dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2021). However, despite these efforts, there is limited knowledge regarding the bycatch of chondrichthyan species, which hampers effective management and conservation efforts in the region  (FAO, 2022). In this study, we adopt a novel approach to examine the spatial co-occurrence between chondrichthyan species and commercial fishing practices using the Adriatic Sea as a case study. We integrate data on fishing effort from the Italian fleets obtained through VMS with predicted species distributions derived from geostatistical Species Distribution Models (SDMs) fitted to data from a fishery-independent bottom trawl survey. Additionally, we analyze and map species richness and the proportion of threatened species. This enables us to assess the overlap between this community and bottom trawl fishing activities, providing insights into the potential impact of fishing.
We extracted a subset of VMS data from 538 vessels utilizing bottom otter trawl (OTB) and beam trawl (TBB) gear types, which are two common fishing methods used along the Italian Adriatic coast. These bottom trawlers were identified through cross-analysis involving logbook data and the EU Common Fleet Register (https://webgate. ec. europa. eu/fleet-europa/index_en). This subset of data was then used to assess the fishing activity of these vessels from 2009 to 2021 using the R package VMSbase (version 2. 2. 1;  Russo et al. , 2014; Russo et al. , 2011a; Russo et al. , 2011b). VMS pings were partitioned into fishing trips by vessel and interpolated to increase their temporal frequency to 10 minutes  (Russo et al. , 2011a). The high-frequency interpolated VMS pings were then joined with the NOAA-Etopo1 database  (Amante, Christopher & Eakins, 2009)  through the R package marmap  (Pante & Simon-Bouhet, 2013)  to estimate the bottom sea depth for each ping.
Finally, to identify fishing activities accurately, fishing set positions were isolated from other vessel states, such as steaming and resting, by applying a combination of speed and depth filters  (Russo et al. , 2014). Thereafter, we filtered the data to include only the period covered by the MEDITS survey (May to September, see below). Subsequently, we computed the yearly aggregated value of trawl fishing effort (i. e. the sum of the fishing hours) for each cell in a 2 x 2 km square grid multiplying the number of fishing set positions by the interpolation frequency (10 minutes). We then filtered this grid by excluding depths not covered by the MEDITS survey, specifically excluding depths < 10 m and > 800 m. Additionaly we excluded the national territorial seas of Slovenia, Croatia, Montenegro and Albania.
Survey data The complete biological dataset considered for this study consisted of the catches of 5,122 bottom trawl hauls that were performed in the Italian and international waters of the Adriatic Sea during the Mediterranean International Trawl Survey (MEDITS). This survey has been conducted annually since 1994 by the Laboratory of Marine Biology and Fisheries of Fano (Italy) in the Northern and Central Adriatic Sea and by Laboratorio Provinciale di Biologia Marina of Bari (Italy)  (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008)  and COISPA Tecnologia & Ricerca (since 2009) in the Southern Adriatic Sea. The survey is generally carried out in the late spring-summer period (May to September, although in some years it has been performed in October-December), and the hauls are located at depths ranging from 10 to 800 m, following a random-stratified sampling scheme based on 5 different depth strata  (Anonymous, 2017; Spedicato et al.
, 2020). The GOC-73 experimental bottom trawl is used as the sampling gear, which has a horizontal opening of 16-22 m and a vertical opening of approximately 2. 4 m. The codend of the trawl is equipped with a 20 mm side diamond stretched mesh. Further information on the sampling procedures, data collection, and analysis can be found in the MEDITS handbook (Anonymous, 2017). We selected and analyzed a total of 4,216 hauls in this study (Appendix S1), after excluding hauls conducted prior to 1999 and hauls conducted during the autumn period (October to December). The decision to exclude these hauls was driven by two factors. Firstly, the availability of the biogeochemicals variables (see section Environmental covariates and model selection) only reaches back to 1999. Secondly, the coverage of the autumn season has been inconsistent over the years.
Ensuring a seasonally homogenous dataset was essential, considering the potential redistribution of chondrichthyan species across seasons  (Manfredi et al. , 2010). Moreover, we excluded species scarcely represented in the data (i. e. , those that occurred in less than 3% of all the trawl hauls considered). Further, we grouped the common smooth-hound (Mustelus mustelus) and the blackspotted smooth-hound (Mustelus punctulatus) together under the category smooth-hound (Mustelus spp. ) because the morphological identification for the Mustelus genus is not straightforward  (Marino et al. , 2018) , and the two species can therefore be misclassified. Additionally, the two species occur together in the Northern Adriatic Sea, display a similar diet  (Di Lorenzo et al. , 2020) , inhabit similar habitats, and the chance of hybridization is not excluded  (Marino et al.
To optimize computational efficiency, we utilized a predictive process modeling framework  (Anderson & Ward, 2019; Latimer et al. , 2009)  for fitting all models. In this framework, we employed the Stochastic Partial Differential Equation (SPDE) approximation  (Lindgren et al. , 2011)  and a triangulated mesh (Appendix S3), generated using the R-package R-INLA  (Rue et al. , 2009) , to represent spatial random fields. Random effects are estimated at the knots (the vertices of the mesh) and are then bilinearly interpolated to data locations. The optimal knot locations were determined using a k-means clustering algorithm, which minimized the total distance between knots and data points. We selected 200 knots for our models after an initial exploration, as this balance between accuracy and computational time was deemed appropriate (Appendix S3).
The models were fitted using TMB  (Kristensen et al. , 2016)  through the Rpackage sdmTMB (version 0. 2. 1)  (Anderson et al. , 2022 [preprint] ) with maximum marginal likelihood and the Laplace approximation to integrate over random effects. We assessed convergence of the models and ensured that the maximum absolute gradient with respect to all fixed effects was < 0. 001, and that the Hessian matrix was positive-definite  (Anderson et al. , 2022 [preprint] ). The model residuals are presented in the Appendix S4-S7.
emodnet. eu/en/bathymetry), respectively. To integrate these environmental covariates with the trawl hauls data, we associated each trawl haul with the nearest spatial point of the variables. In the case of temperature and oxygen variables, we also matched the trawl hauls data with the nearest available measurements in time. Inter-annual variations were accounted for by including year as random intercept. We then asked which combination of covariates resulted in the most parsimonious model, as estimated by the Akaike Information Criterion (AIC). To investigate the relationship with depth, for the most common species (spurdog, small-spotted catshark, and thornback skate), we explored two representations to account for bell-shaped relationships: log(depth) + log(depth 2 ), and depth + depth 2.
The main aim of this study was to evaluate the prognostic value of radiomic approach in pretherapeutic 18 F-fluorodeoxyglucose positron-emission tomography (FDG-PET/CT) in a large cohort of patients with gastro-esophageal junction cancer (GEJC). This was a retrospective monocenter study including 97 consecutive patients with GEJC who underwent a pre-therapeutic FDG-PET and were followed up for 3 years. Standard first-order radiomic PET indices including SUV max , SUV mean , SUV peak , MTV and TLG and 32 textural features (TFs) were calculated using LIFEx software on PET imaging. Prognostic significance of these parameters was assessed in univariate and multivariate analysis. Relapse-free survival (RFS) and overall survival (OS) were respectively chosen as primary and secondary endpoints. An internal validation cohort was used by randomly drawing one-third of included patients.
Materials and methods Patients. Consecutive patients with a histologically proven GEJC who underwent a pre-treatment FDG-PET/CT were retrospectively included from June 2012 to June 2018. Patients with previous GEJ malignancy were excluded. The study was conducted in accordance with the Declaration of Helsinki and was approved by the French Advisory Committee on Information Processing in Health Research (CCTIRS). If patients were alive, they received a written information on the study to express their non-opposition; for deceased patients, an exemption of information was obtained from CCTIRS. Data acquisition. FDG-PET/CT images were acquired on two Biograph mCT PET/CT 64 scanner system (Siemens Healthineers © , Erlangen, Germany) with the same technical setting. Patients fasted 6 h before intravenous injection of approximately 3 MBq/kg of FDG.
Images were corrected for random coincidences, scatter and attenuation using the CT scan data and were smoothed with a Gaussian filter (full-width at half-maximum = 2 mm). The reconstruction transaxial matrix size was 200 × 200 voxels and the voxel size was 4. 07 × 4. 07 × 2 mm. Images analysis. All primary tumors were analyzed using LIFEx software (www. lifex soft. org)  12  on PET imaging. Each primary lesion was segmented by a single nuclear medicine physician using a fixed SUV threshold method consisting in delineating a 3D contour around voxels equal to or greater than 40% of SUV max. In this volume of interest (VOI), intensities of FDG uptake were resampled with an absolute method of 64 discrete values and bounds set to 0 and 30 SUV values, corresponding to the typical range of tumor SUVs encountered in GEJC  6, 7.
Follow-up. Clinical data including age, sex, histological type (SCC or ADC), human epidermal growth factor receptor-2 (HER2) expression, metastatic status and initial pathologic stage according to the American Joint Committee on Cancer classification (AJCC) were recorded. Patients were treated in accordance with standard guidelines after a gastro-intestinal multidisciplinary board and were clinically followed up for at least 1 year to evaluate the relapse free survival (RFS) and overall survival (OS). As recommended by National Comprehensive Cancer Network, European Society for Medical Oncology (ESMO) and French National Society of Gastroenterology, clinical follow-up consisted in a standard digestive examination, including inspection and palpation and an examination of internal structures by a mirror and a flexible endoscope every 2 months during the first year and 3 months during the second year after treatment.
No inner membrane permeabilization was observed when MAC formation was blocked by serum heat-inactivation or addition of a C5 cleavage inhibitor. Taken together, these data indicate that for K. pneumoniae, MAC activity can be quantified by measuring inner membrane damage via Sytox green. Developing colistin resistance sensitizes K. pneumoniae Kp209 and Kp257 to MAC-mediated killing. Since previous studies demonstrated that colistin resistance can affect serum sensitivity of certain K. pneumoniae isolates  8, 11, 12  , we wondered whether there was a role for MAC in this process. First, we used our fluorescent MAC activity assay to investigate the influence of colistin resistance on MAC-mediated killing of K. pneumoniae. To allow a direct comparison, we used three isogenic K. pneumoniae strain pairs that were previously selected during an in vitro evolution experiment, in which three colistin sensitive K.
We analysed the MAC-mediated membrane permeabilization of the two unique Tn::phoQ mutants, which showed reduced membrane permeabilization compared to Kp209_CSTR (Fig. 3b ), indicating that loss of phoQ leads to decreased MAC sensitivity. We also assessed colistin sensitivity of the Tn::phoQ mutants. As colistin disrupts the bacterial cell envelop to kill bacteria, the membrane permeabilization assay could be used to compare colistin sensitivity between Kp209, Kp209_CSTR and the Tn::phoQ mutants (Fig. 3c ). Kp209_CSTR was not permeabilized by colistin, whereas the colistin sensitive Kp209 was. Both Kp209_CSTR Tn::phoQ mutants were permeabilized by colistin, indicating that loss of PhoQ function leads to increased colistin sensitivity of this strain. Taken together these data indicate that enhanced PhoQ activity, as a result of colistin resistance mutations, led to an increased MAC-sensitivity in Kp209_CSTR.
5% paraformaldehyde in PBS for 5 min. Fluorescence was determined via flow cytometry (MACSQuant, Milteny Biotech), acquiring 10,000 events per condition. Flow cytometry data was analysed in FlowJo V. 10. Bacteria were gated based on the forward and side scatter, and AF647 and FITC geometric mean fluorescence intensity was determined for the bacterial population. Serum depletion with bacterium. Bacteria (OD 600 = 1. 0) were incubated in ice cold NHS (20% in RPMI buffer). After a 10-min incubation on ice while shaking, bacteria were pelleted in a cooled centrifuge (2 min at 10,000g) and the bacterium depleted NHS was collected. The depletion steps were preformed trice in total. Antibody depletion was verified via flow cytometry. Polyclonal IgG and polyclonal IgM isolation from NHS. IgG and IgM were isolated from NHS as previously described  20.
RNA-seq was performed in biological triplicates. Sequencing and data analysis. Sequencing was performed by the Montpellier GenomiX platform (MGX, https:// www. mgx. cnrs. fr/) using an Illumina Novaseq instrument. Following quality control, Bowtie2 was used for sequencing reads mapping  50, 51  onto the genome of Kp209 available on the European Nucleotide Archive under accession number PRJEB29521  9  , previously annotated with Prokka. The read count per feature was calculated using Ht-seq count  52. Finally, differential gene expression between the Kp209 and Kp209_CSTR strains was performed using DESeq2  53.
Funding This work was supported by the  Netherlands Organization for Scientific Research (NWO)  through a  TTW-NACTAR Grant  # 16442  (to SvdL and SHMR) and the  European Union 's  Horizon 2020 research programs   787 H2020-EU-ITN-EJD  (CORVOS # 860044  to FM and SHMR). The funders had no role in the study design, data collection and analysis, or preparation of the manuscript.
Data availability The RNA-seq data generated in this study were deposited on the NCBI Gene Expression Omnibus (GEO) and is publicly available under the GSE212413 accession number (https:// www. ncbi. nlm. nih. gov/ geo/ query/ acc. cgi? acc= GSE21 2413). The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Sharing data and resources has revolutionized life sciences, particularly in proteomics, where public data has enabled researchers to reanalyze and reinterpret data in novel ways. However, the lack of comprehensive metadata remains a signi cant challenge to unlocking the full potential of publicly shared data. In response, the Sample and Data Relationship Format (SDRF) Proteomics was developed, However, its complexity presents several challenges. This study investigated metadata annotations in proteomics data sets from the PRIDE database and the corresponding publications, and identi ed major gaps in metadata provision. To bridge this gap, we created a user-friendly, ontology-based Streamlit application, named lesSDRF, that guides users through the annotation process using SDRF. lesSDRF aims to encourage researchers to provide more detailed metadata annotations, leading to greater insights and scienti c advances in proteomics.
Introduction Life sciences represent a vital showcase of the bene ts of open science, as groundbreaking scienti c discoveries are being freely and openly shared by scienti c communities. Prime examples include the Protein Data Bank 1 , the rst draft of the human genome sequence  2  and, more recently, publication of the AlphaFold model for the prediction of protein structures  3. By openly sharing such data and resources, scientists around the world have been able to collaborate and build on each other's work, directly as well as indirectly, thereby accelerating progress and advancing our understanding of biology. Moreover, several of these efforts of sharing by the life sciences community have been widely publicized by general media, as these appeal directly to the general public  4. As a result, the life sciences became a global leader in the open science revolution, a role emphasized by the emergence of many specialized online, public data repositories for global data dissemination.
However, it was only with the establishment of the ProteomeXchange (PX) consortium in 2011 that public data sharing in proteomics started to become widely adopted as general practice  23. PX unites the main proteomics repositories worldwide (including the abovementioned PRIDE and PeptideAtlas) and standardized data submission and dissemination practices in the eld. Importantly, all member repositories are required to comply with the FAIR principles. This widespread access to publicly available proteomics data has since led to the development of widely used data-driven tools such as MS²PIP  24  , DeepLC  25  and Prosit 26. However, it is important to realize that such tools could only be built because they bypassed the need for detailed metadata annotation by solely being based on (several) technical aspects of the data.
As soon as the objectives of a meta-analysis include a deeper biological understanding, any lack of essential biological metadata (e. g. , species or tissue type of the sample) clearly becomes a major obstacle. To address this issue, and to facilitate the capture and dissemination of metadata, the Sample and Data Relationship Format (SDRF) for Proteomics (SDRF-Proteomics) has been developed in 2021, based on the MAGE-TAB le format extensively used by the transcriptomics community  27. The SDRF-Proteomics format consists of a tabdelimited le that allows seamless mapping between data les and their respective sample characteristics. It supports annotation of (i) biologically relevant sample metadata; (ii) the relationship between a sample and its respective data les; (iii) the technical metadata pertaining to the raw data les; and (iv) the factor values outlining the studied variables.
All these properties are encoded as ontology or controlled vocabulary (CV) terms, thus ensuring a structured and standardized representation of metadata  23. Note that we here use SDRF-Proteomics and SDRF interchangeably, as well as ontology and CV terms Despite the potential bene ts of SDRF-Proteomics for capturing valuable metadata, it is not straightforward to use due to the multiple use cases that can support. Indeed, the versatility of the format also results in a complexity that poses a signi cant hurdle for metadata providers, with e. g. factor values and replicates de ned in ways that can be confusing at rst. Moreover, due to the absence of a streamlined method for annotation, users currently need to manually annotate their data using spreadsheet software such as Excel. The extensive use of CV terms further compounds the annotation process, as users may struggle to input the correct terms (e.
g. , due to the use of different ontologies for the same concepts), leading to poor machine readability and inconsistencies in the metadata. Nevertheless, SDRF-Proteomics is already used in ongoing efforts to improve the annotation of existing public data sets, with large-scale initiatives on GitHub focused on the post-hoc annotation of data sets already available in public repositories such as PRIDE  28. These efforts heavily rely on information sourced from PRIDE, and from any associated research articles and supplementary documentation. So far, 220 PRIDE projects have been annotated in SDRF by this process. Of the 9,671 new datasets submitted to PRIDE since the introduction of SDRF as a supported le format in mid-2022, only 156 came with submitter-supplied SDRF les. Taken together, this means that only 3.
9% of all PRIDE public projects currently have SDRF annotation. Indeed, when we assessed the current availability of metadata in PRIDE and accompanying research articles in the context of a large-scale reprocessing effort, we observed a severe lack of comprehensive metadata annotation throughout. While our primary focus was on metadata within PRIDE (as the most popular PX resource), it is worth noting that this dearth of metadata annotation is a common trend for all other proteomics repositories within PX. To help address this critical metadata challenge, we developed a user-friendly SDRF application tool, named lesSDRF, to: (i) provide a streamlined method for users to supply SDRF-formatted metadata annotation; (ii) boost the number of SDRF annotated data sets in the public domain; and (iii) enhance the general completeness and accuracy of metadata in proteomics data sets, which will in turn facilitate (large-scale) data reuse, and help advance scienti c discoveries.
Results We rst evaluated the extent and quality of metadata annotation in publications associated with public proteomics data as part of a large-scale reprocessing effort. To achieve this, seven biotechnology students annotated 241 data sets from the PRIDE database. Details on the data set selection process for this project are available in the Material and Methods section. We gathered the following metadata information from the research articles and their supplementary information: (i) biological metadata: species and tissue/cell line; and (ii) technical metadata: protease, labeling technique, instrument, fragmentation type, number of missed cleavages, precursor ion mass tolerance, fragment ion mass tolerance, variable and xed modi cations. Figure  1  shows the fraction of projects with missing metadata in the corresponding research article and in the PRIDE metadata, respectively, clearly illustrating several major gaps in metadata provision for these data sets.
We also compared metadata annotation found in the paper to metadata found in the corresponding PRIDE project. This comparison revealed a substantial number of discrepancies between the two sources, especially regarding tissue annotation. These discrepancies are due to a lack of detail in the annotations in PRIDE, for example, the "cell culture" term is frequently used without specifying the cell line or tissue of origin of the cultured cells. The high number of incomplete annotations regarding considered protein modi cations in the identi cation process in both sources further indicates how important processrelated metadata is often omitted. The instrument category stood out in having outright incorrect annotations, with six mistakes found, accounting for 2. 5% of the projects. It can thus be clear that, in order to fully reuse a PRIDE data set, researchers must often consult multiple sources, including the repository metadata, as well as the associated paper and its supplementary information, and will also have to resort to additional metadata extraction tools to obtain as complete metadata annotation as possible, all the while keeping a sharp eye out for metadata inconsistencies or even outright contradictions between these various sources.