dataesr/scientific-paragraphs-categorization huggingface dataset:
Proteomics Sample preparation and protein digestion. Triplicates of 35*10 6 DA-FASS synaptosomes were accumulated for proteomic analyses and were compared to triplicates of 35*10 6 SYN synaptosomes. Synaptosomes counts were obtained through the cytometer software BD FACSDiva v9. 0. 1. Both samples were treated in parallel at all steps. Protein samples were solubilized in Laemmli buffer. A small part of each triplicate was analyzed by silver staining using SilverXpress R staining kit (Invitrogen, Cat#LC6100). Protein content was normalized across triplicates to 140 ng (least concentrated sample of the series) and ran onto SDS-PAGE (Sodium Dodecyl Sulfate-Poly Acrilamide Gel Ellectrophoresis) for a short separation. After colloidal blue staining, each lane was cut in 2 bands, subsequently cut in 1 × 1 mm gel pieces.
Ten microliters of peptide digests were loaded onto a 300 µm-inner diameter × 5 mm C 18 PepMap TM trap column (LC Packings) at a flow rate of 10 µL/min. The peptides were eluted from the trap column onto an analytical 75 mm id × 50 cm C18 Pep-Map column (LC Packings) with a 4-40% linear gradient of solvent B in 105 min (solvent A was 0. 1 % formic acid and solvent B was 0. 1% formic acid in 80% ACN). The separation flow rate was set at 300 nL/min. The mass spectrometer operated in positive ion mode at a 1. 8 kV needle voltage. Data were acquired using Xcalibur 4. 3 software in a data-dependent mode. MS scans (m/z 375-1500) were recorded in the Orbitrap at a resolution of R = 120 000 (@ m/z 200) and an AGC target of 4 × 10 5 ions collected within 50 ms. Dynamic exclusion was set to 60 s and top speed fragmentation in HCD mode was performed over a 3 s cycle.
MS/MS scans were collected in the Orbitrap with a resolution of 30 000 and a maximum fill time of 54 ms. Only +2 to +7 charged ions were selected for fragmentation. Other settings were as follows: no sheath nor auxiliary gas flow, heated capillary temperature, 275 °C; normalized HCD collision energy of 30%, isolation width of 1. 6 m/z, AGC target of 5 × 10 4 and normalized AGC target of 100%. Advanced Peak Detection was activated. Monoisotopic precursor selection (MIPS) was set to Peptide and an intensity threshold was set to 2. 5 × 10 4. Database search and results processing. Data were searched by SEQUEST through Proteome Discoverer 2. 5 (Thermo Fisher Scientific Inc. ) against the Mus musculus SwissProt protein database (v2021-02-04; 17,050 entries) added with the green fluorescent reporter (mNeonGreen).
Peaks were detected and integrated using the Minora algorithm embedded in Proteome Discoverer. Proteins were quantified based on unique and razor peptides intensities. Normalization was performed based on the total protein amount. Protein ratios were calculated as the median of all possible pairwise peptide ratios. Background Based pairwise t-test was used to calculate p-values adjusted using Benjamini-Hochberg correction for the false discovery rate. This method assumes that most protein abundances usually do not change in response to any stimulus and automatically determines the range of protein ratios that are essentially constant between conditions and then tests each protein ratio against the median and variance derived from this background population. Quantitative data were considered for proteins quantified by a minimum of two peptides.
The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD027534  99. Meta-Analysis with other databases. Meta-analysis was carried out using databases from the mouse brain proteome  32  , SynGO 33 and DropViz  35. Volcano plots and heatmaps were created using python based bioinfokit  100. Epifluorescence microscopy and image processing. Immuno-stained synaptosomes were imaged using either a Nikon Eclipse NiU (with a ×40/NA 0. 75 dry objective equipped with a sCMOS ANDOR Zyla 5. 5 camera), a Leica DMI8 epifluorescence microscope (with a ×63/NA 1. 4 oil immersion objective equipped with a sCMOS Hamamatsu FLASH 4. 0v2 camera) or a Leica DM5000 epifluorescence microscope (with a ×40/NA 1. 25 immersion objective equipped with a sCMOS Hamamatsu FLASH 4.
0 camera) and Metamorph software. Ten to twenty frames were chosen randomly on each coverslip and imaged. Correlation of synaptosomes' labelling is automated by a homemade macrocommand, using the ImageJ software  101  (SynaptosomesMacro_Randomization, version 10, with functional description and code that can be accessed here https:// github. com/fabricecordelieres/IJ-Toolset_SynaptosomesMacro). The workflow is composed of three steps. First, the images are pre-processed. The original images, trans typed to 32-bits, are centred and reduced: their respective average intensity is subtracted and division by their standard deviation is performed. It is assumed that both signals lay close one from the other: both images are therefore combined into one to serve for synaptosomes' detection. On each pixel, the maximum signal from both channels is retained to produce a new image, subjected to both median filtering and gaussian blurring (3 pixels radius).
Signal quantification is performed by placing a circular region of interest (24 pixels radius) around the centroid, and measuring the integrated intensity. A measurement of the local background is performed through a doughnut-shaped ROI surrounding the measurement circle. All values are logged for both channels, for all retained structures and reported in a "_Pooled_CytoFile. csv" file. Further analysis was performed using the FlowJo and GraphPad PRISM softwares. xy-plots of integrated intensity values are displayed with a quadrant analysis of single or double signal detections. Quadrant gate positions were defined from raw images using the distribution of fluorescence intensities in the ROI. In the range of fluorescence intensities between the background peak and the next peak, we determined the quadrant borders at the values where the ROI start to show a clear punctum of the signal.
For all analyses, randomly chosen particles were displayed in a gallery to give an overview of the population analyzed. For association analysis, a randomization plugin was developed and integrated into the "SynaptosomesMacro_Randomization". The source code is available here: https://github. com/flevet/RandomizerColocalization, while the compiled version can be found here: https://github. com/fabricecordelieres/IJ-Toolset_ SynaptosomesMacro/blob/master/Plugins/RandomizerColocalization_. class. Two separated particles were considered associated if d < 2 µm, with d being the Euclidian distance between their centroid. To statistically determine if these associations were significant or happening by chance, we performed randomization tests. Previously stored particles centroids coordinates were retrieved.
csv" file. Stimulated emission depletion (STED) microscopy. Images were acquired using a Leica TCS SP8/STED3X microscope equipped with a HC PL APO 93×/1. 30 GLYC motCORR -STED WHITE objective and Leica Application Suite X (LAS X) software. We used depletion laser lines at 592 nm for Alexa488 and 775 nm for Alexa594 or ATTO647n fluorophores. A 25% 3D-STED effect was applied to increase Z resolution. Metrology measurements were regularly performed using fluorescent beads to test proper laser alignment. Less than 2 pixels (pixel size = 20 nm) shift between channels was measured. Electron microscopy. Synaptosomes for transmission electron microscopy were fixed right after centrifugation on coverslips with a 1% Glutaraldehyde and 2% PFA (Electron Microscopy Sciences) in 1× PBS solution and kept at 4 °C until further treatment.
The sections were stained with UranyLess® (Chromalys and Deltamicroscopy). Samples were then observed with a Hitachi H7650 transmission electron microscope equipped with a Gatan Orius CCD camera. Synaptosomes were identified by their size (0. 5-2 μm), their shape and the presence of intracellular compartments and organelles such as vesicles. Statistics and reproducibility. Sorts' statistical analysis was performed using twoway mixed design (MD) ANOVA (Fig. 1f  and Supplementary Fig. 5e ). WES' data statistical analysis was performed using Two-way ANOVA and Šídák's multiple comparison testing (Supplementary Table  2 , Figs. 1d  and 7c ). Proteomics data was tested using a Background Based pairwise t-test used to calculate p-values adjusted using Benjamini-Hochberg correction for the false discovery rate and is described in the proteomics section of the methods.
Acknowledgements Our work benefited from the excellent technical support from the central facilities at  Bordeaux university: Bordeaux Imaging Center  ( CNRS UMS 3420 ,  INSERM US4 );  Biochemistry and Biophysics of Proteins ;  Flow cytometry UB'FACSility  ( CNRS UMS 3427 ,  INSERM US5 ); animal care & breeding; Genotyping; proteomics. We thank  Niels Christian Danbolt  for the kind gift of antibodies. We are grateful to  François Georges ,  Philippe Vernier ,  André Callas ,  Nicolas Heck  and  Peter Vanhoutte  for fruitful discussions. Julie Angibaud ,  Lou Bouit ,  Elisabeth Normand ,  Hajer El Oussini ,  Guillaume Dabee  and  Melissa Deshors  provided an excellent technical support. This study benefited from the the  Agence Nationale de la Recherche consortium fundings (IDEX Bordeaux   ANR-10-IDEX-03-02 ;  Labex BRAIN   ANR-10-LABX-43   BRAIN; France Bio Imaging   ANR-10-INBS-04 ) as well as funding to P.
T. and E. H. ( ANR-10-LABX-43   BRAIN Dolipran) , funding to P. T. ( ANR-16-CE16-0022  SynLip) and funding to T. B. ( National Institutes of Health   R01 DA018928 ). E. H. received funding from  Fondation pour la Recherche Médicale  ( ING20150532192 ) and E. H. and D. P. from  ANR  ( DopamineHub - 19-CE16-0003-01 ) and from  Conseil Régional de Nouvelle Aquitaine (ParkSynGref). R. W. holds a fellowship from the  French ministry of research.
In a similar way that Klarman and Gutiérrez-Basulto define a language for contexts, Barkat et al. [21]  separate the expression of context from the ontologies using a specific context language, which is formalized using a UML class diagram. This model makes use of external ontologies to express some elements of a context, such as temporal elements or units of measure. The work of Barkat et al. aims at an automatic construction of data warehouses from a set of user requirements. The expression of context act as a filter from the requirements to the concepts in the ontologies stored in a semantic database containing multiple ontologies. The requirements are mapped with the elements contained in the expression of context, and the concepts and instances resulting from those mappings are extracted from the source ontologies through a set of transformations.
Preliminaries The code for re-running the experiments described in this section and the different version of the ontology that is used to run those tests are available on GitHub (https: //github. com/ThiLou2/ACE, accessed on 31 January 2022). The code is run as follows: python3 ACE_Verification. py ACEn. owl for testing the version "n" of the ontology (the different versions of the ontology with their correponding elements of testing are described in Section 5. 2). The tests produce: • An output ontology ACEn. owl_AceVerified. owl (when testing ontology ACEn. owl); • A log file named ace. log.
It contains the elements with applicable abnormalities. The destination ontology ACEn. owl_AceVerified. owl is the A returned by Algorithm 1. It contains the formulas that are unmarked following Algorithm 3 and does not contains the formulas marked following Algorithm 2 after run-ning the abnormality strategy algorithm, and it is the resulting ontology containing all formulas from Γ satisfying this strategy. It is important to mention that while for the sake of simplicity and code clarity, we gathered all formulas and abnormalities in a single source ontology, this is not a limitation. Multiple source ontologies may be read with the same algorithms and provide the same results as long as they share the concepts, individuals, and abnormalities URIs. In the following, we present the tests conducted with the algorithms and ace tags.
Blockchain solutions providers such as Everledger (everledger. io), Provenance (provenance. org), Bext360 (bext360. com) conducted pilot projects and offered typical use cases that demonstrate blockchain potential in verifying and certifying the origin, authenticity and integrity of products such as diamond, wine buttles, luxury fashion, coffee beans, and medicines  (Kshetri, 2018; Montecchi et` al. , 2019; Lacity, 2018; Tönnissen and Teuteberg, 2020). Enabling traceability, enhancing transparency and establishing product provenance as shown in the above examples are not the only benefits of using blockchain technology. Indeed, blockchain is a distributed ledger (database) through which supply chain partners can interact and create, verify, validate, and securely store various kinds of records such as product information, certificates, localization data, transaction records, data acquired from sensors and other connected devices  (Crosby et` al.
Blockchain technology Blockchain technology can be defined as a peer-to-peer network technology that is used to build and maintain distributed ledgers or databases of records  (Crosby et` al. , 2016; Iansiti and Lakhani, 2017). Parties participating in a blockchain (firms, institutions, individuals, etc. ) can interact with each other and create all kinds of records (product information, certificates, localization data, transaction records, data acquired from sensors, etc. ). Before being stored on the blockchain, records are verified and validated using specific consensus mechanisms  (Crosby et` al. , 2016). Then, records are combined to form a block of data that is linked with previous blocks to form a chain of blocks or a "blockchain". Data in a blockchain is ordered chronologically, every block of the chain contains a hash of the previous blocks, and the whole database is replicated and stored on different nodes of the system  (Crosby et` al.
, 2016). There exist public (permissionless) and private (permissioned) blockchains  (Casey and Wong, 2017). A public blockchain is generally open and allows everyone to have access to the data. A typical example of an open blockchain is the one used to develop Bitcoin. On the opposite, a private blockchain is restrained to a given number of predefined participants who may have different levels of permission to record and access data. Both public and private blockchains are characterized by the implementation of consensus mechanisms to validate data, the use of cryptographic links between the blocks of the chain and the creation of replicates of the whole database in multiple nodes of the network  (Crosby et` al. , 2016; Casey and Wong, 2017). These characteristics offer the guarantee that data recorded on a blockchain is valid, immune against any alteration and protected against the failure of some of the nodes of the system  (Crosby et` al.
Sampling respondents and collecting data We targeted a sample of 160 potential respondents who have sufficient expertise in supply chain management or information technology applications in supply chains. Following similar studies which are based on experts' opinion such as  Agi and Nishant (2017) ,  Bokrantz et` al. (2017)  and  Wang et` al. (2019) , we used two main criteria to select potential respondents: (1) the number of years of experience within the field and (2) the position in the company. Thus, all the members of our targeted sample have at least 5 years of work experience in functions related to supply chain management or information technology management with direct linkages to supply chain processes. All the experts in our panel work in middle or higher management positions in their respective companies, or as confirmed or senior consultants.
In a first step, we sent e-mails to all the 160 potential respondents to give them a full explanation of the objective of the study and ask if they are willing to contribute by filling the matrix of mutual influence between the 20 factors. Then, an Excel file representing the evaluation matrix with the 20 enablers in the rows  and the columns, and the evaluation grid (Table 2) were sent to the 52 respondents who declared their willingness to take part to the study. Data collection took place from mid-January to mid-March 2021. In a limited number of cases, we had to call the respondents and give explanations on the way they should proceed for filling in the Excel sheet. The use of the Excel sheet in collecting answers helped to limit the typing errors, as the data was ready to be exploited directly in Excel.
Assessment of habitat connectivity and habitat amount Land-cover maps of the ZAAr raster database were constructed at a high resolution (5 £ 5 m) from aerial imagery based on nine categories: grassland, grassy strip, maize, cereal, other crops (e. g. , rapeseed), woodland, built area, road and water. We obtained the complete wooded network (i. e. , woodlands and hedgerows) from the Kermap raster database (5 £ 5 m resolution). Fig. 2. Illustration of (A) the 27 winter cereal fields distributed throughout our study site, the LTSER "Zone Atelier Armorique", northwestern France, and the three spatial scales (250 m, 500 m, and 750 m) used to assess (B) habitat amount and (C) habitat connectivity provided by wooded, grassland and cropland elements (see map symbols). The shaded map symbols (urban elements and water) correspond to the elements that were not considered because of their low percentage of total cover in the study area.
We assessed habitat connectivity and habitat amount as provided by three dominant landscape elements in agricultural landscapes: wooded elements (woodlands and hedgerows, 22% of the study area; 2016 Kermap database), grassland elements (grasslands and grassy strips, 33% of the study area; 2017 ZAAr database) and cropland elements (maize, cereal, and other crops, 43% of the study area; 2017 ZAAr database). Urban elements (built areas and roads) and water cover correspond to a network of small villages and small streams. They were not considered because of their low percentage of total cover in the study area (8 and 1% of the study area; 2017 ZAAr database). Habitat connectivity and habitat amount were measured around the centroid of each winter cereal field at three spatial radii scales: 250 m (i.
Resistance values were calculated as the reverse of the proportion of the considered landscape element type in a moving circular window (see Appendix A1 for more details). Then, we calculated the inter-patch distance between each pair of habitat patches from each resistance map as the cumulative cost of the least-cost path using the Graphab 2. 2. 6 software  (Foltête, Clauzel, & Vuidel, 2012). The "proportion of total potential dispersal flux in the landscape" connectivity index  (Saura & Rubio, 2010)  of each selected winter cereal field was calculated for each type of inter-patch distance (i. e. , woodland, grassland and cropland elements) and for each spatial scale considered using Conefor 2. 6  (Saura & Torn e, 2009)  (see Appendix A2 for calculations).
Functional composition of weed assemblages We selected five functional traits linked to (i) dispersal, (ii) establishment and (iii) competition (Table  1  and Appendix A: Table  3 ). Traits were gathered from the TRY  (Kattge et al. , 2011)  and Biolflor  (K€ uhn, Durka, & Klotz, 2004)  databases: seed number per plant (SNPP), type of reproduction (ToR), seed dry mass (SDM), plant vegetative height (PVH) and seed germination rate (SGR). These traits were chosen as potential response traits to habitat amount and habitat connectivity amount (i. e. , linked to dispersal and seedling establishment) that can, in turn, affect the weed diversity and abundance in winter cereal fields through their effect on the establishment and competitive ability of seedlings (i. e. , effect traits). To do so, we restrict our analysis to a set of traits (i) used to be involved in dispersal, establishment and competitive abilities of the seedlings or (ii) involved in trade-offs.
Concerning continuous traits, several trait values for a given trait and for a given species could be available in databases. Thus, trait values with an error risk 4 (cf. Kattge et al. , 2011)  were averaged per trait and per species (see Appendix A3 for details). Trait distributions are summarized in Table  1. The correlations between the five selected traits were either non-significant or low (-0. 35 rho 0. 29, Appendix A: Table  4 ). Continuous traits were then either log-or logit-transformed to correct for skewness. The functional composition of weed assemblages was assessed at the field scale (i. e. , accounting for all species recorded in the ten plots) using the community weighted mean (CWM) trait value. The CWM is defined as the mean trait value weighted by the relative abundance of each species (i.
Weed management For each winter cereal field, we used the diversity and the abundance of weed assemblages at the field scale (i. e. , accounting for all species recorded in the ten plots) as proxies of weed management  (Dainese et al. , 2017). We assessed diversity using total species richness and Pi elou's total evenness using the "vegan" R package. We assessed abundance as the mean total cover. The selected metrics were not or not overly correlated (rho |0. 66|, Appendix A: Table  5 ). The mean total cover was logit-transformed to correct for skewness. None of the selected metrics were spatially auto-correlated (Moran's I test, p > 0. 05). The similarity in weed composition between each pair of winter cereal fields was also measured with the Sørensen index ðS sør ) using the "ade4" R package to obtain additional information on the taxonomic characteristics of weed assemblages.
Because the "ade4" R package computes dissimilarity (d) as ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ffi 1 À S sør p , we applied the following transformation: S sør ¼ ðÀ d 2 þ 1Þ to convert dissimilarity (d) into a similarity index. The higher the Sørensen index ðS sør Þ, the higher the similarity between a given pair of winter cereal field.
We also checked whether and how the effect of connectivity and habitat amount on weed assemblages was affected by rare species. Although removing rare species can diminish our ability to detect the impact of landscape structure on weed assemblages  (Cao, Williams, & Williams, 1998) , we performed all statistical analyses by removing species that were present in fewer than 5% of the winter cereal fields (i. e. , one field), an approximate rule of thumb in multivariate analysis  (McCune & Grace, 2002) , corresponding to 22 species in total that were removed from the dataset. All statistical analyses were performed in R. 3. 5. 1. Model averaging was performed using the "MuMIn" R package.
Characteristics of weed assemblages In total, 80 herbaceous species were recorded. The most common species were Juncus bufonius L. , Poa trivialis L. (both present in 81. 5% of fields), Poa annua L. (70. 4%), Senecio vulgaris L. , and Stellaria media (L. ) Vill. (51. 9%) (Appendix A: Table  6 ). Almost half of the species (48. 8%) recorded was strictly annual (Appendix A: Table  6 ). The similarity (Sørensen index) in weed composition between each pair of winter cereal fields was 0. 32 § 0. 01 (mean § SE), and ranged from 0 to 0. 68. The taxonomic and functional characteristics of weed assemblages are provided in Table  2. Those of weed assemblages after removing rare species from the dataset are provided in Appendix A: Table  7.