiments, except the linker region (residue 98-103). 15 N/  13  C/ 1 H triple resonance experiments on the full-length protein confirmed NMR resonance assignment for the Nand C-terminal domains and enabled the identification of chemical shifts of the linker region. Chemical shifts have been deposited in the BioMagResBank (https://www. brmb. wisc. edu) with accession number 27880. Residual dipolar couplings were measured as differences of 1 J HN couplings between an aligned sample with a 14 mg/mL Pf1 phage suspension (Asla Biotech ) and an isotropic sample of  15  N labelled P1_1-157.
1  J HN couplings were measured as doubles of chemical shift differences in the  15  N dimension of the  15  N-1 H correlation peaks of TROSY-HSQC spectra pair  74  recorded each with 128 complex points inF1, and subsequently zerofilled to 256 complex points using linear forwards prediction. Extraction of the alignment tensor parameters was performed with the Module software. 75  N relaxation datasets were obtained according to published pulse sequences incorporating a heating compensation scheme between T1 and T2 experiments. 76 Experiments were recorded at 20 °C, with a recycle delay of 3 s. Delay times were 1 ms, 100 ms, 600 ms, 900 ms, 1200 ms and 1600 ms for the T1 experiments and 15. 8, 31. 6, 47. 5 and 63. 4 ms for the T2 experiments. Compensatory refocusing periods were introduced in the T1 and T2 experiments so that the spins experience an identical heating in all relaxation experiments.
77 The protein aggregated slowly over time, resulting in a slightly accelerated decay of magnetization with a scaling factor of 0. 94-0. 96 between two successive T2 or T1 experiments as shown in the interspersed 1 D spectra. Estimation of the relaxation times were done by fitting the exponential decay of the peak volumes by three-parameters exponential decay after a rescaling of the corresponding intensities. Estimation of the diffusion tensor was done with the Tensor2 software from T1/T2 ratios. 39 erivation of the mean structure of the whole protein was done by conjoined rigid-body/torsion angle/Cartesian simulated annealing driven by NMR restraints, according to the protocol developed by Clore and co-workers. 78 0] The residues of the [1-96] fragment were held fixed in space, whereas the 105-157 fragment was treated as a rigid body.
After a randomization step of back-bone dihedral angles, atoms of the linker region connecting both zinc fingers (residues 98-104) were given first Cartesian and then torsional degrees of freedom, under residual couplings, restraints derived from 1 H-1 H Noes, and //w restraints derived by TALOS  38  from HN, N, Ca, Cb and C chemical shifts, in addition to covalent and geometrical energetic terms. The coordinates and experimental data have been deposited in the PDB, accession number 6XV2.
EGFP fluorescence  82  was recorded 24 hours post transfection after excitation with an argon laser at 488 nm (fluorescence detection range: 500-550 nm) with a SP8 laserscanning confocal microscope (Leica Microsystems), equipped with 40x water immersion objective. The Leica LAS X software was used to obtain images at a high resolution and without maximum Z-stack intensity projection (1024 pixels for both X and Y dimensions with a physical length of 83 mm, Z dimension being variable in stacks depend on cell size). All data were treated with Adobe Photoshop CS3 software at high resolution. The results are representative of three biological replicates and.
Acknowledgements We are grateful to  Pr Grishin  ( University of Texas, USA ),  Srikrishna Subramania  ( CSIR-Institute of Microbial Technology, India ),  Sam Ireland  and  Andrew Martin  ( Institute of Structural and Molecular Biology, UK ) and  Yue-Ie Hsing  and  Lin-Yun Kuang  ( IPMB Institute, Academia Sinica, Taipei, Taiwan ) for their expertise. We thank the ID23-1 and ID29 beams lines scientists at  European Synchrotron Radiation Facility (Grenoble)  for their excellent support during data collections. The CBS is a GIS-IBIsA platform and a member of the  French Infrastructure for Integrated Structural Biology (FRISBI)  funded by the  French National Research Agency  ( ANR-10-INBS-05  grant). V. P. and F-X. G. PhD interships were funded by the  French Ministry of Education and Scientific Research.
Neural network architecture The number of input and output neurons is fixed in the case of an FFNN architecture and there is no transfer of knowledge from the output to the input layer. In the current study, the NN model is constructed with the Keras (version 2. 8;  Chollet, 2015)  backed by TensorFlow (version 2. 8;  Abadi et al. , 2015)  Python libraries. The Adam  (Kingma & Ba, 2014)  optimization algorithm was used for the training. Grid search was performed to optimize the number of hidden layers, dropout ratio and number of neurons per hidden layer required to efficiently address the complexity of indexing. Details of the Python notebook scripts corresponding to the grid search optimization are provided in the supporting information. The number of hidden layers was varied between one and ten, and it was observed that one hidden layer was not sufficient to obtain the desired accuracy, while anything more than three hidden layers gave no visible improvement and increased the risk of overfitting and the training and prediction times by a factor of 2.
At the end of each epoch both training and validation losses are calculated on the training and validation data sets, respectively. For cubic, hexagonal (not shown here) and tetragonal crystal systems, validation accuracies >95% were reached quickly, whereas for monoclinic and triclinic systems, validation accuracies close to 90% were recorded. For lowersymmetry crystals (more spots are present per Laue pattern), the architecture can be further optimized and higher accuracies should be achievable. However, validation accuracies of $90% are still very good for our purpose. All training and predictions were carried out on a standard 8 core (2. 40 GHz) laptop. Table  1  presents the figures of the NN training. Training a model for higher-symmetry crystals takes seconds to minutes, whereas for a triclinic system it takes approximately 20 min.
The prediction time for all spots in a Laue image for all crystal systems is <1 s. To evaluate the trained model performance on synthetic data, multiple random simulation runs (1000) were performed with single-crystal/polycrystalline copper simulated Laue images. The statistics of these runs are presented in Table  2. Note that the NN training data set included only Laue images comprising one to a maximum of five crystals and yet the NN manages to predict the hkls in a Laue image comprising ten crystals with >90% accuracy. We also observed that the time for prediction increased with the number of spots per Laue image. This can be reduced by employing multiprocessors during prediction, thus making the on-the-fly indexing of Laue patterns a reality. Here we summarize the procedure of LaueNN before application to experimental cases.
(iii) A single NN architecture has been optimized to work for all crystal systems. Generation of training data to train the NN model to higher accuracies takes <20 min and can be done at the start of data collection experiments or beforehand at the user laboratory or the beamline. The Python notebook scripts that describe the various steps from the generation of the training data set to predicting Laue spots are provided in the supporting information. (iv) The NN model provides the prediction of the hkls of spots and not the orientation itself. Prediction of all hkls in a single Laue image takes <1 s. Selected pairs of recognized hkls    Three illustrative cases will be shown: a two-phase material (microcrystal on a substrate), a polycrystalline textured metallic alloy (cubic lattice) deformed in situ, and a polycrystalline material with sub-micrometre-sized crystals and a low-symmetry unit cell.
Discussion As mentioned before, the objective of this work was to introduce a methodology to speed up the indexing of Laue patterns reliably. A feed-forward-based NN, LaueNN, is successfully employed for the prediction of Laue spots. The LaueNN method is available as an open-source GUI under the name LauetoolsNN on the PYPI repository (https://pypi. org/ project/lauetoolsnn/). One could also employ other NN architectures such as the CNN model  (LeCun et al. , 2015)  for the problem of indexing. A CNN-based autoencoder has been proposed recently at the Advanced Light Source beamline  (Song et al. , 2019)  to extract features from Laue diffraction data to aid in the process of indexing. CNN models work directly on the 2D images and can be both memory and CPU demanding. The advantage of CNN models is that they extract features automatically and then classify them.
Future improvements are foreseen by combining the reported single-image analysis with new methods to treat a data set of images based on the relationships between images to index a set of Laue patterns (from a raster scan or in situ monitoring) by means of machine learning tools. The data for the results presented in the article as well as the NN model and complete GUI source code can be accessed via the PYPI repository using pip install lauetoolsnn or via https://github. com/ravipurohit1991/lauetoolsnn. Additional information regarding the reproducibility of the results presented in the article and example Jupyter notebooks are provided in the supporting information.
Data Analysis FT-ICR-MS spectra were processed using Compass Data Analysis 5. 0 (Bruker Daltonics, Bremen, Germany). Each mass spectrum was further internally calibrated using a list of eight known chemical formulas detected in the form of (the internal standard leucine enkephalin)-m/z 556. 276575) for which the mass accuracy values were lower than 200 ppb. Each peak list was exported with a minimum signal-to-noise ratio of 6. Signals related to magnetron and satellite peaks were removed using an algorithm developed by Kanawati et al. [36]. Peak lists were aligned considering a deviation of 0. 5 ppm and m/z values present in more than 50% of the replicate samples were conserved. After replica alignment and filtration, 3362 peaks were used for further statistical analyses. Data normalization and fold-change determination were performed with MetaboAnalyst version 5.
0 (https://www. metaboanalyst. ca/home. xhtml) (accessed on 30 October 2022)  [37]. Missing value imputation was made using substitution, considering half of the minimum positive value found within the data. Intensity data were log-transformed and Pareto-scaled before statistical analysis by multivariate methods. Pathway analysis was also performed in MetaboAnalyst version 5. 0, using the normalized intensity data. Retrieved peak lists were submitted to the MassTRIX 3 server (http://masstrix. org, accessed on 30 May 2022)  [38]  for putative compound identification, using the P. tricornutum available database as a baseline with K and Na adducts. A maximum m/z deviation of 1 ppm was accepted; "KEGG (Kyoto Encyclopaedia of Genes and Genomes)/HMDB (Human Metabolome Database)/LipidMaps without isotopes" was selected for database search.
Compounds with valid KEGG identification were submitted to KEGG P. tricornutum metabolic pathway mapper (https://www. genome. jp/kegg-bin/show_organism?menu_ type=pathway_maps&org=pti) for visualization (accessed on 30 October 2022). All statistical analyses were performed in R-Studio Version 1. 4. 1717. Spearman correlation coefficients and statistical significance between the metabolomic traits and the fluoxetine exogenous concentration were computed using the corrplot package  [39]. Heatmaps and boxplots with probability densities of the data at different values smoothed by a kernel density estimator were computed and plotted using the ggplot2 package  [40]. Nonparametric Kruskal-Wallis tests with Bonferroni correction, for comparisons between the variables and samples exposed to different fluoxetine concentrations, were performed using the agricolae package  [41].