dataesr/scientific-paragraphs-categorization huggingface dataset:
The NHS2 was supported by  NIH  (grants  UM1 CA176726  and  U19 CA148065 ). OBCS was supported by research grants from the  Finnish Cancer Foundation , the  Academy of Finland  (grant numbers  250083  and  122715 , and  Center of Excellence  grant number  251314 ), the  Sigrid Juselius Foundation , the  University of Oulu  general as well as strategic funding, the  University of Oulu Support Foundation  and the special Governmental VTR funds towards  Oulu University  Hospital-based research activities. The ORIGO study was supported by the  Dutch Cancer Society  ( RUL 1997 ( RUL -1505 ) ) and the  Biobanking and Biomolecular Resources Research Infrastructure  ( BBMRI-NL CP16 ). The  PBCS  was funded by  Intramural Research Funds  of the  National Cancer Institute, Department of Health and Human Services, USA.
Genotyping for PLCO was supported by the  Intramural Research Program  of the  National Institutes of Health, NCI, Division of Cancer Epidemiology and Genetics. The PLCO is supported by the  Intramural Research Program  of the Division  of Cancer Epidemiology  and Genetics and supported by contracts from the  Division of Cancer Prevention, National Cancer Institute, National Institutes of Health. The POSH study is funded by  Cancer Research UK  (grants  C1275/A11699 ,  C1275/C22524 ,  C1275/A19187  and  C1275/A15956 ) and  Breast Cancer Campaign  ( 2010PR62  and  2013PR044 ). The RBCS was funded by the  Dutch Cancer Society  ( DDHK 2004-3124  and  DDHK 2009-4318 ). The  SBCGS  was supported primarily by  NIH  (grants  R01CA64277 ,  R01CA148667 ,  UMCA182910  and  R37CA70867 ). Biological sample preparation was conducted the Survey and  Biospecimen Shared Resource , which is supported by  P30 CA68485.
Markham, Little Rock, AR 72205. The  Arkansas Central Cancer Registry  is fully funded by a grant from  National Program of Cancer Registries, Centers for Disease Control and Prevention (CDC). Data on SCCS cancer cases from Mississippi were collected by the  Mississippi Cancer Registry  which participates in the  National Program of Cancer Registries (NPCR)  of the CDC. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the CDC or the Mississippi Cancer Registry. SEARCH is funded by  Cancer Research UK  ( C490/A10124  and  C490/A16561 ) and supported by the  UK National Institute for Health Research Biomedical Research Centre  at the  University of Cambridge. The  University of Cambridge  has received salary support for PDPP from the  NHS  in the  East of England  through the  Clinical Academic Reserve.
SEBCS was supported by the  BRL (Basic Research Laboratory)  program through the  National Research Foundation of Korea funded by the  Ministry of Education, Science and Technology  ( 2012-0000347 ). The  Sister Study  (SISTER) is supported by the  Intramural Research Program  of the  NIH ,  National Institute of Environmental Health Sciences  ( Z01-ES044005  and  Z01-ES049033 ). The  Two Sister Study  (2SISTER) was supported by the  Intramural Research Program  of the  NIH ,  National Institute of Environmental Health Sciences  ( Z01-ES044005  and  Z01-ES102245 ), and, also by a grant from  Susan G. Komen for the Cure , grant  FAS0703856. SKKDKFZS is supported by the  DKFZ. The SMC is funded by the  Swedish Cancer Foundation  and the  Swedish Research Council  ( VR 2017-00644 ) grant for the  Swedish Infrastructure for Medical Population -based Life- course EnvironmentalResearch  ( SIMPLER ).
The  SZBCS  was supported by Grant  PBZ_ KBN_122/P05/2004  and the program of the  Minister of Science and Higher Education  under the name " Regional Initiative of Excellence " in  2019-2022  project number  002/RID/2018/19  amount of financing  12 000 000 PLN. The TNBCC was supported by  SPORE in Breast Cancer  ( CA116201 ), a grant from the  Breast Cancer Research Foundation , a generous gift from the  David F. and Margaret T. Grohne Family Foundation. UBCS was supported by funding from  National Cancer Institute (NCI)  grant  R01 CA163353  (to N. J. Camp) and the  Women's Cancer Center at the Huntsman Cancer Institute (HCI). Data collection for UBCS was supported by the  Utah Population Database (UPDB)  and  Utah Cancer Registry (UCR). The UPDB is supported by  HCI  (including  Huntsman Cancer Foundation, HCF) , the  University of Utah , and  NCI  grant  P30 CA2014.
The UCR is funded by the  NCI's SEER Program , Contract No. HHSN261201800016I , the  US Center for Disease Control and Prevention's National Program of Cancer Registries  (cooperative agreement number  NU58DP006320 ), the  University of Utah and HCF. The UCIBCS component of this research was supported by the  NIH  ( CA58860  and  CA92044 ) and the  Lon V Smith Foundation  ( LVS39420 ). The UKBGS is funded by  Breast Cancer Now  and the  Institute of Cancer Research (ICR), London. ICR  acknowledges NHS funding to the  NIHR Biomedical Research Centre. The  UKOPS study  was funded by The  Eve Appeal  (The  Oak Foundation ) and supported by the  National Institute for Health Research University College London Hospitals Biomedical Research Centre. The US3SS study was supported by  Massachusetts  (KME,  R01CA47305 ),  Wisconsin (PAN ,  R01 CA47147 ) and  New Hampshire  ( LT-E ,  R01CA69664 ) centres, and  Intramural Research Funds of the National Cancer Institute, Department of Health and Human Services, USA.
Poisoning by phycotoxins at the origin of neurological disorders in France We recently conducted a retrospective study of poisoning cases by bivalve shellfish (oysters, mussels and scallops) recorded by the French Poison Control Centers (PCCs) from 2012 to 2019, looking for a link with environmental monitoring data when possible, specifically in order to identify those cases presenting neurological signs compatible with neurotoxic phycotoxins  (Sinno-Tellier et al. , 2022). Among the 619 shellfish poisoning cases recorded by the PCCs between 2012 and 2019, 22% (n = 134) had reported at least one neurological symptom (headache, dizziness or paraesthesia). The review of medical records for the 134 patients led to suspicion of 14 cases of PSP and one case of ASP. Our retrospective study highlighted the difficulties in identifying cases related to neurotoxic phycotoxins by gathering clinical and biological diagnostic confirmations on the one hand, as well as environmental contaminations on the other.
Prospective surveillance of shellfish poisonings by neurotoxins To improve the collection of shellfish poisoning data, the French Agency for Food, Environmental and Occupational Health and Safety (ANSES), French Poison Control Centers (PCCs), the French Research Institute for Exploitation of the Sea (Ifremer) and the French Defense Procurement Agency (DGA) have developed a prospective surveillance of PSP that combines monitoring of poisoning cases (clinical monitoring), of harmful algae and their toxins in shellfish production areas (environmental monitoring), as well as of toxins levels in seafood leftovers and in biological samples from poisoned subjects. First, we have developed a questionnaire to collect all relevant data when neurological signs related to shellfish consumption are reported during the first call to a PCC: clinical signs associated with marine quantity of shellfish ingested, type and location of purchase, and shellfish production area.
The patients should keep and freeze any meal leftovers for subsequent analyses. Measurement of toxin concentrations in unconsumed shellfish are key, especially when poisoning is associated with shellfish harvested from uncontrolled areas. The suspected cases should be referred to the emergency department of a hospital, for collection of biological samples that may be frozen (in particular for a posteriori testing for marine toxins, even those not yet regulated in European countries such as pinnatoxins and brevetoxins). These samples will help to confirm the poisoning. Finally, ANSES has set up daily automatic monitoring of cases of shellfish poisoning recorded in the PCC national database to detect any suspected case the day following the call to the PCC. The aims are to launch appropriate investigations, alert the competent authorities, and inform the population as soon as possible.
Clinical monitoring The monitoring of poisoning cases is carried out by the eight French PCCs. French PCCs provide 24/7 teleconsultations during which physicians or pharmacists with expertise in toxicology conduct a telephone interview with the patient or the healthcare professional who calls for urgent medical advice and/or toxicological expertise. All PCC cases, corresponding to "patients", are collected in the French PCC national database (SICAP) in medical records, corresponding to "events", each event containing either a single case or collective cases related to the same exposure. Each record is related to one or several exposure agents registered in the French National Product and Formulation Database (FNPFD) of the SICAP. When necessary, cases are followed up by the PCC and new information is recorded in the SICAP.
Material 2. 1. Arabidopsis thaliana Inoculated by a Bacteria We consider chlorophyll fluorescence imaging on rosettes of Arabidopsis thaliana ecotype Col0. The experiment consisted of 36 pots of four plants each. Half of the pots were inoculated with water, and the other half with the virulent DC300, a tomato bacteria. We attribute 'Healthy' to the pots inoculated with water and 'Diseased' to the ones with the bacteria. Chlorophyll fluorescence imaging was realized during six days of the experiment: D0, D2, D5, D6, D7, and D8. The same data set was used for automated disease segmentation  [13, 27]. A full description of the experiment is in these two papers. We got interested in F m , the fluorescence after saturating actinic flash, and F 0 , the basal fluorescence before the flash. To build a statistical model of F 0 and F m we manually selected areas located in the limb of the leaves as illustrated in Figure  1.
Arabidopsis thaliana Infected with a Fungal Pathogen We considered a second study aiming to score the development of fungal pathogen symptoms (Botrytis cinerea) on the Arabidopsis thaliana plant  [12]. This is currently the only public data set on chlorophyll fluorescence imaging with diseased plants that we found. The data set can be found in  [29]. It is composed of chlorophyll fluorescences images and RGB images acquired during 96 h post-infection at 0 h, 24 h, 72 h, and 98 h. After checking successfully (not shown) the Gaussianity of the distribution of gray levels in limbs of F 0 and F m images, we computed like for the previous data set the mean and standard deviation associated. The value obtained is provided in Table  3. Here again, one can notice that the standard deviation of the Gaussian distribution is relatively stable over the experiment but only for the healthy plants.
For this fungal disease, spores progressively appear at the surface of the leaves. These spores act as a multiplicative filter that absorbs the incident light. The emergence of these spores may add another source of randomness here. In the following sections, we will refer to the bacteria data set, the chlorophyll fluorescence images associated with the Arabidopsis thaliana inoculated by a bacteria and by fungal pathogen data set, the chlorophyll fluorescence images of the Arabidopsis thaliana infected with a fungal pathogen.
Monte Carlo Experiments To have ground truth in the evaluation of the value of the estimator of PZ, we resorted to the use of simulation of the two empirical data sets both in the healthy and diseased plants. We considered the size of the smallest leaves and the largest ones on our two data sets. We found 10 pixels for the smallest leaves and 80 for the largest ones. Generation of Gaussian distribution for F m and F 0 mimicking the experimental observation of our two experimental data sets given in Tables  2  and 3. The resulting observations of F v /F m were computed. The two proposed non-Gaussian estimators and the Gaussian baselines estimator described in the previous section were computed. Simulations were repeated 5000 times to compute average performance and associated standard deviations.
The results of the Monte Carlo simulation are given in Table  7. The normal distribution and the normal approximation are the methods of estimation when assuming a normal distribution of F v /F m. Bayesian and EM estimation are the two proposed non-Gaussian estimators with PZ distribution. With both measured (Table  6 ) and estimated values (Table  7 ), we calculate the relative error for each day of the experiment. The mean value of the relative error over the six days of the experiment is given in Table  8 , for Healthy and Diseased plants, and per sample size. Table  8. Mean value of the relative error ( % ) for the five days of the experiment per method of estimation and per sample size, N, for Healthy and Diseased plants of the bacteria data set. The maximum value of the relative error was not high: 6% with normal assumptions.
The simulation results are in Table  10. The mean values represent the estimated fourth-order fractional moment with the four methods of estimation: normal distribution assumption, the normal approximation, and the two non-Gaussian estimators, Bayesian and EM. We calculate the relative error per time of experiment with the measured value of the fourth-order fractional moment (Table  9 ) and the estimated values (Table  10 ). We give in Table  11  the mean value of the relative error over all the experiment acquisition times. We see clearly on the fungal data set that the relative error is much higher if one supposes a normal distribution of the ratio: 26% for healthy (N = 10) and 82% for diseased (N = 10) v. s around 6% and 7% for Bayesian and EM estimations. Thus, these results show that using a normal distribution or normal approximation of the ratio F v /F m leads to more or less wrong results depending on how pronounced is the deviation from Gaussianity.
(A39) Thus, +∞ 0 x 2µ-1 exp(-βx 2 ) sinh(γx)dx = 2 -µ (2β) -µ Γ(2µ) Γ(µ) √ 2π γ 2β 1 F 1 1 + 2µ 2 , 3 2 , γ 2 4β. ( A40 ) In the case µ = 0, the above equation becomes: The estimated values of µ X /µ Y are close to 0. 39, and the standard deviations (in brackets) associated with the mean values of the fractional moments of the second order show a very low variation. Thus, we use the second-order fractional moment for the bacteria data set. +∞ 0 x -1 exp(-βx 2 ) sinh(γx)dx = lim µ→0 Γ(2µ) Γ(µ) √ 2π γ 2β 1 F 1 1 2 , 3 2 , γ 2 4β. (A41) Knowing that lim µ→0 µΓ(µ) = 1 then lim µ→0 2µΓ(2µ) 2µΓ(µ) = 1/2. As a consequence, +∞ 0 x -1 exp(-βx 2 ) sinh(γx)dx = π 2 γ 2β 1 F 1 1 2 , 3 2 , γ 2 4β. (A42) We secondly investigated the mean value of the fractional moment of the second order for the CV y between 0.
8 and 1. 3. These values correspond to δ y of the Fungal pathogen data set. It had a high variability for CV y and a poor estimation of the mean value (we don't show the results of this simulation for the sake of readability). We then simulated with a fractional moment of the fourth order (s = 1/4). This corresponds to (µ X /µ Y ) 1/4 = 0. 65. The results of this simulation are in Table  A2. The standard deviations (in brackets) associated with the mean values of the fractional moments of the fourth order show a low variation, and the exact value of 0. 65 is reasonably approximated. Therefore, we propose to use higher fractional moments for higher values of δ y. In our data sets, we will use s = 1/2 for the bacteria data set and s = 1/4 for the fungal pathogen data set. Table  A2. Simulation of the ratio distribution: mean values of the fractional moment of the fourth order and standard deviations (in brackets) for 5000 values of 10 pairs of observations X i /Y i , where X i ∼ N(µ X , σ X ) and Y i ∼ N(µ Y , σ Y ), under varying coefficients of variation (CV), with (µ X /µ Y ).
5, 0. 5 mM Ethylenediaminetetraacetic acid (EDTA), 137 mM NaCl, 10% glycerol, and 0. 2% N-dodecyl β-D-maltoside). After a 10-min centrifugation at 1,000 g, supernatants were collected and boiled 5 min in the presence of β-mercaptoethanol containing Laemmli buffer before being loaded on a 8% SDS-PAGE gel. Electrophoresis, transfer on membrane, and revelation were processed as described above. Flow Cytometry Analysis. Cells were washed twice with flow cytometry buffer (PBS, 0. 5% FBS) and then incubated for 45 min at 10 °C before being labeled with antibodies in flow cytometry buffer. After 3 washes, cells were resuspended in FACS buffer and analyzed by flow cytometry on a Beckton Dickinson (BD) FACSCanto™. Data were analyzed with FlowJo software (Treestar Inc. , CA). For NFAT translocation analysis, T cell nuclei were purified with the Nuclei isolation Kit (NUC201 Sigma-Aldrich), fixed, and permeabilized with the fix/ Perm reagents (from 00-5523-00 eBioscience) and then stained with AF488conjugated anti-NFAT mAb.
After 3 washes, nuclei were resuspended in FACS buffer and analyzed by flow cytometry on a BD FACSCanto™. Data were analyzed with FlowJo software (Treestar Inc. , CA). Spectral Analysis of PhyB Photoswitching. Spectral analysis of PhyB photoswitching was measured with a NanoDrop™ One (Thermo Scientific™). The absorption spectrum of PhyB was measured after 3 min under light exposure at 680 nm. The samples were then exposed to 740 nm light for 1 h before recording a new absorption spectrum. Light-Controlled PhyB/LiTE Pull-Down Assay. Streptavidin-coated magnetic beads (MagStrep "type3" XT beads 5% suspension, Iba) were washed three times in PBS supplemented with 0. 5 mM TCEP with a magnetic separator (Iba). The beads were then incubated with 20 µg/mL of PhyB for 1 h at 4 °C and then washed 3 times before adding the LiTE protein at a concentration of 20 µg/mL.
Statistical Analyses. Quantitative data were reported as means ± SD or ± SEM. Excel (Microsoft) and Prism (GraphPad Software) were used for statistical analyses. The nonparametric Wilcoxon-Mann-Whitney test or the Student t test was used for the evaluation of significance, as indicated in the figures. The sample number (N) refers to the number of biological replicates. n. s. = not significant (P > 0. 05), *P < 0. 05, **P < 0. 01, ***P < 0. 001, and ****P < 0. 0001. Data, Materials, and Software Availability. All study data are included in the article and/or SI Appendix.
ACKNOWLEDGMENTS. We thank  J. M. Ingargiola  from the  Institut des Sciences du Mouvement  and  Dr. S. Mailfert  from the  Plateforme Commune d'Imagerie du Site de Luminy (PICSL) imaging facility  of the  CIML (Centre d'Immunologie de Marseille Luminy) (ImagImm ) for the construction of the optoPlate;  Drs. Leung  and  Rosen  for plasmids encoding PhyB and related enzymes;  Drs. Horner  and  Weber  for advice and plasmid for PhyB production;  A. Formisano  for AF488-H57-597 Fab generation and coupling;  Dr. L. Gauthier  for TA-99 antibody supply and advice;  Torrielli Celia ,  Dr. P. Chames ,  Dr. Y. Hamon ,  Dr. R. Roncagalli , and  Dr. G. Voisine  for advice;  C. Debarnot  for advice in protein production, and  Dr. B. Malissen  and  P. Bousso  for careful reading and advice. We thank the  CIML  cytometry platforms for their technical and methodological support.
We acknowledge the  PICSL imaging facility  of the  CIML (ImagImm) , member of the national infrastructure France-BioImaging supported by the  French National Research Agency  ( ANR-10-INBS-04 ). The manuscript has been revised for the English by Angloscribe, an independent scientific language editing service. This work was supported by institutional funding from the  French INSERM ,  CNRS , and  Aix-Marseille-Université , and program grants from the  French National Research Agency  ( ANR-14-CE14-0014-01  to R. L. ;  ANR-18-CE15-0021-02  to D. M. ,  ANR-17-CE15-0032  to H. -T. H. ) and  SATT Sud-Est  ( SATT N°191702  to D. M. ). M. J. was funded by fellowship from  INSERM/Région SUD/Innate Pharma  ( RSE18003AAA ) and by the  French Foundation for Medical Research  (FRM:  FDT202106013223 ).
Using the MinION device, it is now possible to fully sequence 16S rRNA  [169] [170] [171]  and the 16S-ITS-23S operon  [170, 172] , as well as to process a total of 96 samples in a single run. 16S rRNA metabarcoding data can be used to develop microbial source tracking (MST). These methods enable the identification of fecal pollution sources like human fecal material and contamination from birds or livestock  [173]. Using Bayesian modeling like SourceTracker, it is also possible to estimate the source proportions and uncertainties on known and unknown sources  [174]. For viruses, which lack a universal gene, several approaches have been designed using degenerate primers amplifying a conserved but variable portion of the viral genome as a barcode across a chosen range of viral families. The RNA-dependent RNA-polymerase (RdRp) is often used in environmental virology as it allows the fingerprinting of most families of RNA viruses, but it may lack the necessary resolution to identify specific genotypes inside a given viral genus.
No DOACs are recommended in Child-Pugh score C. In Child-Pugh score B, rivaroxaban is not recommended, while dabigatran, apixaban, and edoxaban should be used with caution  [3] ; however, more data are needed to evaluate how cirrhosis increases the bleeding risk in patients treated with DOACs  [34]. Elderly patients present a particular challenge because of coexisting comorbidities, frailty, and concomitant medications increasing the risk of drug interactions  [35]. A study comparing the risk of GI bleeding with dabigatran, rivaroxaban, or apixaban in patients with AF showed that rates of events for all DOACs increased among patients 75 years or older. However, apixaban had a lower risk of association with GI bleeding in the very elderly than dabigatran or rivaroxaban  [36]. Several scores have been described to assess bleeding risk, especially in patients with AF exposed to long-term anticoagulation therapy.
Idarucizumab is a humanized monoclonal antibody fragment that can be used for the emergency reversal of dabigatran's anticoagulant effect  [68] [69] [70] [71]. However, prohemostatic agents, namely PCC or activated PCC (aPCC) 30-50 U/kg IV, can be administered if a specific antidote is not available. Few data support the use of oral activated charcoal within 6 h of drug intake, especially in the context of overdose  [66]. Regarding factor Xa inhibitors, the US Food and Drug Administration approved andexanet alfa for the reversal of apixaban and rivaroxaban in life-threatening or uncontrolled bleeding under its accelerated approval program in 2018  [72]. The approval was conditional on performance of an ongoing randomized clinical trial (ANNEXA-I NCT03661528). The European Medicines Agency also gave conditional approval in April 2019, and full marketing approval was granted in Japan in March 2022, including for the reversal of edoxaban in patients with lifethreatening or uncontrolled bleeding.
e. , crustaceans and insects). The genome of the model representative Streptomyces coelicolor A3(2) has more than 240 CAZyme-encoding genes  1  , including glycoside hydrolases (GHs) from families 3, 5, 6, 9, 12, and 48 (i. e. , typical cellulases  2  ), in addition to GHs from family 18-20 (i. e. , typical chitinases). Furthermore, S. coelicolor exhibits seven genes encoding lytic polysaccharide monooxygenases (LPMOs) that are all members of the auxiliary activity (AA) family 10 (AA10)  3, 4. The AA class was relatively recently added to the CAZy database and holds redox-active enzymes that assist CAZymes in the degradation of biomass  5. Currently, eight out of seventeen AA families contain LPMOs, viz. families AA9-11 and AA13-17  [6] [7] [8] [9] [10] [11] [12] [13] [14]. LPMOs are widespread in Nature and are best known for their synergistic role with glycoside hydrolases in the conversion of cellulose and chitin  4, [6] [7] [8] 15, 16.