dataesr/scientific-paragraphs-categorization huggingface dataset:
Subsequent network analysis shows common interactions between these last three GO-BP terms (Fig. 4a ). We then compared this proteomic dataset with the previously reported ITM2B interactome purified with the same mouse antibody from rat brains  19. Overall, 124 proteins are in common between our dataset and the 511 rat cerebral proteins identifed in a previous study  19  (Supplementary Table  S8 ). The majority of these common proteins are involved in translation process including the eukaryotic translation elongation factor 1 alpha 1 (EEF1A), the ribosomal protein L12 (RPL12) and the solute carrier family 25 member 11 (SCL25A11) or involved in microtubule processes such as tubulin beta 2A (TUBB2A), tubulin beta 2B (TUBB2B) and tubulin beta-3 class III (TUBB3) (Supplementary Table  S8 ). This comparative analysis strongly suggests that some functions of ITM2B are similar in the brain and the retina.
Among the 150 proteins found in common, we observed proteins associated with the cytoskeleton organization including the adducing 1 (ADD1), the plectin (PLEC) and the vimentin (VIM) (Supplementary Table  S10 ). This comparative analysis supports the reliability of the ITM2B interactome identified with the rabbit antibody. Different ITM2B transcripts are expressed in the human retina. As described before, common interactors as well as distinct clusters of proteins were identified using the two antibodies. One possible explanation to these distinct clusters is given by the difference in the epitopes against which each antibody was raised (Fig. 1 ). Interestingly, a new human ITM2B alternative transcript was recently annotated in Ensembl (ENST00000649266. 1). This transcript harbors a translation initiation codon in exon 2 and is predicted to generate a shorter ITM2B protein (210 amino acids).
However, this finding may also indicate that ITM2B is present in photoreceptor cells. Furthermore, ITM2B immunostaining using the mouse antibody showed ITM2B localization in the photoreceptor layer (Supplementary Fig. S7 ). This is supported by transcriptomic data which suggests the ubiquitous expression of ITM2B in human retina  25, 26. Interestingly, APP and APLP2 previously reported to interact with ITM2B in the brain  17, 18  were also found in our retinal dataset  17, 18. APLP2 is particularly interesting since its deletion in the mouse retina induces a phenotype similar to the retinal dystrophy observed in subjects carrying the ITM2B c. 782A>C, p. Glu261Ala mutation notably with similar electroretinogram alterations  3, 27. The ITM2B-retinal dystrophy mutation could lead to a loss of interaction between APLP2 and the mutant ITM2B inducing a similar phenotype as the Aplp2 -/-mice.
GO-BP analysis of the ITM2B interactome specifically purified with the mouse antibody revealed that beyond microtubule association, most functional pathways are linked to protein translation, organonitrogen compound biosynthetic process and peptide metabolism. These two last functional annotations were found neither in the whole dataset analysis nor in the previous published rat cerebral interactome. ITM2B has never been associated with metabolic processes and this result might suggest a new and specific role of ITM2B in the retina. We next analyzed the ITM2B specific interactome isolated with the rabbit antibody. Most of the GO-BP terms indicate that ITM2B might be involved in mitochondrial functions. While this annotation did not emerge in the previous published proteomic analysis, several mitochondrial proteins were also identified in the ITM2B interactome purified with the mouse antibody in the human retina and in the rat brain.
Quantification of protein abundance variations. Label-free relative quantification was performed in a Between-Subject Study Design using Progenesis-Qi software 4. 1 (Nonlinear Dynamics Ltd, Newcastle, UK). For the identification step, all MS and MS/MS data were processed with the Proteome Discoverer software (Thermo Fisher Scientific, version 2. 2) coupled to the Mascot search engine (Matrix Science, version 2. 5. 1). The mass tolerance was set to 7 ppm for precursor ions and 0. 05 Da for fragments. The maximum number of missed cleavages was limited to two for the trypsin protease. As performed before  31  , the following variable modifications were allowed: oxidation (Met), phosphorylation (Ser, Thr, and Tyr), acetylation (Protein N-term). The Swis-sProt database (02/2017) 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. Protein identifications were validated if at least two unique peptides per protein were identified. Protein abundance measurements were calculated according to the Hi-3 label-free quantification method. Protein variations were validated if their calculated p-values were under 0. 01. In silico analyses and statistics. The list of the 310 proteins highly expressed in the human retina was obtained from the Human Protein Atlas (available online: http:// www. prote inatl as. org). Venn Diaphragms were generated using Venny online tool 32 (version 2. 1). Hierarchical clustering and heatmap were performed by the MEV (Multiple Experiment Viewer software) software (version 4.
9) with a Pearson correlation  33  and the average linkage clustering method. Ontology term enrichment and visualization were obtained using the ClueGo plugin (version 2. 5. 6)  34 within the Cytoscape software (version 3. 7. 1). The following parameters were used for the GO term enrichment analyses: (i) right sided hypergeometric test with a Benjamini-Hochberg correction with a minimum p-value threshold of 0. 01, (ii) reference set with all identified proteins in concerned proteomic experiments with at least 2 unique identified peptides, (iii) GO tree interval from 4 to 8, (iv) GO term fusion activated, (v) GO terms process, function and component from EBI, UniProt and GOA. Volcano plots representing protein abundance variations between control condition and specific antibodies according to p-values were generated with R (version 3.
We will use cumulative risk estimates at postoperative day 28. We chose this 4-week time frame for estimates of risks of thrombosis and bleeding because this is a feasible and frequently selected time frame for prophylaxis  [48, 49]. When the authors present the frequency of events at more than one time point, we will record the number of events up to three months and use the absolute risk closest to 4 weeks. All outcomes will be extracted and analyzed separately for each procedure. Surrogate outcomes such as asymptomatic DVT and the amount of estimated blood loss during the operation are of questionable importance to patients  [50] , and we will not collect data on such events.
Study selection and data abstraction Literature search, study selection, application of eligibility criteria, and data abstraction We will perform comprehensive searches separately for general and gynecologic surgeries with the aid of an information specialist (R. J. C. ). A combination of keyword and medical subject headings (MeSH) search will include the post-operative complications and VTE block combined with the surgery block (including the target surgery subfield terms combined with the target procedure and body part terms). Our searches will include Embase, MEDLINE, Web of Science, and Google Scholar from January 1, 2004, until the present for general surgery and from January 1, 2000 until the present for gynecologic surgery. We limit our searches to contemporary studies, because the baseline risks of VTE and bleeding have likely changed over time  [51] [52] [53].
The update search is planned for 1 year before estimated publication date of results articles. We will also search for any reviews (found by our search) that may include observational studies of our interest. We will review these for possible eligible studies and add identified studies to selection process. We will further identify articles for procedures by reviewing the reference lists of the included studies. Finally, we will include articles that were not yielded by the search but are known to the experts in the panel to the full text screening. We developed piloted, standardized data forms for screening articles and data extraction. Methodologically trained reviewers will apply these forms, guided with written instructions, to screen study reports for eligibility and extract data from eligible reports.
The reviewers will conduct pilot screening and data extraction exercises to achieve a high level of agreement. Reviewers will screen abstracts in DistillerSR (Evidence Partners, Ottawa, Canada), an online systematic review software program. Reviewers will first screen titles and abstracts to select papers for full-text assessment and then full-text papers to confirm the eligibility of the articles (Fig. 1 ). All screening and data extraction will be completed independently and in duplicate. In every full-text screening pair, there will be at least one clinician expert (general/ abdominal surgeon in general surgery and gynecologic surgeon in gynecologic surgery). An adjudicator (lead author or clinician-methodologist) will resolve any disagreement on eligibility or data extraction. Because of the very large number of studies, our data extraction will be conducted in two phases.
In the first phase, we will assess the risk of bias and classify studies as having very low, low, moderate or high risk of bias ("Assessment of risk of bias and outliers (final selection of eligible studies)" section). In this first phase, we will also extract data regarding procedure characteristics: procedure name, number of patients and outcomes reported for each procedure. In the second phase, (i) if we have at least 1000 patients and five articles for a target procedure in studies with very low and low risk of bias, we will exclude studies with moderate and high risk of bias; (ii) if we have at least 2000 patients and 10 articles for a certain target procedure in studies with very low, low, or moderate risk of bias, we will exclude studies with high risk of bias; and (iii) in other situations, we will use all studies irrespective of their risk of bias.
Because the baseline risks of VTE and bleeding are likely to have changed over time  [61] [62] [63] , older studies present potential risk of bias when estimating the present baseline risks. The complication estimates depend on the length of follow-up and studies that do not clearly define the time period of follow-up may therefore be biased. In addition, the details of thromboprophylaxis are sometimes insufficiently reported in available studies, which may also bias the results. We will estimate the thromboprophylaxis used based on information from the authors and, if not directly provided, on data on the type of thromboprophylaxis typically used in the country during the study period. Since the exclusion criteria include atypical and selected populations, we will not assess risk of bias resulting from the exclusion of patients.
We will record the timing of VTE by day as reported in each individual study. We will then model the timing of VTE for each individual study using natural cubic spline interpolation and R data analysis language. We will pool these models to a single model using the random effects inverse variance method. In our main model, we will adjust for the thromboprophylaxis use. In addition, we will perform a secondary analysis without any adjustments as well as sensitivity analyses in which we will revise the reported results in each study modeling what would have happened under various conditions of thromboprophylaxis. We will perform three such sensitivity analyses by assuming (i) 1 week of thromboprophylaxis for all patients, (ii) 2 weeks of thromboprophylaxis for all patients, and (iii) 3 weeks of thromboprophylaxis for all patients.
Calculating risks To produce up-to-date information, we will extract data from contemporary observational studies. However, the contemporary studies representing the current rates of VTE often include patients that receive thromboprophylaxis. In addition, the decreasing incidence of VTE, linked to improving surgical techniques, adopting early mobilization, and extended prophylaxis as the standard of care  [51] [52] [53] , represents a challenge in estimating the efficacy of thromboprophylaxis. To adjust the reported risks of VTE and bleeding for the use of thromboprophylaxis, we will estimate the effectiveness of prophylaxis. To achieve this, we will update our previously conducted meta-analyses of placebocontrolled randomized trials on the relative risks of VTE and bleeding among those who receive prophylaxis  [11, 12, 38].
To estimate the proportion of patients aged 75 or more, we will first estimate the mean/median age and SD for a target procedure, and then assume a normal distribution and calculate the proportion of patients aged 75 or more. To estimate the proportion of patients with body mass index 35 or more, we will first estimate the mean/median age and SD for a target procedure, and then use population-based data on distribution of body mass index and calculate the proportion of and patients with body mass index 35 or more. We will calculate the proportion of those with a personal history of VTE by using population-based study providing cumulative risks of personal history of VTE by age. Finally, we will estimate the proportion of patients with family history of VTE by using data from Michigan Surgical Quality Collaborative study  [66].
Discussion Our work will use rigorous methods to make the best use of available observational data. We have specified explicit eligibility criteria, conducted comprehensive searches, and created an instrument for risk of bias assessment specific for this review. Teams of two reviewers will independently assess eligibility, risk of bias, and extract data. At least one clinician expert will assess the eligibility for every article in the full text screening phase. The lead author or a clinician-methodologist will adjudicate any discrepancies and we will send our consensus of data extraction to the original authors of each article for confirmation or correction. We will carefully address the many challenges involved in generating baseline risks of VTE and bleeding from low-quality observational data.
We will conduct meta-analyses to adjust for the use of thromboprophylaxis and differences in the length of follow up. We will also take into account patient risk factors of VTE and bleeding outcomes. We will exclude studies that would bias our results and, at the same time, find balance for the amount of data for estimates. Our research group has earlier performed this kind of endeavor in urologic surgery  [11, 12]. This work informed the first ever procedure-specific thromboprophylaxis guideline in any field of surgery, endorsed by the European Association of Urology. Our current project will explore this very frequent clinical dilemma in two large-scale surgical specialties: general abdominal and gynecologic surgery. We will implement improvements to the prior endeavor methods, including conducting more comprehensive literature searches and updating the VTE risk over time model.
Accurate and reliable bathymetric data are needed for a wide diversity of marine research and management applications. Satellite-derived bathymetry represents a time saving method to map large shallow waters of remote regions compared to the current costly in situ measurement techniques. This study aims to create very high-resolution (VHR) bathymetry and habitat mapping in Mayotte island waters (Indian Ocean) by fusing 0. 5 m Pleiades-1 passive multispectral imagery and active ICESat-2 LiDAR bathymetry. ICESat-2 georeferenced photons were filtered to remove noise and corrected for water column refraction. The bathymetric point clouds were validated using the French naval hydrographic and oceanographic service Litto3D ® dataset and then used to calibrate the multispectral image to produce a digital depth model (DDM).
A second relevant study used ICESat-2 bathymetric measurements, down to 18 m depth, to calibrate and validate Sentinel-2 imagery at 10 m pixel size  [17]. This spatial resolution nonetheless remains limiting for some applications (e. g. , marine ecology, navigation). Our paper aims to create a higher resolution digital depth model (DDM) by fusing active ICESat-2 bathymetric soundings and 0. 5 m Pleiades-1 passive MS imagery in order to provide very high-resolution (VHR) satellite-based bathymetry and habitat maps of the coral reefscapes in Mayotte. First, a density-based algorithm was implemented on ICESAt-2 ATL03 L2 dataset to remove the noise in photon data and detect the water surface. The noise arises from several sources, including the laser pulse being scattered by the atmosphere, the solar background noise effects, and the detector dark noise.
Bathymetric data were used to remove the effect of the water column and generate a digital albedo model (DAM) to classify benthic habitats  [24] [25] [26] [27]. Finally, the vertical accuracy of the predicted depths was assessed by comparing the bathymetric data to the French naval hydrographic and oceanographic service SHOM bathymetric LiDAR and multibeam echosounder reference dataset (Litto3D ® ). Classification performances were evaluated using a confusion matrix.
Litto3D ® Reference Dataset The French Oceanographic and Hydrographic Marine Service (SHOM) and the French National Institute for Geographical and Forest Information (IGN) conducted a joint altimetric and hydrographic survey of Mayotte from 2003 to 2010. Most of the island was mapped using the airborne topographic and bathymetric LiDAR and multibeam echosounder. The resulting Litto3D ® product provides soundings located in a three-dimensional geometric reference system with high spatial resolution and a land-sea continuum (data are available for free from https://diffusion. shom. fr/, last accessed: 28 December 2021). Data extracted from this dataset, corresponding to the ICESat-2 ground track, and used for comparison, include bathymetric points acquired using the bathymetric Li-DAR and multibeam echosounder.
Litto3D ® Reference Dataset The French Oceanographic and Hydrographic Marine Service (SHOM) and the French National Institute for Geographical and Forest Information (IGN) conducted a joint altimetric and hydrographic survey of Mayotte from 2003 to 2010. Most of the island was mapped using the airborne topographic and bathymetric LiDAR and multibeam echosounder. The resulting Litto3D ® product provides soundings located in a three-dimensional geometric reference system with high spatial resolution and a land-sea continuum (data are available for free from https://diffusion. shom. fr/, last accessed: 28 December 2021). Data extracted from this dataset, corresponding to the ICESat-2 ground track, and used for comparison, include bathymetric points acquired using the bathymetric LiDAR and multibeam echosounder.
Litto3D ® soundings are provided in a cartesian coordinate system in the horizontal plane and with orthometric heights. The point cloud density is constrained by the acquisition method over a specific area and the gridded model is provided with a spatial spacing of either 1 m or 5 m. Specification regarding the positioning and the geodesy of the Litto3D ® dataset are presented in Table 1  [34]. A local geoid, RGM04, was used as a reference for this dataset. This dataset was used during the validation phase to measure the accuracy of the SDB, but Litto3D ® data were not used as calibration points for the models.
This study uses the 3rd version of the L2 ATL03 georeferenced photons (data publicly available at https://search. earthdata. nasa. gov/search, last accessed: 28 December 2021)  [37]. Data about each photon are provided with the latitude, the longitude, and the height relative to the WGS84 ellipsoid as well as other ancillary information. Considering that ICESat-2 was not designed to study the sub-surface water or the bottom topography, it is necessary to include in the analyses a correction for refraction bias induced by the water column. We selected the ICESat-2 track acquired on the date closest to the acquisition date of the MS imagery. The two datasets were acquired 10 days, 10 h and 33 min apart. Then, the specific study area in Mayotte was selected based on the range of depths for which calibration data were available.
Noise Removal and Detection of the Sea Surface In the dataset, noise corresponds to sparse points with a low spatial density compared to the sea surface and seabed clusters. Georeferenced photons likely to be noise were removed, and photons associated with the sea surface were identified. Here, a density-based spatial clustering method was used, which is an unsupervised learning method used to identify clusters in a dataset. The method is based on the premise that each cluster is defined as a region of points with a given density and spatially isolated from other groups by areas of lower density. The DBSCAN algorithm used scanned the entire dataset and established a search radius on each point successively. The point considered during a given step is a "core point". DBSCAN allows the users to specify a search radius size according to two criteria: The search circle radius ϵ and the minimum number of points MinPts.
Noise Removal and Detection of the Sea Surface In the dataset, noise corresponds to sparse points with a low spatial density compared to the sea surface and seabed clusters. Georeferenced photons likely to be noise were removed, and photons associated with the sea surface were identified. Here, a density-based spatial clustering method was used, which is an unsupervised learning method used to identify clusters in a dataset. The method is based on the premise that each cluster is defined as a region of points with a given density and spatially isolated from other groups by areas of lower density. The DBSCAN algorithm used scanned the entire dataset and established a search radius on each point successively. The point considered during a given step is a "core point". DBSCAN allows the users to specify a search radius size according to two criteria: The search circle radius and the minimum number of points MinPts.
Once a criterion is no longer satisfied, the algorithm begins a new classification group  [41]. Previous studies successfully implemented DBSCAN on the ICESat-2 dataset of islands located in the south of China and in the Bahamas. One particular study provides formulas to configure the MinPts and the radius parameters of the DBSCAN algorithm  [17]. In the present research, the search radius was manually chosen by the user to guide the clustering process and optimize the results. In addition, the MinPts parameter is defined by Equation (  1 )  [17]  (this formula is suited for a study of a water column whose depth is not expected to exceed 60 m). MinPts = 2SN 1 -SN 2 ln 2SN 1 SN 2 , ( 1 ) where SN 1 is the number of expected photons corresponding to signal and noise and defined by Equation (  2 ): SN 1 = π 2 N 1 hl , (2) where N 1 is the total number of photons (both signal and noise), h is the vertical range and l is the along track range.
SN 2 is the expected noise photons number and is defined by Equation (3): SN 2 = π 2 N 2 h 2 l , (3) where N 2 corresponds to the number of photons in the layer with the fewer bathymetric photons, while h 2 is the height of the corresponding layer  [17]. The variable MinPts is constrained to a value no lower than 3. If the previous formula provides a value lower than this threshold, then MinPts was set to 3  [17]. This algorithm might not be optimal in the present situation, since the dataset contains isolated photons from the seabed which could be identified as noise. Considering the small number of photons from the seabed, it was decided not to optimize the noise cleaning process, even if it meant that some manual cleaning had to be done. Therefore, the remaining noise points were removed manually using GlobalMapper software 22.