dataesr/scientific-paragraphs-categorization huggingface dataset:
In this late region the synteny of genes encoding structural proteins of the SPP1 virion and late lysis genes is conserved when compared to the genomes of other Siphoviridae (Figure  1 ). The function of other SPP1 ORF products is poorly understood. Genome deletions or inversions showed that ORFs 3-5, 7, 12, 14, 31. 2, 32, 33 and 42-53 are non-essential (Figure  1 ;  [26, 35, 53, 55, 83, 88] ; this work; P. T. , unpublished) which corresponds to 19% of the SPP1 genome. Among these, a function was assigned experimentally only to the products of genes 7, 12 (see Section 3. 8) and 44 (see Section 3. 5) while bioinformatics revealed a putative role for gp32 and gp33 (see Section 3. 4). Within the SPP1 genome regions not tested by deletion analysis, the (putative) function of the products from ORFs ) whose products are unrelated to protein sequences in databases.
We privilege the hypothesis that gp33 accumulates in the cytoplasm and its release upon lysis acts on the wall of the infected bacterium to facilitate lysis and/or on the envelope of other B. subtilis cells to facilitate their subsequent infection. PE5 controls expression of 14 ORFs including genes 25, 26 and 26* involved in cell lysis (see Section 3. 6). Most of the other ORFs encode short polypeptides of unknown function. Gp29 and its homologous hypothetical protein of Geobacillus phage GBK2 give strong hits in HHPred to DNA-binding proteins with a winged helix fold like transcriptional regulators and excionases. ORFs 30, 31. 1, and 31. 2 products have predicted transmembrane segments suggesting an early role in the cytoplasmic membrane during infection. Gp31. 1 and gp31. 2, which are encoded by contiguous ORFs, are homologous to numerous proteins of Bacillus spp.
subtilis phage SPO1 HNH homing endonuclease I-HmuI (PDB accession number 1U3E)  [128]. Proteins containing the HNH motif carry out intron homing in phages T4, SPO1 and SP82  [129, 130] , and are essential for DNA packaging in a group of cos-phages  [131, 132]. In other phages they play dispensable functions like in Staphylococcus aureus phage 80α  [133]. Gp37. 3 is a small basic polypeptide whose amino acid sequence and structure prediction show relatedness to numerous bacterial and phage DNA binding proteins. SPP1 gp34, gp36. 1, gp37. 3 together with gp29, and possibly other early polypeptides that did not deliver robust bioinformatics hits yet, are strong candidates to participate in SPP1 DNA metabolism and in gene expression regulation processes calling for further research. Gene 37. 1 that is found within this set of genes codes for an enzyme with a distinct function.
Gp37. 1 is highly homologous to poly-γ-glutamate (γ-PGA) hydrolases from phages of Gram-positive bacteria, mainly infecting Bacillus spp. like vB BsuM-Goe3, BSP10, Grass, BSNPO1, PM1, phiNIT1, PBS1, AR9 and others. HHPred provided a strong hit to the structure of the γ-PGA hydrolase PghP from phage phiNIT1 (PDB accession number 3A9L). The supposed role of the enzyme in phages is to degrade the γ-PGA polymer layer. This layer forms a shield at the surface of several microorganisms, mainly Bacillus spp. , to protect them from environmental attacks as diverse as phagocytosis or phage infection  [134, 135]. Release of γ-PGA hydrolase from phage-infected bacteria would thus open the way for infection of new host bacteria protected by a γ-PGA layer. Experimental demonstration of γ-PGA hydrolase activity from B.
The SPP1 Genes Set. II. DNA Replication Early Genes The bacteriophage SPP1 DNA replication is a well-characterized process. SPP1 was reported to have two origins of replication, oriR and oriL (Figure  1 ), localized ~13 kbp apart in the circularized genome. The two sequences have a similar organization composed of direct repeats and an AT-rich region that acts as a DNA unwinding element (DUE)  [102]. The SPP1 origin binding protein gp38 binds to the direct repeats of oriL, positioned within gene 38 (Figure  1 ), and of oriR with nanomolar affinity  [102]. Replication initiation requires gp38, the helicase loader gp39  [103, 104]  (PDB accession number 1NO1), and the replicative helicase gp40 that belongs to the DnaB family  [103, 105, 106]  (PDB accession number 3BGW). After melting of the origin sequence and unwinding, the gp40 hub interacts with the host DnaG  [107, 108]  and with the DnaX subunit of the clamp loader  [109].
Gp22 has homology to a group of Bacillus spp. putative proteins and its structure (PDB access code 2XC8) reveals a fold similar to a domain of the tail receptor binding protein from lactococcal phage p2  [96]  (see  Section 3. 8). The structure of the gp23. 1 hexamer was also determined (PDB access code 2XF7) but provided no conclusive insight on protein function  [97]. Gp23, gp23. 1 and gp24 gave no strong homology hits to proteins in the databank. The role of the dispensable short ORFs 3, 4, 5 and 14 that separate essential genes for SPP1 capsid assembly is not known. The products of ORFs 3 and 4 show homology to a hypothetical protein of Bacillus phages vB_BsuM-Goe3 and PM1, respectively, while gp5 has similarity to a large number of bacterial and phage uncharacterized proteins. ORF 14 is an orphan.
One of the 12 vertexes of the procapsid icosahedron is a specialized structure defined by presence of a cyclical dodecamer of the portal protein gp6  ([20,60,75-81] ; PDB access code 2JES). Assembly of the procapsid initiates most likely at the portal vertex by co-interaction between gp6, gp11 and gp13. In absence of gp6, gp11 and gp13 assemble procapsids of normal and of a smaller size, revealing that the portal ensures correct size determination of the procapsid  [86]. Co-production of gp6, gp11 and gp13 is sufficient and necessary for assembly of procapsids functional for DNA packaging, showing that they are the minimal set of essential components to build these structures  [86]. Gp6 makes a strong interaction with gp7 that targets this protein to the procapsid interior in a few number of copies  [84].
The terminase is composed of the small subunit (TerS) gp1 that binds to pac [57,  [63] [64] [65] [66]  (PDB access code 3ZQQ of gp1 from the SPP1-related phage SF6) and of the large subunit (TerL) gp2, a two-domain protein with ATPase and nuclease activities [57,67,70-72] (PDB access codes 2WBN and 2WC9 of the gp2 nuclease domain). Re-analysis of gene 1 sequence showed that gp1 is shorter (Table  1 ) and more similar to the TerS from other SPP1-related phages  [118]  than initially reported. The pac sequence overlaps the PL1/PL2 promoters (see Section 3. 2), the RBS, and the sequence of gene 1 encoding the gp1 DNA-binding domain (Figure  1 )  [63]. Gp1 binds to the pacL and pacR regions of pac  [63, 68]  and recruits gp2 for making a double-strand cleavage within pacC, a sequence that is flanked by pacL and pacR  [70].
Disassembly of the DNA packaging motor is coordinated with sequential binding of the head completion protein gp15 (PDB access code 2KBZ) to gp6, extending the portal channel, and of gp16 (PDB access code 2KCA) that closes the channel, preventing release of the packaged DNA  [60, 78, 89]. The gp6-gp15-gp16 complex is named connector. The SPP1 long non-contractile tail has a building plan similar to the long tails of Siphoviridae and Myoviridae. These structures are structurally related to a variety of cellular, tube-like, delivery devices used in bacterial warfare like phage tail-like bacteriocins (PTLBs), phage tail-like complexes that confer toxicity against eukaryotic cells, or type VI secretion systems  [156, 157]. The SPP1 tail features a host adsorption apparatus that binds selectively to the B.
subtilis surface and promotes DNA transfer across the bacterial envelope. This ~31 nm-long structure binds selectively to glycosylated teichoic acids [21], which facilitates its subsequent strong interaction with the membrane protein YueB  [23, 158] , committing the phage particle to infection. The known components of the adsorption apparatus are the tail fiber (or Tal) gp21  [93] , which binds YueB  [95] , and the distal tail protein (Dit) gp19. 1 (PDB access code 2X8K)  [94]. The gp19. 1 hexamer forms a complex with the trimeric gp21 amino terminus that closes the tail tube in the virion and opens at the beginning of infection  [93]. Gp19. 1 defines one end of the ~160 nm-long tail tube built by a helical array of the TTPs gp17. 1/gp17. 1* that are found at a ratio of ~3:1 in the tube  [91].
Similar tubes can be formed exclusively by gp17. 1 in vivo  [91]  and in vitro  [92]. The TTPs form hexameric rings organized around the tape measure protein (TMP) gp18  [16]. The tail tube of sipho and myoviruses is tapered by (a) tail completion protein(s), which provide(s) the interface for tail attachment to the connector. In SPP1 it is the tail-to-head joining protein (THJP) gp17 (PDB access code 2LFP) that interacts with the capsid connector in the viral particle  [32, 90]. The connector-tail completion proteins structure is named head-to-tail interface  [81]. SPP1 tail assembly is anticipated to follow the pathway of phages with long tails typified by T4  [159]  and lambda  [160]. In such case, the tail adsorption apparatus is formed first providing a platform for initiation of gp17.
Funding: Research in our teams was funded by the  CNRS ,  INRA ,  MINECO  grant  BFU2015-67065-P  (to S. A. ),  ANR  grants  09-BLAN-0149-0  (to P. T. ),  ANR-12-Blanc-BSV3-0021  (to P. T. and R. C. L. ),  ANR-15-CE11-0 010-01  (to P. T. and R. C. L. ), and an  "Equipe de la Fondation Médicale (FRM) " grant (to P. T. ).
Methods This study analyses data collected from 2011 (the date of creation of the IHU-MI) through the completion of this article in February 2018. The number of students by level of graduation and by country of origin per year was sorted from our administrative database of students and scientific visitors during the study period from the team dedicated to antibiotic resistance research (JMR team). Students from the Mediterranean basin and Africa were counted from this primary list and were sorted by level of graduation (master's degree, PhD, postdoc and scientific visitors) and by country. Because some students stayed at our institute both for master's and PhD courses, we deduplicated the total count. The number of students present per year in the team was also calculated from this list to show the student kinetics of reception per year.
All students facing antibiotic resistance in their country came with their own isolates to analyse them as a course training. Most of them continued to collaborate with our institute, resulting in real-time surveillance of antibiotic resistance according to their field of research (humans, animals or the environment). Initially there was no rationale for the recruitment and analysis of the samples because no data existed at the beginning of this network. Now, however, the follow-up of antibiotic resistance is mainly focused on the current antibiotic resistance situation. For each epidemiologic study, the number and type of samples and/or bacterial isolates and the country of origin were counted, and data were presented in a single table, with all data provided by country. Antibiotic resistance for each sample or bacterial isolates was studied using the same procedure.
Then AST results were interpreted according to European Committee on Antimicrobial Susceptibility Testing guidelines  [7]. Genotypic identification of resistance genes were screened by real-time quantitative PCR and confirmed by standard PCR and sequencing when necessary, and sequences were analysed using ARG-ANNOT software  [8]  to identify the specific antibiotic resistance gene. Multilocus sequence typing was performed to evaluate genetic relatedness of strains. If necessary, a whole genome sequence study was performed to obtain the complete resistome of a strain  [9]  or to describe the genetic environment of an antibiotic resistance gene  [10, 11]. Each student hosted by our institution received specific training for the study of antibiotic resistance (MALDI-TOF MS, AST, molecular training, genomics, bioinformatics) and presented the progress of their work and their results every week so that we could prepare tables and figures to be used for publication.
Differences Between GBD 2015 and GBD 2013 General methods for GBD 2015 and prior GBD studies have been described previously. 1, 12 Here, we present methods and results specific to the GBD 2015 cancer estimation. The general framework for the cancer estimation in GBD 2015 has remained similar to GBD 2013, exceptions are detailed below. 13 The GBD 2015 study is compliant with the newly developed Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). 14 A chart detailing fulfillment of GATHER requirements is provided in eTable 1 in the Supplement; flowcharts and a detailed description for each estimation step are also available in the eAppendix and in the numerous eTables and eFigures in the Supplement. Box 1 includes a list of the figures and tables in this article. Further details about methods and data sources are provided in the eAppendix, eFigures, and eTables in the Supplement.
Box 2 contains a list of the supplementary figures and tables. Additional information is available from the authors in Web Tables  1  through 3 ; the web addresses for these items are listed in Box 3. Hereinafter, citations to Web Tables are for those given in Box 3. Data sources for GBD 2015 are listed in eTable 2 in the Supplement, including which new sources were added compared with GBD 2013. Relevant changes in the estimation strategy since GBD 2013 include the addition of 7 territories (American Samoa, Bermuda, Greenland, Guam, Northern Mariana Islands, Puerto Rico, and the US Virgin Islands), which previously were only included in the GBD regional totals. Results for the United Kingdom are reported for Northern Ireland, Scotland, Wales, and England). Changes to the GBD causes include dividing "leukemia" into acute lymphoid leukemia, chronic lymphoid leukemia, acute myeloid leukemia, and chronic myeloid leukemia.
For GBD 2015, SDI quintiles were used to group countries that are similar based on their development status. Locations were grouped into quintiles based on their SDI value in 2015. Quintile cutoffs were based on the distribution of geography-years from 1980 to 2015 with the exception of populations smaller than 1 million. eFigure 4 and eTable 8 in the Supplement show the SDI quintile for each country. As for every GBD study, the full time series estimated for each GBD cycle supersedes prior GBD studies. For GBD 2015, the full time series from 1990 to 2015 was estimated. We focus here on changes over the last decade. Estimates before 2005 as well as additional results can be found online (https://vizhub. healthdata. org/gbd-compare/). To maximize data availability and take advantage of cancer registry data in countries with scarce mortality data, incidence data from cancer registries were transformed to mortality estimates through the use of separately estimated MI ratios.
Modeling of the MI ratios is described in detail in the eAppendix in the Supplement. In brief, the estimation followed a 3-step approach, the creation of logit random effect models, spatiotemporal smoothing, and Gaussian process regression. A final model was selected based on out-of-sample validation. Updated cancer registry data for GBD 2015 was obtained from the GBD collaborator network or downloaded from publically available sources. All data sources used for MI ratio estimation, as well as those used for incidence data transformed to mortality estimates, are listed in the eAppendix and eTable 2 in the Supplement. For cancer estimation, 333 513 site-years were used from vital registration systems, 785 site-years from verbal autopsy, 619 site years from surveillance data, and 69 013 site years from cancer registry data.
The number of site-years used by source type and by cancer can be found in eTable 3 in the Supplement. All data sources were extracted at the most detailed cause-and age-specific level and mapped to the GBD cause list. Codes from the International Classification of Diseases, Ninth Revision (ICD-9), and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10), for each GBD cancer group as well as a description of data processing steps can be found in the eAppendix in the Supplement. The 32 cancer groups, together referred to as the "all cancer" group, encompass all malignant neoplasms as defined in the ICD-10 except for nonmelanoma skin cancer (NMSC, ICD-10 code C44) and Kaposi sarcoma (ICD-10 code C46). Although NMSC is the most common cancer in many populations, most cancer registries do not include NMSC, which necessitates different estimation methods from the cancers presented here.
16 Deaths due to Kaposi sarcoma are not separately included because these were attributed to human immunodeficiency virus/AIDS in the GBD study. The combined data on individual causes of death were used as input for the Cause of Death Ensemble model (CODEm). 17 Covariates used for each cancer are listed in the eAppendix in the Supplement. Individual cause mortality estimates from CODEm were constrained to fit independently modeled, all-cause mortality estimates using the tool CodCorrect. 1  We calculated YLLs by multiplying each death with the life expectancy for that age taken from a normative life table; life expectancy at birth was 86. 5 years, which is based on the lowest observed death rate in each 5-year age group in populations over 5 million. 1 inal mortality estimates were transformed into incidence estimates using modeled MI ratios.
Since data sources including stage distribution and treatment approaches are not available for most countries, the simplifying assumption of a constant duration of the diagnosis and treatment, metastatic/disseminated, and terminal phase for all ages, over time, and all countries was made. After dividing total prevalence into these 3 sequelae, we attributed the remaining prevalence to the remission phase. To calculate YLDs, the prevalence for each sequela was multiplied with a disability weight. Additional disability was estimated for procedures and procedure-related morbidities associated with the treatment of breast, larynx, colorectal, bladder, and prostate cancer (mastectomy, laryngectomy, stoma, urinary incontinence, and impotence) under the assumption that these are major disabling sequelae after cancer treatment.
Limitations As in prior GBD studies, our estimates depend on the quality and quantity of the data sources available to inform the estimates. Because of the lag time for data reporting, estimates for 2015 are mainly based on data and trends from recent years. For many countries, data sources for informing cancer burden estimation are still sparse, and the GBD estimates rely heavily on covariate selection in the models and regional patterns. Only an estimated 38% of deaths worldwide were registered in 2012. 66 Alternative data sources for cancer mortality, such as verbal autopsy, are substantially less reliable. 67 To overcome the limitation from lack of data sources, the GBD study includes cancer incidence data from registries in the mortality estimation. Cancer registration has a long tradition in many countries.
Cancer mortality estimates are predominantly based on vital registration data, cancer registry data, and to a much lesser extent other data sources. If a large proportion of deaths are miscoded, the redistribution of these so-called garbage codes can substantially affect mortality estimates. Since GBD cancer incidence estimates are based on mortality estimates, garbage code redistribution directly affects cancer incidence. Misclassification of metastatic sites as primary cancer sites (eg, liver, lung, brain) is another source of potential bias, especially in locations with limited diagnostic resources. Changes in coding practices or coding systems may also have an effect, even though mapping to the GBD causes list includes adjustments to account for different coding systems. 69 ervical and uterine cancer incidence rates are potentially overestimated in the GBD in locations where hysterectomies are common, since rates are calculated without adjustments for the population at risk.
For GBD 2015, we have updated the MI (mortality to incidence) ratios and used out-of-sample validation of a set of potential models to choose the most appropriate MI model. However, in young age groups with sparse data, and in areas where no matching mortality and incidence data exist (which is the case for most countries in Sub-Saharan Africa), the MI model is based on the combination of trends for older age groups as well as covariate selection. The addition of the leukemia subtypes for the GBD 2015 study is a necessary step; aggregating "leukemia" into a single entity is appropriate from neither a public health nor a clinical perspective. However, when interpreting the results, it is important to recognize that those data sources for the MI ratios that determine incidence for leukemia subtypes are mostly from high-income countries.
For conventional fluorescence microscopy, the sample was imaged on a Zeiss LSM 800 confocal laser scanning microscope at 630-fold magnification using a far-red filter (Cy5). The images were analyzed using the ZEN 2. 1 lite software. For superresolution microscopy (STORM), the samples were imaged using an ELYRA PS1 microscope (Zeiss), using a 100× Plan-apochromat objective (NA 1. 46 oil immersion). A series of 2000 cycles were acquired in total internal reflection microscopy (TIRF) mode for each experimental measurements using 642 nm laser (100% power) and 405 nm laser (1% power during integration). For visualization, "Localization Precision" (20-35 nm) and "Chi Square" (1. 5-3) filters were used. Images were segmented using CellProfiler (v2. 2. 0)  27  to identify fluorescent spots and measure shape and intensity.
All procedures were carried out in accordance with the "Measures on Ethical Review over Biomedical Research Involving Human Subjects" (Ministry of Health, China, January 11, 2007). All the samples were collected from patients after obtaining informed consent, and the data from these patients were used in this study only. Clincal Samples. Fifty-four clinical samples were collected in March 2018 from Adicon Clinical Laboratories (Hangzhou, China). DNA was extracted from 200 μL of serum using a magnetic beadsbased method according to the provided instructions from the kit (Sansure Biotech, China). The extracted HBV-DNA was characterized with a quantitative PCR diagnostic kit (Sansure Biotech, Changsha, China) on an ABI 7500 real-time PCR system (2 min at 95 °C, followed by 45 cycles at 95 °C for 10 s and 60 °C for 30 s).
Acoustic Emission (AE) Acoustic emission monitoring was performed on openhole samples by using the AE system from Mistras Group. Two sensors Micro-80 with a resonant frequency of 300 kHz and an active surface diameter of 10mm were used. They were placed at the gauge extremities of the specimen using silicon grease as the coupling agent. Sensors are separated with a distance of 100 mm between their centers. The amplitude threshold has been chosen equal to 35 dB. Table  2  shows the settings of the AE system used. Each test was preceded by a data acquisition calibration step. Using a pencil lead break procedure, the acoustic wave speed as well as the attenuation phenomenon were measured. For the latter, the lead breakage operation was repeated several times between the two sensors, at regular intervals (Hsu-Nielsen method).