dataesr/scientific-paragraphs-categorization huggingface dataset:
The coordinates of these maxima were weighted considering all pixel intensity in a 200 nm radius, and exported in a text file. These points were positioned both on RIM reconstructed image and on its spatial derivative version created thanks to ImageJ Find Edges tool. From these locations, 8 radius profile were traced with length 1 µm and width 100 nm on both images, and intensity values along this line were extracted and stored in a text file. A dedicated Python script was then written to extract statistical data. Core radii were computed by detecting the first intensity maximum along the Find Edges profile. Intercore distances were computed thanks to weighted coordinates following the same Delaunay algorithm already described in the SEM analysis section (Figure  S4 ). For the analysis of two-color acquisitions, actin cores were detected following the same procedure as described for the analysis of the actin network.
Then, both signals were extracted along 1. 5 µm long and 100 nm wide lines drawn so that each core coordinates were placed at the middle of the line, and its orientation either followed the local curvature of the sealing zone, or was transverse. This process was repeated on the reconstructed actin image, the actin image after spatial derivation with ImageJ Find Edges tool and the protein image. All data were extracted and stored in a text file to be read by a dedicated Python script. This script computed the median core size for all data thanks to Find Edges signals, in order to establish a normalized profile length. Then, both actin-and proteinassociated signals were interpolated along this new axis to yield comparable intensity profiles. Median profiles were eventually computed (Figure  S5 ).
The dynamic characteristics of actin were assessed by extracting the time-dependent signals in a circular selection or radius 100 nm around each core, and storing them in a text file. A dedicated Python script was developed to compute the distance between all core pairs in the same cell from, the Fourier spectrum associated with each signal and the Pearson cross-correlation between two signals in the same cell. Natural frequencies were identified as the frequencies associated with Fourier coefficients greater than a threshold value proportional to the median value for Fourier coefficients over the spectrum. Pearson coefficients were eventually sorted according to the distance between the core pair coordinates. In order to obtain graphical representations of the local evolution of fluorescence over time, as shown in Figure  3 , 5 and Movies3, 5, the films were processed using ImageJ as follows.
Acknowledgements The authors are grateful to  Myriam Ben Neji  for isolation of human blood monocytes and  Isabelle Fourquaux  from  TRI imaging facility  for SEM preparation. The authors also acknowledge  Anne Blangy ,  Alessandra Cambi ,  Amsha Proag  and  Olivier Destaing  for helpful discussions. This work has been supported in part by l 'Agence Nationale de la Recherche  ( ANR16-CE13-MechanOCs ), l' Université de Toulouse , la  Région Occitanie , la  Fondation pour la Recherche Médicale  ( FRM DEQ2016 0334894 ),  INSERM Plan Cancer and Human Frontier Science Program  ( RGP0035/2016 ).
The cDNA encoding GABARAP was produced by PCR from a pcDNA 3. 1 GFP-GABARAP vector. The primers used contained overhang sequences for the enzyme restriction sites BamHI (BamHI-GABARAP-F) and XhoI (XhoI-GABARAP-R). The cDNA encoding human GABARAPL1 was isolated from MCF7 and was Cells 2022, 11, 2782 3 of 17 amplified by PCR using the following primers, including overhang sequences for BamHI (BamHI-GABARAPL1-F) and XhoI (XhoI-GABARAPL1-R). The cDNA encoding Rattus norvegicus LC3B (GenBank ID: U05784) was amplified by PCR with primers, including overhang sequences for BamHI (BamHI-LC3B-F) and XhoI (XhoI-LC3B-R), and cloned into the ptfLC3 vector (gift from the Tamotsu Yoshimori's Laboratory). Hybridization of specific overlapping primers (myc 1 and 2) in water at room temperature was performed to create compatible BamHI sites between the OVA and ATG8 sequences.
The cDNA encoding human GABARAPL1 was isolated from MCF7 and was amplified by PCR using the following primers, including overhang sequences for BamHI (BamHI-GABARAPL1-F) and XhoI (XhoI-GABARAPL1-R). The cDNA encoding Rattus norvegicus LC3B (GenBank ID: U05784) was amplified by PCR with primers, including overhang sequences for BamHI (BamHI-LC3B-F) and XhoI (XhoI-LC3B-R), and cloned into the ptfLC3 vector (gift from the Tamotsu Yoshimori's Laboratory). Hybridization of specific overlapping primers (myc 1 and 2) in water at room temperature was performed to create compatible BamHI sites between the OVA and ATG8 sequences. The plasmid that encoded the OVA242-386-Myc-ATG8 (242OMATG8) proteins that expressed a deleted variant of OVA were designed by PCR using a forward primer, including overhang sequences for NheI (NheI-242OVA-F), and the precedent reverse primers, including overhang sequences for XhoI.
3, 13 Therefore, additional large-scale studies with high-quality prediagnostic epidemiologic, biomarker, and clinical data are needed to comprehensively examine the MetS and gastrointestinal cancer association. In addition, the availability of extensive genotyping data in large cohorts allows polygenic risk scores (PRS) to be derived, which may improve prediction of cancer risk at the population level,  14  particularly where there is evidence suggesting an interaction between PRS and lifestyle or environmental risk factors. To our knowledge, the interaction between PRS and MetS and its association with cancer risk has not previously been examined. In this study, we leverage the wealth of molecular measurements available in the United Kingdom (UK) Biobank prospective cohort to investigate the associations between MetS and risk of gastrointestinal cancers (overall, colorectal, pancreatic, esophageal adenocarcinoma and squamous cell carcinoma, stomach cardia and non-cardia, HCC, and IBDC).
Physical measurements, including weight, height, and waist circumference, were taken. Systolic and diastolic blood pressure (BP) was measured from 2 separate automated readings and an average taken. Around 20,000 participants attended a repeat assessment visit between 2012 and 2013. Exclusions were made for prevalent cancer at recruitment (n ¼ 30,296), missing MetS component data (n ¼ 106,344), and voluntary withdrawal from the study (n ¼ 44), leaving a final sample of 366,016 participants.
Assessment of Cancer Outcome Incident cancer cases and cancer cases recorded first in death certificates within the UK Biobank cohort were identified through linkage to national cancer and death registries. Complete follow-up was available through March 31, 2016, for England and Wales and October 31, 2015, for Scotland. Cancer incidence data were coded using the Tenth Revision of the International Classification of Diseases. Gastrointestinal cancers included colon cancers (C18), rectal cancers (C19-20), esophageal adenocarcinomas and squamous cell carcinomas (C15), gastric cardia (C16. 0) and non-cardia (C16. 1-16. 6) cancers, pancreatic cancers (C25), hepatocellular carcinomas (C22. 0), and intrahepatic bile duct cancers (C22. 1).
10 relative to the 1000 Genomes reference population, and palindromic SNPs with MAF !0. 45. Genotypes for risk SNPs were extracted for each chromosome from imputed UK Biobank genotyping data using plink2 software, converted to dosages, and inverse variance weights applied for risk alleles. PRS for individuals were calculated as the sum of these weighted dosages. Participants were stratified into low, medium, and high PRS groups using 20th and 80th percentile cut points.
In particular, we solved the structure of this complex in four different crystal forms, obtaining unprecedented structural data on a secondary interaction of the TBA-like aptamer with thrombin exosite II. Interestingly, the crystal packing analysis has revealed the ability of the aromatic appendages to make contacts with hydrophobic patches of the protein surface thanks to their solvophobic behavior. These data clearly suggest that the introduction of hydrophobic portions in an oligonucleotide sequence represents a powerful strategy to shape the contact surface with the target molecule. Spectroscopic, calorimetric, and electrophoretic studies were also performed to deeply investigate in solution the folding and binding properties of this TBA analogue.
However, EMSA analysis revealed a similar behavior for TBA and TBA-NNp/DDp, pointing also for the modified aptamer at the formation of 1:1 protein-aptamer com- and presence (+) of thrombin (Thr); 130 pmol of each aptamer was incubated with 130, 260, or 65 pmol of the protein, thus obtaining final 1:1, 1:2, or 1:0. 5 aptamerprotein molar ratios, respectively. Gels were run, under native conditions, at constant 100 V for 55 min at r. t. in TAE 1X buffer. Lane 1: TBA, lane 2: TBA-thrombin 1:1 complex, lane 3: TBA-thrombin 1:2 complex, lane 4: TBA-thrombin 1:0. 5 complex, lane 5: TBA-NNp/DDp, lane 6: TBA-NNp/DDp-thrombin 1:1 complex, lane 7: TBA-NNp/DDp-thrombin 1:2 complex, lane 8: TBA-NNp/ DDp-thrombin 1:0. 5 complex, lane 9: thrombin. plex. Taken together, these binding data may be interpreted as due to the influence of the end groups that in addition to the more specific interactions, drive toward nonspecific clustering of protein-aptamer molecules, whose contribution depends on the experimental conditions.
0000 7] [58] [59]  The phase problem was solved by molecular replacement using Phaser MR  60  from the CCP4 package  58  and the coordinates of the native protein (PDB code: 1PPB),  61  as a search model. Refinement and manual model building were carried out using REFMAC5  58, 62  and Coot 63 programs, respectively. Detailed statistics on data collection and refinement are reported in Table  S1. Structures were validated using the PDB validation server (https://validate-rcsb-1. wwpdb. org/) and Coot routines. 63 The coordinates of the structures were deposited in the Protein Data Bank (PDB codes: 7ZKL, 7ZKM, 7ZKN, and 7ZKO). Thrombin-TBA-NNp/DDp interface areas and interactions were examined using the PISA program  64  available online (https://www. ebi. ac. uk/pdbe/pisa/). Molecular graphics figures were generated with PyMOL (DeLano Scientific, Palo Alto, CA, USA).
After DNA staining, the gels were washed in water and stained again with Colloidal Coomassie G-250 to visualize the bands of the free and aptamer-bound protein. Each experiment was performed in triplicate. The intensity of the DNA bands on the gel was then calculated by using the FiJi software and normalized with respect to the free, untreated aptamer. Percentages of the oligonucleotide bands are reported as mean values ±SD for multiple determinations. The statistical significance of replicates was analyzed using Student's t test with *p < 0. 1, **p < 0. 01, or ***p < 0. 001 compared with untreated oligonucleotides.
ACKNOWLEDGMENTS The authors thank  Elettra Sincrotrone Trieste  for beamtime (proposals  20205526  and  20205550 ), and the staff of XRD2 beamline for technical assistance during data collection. R. T. was supported by a fellowship from the  Univ. of Naples Federico  II Research Grant  ( Finanziamento per la Ricerca di Ateneo ,  Progetto LipAptaNanoSens ,  000008 ALTRI_CdA_75_2021_FRA_LINEA_B ). K. P. C. thanks the  Univ. of Montpellier  for the award of a research studentship. F. M. is a member of  Inserm. D. M. thanks  Associazione Italiana per la Ricerca sul Cancro (AIRC)  for funding (IG2020 n. 25046). C. R. was supported by a fellowship from  AIRC. Funding for open access charge:  Department of Chemical Sciences, University of Naples Federico II  and  AIRC  (IG2020 n. 25046 to D. M. ).
Objective: The purpose of this study was to examine the relationship between project features and the success of cancer crowdfunding campaigns and to determine whether text features affect campaign success for various cancers. Methods: Using cancer-related crowdfunding projects on the GoFundMe website, we transformed textual descriptions from the campaigns into structured data using natural language processing techniques. Next, we used penalized logistic regression and correlation analyses to examine the influence of project and text features on fundraising project outcomes. Finally, we examined the influence of campaign description sentiment on crowdfunding success using Linguistic Inquiry and Word Count software. Results: Campaigns were significantly more likely to be successful if they featured a lower target amount (Goal amount, β=-1.
Statistical Modeling Through Penalized Logistic Regression We used penalized logistic regression (PLR) based on the glmnet package  [29]  in R programming language to explore the relationship between each project feature and crowdfunding success. PLR adds regular optimization compared with traditional logistic regression, controlled by the parameter α to use L1 regularity (α=1; lasso regression) or L2 regularity (α=0; ridge regression). The use of L1 regularity is also known as lasso regression. Feature selection can be realized by limiting the sum of the absolute values of the estimates, such that some coefficients are equal to zero. First, because of the large number and collinearity of English phrases in our sample, we used lasso regression to retain important phrase features and set the other relevant features to zero.
Linguistic Inquiry and Word Count-Based Sentiment Analysis The social and psychological tone or sentiment of a GoFundMe campaign may also impact its success. Research suggests that the Linguistic Inquiry and Word Count (LIWC) software is ideal for identifying emotions in language  [30]. In addition, LIWC can reveal more than just the emotions underlying it, it can even identify the social status, motives, and gender of the respondent  [31]. The software can uncover individuals' emotions, thinking styles, and social concerns  [30]. Therefore, we used LIWC to capture the fundraiser's emotional, cognitive, and structural components present in the GoFundMe campaigns' descriptions. Using LIWC version 2022, we focused on 22-word categories  [13] , including affective, social, cognitive, perceptual, biological, drive-related, and personal words.
Limitations and Strengths Although crowdfunding websites such as GoFundMe offer an opportunity to observe a wide range of thoughts and behaviors, our data have some limitations. First, people who earn a greater income and are fully insured might not need money from donations for cancer treatment. Therefore, they may not have created campaigns on crowdfunding websites. Second, the data included only US-based samples. It is possible that individuals in countries with universal health systems (eg, Canada) may not experience the same financial burden for cancer treatments. They may also describe their medical campaigns in a different manner than people in the United States, leading to different predictors of campaign success. In addition, we did not analyze the cultural background of the individuals requesting funds through crowdfunding nor did we assess donors' cultural backgrounds.
(1988) ;  Moulton et al. (2000) ;  Fontana et al. (1993) ; Flamm 37 et al. (2001) ) and iii) considering the whole folding space  (Hofacker et al. 38 (1994) ;  Bonhoeffer et al. (1993) ;  Giegerich et al. (2004) ). Among them, 39 the most frequently applied are those working on single structures, such as  pairs are summed up, the number of introduced gaps is added and the resulting sum is normalized by the total number of cells containing 1 in both matrices, in order to find the final AptaMat distance  (Figures S2 and S3, Supplementary Material). A weighted AptaMat distance can also be computed to consider simultaneously multiple alternative conformations. We applied our approach to i) 5 examples taken from the work by  Ivry et al. (2009)  to make a direct comparison with the Hamming distance, RNAdistance and DoPloCompare and ii) to 5 structures of aptamers taken from the Protein Data Bank  (Berman et al.
Clustering RNAs from RFAM families In order to further challenge AptaMat, we tested its ability in clustering sequences belonging to different RFAM families. We considered only RFAM families with experimental 3D structures to avoid a bias related to the secondary structure prediction, even if we might end up with incomplete structures, because of missing residues at the extremities, generating subclusters. For families having a high number of experimental structures available, such as tRNA and the bacterial small subunit ribosomal RNA, we randomly selected 30 structures. In addition, we discarded the families with less than 10 available structures. The final dataset consisted in 291 structures and 14 RFAM families (Table  S3 ). We used the affinity propagation method  (Frey et al. (2007) ) to perform the clustering (see Methods section for further details), since it does not need to preliminary fix the number of clusters.
more alternative structures were used to perform the 219 comparison. The alternative structures for the examples taken from Ivry 220 et al. (2009) were obtained from the same article, while for those taken from 221 the PDB database we used 6 different ssNA secondary structure prediction 222 tools, namely Mfold (Zuker. (2003) ), LinearFold (Huang et al. (2019) ), 223 CentroidFold (Hamada et al. (2009) , RNAfold(Gruber et al. (2008b)), 224 RNAstructure (Reuter et al. (2010) ), and MC-Fold (Parisien et al. (2008))  225 to obtain at least two different secondary structures for each ssNA. This 226 was achieved when the prediction tools were not able to correctly ), the BP distance and RNAdistance from the ViennaRNA package 232 (Hofacker et al. (2003) ). The first computes the distance between two 233 ssNAs structures of same length L, by calculating 234 Hamming (S A , S B ) = N diff /L (3) where N diff is the number of unmatched positions between the two strings 235 corresponding to the dot-bracket notation of the compared structures.
Clustering 286 To fully challenge AptaMat, we evaluated its classification ability by 287 performing a clustering approach on RNA structures belonging to different 288 RFAM families (Kalvari et al. (2021); Berman et al. (2000)). To avoid the 289 potential bias induced by the secondary structure prediction method, we 290 selected among the RFAM families those having more than 10 complete 291 experimental 3D structures (see Table S3 for a complete list of the 292 retained RNAs) and we verified the absence of structural issues within 293 their 3D structures. For families with more than 30 complete structures we 294 randomly chose 30 structures in order to have a balanced dataset. For the 295 other families we kept all the available structures. The resulting dataset 296 consists in 14 RFAM families and 291 sequences.
metrics. SsNA 6 (PDB ID: 1NGO) has a simple hairpin/stem loop structure (Figure S8). The alternative structure (b) obtained by CentroidFold is correctly considered by the used metrics as the closest to the experimental structure (Hamming distance = 0. 074, default RNAdistance = 2, BP distance = 2, and AptaMat = 0. 091). AptaMat then indicates that the alternative structure (d) obtained by MC-Fold is closer to the reference (AptaMat distance = 0. 20) than the alternative structure (c) obtained by RNAfold (AptaMat distance = 0. 22), since the former only misses two pairs of bases (T5-G23 and T6-G22) while maintaining the overall structure. Conversely, structure (c) has 2 additional base pairs that lead to the loss of the characteristic loop of 1NGO (FigureS8). On the opposite, the Hamming distance fails in finding this difference, and the default RNAdistance suggests the opposite trend, with structures (c) and (d) having an RNAdistance of 6 and 8, respectively.
DD more alternative structures were used to perform the 219 comparison. The alternative structures for the examples taken from Ivry 220 et al. (2009) were obtained from the same article, while for those taken from 221 the PDB database we used 6 different ssNA secondary structure prediction 222 tools, namely Mfold (Zuker. (2003) ), LinearFold (Huang et al. (2019) ), 223 CentroidFold (Hamada et al. (2009) , RNAfold(Gruber et al. (2008b)), 224 RNAstructure (Reuter et al. (2010) ), and MC-Fold (Parisien et al. (2008))  225 to obtain at least two different secondary structures for each ssNA. This 226 was achieved when the prediction tools were not able to correctly ), the BP distance and RNAdistance from the ViennaRNA package 232 (Hofacker et al. (2003) ). The first computes the distance between two 233 ssNAs structures of same length L, by calculating 234 Hamming (S A , S B ) = N diff /L (3) where N diff is the number of unmatched positions between the two strings 235 corresponding to the dot-bracket notation of the compared structures.
2. 4 4 Clustering 286 To fully challenge AptaMat, we evaluated its classification ability by 287 performing a clustering approach on RNA structures belonging to different 288 RFAM families (Kalvari et al. (2021); Berman et al. (2000)). To avoid the 289 potential bias induced by the secondary structure prediction method, we 290 selected among the RFAM families those having more than 10 complete 291 experimental 3D structures (see Table S3 for a complete list of the 292 retained RNAs) and we verified the absence of structural issues within 293 their 3D structures. For families with more than 30 complete structures we 294 randomly chose 30 structures in order to have a balanced dataset. For the 295 other families we kept all the available structures. The resulting dataset 296 consists in 14 RFAM families and 291 sequences.
Consistently, in the cryo-EM map of NTCP EM -Mb91, we observed extra density that partially occupies the pore on the cytoplasmic side, wedged in the crevice between TM6 and TM9 and in close proximity to conserved residues in the X motifs (Fig. 3a , Extended Data Fig. 7 ). Our cryo-EM sample included both Na + and substrate taurocholate, and the density probably corresponds to a substrate molecule bound to NTCP EM. However, the lack of molecular features in the density precluded unambiguous determination of the bound molecule. It is worth noting that the NTCP EM -Mb91 complex structure was determined from samples in detergent solutions, raising the possibility that detergent molecules could have facilitated the open-pore state. To shed light on this question, we determined the cryo-EM structure of NTCP EM -Nb91 complex reconstituted in nanodiscs.
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Then, cells were resuspended and lysed using Pierce IP lysis buffer (Thermo Fisher). Finally, lysates were centrifuged (13,000g for 10 min), and transferred to black 96-well flat-bottom plates (Grenier), and quantified by fluorescence in a micro-plate reader (CLARIOstar-Plus) using excitation at 454 nm and emission of 570 nm. Three biologically independent experiments were quantified in triplicate samples. Nb titrations data were fitted in Prism 8. 0. 1 (GraphPad) to the following dose-respond curve: Myr-preS1 48 -GFP binding to NTCP constructs was assayed in HEK 293F cells, grown and transfected with 1µg ml -1 DNA using the protocol described above. Forty-eight hours after transfection, cells were washed with pre-warmed PBS, and ~1 million cells were pelleted and resuspended in 1 ml of PBS.