dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors thank technical assistance from the  John Innes Centre Horticultural Services , and helpful discussions with members of the  Zipfel and Malone laboratories. SP is funded by a studentship from the  Norwich Research Park. Research in the Malone and Zipfel laboratories is supported by  BBSRC   Institute Strategic Program  Grant  BB/J004553/1. The  Zipfel laboratory  is further supported by the  Gatsby Charitable Foundation. The work done at LIPM, France was supported by the  Laboratoire d'Excellence (LABEX)   TULIP  ( ANR-10-LABX-41 ). JAD thanks the  John Innes Foundation  for an Emeritus fellowship. Dr Zipfel  collaborates with the 2Blades Foundation to develop commercial and charitable.
Study Design This is a retrospective registry-based analysis on behalf of the ALWP of the EBMT. The EBMT is a nonprofit, scientific society representing >600 transplant centers, mainly in Europe, that are required to report all consecutive stem cell transplantations and follow-ups once a year. Data are entered, managed, and maintained in a central database; each EBMT center is represented in this database. Audits are routinely performed to determine the accuracy of the data. Patients provide informed consent authorizing the use of their personal information for research purposes. Patients were eligible for the study if they had received first alloSCT for AML in CR1 or CR2 after 1 of the following conditioning regimens: (1) BuFlu (ie, fludarabine 5 £ 30 mg/m 2 , busulfan 8 £. 8 mg/kg body weight with or without antithymocite globulin); (2) FLAMSA-TBI (ie, fludarabine 4 £ 30 mg/m 2 , amsacrine 4 £ 100 mg/m 2 , cytarabine 4 £ 2000 mg/m 2 , 400-cGy TBI, cyclophosphamide 2 £ 40 mg/kg body weight [2 £ 60 mg/kg body weight in case of unrelated donors (UDs)], antithymocite globulin 3 £ 10 mg/kg body weight [3 £ 20 mg/kg body weight in case of UDs]); or (3) FLAMSA-Bu, in which TBI is replaced by busulfan 8 £.
8 mg/kg body weight. Matched related donors or UDs with up to 1 mismatch (9/10) at antigen or allele level were acceptable. For all patients, information on cytogenetic risk at first diagnosis had to be available. Cytogenetic abnormalities were classified according to the Medical Research Council classification system  [13, 14]. Transplants were done in 134 centers between January 2005 and June 2016. Data were taken from the EBMT registry.
All tests were 2 sided. The type I error rate was fixed at. 05 for determination of factors associated with time to event. Analyses were performed using R statistical software version 3. 2. 3 (R Foundation for Statistical Computing, Vienna, Austria). PS analysis was performed using the mnps function of the twang package  [20]. Estimation of weighted outcomes at 2 years was done using the rms and npsurv packages, and weighted Cox was done using the survey package.
Molecular Cloning of Plasmids The primers and nucleotide sequences of all constructs used in this study are given in Tables  S3, S4. The soybean CSN5a-like ortholog was identified by BlastP search with the A. thaliana CSN5a protein sequence (accession number AT1G22920; Uniprot Q8LAZ7) as the query sequence by using the Phytozome website (version 10. 0, phytozome. jgi. doe. gov). We identified the soybean CSN5a-like alias Glyma. 04G075000. 1 (Sequence ID KRH61926. 1; Uniprot A0A0R0K5E1) by considering the score, similarity and identity percentages of hits with E-value < 1 e -10. The software Clustal X v2. 1 was used to align CSN5 amino acid sequences from different species, including the Arabidopsis CSN5 isoforms and soybean CSN5a-like (Figure  S1A ). The phylogeny tree reconstruction has been built with the same CSN5 protein sequences using Phylogeny.
fr (http://www. phylogeny. fr/). The APEX-tagged GmCSN5 construct (named APEX-GmCSN5 for convenience) was synthesized and cloned (Epoch Biolabs, USA, Houston) into the derived binary pGreenII vector  (Hellens et al. , 2000)  to generate the plasmid pGIIH-azul-Strep-CSN5-APEX. The binary plasmid carried the blue fluorescent protein (BFP) for use as a positive reporter gene for plant transformation; it was constitutively expressed under the CaMV 35S promoter and specifically targeted to the mitochondria. With the aim to generate distinct plasmids expressing MiPM -SP gene, we identified the MiPM -SP gene sequence (sequence ID Minc19205) from the public database Wormbase Parasite (http://parasite. wormbase. org/ index. html). The partial open reading frame (ORF) of MiPM -SP was synthesized and cloned into the pBSK vector  (Epoch Biolabs, Houston).
Mass Spectrometry Analysis and Protein Identification The precipitated proteins were digested in-solution with trypsin and thereafter subjected to mass spectrometry analysis by methods previously described  (Murad, 2011). Briefly, the tryptic fragments were separated using a 2D NanoUPLC system coupled into a SynaptG2 mass spectrometer (WATERS). NanoUPLC-MSe data were collected in an alternating low (3 eV) and elevated energy (ramp from 12 to 45 eV) mode of acquisition. The MS data was assessed by the Protein Lynx Global Server (PLGS) software (version 2. 5. 2) with Expression version 2 installed and conFigd to search proteins from the available protein sequence databases of N. tabacum (http://solgenomics. net) and M. incognita (http://www6. inra. fr/meloidogyne_incognita; INRA Meloidogyne resources).
The criteria for the identification of proteins also included the detection of at least three fragment ions per peptide, six fragments per protein, the determination of at least one peptide per protein, and the identification of the protein was allowed with a maximum 4% false positive discovery rate. MS data were filtered by excluding any proteins identified by peptides in the control pull-down (GST alone) and in GST-MiPM -SP. The putative interacting proteins identified by affinity-based systems coupled with MS were next characterized by in silico analyses. To determine the identity of the prey proteins, peptide sequences were manually annotated by BlastP search from public databases to confirm their identity by using INRA Meloidogyne resources and Wormbase Parasite (http://parasite. wormbase.
org/index. html) concerning nematode proteins and using National Center for Biotechnology Information (NCBI) for plants. The nematode protein candidates were subjected to a pipeline of available software (TargetP, TMHMM2. 0, and Phobius) to predict the signal peptide and its respective cleavage site  (Krogh et al. , 2001; Emanuelsson et al. , 2007; Kall et al. , 2007). The physiological and chemical properties of M. incognita candidates were subjected to the Protparam tool of ExPaSy and the Genesilico Metaserver (http://web. expasy. org/protparam/; https://genesilico. pl/meta2) (Kurowski, 2003; Gasteiger et al. , 2005).
6%, respectively from the publicly available database Phytozome (v. 10). Given difficulties to generate soybean transgenic lines in our laboratory, we took advantage that CSN5a is widely conserved across multiple plant species  (Figures S1A,B; Lozano-Duran et al. , 2011)  to use Nicotiana tabacum as valuable surrogate system for the generation of stable transgenic lines expressing the isoform CSN5a-like gene of soybean (named thereafter GmCSN5). Herein, we designed the construct to allow the capture of tagged-GmCSN5 protein complexes based on biochemical and proteomic strategies, respectively called StrepTrap-AP and APEX proximity-based labeling (Figure  1A ). Given the solved crystal structure of the human CSN5 in the CSN complex  (Echalier et al. , 2013) , we modeled the structure of the GmCSN5 in fusion with APEX and within the HsCSN complex (Figures  S2A, B , Video S1).
Identification of Candidate Proteins Interacting With APEX-GmCSN5 We first validated the molecular characterization of transgenic T2 N. tabacum lines (i. e. , 11. 7 and 11. 11) stably expressing APEX-GmCSN5 by the evaluation of its protein expression in the roots before and after nematode infection (data not shown). In an attempt to identify the proteins in close proximity to GmCSN5, we examined the feasibility of the APEX system in plants. The methodology and the results obtained are described in the supplemental data section (Figure  S3 ). In an effort to identify interacting partners of GmCSN5, the APEX system was unsuccessful. We next assessed whether AP-MS was suitable at least to identify APEX-GmCSN5 in interaction with tobacco proteins related to CSN complexes. Proteins associated to APEX-GmCSN5 were purified by StrepTrap approach from transgenic tobacco roots infected with M.
The MiPM expression level was evaluated by quantitative RT-PCR at different stages of development of M. incognita, i. e. , eggs, pre-infective second juveniles (pre-J2), parasitic second juveniles (par-J2; 3 dai), third-and fourth-stage juveniles (J3/J4; 15 dai) and females (25 dai). Fold change values were calculated by the 2 -(-Ct) method, and the data were normalized according to the geometric average of two reference genes (MiActin and Mi18S) and relative to the expression in eggs  (Livak and Schmittgen, 2001; Arguel et al. , 2012). Each column represents the mean and standard deviation of four independent biological experiments. conditions. Taken together, these results underline that the function of MiPM in plant immunity remains inconclusive.
GmCSN5 Interacts With the Nematode MiPM Protein We next verified the interaction between GmCSN5 and MiPM. We first performed a GST pull-down assay using the recombinant protein GST-MiPM -SP or GST alone (as negative control) with the whole lysate of M. incognita-infected root extracts overexpressing APEX-GmCSN5. The production and purification of the recombinant proteins are presented in the Figure  S5. Eluted proteins associated with GST-MiPM -SP or GST were separated and visualized by silver-stained SDS-PAGE (top panel; Figure  3A ). GST-MiPM -SP -bound resin, but not the GST control, provided enrichment in APEX-GmCSN5 detected by western blot with the strep-tag II antibody (bottom panel; Figure  3A ). These data suggest that MiPM is associated with APEX-GmCSN5 protein complexes. To further identify host proteins that interact with MiPM, we subjected proteins co-purified with GST-MiPM -SP to NanoUPLC-MSe analysis from two independent biological repetitions (Figure  3B ; Table  S2 ).
Remarkably, preliminary biophysical analyses showed that MiPM is a stable protein at various pH values (data not shown) and can support some biochemical modifications, such as the conjugation of a fluorescent probe. Some proteomic approaches conjugated with proximity labeling, such as click chemistry, have proven to be useful and efficient for identifying proteins implicated in a specific endocytosis pathway  (Tanaka et al. , 2012; McFedries et al. , 2013). Thus, the future identification of MiPM interaction proteins during cell entry by such approaches might contribute to a better understanding of its mode of action. agencies CAPES and CNPq. CB was supported by postdoctoral fellowship funded by CNPq Science without Frontiers (Project number: 401868/2013-3). The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.
ACKNOWLEDGMENTS The authors are grateful to EMBRAPA, CAPES, CNPQ, and  FAPDF  for their constant support of our scientific research. We acknowledge  Dr. Florence Vignols  ( INRA Montpellier, France ) for the plasmids used for the BiFC and subcellular localization assays and  Dr. Diana Fernandez  ( IRD Montpellier, France ) for the plasmid encoding INF1.
However, a map of certified forests is lacking, although mapping would be essential to locate the percentage of forest that are certified to be sustainably managed. The study mapped forest certification across 43 European states, according to 499 FSC and 284 PEFC reports and assessed the proportion of certified forest area on public and private land and the rate of increase. This research was carried out collecting information on European certified forest companies/owners and locating geographically their forests at sub-national level (regions, NUTS 2). The database of the Joint COST Action FACESMAP/UNECE/FAO was an important data source. At European level, about six percent of the forest is certified under FSC scheme, while about seven percent under PEFC scheme. As forest certification is a useful tool to manage forests aiming at the integration of economic, ecological and social sustainability, the knowledge of the location and area of certified forest in Europe could be important in motivating decision makers to increase these sustainably managed areas.
Material and methods In order to develop the maps, a large set of data from European FSC and PEFC reports was collected and elaborated in a GIS (Geographic Information System) environment at the NUTS (Nomenclature of Territorial Units for Statistics) 2 level (i. e. , Regions). The NUTS classification is a hierarchical system for dividing the economic territory of the EU for the purpose of the collection, development and harmonisation of European Union regional statistics, socioeconomic analyses of the regions and the framing of EU regional policies. The research process was divided in three stages. In the initial stage, a shapefile for all countries of the European region was created. In the second one, the database of certified forests in Europe (from the geographical point of view) for both FSC and PEFC was built, and in the final stage, the data were elaborated and the map of certified forests in Europe was completed.
iForest -Biogeosciences and Forestry Forest certification map of Europe ing to the second level of NUTS classification, were used. The NUTS geo-data file was downloaded from the European Commission website (2010 update -"European NUTS layer") and includes 36 countries (Tab. 1). For countries not covered by NUTS geo-data, sub-national boundaries were taken into account. In this case, to identify the exact location of the forests, a code ("European boundaries layer" -shapefile), similar to that of the NUTS code, was created for each area (Tab. 2). For Russia, only the European part was considered (up to the Urals mountain range). Subsequently, to identify the different boundaries of European countries, a new shapefile was created by the integration of the "European NUTS layer" and "European boundaries layer" at sub-national level using the QGis Software.
The new shapefile includes all countries present in European area (Fig. 1 ). The data of certified forests areas were collected in June 2014 from FSC (http://in fo. fsc. org/) and PEFC (http://www. pefc regs. info/) on-line database, and were imported into a spread sheet. The structure of the database was made according to the data present on freely available public reports. In fact, FSC certification requires forest managers and auditors to make auditing and monitoring reports publicly available  (FSC 2014)  In this study, the certified area according to the two schemes were maintained separated because the exact location of the forest is given in the FSC reports but not in the PEFC reports. In the latter, the location of the forest owner is reported, but without information on the ownership type.
000, Minimum Mapping Unit: 25 hectares). The CLC maps are available for: Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Denmark, Estonia, Finland, France, Germany, Hungary, Ireland, Italy, Lithuania, Luxembourg, Netherlands, Norway, Poland, Portugal, Romania, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey and United Kingdom. Instead, data from Forest Europe (Forest Europe/UNECE/FAO 2011) and FAO (FRA 2005) on the total forest area was respectively considered for Russia and Belarus. The CLC classes related to forests and Others Wooded Land (OWL) categories were taken into account for each European Country and then joined into a single layer. iForest 11: 526-533 528 Fig. 1  -Shapefile from two layers: "Official European NUTS layer" and "European boundaries layer".
Globally, the annual growth in area of certified forests has been quite constant. This report analysed the situation in European countries. Within the FSC database, the public report summary of the main audit and sometimes others documents (surveillance audit, notes, etc. ) are made available, while in the PEFC database only the main data of the certification are shown. A critical issue into the study process was to define the exact location of the certified forests. On the FSC online database, summaries of audit reports are publicly avail- The forest certification map of Europe is certainly a useful starting point for understanding the network of the certified forests. Keeping it up to date, the map will allow to quantify geographically location changes over time of the forest certified and to assess if management options, property or other factors may affect the spread of forest certification in the future.
Patients and methods In this open-label, multicentre, uncontrolled, Phase II study ( ClinicalTrials. gov NCT00896480),patients received ≤24 doses of MAGE-A3 immunotherapeutic (4-cycle schedule). At screening, two skin lesions were biopsied for MAGE-A3 expression analysis and presence/absence of a previously identified gene signature (GS) associated with favourable clinical outcome. Clinical activity was assessed in terms of clinical response, time-totreatment failure (TTF) and progression-free survival (PFS). Adverse events (AEs) and serious AEs (SAEs) were recorded. MAGE-A3-specific immune responses were assessed. Clinical activity and immunogenicity were analysed overall and separately in patients with 2/2 (GS+/+), 1/2 (GS+/-) or 0/2 (GS-/-) biopsies presenting GS. Results Of 49 screened patients, 32 had MAGE-A3positive tumours; 24 (8 GS+/+, 8 GS+/-, 8 GS-/-) were treated.
MeTHods study design, objective and treatment This study was an open-label, multicentre, uncontrolled, descriptive, exploratory Phase II study with a single study group conducted between 2009 and 2014 in six centres in Belgium and France ( ClinicalTrials. gov NCT00896480). Patients with MAGE-A3-positive metastatic melanoma received up to 24 doses of MAGE-A3 immunotherapeutic administered according to a 4-cycle schedule (online supplementary figure  S1 ). The total duration of the treatment for each patient from screening to the end of cycle 4 was approximately 4 years. Continued treatment in cycles 2, 3 and 4 depended on an adequate clinical response at the end of the respective previous cycle. Treatment response qualifying patients to receive further MAGE-A3 immunotherapeutic administrations was: objective response (ie, complete response (CR) or partial response (PR)), stable disease (SD), mixed response (MR) and slow progressive disease (SPD) (for definitions refer to online supplementary materials).
SPD and MR allowed continuing the treatment even in the event of progressive disease (PD). At screening, two skin lesions were biopsied for MAGE-A3 expression analysis and presence or absence of a GS predicting favourable clinical outcome. 36 The list of the 100 probesets (84 genes) used in the GS  36  is shown in online supplementary table  S1. Demographic and laboratory data were collected in electronic case report forms (eCRFs). The objectives of this study included evaluation of clinical activity, safety and immunogenicity of MAGE-A3 immunotherapeutic; clinical activity and MAGE-A3 immunotherapeutic is composed of the MAGE-A3 protein and the AS15 immunostimulant. Patients received 0. 5 mL of MAGE-A3 immunotherapeutic by intramuscular injection in the deltoid or lateral regions of the thigh, alternately on the right and left side.
For non-compliant patients, all data collected after protocol violation were eliminated from the ATP immunogenicity analyses. The best overall response was analysed in terms of number and proportion of patients falling into each category, by GS result. Objective response rate was defined as the proportion of patients whose best overall response was PR or CR. Disease control rate was defined as the proportion of patients whose best overall response was CR, PR, SD or SD/PR. Kaplan-Meier curves were used to describe TTF and PFS by GS status and overall, and with median TTF or PFS, respectively and 95% CI. Details on immune response analysis are provided in online supplementary materials. ResulTs study patients, MAGe-A3 expression, gene signature and treatment compliance Forty-nine patients were screened, 24 patients were recruited and included in TTP and 3 patients completed the study; nine patients (2 GS+/+, 4 GS+/-and 3 GS-/-) were excluded from ATP population (figure  1 ).
0%), influenza-like illness (29. 0%), nausea (25. 0%), headache (25. 0%), asthenia (21. 0%) and fatigue (21. 0%). Two SAEs were reported. One patient reported severe grade 3 cardiac thrombosis 27 days after the last dose of MAGE-A3 immunotherapeutic with concurrent heart disorder. The patient was subsequently treated with clopidogrel bisulfate, furosemide, bisoprolol fumarate and heparin sodium. This SAE assessed by the investigator as unrelated to the study treatment was resolved 18 days after its onset. The second SAE was a case of malignant melanoma. Following administration of the last dose of the study treatment, the patient developed malignant neoplasm of cheek mucosa. This SAE was considered by the investigator as unrelated to the study treatment. The patient underwent a partial resection and was awaiting a further surgery at the time of the data lock point of this study.
Acknowledgements The authors thank  Vincent Brichard ,  Olivier Gruselle ,  Bruno Salaun  and  Anne Dompmartin  for their contribution to the study. They also thank  Urszula Miecielica  ( XPE Pharma & Science ) for providing medical writing services,  Houda Khamis  and  Maria Ana De la Grandiere  ( XPE Pharma & Science , on behalf of  GSK ) for editorial support in preparing this manuscript.
Intention decoding First, we collected the EEG data from each trial and aligned it to the intention start (auditory) cue presentation time. The cue aligned data were divided into two classes labeled according to whether the cue was from the right (RHT-CUE) or left (LFT-CUE) speaker, that is, whether the participants thought of turning right or left. We then considered different time periods of EEG data (either 500, 1000, 1500, 2000, or 2500 ms of data from the cue presentation) and tried to decode the participant intention using the absolute magnitude of the EEG signals across these time periods (collected at 512 Hz) as features (we will discuss results of a frequency-based decoding later). Because of its robustness to overfitting, a sparse logistic regression (SLR) (28) decoder was chosen, and trained on part of the data (80% of randomly selected trials) to classify the turning direction the participant thinks of, and tested on a test set (remaining 20% trials).
Prediction error decoding Next, we aligned the EEG data to the start of the GVS. Crucially, this time, we collected the data into two classes that were labeled as either MATCHED, if the cue and GVS directions corresponded, or MISMATCHED, if the cue and GVS directions did not correspond. Note that both the MATCHED and MISMATCHED classes contained In each trial, using stereo speakers and a "high"-frequency beep, the participants were instructed to imagine turning either left or right while sitting on a rotating chair. A subliminal GVS was applied 2 s after the end of each cue, randomly corresponding to turning either right or left. This was followed by a rest period of 3 s cued by a "low"-frequency beep (stop cue). Fig. 2. Decoding performance summary. The across-participant median decoding performance when decoding for the direction in which a participant wants to turn (that is, the cue direction) is shown in red and pink, whereas decoding for a MATCHED/MISMATCHED participant intention and applied GVS is shown in black.