dataesr/scientific-paragraphs-categorization huggingface dataset:
INTRODUCTION Intravesical instillation of bacillus Calmette-Guerin (BCG) is the most effective adjuvant treatment of non-muscle invasive bladder cancer (NMIBC) after transurethral resection of the bladder (TURB)  [1] [2] [3] [4] [5] [6] [7]. Side effects are usually mild and transient, although localized (1%) or systemic (<1% to 4. 7%) mycobacterial infections can occur  [8] [9] [10]. These often lead to early treatment discontinuation, and thus negatively impact the disease course  [11]. Among the eight available BCG-marketed strains, BCG Connaught, TICE, and Rijksinstituut voor Volksgezondheid en Milieuhygiene (RIVM) are the most commonly used. Data is conflicting regarding the strains' efficacy rates, and to date no difference has been identified in their safety profiles  [12] [13] [14] [15] [16] [17].
Our study has a low sample size and limitations inherent to its retrospective design, namely missing data, loss to follow-up, and the lack of standardized diagnostic and therapeutic procedures. As there is no proper code for the diagnosis of BCG infection in the medical information system database, it is also likely that our study was not a comprehensive survey of all incident cases of BCG infection occurring in France over the study period. Yet, this data arising from complementary collaborative networks supports the implementation of both basic and clinical studies to better decipher the interplay between BCG strains (and instillation devices) and patient outcomes. Hence, prospective studies are needed, especially as BCG Connaught production resumed at the end of 2016. As the batch number was reported in the medical files of only 15/40 patients of the present series, our findings underline the opacity of our current drug traceability system, which should be considerably improved.
Acknowledgements We are grateful to the  Association Française d'Urologie (AFU) ,  Société de Pathologie Infectieuse de Langue Française (SPILF) ,  Société de Pneumologie de Langue Française (SPLF)/Groupe pour l'Enseignement et la Recherche en Pneumo-Infectiologie (GREPI) ,  Amicale des Jeunes Internistes (AJI) , and  Centres de Lutte Antituberculeuse (CLAT)  for helping with the case-by-case identification and data collection.
Background: Austria has been among the main European countries hosting incoming asylum seekers since 2015. Consequently, there was an urgent need to predict any public health threats associated with the arriving asylum seekers. The Department of Surveillance and Infectious Disease Epidemiology at the Austrian Agency for Health and Food Safety (AGES) was mandated to implement a national syndrome-based surveillance system in the 7 reception centers by the Austrian Ministry of Interior and Ministry of Health. Objective: We aimed to analyze the occurrence and spread of infectious diseases among asylum seekers using data reported by reception centers through the syndrome-based surveillance system from September 2015 through February 2018.
Methods: We deployed a daily data collection system for 13 syndromes: rash with fever; rash without fever; acute upper respiratory tract infection; acute lower respiratory tract infection; meningitis or encephalitis; fever and bleeding; nonbloody gastroenteritis or watery diarrhea; bloody diarrhea; acute jaundice; skin, soft tissue, or bone abnormalities; acute flaccid paralysis; high fever with no other signs; and unexplained death. General practitioners, the first professionals to consult for health problems at reception centers in Austria, sent the tally sheets on identified syndromes daily to the AGES.
Case Finding and Case Reporting In Austrian RCs, health care staff, including general practitioners and nurses, are the first professionals to consult for health problems of asylum seekers. The health care staff assessed the presence of syndromes, as defined in Table  2 , during the medical entry examination (active case finding) and any other consultation required by asylum seekers during their stay at the RC (passive case finding). The RC's SbSS focal person accordingly filled in a tally sheet, which listed the 13 syndromes with their clinical definition by age group (0-4, 5-14, 15-44, 45-64, ≥65 years) and was asked to send it on a daily basis to the SbSS project manager at the AGES by fax, email, or an online data entry form.
Data Analysis We adapted and modified the model for signal and alert generation as described by Napoli et al and applied in refugee RCs in Italy  [16]. We determined the number of observed (reported) daily cases for each syndrome (syndrome-specific ODCs) and RC, including zero reporting. By calculating the 1-sided moving average of the syndrome-specific ODCs of the previous 14 days with the formula shown in Figure  1 , we determined the expected daily cases (EDCs). We created a threshold for the expected daily cases (ETH) by adding 2 times the standard deviation to the EDCs (ie, ETH t = EDCs t + 2 × SD t ). We measured the ODCs daily against the ETH for each syndrome. A signal was generated when the number of syndrome-specific ODCs exceeded the ETH for the syndrome, and an alert was defined as signals occurring over 2 consecutive days.
We analyzed the data and generated outputs (syndrome-specific number of signals and alerts) automatically on a daily, weekly, and monthly basis using R  [17]. We shared the weekly reports with the Federal Ministry of Health and the monthly reports with the contact person at each RC. In case the RCs did not send the daily report, the manager of the SbSS called the contact person at the respective RC to follow up on the status of the report. In case of a signal, the SbSS manager at the AGES immediately called the respective RC contact person to clarify the underlying diseases and to verify the cluster of cases. In case of an alert, the AGES immediately informed the Ministry of Health. Formula for the expected daily cases (EDCs) at day t defined as the 1-sided moving average of the syndrome-specific observed daily cases (ODCs) of the previous 14 days.
Acknowledgments We would like to acknowledge and thank (1) all the health care staff at the reception centers and  ORS Service , the emergency health services (144-Notruf Niederösterreich) and the  Austrian Red Cross  for reporting to the SbSS; (2) the  Ministry of Health , the local and regional staff of the  Ministry of Interior , and the local and regional public health authorities for supporting the SbSS activities; and (3)  Dr Birgit Negedly  for supporting and helping in initiating, piloting, training, and implementing the SbSS at the RCs.
Genome assembly and rearrangement identification We generated our Pieris napi (Linnaeus, 1758) (Pieridae) genome using DNA from inbred siblings from Sweden, variable fragment size libraries [180 base pairs (bp) to 100 kb, N50 length of 4. 2 Mb, and a total length of 350 Mb; notes S1 and S2], and a high-density linkage map (https://github. com/JasonAnthonyHill/Piers_napi) computed using 275 full-sib larvae, which placed 122 scaffolds into 25 linkage groups, consistent with previous karyotyping of P. napi  (19, 20)  The total chromosome level assembly was 299 Mb, comprising 85% of the total assembly size and 114% of the k-mer estimated haploid genome size, with 2943 scaffolds left unplaced (note S1). Unplaced scaffolds either were too small to have informative markers or were alternatively assembled haplotypes whose sequence was contained within a larger scaffold (note S1).
mori-like chromosomes and rarely supporting P. napi-like chromosomes, two recently published, independent assemblies of the congener P. rapae  (23, 24)  exhibited many P. napi-like scaffolds (Fig. 3B ), suggesting that this rearrangement may be shared across Pieris. We further examined how different stages of our P. napi assembly captured these rearrangements and found an increase in number of rearrangements observed with increasing assembly contiguity (N50; Fig. 3B ). We note that our analysis here only interrogated genomes for their support for either B. mori-or P. napi-like chromosomal structure, as future work will investigate to what extent similar approaches can be used upon these datasets to detect other types and rates of genome reorganization. Last, we expected the extensive genomic reorganization observed within Pieris to be associated with taxa recently experiencing chromosomal instability, such as fission and fusion events.
DNA sequencing and genome scaffold assembly Pupal DNA was isolated from a fourth-generation inbred cohort that originated from a wild-caught female collected in Skåne, Sweden, using standard salt extraction  (35). Illumina sequencing was used for all data generation used in genome construction. One paired end (PE) and the two MP libraries were constructed at the Science for Life Laboratory (SciLifeLab), the National Genomics Infrastructure, Sweden, using one polymerase chain reaction-free PE DNA library (180 bp) and two Nextera MP libraries (3 and 7 kb) all from a single individual. All sequencing was done on Illumina HiSeq 2500 High Output mode, PE 2 × 100-bp by SciLifeLab. Two additional 40-kb MP fosmid jumping libraries were constructed from a sibling used in the previous library construction.
The 3-and 7-kb MP pair libraries were filtered for high-confidence true MPs using NextClip v0. 8  (38). All read sets were then quality-filtered, and the ends were trimmed of adapters and low-quality bases and screened of common contaminants using BBDuk v37. 51  (39). Insert size distributions were plotted to assess library quality, which was high (fig. S6 ). The 180-bp, 3-kb, and 7-kb read datasets were used as input for AllpathsLG r50960  (40)  for initial contig generation and scaffolding (note S1). AllpathsLG was run with haploidify = true to compensate for the high degree of heterozygosity. A further round of superscaffolding using the 40-kb library alongside the 3-and 7-kb libraries was done using SSPACE v2  (41). Last, the assembly was scaffolded a third time and error-corrected using the Chicago read libraries and the HiRise software pipeline.
Linkage maps RAD-seq data of 5463 single-nucleotide polymorphism (SNP) markers from 275 full-sib individuals, without parents, were used as input into Lep-MAP2  (44). The RAD-seq data were generated from nextRAD technology by SNPsaurus (Oregon, USA) (note S10). To obtain genotype data, the RAD-seq data were mapped to the reference genome using BWA mem  (45) , and SAMtools  (46)  was used to produce sorted bam files of the read mappings. On the basis of read coverage (SAMtools depth), Z chromosomal regions were identified from the genome, and the sex of offspring was determined. Custom scripts  (47)  were used to produce genotype likelihoods (called posteriors in Lep-MAP) from the output of SAMtools mpileup. The parental genotypes were inferred with the Lep-MAP2 ParentCall module using parameters "ZLimit=2 and ignoreParentOrder=1," first calling Z markers and second calling the parental genotypes by ignoring which way the parents are informative (the parents were not genotyped, so we could not separate maternal and paternal markers at this stage).
Scripts provided with Lep-MAP2 were used to produce a linkage file from the output of ParentCall, and all singleparent informative markers were converted to paternally informative markers by swapping parents, when necessary. Filtering by segregation distortion was performed using the Filtering module. Following this, the SeparateChromosomes module was run on the linkage file, and 25 chromosomes were identified using a logarithm of the odds (LOD) score limit of 39. Then, the JoinSingles module was run twice to add more markers on the chromosomes with an LOD score limit of 20. Then, SepareteChromosomes was run again but only on markers informative on single parents with an LOD limit of 10 to separate paternally and maternally informative markers. Fifty-one linkage groups were found, and all were ordered using the OrderMarkers module.
On the basis of likelihood improvement of marker ordering, paternal and maternal linkage groups were determined. This was possible as there is no recombination in females (achiasmatic meiosis), and thus the order of the markers does not improve likelihood on the female map. The markers on the corresponding maternal linkage groups were converted to maternally informative, and OrderMarkers was run on the resulting data twice for each of the 25 chromosomes (without allowing recombination in female). The final marker order was obtained as the order with the higher likelihood from the two runs. A maternal marker map was constructed from a different family of P. napi in which a female from Abisko, Sweden, was crossed with a male from Catalonia, Spain. Genomic DNA libraries were constructed for the mother, father, and four offspring (two males and two females).
RNA libraries were constructed for an additional six female and six male offspring. All sequencing was performed on an Illumina HiSeq 2500 platform using High Output mode, with PE 2 × 100-bp reads at SciLifeLab (Stockholm, Sweden). Both DNA and RNA reads were mapped to the genome assembly with bbmap using default settings. SAMtools was used to sort read mappings and merge them into an mpileup file (note S6). Variants were called with BCFtools (48) and filtered with VCFtools  (49). Linkage between SNPs was assessed with PLINK  (50). A custom script was used to assess marker density and determine sex-specific heterozygosity.
Chromosomal assembly The 5463 markers that composed the linkage map were mapped to the P. napi HiRise assembly using bbmap  (39)  with sensitivity = slow. Reads that mapped uniquely were used to identify misassemblies in the HiRise scaffolds and subsequently rearrange those fragments into the correct chromosomal order. Fifty-four misassemblies were identified, and a total of 115 fragments were joined together into 25 chromosomes using a series of custom R scripts and the R package Biostrings  (51). Scaffold joins and misassembly corrections were validated by comparing the number of correctly mapped MPs spanning a join between two scaffolds. MP reads from the 3-, 7-, and 40-kb libraries were mapped to their respective assemblies with bbmap (po = t, ambig = toss, kbp = t). SAM output was filtered for quality (mapq > 20 and properly paired), and a custom R script was used to tabulate read spanning counts for each base pair in the assembly.
Data used were genome assemblies and annotated protein sets downloaded for 24 species of Lepidoptera from Lepbase v4  (57)  and other sources (table  S2 ). For the first analysis, we used a BLAST-like approach, with each target species protein set aligned to its own genome as well as to the P. napi protein set using Diamond v0. 9. 10 (  58 ) with default options. The proteingenome comparison was used to assign each target species gene to one of its assembled scaffolds, while the protein-protein comparison was used to identify RBHs between the protein of each species and its ortholog in P. napi and B. mori. Using this information, we used a custom R script to examine each assembly scaffold for evidence of synteny to either P. napi or B. mori. First, each scaffold of the target species genome was assigned genes based on the proteingenome BLAST results, using its own protein set and genome.
mori SCOs rather than the P. napi protein set for alignment. In addition, this analysis used a much more restricted set of proteins that had been filtered extensively for high-quality orthology assignment, which reduced detection of SCOs while increasing accuracy. First, P. napi proteins were extracted from the genome using the GFF annotation file, via the program gffread from the cufflinks software package v. 2. 2. 1  (59). The resulting proteins were then filtered for those having a start, stop, and no internal stop codons. Then, a reciprocal best BLAST (RBB) was conducted upon these P. napi proteins against the B. mori SCOs, with BLASTP via Diamond v. 0. 9. 10, with results filtered (eval <0. 00001, bitscore >100) before RBB comparison using custom scripts. This resulted in n = 1835 SCO proteins.
The chromosomal locations and start positions of each protein were then added to the resulting RBB table for the two species. Then, for each species genome, these B. mori SCOs were aligned to the genome using SPALN, a high-performing protein to genome alignment software  (60). These results were then filtered to only consider scaffolds where >3 SCOs per B. mori chromosome were identified (e. g. , scaffolds having five B. mori SCOs from five different chromosomes were ignored). Then, scaffolds were queried for instances where they had SCOs from only 1 B. mori chromosome, with >1 P. napi chromosome, which were then considered B. mori-like scaffolds. Those with one P. napi chromosome and >1 B. mori chromosome were considered P. napi like. If scaffolds only contained SCOs from one chromosome of B.
Pieridae chromosomal evolution Chromosomal fusions and fissions were reconstructed across the family Pieridae by placing previously published karyotype studies of haploid chromosomal counts into their evolutionary context. There are approximately 1000 species in the 85 recognized genera of Pieridae, and we recently reconstructed a robust fossil-calibrated chronogram for this family at the genus level  (25, 26). We placed the published chromosomal counts for 201 species  (6, 61)  on this time-calibrated phylogeny, using the maximal reported haploid chromosomal count per species when more than one was recorded, with ancestral chromosomal reconstructions for chromosome count, treated as a continuous character, using the contMap function of the phytools R package  (62). For instances where there was more than one species per genus, all species per genus were treated as a polytomy 5 million years old.
Estimation of chromosomal rearrangements A table of the identified SCOs between B. mori and P. napi, including their start locations and chromosomal location, was filtered to remove potential false positives (i. e. , any translocated SCO that appeared once within a given collinear block set of four genes) and then collapsed to remove any local chromosome inversions, so that we would detect only the evolutionary dynamics among the collinear blocks. We then further simplified the dataset to have only one gene represent each collinear block (again, to ensure that we were only detecting the large-scale events). The resulting table was then used as input for the software GRIMM v. 2. 01  (66)  to detect an optimal scenario of rearrangements between the two genomes with genes unsigned (options "-s -u").
Annotation of P. napi genome Genome annotation was carried out by the Bioinformatics Short-term Support and Infrastructure (BILS, Sweden). BILS was provided with the chromosomal assembly of P. napi, and 45 RNA sequencing read sets representing three different tissues (head, fat body, and gut) of seven male and eight female larvae from laboratory lines that were separate from the ones used for the initial sequencing. Sequence evidence for the annotation was collected in two complementary ways. First, we queried the UniProt database (67) for protein sequences belonging to the taxonomic group of Papilionoidea (2516 proteins). To be included, proteins gathered in this way had to be supported on the level of either proteomics or transcriptomics and could not be fragments. In addition, we downloaded the UniProt/ Swiss-Prot reference dataset (downloaded on 15 May 2014) (545,388 proteins) for a wider taxonomic coverage with high-confidence proteins.
Acknowledgments: We acknowledge support from  SciLifeLab , the  National Genomics Infrastructure, NGI, and Uppmax  for providing assistance in massive parallel sequencing and computational infrastructure. We also thank  SNPsaurus  for support in nextRAD sequencing for linkage map construction. Funding: Funding was provided by the  Swedish Research Council  [VR grant numbers  2012-3715  (to S. N. ),  2010-5341  (to S. N. ), and  621-2012-4001  (to C. W. W. )], the  Academy of Finland  [grant number  131155  (to C. W. W. )], the  Knut and Alice Wallenberg Foundation  (grant number  2012. 0058 ), and  MINECO-FEDER  [project number  CGL2016-78093-R  (to J. C. C. )]. Author contributions: C. W. W. , S. N. , and J. H. designed the project. C. W. collected and inbred the material. J. H. led the genome assembly.
tical and size data, while the spectral SSA was directly retrieved from β sca and β abs measurements. Dust is assumed to be spherical in the whole data treatment, which introduces a potential source of uncertainty. Our results show that the imaginary part of the refractive index (k) and the SSA vary widely from sample to sample, with values for k in the range 0. 0011 to 0. 0088 at 370 nm, 0. 0006 to 0. 0048 at 520 nm, and 0. 0003 to 0. 0021 at 950 nm, as well as values for SSA in the range 0. 70 to 0. 96 at 370 nm, 0. 85 to 0. 98 at 520 nm, and 0. 95 to 0. 99 at 950 nm. In contrast, the real part of the refractive index (n) is mostly source (and wavelength) independent, with an average value between 1. 48 and 1. 55. The sample-to-sample variability in our dataset of k and SSA is mostly related to differences in the dust iron content.
In spite of this sensitivity, present climate models adopt a globally constant spectral complex refractive index (and SSA) for dust and hence still implicitly assume the same dust mineralogical composition at the global scale. This is mainly due to the lack of a globally consistent dataset providing information on the geographical variability of the dust scattering and absorption properties (e. g. ,  Samset et al. , 2018). Reference values for the refractive index are usually taken from  Volz (1972) ,  Patterson et al. (1977) , D  'Almeida et al. (1991) ,  Shettle and Fenn (1979) ,  Sokolik et al. (1993) ,  Sinyuk et al. (2003) , or OPAC (Optical Properties of Aerosols and Clouds;  Hess et al. , 1998; Koepke et al. , 2015). A parameterization of the spectrally resolved dust refractive index as a function of the mineralogical composition of the particles is desirable to replace the globally constant values in current climate models, in particular for those models that started to incorporate the representation of dust mineralogy into their schemes  (Scanza et al.
, 2003; Schladitz et al. , 2009; Formenti et al. , 2011; Rocha-Lima et al. , 2018). For the different dust samples, C trunc ranged between 1. 2 and 1. 7 and decreased with wavelength and the dust residence time in the chamber, following the relative importance of the coarse component in the dust population. The uncertainty on C trunc , calculated by repeating the optical calculations by using the size distribution of dust within its error bars as input to the optical code, is less than ±5 % at all wavelengths. In order to assess the consistency of the derived truncation correction, we made a sensitivity study in which we recalculated C trunc by varying the refractive index used as input to the Mie calculations in the range of n and k values obtained in this study (i. e. , values at 10 % and 90 % as reported in Table  5  for the whole dataset; that is, n between 1.
75 ± 0. 10, as determined by SMPS-SkyGrimm comparison in their overlapping range (see DB17). Figure  1. Flowchart illustrating the procedure for data treatment and retrieval of the physical and chemical (size, composition) as well as spectral optical properties (single-scattering albedo (SSA) and complex refractive index) of mineral dust aerosols. In red we mention the different corrections performed and the values adopted in the calculations. The α s symbol in the scheme indicates the SAE 450-550 and the SAE 550-700 Ångström exponents used to extrapolate the nephelometer data at the aethalometer wavelengths. The α(λ)β sca (λ) term accounts for the fraction of the measured attenuation due to side and backward scattering and not to light absorption. The Collaud Coen correction scheme has been recently shown to yield quite accurate values of the absorption coefficients and absorption Ångström exponents from aethalometer data  (Saturno et al.