dataesr/scientific-paragraphs-categorization huggingface dataset:
g. , PDB: 4HEA, PDB: 2ZW3_A). Cases with symmetry mismatch proved to be inscrutable to all the abovementioned algorithms. Such cases included the structures of the AMPA (PDB: 4U1W) or NMDA (PDB: 4PE5) receptors, which besides the readily-detectable global two-fold symmetry of the complex, also exhibit two unrelated and distinct pseudo-symmetries relating the chain fragments in the membrane-embedded and extracellular components of the complex (Fig. 2  in  [17] ). These symmetries presented a challenge for AnAnaS and BioJava because neither method considers repeats formed by a fraction of a chain. Among the internalsymmetry detection methods, SymD is inherently unable to detect more than one symmetry in a structure, while CE-Symm can only detect multiple symmetries simultaneously if those symmetries are hierarchical, i.
e. , they relate sub-regions of the same symmetric repeats. These limitations of the internal-symmetry detection algorithms also prevented them from identifying two distinct symmetries within a single protein chain that cannot be related to one another, such as those found in the protomer of a glutamate transporter homolog (GltPh, PDB: 2NWX_A; Fig. 2B ). One of the main challenges for internal symmetry algorithms is detecting the full extent of repeats. First, since there is often much more sequence and structure divergence between repeats in internal symmetry than in quaternary symmetries, setting appropriate thresholds for determining whether a protein structure is symmetric and which residues are involved can be difficult. This issue was apparent for protein structures in which a pronounced structural divergence between repeats is of functional significance, such as in vcINDY (PDB: 4F35_C)  [18, 19].
Second, CE-Symm, due to its focus on detecting multiple, hierarchically-related symmetries within a structure, might only identify some of the residues of the repeats related by a given symmetry axis because the remainder do not satisfy the criteria of a second symmetry axis. Such an outcome was observed not only within protein chains (e. g. PDB: 3NCY_A), but also for complexes that exhibit both quaternary and internal symmetry (e. g. PDB: 1OTS; Fig. 1D ). In conclusion, the MemSTATS data set provides a solid foundation for evaluating both the overall performance of symmetry detection algorithms and the specific challenges in membrane protein structures that each method can address. However, the limitations of the dataset, including its relatively small size, should be kept in mind when interpreting the statistical significance of results obtained thereon.
For example, at the quaternary level, more than one complex within the dataset has similar architecture (e. g. , there are multiple examples with C4 quaternary symmetry), and therefore the specific issues that this architecture presents will be upweighted when judging the overall performance of an algorithm. Furthermore, only a few multi-chain proteins have no quaternary symmetry, while few betabarrel proteins have no internal symmetry; this imbalance means that measures such as accuracy should be avoided or used with care. We also note that the determination of internal structural symmetry in beta-barrels is highly ambiguous, because it is not fully apparent how the symmetry might relate to function; these cases were included in the dataset mainly for completeness. The abovementioned issues are unlikely to be solved by the increasing number of membrane protein structures since it is likely that they are intrinsic to the types of symmetries that can be encountered within the membrane environment.
TEMAS Step 1: Global Search Our new tool allows choosing a date range for the study and entering the search keywords (labeled "Global Search"). An advanced search is possible since it respects the PubMed search syntax. The number of PubMed answers matching the search criteria is displayed. All the abstracts are retrieved using the package rentrez  14  of R software, which provides an interface to the NCBI's EUtils API. It enables users to search databases like PubMed (which is, here, the default database), processing the results of those searches, and loading data into their R sessions. The result of this research can be downloaded as a file in CSV format. We chose a time period from 2006 to 2017 to perform our documentary search  15. We used an advanced search with the following syntax: A first sort allows removing PubMed results without abstracts (letter to the editor, teaching case, erratum).
𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑟𝑎𝑡𝑒 =      ℎ   (,. )      ℎ  (,. ) 𝑟𝑒𝑐𝑎𝑙𝑙 𝑟𝑎𝑡𝑒 = 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑡 𝑎𝑟𝑡𝑖𝑐𝑙𝑒𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑇𝐸𝑀𝐴𝑆 𝑑𝑎𝑡𝑎𝑏𝑎𝑠𝑒 𝑛𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑡 𝑎𝑟𝑡𝑖𝑐𝑙𝑒𝑠 𝑖𝑛 𝑡ℎ𝑒 𝑅𝑆 𝑑𝑎𝑡𝑎𝑏𝑎𝑠𝑒 Precision is the fraction of relevant instances among the retrieved instances. Recall is the fraction of the total amount of relevant instances that were actually retrieved. Both precision and recall are therefore based on an understanding and measure of relevance. The measurement of precision and recall rates requires a qualified individual to inspect the output from a search and to address the output into two groups of articles: relevant and not relevant. The recall rate cannot be calculated for the RS. Indeed, all articles in the PubMed database should be read to calculate the RS recall rate. The recall rate of a PubMed search is not 100% since there may be relevant articles that are not extracted by the RS.
TEMAS step 2: First sort We excluded articles without abstract, and we defined the following terms as off topic since they were downstream the screening procedure: -"mastectom" for mastectomy, mastectomies. -"metasta" for metastatic, metastasis. -"chemot" for chemotherapy, chemotherapies -"surger" for surgery and surgeries -"surgic" for surgical Among the articles extracted in Step 1, we excluded 954 articles without abstract and 4,582 off-topic articles. We thus obtained a database of 10,046 classifiable articles.
Patients and study design This monocentric study (Paoli-Calmettes Institute, Marseille, France) included forty-eight patients with newly diagnosed non-acute promyelocytic leukemia AML from the HEMATOBIO cohort (NCT02320656). Patients were aged 19 to 80 years (mean ±SD = 54. 0±14. 0). All patients received cytarabine and anthracycline-based induction chemotherapy as previously described. 43 Eight patients (16. 7%) were treated with allogeneic stem cell transplantation as a consolidation therapy. Patients' characteristics are summarized in Table  1.
Algorithm-based high-dimensional analysis NK cells were manually defined as CD13 -CD33 -CD34 -CD45 + CD3 -CD19 -CD56 + or CD13 -CD33 -CD34 -CD45 + CD3 -CD19 -CD56 -CD16 + and exported using FlowJo V10. 6. 2. The gating strategy is displayed in Supplemental Figure  1. Data were arcsinh-transformed with a cofactor of 5. NK cell populations were automatically defined using hierarchical stochastic neighbor embedding (h-SNE) analysis (Cytosplore V2. 2. 1) with default settings (30 perplexity and 1,000 iterations). 45 For the h-SNE analyses, consensus files were generated for each group of patients with a fixed number of NK cells in order to obtain a representative and balanced view of all patient groups. NK cell trajectory inferences were performed using the Wishbone algorithm. 32 The library was updated for Python V3.
ACKNOWLEDGMENTS The authors thank the  CRCM cytometry core facility  as well as the IPC/CRCM/UMR 1068  Tumor Bank , which operates under authorization # AC-2007-33 granted by the  French Ministry of Research (Ministère de la Recherche et de l'Enseignement Supérieur). Louise Ball from Angloscribe , an independent scientific language editing service, provided drafts and editorial assistance to the authors during preparation of this manuscript. This work has been financially supported by the  INCa  (grant  2012-064/2019-038  for D. O. , and A. T. ), the  Fondation de France  (grant  00076207  for A. S. C), the  SIRIC Marseille  (grant  INCa -DGOS-INSERM 6038 ), the  Cancéropôle PACA  (grants K_CyTOF 2014 and AML_CyTOF 2016 for J. A. N. ), the  GS IBiSA  and the  Agence Nationale de la Recherche  ( PHENOMIN  project for M.
Acknowledgments We thank the  National Administration of Laboratories and Institutes of Health-Dr. Carlos Malbrán Institute  for providing immune plasma; nurses at the intensive care unit of  Saint-Pierre University Hospital  and members of the infection control team ( Stéphanie Francenne ,  Françoise Antoine , and  Salma Flis  for providing continuous effort and dedication; laboratory staff at the  Laboratoire Hospitalier Universitaire de Bruxelles, Universitair Laboratorium Brussels , and the  Bernhard Nocht Institute for Tropical Medicine , particularly  Stefanie Ruben ,  Alexander Schlaphof ,  Birgit Muntau , and  Petra Emmerich  for providing contributions to laboratory management of the patient;  Andres Mangiarotti ,  Micaela Paz ,  Paul Pardon , and the  Vaccine Provision and Prevention Service  at the  National Institute for Public Health and the Environment (Bilthoven, the Netherlands ) for providing favipiravir; and all experts consulted worldwide for providing assistance during the study.
Sampling and Analyses of TEIs The data presented herein was collected primarily during two cruises in 2015 associated with the Arctic GEOTRACES program. The U. S. GEOTRACES GN01 (HLY1502) cruise was held aboard the USCGC Healy, while the German GEOTRACES GN04 (PS94) cruise was on the R/V Polarstern (Fig. 1 ). All sampling and analyses were conducted according to pre-established GEOTRACES approved protocols (for TEIs)  (Cutter et al. , 2014)  and/or GO-SHIP approved protocols (for non-TEIs)  (Hood et al. , 2010). To further ensure quality of TEI data across participating laboratories, extensive intercalibration efforts were taken in accordance with GEOTRACES protocols  (Cutter, 2013). For example, the GN01 and GN04 cruises both occupied the same station within two weeks of each other (GN01 station 30 and GN04 station 101), which enabled investigators to intercompare results for their respective TEIs.
CTD/rosette data and methodologies for PS94 are available via the PANGEA database  (Ober et al. , 2016a (Ober et al. , , 2016b;; Rabe et al. , 2016b Rabe et al. , , 2016a)). The GN01 CTD/rosette procedures are stored on the BCO-DMO database  (Landing et al. , 2019a (Landing et al. , , 2019b)). Detailed methodologies can be found in the publications where the original TEI data were first reported (in case of Hg:  Lamborg et al. , 2016 , Heimbürge et al. , 2015 , Agather et al. , 2019). The GN01 and GN04 Hg species data were intercalibrated at the crossover station. Total Hg concentrations agreed well for the majority of sampling depths. The tHg and MeHg data have been intercalibrated and validated by the GEOTRACES standards & intercalibration committee.
Acknowledgements This study would not have been possible without the dedication of the captains and crews of the  USCGC Healy and R/V Polarstern. The authors also thank the many members of the shipboard scientific parties, including the chief scientists, who enabled sample collection via the various CTD rosette and in situ pumping systems, as well as the sample analyses on the ship and back in their home laboratories. Funding for Arctic GEOTRACES was provided by the  U. S. National Science Foundation ,  Swedish Research Council Formas ,  French Agence Nationale de la Recherche and LabexMER, Netherlands Organization for Scientific Research, and Independent Research Fund Denmark.
Data from GEOTRACES cruises GN01 (HLY1502) and GN04 (PS94) have been archived at the Biological & Chemical Oceanography Data Management Office (BCO-DMO; https://www. bco-dmo. org/deployment/638807) and PANGAEA (https://www. pangaea. de/?q=PS94&f. campaign%5B%5D=PS94) websites, respectively. The inorganic carbon data are available at the NOAA Ocean Carbon Data System (OCADS; doi:10. 3334/CDIAC/OTG. CLIVAR_ARC01_33HQ20150809).
Exposure levels Evidently, most of the available data on workplace exposure levels concern dust, endotoxins and (1 → 3)-β-dglucans. Organic dust is frequently used as a marker of exposure to bio-aerosols whereas information regarding levels of other airborne exposures is scarce. Readers interested in such studies are recommended to look elsewhere  [37, 113]. Overall, studies have shown great variations in personal exposures both between and within different farm types (Table  5 ). Average personal concentrations of dust are reported to range between 0. 2 and 11. 2 mg m -3 with content of endotoxin and glucan concentrations averaging between 13 and 9609 EU m -3 and 223 and 10,300 ng m -3 , respectively. Pig and poultry farmers are the highest exposed, whereas mixed production and mink-farmers are the lowest exposed, irrespectively of the agent concerned.
One of the major contributors of malaria-related deaths in Sub-Saharan African countries is the limited accessibility to quality care. In these countries, malaria control activities are implemented at the health-district level (operational entity of the national health system), while malaria readiness indicators are regionally representative. This study provides an approach for estimating health district-level malaria readiness indicators from survey data designed to provide regionally representative estimates. A binomial-hierarchical Bayesian spatial prediction method was applied to Burkina Faso Service Availability and Readiness Assessment (SARA) survey data to provide estimates of essential equipment availability and readiness for malaria care. Predicted values of each indicator were adjusted by the type of health facility, location, and population density.
SARA survey data provide crucial information to ensure that health facilities are resourced and equipped to deliver essential care to the population. However, most health programs to prevent or reduce diseases are implemented at the health-district level (operational unit), whereas the SARA survey provides service-specific readiness estimates with exclusive focus on national and regional rates. In Burkina Faso, like other SSA countries, the new Global Technical Strategy for Malaria 2016-2030 approach involves transforming malaria surveillance through the District Health Information System (DHIS) into a core intervention  [9]. The WHO further recommends malaria surveillance through the DHIS to be integrated into national and local (health district) malaria control strategies. Therefore, understanding the availability and readiness of health systems at the local scale (health district, community, or village) is necessary to assess whether health programs are progressing as planned or whether adjustments are needed at the operational unit of the health system  [10].
Study Design, Sample, and Sampling Procedure The study was designed as a health facility-based cross-sectional survey conducted between October and November 2014. This survey included both public and private health facilities across the three levels of health system organization (central, intermediate, and peripheral) and different locations (rural or urban). The sampling procedure used in this survey was reported previously  [16]. Briefly, the study used stratified sampling with simple random sampling applied within each stratum, so that the indicators were representative at the regional level. For our study, the analysis included data collected at the health-district level. A total of 753 health facilities located in 70 health districts in the 13 regions of the country were included in the survey, representing 37.
Data Collection and Processing In this analysis, we assessed (1) the general operational capacity of services, with a focus on the availability of essential equipment, and (2) the readiness to provide malaria case management. The data were collected using two methods: Face-to-face interviews with the heads of health facilities or any other relevant health personnel, and direct observation to verify the availability, functionality, and use of the key items. The assessment of the availability of essential equipment included weighing scale for children and adults, medical thermometer, stethoscope, tensiometer, latex gloves for physical exam, and a light source. To assess the service availability for malaria management, the following items were included: Malaria diagnosis by clinical symptoms, malaria diagnosis by rapid diagnostic test (RDT), malaria diagnosis by microscopy, malaria treatment, intermittent preventive treatment during pregnancy (IPTp), national guidelines for malaria treatment and IPTp, first-line antimalarials in stock, and RDT availability.
In the analysis, the variable "health facility provides malaria diagnosis and treatment services" was not included as all (100%) health facilities surveyed provided malaria diagnosis and treatment services. In brief, the availability and readiness indicators were binary variables, taking a value of 1 if the key item was available and in a functional state at the health facility, and 0 otherwise  [8, 16]. Data were recorded on a paper questionnaire. After verification and validation, data were entered electronically in a database designed as a Census and Survey Processing System (CSPRO).
Hierarchical Bayesian Spatial Modeling (HBSM) In this study, four steps for modeling the SARA survey data were used to create a HBSM framework model for the data. • Step 1: Model for the Data As mentioned above, the availability or readiness variables were binary (denoted Y), each with its own Boolean-valued outcome, i. e. , success (Y is equal to 1 with probability p) or failure (Y is equal to 0 with probability q = 1p). The Burkina Faso area comprises a set of K = 1,. , 70 non-overlapping health districts S = {S 1 ,. , S K }, and availability or readiness indicators are recorded for each health district. Let n k be the number of health facilities in the health district K (S k ). In each S k , the variable Y is tested (or measured) n k times, and the number of successful trials among n k tests is counted.
3%, whereas about threequarters of health districts had a percentage less than 46. 5% in terms of disposal of artesunate parenteral form. More than three-quarters of districts had a high rate (>91. 2%) in terms of providing IPTp. Health districts located in major cities, such as Ouagadougou and Bobo-Dioulasso, had the lowest rates regarding the ACT in stock. The percentage of health districts with no ACT in stock on the day of survey ranged from 2. 4% to 12. 4%. The shapefile was obtained from the "Base Nationale de Découpage du territoire" of Burkina Faso (BNDT, 2006). The Service Availability and Readiness Assessment data for modelling were obtained from the Ministry of Health of Burkina Faso.
Discussion To date, the Service Availability and Readiness Assessment (SARA) surveys were carried out in Burkina Faso to generate estimates on basic equipment availability and the readiness to provide specific health care (such malaria case management) for the whole population including pregnant women, mothers, and children at regional level (with smaller sample size). This paper provides an alternative method based on HBSM for optimal estimation of subnational indicators drawn from health facility-based survey data with a much smaller sample size. Since local administrators need information on the operational scale for planning purposes, this alternative method, using advanced statistical methods applied to SARA survey data, offers a useful method for countries with limited resources. This method has been used in other areas to estimate indicators at the subnational level from samples drawn for national or regional estimates  [17, 18].
The authors are sorry to report that some of the validation data used in their recently published paper  [1]  were incorrect. The field biochemistry data considered for summer 2013 were associated with incorrect geographic locations corresponding to a previous campaign. The samples from the three biochemistry dates used in this study (summer 2013, fall 2013, summer 2014) were collected from the same trees each time. This led to incorrect analysis of the biochemistry field data for summer 2013, as well as incorrect selection of the summer 2013 pixels needed to confront estimations with our method with in situ data. After examination of the correct summer 2013 data, two points (related to two specific trees) showed inappropriate variation of both leaf chlorophylls a+b content(C ab ) and leaf carotenoids content (Car) between summer and fall, with a significant increase when it should have been decreasing.
38 µg/cm 2 , respectively), besting even soil-adjusted VI. When compared to field measurements from all dates, most GM_94b-estimated points are very close to the first bisector, and only one point (pink from summer 2013) is greatly underestimated (Figure  10a ). For Car, at q = 400, the best method is also clear: RMSE INT CAR is the only method to present a low RMSE, a low STDB, and a high R 2 (1. 34 µg/cm 2 , 1. 06 µg/cm 2 and 0. 59, respectively. See Table  9 ). The RMSE INT CAR method showed the best performances overall, with estimated values very close to the first bisector (Figure  10b ) for all seasons. " Section 4. 2. , update: "Indeed, in Section 3. 3's Table  7 , both GM_94b and gNDVI indices could be identified as optimal depending on the date. However, when considering the complete dataset, which includes summer and fall data, GM_94b outperforms gNDVI significantly with a lower RMSE and considerably higher R 2 (Table  9 ).
" to: "Indeed, in Section 3. 3'sTable 7, TCARI/OSAVI, GM_94b and gNDVI indices could be identified as optimal depending on the date. However, when considering the complete dataset, which includes summer and fall data, GM_94b outperforms the others with a lower RMSE and higher R 2 (Table  9 ). " Section 4. 2. , update: "Indeed, in Section 3. 3's Table  7 , both GM_94b and gNDVI indices could be identified as optimal depending on the date. However, when considering the complete dataset, which includes summer and fall data, GM_94b outperforms gNDVI significantly with a lower RMSE and considerably higher R 2 (Table  9 ). " to: "Indeed, in Section 3. 3'sTable 7, TCARI/OSAVI, GM_94b and gNDVI indices could be identified as optimal depending on the date. However, when considering the complete dataset, which includes summer and fall data, GM_94b outperforms the others with a lower RMSE and higher R 2 (Table  9 ).
" Section 4. 3, update: "Carotenoid estimations did not perform that well, even though the estimation RMSE was low Section 4. 2. , update: "Indeed, in Section 3. 3's Table  7 , both GM_94b and gNDVI indices could be identified as optimal depending on the date. However, when considering the complete dataset, which includes summer and fall data, GM_94b outperforms gNDVI significantly with a lower RMSE and considerably higher R 2 (Table  9 ). " to: "Indeed, in Section 3. 3's Table  7 , TCARI/OSAVI, GM_94b and gNDVI indices could be identified as optimal depending on the date. However, when considering the complete dataset, which includes summer and fall data, GM_94b outperforms the others with a lower RMSE and higher R 2 (Table  9 ). " Section 4. 3, update: "Carotenoid estimations did not perform that well, even though the estimation RMSE was low (RMSE = 2.
Tidal Stream Turbine Experimental Data-Based Tests The proposed HOS-based biofouling diagnosis approach is tested on an experimental dataset issued from the Shanghai Maritime University TST plateform. This plateform consists of 230 W/8 pole pairs direct-drive PMSG-based TST operating in a water tunnel emulating waves and turbulences (Figures  9  and 10 )  [13, 14]. Besides, 19 processing features were handled under varying operating conditions (wave and turbulence, Figure  11 ). The biofouling (imbalance fault) was emulated by attaching wire ropes on the rotor blade (Figure  10 ). Experimental data collection was made under the following operating conditions: TST blades rotation was set at 120 rpm, the supply f 1 [H z].