dataesr/scientific-paragraphs-categorization huggingface dataset:
8; therefore, we tried to combine the data sets with that obtained from LADI-III (occupancy 1. 0) using XPREP. The internally scaled but unmerged data sets were scaled to each other and then merged. The MaNDi data set was assigned a scaling B factor of 58 A ˚2 relative to the LADI-III data. The resulting data set was of acceptable quality [R merge (I) = 20. 6%], with much improved completeness (95%), particularly at high resolution (90%), and multiplicity (13). These improved statistics did not result in a dramatic improvement in the nuclear density maps, but the resulting more complete data set will result in more reliable refinement and better statistics. Furthermore, this shows proof of principle that pseudo-Laue and TOF Laue data sets from a reactor-based neutron source and a spallation source, with very different detector technologies, can be successfully combined.
Acknowledgements We wish to thank  Lund Protein Production Facility  ( LP3 ), in particular  Annika Rogstam , for the production of some of the batches of deuterated galectin-3C used in this work. We acknowledge the contribution of  Joakim Brorsson  and  Oskar Aurelius  to exploring large crystal growth by repeated macroseeding, and we thank  Susana Teixeira  for collecting the room-temperature X-ray data for the lactose-1 crystal. FM was supported by a PhD fellowship from the  European Spallation Source  to DTL and UR. RK was supported by a project grant from the  Knut and Alice Wallenberg Foundation   KAW 2013. 022 ). The work was also funded by two seed grants from the  Natural Science Faculty at Lund University  to DTL. Research at ORNL's Spallation Neutron Source was sponsored by the  Scientific User Facilities Division, Office of Basic Energy Sciences, US Department of Energy.
Pfs47 polymorphism in P. falciparum isolates from Africa. To examine whether Pfs47 polymorphism determines the outcome of infections with natural P. falciparum isolates, we explored genetic diversity in the immune-variable region that was proposed to underlie complement susceptibility of African P. falciparum strains  26. To this end, the region of interest of Pfs47 was PCR-amplified and sequenced from the NF54, NF165 and NF166 strains and from field isolates from Cameroon  27. In addition, we compared Pfs47 sequences of laboratory and field P. falciparum isolates of diverse geographic origins available in the public databases (MalariaGen project, PlasmoDB). The Pfs47 sequences of NF166 and NF165 displayed strong similarities with other African strains (S2 Table) but were markedly different from parasite strains from Latin America and South-East Asia and our reference P.
Specific primers were used; BVS300 fwd: '5-TACATTCAAATAACTCAGAGGGTAAC-3′ and BVS301 rev: '5-GTTTGTGTATATTTACCTTACATTTATCTCC-3′ to amplify the Pfs47 gene fragment of 3,319 bp. HotStar HiFidelity Polymerase (Qiagen, NL) and long PCR amplification program was used; 5 min at 94 °C and then 30 s at 94 °C, 60 s at 48 °C, 10 min at 62 °C for 30 cycles and finally 15 min at 68 °C. Amplification products were then analysed on 1% agarose gel and purified using QIAquick Gel Extraction Kit (Qiagen, NL) or AMPure PCR DNA purification kit (Agencourt, MA). Sequence analysis was performed in triplicate or quadruplicate between nucleotides 0-417 using primer BVS202 rev 5′ -CCGTTTTACTATTATCACATCTAC-3′ and between nucleotides 500-860 using primer MWV325 fwd 5′ -GTAGATGTGATAATAGTAAAACGG-3′ (based on data of other P.
Statistical analysis. Statistical analyses were performed in STATA version 12 (Statacorp, College Station, Texas, US). The effect of TEP1 knockdown was determined by comparing oocyst burden after dsTEP injection and dsLacZ injection. For this, a negative binomial regression model was fitted to the oocyst count data per parasite isolate and per mosquito strain using random-effects and conditional fixed effects overdispersion models (xtnbreg command in Stata), combining data from all experiments and adjusting for correlations between observations from the same experiment. A potential effect of injection on oocyst development was tested by comparing oocyst burden after dsLacZ with that in noninjected mosquitoes, using the same approach.
Acknowledgements The authors are grateful to  Jolanda Klaassen ,  Astrid Pouwelsen ,  Laura Pelser-Posthumus  and  Jacqueline Kuhnen  for their work on mosquito breeding and handling of the infected mosquitoes. The authors also would like to thank  Bas E. Dutilh  and  Nathalie de Wagenaar  from the  CMBI  for providing Pfs47 sequence data. This work was supported by funds from the  European Union 's  Seventh Framework Program  ( FP7/2007-2013 ) under grant agreements  N°242095  (EVIMalar) and N°223601 (MALVECBLOK). T. Bousema is further supported by the  Netherlands Organization for Scientific Research  ( NWO Vidi 016. 158. 306 ).
Statistical methods As a first step the diagnostic performance of the three immunodiagnostic tests was determined using carcass visual and incisional examination as the "gold standard". In addition, receiver operator characteristic (ROC) curve analysis was performed. "Doubtful results" were removed from the dataset (8 for HP10 Ag-ELISA and 1 for B158B60 Ag-ELISA). The statistical analysis was carried out in R v3. 0. 3 (R development core team, 2008) using the caret and pROC packages. However, carcass inspection is not a true gold standard for validation of diagnostic tests for porcine cysticercosis unless complete carcass dissection and enumeration of cysts is carried out, which is rarely logistically and economically feasible. Thus, a Bayesian approach (Markov chain Monte Carlo [MCMC] simulation with Gibbs sampling) was adopted to estimate test sensitivity and specificity in the absence of a gold standard  (Berkvens et al.
, 2006; Branscum et al. , 2005). To maximise the number of samples with complete test results, EITB results were excluded from the analysis. Data from groups 1 and 2 on carcass inspection and the two Ag-ELISAs were included in the analysis (n=117). As both ELISAs detect circulating parasite antigens, an assumption of conditional dependence between these two tests was made and two co-variance parameters were included in the model  (Branscum, 2005). In contrast, carcass inspection was assumed to be conditionally independent of both ELISAs due to a biologically different outcome being measured (i. e. visible pathology rather than antigen). An initial model was constructed which included prior information about sensitivities and specificities of the three tests. The BetaBuster software (http://www.
epi. ucdavis. edu/diagnostictests/betabuster. html) was employed to calculate beta (α, β) distributions based on published estimates (see Table  2 ). Two models (Models 1 and 2) were run using two sets of priors for the sensitivity and specificity of B158B60 Ag-ELISA, based on two different studies conducted in Africa  (Dorny et al. , 2004; Krecek et al. , 2011). The priors for the other diagnostic tests were the same in the two models. The Bayesian models were run using the WinBUGS software (v14)  (Lunn et al. , 2000). An initial burn-in of 5,000 iterations was discarded, and followed by 50,000 further iterations. The median and 95% credibility intervals of the posterior distributions of the parameters of interest were obtained using MCMC with Gibbs sampling. Model convergence was assessed by running five chains simultaneously and visually inspecting time-series plots for each parameter.
However, the HP10 Ag-ELISA was less specific than EITB and B158B60 Ag-ELISA (see Table  3 ). When ROC curve analysis was carried out, the area under the curve (AUC) was 0. 916 for the EITB, 0. 971 for the B158B60 Ag-ELISA, but 0. 802 for the HP10. Of the 117 pigs for which complete results were available for three diagnostic tests (carcass visual and incisional examination, B158B60Ag-ELISA and HP10 Ag-ELISA), there was full agreement between all three tests in 111 cases (94. 8%). The cross-classified results of the three tests are presented in Table  4. This dataset was used to estimate the sensitivity and specificity of the three diagnostic tests using Bayesian analysis. The results are summarized in Table  5. Similar estimates of diagnostic test performance were generated by the two models based on different prior distributions for the sensitivity and specificity of B158B60 Ag-ELISA.
Acknowledgements We are extremely grateful to the technical staff of the  FOFIFA-DRZV , and especially  Mr Samuel Rakotonindrina  for his invaluable work in the pig slaughterhouses. We thank  Denis Limonne ,  LDBio Diagnostic , for his involvement in the EITB analysis. We are grateful to  Raf Berghmans ,  Advanced Practical Diagnostics NV (ApDia ltd. )  for providing the Ag-ELISA kit material. Equally, we warmly thank  Dr Michael Parkhouse  for his technical assistance regarding the HP10 Ag-ELISA method.
For the detection of IGF-I mRNA (Figure  1a ) and peptide (Figure  1b ), we used T-cells from a defined Channel catfish (Ictalurus punctatus) T-cell line G14D established by Norman Miller, University of Mississippi Medical Center, Jackson, MS and co-workers  [49]. Cells were purchased from ATCC (Cat. No: CRL-2759,  [49] ) and cultured according to protocol  [50]  in incomplete channel catfish medium supplemented with channel catfish serum kindly provided by Professor Miller. For real-time PCR (Figure  1a ), primers and probes were created based on mRNA sequences of I. punctatus IGF-I (GenBank Access No: NM_001200295. 1  [51] ), and O. mossambicus 18S rRNA (GenBank Access No: AF497908. 1) and obtained from Microsynth as described for other species and tissues (e. g. ,  [42, 43, 47, 52, 53] ).
Acknowledgments: The authors are grateful to  Norman W. Miller  and his team,  University of Mississippi Medical Center, Jackson, MS, USA  for helpful support with the cell culture. The data are based on previous studies supported by the  Swiss National Science Foundation  and the  Foundation at the University of Zurich/Baugarten Stiftung. Recent work is supported by the Prof. Dr. med. Karl und  Rena Theiler-Haag Foundation. Special thank is due to  Helmut Segner ,  Institute for Fish and Wildlife Health, University of Bern, Switzerland  for critical reading of the manuscript and lively discussions.
1b, c ). In addition, we observed that the levels of anti-collagen IgG2a antibodies were reduced in CIA mice treated by apoptotic cells (Fig. 1d ). Because apoptotic cell injection allowed the control of the progression of arthritis, we then evaluated whether injecting a higher number of apoptotic cells would increase this effect. Again, CIA mice with a score of 7-8 received 5 or 15. 10e6 apoptotic cells, or vehicle, and the arthritis clinical score was evaluated daily. We did not observe any additional benefit in the reduction of the arthritis clinical score when more apoptotic cells were infused (Fig. 1e ). Indeed, the injection of three times more apoptotic cells did not further decrease the severity of arthritis (Fig. 1e ). This confirms our previous data showing that apoptotic cell injection can alleviate arthritis  [11]  and reinforces the fact that 5.
10e6 apoptotic cell infusion is sufficient to affect the inflammatory response  [11, 17, 20, 22]. Fig. 1  Apoptotic cell injection modulates the severity of collagen-induced arthritis (CIA). CIA mice received 5. 10e6 apoptotic cells (ApoCell), or 15. 10e6, or vehicle (CIA), when they demonstrated an arthritis score of 7-8 and were monitored daily (a, e). Data are expressed as mean of group ± SEM from 5 to 10 individual animal from one to two independent experiments; *p < 0. 05, **p < 0. 01 and ***p < 0. 001 (two-way analysis of variance with Sidak's multiple comparisons test). At the time the mice were killed, joint cuts were stained with HES, observed at × 25 and × 100 magnification (b), histological lesions were scored in the rear ankles (c) (8 to 9 mice from two independent experiments; no difference, Mann-Whitney test), and anti-collagenase IgG2a antibodies were quantified in the serum (d) (19 to 25 mice per group from four to five independent experiments; *p < 0.
2b ). This strongly confirms that reduced T cell proliferation associated with apoptotic cell injection was related to a collagen-specific immunomodulation. Fig. 2  Apoptotic cell injection favors the induction of collagen-specific T regulatory cells (Treg). Cells from the lymph nodes of collagen-induced arthritis mice (CIA) receiving or not receiving apoptotic cell injection (+ApoCell) were harvested at sacrifice and cell proliferation was assessed through 5-Bromo-2'-deoxyuridine (BrDU) incorporation and counting in the presence of increasing concentrations of collagen antigen, Mycobacterium tuberculosis (MBT), or CD3-specific antibodies, as indicated (a-c). Data are shown as mean ± SEM from one representative experiment out of three with five mice per group; *p < 0. 05, **p < 0. 01, ***p < 0.
001, vs. CIA group (nonparametric unpaired t test). In addition, cellularity was assessed, and CD4 + T cell and Foxp3 + Treg percentages and absolute numbers in the spleen (d, e) and lymph nodes (LN) (f, g) were determined. Data are shown as means from seven independent experiments; each experiment includes five mice per group; *p < 0. 05, **p < 0. 01 (parametric paired Student t test). Suppressive assays were performed using Treg isolated from the spleens of the previous treated and untreated CIA mice, and added at a different ratio into collagen-specific or MBT-specific co-cultures, or in CD3/CD28-stimulated T cell cultures, and responder T cell proliferation was assessed by BrdU incorporation and counting (h). Data are shown as mean ± SEM from one experiment with five mice per group; **p < 0.
2g ) were decreased in CIA mice receiving apoptotic cells. The role of Treg in collagenspecific immunomodulation was further investigated using sorted Treg, added into collagen-specific or MBT-specific co-cultures or CD3/CD28-stimulated T cell culture, at a different ratio. Our data showed that only Treg sorted from apoptotic cell-treated CIA mice allowed the suppression of the collagen-specific T cell response, but not the Treg sorted from untreated CIA mice (Fig. 2h , left panel). In addition, the suppressive activity of the Treg sorted from apoptotic-cell-treated CIA mice was restricted to collagen antigen and not extended to MBT antigen, as Treg from CIA or apoptotic-cell-treated CIA mice demonstrated the same ability to limit both MBT-specific co-culture and CD3/CD28-stimulated T cell culture proliferation (Fig.
6b ). Also, anti-TNF antibody treatment favored a higher number of spleen Treg, that were maintained in the presence of apoptotic cells (Fig. 6c ). So far, our data demonstrate that apoptotic cell injection with TNF neutralization strongly reduces arthritis severity and allows an increased number of spleen Treg. Fig. 4  Apoptotic cell-induced immunomodulation in mice with collagen-induced arthritis (CIA) is dependent on transforming growth factor (TGF)-β. CIA mice that received or did not receive apoptotic cells, with or without anti-TGF-β blocking antibody were scored daily (a). Data are shown as mean ± SEM of five mice per group from one of two representative experiments; *p < 0. 05, **p < 0. 01 (Friedman test analysis of variance (ANOVA) with Dunn's multiple comparisons test). Anti-collagen IgG2a antibodies (Ab) were quantified in plasma (b).
Data from 5 to 16 mice from two to three independent experiments (no difference, Kruskal-Wallis test ANOVA with Dunn's multiple comparisons test). Lymph node cells were collected, cultured, and T cell proliferation in response to collagen protein or CD3-specific Ab stimulations was evaluated (c). Data are shown as mean ± SEM from one of two representative experiments; *p < 0. 05, **p < 0. 01 (nonparametric unpaired t test). Percent and absolute number of Foxp3 + T regulatory cells (Treg) were evaluated in the spleen (d) and suppressive assays were performed by isolating and adding Treg at a different ratio into collagen-specific cultures and responder T cell proliferation was assessed (e). Data are shown as mean ± SEM from one experiment (d Kruskal-Wallis test ANOVA with Dunn's multiple comparisons test; e Friedman test ANOVA with Dunn's multiple comparisons test).
Data are shown as mean ± SEM of five mice per group from one of two representative experiments; *p < 0. 05 vs. CIA, ***p < 0. 001 (two-way analysis of variance (ANOVA) with Sidak's multiple comparisons test). Anti-collagen IgG2a antibodies (Ab) were quantified in plasma (b). Data from 5 to 16 mice from two independent experiments plus mean (bar graph); *p < 0. 05 (Kruskal-Wallis test ANOVA with Dunn's multiple comparisons test). Lymph node cells were collected, cultured and T cell proliferation was evaluated in response to collagen antigen or CD3-specific Ab stimulations (c). Data are shown as mean ± SEM from one of two representative experiments; *p < 0. 05, **p < 0. 01 (nonparametric unpaired t test). T regulatory cells (Treg) were isolated and added at different ratio into co-cultures specific for collagen and responder T cell proliferation was assessed (d).
A dysregulated intricate interplay between T cells, macrophages and inflammatory cytokines is implicated in RA pathophysiology. Here, we used a mouse model of arthritis that recapitulates RA  [27]. We report in this model that i. v. administration of apoptotic cells is able to treat ongoing arthritis and is associated with the reprogramming Fig. 6  Apoptotic cell injection and anti-TNF therapy synergize to control ongoing arthritis. Collagen-induced arthritis (CIA) mice received or did not receive apoptotic cells, with or without anti-TNF blocking antibody and were scored daily (a). Data are shown as mean ± SEM of five mice per group from one experiment; *p < 0. 05 (two-way analysis of variance (ANOVA) with Sidak's multiple comparisons test). Lymph node cells were collected, cultured, and T cell proliferation was evaluated in response to collagen protein or CD3-specific antibody (Ab) stimulations (b).
Data are shown as mean ± SEM from one experiment; *p < 0. 05, **p < 0. 01 vs. CIA (nonparametric unpaired t test). Percentages and absolute numbers of Foxp3 T regulatory cells (Treg) were evaluated in the spleen (c). Data are shown as individual mouse plus mean (black bar) from one experiment; *p < 0. 05, **p < 0. 01 (Kruskal-Wallis ANOVA test with Dunn's multiple comparisons test). Plasmacytoid dendritic cells (pDC), conventional dendritic cells (cDC) and macrophages (Macro) were isolated to evaluate their response to toll-like receptor (TLR) ligand stimulation through evaluation of CD40 costimulatory molecule expression (d) and cultured with naïve allogeneic CD4 + T cells to determine Treg polarization (e). Data from individual mouse plus mean (black bar) from one experiment; *p < 0. 05, **p < 0.
Funding This study was supported by the  Arthritis Fondation Courtin  (to SP), the  Etablissement Français du Sang  ( 2011-05  and  2014-02 , to SP), the  Fondation pour la Recherche Médicale  ( DBS20131128447  to SP), the  Région de Franche-Comté  (to SP and PS) and the  Agence Nationale de la Recherche  ( Labex LipSTIC ,  ANR-11-LABX-0021 ).
Modelling strategy As dendritic cell activation is key to the initiation of immune responses, we investigated whether transcriptome data from sorted spleen dendritic cells (DCs) sampled 6 hours after immunization could be predictive of the antigen-specific T-cell response measured several days later, at the peak of the response. To address this question, we devised a stepwise modelling scheme. DC-sorted transcriptome datasets were initially produced for 19 vectors on the Codelink platform, corresponding to 7 different vaccine platforms, for which the antigen-specific T-cell response was also measured (S1 Table ). The rationale for looking at signatures instead of individual genes was motivated by (i) the need to detect slight gene expression modifications (captured as the overall expression changes of correlated genes), (ii) the technical constraints of working on different microarray platforms (CodeLink, Illumina and Affymetrix), and (iii) the objective of producing a predictive model working across microarray platforms.
Thus, our modelling scheme was based on our recently described strategy for signature discovery, using independent component analysis (ICA) followed by gene set enrichment analysis (GSEA)  [20]. This allows circumventing the limitations due to the use of different platforms when analyzing individual gene expressions, by comparing statistical signature's enrichment across datasets. ICA is an unsupervised algorithm extracting independent components Y from original datasets X by searching for the demixing matrix W: Y ¼ X Â W W matrix is calculated by maximizing the non gaussianity of the components measured as the negentropy J: JðyÞ ¼ Hðy Gauss Þ À HðyÞ; where H(y) and H(y Gauss ) are the Shannon entropy for a vector y and a random Gaussian vector with same variance as y  [21]. The use of ICA to analyze microarray data is justified by the hypothesis that X is a mix of signals from underlying cellular pathways.
Therefore, columns of Y contain a summary of gene contributions in the extracted components. The RNA expression value of a gene is thus the superposition of several signals of this gene in each component which add up. From each component y, two reduced gene sets can be extracted by selecting genes with critical contribution on both sides of the distribution  [22]. We first performed ICA on the 19 available datasets, yielding 210 molecular signatures characterizing the variability within each dataset, and likely linked to vector properties. We then analyzed the differential gene expression between the controls and the tested vectors using bootstrapping  [23, 24] , in order to increase the model's sensitivity. Bootstrapping consists in sampling series of additional datasets by randomly drawing samples with replacement of equal size from an original dataset, as described in Fig 2.
We sampled 100 consecutive bootstrapped datasets from each of the 19 original datasets and generated 100 corresponding ranking lists of genes based on modified t-test statistics. The previously identified signatures were then tested for their behavior vis-à-vis the gene lists using GSEA, generating normalized enrichment scores (NES). Molecular signatures from GSEA software (>5000) were added at this step in order to increase the efficiency of the normalization procedure. NES of molecular signatures from ICA were then extracted for the next steps. This yielded a matrix, containing 1900 columns (100 bootstrapped datasets for each of the 19 original datasets) and 210 lines (the number of calculated NES). This matrix was then used to create random forest (RF) classification models (Fig 2 ). NES values and T-cell response classification were used as predictors and dependent variables, respectively, in the randomForest package, which as output provides classification results and associated probabilities for each T-cell response class.
Model stability and confidence An initial predictive model was built with 9 vector datasets (in red in Fig 1B ) for which the antigen-specific T-cell responses were available (900 bootstrapped datasets and 100 signatures). Predictions of 10 additional datasets, including independent experiments done with the same or different batches of these vectors, were very consistent (see Tables  1  and S2 ). The model sensitivity for the "Weak" and "Strong" vector classes (respectively equal to the specificity for the "Strong" and "Weak" classes) are 0. 89 and 0. 98, respectively. The positive predictive value responses were evaluated at days 5, 7 and 10 in groups of 3-5 vaccinated mice. The normalized "CD8 T-cell expansion" value was calculated as the average of the peak response for each mouse against the value obtained for the internal standard experimental group (rAd_1).