dataesr/scientific-paragraphs-categorization huggingface dataset:
Orientation analysis We worked on greyscale confocal images of actin fibers and cell membranes, acquired after phalloidin and β-catenin staining respectively. The alignment was corrected by rotation of 2. 5 degrees so that the sidewalls of the channel precisely correspond to the verticality (represented by 90°). We used the OrientationJ set of plug-ins available on ImageJ Software. OrientationJ Analysis created color-coded overlays which pixel hue corresponded to the angle of local staining orientation. OrientationJ Distribution computed in a spreadsheet the numeric orientation values ranged from 90° to 90° relative to the horizontal. We converted this range from 0 to 180° to match the verticality (corresponding to the channel axis/ flow direction) with 0°. The number of structures in each direction was next plotted as angle histogram using the rose function in Matlab.
Protein concentrations were determined for each replicate using BCA Protein Assay kit (ThermoFisher scientific). Normalization of fluorescence intensities to the quantities of total proteins avoided potential variation in cell number. Three experiments including three replicates were independently performed for each condition. The difference in cell accumulation of P-gp or MRP substrates with and without specific inhibitor reveals the activity of each transporter in the substrate efflux. GraphPad Prism software was used for statistical analysis and graphical representation. Numerical results are given as means ± standard deviation. Data were treated using two-way ANOVA and posthoc Tukey's test. Statistical significance was accepted for *p < 0. 05; **p < 0. 01; ***p < 0. 001.
Images in Fig. 1a  confirmed our previous observation because cells appeared more elongated (fusiform) under flow, as compared to cells that remained in static condition. Based on these images, we performed an orientation analysis with ImageJ software in order to quantify the directionality of the cells. Angular distributions were computed from 0° to 180°, with 0° and 180° being the channel axis (corresponding to the flow direction), and 90° corresponds to the perpendicular of the channel/flow axis. Our results in Fig. 1b  showed that cells under static condition had no preferential orientation. In contrast, cells exposed to SS exhibited a significant preferential alignment from 100° to 130° relative to the flow axis. Then, the effect of SS on the actin cytoskeleton was investigated. In static culture, F-actin fibers visually appeared randomly oriented at any time, without homogeneous orientation (Fig.
We focused our analysis on the common responses at 5 and 10 dyn. cm -2 versus static condition and (d). Cells were seeded in Ibidi microslides and cultured either in static condition or under a shear stress of 5 dyn. cm -2 for 72 h. Staining was performed with an antibody against the cell membrane protein -catenin, phalloidin targeting filamentous actin (F-actin). Nuclei were stained in blue with Hoechst 33,342 in order to compare images with a similar confluence. Images were acquired by confocal microscopy, and each image is representative of 4 images acquired in random locations. Four images per condition were analyzed with OrientationJ in ImageJ software. This plug-in gives the orientation of each pixel from 0° to 180° relative to the channel axis (i. e. the flow direction). 0° and 180° correspond to the channel/ flow direction, whereas 90° is the perpendicular to the flow.
Activation of the pentose phosphate pathway (PPP) for NADPH regeneration NADP-dependent malic enzyme (ME1), glucose-6-phosphate dehydrogenase (G6PD), and 6-phosphogluconate dehydrogenase (PGD) are enzymes involved in the oxidative phase of pentose phosphate pathway (PPP) by generating NADPH, whereas transketolase (TKT) and transaldolase (TALDO1) catalyze the non-oxidative phase for the synthesis of nucleotides and glyceraldehyde-3-phosphate to restart the oxidative phase. All these proteins were upregulated by SS in our dataset (Additional file 2: Table  S3 ). Moreover, PPP (both oxidative and non-oxidative phases) was predicted to be activated by SS according to IPA, with an activation z-score > 2. NRF2 was indeed described to positively regulate the PPP  [55]. NADPH is used for antioxidant mechanisms.
Acknowledgements The authors greatly thank  Technologie Servier  for granting funding. The authors greatly acknowledge the Cell and  Tissue Imaging  ( PICT-IBiSA ),  Institut Curie , member of the  French National Research Infractucture France-BioImaging  ( ANR10-INBS-04 ), and particularly  Vincent Fraisier  and  Anne-Sophie Mace  for their expert technical help. The authors also acknowledge  Cedric Broussard  from the  3P5  proteom'IC facility,  Université de Paris, Institut Cochin , and  Cerina Chhuon  and  Chiara Guerrera  from  3P5  proteom'IC facility,  Université de Paris, Necker. This work was supported by the  DIM Thérapie Génique Paris Ile-de-France Région ,  IBiSA , and the  Labex GR-Ex.
Availability of data and materials The mass spectrometry proteomics data have been deposited to the Pro-teomeXchange Consortium via the PRIDE  [1]  partner repository with the dataset identifier PXD030493. Datasets are available at: Project Name: Proteomic profiling of hCMEC/D3 cells exposed 3 days to laminar physiological shear stress. Project accession: PXD030493. Project DOI: Not applicable. Reviewer account details: Username: reviewer_pxd030493@ebi. ac. uk. Password: 2ejuEa5r. [1]  Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, Inuganti A, Griss J, Mayer G, Eisenacher M, Pérez E, Uszkoreit J, Pfeuffer J, Sachsenberg T, Yilmaz S, Tiwary S, Cox J, Audain E, Walzer M, Jarnuczak AF, Ternent T, Brazma A, Vizcaíno JA (2019). The PRIDE database and related tools and resources in 2019: improving support for quantification data.
Current Status and On-Going Efforts We outline the on-going efforts in two distinct research areas in tectonophysics toward ICON science: (a) global databases for geochronology, geochemistry, petrology, mineralogy, the standardization and reassessment of the global heat flow database; and (b) biennial conferences bringing together analog and computational tectonic modeling. Tectonophysics aims to understand the mantle and lithospheric processes that shape our planet. This field integrates geophysical, geological and geochemical data from both quantitative studies (model simulations and experiments) and field observations. Therefore, a vast variety of individual databases exists among the different subdisciplines such as geochronology, heat flow, and geochemistry; and in many cases, they lack standardized protocols and quality control or use different methodological approaches.
In recent years, efforts toward ICON science in distinct research areas in tectonophysics have been made to agree on concentrating and integrating data, standardized protocols and quality criterias. Furthermore, biennial conferences bringing together analog and computational tectonic modeling strengthen the knowledge transfer between disciplines. An example of an active ICON initiative is EarthChem, a community-driven, NSF-funded, portal for multiple disciplines, whose purpose is to integrate different databases into one portal, offering free data access to researchers, publishers, funding agencies, educators, and students (Figure  1 ). In doing so, they support networking by grouping the data, and aiming to build communities around specific themes, so, although there is no reference that focuses on "best practice" these research communities can build such best practices focused on data management and open data sharing, another of the ICON science characteristics.
In the case of the Global Heat Flow Database (Figure  1 ), for example, the different industry collaborators provide thermal data which is then reviewed, digitized, and coordinated under one project, led by academia, to develop an accurate data set for multidisciplinary use. Moreover, the industry funds through R&D research projects within the heat flow community to better understand thermal processes and actively participates in ongoing research and publications. The database is currently in the process of transformation into a modern database framework. The database was reviewed last year in terms of structure and quality criteria by scientists experienced with heat flow data  (Fuchs et al. , 2021). This agreement builds the basis for the on-going reassessment of the database. Converging conferences must be among what the communities can do with coordinated and integrated data such as EarthChem.
Integration ICON initiatives encourage the integration of processes across traditional disciplines in different spatial and temporal scales  (Goldman et al. , 2021). We understand this as the need for boosting the spatio-temporal transdisciplinarity from these three main sciences, which requires overcoming the challenge it includes. As an example, the thermal data are published in repositories for different research areas (e. g. , Geology, Geophysics, Biology, etc. ). However, each of the disciplines pursues only part of the data and therefore a global standardization protocol is required in order to create a common database structure. As well, the integration of analog experiments and numerical simulations has low demand and obscure feasibility. Even AMTP and GeoMod aim to enhance coordination of the two research methods and improve reproducibility but not to integrate them.
, 2016). However, the ways to remove those identified sources have yet to be formulated in a systematic way. The agreement on the heat flow database standardization opened new ways to compare and characterize the individual data entries and created a better idea of data reliability and quality. The review of the database is a collaborative work open to each scientist. The newly established protocols and methods initiated further programs that were coordinated by different task forces. Each of these task forces are responsible for coordinating parts of the database and are organized throughout the heat flow community, with the goal of more collaboration and improvement in quality of the generated data resulting from the consistency of the global database.
, 2016). An outstanding challenge is how to identify and disseminate the best practices across the diverse communities in tectonophysics. Active promotion of the existing protocols and software tools that share goals with ICON science might be a good start. For instance, it would be beneficial for tectonic modelers to assess a rubric developed by the EarthCube Research Coordination Network project "'What about model data?' Determining best practices for preservation and replicability" (https://modeldatarcn. github. io/). Designed to provide simulation data management best practices, the rubric directly addresses the motivation of AMTP workshops. Also, well-structured and financially well-supported groups could take the lead in distributing data and experimental protocols through their existing networks or by expanding them.
Network The construction of open-access databases in Tectonophysics reflects one of the steps this field is taking into providing open access to data that has been privately compiled in the past through better communication using workshops and networking. When open data, software, and models are exchanged through networks of various scopes, the discriminating effects from the financial discrepancy will be mitigated. Nevertheless, individual research disciplines still need to strengthen their communication in terms of collecting data. Not only data of specific interest to an individual sub-field but also additional data for potentially broader interest should be collected to satisfy the protocols of the database and therefore a broader usage for the entire community. Despite this, the panorama is satisfactory when taking a look at the increase in the number of individual database users.
For example, the available statistics of data usage in the EarthChem database portal (https://www. earthchem. org/about/statistics/ecl-statistics/) show that from May 31, 2012 to May 31, 2019, there was an increase in data download numbers which correlates with the number of data submissions to the portal. This statistic demonstrates that in the last decade there has been an increasing interest in the use of databases such as this one. Another beneficial impact of exchanging open data through networks of various scopes is the mitigation of discriminating effects from the financial discrepancy between countries and irregular geopolitics.
Coordinated There is an effort being made to coordinate data through databases and organized systems, however, such coordination is usually focused on a single country or region, rather than a global effort (Figure  1 ). Additionally, several manuscripts have been published in scientific journals, for example, in the realm of thermochronology, advocating for organized protocols when it comes to data reporting, outlier flagging, or data modeling decisions (e. g. ,  Abbey et al. , 2020; Flowers et al. , 2015; Flowers & Kelley, 2011; Murray et al. , 2020; Sousa & Farley, 2020).
Open Although new data, software, and/or models are published and often made fully public, they are not often well advertised or are unequally advertised. For instance, researchers will often create a tool, publish it in a manuscript, and provide a link to an accessible site like GitHub. And yet, very few people know about or use the tool, and in fact a few years later another researcher will remake something very similar. In addition, these available resources commonly lack user guides or manuals that help explain how and when to use the new items. Consequently, researchers often end up re-creating similar resources.
Bringing ICON in as Second Nature Here, we emphasize some key challenges to implementing ICON principles in tectonophysics and put forward possible solutions as lists of top-down and bottom-up actions. Some key challenges to implementing ICON principles in tectonophysics include: 1. The desire of each researcher to share their research and lose the prima donna attitude especially characteristic of the former generations (affects openness and integration) 2. Finding methods, formats, and styles that everyone can agree on (affects openness, integration, and coordination) 3. The time and effort spent on loading the data onto the maintained shared data sites (affects coordination, openness, and networking) 4. Maintenance of the shared databases, and making sure people are using them correctly and that new data is navigable and searchable after it has been loaded (affects coordination and networking) Possible solutions or beginnings to solutions might include top-down actions like: 1.
Required training on data sharing (and workshops). Many research institutions and organizations already require training on scientific merit and integrity, so they could add a component on how to report and share new data and models through maintained infrastructure and databases 2. Providing incentive. Approved databases could have some form of credit that can be used to track individual contributions, for example, citable DOI's providing recognition for data contribution in addition to article publications. This is beginning to take hold as repositories like Zenodo and EarthChem add citable doi information connected to data 3. Fostering the careers of researchers and laboratories, especially early career scientists, which promote ICON practices 4. Encouraging infrastructures to follow ICON principles through financing implementation costs from national or international research calls On the other hand, bottom-up ways to disseminate ICON ideas could involve: 1.
Call to Action: Tectonophysics Can Embody ICON! Here Is Where We Begin Recently, journals (e. g. , Tectonics: AGU) have begun to require that all data associated with a submitted manuscript must be housed in a database. More journals should adopt this requirement and focus on publicly accessible databases, they could (a) define ICON, (b) explain why this is a new and good practice and why they support ICON, and (c) provide a list of the existing open and shared databases, perhaps compiled from this collaborative special publication, with links to them. As researchers, we can be more transparent about the data we are collecting. Sending out a call to established listservs to see what similar data already exists will not only begin the process of networking and integration but can aid in coordinated efforts to consolidate and elaborate on existing data.
Participants are asked to report each food, beverage, and composite dish consumed from midnight to midnight, including the portion size/quantity, estimated with the help of validated photographs  38  , the preparation method or recipe used, and each meal setting (place, time, etc. ). Next, NutriNet-Santé's own food composition table including > 3500 items is used to estimate mean daily calorie and nutrient intake  39. All collected dietary data are weighted in order to respect the 5:7 and 2:7 ratios of weekdays and weekend days. For this analysis, each participant's dietary carbohydrate intake was averaged across a minimum of two 24-h dietary records completed within a 2. 5-year window around the baseline anxiety assessment date (described below); it was energy-adjusted using the residual method  40.
5, normal weight: 18. 5-24. 9, overweight: 25. 0-29. 9, and obese: ≥ 30. 0 kg/m 2 ). The International Physical Activity Questionnaire-Short Form assessed sedentariness (minutes/d spent sitting) and physical activity levels based on an established scoring protocol (low, moderate, high)  47. As all of the above questionnaires are administered at baseline and annually thereafter, we used covariate data collected within a 2. 5-year window around the baseline STAI-T completion date. Data on medication use for mental disorders (anxiety, addictive disorders, bipolar disorder, depression, anorexia nervosa, memory impairment, or sleep disorders) were obtained from the self-reported annual health status questionnaire. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression scale (CES-D)  48  which is administrated every 2 years; thus, we used data closest to the baseline STAI-T date.
We handled missing values for any covariates in the main model using the Multiple Imputation by Chained Equations method (20 imputed data sets)  52. All tests were two-sided and p < 0. 05 was considered as evidence for statistical significance. SAS version 9. 4 (SAS Institute, Inc. , Cary NC, USA) was used for all statistical analyses. Sensitivity analyses. Three sets of sensitivity analyses were conducted to test the robustness of the main results. In the first sensitivity analysis, an adjustment for self-reported prescribed medication use for mental disorders was added to the main model. In the second sensitivity analysis, an adjustment for presence of depressive symptoms and likelihood of eating disorders (ED) was added to the main model, because of potential associations not only between carbohydrate intake and depression  53  and ED  54  , but also between these mental disorders 1.
Ethical standards. The NutriNet-Santé cohort study is conducted according to the Declaration of Helsinki guidelines. It was approved by the Institutional Review Board of the French Institute for Health and Medical Research (INSERM # 00000388FWA00005831) and by the National Commission on Informatics and Liberty (CNIL # 908450 and # 909216). NutriNet-Santé is registered at: https:// clini caltr ials. gov/ ct2/ show/ NCT03 335644. Electronic informed consent was obtained from all volunteers prior to inclusion in the cohort.
Description of sample. In the final sample (N = 15,602), 73. 8% of the participants were female and the mean age was 53. 8 ± 13. 1 years. Participants included in the analysis were generally older, more likely to be male, retired, and never smokers, to report higher levels of both physical activity and educational attainment, and were less likely to be obese, current smokers, or to live alone compared to those excluded from the analysis (all p < 0. 0001; data not tabulated). The distribution of participants according to anxiety status was as follows: "None" n = 8939 (57. 3%); "Transient" n = 1956 (12. 5%); "Onset at follow-up" n = 1200 (7. 7%); "Persistent" n = 3507 (22. 5%). Mean follow-up time in the study was 5. 4 ± 0. 9 years. Table  1  presents the participants' characteristics across anxiety status evolution.
Acknowledgements The authors thank  Dr. Françoise Radat  for her involvement in the assessment of general anxiety in the NutriNet-Santé cohort. The authors also thank  Cédric Agaesse  (dietary data manager),  Alexandre De-Sa  and  Rebecca Lutchia  (dietitians);  Younes Esseddik  (IT manager),  Thi Hong Van Duong ,  Régis Gatibelza ,  Jagatjit Mohinder  and  Aladi Timera  (computer scientists);  Julien Allegre ,  Nathalie Arnault ,  Laurent Bourhis ,  Nicolas Dechamp  (biostatisticians), and  Fabien Szabo de Edelenyi, Ph. D. (biostatistics team manager);  Maria Gomes  (NutriNet-Santé participant support);  Paola Yvroud, M. D. (health event validator); and  Nathalie Druesne-Pecollo, Ph. D. (operations coordinator) for their technical contribution to the cohort. Finally, the authors thank all the volunteers of the NutriNet-Santé cohort.
Funding The  NutriNet-Santé study  is supported by the  French Ministry of Solidarity and Health , the  National Agency for Public Health  ( Santé Publique France ), the  National Institute for Health and Medical Research (INSERM) , the  National Research Institute for Agriculture, Food and Environment (INRAE) , the  National Conservatory of Arts and Crafts (CNAM) , the  Center for Epidemiological Research and Statistics (CRESS)  and  Sorbonne Paris Nord University. Junko Kose  and  Margaux Robert  are supported by doctoral fellowships provided by the  French Ministry of Education via Sorbonne Paris Nord University. Pauline Duquenne is supported by the  French National Research Agency  (grant #  ANR-21-CE36-0003 ). Charlotte Debras is supported by a doctoral fellowship provided by the  French National Cancer Institute (INCA).
Photophysical properties Detailed photophysical properties of all compounds, in solution as well as in the solid state, are depicted in Figure  2. Absorption and emission spectra of the dicationic compounds (1M-3M) were measured in MeCN as well as in water. For 3M, photophysical measurements were also carried out in MeOH. For the neutral compounds, absorption and emission spectra were measured in MeCN and toluene for 1 and 3, and MeCN for 2. Key photophysical data are summarised in Table  1. In order to provide a comparison of their absorption and emission characteristics, the data measured in MeCN are considered. The absorption and emission characteristics of 1 and 2 are very similar. They absorb at 335 nm (ɛ = 37100 M À 1 cm À 1 ) and 333 nm (ɛ = 34200 M À 1 cm À 1 ), respectively, and emit (λ em ) at 342 nm and 340 nm, respectively.
Theoretical calculations In order to rationalize the observed spectroscopic and electrochemical (see below) properties, in-depth density functionaltheory (DFT) and time-dependent density functional-theory (TD-DFT) calculations were carried out. Geometry optimizations and vibrational frequencies calculations were first performed at the PBE0/GD3BJ level using the 6-31G* basis set with the Gaussian09 program (see computational details in the Experimental section). Then, TD-DFT computations of the UV-Vis, as well as the circular dichroism (CD) spectra were carried out at the same level of theory, and also using the CAM-B3LYP functional for comparison. Selected experimentally determined (crystallographic) and theoretically optimized structural parameters are listed in Table  S2. Overall, the optimized structural parameters for all the dications (1M, 2M, 3M) match very well with the crystallographically determined parameters.