dataesr/scientific-paragraphs-categorization huggingface dataset:
The interaction of the generated particles with the detector and its response are implemented using the Geant4 toolkit  [20]  as described in Ref. [21]. The trigger  [22]  consists of a hardware stage, based on information from the calorimeter and muon systems, followed by a software stage, which performs a full event reconstruction. The candidates used in this analysis are triggered at the hardware stage either directly by one of the particles from the B candidate decay depositing a transverse energy of at least 3. 6 GeV in the calorimeters, or by other activity in the event (usually associated with the decay products of the other b-hadron decay produced in the pp → b b X interaction). Inclusion of the latter category increases the acceptance of signal decays by approximately a factor two.
Selection efficiencies are determined from simulated samples generated at a centre-of-mass energy of 8 TeV. The ratio of selection efficiencies is found to be r sel = 1. 111 ± 0. 019, where the uncertainty is from the limited sample sizes. To first order, effects from imperfect simulation should cancel in the ratio of efficiencies. In order to assign a systematic uncertainty for a potential deviation of the ratio of efficiencies in 7 TeV data with respect to 8 TeV, the B + → K 0 S π + and B + → K 0 S K + simulated events are reweighted by a linear function of the B-meson momentum such that the average B momentum is 13% lower, corresponding to the ratio of beam energies. The 0. 7% relative difference between the nominal and reweighted efficiency ratio is assigned as a systematic uncertainty.
agencies: CAPES, CNPq, FAPERJ and FINEP (Brazil); NSFC (China); CNRS/IN2P3 and Region Auvergne (France); BMBF, DFG, HGF and MPG (Germany); SFI (Ireland); INFN (Italy); FOM and NWO (The Netherlands); SCSR (Poland); MEN/IFA (Romania); MinES, Rosatom, RFBR and NRC "Kurchatov Institute" (Russia); MinECo, XuntaGal and GENCAT (Spain); SNSF and SER (Switzerland); NAS Ukraine (Ukraine); STFC (United Kingdom); NSF (USA). We also acknowledge the support received from the ERC under FP7. The Tier1 computing centres are supported by IN2P3 (France), KIT and BMBF (Germany), INFN (Italy), NWO and SURF (The Netherlands), PIC (Spain), GridPP (United Kingdom). We are thankful for the computing resources put at our disposal by Yandex LLC (Russia), as well as to the communities behind the multiple open source software packages that we depend on.
Treatment Once MDR-TB is confi rmed, staff from the district or provincial dispensary contact the patient and enter the data into the Category 4 MDR-TB Register. MDR-TB treatment is standardised using a regimen comprising kanamycin (KM), capreomycin (CPM), ofl oxacin, levofl oxacin, ethionamide, prothionamide, cycloserine (CS), and para-aminosalicylic acid (PAS). The drugs are selected by the case management committee of the NCCD, and treatment is administered according to national guidelines (Table  2 ). 4  Side effects are monitored clinically. In the case of KM resistance or ototoxicity, CPM is used. In the case of CS intolerance, PAS is added to the regimen. Treatment is generally initiated in the MDR-TB ward. The patient is admitted to the ward and receives treatment for at least 2 months after culture conversion, after which the patient is transferred to the district or provincial dispensary for continuation of treatment for a period of at least 18 months until completion.
Myocardial Function Freshly excised mouse hearts were immediately mounted onto a Langendorff apparatus, perfused at constant coronary flow (2. 5 mL. min 21 ) with Krebs-Henseleit bicarbonate buffer and paced at 10 Hz. A latex balloon, inserted into the left ventricle, was filled with an aqueous solution to achieve a left ventricular end-diastolic pressure of 6-8 mmHg. The balloon was linked to a pressure transducer connected to ML118 bridge amplifier that fed into a Powerlab 8 SP high-performance data acquisition system (ADInstruments Ltd. by Phymep, Paris, France). Isovolumic contraction was appreciated by measuring left ventricular developed pressure (LVDP) and its first derivatives. Data were obtained with rising concentrations of the b-adrenergic receptor agonist isoproterenol (0, 1, 10 and 100 nM).
Real-time PCR and RT-PCR Real-time PCRs and RT-PCRs were performed using Eppendorf Realplex S2 (Eppendorf, Le Pecq, France) and Mesa Blue qPCR Master Mix Plus for SYBR assay (Eurogentec, Angers, France). PCR primers used for determination of mitochondrial DNA copy numbers were Ppia (nuclear-encoded gene), mtCOII (mtDNA-encoded gene) and Nd1 (mtDNA-encoded gene) and are presented Table  S1. Primers used for RT-qPCR were against Mif, Slc2a1, Pfkp, Pdk3 and Pgc-1a (Table  S2 ). b-actin was used as an internal control (Table  S2 ). Realplex software was used to quantify differences in gene expression. Results are expressed as fold expression.
TB screening and diagnosis before starting ART is important, since the initiation of ART shortly after the initiation of TB treatment improves survival, particularly in patients with low CD4 cell counts  [15] [16] [17]. In a survey of 15 ART programs in 12 countries in Africa, the Caribbean, Central and South America, as well as Asia, we previously showed that the capacity of programs to diagnose TB was often limited in 2008  [18]. Since then, new diagnostic tools for rapid TB diagnosis have been introduced and have shown promising results  [19, 20]. In 2012, we undertook a large scale survey on the integration of TB and HIV services, diagnostic, screening, preventive and treatment practices within the International epidemiologic Databases to Evaluate AIDS (IeDEA) collaboration. Here, we report the results on the availability and use of TB diagnostics across adult ART programs in low-and middle-income countries, using both program-level and patientlevel data.
Methods IeDEA includes networks of ART programs in low-and middleincome countries from Africa, Asia, the Caribbean, Central and South America, as well as North America  [21] [22] [23]. Data are collected at each site as part of routine monitoring at program enrollment and each follow-up visit. We surveyed ART sites in low-and middle-income countries participating in IeDEA across Africa, Asia, the Caribbean, Central and South America between March 1 and July 1, 2012. A total of 71 sites treating adults or children were invited and 58 sites (81. 7%) participated, including 11 pediatric sites treating children only. See File S1 and Table  S1  in Supporting Information for a list of investigators and sites.
Data collection Representatives from participating IeDEA regions and the TB and pediatric working groups of IeDEA developed this project. The survey was written in English, translated into French and Spanish, and pilot-tested in both languages. Study data were collected and managed using REDCap (Research Electronic Data Capture) electronic data capture tools (see https://redcap. vanderbilt. edu/) [24]. The survey consisted of three components: Section A was an online questionnaire on integration of services, diagnostic, screening, preventive and treatment practices related to TB at the sites; Section B included eight clinical scenarios on TB and HIV management to assess the practices of treating clinicians; and Section C requested routinely collected data on consecutive TB patients seen during the study period.
Section A was completed by local data managers or medical staff. Data collected included site characteristics (level of care, urban or rural setting, number of HIV patients actively followed-up, number of TB cases seen), diagnostic and screening practices, infection control measures, treatment schemes, prevention and prophylaxis practices, integration of HIV and TB care, costs to patients and TB case definitions. Availability was assessed as access to the provider on site, within 5 km, within 10 km, within 20 km, within 30 km, within 40 km, within 50 km, more than 50 km, or not available. In Section B, treating physicians and clinical officers at the sites were asked to address clinical scenarios for hypothetical TB patients presenting at their clinic (Table  S2 ). Treating physicians were instructed that the answers should reflect current practices at their treatment program and not necessarily the best practice available.
Sections A and B consisted of one questionnaire per site. In Section C, we requested data on HIV-infected TB patients consecutively seen from the start of the study period on March 1 through to July 1, 2012, up to a maximum of 50 patients per site. TB cases were defined based on the local case definition; all received anti-TB treatment. Collected data included age, sex, pregnancy status at time of TB diagnosis, date of enrollment into HIV care, date of TB diagnosis, start date of TB treatment, prescribed TB regimen, start date of ART, CD4 cell count, WHO clinical stage at ART start, date of any previous TB episode, type of screening, type of episode (new case, relapse, treatment after failure, etc. ), disease manifestation, sputum microscopy result, culture result, nucleic acid amplification test result, clinical signs and symptoms (coughing, night sweats, fever, weight loss), use of tuberculin skin testing (TST) and isoniazid preventive therapy (IPT).
Statistical analyses The present analysis included ART programs treating patients aged 16 years or older, and focused on TB diagnostics and screening practices. Descriptive statistics were used to compare characteristics of sites and TB patients. Variables of interest included program-level characteristics (such as level of care, setting, cumulative HIV cohort size, or availability of diagnostics), clinical decision making using hypothetical clinical scenarios (such as decision to conduct sputum smear microscopy, culture, or chest X-ray), and individual-level patient data (such as age, sex, WHO clinical stage, on ART or not when TB treatment started, use of sputum smear microscopy, culture, Xpert MTB/RIF, or chest Xray to diagnose TB). We distinguished between the availability of diagnostics (''access'') and how diagnostic tools were used at the patient level at sites with access to the tools (''use'').
Ethics statement Data were collected through IeDEA cohorts. Ethics committees and/or Institutional Review Boards in all host countries approved the collection and transfer of anonymized data (see File S2 in Supporting Information for a complete list). Where requested per local regulations informed consent was provided. In addition, the Vanderbilt University Health Science Committee, Nashville, Tennessee (USA), the Ethics Committee of the University of Bern (Switzerland), and the University of Cape Town (South Africa) approved the analyses of these observational data for this specific project.
Study population The 3C study is a prospective cohort study that recruited community dwelling older people aged 65 years or over from electoral rolls in three French cities (Bordeaux, Dijon, Montpellier) in 1999-2001. 31 The first six years of follow-up were common to the three study centres; subsequently, each centre had specific aims. The study reported here is based on data from Dijon (n=4931; response rate 35%), where investigators were particularly interested in physical function and disability. After the baseline examination (wave 0), participants were interviewed in person after two (wave 1, 2001-02), four (wave 2, 2003-04), seven (wave 4, 2006-07), nine (wave 5, 2008-09), and 11 years (wave 6, 2010-12); wave 3 (2005-06) consisted of a self administered questionnaire that did not include disability measures.
Unhealthy behaviours Data on health behaviours came from the baseline questionnaire. We categorised behaviours as healthy/unhealthy a priori on the basis of previous findings; we did sensitivity analyses to ensure that findings were robust to categorisations used in the analysis. Physical activity was assessed through questions on frequency of daily walking and exercise (for example, gym, swimming, cycling) and categorised as high (walking more than one hour a day and exercising more than once a week), low (walking less than one hour a day and exercising less than once a week), and intermediate (all others). We considered low or intermediate physical activity levels to be unhealthy behaviours. Consumption of fruit and vegetables was assessed through questions on frequency of consumption of raw and cooked fruit and vegetables.
Covariates Baseline sociodemographic measures considered to be potential confounders included age, sex, education (no education/primary school, secondary school, high school/university), and marital status (married, divorced/separated/widowed, single). On the basis of previous literature, we identified characteristics that may mediate the relation between unhealthy behaviours and disability. Cardiovascular disease, stroke, diabetes, cognitive and visual impairment, dyspnoea, and depression have been associated with the hierarchical indicator of disability. 36 epressive symptoms, falls, and hearing/visual impairment have been associated with greater risk of incident disability in basic activities of daily living in women. 39 1] [42] [43]  These covariates were assessed at baseline and each wave of data collection.
Statistical analysis We described participants' characteristics as a function of disability status at the end of the follow-up and the number of unhealthy behaviours at baseline. Disability was assessed at each wave, but the precise date of onset was unknown. In addition, participants could have become disabled between two visits and died before the next visit without having been seen and identified as being disabled. To take into account the interval censored nature of our data and competing risks of death, we used a multistate model with transitions between three states (disease-free, disabled, dead) and a Weibull distribution for the hazards. This approach takes interval censoring into account and allows those who die between two waves of data collection to become disabled before dying.
Time to event analyses require a binary outcome; to take into account the ordinal nature of the disability indicator, we used multinomial logistic regression at each wave, with the hierarchical score as the dependent variable. We examined the extent to which the association between unhealthy behaviours and disability was explained by mediators (body mass index, cognitive function, depressive symptoms, trauma, chronic conditions, cardiovascular disease and risk factors) by estimating the percentage reduction as 100×(β model We did analyses with SAS 9. 3 and the R (2. 14) SmoothHazard package. P values are two sided, and we considered those of 0. 05 or below to be statistically significant.
Results At baseline, 488 (9. 9%) of 4931 participants were disabled and excluded from the analyses; a further 195 participants dropped out of the study before wave 1 and did not die during the follow-up. These participants were less physically active (17. 7% v 24. 7% in the high category; P=0. 05) and less likely to consume alcohol lightly to moderately (49. 5% v 63. 7%; P<0. 001) than were those who remained in the study; no major differences existed in age, sex, diet, smoking, and number of unhealthy behaviours (all P>0. 05). Moreover, 266 participants had missing data on at least one health behaviour (n=254) or the outcome (n=12). Our analyses are therefore based on 3982 participants (2410 (60. 5%) women) (supplementary figure A). Compared with participants included in the analyses, those excluded (n=461) were older (75.
Introduction Tuberculosis (TB) still remains as a major, global public health problem. Worldwide, 10 million new cases of active TB and 1. 8 million associated deaths are estimated to occur annually  [1]. As judged by the information from available strain genotype databases  [2, 3, 4] , these impressive figures imply the existence and the circulation of at least tens of thousands strains of the causative agent Mycobacterium tuberculosis and its close relatives of the M. tuberculosis complex, such as Mycobacterium bovis and Mycobacterium africanum. Recent studies have shown an association between strain genetic lineages and TB patient geographic origin  [2,5,6,7,8,]  revealing a strong phylogeographical structuring of the pathogen. This association has been hypothesized to reflect the potential contribution of social factors limiting contacts between host population groups in some settings and/or potential adaptation of specific M.
Study population This study initially included 328 isolates that represent approximately 35% of the total estimate of drug resistant TB cases in the country during the study period. Six isolates were excluded from the study dataset based on the detection of double alleles in two or more MIRU-VNTR loci, indicating the simultaneous presence of two independent strains potentially due to mixed infection or contamination. The nationalities of the study subjects were composed of 51. 9% Saudis and 48. 1% non-Saudis. The non-Saudi population enrolled in the study comprised patients from 19 countries, mainly from Asia and Africa (Table  1 ). Of the total 214 (66. 5%) cases were classified as ''New'' and 108 (33. 5%) were ''previously treated'', according to standard WHO guidelines  [19, 20]  (Table  2 ).
Phylogenetic distribution Analysis of the congruence between the MIRU-VNTR typing and the spoligotyping data for prediction of the phylogenetic lineages of the isolates was first performed by constructing a neighbor-joining tree based on MIRU-VNTR data followed by visual inspection of typical spoligotype signatures of the resulting strain groupings. The results revealed expected, consistent groupings, as seen from the fact that most isolates within the different groups shared typical spoligotype patterns and variants thereof (see e. g. the monophyletic grouping of the isolates with a typical Beijing spoligotype) (Figure  S1 ). In a second step, the MIRU-VNTR and spoligotyping data were submitted to MIRU-VNTRPlus, and subjected to a bestmatch, followed by a tree-based analysis, using the genotypes included in the database as references  [19].
This analysis revealed the existence of 12 genetic lineages, which included almost all the known major branches of M. tuberculosis represented in MIRU-VNTRPlus database. Among these ones, Delhi/CAS (21. 1%), EAI (11. 2%) and Beijing (11. 2%) lineages were the most prominent ones as well as the Ghana branch of the Euro-American super-lineage (14. 9%) (Figure  S1 ). Almost all other branches of this super-lineage were also detected, including Haarlem (10. 6%), Cameroon (7. 8%), and LAM (7. 5%). Of note, we additionally found 15 (4. 7%) cases of M. bovis, recognized as BCG Danish based notably on the identification of typical variant allele of locus 0580 (alias MIRU 04)  [22, 23].
Discussion Two main findings were obtained from the present study. Firstly, a particular broad distribution of M. tuberculosis strain lineages was observed among the 322 isolates collected from 8 regions of Saudi Arabia. This is completely in line with intense human pilgrimage and cosmopolitan migration into the country. Secondly and less expectedly, we observed very similar strain lineage distributions among Saudi and non-Saudi TB patients and a majority of strain molecular clusters were shared by patients of both groups, in striking contrast to large immigration centers of the Western hemisphere where native and foreign-born TB patients show more specific, respective strain populations. Genotyping allowed us to detect in this strain collection almost all the major branches of M. tuberculosis represented in MIRU-VNTRPlus database.
Data analysis The drug susceptibility results were defined into three major groups, an isolate resistant to single drug (mono drug resistance), more than one drug (poly resistance) and resistance to at least both  INH and RIF (MDRTB). A patient who received anti-tuberculosis therapy for. 1 month was considered as ''previously treated'', and for ,1month as ''New''. The spoligotyping results were converted into numerical octal codes. The alleles of MIRU-VNTR types were initially determined by using Genemapper V-4. 0 (Applied Biosystems, CA, USA) and data were compiled by the MIRU-VNTR Data manager (Genoscreen, Lille, France). MIRU-VNTR and spoligotyping data were submitted to the international online database (www. miru-vntrplus. org) to assign the lineages according to the previously described strategy, combining best-match-and tree-based analysis  [3, 21].
Molecular clustering of the isolates was determined by constructing a dendogram based on the combination of MIRU-VNTR and spoligotyping data, using the unweighted pair group method using average linkages and the categorical coefficient. A strain cluster was defined as two or more isolates sharing completely identical genotypes. The statistical analysis of the results was carried out by using the software package SPSS V-19. 0 (IBM, USA). Associations with mono, poly or multidrug resistance were calculated by chi square analysis. The p value,0. 05 was considered as statistically significant. provinces/study sites are illustrated. The areas of Jizan, Al-Baha and Medina were clubbed as ''Others'' as the respective isolate numbers were very low. The lineages TUR, NEW-I, Uganda-I, S, X, URAL are also labeled together as ''Others'' for the same reasons.
As shown above, for both Gαi1-Rluc (Figure  3A ) and Gαo-Rluc (Figure  3C ) we observed a basal BRET signal and trypsin promoted a rapid BRET increase in the first seconds of stimulation and the signal was stable for ~4 min before its slow decline in a time-dependent manner. The kinetic analysis using "Plateau followed by one phase decay" equation of Prism GraphPad software resulted in decay t 1/2 values close to 10 min (Table  1 ) for both Gαi1-Rluc (Figure  3B ) and   Gαo-Rluc (Figure  3D ). This analysis demonstrates a reversible trypsin-induced BRET increase reflecting a rapid activation of PAR2-Gαi1 and PAR2-Gαo complexes which is then likely followed by their desensitization. In contrast, a very low BRET signal was measured between Gα12-Rluc and PAR2-YFP as expected (Figure  3E ) consistent with the data in Figure  1A.
For each RT-PCR assay the agarose gel electrophoresis analysis revealed a single band of expected size. For each PCR product obtained, both strands of the amplicon were sequenced using RT-PCR and sequencing primers, previously described  [7]. For each sequencing reaction the electropherogram was analysed by Chromas lite software V2. 01 package (available at www. technelysium. com. au) and the nucleotide sequences were aligned and confirmed using BLAST software (http://www. ncbi. nlm. nih. gov/blast). The human USUV complete genome sequence (designated Bologna/09) was deposited in GenBank with the following accession number: HM569263. Multiple alignment of the USUV complete genome sequences derived from the published isolates, was generated with ClustalW software (version 2, available at: http://www.
Phylogenetic Analysis Phylogenetic analysis was performed by aligning the complete nucleotide sequences of the poly-protein coding regions of the Bologna/09 strain with the complete coding region of the USUV isolates available in GenBank (AY453411, JQ219843, EF206350, HE599647, JF266698, AY453412). Phylogenetic trees were inferred using nucleotide alignment  [32]. Neighbor joining (NJ) method  [30]  was performed and implemented in MEGA software (v 4. 1)  [33]. The most parsimonious tree incorporated 1000 bootstrap replications.