dataesr/scientific-paragraphs-categorization huggingface dataset:
A listing of these unresolved alleles can be found at http://www. ebi. ac. uk/imgt/hla/ambig. html under database release 2. 21. 0. A few DRB1 alleles differing in the last three codons of exon 2 were also not distinguished. The HLA-B alleles of a subset of Native Americans (n = 148) were sequenced in Dr. Meyer's laboratory as part of another project. After resolution of discrepancies, typing for four samples (2. 7%) was corrected. For most analyses (see below) we transformed the allele calls to two-field allele definitions, where the first field identifies the serological group and the second field defines the peptide sequence, specifying HLA proteins. This results in a reduction in the amount of information about the molecular level definition of the allele's sequence, but allowed us to compare our dataset with that of previously published work, which is almost exclusively at the two-field level.
The mapping between the molecular-level and two-field level of resolution was one-toone for all but a small number of alleles (HLA-A: 24:03:01G 24:03:02; HLA-B: 39:06:01,39:06:02, 51:13:01, 51:13:02, 52:01:01G, 52:01:02; HLA-DRB1: 04:05:01, 04:05:04), implying that the information loss in minimal. For a subset of the analyses we used sequence information available from our typing to carry out tests that use this level of information (tests for equilibrium-neutrality that explore the site-frequency spectrum). The genotype data used in this study are available in S1 Table and at the Allele Frequency Net Database (AFND) repository for immune-related gene polymorphisms in worldwide populations. The AFND accession numbers for the 24 populations in this study are 3692-3715. For example, the TundraNentsi population from Siberia can be accessed at http://www.
Data analyses Population variability. We used the PyPop (v. 0. 7. 0) software package  [31, 32]  to estimate summary statistics (the number of alleles (k), and sample heterozygosity (H)) and to test for deviation from Hardy-Weinberg proportions (HWP) using an exact test  [33]. Tests of neutrality. We tested for departure from neutrality-equilibrium conditions using two methods, which capture different aspects of the time scale for selection. We used the Ewens-Watterson test  [34, 35]  implemented in PyPop to test for deviations from neutrality in the direction of balancing selection. The full molecular-level dataset was analyzed using Tajima's-D, as implemented in Arlequin (v. 3. 5) [36]. For each method deviations in the direction of positive selection are indicated with p-values close to one.
An empirical p-value was computed as the proportion of the 678 microsatellite loci with a higher F ST than HLA. Haplotype frequencies. Haplotype frequencies were estimated using the EM algorithm  [37]  as implemented in PyPop. Allele sharing between the Americas and other world regions. For each allele in our dataset (at the two-field or peptide level of resolution), we compared the mean frequency across the Native American populations with the mean frequency from 364 non-Native American populations from a published meta-analysis of 497 worldwide populations  [21]  representing approximately 66,800 individuals. These mean frequencies, computed after omitting migrant populations and those whose continental origin was classification was uncertain, were used to estimate a ratio of frequencies between the Americas and other world regions.
Second, we performed a similar classification comparing allele frequencies in the Native American and non-Native American populations of the Solberg et al. [21]  study, so as to validate the findings obtained from our dataset. If an LFD allele was poorly supported in both datasets, we removed it from our final set of classified alleles (21 LFD alleles were poorly supported-3 HLA-A, 13 HLA-B, and 5 HLA-DRB1). This yielded a final list of alleles found in our Native American data which were classified as either endemic or LFD. Because the HLA typing for the Solberg et al. [21]  dataset was carried out over an extended period of time, it is possible that the same allele could have received different names, depending on when it was typed, since earlier methods may not have been able to unambiguously distinguish among certain alleles.
HLA alleles within the Americas For the 23 Native American populations and one Siberian we identified 36 alleles at HLA-A, 80 at HLA-B, 29 at HLA-C, and 38 at DRB1, at the two-field level of resolution (S1 Table ). This dataset was used in all analyses, with the exception of neutrality tests, which were carried out on a version of the data which was recoded at the molecular level (by assigning DNA sequences corresponding to each allele).
To distinguish such identify by state from common origin, it will be necessary to either survey a longer portion of each locus, or their haplotypic contexts. Despite the evidence for selection seen in our dataset, we found that many features of HLA variation are accounted for by the recent demographic history of these populations. This is a recurrent feature of human evolutionary history, and there are in fact few cases where selection overrides the patterns of genetic differentiation which originate due to demographic processes  [23]. Specifically, we show that the abundance of alleles which are unique to America (or much more common in this continent than others) is to a large degree explained by demographic history, as are overall levels of polymorphism. Thus, despite the intense selection pressures acting on HLA loci, recent demographic history has played a substantial role in shaping their overall patterns of variations within America.
All relevant data are in S1 Table and in the Allele Frequency Net Database (AFND) repository for immune-related gene polymorphisms in worldwide populations. The AFND accession numbers for the 24 populations in this study are 3692-3715. Full URLs are listed in the file S1 Table. A listing of the 24 hyperlinked accession URLs can also be found at https://genevol. ib. usp. br/software/ along with the primer.
Data Analysis Data were collected at baseline (at DRV/r initiation date) and at all subsequent clinic visits or hospitalizations. Demographic characteristics (age, gender), comorbidities, mode of infection, hepatitis B and C serological status, AIDS stage, previous antiretroviral treatments, and previous virological failure were considered. Virological success (VS) was defined as plasma viral load (pVL) <50 copies/mL after DRV/r initiation. Virological failure (VF) was defined (i) for ARV-naïve PWH as 2 consecutive pVL >50 copies/mL or 1 pVL >1000 copies/mL after 1 pVL <50 copies or no pVL <200 copies/mL at 6 months or <50 copies/mL at 1 year after DRV/r starting; (ii) for ARV-experienced PWH switching to DRV while VS as 2 consecutive pVL >50 copies/mL or 1 pVL >1000 copies/mL; (iii) for ARV-experienced PWH switching to DRV while failing therapy as 2 consecutive pVL >50 copies/mL or 1 pVL >1000 copies/mL after 1 pVL <50 copies or no pVL <50 copies/mL at 6 months.
Statistical Analysis The Aalen-Johansen estimator was used to estimate the cumulative incidence of VF and VS up to M36, considering DRV/r discontinuation, loss to follow-up, and death as a competing risk. Comparisons of patients' characteristics at baseline and at VF were carried out by the Student t test for quantitative variables and by the chi-square test for qualitative variables. All patients were compared at baseline according to VF, and patients with VF were compared according to presence of DRV RAMs at DRV initiation. P values <. 05 were considered statistically significant. All analyses were performed with SAS 9. 4 (SAS Institute, Cary, NC, USA).
DISCUSSION Clinical studies have described the long-term efficacy and safety of DRV/r in randomized trials with screened patients, but there are few data available on DRV resistance in real-life settings. In our study, we estimated the probability of virological failure and the emergence of DRV resistance-associated mutations in patients included in the large ANRS CO3 Aquitaine Cohort. We showed that VF occurred for 18. 5% of patients treated with a DRV-based regimen. Cumulative incidence rates of VS at 36 months in ARV-naïve patients and ARV-experienced patients who were virologically controlled were 89. 4% and 81. 8%, respectively. These results were close to those provided by the FHDH, which studied DRV/r use in France between 2012 and 2016  [17]. The FHDH showed that the 4-year cumulative incidence of VS was 80.
Altogether, our data indicate that DRV/r could provide a sustained virological response even in patients with preexisting PI RAMs, as previously shown  [5]. We observed a very limited emerging resistance to DRV. Only 6 patients who were heavily pretreated, out of 1458 (0. 4%), developed DRV RAMs while on DRV. These results are  consistent with findings previously described in clinical trials (TITAN and POWER 1 and 2 studies). Similar data from the UK Collaborative HIV Cohort combined with the HIV Drug Resistance Database showed that 2. 8% of participants developed emergent DRV RAMs  [25]. All of the patients with additional DRV RAMs at failure had baseline genotypes indicating the presence of at least 1 DRV RAM. The patterns of resistance showed the well-identified mutations: V32I, L33F, I84V, I54M/L, 76V, 54L, 50V, T74P.
Bacterial length was determined from the images using Image J software. The mean of five measurements for each isolate was used for analysis. Boxes represent the interquartile ranges, lines within boxes represent medians and whiskers represent minimum and maximum values. P-values < 0. 05 were considered statistically significant. rho = -0. 9034). Isolates from women with non-optimal microbiota produced significantly lower amounts of D-lactate (p = 0. 0017) and lactic acid (p = 0. 016) in bacterial culture compared to those from women with optimal microbiota (Fig. 5 ). However, neither D-nor L-lactate production differed significantly between species (Supplementary Fig. 4 ).
Methods Study design and sample selection. We carried out a cross-sectional observational study to assess the immunoregulatory properties of lactobacilli isolated from cervicovaginal secretions collected from young women who participated in the Women's Initiative in Sexual Health (WISH) study in Cape Town, South Africa  8. The parent study cohort comprised 149 women (16-22 years) and the present sub-study included 32 women. Demographic data was collected from the women by questionnaire and vulvovaginal swabs were collected for detection of STIs by nucleic acid amplification tests (HSV-1, HSV-2, Mycoplasma genitalium, Trichomonas vaginalis, Neisseria gonorrhoeae, Chlamydia trachomatis and Treponema pallidum), while candidiasis and BV were assessed by Gram stain, microscopy and Nugent scoring.
IL-6, IL-8, IL-1α, IL-1β, IP-10, MIP-3α, MIP-1α and MIP-1β concentrations were measured using a Magnetic Luminex Screening Assay kit (R&D, Minneapolis, Minnesota). We used a Bio-Plex Suspension Array Reader to collect data and a 5-parameter logistic regression to calculate cytokine concentrations from the standard curves using BIO-plex manager software (version 4; Bio-Rad Laboratories Inc, Hercules, California). Cytokine concentrations below the detectable limit were assigned the value of half the lowest recorded concentration of that cytokine. To confirm VK2 cell viability following bacterial stimulation, we used the Trypan blue exclusion assay. Viable and dead cells were counted using a light microscope. VK2 cell viability was expressed as a percentage of viable cells in relation to the total number of cells counted.
After binding, the beads were washed four times with 95% ACN. Protein was digested by incubation with trypsin for four hours and the supernatant containing peptides was removed and dried down. Liquid chromatography tandem mass spectrometry analysis (LC-MS/MS) was conducted with a Q-Exactive quadrupole-Orbitrap (Thermo Fisher Scientific, USA) coupled with a Dionex UltiMate 3000 nano-HPLC system. Raw files were processed with MaxQuant version 1. 5. 7. 4 against a database including the Lactobacillus genus and common contaminants. Statistical analysis. Data was analysed using STATA Version 12 (StataCorp, College Station, Texas), GraphPad Prism version 7 (GraphPad software, San Diego, California) and R Version 1. 1. 447 (The R Foundation for Statistical Computing, Vienna, Austria). Unsupervised hierarchical clustering was used to evaluate overall cytokine production in response to the isolates.
(D) Relative bacterial size. Single colonies were picked from Lactobacillus cultures (n = 64) and smears were prepared on microscope slides and Gram-stained before taking images at 1,000x magnification. Bacterial length was determined from the images using Image J software. The mean of five measurements for each isolate was used for analysis. Boxes represent the interquartile ranges, lines within boxes represent medians and whiskers represent minimum and maximum values. P-values < 0. 05 were considered statistically significant.
Acknowledgements This work was supported by the  South African National Research Foundation  (NRF;  PI L. Masson ) and the  South African Medical Research Council  (SAMRC; PI L. Masson). The WISH cohort was supported by the  European and Developing Countries Clinical Trials Partnership (EDCTP)   Strategic Primer grant  ( SP. 2011. 41304. 038 )and the  South African Department of Science and Technology  ( DST/CON 0260/2012 ; PI J. S. Passmore). The  Poliomyelitis Research Foundation (PRF) of South Africa  ( 15/23 ;  PI J. S. Passmore ) funded initial bacterial culture and isolation from the WISH cohort. M. T. M. was supported by the  Poliomyelitis Research Foundation (PRF) ,  NRF Innovation  and the  Letten Foundation. L. M. was supported by the  National Research Foundation (NRF) of South Africa  and the  Carnegie Corporation.
#9101) to assess Erk1/2 kinase activation. (This antibody is specific for the phosphorylated Thr202/Tyr204 residues of activated Erk1/2, which are a hallmark of Erk1/2 kinase activation. ). Samples were then washed twice in the FACS buffer and incubated with anti-mouse IgG1-PE (BD Biosciences, ref. #550083) and anti-rabbit IgG-AlexaFluor647 (ThermoFisher Scientific, ref. #A21245) conjugates. Finally, cells were washed, resuspended in the FACS buffer and analyzed with a MacsQuant flow cytometer (Miltenyi Biotech). Data analysis was performed using FlowJo 10 (FlowJo, LLC). Statistical significance was assessed with a one-way ANOVA test using GraphPad Prism software. Antibody labeling Unlabeled antibodies were labeled with Alexa Fluor 405, Alexa Fluor 568, Alexa Fluor 647 according to need. For this, unlabeled antibody molecules in PBS buffer were reacted with the NHS ester of the corresponding dye in a 1:10 ratio in presence of 0.
52) and blinking buffer (1. 35), respectively. To find the location of the tips of microvilli, we developed an appropriate algorithm based on MATLAB. First, the ''Laplacian of a Gaussian'' (LoG) filter was applied to the TIRF images, and the processed images were then converted to binary images by setting a threshold. The binary images were segmented using the function ''bwlabel. '' To include the maximum number of microvilli, the binary image acquired from the data recorded at the smallest angle of incidence was considered for segmentation first, and then data from the image recorded at the largest angle of incidence was combined to separate individual microvilli. Finally, segmented areas obtained from images taken at angles 65. 5 , 66. 8 , and 68. 2 were combined and the mean of the dz of each pixel was calculated and used to generate the LocTips map (Figure  S1 ).
We then used the 'boundary' function of MATLAB to encapsulate this central region (Figure  S6F ), which could take different shapes, depending on the shape of the specific microvillus and how it sat with respect to the surface. We then plotted concentric closed curves of a similar shape and increasing size (Figure  S6G ), and calculated the number of molecules in each concentric structure, from which we obtained the cumulative fractional increase. This value was normalized by the cumulative fractional increase of the area, to obtain the dCount/dArea plot. The more a protein localized to the microvillar region, the steeper was the slope of this plot.
3D SLN setup Three-dimensional super resolution images of the actin cytoskeleton were collected on a Vutara SR352 (Bruker) microscope based on the single-molecule localization biplane technology, using a 60x silicon oil immersion objective (1. 3 NA). Alexa Fluor 647 phalloidin labeled actin was excited using 640 nm laser (5 kW/cm2), and 45,000 frames were acquired with an acquisition time of 20 ms per frame. Imaging was performed in the presence of the imaging buffer (7 mM glucose oxidase (Sigma), 56 nM catalase (Sigma), 5 mM cysteamine (Sigma), 50 mM Tris, 10 mM NaCl, 10% glucose, pH 8). Data were analyzed and visualized by the Vutara SRX software.
ACKNOWLEDGMENTS We thank  Drs. Sandeep Yadav ,  Alexander Vaskevich , and  Yunmin Jung , as well as  Mr. Francesco Roncato , of the  Weizmann Institute of Science , for their kind advice and experimental help during the project. We also thank  Dr. Keir Neuman  ( NIH ) for kindly sharing with us fluorescent nanodiamond samples. The SEM studies were conducted at the  Electron Microscopy Unit of the Weizmann Institute of Science. G. H. is the incumbent of the Hilda Pomeraniec Memorial Professorial Chair.
Both methods showed the same band around 55 kDa, only in immuno-precipitated material from mo-DC. This observation confirms that the VHH D4 binds LSP-1. LSP-1 is an F-actin binding protein reported to be expressed in all leukocytes  (Pulford et al. , 1999). Using our previously generated transcriptomic data  (Goudot et al. , 2017) , we confirmed that LSP1 was expressed at similar levels in ascites mo-DC; ascites macrophages; in vitro mo-DC and mo-Mac generated with M-CSF, IL-4, and TNFa; in vitro mo-DC generated with GM-CSF and IL-4; and blood monocytes (Figure  4A ). Using transcriptomic data from the Human Cell Atlas, we also observed that LSP1 was expressed in all blood leukocytes, with myeloid cells displaying the highest levels (Figure  4B ). To confirm the intracellular expression of LSP-1, we performed flow cytometry on permeabilized cells using either a commercial anti-LSP1 antibody or VHH D4.
After washing and shrinking the gel pieces with 100% (vol/vol) MeCN, we performed in-gel digestion using trypsin (Roche) overnight in 25 mM NH4HCO3 at 30 °C. Peptides extracted from each band were analyzed by nanoLC-MS/MS using an Ultimate 3000 system (Dionex, Thermo Scientific, Waltham, MA) coupled to a TripleTOF TM 6600 mass spectrometer (ABSciex). For identification, data was searched against the Swissprot fasta database containing Homo Sapiens (2014_10, 20194 sequences) using Mascot TM (version 2. 3. 02) and further analyzed in myProMS  (Poullet et al. , 2007). The maximum false discovery rate (FDR) calculation was set to 1% at the peptide level for the whole study (QVALITY algorithm). Only proteins found in two experiments and not in the control IPs were considered candidates. Only one candidate was identified (P33241, Lymphocyte-specific protein 1), with scores of 403.
47 and 499. 61, and coverage of 23. 9% and 26. 3%, for the two bands, respectively. Data is available via ProteomeXchange (identifier PXD015647)  (Vizcaino et al. , 2016). Supplemental References  Durand, M. & Segura, E. 2016. Dendritic Cell Subset Purification From Human Tonsils And Lymph Nodes. Methods Mol Biol, 1423, 89-99.
Nationale de la Recherche  ( ANR-10-LABX-0043 ,  ANR-CHIN-0002 ,  ANR-10-IDEX-0001-02 PSL ),  Institut Curie  ( CIC IGR-Curie 1428 ), the  European Research Council  ( 2013-AdG N 340046 DCBIOX ), and  Canceropole I ˆle-de-France  ( 2017-1-EMERG-56-ICR-1 ). The authors wish to thank  A. Coillard  for technical assistance, the  Flow Cytometry Platform of Institut Curie  for cell sorting and the  TAb-IP platform of Institut Curie  for the production of the VHH.
Statistical methods Data were analysed using R v3. 6. 0. Plots of titres across age and space were used to evaluate the distribution of data, and non-parametric statistical tests were used to identify regions or age groups that differed significantly (e. g. the two-sided Mann-Whitney for continuous variables, Fisher's exact test for categorical variables). Aggregated data are available upon request and at https://github. com/apwez/Pertussis_Madagascar.
Estimating the force of infection across years The data consist of age and IgG serostatus for individuals sampled in 2016 across ages and locations, including seropositive individuals (defined as Y i,t,j = 1, corresponding to a quantitative titre >5 IU/ml, where i indexes individual observations, t indexes the year, and j the location) and seronegative individuals (Y i,t,j = 0). To be seropositive at any age requires having been infected at some prior age, or vaccinated. In the absence of vaccination, if the force of infection, l j,t , captures the rate at which susceptible individuals become infected in location j in year t, we can use a catalytic model to frame the probability that an individual i is immune at age a in year y  [26]  as: p i,j (a, y) = 1 -exp - y y-a f i,t l j,t , where a is the age of the focal individual in year of measurement y, and f i,t reflects the fraction of exposure in year t that individual experienced, which will be <1 for individuals sampled part way through that year, or individuals still protected by maternal immunity (assumed negligible here, since all individuals are aged >6 months).
Acknowledgements. The authors thank the  PERILIC project for initiating the study and for being a source of inspiration  for this research;  RASOLOHERY Andriamihaja ,  Santé Géomatique , Epidemiology and  Clinical Research Unit, Institut Pasteur de Madagascar  who helped us for the map;  RANARIJESY Marc Rovatiana , project  Manager, Bacteriology Experimental Unit ,  Institut Pasteur de Madagascar. Financial support. AW and CJEM were supported by the  Wellcome Trust  ( 106866/Z/15/Z ). AW was also supported by a  Career Award at the Scientific Interface  by the  Burroughs Wellcome Fund. Data availability statement. Data and code are available at: https://github. com/apwez/Pertussis_Madagascar.
2a ). To determine goblet cell coverage (%), an in-house script was used (Image J 1. 47v) for automated goblet cell separation onto a white background (Fig. 2b ) and determination of the total area covered by goblet cells. The area of the dermis was then measured on the original image using the freehand-draw tool (Fig. 2c ) to calculate goblet cell coverage as follows: Goblet cell coverage (%) = (total area of goblet cells ÷ dermal tissue area) x 100.
Prior analyses, proportions were arcsintransformed; datasets were checked for normality using the Kolmogorov-Smirnov test and for homogeneity of variance using Levene's test. Where differences occurred, post-hoc analyses were carried-out using Bonferroni-corrected t-test. These statistical analyses were applied using IBM® SPSS® Statistics v24. Linear regression between relative skin mucus level pre-challenge (T 1 ) and sea-lice count at T 2 (7-day post challenge) were conducted using SigmaPlot v11. 0 to test the significance of the linearity and determine the adjusted R-squared (R 2 ) value of the regression model using relative skin mucus level as an independent variable and sea-lice count as a dependent variable. A significance level of 5% (p <. 05) was applied, data are presented as mean ± SEM of replicates tanks.