dataesr/scientific-paragraphs-categorization huggingface dataset:
V. ACKNOWLEDGMENTS This study was supported in part by the  Center for Nonlinear Studies at Los Alamos National Laboratory , which is operated by  Triad National Security, LLC  under the auspices of the  National Nuclear Security Administration of U. S. Department of Energy  under Contract No. 89233218CNA000001. This work was also supported in part by  ANR  grants  Cy-berCircuits  ( ANR-18-CE91-0002 ),  MEMIP  ( ANR-16-CE33-0018 ), and  Cogex  ( ANR-16-CE12-0025 ), by the  H2020 Fet-Open COSY-BIO  grant (grant agreement no. 766840 ) and by the  Inria IPL  grant COSY. ZRF thanks  Huy Vo  and  Anatoly Zlotnik  for helpful discussions regarding the presentation of this work. This manuscript has been authored by  UT-Battelle, LLC  under Contract No. DE-AC05-00OR22725  with the  U. S. Department of Energy.
Components of the Notch pathway are also expressed during odontogenesis and play an essential role in the patterning and formation of dental tissue matrices  [15, 17, 32]. Our previous findings have shown that Jagged2-mediated Notch signaling is required for proper tooth morphology  [30]. Our previous studies have shown that Jagged1 is expressed from the very first stages of odontogenesis in the developing dental epithelium  [14]. Loss of Jagged1 is embryonically lethal due to defects in the embryonic and yolk sac vasculature remodeling  [29]. Therefore, to investigate the role of this gene in dental epithelium, we used a tissue-specific deletion strategy (K14Cre;Jagged1 fl/fl ; GenBank accession number AF171092). We examined the effects of Jagged1 loss in tooth epithelium and its involvement in tooth crown shape modifications.
This method has been proven to be very useful for very precise imaging of small elements as teeth  [35]. Three-dimensional renderings were then performed using VG Studio Max 2. 0 software. Dental morphological variations in location, size and interconnection of cusps were analyzed in the WT and K14Cre;Jagged1 fl/fl mice. Length (L) and width (W) for each molar were extracted at 0. 001 mm using the LAS Core software (Leica ® , 4132 Muttenz, Switzerland). The Student's t-test and Fischer's F-test was used on each dental measurement (W, L and d1-d5) for WT and K14Cre;Jagged1 fl/fl mice to check mean and variance equality. In order to quantify the shape variations within M 1 and M 1 mesial parts, five other distances (d1-d5) were measured using LAS Core. Overall tooth shapes were investigated by using an outline analysis.
By registering the relative size and position of each cusp, this method appears suitable for tooth shape study. Fourier methods, notably Elliptic Fourier Transform (EFT), allow the description of complex outlines approximating them by a sum of trigonometric functions of decreasing wavelength (i. e. , harmonics). x and y coordinates of 64 points equally spaced along dental outline were calculated to quantitatively describe the shape of M 1 and M 1. We applied EFTs to these data using EFAwin software (version 11794, New York State University at Stony Brook, NY 11794, USA)  [36] , extracting Fourier coefficients from the original outline, and normalizing these shape variables. This method considers the separate Fourier decomposition of the incremental change in x and y coordinates as a function of the cumulative length along the outline  [37].
Statistical Analyses We performed the Student's t-test and Fischer's F-test on each dental measurement (W, L and D1-5) for WT and K14Cre;Jagged1 fl/fl mice to respectively check mean and variance equality. A principal component analysis (PCA) allowed the evaluating of a possible outline variation between WT and K14Cre;Jagged1 fl/fl mice. Variables were represented by the coefficients of each harmonics previously selected for M 1 and M 1 (respectively 32 and 16). A multivariate analysis of variations (MANOVA) allowed the researching of a potential significant difference between WT and K14Cre;Jagged1 fl/fl mice cohorts. This test included the coordinates of first axes of the PCA for which the sum met at least 95% of the total variation. These data were previously rank transformed since they did not fulfill the required parameters (i.
Data Analysis Reads were quality checked with FastQC. Sequencing adapters were removed with Trimmomatic  [41]  and reads were hard-trimmed by 5 bases at the 3 end. Successively, reads at least 20 bases long, and with an overall average phred quality score greater than 10 were aligned to the reference genome and transcriptome of Mus Musculus (FASTA and GTF files, respectively, downloaded from Ensembl, GRCm38) with STAR v2. 5. 1 [42]  with default settings for single-end reads. Distribution of the reads across genomic isoform expression was quantified using the R package GenomicRanges  [43]  from Bioconductor Version 3. 0. Differentially expressed genes were identified using the R package edgeR  [44]  from Bioconductor Version 3. 0. A gene is marked as DE if it possesses the following characteristics: (i) at least 10 counts in at least half of the samples in one group; (ii) p ≤ 0.
3D Morphing of Tooth Geometries and Computation/PREDICTION of Human Jagged1-Mutant M 1 The 3D morphing of all tooth geometries was conducted in ANSA by BETA CAE Systems A. G. (CH-6039 Switzerland) and the methodological approach can be broken down into the following 5 steps. Tooth surface generation. Average tooth morphologies were computed for the M 1 of WT rodent (based on two characteristic teeth), a mutant one (considering nine Jagged1-mutant crown morphologies) and a human M 1 (resulting from 246 molars). This process facilitated the detection and association of the prevalent features, to compute realistic reference models. Especially for the human molar M 1 , a large existing database was available, which was used as the starting point for calculating the respective average tooth morphology.
The data set is based on a set of existing impressions of carious-free and intact tooth surfaces of West European young people within the ages from 16 to 20 years. From the impressions, stone replicas were made, and the clinical visible parts of their surfaces were measured with a 3D-scanning device. The resolution of the measuring process was 50 µm × 50 µm (x,y). All data sets were aligned in position and orientation within the same coordinate system: For this, a representative molar with an appropriate orientation was chosen. All other molars were superimposed with this representative molar by a least-square fitting routine (Match3D)  [45] [46] [47]. This guaranteed that all occlusal features had the same orientation. The pattern of cusps and grooves varies considerably across individuals, even though some morphological properties, such as the overall layout of the main cusps and fissures, are shared by all samples.
Acknowledgments: We thank the staff of the  Institute of Oral Biology (University of Zurich ) for technical assistance. The authors would like to thank  BETA CAE Systems A. G. for providing them with the  CAE pre-processor ANSA , used during surface modeling and morphing of the teeth. The authors would also like to thank the  Functional Genomics Center Zurich  ( University of Zurich ) for helping with the bioinformatics analysis of the RNA sequencing data. We warmly thank  P. Tafforeau  ( ESRF, Grenoble, France ) for access to beamline BM5 and contribution to data acquisition.
Objectives: Cue-reactivity is a critical step leading to the emergence of addictive psychology and the triggering of addictive behaviors within the framework of addiction theory and is considered a significant risk factor for addiction-related behaviors. However, the effect of cue-reactivity targeted smoking cessation intervention and the cue-reactivity paradigms used in the randomized controlled trials varies, which introduces more heterogeneity and makes a side-by-side comparison of cessation responses difficult. Therefore, the scoping review aims to integrate existing research and identify evidence gaps. Methods: We searched databases in English (PubMed and Embase) and Chinese (CNKI and Wanfang) using terms synonymous with 'cue' and 'tobacco use disorder (TUD)' to April 2023, and via hand-searching and reference screening of included studies.
Search strategy The search was conducted by combining subject terms and free words, using the Chinese search terms "smoking, cigarette addiction, tobacco addiction, cue" in the China National Knowledge Infrastructure (CNKI) and WanFang databases, the English search terms "tobacco, nicotine, cue" in the PubMed and EMBase databases. The main search of the database was performed in July 2021, the last update was in May 2022, and the final search of all databases was performed in April 2023. The specific search strategies for the four databases are described in Supplementary Table  S1.
After entering the retrieved literature titles into Endnote X9 for deduplication, a two-step review strategy was adopted: (1) title/ abstract level;  (2)  full-text level. The two authors performed independent screening exercises. Disagreements between two authors (Luo and Gan) that emerged during the literature selection process would be discussed or consulted with a third author for consensus. Data for final inclusion in the literature were extracted and summarized in standardized tables. First author, time of publication, study sample and context, stimulus material, cue-reactivity paradigms, type of intervention, follow-up time, outcome measures and effects were extracted and recorded. The quality of evidence for each study and a formal risk of bias was not assessed. The data were aggregated and reported according to key themes.
Janse Van Rensburg et al. ( ) (86) Image Vision (in vitro cues) 2009a The 60 images (smoking and neutral images) were randomly presented in each scanning session and for each participant using E-prime software. Images were viewed on a screen placed at the foot of the scanner via a mirror mounted on the head coil. Each image was presented for 3 s. A button, placed in each of the participant's hands, was pressed upon presentation of the image to ensure attentional focus. The button-press for smoking or neutral images was randomized for hand dominance between participants. After each image, a white screen with a black fixation cross was presented for a randomly determined period of 8, 10, or 12 s, and participants were asked to view this between smoking images to remain focused. The duration of image presentation and the inter-stimulus-interval chosen fall between those reported in other event-related studies in this area.
Different sets of pictures were used across the three assessment sessions to minimize habituation. Pictures were displayed for 6 s each on a 20″ computer monitor located 2. 5 ft. in front of participants, controlled by software that synchronized cue presentations with physiological data collection. Participants viewed a randomized sequence of 12 smoking-related and 12 neutral images on a 20-inch computer monitor. The sequence for each of the 24 images consisted of a 2-s baseline period, a 6-s picture-viewing period, followed by a subjective craving rating obtained via computer (0-20 scale). A variable 12-20 s intertrial interval separated the end of each rating period from the start of the next trial. An established picture-viewing paradigm was adapted to assess subjective, behavioral, and physiological responses to 30 affective pictures (10 pleasant, 10 unpleasant, and 10 neutral) and 10 smoking-related pictures.
Thus, each type of probe appeared in the location of the smoking-related and neutral picture with equal frequency. Each block of trials was presented in a new random order for each participant, using EPrime version 2. To establish baseline parameters, participants listened, through headphones, to a relaxation script, heart rate, skin conductance, left corrugator electromyogram were measured and subjects completed assessments of baseline emotional state and craving. This procedure was then repeated with two counter-balanced personalized smoking and two standard neutral scripts, 40 s each, beginning with a neutral script. Each script presentation consisted of four sequential 30-s periods: baseline, read, imagery, and recovery. Subjects were instructed to listen carefully during the playing of the scripts and to attempt to imagine as vividly as possible each experience as it was presented (read period) and, on script termination, to continue to imagine the experience from beginning to end (imagery period) until they heard a tone.
They were further instructed to stop imagining the script at the tone and to relax (recovery period) until a second tone was heard. Participants were allowed to touch a pack of their favorite cigarette brand and their lighter for two minutes. Two opaque pitchers were placed on the participant's desk at the beginning of the session. Hidden under one pitcher was a glass and a bottle of spring water. An unopened pack of their favorite brand of cigarettes, lighter, matches, and an ashtray were hidden under the second pitcher. During the cue session, participants were first shown the water bottle and asked to look at, hold and sniff it, and take a drink of the water inside. After a 5-min relaxation period, participants were visually exposed to the smoking cues. A research nurse subsequently instructed participants to open their pack of cigarettes and take one out.
(Continued) Participants were shown one of three equivalent picture sets. Each set included 96 pictures: 24 pleasant (8 erotica, 8 romantic, 8 pleasant objects), 24 unpleasant (8 mutilations, 8 sad, 8 unpleasant objects), 24 neutral, and 24 cigarette-related images. The images were selected from the international affective picture system and from other sets used in previous studies. At each visit, participants saw a different picture set and the order of presentation was randomized across participants. At each visit, the frequency of each order presentation was similar across the three medication groups. Participants viewed the picture slideshow on a plasma television screen at a viewing distance of approximately 1. 5 m. E-Prime software, running on a Pentium 4 computer, controlled the picture presentation.
Furthermore, our literature search strategy and limited database selection may have resulted in the omission of literature that met the inclusion criteria, thus preventing this review from providing a comprehensive overview of current advances in smoking cessation therapy based on CR. And we only searched for publications in English and Chinese, which led to missing literature in other languages and further contributed to the abovementioned problems. Finally, there is also a limitation with regards to the differences among the included articles in gender/sex ratio, ethnicity or region or origin or diagnostic criteria of the study participants, sample size of the individual studies, as well as the statistical methods, resulting in significant heterogeneity among various studies. Therefore, we did not statistically test for the overall efficacy, which is also a limitation for a descriptive and comparative approach we adopted here.
Conclusion This paper reviews the effects of various cue-reactivity-targeted smoking cessation therapies and types of cue-reactivity paradigms to understand the role of cue-reactivity in smoking cessation diagnosis and treatment. It proposes that, given that current studies are still inadequate in terms of homogeneity and lack repeated validation, cue-reactivity can be conducted in the future by constructing a standard library of smoking cue materials and conducting cue-reactivity causal analysis in order to adequately screen for causes of addiction persistence. In summary, the following problems remain: (1) it is still challenging to find specific targets among the factors influencing cue-reactivity, and it cannot be ruled out that they are due to a combination of factors, so causality studies need to be strengthened; (2) the specificity of the indicators can be enhanced by expanding the sample size, strengthening the homogeneity of the sample, standardizing the parameters of the cue-reactivity paradigms, increasing the years of follow-up, and standardizing statistical methods; (3) there is a lack of a unified and standardized database of smoking cues worldwide, and the construction of a database of smoking cues would be a worthwhile endeavor to facilitate repeat trials and the reliability of final scientific findings.
Coding and long non-coding genes were identified using gencode annotation (v32), while unannotated RNAs were recovered with Scallop assembler  35  (Figures 1D-1F, S2A, and S2B; Tables  S1-S3 ). Principal component analysis (PCA) of polyA+ transcriptomes segregated S + cells from S À and mock-infected ones (Figure  1D ). This segregation based on S expression represented around 92% of the transcriptomic differences between the samples (Figure  1D ). Only subtle differences (2. 5%) distinguished bystander and mock-infected cells (Figure  1D ), suggesting that the transcriptional landscapes of these 2 cell populations were very similar. An absence of response of S À cells was unexpected since cytokines, which are commonly secreted by virally infected cells, activate an antiviral state in bystander cells through surface receptors.
38  Among the top downregulated lncRNAs, we identified HOXA-AS2 and NKILA, which are negative regulators of NF-kB signaling, in endothelial cells and breast cancer cell lines, respectively. 39, 40  Altered expression of WAKMAR2, HOXA-AS2, and NKILA in infected cells could thus play a role in viral-associated inflammation. From the 1,400 unannotated transcripts we detected using Scallop assembler,  35  around 800 unannotated transcripts were also differentially expressed in S + cells as compared to S À ones (Figures  1E  and 1F ; Table  S3 ). In agreement with the PCA (Figure  1D ), volcano plots and heat maps revealed that S À bystander cells and mock-infected control cells exhibited very similar transcriptomic profiles (Figures  1E  and 1F ; Figures  S2A  and S2B ). Only around 170 polyA+ transcripts were differentially expressed in S À cells as compared to mock-infected ones (Figure  1E ; Tables  S1-S3 ).
Separating lung cells based on the expression of the viral S protein improved discovery of DEGs To compare our differential deep analyses with known datasets, we analyze publicly available polyA+ RNA-seq raw data of unsorted A549-ACE2 infected with SARS-CoV-2 at an MOI of 0. 2. 17  Viral reads represented around 50% of the total number of reads in these unsorted bulk population of cells,  17  which was expectedly less than in A549-ACE2 cells positive for S protein (Figure  1C ). The 2 analyses shared 150 upregulated protein-coding genes and 238 downregulated ones (Figure  2A ; Table  S4 ). The vast majority (about 80%) of the downregulated mRNAs that we identified were classified as ''unchanged'' in the analysis of unsorted cells (Figure  2A ; Table  S4 ). This was also true when comparing our dataset with polyA+ RNA-seq raw data of unsorted A549-ACE2 infected with SARS-CoV-2 at an MOI of 2 (Figure  S3A ).
17  We also compared DEGs identified in our analysis with the ones recovered from scRNAseq studies performed on A549-ACE2 cells in which infected cells were identified based on the presence of multiple viral RNA transcripts. 36  The 2 datasets shared around 30% of downregulated genes and 5% of upregulated ones (Figure  S3C ). Over 88% of upregulated genes and 68% of downregulated genes identified by our analysis were missed by the scRNAseq analysis, including key genes that we later identified as regulators of viral replication (e. g. , NFKB1 and CYLD) (Figure  S3C ). Our analysis recovered about 11 times more upregulated genes and 3 times more downregulated genes that this scRNA-seq analysis (Figure  S3C ). By contrast, 3. 4% of upregulated and 0. 6% of downregulated genes were found by scRNA-seq only.
Genes were defined as expressed when they were represented by at least 10 normalized reads in each replicate. The solid lines and central overlap show the genes that appear in both datasets while dashed gray zones outline genes detected in only one of the two datasets. (B) RT-qPCR quantification of viral genome copy number per mg of total RNA extracted from A549-ACE2 cells infected by SARS-CoV-2 at an MOI of 1, analyzed either in bulk (left side of graph, n = 2 independent experiments, line at mean) or post-sorting based on Spike protein expression, allowing distinction between productively infected and bystander subpopulations (right side of graph, n = 3 experiments, line at mean). (C and D) RT-qPCR quantification of mRNA, lncRNA, and unannotated-RNA, that were identified as upregulated (C) or downregulated (D) upon infection by SARS-CoV-2 in the RNA-seq analysis, in total RNA extracted from A549-ACE2 cells infected with SARS-CoV-2 at an MOI of 1, analyzed either in bulk (left side of graph) or post-sorting based on Spike protein expression (right side of graph, normalized fold change over mock-infected, n = 3 independent experiments, ratio-paired t-test, line at mean G SEM).
*p < 0. 05, **p < 0. 01, ***p < 0. 005, ****p < 0. 0001, and ''ns'' indicates not significant. (E) Top 10 enriched GO terms for Biological Process (BP) and KEGG pathways from DAVID database ranked by the adjusted p value (Benjamini), for upregulated mRNAs identified in RNA-seq comparison between infected vs. mock cells. See also Figures  S3  and S4. ITGAM, and TRAF1), two lncRNAs (WAKMAR2 and AL132990. 1), and one unannotated transcript (XLOC_007519) that were identified among the most upregulated RNAs in S + A549-ACE2 cells (Figures 2C, S4A, and S4B; Tables  S1-S3 ). The abundance of IL32 mRNA did not increase significantly in the infected bulk population, as compared to mock-infected cells (Figure  2C ). By contrast, IL32 mRNA levels increased around 30-fold in S + cells, compared to those in mock-infected cells (Figure  2C ).
Limitations of the study We limited our analysis on polyA+ RNAs to circumvent the potential issue of measuring intermediates of degradation in cells infected by SARS-CoV-2, since these exhibit large defects in RNA splicing, termination and decay. 43  Moreover, performing deep polyA+ transcriptome analyses allowed to compare our results with other published dataset,  17  including scRNA-seq dataset,  36  which were all obtained by sequencing polyadenylated RNAs. Yet, as all polyA+ RNA-seq analyses, our study did not provide sequences for circRNAs, RNAs with short polyA tails, or non-polyadenylated transcripts resulting from classic RNA processing (for instance, snoRNAs, histone mRNA) or polyA-.
Flow cytometry Cells were detached with trypsin, washed with PBS and fixed in 4% PFA for 30 min at 4 C. Intracellular staining was performed in PBS, 2% BSA, 2mM EDTA and 0. 1% Saponin (FACS buffer). Cells were incubated with antibodies recognizing the NTD domain of the spike protein of SARS-CoV-2 (anti-NTD Cv2. 8162, a kind gift from Dr. Hugo Mouquet, Institut Pasteur, Paris, France  89  or anti-Spike 1A9, GeneTex GTX632604) and subsequently with secondary anti-human AlexaFluor-647 antibody (1:1000, A21455 Thermo) for 30 min at 4 C. Data were acquired using Attune NxT Acoustic Focusing Cytometer (Thermo Fisher) and analyzed using FlowJo software.
RNA-sequencing analysis of sorted cells Dataset consists of 9 paired-end libraries (150 nt), with 3 conditions: mock, bystander and infected cells. Adaptors were trimmed with Trim Galore v0. 6. 4  92  (wrapper for cutadapt v2. 10 101 and FastQC v0. 11. 9  93  ), with options -stringency 5 -trim-n -q 20 -length 20 -paired -retain_unpaired. Reads were mapped to a reference containing human genome (hg38), SARS-CoV-2 (NC045512. 2) and ERCC sequences. STAR v2. 7. 3a  94  was used to map the reads, with default parameters. Bam files were then filtered using SAMtools v1. 10 95 to retained reads flagged as primary alignment, and with mapping quality >30 (option -q 30 -F 0x100 -F 0x800). Read coverage was computed for each strand with bamCoverage (deep-Tools v3. 5. 0 102 ) with options -binSize 1 -skipNAs -filterRNAstrand forward/reverse.