dataesr/scientific-paragraphs-categorization huggingface dataset:
E. R. is supported by the  French National Research Agency  under the program " Investissements d'avenir "  Grant Agreement LabEx MabImprove :  ANR-10-LABX-53  and by " ARD CVL Biom edicaments. "E. D. , N. C. , G. G. , and F. S. also acknowledge the projects CHemBio ( FEDER-FSE 2014-2020-EX003677 ),  Techsab  ( FEDER-FSE 2014-2020-EX011313 ), the  Reseau Th ematique de Recherche RTR Motivhealth  ( 2019-00131403 ), and the  Labex  programs  SYNORG  ( ANR-11-LABX-0029 ) and  IRON  ( ANR-11-LABX-0018-01 ) for financial support. A. I. was funded by the  Japan Society for the Promotion of Science KAKENHI  grants  21H04791  and  21H05113 ; the  Moonshot Research and Development Program   JPMJMS2023 ; the  FOREST   JPMJFR215T  from the  Japan Science and Technology Agency ; the  PRIME JP19gm5910013 , the  LEAP JP20gm0010004 , and the  BINDS JP20am0101095  from the  Japan Agency for Medical Research and Development ; the  Daiichi Sankyo Foundation of Life Science  and the  Uehara Memorial Foundation.
3 mm and perfect fit in the bone defect (Figure  1A ). First, an STL model was created using SolidWorks 2020 (Dassault Systèmes, United States). Then, the software Ultimaker Cura 4. 6 (Ultimaker, Netherlands) was used to create a G-code file, which was further tested and modified until the desired dimensions and porosity, assessed with a caliper and binocular, achieved, and reproduced.
X-Ray Diffraction XRD scans were recorded for phase Identification and quantification of the "Pre-mineralized" scaffold using D8 Advance with DaVinci X-ray diffractometer (Bruker, United Kingdom). Instrument was operated with flat plate θ/θ geometry and Ni-filtered Cu-Kα radiation at 45 kV and 40 mA (Kα1 = 1. 54059 Å, Kα1 = 1. 54442 Å)  (Elsharkawy et al. , 2018). The values were recorded from 5 °to 70 °with a step size 0. 02 °, and data were obtained at step time of 1,600 s. PDF4 database (ICDD, USA, release 2014) was used for comparison.
Data presented at mean ± SD (n = 6). In (B) * represents significant difference p < 0. 05 between "Uncoated" and "Pre-mineralized" scaffold, estimated using one-way ANOVA in GraphPad Prism ver. 6 software. compared to "ELR only" (Figure  4B ). Moreover, we did not observe any signs of fibrous tissue formation at the implant-tissue interface on all our scaffold types (i. e. , "Uncoated," "ELR only," and "Pre-mineralized") (Figures  4A, B ). This is one of the characteristic of osseointegrated implants  (Shah et al. , 2019). Fibrous tissue formation is a surface responsive behavior. For instance, stiff surfaces can activate myofibroblasts (a scar-forming cell type) that leads to fibrous formation around the implant  (Noskovicova et al. , 2021b) , thus, blocking implant-tissue integration  (Noskovicova et al.
Introduction Language pro ciency is not considered as the nal objective of language education, but it is observed as a tool to accomplish interpersonal and intercultural goals. Therefore, the aim of EFL learning is to enable better communication and understanding among learners. The objective of the current research was to collect data on several commonly used measures of communication orientations, namely communication anxiety, communicative competence, willingness to communicate, and international posture among Iranian EFL and Malaysian ESL learners. There is an increasing awareness of the importance of context in English as a foreign language (EFL) pedagogy  (Wedell & Malderez, 2013)  and its in uence on the provision and success of education systems around the globe. As  Yashima (2002, p.
Procedure The process of administering the instruments of the present study so as to run the research went through successive stages as follows: Questionnaires containing measures of the aforementioned communication anxiety, communicative competence, willingness to communicate, and international posture in English language were simultaneously administered to Iranian EFL and Malaysian ESL learners. Since the target Iranian EFL university students were easily available, the researcher was able to collect the required data in the determined time limit. However, the access to the target Malaysian ESL university students was a bit di cult since the researcher resided in Iran and had no easy access to them. By the way, through the help of a university faculty member in the university Putra Malaysia, as a research assistant, she could collect the required data in ESL context but in a longer period of time.
The questions in various forms and scales were keyed in Microsoft Forms (Figure  3. 1) an online survey creator and part of O ce 365 which was released by Microsoft. The different questionnaires had their own designated scales, and the respondents needed to make the options in Likert Scale form, determine the percentage, or decide on 1-10 scale. This online Software made it possible for the researcher to export the collected data to Microsoft Excel, and then enter them into SPSS Software to be analyzed. The respondents in both groups were asked to click on the separate provided links and start answering the questions. After nishing the job, they were required to click on the submit button within the set time limit. The concept WTC in English as well as communication anxiety, communicative competence, and international posture were considered an indicator variables, so to specify each indicator variable, values of all the items were aggregated.
Values of negative items were also reversed before aggregation. After the researchers collected the required data for the study, they scrutinized them for any missing information. Based on the collected information, they had to leave out three cases from each group, as some sections of the questionnaires were left untouched and without answers. Hence, the number of questionnaires for analysis was determined to be 62 in each group. The data collection procedure took about eight weeks to be accomplished. In the process of analyzing the collected data, after collecting and tabulating them, all the data from the scales were run through the Statistical Package for Social Sciences (SPSS v. 23) to work out descriptive statistics. The participants were asked to complete the questionnaires. Hence, the collected data, after being converted into a computer le, were screened and cleaned to gain a fully-crossed dataset.
Findings In the phase of data analysis, descriptive statistics was described by measures, such as mean, standard deviation, minimum and maximum obtained data values, while inferential statistics through independent samples t-test analyzed the collected data and assessed the hypotheses. It should be noted that the required statistical processing of the data were performed using SPSS Software, version 23. As shown in the Table  4. 1, in the present study, the data from a total of 62 university English students were involved in data analysis. Before running independent samples t-test, the Anderson Darling test was used in order to check for the normality of the collected data. It tested the normality of the data related to the corresponding scores of respondents in Question 1. According to the above graphs, resulting in the assumption of normal distribution of data to be met.
Station 2 (Sediment Flux) Station 2 is located at Villelongue de la Salanque (4. 5 km upstream from the outlet). Measurement devices include a pumping system and a refrigerated sampler Teledyne ISCO 6712FR allowing hourly surface water sampling. The suspended particulate matter (SPM) contained in the water samples was analyzed using a Malvern Mastersizer 3000 after five minutes of ultrasonification to determinate the primary particle size distribution (PSD). The estimation of PSD was performed using the Mie theory to describe suspended particles between 0. 01 and 3500 µm. Each sample was measured five times, and data were then averaged. To determine mass concentrations, water samples were filtered in triplicate on preweighed 0. 7 µm GF/F Whatman filters. Then, the filters were rinsed with deionized water, dried at 50 • C, and re-weighed.
The data were averaged to determine the absolute mass concentration of SPM and uncertainties in the measurements. The sand mass concentration was then calculated by applying the percentage of sands determined by the PSD on the absolute mass concentration of SPM. A least squares regression method was used to estimate the relationship between the SPM and sand mass concentration in suspension (g L -1 ) with the water discharge (m 3 s -1 ) (Equations (  1 ) and (  2 )). [SPM] = 1. 8946 (± 0. 088) × water discharge r 2 = 0. 90  (1)  [Sand mass concentration in suspension] = 0. 2247 (± 0. 018) × water discharge r 2 = 0. 76 (2) These relations were finally applied to calculate the total SPM and sand flux, in terms of both whole mass and volume (a density of 1. 6 t m -3 was used for sand [55]), occurring during the event in the river.
Pressure is transformed into depth and then into water level, which was adjusted from the French National Geodesic referential (NGF) using in situ bathymetric data. The vertical velocity profile, with 30 cells of 10 cm each, was re-processed for beam-mapping using the Storm software (©Nortek). The current profiler of Station 4 (Figure  2c ) is an ADCP AWAC 1 MHz (© Nortek) fixed on the bottom and oriented upwards, providing time series averaging 60 s of measurements of pressure, temperature and vertical velocity profiles (intensity and direction) every 30 min. Water depth was derived from pressure using the Thermodynamic Equation Of Seawater equations (TEOS-10)  [60]. The water level was then adjusted to the tide gauge measurements of Port-Vendres (https://data. shom. fr/, accessed on 24 November 2021) to obtain a time series based on the NGF referential.
The vertical velocity profile, consisting of 10 cells of 1 m, was re-processed for beam-mapping using the Storm software ( © Nortek). The AWAC also provides wave data (HS and direction) every 30 min (510-s burst average). A calibration of the optical signal of the OBS was performed in the laboratory using in situ samples collected at a depth of 10 m close to Station 4, in order to estimate SPM concentrations. Samples were used to establish a concentration range from 0 to 0. 1, 0. 1 to 1, 1 to 10 and 10 to 15 g L -1 in intervals of 0. 05, 0. 1, 1 and 5 g L -1 , respectively. Each concentration was agitated and exposed at the OBS connected to a multimeter. The second-order polynomial regression method was used to estimate the relationship between the mass concentration of SPM (g L -1 ) and the voltage of OBS (Equation (  3 )).
The vertical velocity profile, consisting of 10 cells of 1 m, was re-processed for beam-mapping using the Storm software (©Nortek). 𝑆 ℎ𝑖𝑔ℎ = 𝜂 + 0. 35𝛽 𝑓 (𝐻 0 𝐿 0 ) 1 2 ⁄ (4) The AWAC also provides wave data (H S and direction) every 30 min (510-s burst average). A calibration of the optical signal of the OBS was performed in the laboratory using in situ samples collected at a depth of 10 m close to Station 4, in order to estimate SPM concentrations. Samples were used to establish a concentration range from 0 to 0. 1, 0. 1 to 1, 1 to 10 and 10 to 15 g L -1 in intervals of 0. 05, 0. 1, 1 and 5 g L -1 , respectively. Each concentration was agitated and exposed at the OBS connected to a multimeter. The secondorder polynomial regression method was used to estimate the relationship between the mass concentration of SPM (g L -1 ) and the voltage of OBS (Equation (  3 )).
Collection and Analysis of Topographic/Bathymetric Data The LiDAR dataset is composed of two surveys on 04 November 2019 and 28 January 2020, conducted by a LiDAR YellowScan VX20 mounted on a DJI M600 UAV. N-S and E-W amplitudes for the LiDAR flight were 850 and 250 m, respectively (0. 215 km 2, ) with a final resolution of 10 cm. The Z accuracy was ±0. 025 m. Additional topographic data were monitored using a kinematic DGPS Trimble R6, with an accuracy of ±0. 05 m, on 15 November 2019 and 30 January 2020. Two bathymetric surveys were carried out on the river side of the mouth on 15 November 2019 and 30 January 2020, and two others were conducted in the nearshore area on 08 April 2019 and 13 February 2020. A single-beam echosounder Tritech PA500 coupled to a RTK-GNSS antenna Trimble R6 or R8 was used, working in VRS mode, through Hypack software (Z accuracy of ±0.
1 m). Cross-shore and longshore survey lines were measured from 300 m upstream to the river mouth to a depth of -14 m in the nearshore zone. Outliers and spikes were manually removed from the output files and smoothing was performed (7 times the wave period) using Hypack ® software. To compute the volume changes, Digital Elevation Models (DEMs) were generated using a GIS software package (ESRI ArcGIS ® ) with the natural neighbor interpolation method.
From 21 to 24 January, a second decrease in atmospheric pressure occurred, with moderate winds (up to 12 m s -1 ) and a shift from an easterly to an S-E direction. After 24 January, the atmospheric pressure stabilized around 1022 hPa, and the winds became weak. Precipitation, mostly concentrated on 21 January, led to rapid increase in the Têt River discharge from 5 to 1000 m 3 s -1 (22 January at 16:00) (Figure  4c ). After a decrease in the river discharge over a small number of hours, a second peak reaching 1280 m 3 s -1 was observed on 25 January at 12:00 H (according to the HYDRO database, the return period of this flood event was estimated to be more than 50 years). Finally, the water discharge decreased progressively until to 26 January. The HS was below 1. 5 m (East to N-E direction) until 20 January at 08:00 (Figure  4d ).
From there, a rapid increase was observed, reaching 6. 3 m (according to the CANDHIS database, the return period was estimated to 15 years for the zone,  [63] ) 21 January at 12:00. The decrease in HS started on 22 January at 07:00 and lasted until 26 January, with waves coming from the southeast. The meteorological conditions first indicated a decrease in atmospheric pressure between 18 January at 09:00 UTC and 19 January (1031 to 1022 hPa) (Figure  4a ), accompanied by fairly weak easterly winds (2. 3 to 5. 4 m s -1 ) (Figure  4b ). Until 21 January, a rapid increase in atmospheric pressure (to 1036 hPa) was observed without significant change in wind characteristics. From 21 to 24 January, a second decrease in atmospheric pressure occurred, with moderate winds (up to 12 m s -1 ) and a shift from an easterly to an S-E direction.
After 24 January, the atmospheric pressure stabilized around 1022 hPa, and the winds became weak. Precipitation, mostly concentrated on 21 January, led to rapid increase in the Têt River discharge from 5 to 1000 m 3 s -1 (22 January at 16:00) (Figure  4c ). After a decrease in the river discharge over a small number of hours, a second peak reaching 1280 m 3 s -1 was observed on 25 January at 12:00 H (according to the HYDRO database, the return period of this flood event was estimated to be more than 50 years). Finally, the water discharge decreased progressively until to 26 January. The H S was below 1. 5 m (East to N-E direction) until 20 January at 08:00 (Figure  4d ). From there, a rapid increase was observed, reaching 6. 3 m (according to the CANDHIS database, the return period was estimated to 15 years for the zone,  [63] ) 21 January at 12:00.
Acknowledgments: We thank  Météo France  for supplying meteorological data, the  DREAL Occitanie  for hydrologic data and  CEREMA/DREAL Occitanie/Observatoire Océanologique de Banyuls  for wave data. We thank  P. Feyssat  and  O. Raynal  for the acquisition of post-Gloria bathymetry and  N. Valentini  for the X-Beach modeling. Thanks to the three anonymous reviewers, and the journal editor for their constructive and thoughtful comments and suggestions.
In Drosophila, dSfxn1/3 overexpression counteracts the loss of dopaminergic neurons in a PD model, suggesting a neuroprotective role of SFXN  [11]. Furthermore, SFXN3 mutations lead to retinal degeneration in mice  [12]. For two decades, SFXN were presumed to be metabolite transporters on the basis of in silico structural analysis. For example, S. cerevisiae Fsf1 (YOR271cp) was proposed to be an alpha-isopropylmalate carrier though no experimental data has ascertained this function  [13]. Since a mitochondrial iron overload was seen in flexed-tail mice carrying a mutation in the SFXN1 gene, SFXN1 was suggested as functioning in iron homeostasis. It was thus postulated that SFXN proteins mediated the mitochondrial uptake of a metabolite required for iron metabolism  [14]. Indeed, increased mitochondrial iron levels and deficient heme biosynthesis were reported in SFXN2 and SFXN4 KO human cells  [15, 16].
Collectively, these data have shed light on the role of the SFXN family in regulating essential metabolic pathways and mitochondrial activities. Despite the recent progress, still little is known about the molecular functions of the SFXN family. The knowledge of the SFXN interactome may assist in elucidating the functions of these transporters, specifically the mechanisms by which they regulate mitochondrial activities. The main objective of our study was to uncover the physical partners of the human SFXN1 because (i) SFXN1 is the most conserved member of the SFXN family, and (ii) it appears as a main regulator of mitochondrial function through its involvement in OCM. Upon large-scale survey of the SFXN1 interactome, via coIP-MS/MS on mitochondria of MCF7 human cells, we identified two proteins related to neurodegeneration, namely ATAD3 and the multifunctional enzyme 17β-hydroxysteroid dehydrogenase (17β-HSD10).
Immunofluorescence Staining 1 × 10 6 cells were plated on 60 mm-diameter Petri dishes containing coverslips and fixed 24 h post-seeding, at a confluence of 60%, with 3. 7% cold paraformaldehyde (PFA) for 20 min. Cells were washed once with PBS and then permeabilized with 0. 5% Tween20 for 30 min. Nonspecific sites were blocked with 1% PBS-BSA for 30 min. immunostaining, and then cells were incubated for 60 min with primary or secondary antibodies in 3% PBS-BSA. Coverslips were mounted with ProLong Gold Antifade Mountant (Invitrogen). Imaging was performed using Leica TCS SPE or Leica TCS SP8 confocal microscopes (CYMAGES imaging facility, UVSQ). Image analysis was performed using ImageJ software.
Imaging and Statistical Analysis for Co-Localization Images were taken under a Leica SP8-X confocal microscope using a x63 oil immersion objective with 1. 4 NA, 1024 × 1024 resolution and Voxel size x = 0. 18, y = 0. 18, z = 0. 3. The cell of interest was cropped and properly denoised (background subtracted and filtered with a Gaussian blur filter), and individual pixel intensity in three consecutive stacks was extracted with Fiji package  [22]. Data were processed, and graphs were generated in Microsoft Excel (2019), R package (R Core Team, 2020) and GraphPad Prism 8. 0. Co-occurrence (Manders's overlap coefficient  [23] ) and correlation (Pearson's correlation coefficient or r) values were calculated as reviewed in  [24].
Sample Preparation for Proteomic Analyses Enriched mitochondrial fraction from MCF-7 cells were prepared as described above. Protein content was estimated by Bradford assay. Proteins (250 µg per condition) were subjected to immunoprecipitation using magnetic beads (Bio-Adembeads, PAG 0463, Ademtech, Pessac, France). Briefly, 250 µg of enriched mitochondrial fraction was incubated in lysis buffer (20 mM Tris pH 8. 0, 150 mM NaCl, 0. 5% NP40, 0. 5% deoxycholate complemented with anti-protease cocktail) supplemented with 2 µg of a rabbit anti-SFXN1 antibody (HPA063745, Sigma Aldrich, Saint-Quentin-Fallavier, France) or a rabbit IgG control antibody (Cat#12-370, Merck Millipore, Overijse, Belgium) for 30 min at RT at 1000 rpm on a tube rotator. After washing and equilibration in lysis buffer, 20 µL of magnetic beads were added to the immune complex and incubated 30 min under agitation (1000 rpm at RT).
Proteomic Data Analysis All MS and MS/MS data were processed with the Proteome Discoverer software (Thermo Scientific, version 2. 2) coupled to the Mascot search engine (Matrix Science, version 2. 5. 1). The mass tolerance was set to 6 ppm for precursor ions and 0. 02 Da for fragments. The maximum number of missed cleavages was limited to two for the trypsin protease. The following variable modifications were allowed: oxidation (Met), phosphorylation (Ser, Thr or Tyr) and acetylation (protein N-terminus). The SwissProt database (2017_12 release) with the Homo sapiens taxonomy was used for the MS/MS identification step. Peptide identifications were validated using a 1% FDR (false discovery rate) threshold calculated with the Percolator algorithm. Proteins for which validated peptides were identified solely in the SFXN1 immunoprecipitation and not in the control were considered potential SFXN1 partners.
Biological Process Analysis Using PANTHER Protein localization was determined using the gene ontology annotation  [26, 27]. Potential SFXN1 partners were submitted to the online PANTHER classification system using the PANTHER Overrepresentation test (released 20171205) with the whole annotation for Homo sapiens in the Gene Ontology database as reference set  [28]. The overrepresented biological processes with a FDR < 0. 05 were assumed to be related to the biological role of the SFXN1. Proteins that may not interact with SFXN1 were also submitted to the same analysis pipeline to further evaluate the specificity of the biological process determined.
Analysis of SFXN1 Interactome Using STRING STRING version 11. 0  [29]  was used to model SFXN1 interactome with the following basic settings: experiments, database, co-expression and co-occurrence were chosen for active sources; the value for the minimum required interaction score was set at high confidence (0. 700), and no more than 5 interactions for the first shell and none for the second shell were parameters chosen for the max number of interactors to show. The network generated can be retrieved at the following permalink: https://version-11-0. string-db. org/cgi/network. pl?networkId=jUrnsUFnafB2 (accessed on 1 november 2019). Functional enrichments for GO terms linked to cellular component permitted to retrieve mitochondrial proteins (GO:0031966), membrane mitochondrial proteins (GO:0005739) and inner membrane mitochondrial proteins (GO:0005743).
Metascape Analysis of SFXN1 Physical Partners A list containing the Uniprot identifiers for the 96 proteins found in our co-IP-MS/MS experiment was submitted to the Metascape webtool (https://metascape. org/gp/index. html#/main/step1 (accessed on 6 December 2021)). Express Analysis was chosen for enrichment and clustering analysis. Metascape Express analysis consists of an automated analysis workflow beginning with identifier conversion and followed by gene annotation, membership search and enrichment analysis. For the meta-analysis conducted to compare enriched terms related to mitochondrial proteins and SFXN1 partners, we submitted a list of 1484 proteins selected from the IMPI database (IMPI-2021-Q4pre) containing 1357 genes encoding verified mitochondrial proteins with "gold standard" evidence of mitochondrial localization and 127 encoding mitochondrial associated proteins with evidence of mitochondrial localization but lacking visual confirmation (https://www.
mrc-mbu. cam. ac. uk/ research-resources-and-facilities/impi (accessed on 27 July 2022)). To construct the protein-protein interaction network for our 96 candidates, Metascape "Express Analysis" mode was chosen. It utilizes the "Physical Core" dataset, which contains only 2/3 of the highest-scoring corresponding STRING data. The MCODE algorithm automatically extracts densely connected protein complexes from our list of SFXN1 partners.