dataesr/scientific-paragraphs-categorization huggingface dataset:
Agnathan-specific stat genes shed light on the origin of vertebrate STAT transcription factors The repertoire of stat genes is generally more diverse in Vertebrates than in other Metazoans  (Liongue et al. , 2012) , likely due to the two cycles of WGD that occurred in the early evolution of this lineage. To get insight into the early steps of stat evolution in vertebrates, we analysed genomes from cartilaginous fish (i. e. , Chondrichthyans) and Agnathans. Orthologs of all vertebrate stat were found in cartilaginous fish (Table  3 , Fig. 5A ). Stat1, 2, 3, 5 and 6 have been annotated in most species of sharks and rays for which a genome is available (Table  S1 ). A typical stat4 genomic sequence was not detected in shark genomes except in the whale shark Rhincodon typus (Genbank ID XP_020376005).
An EST was also found in the dogfish shark Squalus acanthias (Genbank ID EE627912). Phylogenetic analysis confirmed that stat genes from Chondrichthyans have human orthologs (Fig. 5A ). In contrast, the list of stat genes was different in Agnathans: in two species, the sea lamprey Petromyzon marinus and the hagfish Eptatretus burgeri, phylogenetic analysis identified orthologs of human STAT3 and STAT5 (Fig. 5A ). Two other stat sequences clustered with group1-4 (later referred as "stat1-4") and group5-6 (later referred as "stat5-6") respectively but could not be assigned to a particular set, suggesting that the stat repertoire of "modern" vertebrates was consolidated and standardized in Gnathostomes. Additionally, the genomic neighborhood of   (Dehler et al. , 2019). Data are on a log scale and represent the average Standard deviation (N = 3).
Data from genome assemblies in Ensembl release 100 (Human GRCh38. p13 and Trichoplax adhaerens ASM15027v1). 8. Evolutionary pathways of stat genes in Deuterostomians. WGD are indicated by "X", and the date indicated for the most recent events. Salmonid stat genes for which paralogs have not been retained are boxed in red. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article. ) pfam to look for assembly problems and fragmentary sequences. MegaX software was used to carry out phylogenetic analyses and confirm the homology relationships between sequences. The evolutionary history was inferred by using the Maximum Likelihood method and JTT matrixbased model. The bootstrap consensus tree inferred from 1000 replicates was taken to represent the evolutionary history of the taxa analysed.
Acknowledgements We thank  Luc Jouneau  for help with transcriptome analysis and annotation. This work was funded in part by the  VetBioNet  project ( EU Grant Agreement INFRA-2016-1 731014 ) and by institutional grant from  INRAE. BC, PB, SB and LDP carried out in silico analyses. BC, PB and LDP wrote the manuscript. All authors read and approved the final manuscript.
Patients and study design We conducted a retrospective study that included, from March 1 to April 20, 2020, all consecutive patients with COVID-19 pneumonia who had a CT scan for diagnosis and/or evaluation of the severity of lung lesions. All patients were recorded in a database in two large university hospitals in Paris, France: Groupe Hospitalier Paris Saint-Joseph (GHPSJ) and Hôpital Européen Georges Pompidou (HEGP). Patients were included according to the following inclusion criteria: patients over 18 years of age, admitted for acute COVID-19 pneumonia and who underwent a chest CT at baseline for rapid triage assessment at the emergency room and/or in wards during their hospitalization. Diagnosis of SARS-CoV-2 infection was confirmed by a positive result of a reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assay and/ or typical CT findings of COVID-19 pneumonia.
Clinical and laboratory data All data were extracted from our computed medical record (Dx-Care® MEDASYS, France) by distinct investigators independently. All data were confidentially collected and coded according to the local cohort IRB-approved statements. The database was frozen for statistical analysis on May 20, 2020. The computed file used for this research was implemented in accordance with French regulations and European regulations (GDPR). Demographic and medical characteristics, including age, sex, body mass index (BMI), history of venous or arterial thrombosis, tobacco use, and anticoagulant treatment at admission and before the diagnosis of PE were available in medical records. During follow-up, maximal oxygen flow required or the need of invasive mechanical ventilation (IMV) were recorded.
Statistical analysis Cases and controls were matched in a 1:2 ratio on the basis of age, sex and center. For each patient of the PE group, a greedy-matching algorithm was used to select the control patients who most closely matched that patient in terms of the three matching factors  [23]. This resulted in 2 different case-control studies. The first one compares the PE group to a control group sampled in the whole database of COVID-19 patients who had a CTPA and did not have PE (CTPA controls). The second one compares the PE group to a control group sampled in the whole database of COVID-19 patients who had an unenhanced CT-scan (CT controls). Categorical variables are presented as number of patients (percentages) and quantitative variables as median (interquartile range [IQR]). All percentages were calculated for available data for each variable.
Patient Population We retrospectively performed semiquantitative and radiomic analysis in the 68 Ga-DOTATOC PET/CT image of two patients both presenting with NET liver metastases, selected from a retrospective study approved by the Local Ethical Committee (IRB protocol: CS2/477) of AOU Città della Salute e della Scienza. Both patients were treated with PRRT in a clinical trial (EUDRACT 2015-005546-63) approved by the Ethical Committee of "Area Vasta Emilia Nord" (AVEN) of the "Azienda USL-IRCCS of Reggio Emilia, Italy. " Patient A had metastatic disease by G3 NET of rectum and patient B by G3 NET of the pancreas. Both patients underwent surgery as primary therapy (pT2N0, Ki67 10%) plus somatostatin analog (lanreotide) administration. Both patients developed liver metastases and treated with multiple radiofrequency ablations, subsequently with chemotherapy after progression, and later with everolimus.
Quantitative Imaging Analysis Radiomic analysis was performed by manually contouring the VOI by one operator (VL) using the software LIFEx v. 5. 10 (IMIV/CEA, Orsay, France) (  16 ). An absolute intensity rescaling factor of 0-60 of the SUV of the VOI was applied (64 bins, 0. 95 fixed bin width). A total of 38 features were extracted: six conventional PET parameters, six descriptors of the image intensity histogram (skewness, kurtosis, excess kurtosis, energy, entropy-log2, and -log10), four shape-based features, 22 second-order statistics texture signatures from all VOI >64 voxels (gray-level co-occurrence matrix, GLCM; gray-level run length matrix, GLRLM; gray-level zone length matrix, GLZLM; and neighborhood gray-level different matrix, NGLDM). The compliance of Lifex feature calculation formulas with the IBSI standard was verified  (17).
Transcriptional Reprogramming of Selected Genes The above-mentioned hypotheses were further investigated by the analysis in L1, L4 and L6 leaf midribs of the expression of genes encoding either stress markers or involved either in the glyoxylate cycle, photorespiration or sugar transport and metabolism (Table  S4 ). Selected genes for glyoxylate cycle and photorespiration were coding for glycolate oxidases (GLO00/GOX2, GLO40/GOX1 and GLO50/GOX3); glyoxylate reductases (GLYR1 and GLYR2); isocitrate lyase (ICL) and malate synthase (MLS). Genes coding for callose synthases (CAS2 and CAS7) and acidic pathogenesis-related (PR) proteins that are hallmarks of SA-mediated defenses (PR1a and PR2a) were included as stress markers. CAS7 was reported to be the only callose synthase gene, out of eight retrieved from the NCBI database, to show an increased expression in symptomatic tomato leaves in comparison to noninfected ones  [42].
For sugar transport and metabolism, we included genes coding for fructokinases (FRK1, FRK2 and FRK3); sucrose synthases (SUS1 and SUS3) and SWEET sugar facilitators (SWEET2a, SWEET5b, SWEET10c, SWEET11a and SWEET12a). A gene coding for a phloem marker (Phloem protein 2, PP2) was added as well. The expression of most genes varied depending on the leaf (Table  S5 ), so the responses were analyzed per leaf level. A strong correlation (R > 0. 7, p < 0. 001) was found between the expression of CAS7, PP2 and FRK3 (Table  S6 ). In noninfected plants, differences were observed in the transcript levels for FRK2 and FRK3 in L1 leaves and for GLO00/GOX2 and GLYR2 in the L4 leaves in SUT1and SUT2-AS plants, compared to WT plants (Figure  S3 ). The data indicated that the downregulation of SUT1 and SUT2 affected the sugar metabolism and photorespiration, depending on the leaf level.
Identification of Tomato Gene Sequences and Design of Primers Tomato sequences were retrieved from the NCBI (http://www. ncbi. nlm. nih. gov/) and Phytozome (https://phytozome. jgi. doe. gov/pz/portal. html) databases  [87] , and the expression pattern was analyzed on the Bio-Analytic Resource  [88]  (http://bar. utoronto. ca/efp_tomato/cgi-bin/efpWeb. cgi). Information about selected genes is reported in Supplementary Table  S4 , and the primers used in real-time qPCR experiments are listed in Supplementary Table  S7. The efficiency of each primer couple was evaluated as described in Pfaffl  [89].
Acknowledgments: We thank  Christina Kühn  for the gift of the LeSUT1-AS and LeSUT2-AS seeds. We thank  Catherine Jonard ,  Solenne Berardocco ,  Sylvain Déchaumet  and  Nathalie Marnet  for the metabolite profiling at P2M2-IGEPP (INRAE, Rennes). We thank  Priscilla Montfalet  for the initial development of the normalization method of the phloem sap exudates;  Françoise Gilard  for the initial analysis of the phloem sap-enriched exudates and  Michael Hodges ,  Xavier Foissac  and  Catherine Bellini  for stimulating discussions.
| Design We conducted a retrospective before-after study to assess potential associations between implementation of the HPAP and outcomes while on the waiting list and after transplantation in patients with PAH-CHD. We included consecutive patients with PAH-CHD listed for HLTx or DLTx at a single tertiary center between 1997 and 2016, that is, 10 years before and 10 years after implementation of the HPAP. The study center was the Marie Lannelongue Hospital, which is a referral center for the surgical treatment of PAH and for the management of complex CHD (M3C network). All transplantations were performed at the Marie Lannelongue Hospital. All patient data were collected prospectively by the transplantation team and entered into a database, which was reported to the French Data Protection Authority (CNIL #1154338, April 27,   2006).
Data derived from our local database were linked to the CRISTAL database. CRISTAL is a national database initiated in 1996 and administered by the Agence de la Biomédecine. It prospectively collects data on all organ-transplant recipients in France, together with their outcomes and donor characteristics. Recipient and donor data are entered into the registry by transplant and procurement centers, respectively. Data collection is mandatory. The study was conducted in compliance with the Declaration of Helsinki and was approved by our institutional review board (#CCML 2016-012), which waived the need for informed consent in compliance with French legislation on retrospective studies.
In children, HPAP status is conferred for an unlimited period. 18, 21, 22, 23 e patients were classified according to the Nice classification. 19, 24 We included patients with ES, a prevalent systemic-topulmonary shunt, PAH and a history of surgery to correct or palliate a congenital heart defect, or segmental PH (e. g. , pulmonary atresia with ventricular septal defect). The CHDs were classified as simple (e. g. , single pretricuspid or posttricuspid shunt) or complex. 17 Heart failure was defined according to international guidelines based on the physical examination, transthoracic echocardiography findings, and biomarkers. 25 llow-up information was entered into the study database during outpatient clinic visits and readmissions. For the outcome analysis, follow-up of surviving patients was censored on December 31, 2019.
Comparisons of continuous variables relied on Student's t test for independent samples if its normality assumptions were satisfied (Shapiro-Wilk test) and on the Welch t test (if variances were heterogeneous) or the Mann-Whitney U test otherwise. First, baseline data were described in the overall population and in each of the three groups. Baseline data and donor data were described for the patients who underwent transplantation during the study period. Baseline variables associated with the incidence of transplantation were analyzed using the Fine and Gray regression model, with death without transplantation and being delisted as competing risks. 26 sociations between baseline variables and death on the waiting list were assessed using a Cox proportional hazards model with right censoring at the time of transplantation, delisting, or study completion.
Only univariate analyses were done given a low event rate and a small sample size. Kaplan-Meier survival curves with time since listing were plotted using months as the time scale. In transplanted patients, Kaplan-Meier survival curves with time since transplantation were plotted using years as the time scale. Differences in survival according to period of listing were assessed using the Log-rank test. All reported P values are two-sided. Values of p <. 05 were considered statistically significant. Risk estimations are reported with their 95% confidence intervals (95% CIs). Statistical analyses were performed using Stata ® 11. 2 software (StataCorp, College Station, TX) and R software v3. 6. 1. Table  1  reports the main patient features at listing. Eight (11. 9%) patients were listed before the age of 18.
Study design A multicenter cross-sectional study was conducted between January and June 2019 among Lebanese surviving stroke patients previously diagnosed in Beirut and Mount Lebanon hospitals and rehabilitation centers. Participants were recruited from hospitals and clinics in Beirut and Mount Lebanon regions, notably: Ain w Zein Hospital, Sacre Coeur Hospital, Zahraa Hospital, RHUH, Abou Joaude Hospital, Beit Chabeb Rehabilitation Center. Telephone interviews were made to retrieve data on medication adherence post-stroke and its potential barriers. The questions were answered by the patients themselves or by their relatives or caregivers. The study protocol was reviewed and approved by the Institutional Review Board of the Lebanese International University-Beirut and the aforementioned hospitals.
Data collection and outcome measures Data extraction was carried out via telephone interviews using a well-structured questionnaire written in Arabic with the patient. If the patient was unable to communicate because of physical and/or mental disabilities, information was obtained from relatives or caregivers. Therefore, 41, 48 and 11 questionnaires were completed by stroke survivors, patient's relatives and caregivers respectively. The questions were divided into 5 major sections that addressed the following topics: Socio-demographic and socio-economic characteristics: age, sex, place of residence, marital status, number of children, the age of patient's responsible, level of education of the patient and his responsible, professional status, number of residents at home, number of rooms and type of health insurance, Medication related variables: Treatment post stroke upon discharge (date of stroke diagnosis, list of medications) and medication adherence were assessed.
Statistical analysis Collected data was coded, introduced and entered on the software Statistical Package for Social Sciences (SPSS) version 22 (SPSS TM Inc. , Chicago, IL USA). The Cronbach alpha was used to assess the reliability of all scales. The bivariate analyses were conducted taking the LMAS as the dependent variable. Correlation coefficients were used to evaluate the relationship between 2 quantitative variables; the Student t-test and the ANOVA test for comparison of means of the quantitative variables with a normal distribution and variances homogeneous whereas the test of Wilcoxon or Kruskal Wallis for quantitative with abnormal distribution variables. Multivariate linear regression analyses were conducted using variables with p < 0. 2 in the bivariate analysis  27, 28  ; potential confounders were eliminated only if p > 0.
Limitations and strengths Multiple limitations are to be taken into consideration when interpreting our results. First, compliance was assessed using self-report, hence the memory and reliability of each participant may have influenced data obtained upon telephone follow-up, which could lead to information bias. Moreover, the scale used to evaluate compliance has not been validated in stroke patients. Also, it did not particularly enquire about adherence to stroke medications. Nonetheless, self-report compliance measures are commonly greatly associated with objective measures,  43  and have demonstrated to be dependable predictors of poor outcomes in numerous studies. 44, 45 urther, the survey was limited to the Beirut and Mount Lebanon regions, which may have led to patient selection bias.
Related Work Weakly supervised object detection (WSOD). In this setting, all training images have image-level class labels. A major approach is to formulate it as multiple instance learning (MIL)  [1] , considering each training image as a "bag" and iteratively selecting high-scoring object proposals from each bag, treating them as ground truth to learn an object detector. MIL fails in large-scale datasets with poor initial solutions. Many works thus focus on providing robust initialization, using either hand crafted or CNN features  [12, 46, 39]. Bilen and Vedaldi  [4]  propose weakly-supervised deep detection network (WSDDN), which pools region-level scores into image-level class probabilities and enables endto-end learning from image-level labels. Building upon WSDDN, Zhang et al. [58]  propose a zigzag strategy to discover reliable object instances and train them by curriculum learning  [2].
It is also common to use extra data to transfer knowledge from a source domain and help localize objects in the target domain  [36, 39, 53]. Large-scale weakly-supervised web images  [13, 51]  and videos  [44, 26]  with noisy labels are also common as extra data. In this work we only use a few weakly-labeled images. We do not even use the image-level labels from the standard WSOD training set. Semi-supervised learning. There are several works that assume a few images are annotated with object bounding boxes and the rest still have image-level labels as in WSOD  [50, 18, 19, 57]. These are often called semisupervised  [50, 57]. However, semi-supervised may also refer to the situation where some images are labeled (at image-level or with bounding boxes) and the rest have no annotation at all  [6, 31].
Experimental Setup Datasets. We construct the support set G by collecting the top-ranking images per class from Google Image Search. We choose the standard object detection datasets PASCAL VOC 2007 and 2012  [8]  for the benchmark sets. They have 20 object classes in total. Each contains a trainval set and a test set. For VOC 2007, the trainval set has 5011 images while test set 4952 images. For VOC 2012, the size of trainval and test sets are 11540 and 10991, respectively. Importantly, we do not use any labels, not even image-level labels. We use the trainval sets as X to train the object detector. We evaluate this detector on the test set. Networks. We choose by default VGG16  [43]  as our student U, which is consistent with most weakly-supervised detection methods  [4, 48, 49, 38, 47]. As for teacher network T (feature extractor φ), since it is not used at inference time, we choose the more powerful ResNet-152  [15].
METHOD CorLoc OICR  [48]  62. 1 ZLDN  [58]  61. 5 PCL  [47]  63. 2 NSOD 58. 7 NSOD (VOC07+12) 59. 9 Table  5. Detection CorLoc on the trainval set of PASCAL VOC 2012. All compared methods  [48, 47, 58]  utilize the image-level labels in the benchmark set while our NSOD does not. tection methods  [4, 48, 49, 47, 38]  in Table  2. For a fair comparison, all these methods use the same VGG16 backbone as we do, without bells and whistles. Recall that NSOD requires no annotation on the benchmark set X, while weakly-supervised methods assume image-level labels for all images in X. One directly competing method is PCL trained on ground truth image-level labels in X. Despite using no annotation on X, NSOD achieves an mAP that is only 5. 5% below that of PCL (38. 0 vs. 43. 5). Most importantly, our method naturally improves simply by using more unlabeled data for X: on VOC07+12, NSOD is only 1.
Animals and ethic statements For immunogenicity assays, six weeks old BALB/c mice were purchased from Charles River (Saint Germain Nuelles, France) and housed under conventional conditions (DBV Technologies, Montrouge, France, agreement number #A92-049-02). Experiments were performed according to the European Community rules of animal care, and with permission of the French government (authorization #5302). Animals had free access to breeding pelleted diet and to automatic drinking water dispenser. For challenge experiments, BALB/c mice were purchased from Envigo (Frederick, MD, USA), housed in large polycarbonate cages and fed a standard diet of rodent chow and water (Sigmovir Biosystems, Rockville, USA). The colony was monitored for the absence of antibodies to adventitious respiratory viruses and other common rodent pathogens (data not shown).
D. value twice that of blank wells (where diluent buffer was used in place of sera or BAL) 2. 6. Dosage of F-specific antibodies by ELISA 96-well plates were coated with F protein purified from RSV/ A2-infected cells (SinoBiological Inc. ). Plates were incubated in blocking solution for 1 h at RT and washed. Serially diluted sera along with positive and negative controls were added to wells and incubated for 1 h at RT. Plates were washed and incubated with rabbit anti-mouse IgG1 or IgG2a (Invitrogen, 1:4000) for 1 h at RT. Plates were washed and incubated with HRP conjugated Goat anti-rabbit IgG1 or IgG2a (1:4000) for 1 h at RT. Following additional washes, HRP was revealed and O. D. were recorded as described in Section 2. 5. Data were presented as Log10 of O. D.
Dosage of RSV-neutralizing antibodies by plaque reduction assay Heat inactivated sera were diluted 1:10 in Eagle's Minimal Essential Medium (EMEM) and serially diluted further 1:4. Diluted sera were incubated for 1 h at RT with RSV/A2 (25-50 Plaque-Forming Units (PFU)) and inoculated in duplicates onto confluent monolayers of HeP-2 cells in 24-well plates. Following one-hour incubation at 37 °C and 5% CO 2 , each well was overlaid with 0. 75% of methylcellulose medium. Following 4 days at 37 °C and 5% CO 2 , overlays were removed, and cells were fixed with 0. 1% crystal violet. The corresponding reciprocal neutralizing titers were determined at the 60% reduction end-point of the virus control using the statistics program ''plqrd. manual. entry".
Quantitative real-time (qRT-)PCR Messenger RNA (mRNA) was extracted from homogenized lung tissue using the RNeasy purification kit (QIAGEN). One mg of total mRNA was used to prepare cDNA using Superscript II RT and oligo dT primer (Invitrogen). For the qRT-PCR reactions for b-actin, IL-5, IL-13, IFN-c and IL-2, iQTM SYBR Green Supermix (BioRad) was used in a final volume of 25 mL, with final primer concentrations of 0. 5 mM (Sequences of specific primers are available in Table  S1 ). Reactions were set up in duplicates in 96-well trays. Amplifications were performed on an iCycler (Bio-Rad) for 1 cycle of 95 °C for 3 min, followed by 40 cycles of 95 °C for 10 s, 60 °C for 10 s, and 72 °C for 15 s. Baseline cycles and cycle threshold (Ct) were calculated by the iQ5 software in the PCR Base Line Subtracted Curve Fit mode.