dataesr/scientific-paragraphs-categorization huggingface dataset:
coli cells and exhibit more potent efflux inhibition activity. 900 Later, Darzynkiewicz et al. 901  investigated the potential binding sites of compound 9 and found that it likely binds to a site situated between the lipoyl and β-barrel domains of AcrA. The β-barrel domain of AcrA has been previously shown to be involved in RND binding, and the lipoyl domain is involved in self-association; therefore, AcrA inhibitors may function to prevent the functional assembly of AcrAB-TolC. 902 In a recent study, Green et al. 903  searched the ZINC15 database for compounds with physicochemical properties matching those of existing EIs and Gram-negative antibiotics. Subsequently, compounds with matching criteria were computationally docked onto AcrA of E. coli at the previously reported binding site of compound 9, as well as several other sites that when disrupted have been reported to disrupt efflux.
Trimeric assembly based on PDB ID 6T7S. 270 PN and PCperiplasmic or porter domain N-terminal and C-terminal subdomains; DN and DCdocking/funnel domain N-and C-terminal subdomains, respectively. See section 5. 1 and Figures 5 and 6 for details. (D) A representative structure of the Major Facilitator Superfamily (MFS) transporter with 14-transmembrane helices based on the POT-family member PepTSo from Shewanella oneidensis. PBD ID 4UVM, 271 monomer presented. See Section Y for details. (E) A dimeric structure of the MacB transporter from E. coli based on PDB ID 5NIK. 272 (F) Representative structure of the ABC transporters involved in the formation of the type 1 Secretion system (T1SS), on the example of Hemolysin alpha exporter HlyB. Dimer structure based on a homology model derived from the peptidase containing ABC transporter PCAT1 structure (PBD ID 4RY2).
Molecular morphing movie illustrating a possible transition from the OMF-free state to the OMF-engaged state (zip) pathogen Acinetobacter baumannii). M. S. Kuth is a recipient of a fellowship from the Friedrich-Naumann-Stiftung fur die Freiheit. Work in the Picard lab is supported by the 'Initiative d'Excellence' program from the French State (Grant 'DYNAMO', ANR-11-LABEX-0011-01) and by ANR (ANR-19-CE44-0015-01). Work in the Blair lab is supported by a BBSRC David Phillips Fellowship to J. M. A. B. (BB/M02623X/ 1). I. A. was funded by the MIBTP2 BBSRC BB/M01116X/1 at the University of Birmingham. Molecular graphics in Figures 1, 4, 7, and 9 were created with UCSF Chimera, developed by the Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco, with support from NIH P41-GM103311.
ACKNOWLEDGMENTS We thank  Dr. Martyn Symmons  for discussion, advice, and help with topology diagrams throughout and  William Batista Dos Santos  for input on the OMF section. The research in the Bavro lab was partially supported by funding from  BBSRC  ( BB/N002776/1 ). Work in the Pos lab is supported by the  German Research Foundation  ( DFG-SFB 807 ,  Transport  and.
aeruginosa. Trimeric assembly based on PDB ID 6T7S. 270 PN and PCperiplasmic or porter domain N-terminal and C-terminal subdomains; DN and DCdocking/funnel domain N-and C-terminal subdomains, respectively. See section 5. 1 and Figures 5 and 6 for details. (D) A representative structure of the Major Facilitator Superfamily (MFS) transporter with 14-transmembrane helices based on the POT-family member PepTSo from Shewanella oneidensis. PBD ID 4UVM, 271 monomer presented. See Section Y for details. (E) A dimeric structure of the MacB transporter from E. coli based on PDB ID 5NIK. 272 (F) Representative structure of the ABC transporters involved in the formation of the type 1 Secretion system (T1SS), on the example of Hemolysin alpha exporter HlyB. Dimer structure based on a homology model derived from the peptidase containing ABC transporter PCAT1 structure (PBD ID 4RY2).
Risk factors for sink contamination and splashing The local infection control practitioners provided information on the use of each studied sink (use for waste disposal and/or for handwashing), on factors that may contribute to contamination of clinical areas near to the sinks (visible splashes during sink use, presence of a physical barrier and a distance <2 m between sink and bed) and on the sink disinfection procedure potentially implemented in the ICU (product used, disinfection frequency). The data were collected using a standardized questionnaire, centralized and analysed by the SPIADI team. Four potential risk factors for sink contamination and splashing were defined: a lack of daily (or three times a week) 2. 6% bleach and, using the median values observed for the 73 ICUs, a local sink contamination rate exceeding the contamination median value, a prevalence of sinks with a distance <2 m and no barrier between sink and bed exceeding the prevalence median value, and a prevalence of sinks with visible splashes exceeding the prevalence median value.
Acknowledgements The SPIADI ICU collaborative group comprises : the local infection control physicians :  Hanane ABDOUSH ,  Serge ALFANDARI ,  Alexandra ALLAIRE ,  Lauriane ALOE ,  Anaïs ANDREO ,  Eliane ANTOINE ,  Caroline AUREL ,  Abdebalki AZAOUZI ,  Val erie BARRY-PERDEREAU ,  Yasmina BERROUANE ,  Sylviane BLAISE ,  Mathilde BLANIE ,  S everine BONJEAN ,  Guy-Claude BORDERAN ,  Myriem BOUNOUA ,  C eline BOURIGAULT , Val erie BREAN, Agn es CECILLE,.
svFCS Data Recording and Analysis Prior to the measurements on the cells, the waist size was calibrated using rhodamine 6G solution as described in Mailfert et al. [25, 26]. For the measurement of the living cells, the 488-laser beam power was adjusted to 3-4 µW. The GFP-expressed cells were identified under widefield fluorescence illumination. The low fluorescence signal cells were preferable as the ACF is inversely proportional to the number of diffusing fluorescent molecules within the confocal spot. High fluorescence signal results in flat ACFs, which are difficult to fit and thus correctly calculate the mean diffusion time (t d ) and mean number of diffusing molecules (N). In the next step, the XY confocal image and z scan of the selected cells were performed. The separation between the top and bottom plasma membrane was achieved.
with P. brassicae , or a combination of both stresses. A total of 67. 6 Gb of transcriptome paired-end reads were filtered, mapped onto the B. napus reference genome Darmor-bzh and used for identification of differentially expressed genes and gene ontology enrichment. The raw reads are available under accession PRJNA738318 at NCBI Sequence Read Archive (SRA) repository. The dataset is a resource for the scientific community exploring seed plasticity. © 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license ( http://creativecommons. org/licenses/by-nc-nd/4. 0/ ).
Subject Agricultural and Biological Sciences Specific subject area Omics: Transcriptomics Plant Science: Plant Physiology Type of data Tables Figures How data were acquired RNA samples were sent to the BGI, Hong Kong ( https://www. bgi. com ), for library construction using the QuantSeq 3 mRNA-Seq Library Prep Kit REV (Lexogen) and sequencing using an Illumina HiSeq40 0 0 platform that generated an average 9. 38 M of 100 bp paired-end reads per sample. Data format Filtered raw reads (FASTQ) Analysed RNA-seq data files (counts and DEG lists) Parameters for data collection Total RNAs were extracted from seeds of Brassica napus (cv. "Express"). Seed-bearing mother plants were grown under (1) standard conditions, (2) water shortage, (3) clubroot infection after inoculation at 7 days after germination with Plasmodiophora brassicae (isolate eH), or (4) combination of both clubroot infection and water shortage.
Value of the Data • The present study reports a transcriptomic dataset from developing seeds of B. napus harvested from plants grown under four environmental conditions (standard watering/drought with or without inoculation by P. brassicae, and inoculation by P. brassicae alone). • These data are useful for the scientific community working on the impact of single or combined abiotic and biotic constraints on seed quality. • Presented RNA-seq data provides a key resource to unravel the transcriptomic programs that support the seed development under standard conditions or environmental stresses. The data will provide candidate genes including regulatory factors to predict seed quality under various conditions. • Further analyses of the transcriptome dataset described here will provide useful information to accelerate the development of stress-resistant oilseed rape cultivars.
Data Description This paper describes a transcriptomic dataset of developing seeds of Brassica napus (cv. "Express") that were produced under drought stress and/or Plasmodiophora brassicae infection. Among the abiotic stress, drought is one of the main threats to the crop productivity  [3]  and genetic improvement of oilseed rape with respect to drought tolerance requires the exploration of drought-response genes during seed development in particular. On the other hand, clubroot is a major soil-borne disease of Brassicaceae species  [4]  that is caused by the telluric biotroph protist P. brassicae. It induces the formation of swellings or galls on the root, ultimately leading to the premature death of the plant. In addition, combinations of multiple stresses have been shown to trigger complex plant responses at the transcriptome, cellular and physiological levels that significantly differ from the responses to individual stresses  [5].
This prompted us to design an experiment to generate RNA-seq data from seeds of oilseed rape plants grown under combinations of abiotic and biotic constraints, resulting in the four treatments presented in Fig. 1  and hereafter: (1) standard watering and no P. brassicae inoculation (C, control); (2) water shortage (WS); (3) clubroot infection by inoculating 7-day old seedlings with resting spores of P. brassicae (Pb); and (4) a combination of clubroot and water shortage (Pb + WS). Immature seeds were collected at different timepoints during seed development resulting in a total of 72 samples for paired-end RNA-sequencing. Table  1  displays the quality of the transcriptome dataset. An average of 9. 39 M paired reads (2 × 100 bp each) was obtained per sample with good quality scores (Phred scores ≈ 33 or 26 for reads 1 or reads 2, respectively) of which 82.
RNA seq data analyses Processing of the RNA-seq data was carried out using the adapted nf-core RNA-seq pipeline version v1. 4. 2 ( [9]  ; https://github. com/nf-core/rnaseq ). Briefly, the pipeline was based on Nextflow v19. 07. 0  [10]  and processed data using the default options. Read mapping onto the reference genome Darmor-bzh -v4  [2]  was performed with Hisat2 v2. 1. 0 [11]  , and transcript quantification with featureCounts v1. 6. 4  [12]  according to the Darmor-bzh -v5 gene annotation  [2]. The pipeline outpout files were plotted and summarized with multiQC tools v1. 7  [13]. For gene expression analyses, we used the AskoR R library  [14]  with the following steps: (1) data filtering, (2) data normalization, (3) visualization of correlations, and (4) differential expression analysis.
Acknowledgments We thank all our colleagues from  IGEPP ,  INRAE Le Rheu (France) , especially  J. P. Constantin ,  L. Charlon ,  F. Le Tertre  and  P. Rolland  for supplying excellent greenhouse services,  P. Glory  and  C. Lariagon  for assistance with P. brassicae infection, and  I. Glais ,  A-S. Grenier  and  M. Boudet  for help with RNA-seq data management. We are grateful to the  BrACySol biological resource center (INRAE Ploudaniel, France)  for kindly providing us with seeds of Express. The BGI sequencing platform ( https://www. bgi. com ) and the GenOuest bioinformatics platform ( https://www. genouest. org/) are acknowledged for access to their facilities. This research was funded through an  ANR grant  ( ANR-14-JFAC-007-01 ) in the framework of the  SYBRACLIM  project ( FACCE-JPI-ERA-NET + CLIMATE SMART AGRICULTURE  project call) and a grant from  INRAE  to  J.
The terms are encoded with contextualized embeddings and classified via cosine similarity and softmax. A first step uses a subset of the terminology to finetune the embeddings and train the model. A second step adds the entire target terminology, and the model is trained further with hard negative selection and softmax sampling. Results: On two corpora from the Quaero FrenchMed benchmark, we show that our approach can lead to good results even with no labeled data at all; and that it outperforms existing supervised methods with labeled data. Discussion: Training the system with both French and English terms improves by a large margin the performance of the system on a French benchmark, regardless of the way the embeddings were pretrained (French, English, multilingual). Our distantly supervised method can be applied to any kind of documents or medical domain, as it does not require any concept-labeled documents.
We take advantage of the latest progress in neural network optimization, which allows us to cast the normalization task as a standard, highlymulticlass, classification problem. In contrast, other recent deep learning-based approaches work around the problem by converting the task into a nearest neighbor problem and must insert all the available synonyms in the search space. On two corpora from the Quaero FrenchMed benchmark, we show that our approach can lead to good results even with no labeled data at all (distant learning from the terminology only). It outperforms existing methods, including those using machine translation, with labeled data (distant + supervised learning). We also designed a set of experiments showing the contribution of multilingual models and resources compared to monolingual.
We tackle this difficulty by making use of developments in neural network optimization over the last years, such as Adam  [29] , BatchNorm  [30] , or the recent advances on the face identification problem  [31, 32, 33]. Indeed, face identification is similar to medical normalization in terms of the number of target classes and the low number of examples per class. Medical named entity representation learning has closely followed the general domain NLP research by using pretrained models such as Word2Vec  [34] , Elmo  [35] , BERT, and its multilingual variant  [17]. The latter processes the input text, regardless of its language, to compute contextualized token representations. The normalization of medical entities in languages other than English has so far relied mainly on the translation of English synonyms into the target language  [18, 4] , or conversely, the translation of entities into English  [36, 23].
Quaero FrenchMed corpora The Quaero FrenchMed corpus  [11]  consists of two sets of textual documents in French, annotated with concept CUIs from the 2014AB version of the UMLS: • Titles of research articles indexed in the MEDLINE database • Information on marketed drugs from the European Medicines Agency (EMEA) Unlike other normalization corpora such as NCBI  [38]  or BC5CDR  [39] , the annotated concepts were not limited to vocabularies such as SNOMED, MeSH, or OMIM. However, they were limited to 10 of the 15 UMLS semantic groups. We Table  2  presents general corpus statistics including the number of annotated mentions (i. e. , text spans linked to UMLS concepts within the documents), the number of unique mentions, the number of unique concept CUIs, as well as the rate of mentions in each corpus that are linked to a concept with at least one synonym in French in the terminology.
Given a dataset, , ,. , our goal is to build a CUI classifier, i. e to learn a probability distribution to predict the concept of each mention ∈ : * | ; ; where represents the parameters of the encoder (detailed below), which goal is to map a mention to a dense vector space, and represents the embeddings of the concepts in this space, that have the same semantic group as the mention. Our model is a classification model built on top of a sequence to sequence encoder, and we chose  (1)  pretrained Transformer  [41]  models in our experiments. We call this model MLNorm (for multilingual normalization). The model is described in this section and illustrated in Figure  1.
Experimental setups We performed two main sets of experiments with our model MLNorm, that we call "distantly supervised" and "fully-supervised. " In the first setup (systems S1 and S2 in Figure  2 ), we used only distant supervision from the UMLS, and no direct concept supervision from the available, labeled datasets from Medline and EMEA. These systems then do not suffer any potential bias from the corpus specificities and do not benefit from the redundancy of mentions within the labeled datasets. In the second setup (S1sup and S2sup), we add mentions and labels from the Medline and EMEA training sets, thus enabling comparison with state-of-the-art fully-supervised approaches using these data. Step 2 experiments use the English concepts from the five sources ("EN5") described in Section 3.
Hyperparameter selection For all these experiments, we trained on the training sets and evaluated on the test sets from Quaero FrenchMed datasets (Medline 2015 and 2016, EMEA 2015 and 2016). We chose the hyperparameters by selecting the best-performing values on the training set of Quaero in the distant supervision setting. We run our models on a 20 Go Tesla P40 GPU, except the 1-step experiment which required a 30Go Tesla V100 GPU.
5 and S2 with a threshold of 0. 1. We finetuned the multilingual pretrained BERT model (bert-base-multilingual-uncased) in all our experiments, unless specified otherwise. We report our main results on the test datasets from the challenges Quaero FrenchMed 2015 and 2016 (Table  3 ), as well as the results of our additional experiments described in previous Section (Table  4 ), using the traditional metrics precision, recall and F1-measure (harmonic mean of precision and recall). We also give some examples of the distantly supervised system's output (MLNorm S2), that highlight its multilingual capacities (Table  6 ).
Sequencing of HuNoVs The norovirus was sequenced at the Plateforme d'Analyses Génomiques of the Institut de Biologie Intégrative et des Systèmes (PAG-IBIS, Université Laval, Québec, QC, Canada) using the BigDye™ Terminator v3. 1 cycle sequencing kit (Applied Biosystems, Foster City, CA, USA), an ABI 3500 series analyzer (capillaries 50 cm), and Chromas v2. 6. 6; and it was aligned using BioEdit v7. 0. 5. 3 and BLAST with the Norovirus Genotyping Tool v2. 0 (https://www. rivm. nl/mpf/typingtool/norovirus/ (accessed on 16 December 2021)). The sequence of NoV GII. 4 used in this study can be retrieved using the GenBank number MZ357344.
Shifts in surface charge, therefore, could explain the differences in the results obtained with strawberries and raspberries. It was shown that at a pH of 4. 0, HuNoV particles tend to aggregate  [27] , which could make capture by ApoH more difficult. If the problem were this simple, adjusting the pH to neutrality before adding the ApoH beads to the suspension of the virus eluted from fruits should improve the results. Another important factor may be the detersive power of the CV-1 buffer compared to the TAS buffer used with the ApoH CaptoVIR kit (Catalog number #MP10022-100T, ApoH Technologies, La Grande Motte). The composition of this commercial buffer is a trade secret. The CV-1 buffer appeared to dislodge the viruses more effectively from the berries (data not shown). Additional studies are required to confirm these explanations.
Acknowledgments: We thank the  Georges L. Dumont University Hospital Centre in New Brunswick  for kindly providing us with human norovirus samples. We also thank the  Institute of Nutrition and Functional Foods (INAF) , the  Conseil de la transformation alimentaire du Québec (CTAQ) , and the  Initia Foundation  for graduate scholarships. Special thanks to  Coralie Goetz  and  Stephen Davids  for proofreading this article.
Acknowledgments: The author thanks  Christiane Brahimi-Horn  for editing this manuscript, the  Conseil Départemental des Alpes Maritimes , the  Institut National du Cancer (INCa)  (PHRC;  STALK-LUNG01 13-APN-01 ), the  Ligue Départementale de Lutte contre le Cancer des Alpes Maritimes , and the  Canceropôle PACA.
The Cdc50 subunit of Drs2 is in pink. (PDB codes used for this figure, nhTMEM16F: 4WIT, Drs2/Cdc50: 7OH7 (closed), 7OH6 (PS-occluded). The figure was generated with ChimeraX  (Pettersen et al. , 2021). in caveolae  (Fairn et al. , 2011b). The relationship between PS and caveolae is two-sided: on the one hand, caveolae control PS clustering as knock-down of caveolin-1, the main structural component of caveolae, significantly increases PS clustering at the PM of BHK cells  (Ariotti et al. , 2014) ; on the other hand, both PS sequestration using a tandem Lact-C2 domain or stimulation of PS scrambling depletes caveolae. Such effect is specific to PS because selective depletion of PI(4,5)P 2 or PI(4)P using a rapamycin inducible pseudojanin construct did not markedly induce disassembly of caveolae at the PM  (Hirama et al.
Study design and Participants EPIC is a large on-going multicentre prospective cohort study consisting of 521,324 adults (366,521 women and 153,437 men) mostly aged 35-70 years from whom diet, and lifestyle data were collected at baseline. The objective of this cohort is to investigate the role of diet, lifestyle, metabolic factors and genetics in cancer development as well as other chronic diseases in a European sample. The participants were enrolled between 1992 and 2000 from 23 centres in 10 European countries: Denmark, France, Germany, Greece, Italy, The Netherlands, Norway, Spain, Sweden and the United Kingdom  13. The rationale, study population and data collection have been described elsewhere  14. All participants provided written informed consent and the ethical review boards from the International Agency for Research on Cancer (IARC) and from all local centers approved the study.
In order to study associations between dietary AA intakes and blood levels DQ data and AA blood concentrations from 3,768 men and women who served as controls in any of five EPIC nested case-control studies on colorectal (n = 491)  15  , hepatobiliary (n = 327)  16  , kidney (n = 633)  17  , breast cancer (N=796)  18  and prostate cancer (n = 1,521)  19  were used (Figure  1 ). In addition, data from 334 men and women with 24-HDR data and plasma AA information were analysed. Only control participants were included in this study to avoid any potential bias due to metabolic changes induced by pre-clinical cancer development at the time of blood collection in participants that were subsequently diagnosed with cancer (i. e. , cases from the nested studies). None of the control participants analysed were included in multiple case-control studies, therefore avoiding duplication.
Assessment of diet For a total of 514,487 participants, the diet was assessed at study baseline using validated country/centre-specific methods, including food frequency questionnaires (FFQs) spanning the previous 12 months  13. In most centres, FFQs were self-administered, with the exception of Ragusa (Italy), Naples (Italy) and Spain, where face-to-face interviews were performed. Extensive quantitative FFQs were used in northern Italy, the Netherlands, Germany and Greece that were structured by meals in Spain, France and Ragusa. Semi-quantitative FFQs were used in Denmark, Norway, Naples (Italy), Umeå (Sweden) and the United Kingdom, while a nonquantitative FFQ was combined with a 7-day record on hot meals in Malmö (Sweden)  13  In addition, a computer-assisted single 24-HDR interview program (EPIC-soft) was used to conduct interactive, face-to-face dietary interviews following a highly standardised procedure.