dataesr/scientific-paragraphs-categorization huggingface dataset:
Data gathered in November 2011 after the first phase of the intervention were used to assess the impact of the school bednet distribution and malaria education programme, while outcomes collected in February 2012 after the second phase of the intervention were used to evaluate the combined impact of the IPCs treatment, bednet distribution and malaria education programme. To preserve blinding, staff responsible for measuring study outcomes were unaware of group allocation, and data analysis was undertaken by a statistician with no prior involvement in the trial.
biomedical outcomes Biomedical outcomes were assessed, on the day following the cognitive assessments, in a randomly selected subset of 38 schools. The biomedical sample included the full age range of primary school pupils (age 5-18 years) and comprised the 15 pupils already enrolled in the cognitive cohort, plus an additional 35 pupils randomly selected from among the rest of the school (excluding the class from which the longitudinal sample had been drawn). The sample was drawn on the day of the survey using a number counting system, as described above, with the total number of boys (or girls) divided by 35 to yield a 'selection number'. This sampling procedure was undertaken at baseline and repeated afresh in November 2011 and February 2012. In this manner, data were collected at each time point on approximately 1900 students (50 per school, 950 in each study arm).
As a result of the selection procedure, the biomedical dataset available for analysis included some data collected from the same 570 children evaluated at all three time points (15 per school in cognitive cohort) and a larger majority of data collected from mainly different individuals at each time point (1330 children; 35 per school). The data available from each time point are summarised in figure  1. Finger-prick blood samples were taken to assess haemoglobin concentration (Hb) with a portable photometer (Hemocue, Angelholm, Sweden). Anaemia was defined according to WHO age-specific thresholds: Hb <110 g/L for children aged less than 5 years, Hb <115 g/L for children aged 5-11 years, Hb <120 g/L for children aged 12-14 years and female children >15 years and Hb <130 g/L for boys aged >15 years.
27 Thick and thin blood smears were prepared for malaria parasites, stained for 15 min with 10% Giemsa and examined by light microscopy by an experienced research technician. Slides were declared negative after examination of 100 negative high-powered fields. Microscopy was repeated on a 10% random sample of slides for quality assurance. Height (cm) and weight (kg) were measured and anthropometric indices height-for-age, weight-for-age, weight-for-height, and body mass index, calculated using the WHO AnthroPlus software. 28 Data on malaria knowledge and bed net use, as well as covariates such as household socioeconomic indicators and parental literacy, were obtained through a questionnaire administered to students in classes 3-6 at the time of the biomedical surveys.
statistical analysis For each biomedical outcome (anaemia, mean Hb, Plasmodium infection), separate statistical models were fitted to assess (1) the impact of the school bed nets and malaria education programme (using data from the first follow-up survey in November 2011) and (  2 ) the combined impact of the IPCs treatment, bed nets and malaria education programme (using data from the second follow-up survey in February 2012). For the cognitive outcomes (sustained attention, vocabulary, writing, numeracy and visual search skills), only the full intervention including the IPCs treatment was evaluated. For each outcome, data from baseline and the relevant follow-up were analysed in a single statistical model irrespective of whether students had provided data at both or just one of these time points.
Acknowledgements The authors would like to thank  Souleymane Toure ,  Tom McComack  and  Dr Aissata Male  ( Save the Children, Mali );  Dr Teme  (deceased),  Dr Issa Guindo  and  Dr Drissa Ouattara  ( Ministry of Health, Sikasso );  Bintou Kone ,  Salim Salihou Toure ,  Sinaly Togola  and  M. Togora  ( Ministry of Education, Sikasso ) as well as the numerous Save the  Children Community Development Agents in Sikasso  and other staff who supported the implementation of the project and/or assisted in the training of teachers. We are also grateful to  Elizabeth Allen  ( London School of Hygiene & Tropical Medicine ) for her statistical advice and guidance. The trial was funded by  Save the Children. SEC was supported by a  Career Development Fellowship  from the  Wellcome Trust  [ 0 84 933 ].
The Subtyping Distance tool (SUDI) from Los Alamos database (https://www. hiv. lanl. gov/content/sequence/SUDI/sudi. html) was used to calculate genetic distances and determine if any new distinctive clade should most appropriately be considered a new sub-subtype. The input alignment consisted of our HIV-1/M reference sequences plus an HIV-1/N sequence as out-group reference (strain N_95CM. YBF30). Finally, exhaustive local alignment searches using BLAST were done to identify sequences with high similarity to the newly generated near full-length genomes.
Reconstruction of dated phylogenies We screened Los Alamos database to generate an HIV-1 subtype C reference dataset. We retrieved all available HIV subtype C fullgenome sequences with known year and geographic location and retained one sequence from each patient. We conducted preliminary ML phylogenetic analysis using FastTree v. 2  (Price et al. 2009 (Price et al. , 2010) )  and a GTR þ 4C nucleotide substitution model to identify clonal records, and narrowed the number of sequences to be representative of countries but particularly of years (Supplementary Table  S1 ). This down-sampling reduced computational burden and comprised representative subtype C sequence samples (n ¼ 92) isolated between 1986 and 2014. We also included subtype A/CRF02_AG reference sequences (n ¼ 24; eight sequences from CRF02_AG, eight sequences from subtype A1, and two sequences from every other A sub-subtype) to account for the similarity of some regions of the novel strains with these subtypes.
We generated profile alignments with Mafft v. 7  (Katoh and Frith 2012, 2013) , followed by rounds of automated and manual refining in Muscle v. 3. 8. 31  (Edgar 2004a,b)  and Mesquite v. 3. 2  (Maddison and Maddison 2002) , respectively; this final alignment comprised 124 sequences. In order to date relevant evolutionary events, time-scaled evolutionary analyses were done using either ML analysis as implemented in PhyML  (Guindon et al. 2010 )-where branch lengths were converted into units of real calendar time using Least-Squares Dating in LSD-0. 3beta (To et al. 2016 )-or using Markov chain Monte Carlo (MCMC) sampling, as implemented in BEAST v1. 8. 3 software package  (Drummond et al. 2012). We also used the BEAGLE parallel computation library to enhance the speed of the likelihood calculations  (Suchard and Rambaut 2009).
Reconstructions were done using gag, pol and env genes-A partitioned analysis (only the demographic model was shared) in BEAST or individually in PhyML. Regression of rootto-tip genetic distance against sampling time was used to explore temporal signal and data quality using TempEst v. 1. 5 (Rambaut et al. 2016). All datasets were analyzed using a GTR þ 4CþI nucleotide substitution model. For BEAST, we used a relaxed uncorrelated lognormal molecular clock model in order to infer the timescale of HIV evolution while accommodating among-lineage rate variation  (Drummond et al. 2006)  and a Bayesian skygrid model as a non-parametric coalescent tree prior  (Gill et al. 2013). For each dataset, two to six MCMC chains of 80-200 million steps were computed. Samples were combined and diagnosed using visual trace inspection and calculation of effective sample sizes in Tracer  (Rambaut et al.
2014). We performed further analyses using the partial p51-RT gene in order to include 160 additional sequences from our surveillance studies in the DRC  (Vergne et al. 2000; Vidal et al. 2006; Muwonga et al. 2011; Boillot et al. 2016). In this analysis, we placed an informative prior on the root based on our previous findings (this partial p51-RT dataset comprised 284 sequences). These sequences were sampled between 2002 and 2013 from the capital city of Kinshasa (n ¼ 35), Kongo-Central (n ¼ 4), Kasai-Oriental (n ¼ 14), Tshopo and North Kivu (n ¼ 31), and Haut-Katanga (n ¼ 65) regions; for 11 additional sequences, the specific region of origin in the DRC was unknown. In addition, we performed analyses excluding the A/CRF02_AG reference datasets and nearly identical sequences from the partial p51-RT dataset to describe only the subtype C viral population dynamics; this down sampling resulted in 245 sequences.
Using the subtype C subset, we also performed demographic model testing (exponential, logistic and two-phase exponential-logistic growth) via path sampling and stepping-stone (100 steps with two million iterations each) that resulted in the exponential-logistic growth as the best-fit parametric coalescent tree model (Supplementary Table  S2 ). Therefore, we also used the two-phase exponential-logistic growth to estimate the population growth rate of subtype C for each period and placed an informative prior on the time of transition between them based on previous HIV-1/ M findings  (Faria et al. 2014). Finally, given that our preliminary analysis suggested that the envelope gene of some novel sequences was different from those lineages of CRF02_AG described in the literature and also that our reference datasets was only a small representative of this group, we decided to further explore the phylogenetic relationships of the novel strains in the envelope gene by including a bigger set of samples from subtype A (n ¼ 160) and CRF02_AG (n ¼ 121), representative of countries and years.
2016). Another near-full-length genomic sequence was sampled in 2002 in the Kwilu region, DRC (strain CG-0151-02V, accession KY392767) and originally described as divergent subtype C  (Rodgers et al. 2017 ). LA08SySa and CG-0151-02V have a similar genomic organization as CRF92_C2U and grouped within the respective sub-subtype C2 clade (Supplementary Fig. S1 ). Finally, we found an env sequence (strain VI1358, accession HQ912711) that was collected in 1994 from an infected individual who regularly attended a clinic in Belgium and who was from sub-Saharan Africa  (Balla-Jhagjhoorsingh et al. 2011). Further searches in our laboratory database found eight additional sequences from the DRC, which clustered within the C2 clade in the V3-V5 env gene (n ¼ 6; aLRT ¼ 0. 90) or the partial p51-RT regions (n ¼ 2; aLRT ¼ 0.
99) (Supplementary Table  S3 ). Five were from Mbuyi-Mayi, the remaining were from Kinshasa, Goma and one unknown location. Therefore, a total of fourteen C2 strains were detected in the DRC from 1997 to 2012. Taking into account all unique subtype C sequences from the DRC deposited in Los Alamos database and in our own laboratory database (n ¼ 278), we found that sub-subtype C2 represent 5 per cent of the subtype C strains in the DRC.
Early lineage diversification of subtype C in the DRC We investigated the evolutionary history of subtype C using a representative time-stamped dataset (n ¼ 92) in different genomic regions (Although we referred to them as either gag, pol or env, we used the non-recombinant regions as delineated in the top-left panel of Fig. 2 ) and the partial p51-RT. Root-to-tip divergence analysis for each dataset are presented in Supplementary Fig. S5. The estimated mean evolutionary rates for gag', gag, pol and env regions were, respectively, 1. 87 Â 10 À3 (95% HPD (highest posterior density)¼ 1. 64 Â 10 À3 , 2. 09 Â 10 À3 ), 1. 27 Â 10 À3 (95% HPD ¼ 1. 03 Â 10 À3 to 1. 52 Â 10 À3 ), 1. 21 Â 10 À3 (95% HPD ¼ 1. 10 Â 10 À3 to 1. 33 Â 10 À3 ) and 2. 45 Â 10 À3 (95% HPD ¼ 2. 26 Â 10 À3 to 2. 65 Â 10 À3 ) nucleotide substitutions per site per year.
Extensive diversity of subtype C in DRC Further phylogenetic analyses using a comprehensive partial p51-RT sequence dataset for subtype C from our surveillance studies in the DRC (n ¼ 160) (Fig. 3 , left panel)  (Muwonga et al. 2011; Boillot et al. 2016)  showed that strains 120109035 and VIR91 (this strain has an epidemiological link with VIR90) grouped within sub-subtype C2 as suggested above (Supplementary Table  S3 ). This analysis also revealed that strain LBTB028 (accession AM041013) grouped with strain CG-0373-02, corroborating our previous observation of the later   1946 (1938-1954) 1948 (1943-1954) 1949 (1937-1956) 1956 (1946-1962) 1952 (1947-1957) 1957 (1948-1964) 1953 (1947-1960) 1952 (1941-1960) 1950 (1944-1954) 1948 (1944-1953 )  1926 (1914-1935) 1921 (1916-1929) 1934 (1922-1946) 1936 (1924-1946) 1935 (1927-1942) 1941 (1929-1950) 1933 (1923-1944) 1932 (1918-1944) 1933 (1926-1940) 1932 (1926-1938 )  1928 (1915-1940) 1921 (1911-1929) 1928 (1919-1936) 1933 (1919-1944) 1928 (1914-1938) 1924 (1907-1937).
Tumor microenvironment including endothelial and immune cells plays a crucial role in tumor progression and has been shown to dramatically influence cancer survival. In this study, we investigated the clinical relevance of the gene expression of key mediators of angiogenesis, VEGF isoforms 121, 165, and 189, and their receptors (VEGFR-1 and R-2) in a cohort of patients (n = 37) with relapsed/refractory diffuse large B-cell lymphoma (DLBCL) from the Collaborative Trial in Relapsed Aggressive Lymphoma (CORAL). In patients with ABC-like DLBCL, but not in patients with GCB-like DLBCL, low VEGF 121 expression was associated with a significantly better survival than in those with high VEGF 121 level: 4-year overall survival at 100% vs 36% (p =. 011), respectively. A specific gene signature including 57 genes was correlated to VEGF 121 expression level and was analyzed using a discovery process in 1,842 GSE datasets of public microarray studies.
This gene signature was significantly expressed in other cancer datasets and was associated with immune response. In conclusion, low VEGF 121 expression level was significantly associated with a good prognosis in relapsed/refractory ABC-like DLBCL, and with a well-conserved gene-expression profiling signature related to immune response. These findings pave the way for rationalization of drugs targeting immune response in refractory/relapsed ABC-like DLBCL.
The specific gene signature is conserved in public cancer datasets From the GEO (Gene Expression Omnibus) database, we considered human microarray platforms having a sufficient coverage of our gene set: GPL96 (84%), GPL570 (94%) and GPL571 (84%), and we retained 1,842 GSE (GEO Series) from these platforms. In these series, we searched for pairs of samples with the same trend of expression for our 57 genes, according to an already published method that identified from several different studies a common gene signature associated with tolerance to renal allograft  [22]. For each pair of samples, we computed a p-value corresponding to the probability to observe the same trend by chance. The resulting pair of samples was ranked according to its p-value. We selected the 250 best pairs, with a threshold corresponding to a proportion p of positive expression changes greater or equal to 90%, a p-value less than 1.
9 x 10 -8 and an adjusted p-value for multiple comparisons less than 3. 0 x 10 -5 (using Holm method). We performed a text mining processing of the annotations of our GSE and compared the term occurrences between the series related to our selection and the remaining series. Using a Fisher test, we could notably associate to our selection the following significant terms: tumor, carcinoma, immunity, lymphocyte. Figure  3  shows the existence of this signature in two DLBCL studies (GSE10846  [20] , Figure  3A ; E-TABM-346  [23] , Figure  3B ) and two solid cancers studies (Breast cancer GSE1561  [24] , Figure  3C ; Adult Male Germ Cell Tumors GSE3218  [25] , Figure  3D ). This shows that more than half the genes are highly correlated in these studies and constitute a robust gene signature.
It is interesting to note that blockade of VEGFR2 selectively increased blood flow in VEGF 120expressing tumors but not in those expressing VEGF 188  [49, 50]. In conclusion, tumor microenvironment and angiogenesis in DLBCL are differently orchestrated in the ABC-like subtype and in the GCB-like subtype. VEGF 121 expression level has a major impact on survival of patients with refractory/relapsed ABC-like DLBCL and is strongly associated with an immune response. This were found in public data sets. The signature was found in two DLBCL studies (GSE10846  [20]  Figure  3A ; E-TABM-346  [23]  Figure  3B ) and two positive studies found in GEO database by the previous strategy (Breast cancer GSE1561  [24]  Figure  3C ; Adult Male Germ Cell Tumors GSE3218,  [25]  Figure  3D ). www. impactjournals.
MATERIALS AND METHODS The 37 patients studied were a subset of the 477 patients included in the CORAL study  [11]  which enrolled patients aged 18 to 65 years old presenting a relapsed/ refractory CD20+ DLBCL, to compare the efficacy of R-ICE and R-DHAP followed by autologous stem cell transplant (part 1) and to test maintenance with or without rituximab (part 2)  [51]. The study was registered under European Union Drug Regulating Authorities Clinical Trials (EudraCT) No. 2004-002103-32 and ClinicalTrials.
VEGF and VEGF receptors evaluation Expression of 5 angiogenic biomarkers including VEGF (isoforms 121, 165, and 189), and their receptors (VEGFR-1 and R-2) was assessed by quantitative qRT-PCR after total RNA extraction and cDNA synthesis from frozen tumor samples, using Perfect-master Mixprobe (Anygenes, Paris, France) on Light-cycler (Roche Diagnostics, Meylan, France) as previously described  [53]. The expression levels of the transcripts were normalized to the housekeeping PPIA (peptidylpolyl isomerase A) and TBP (TATA-box binding protein) gene transcripts. Gene set assays were designed using Primer-Express software (Applied Biosystems, Foster City, CA, USA). Primers and probes sequences are listed in Table  5. Gene expression levels were determined using standard calibration curves prepared form gene-specific PCR products.
Gene expression profiling From the 37 patients, 47 biopsies samples (20 primary biopsies, 17 relapse biopsies and 5 matched cases) were included in the GEP analysis using the Agilent Whole Human Genome microarray (G4112F) (Agilent Technologies, Mississauga, ON, USA). The microarray procedures are previously described  [10]. Briefly, total RNA quantity and initial quality were estimated with a NanoDrop ® ND-1000 spectrophotometer, and RNA quality was further assessed by electrophoresis with the Agilent 2100 Bioanalyzer (Agilent Technologies). Data have been submitted to the GEO (GSE26812). After raw data normalisation using Lowess method  [56] , genes with low expression (inferior to median value of the sample) in more than two-third of the samples were rejected. On the 44,000 probes of the microarray, only 14,455 probes went through the filtration step.
Validation of the signature in public microarray databases To validate our gene list (L), we mined a large collection of public microarray studies stemming from the public repository GEO. We aimed to validate the coordinated expression trend found in our dataset by systematically analyzing the variation of gene expression among GEO samples. The principle of this discovery process consists in observing the propensity of our gene set to follow the same differential expression between two biological situations. More precisely, we considered a pair of samples (s i , s j ) and observed the proportion p of positive expression change between samples s i and s j. Let s i k (resp. s j k ) be the expression value of the k th L-gene observed in sample s i (resp. s j ). The proportion p is defined by the ratio of two numbers: the number of occurrences of positive values of the difference s i k -s j k ; the number of L-genes.
A perfect coordinated expression change would correspond to a value p = 1. At the opposite, one could expect that independent and identically distributed expression values would result in a value p close to 0. 5. Assuming that the sign of s i k -s j k stems from a fair Bernoulli experiment, we computed the probability to observe the same proportion p by chance. We thus obtained a p-value measuring the fitness of a sample pair according to our gene expression signature. As public datasets are poorly annotated, phenotypes associated to samples are not always clearly determined. Therefore, we followed an unsupervised approach based on the systematic analysis of all pair samples stemming from a GEO dataset. For each GEO study, we followed the same procedure: firstly, samples were preprocessed using rank-based normalization  [59].
Then, we computed the p-value for all possible sample pairs. The more significant sample pair was retained as well as its p-value. This discovery process yielded a collection of GEO series ranked by their sample pair p-values. We selected a set of representative datasets and performed a text mining processing of their annotations (title and summary sections). Using a Fisher test, we explored the most significant terms by comparing the term occurrences between the series related to our selection and the remaining series.
Functional characterization of the discriminating genes To identify conserved patterns of co-expression among public microarray datasets, we used a bioinformatics tool called MADCOW  [26]. Given a userspecified gene, this online resource extracts strongly coexpressed genes in GEO datasets. More specifically, this tool provides a list of neighbors, a neighbor being a gene having a correlation significantly higher than expected by chance (p-value threshold of 10 -4 ). The resulting list comprises a selection of 200 best neighbors (i. e. presenting the highest occurrences in the scanned microarray studies). From this tool, we identified a set N of neighbors stemming from queries based on our gene signature L. These results have been modeled as a Boolean matrix m(i,j), were g i defines a L-gene, g j a gene belonging to N and m(i,j) a Boolean indicating if g j is a neighbor of g i.
150 charts from centre 1 and 178 charts from centre 2 were reviewed. Inclusion criteria:1. Definitive diagnosis of KD as per AHA guidelines2. Received IVIG at AHA recommended dose 3. Received ASA (3-5 mg/kg at centre 1 and 80-100 mg/kg in initial phase at centre 2)Charts with incomplete information were excluded. Data from both centres was entered into the REDCAP database and exported to SPSS version 22. 0 for analysis. Data analysis: Baseline characteristics were compared with descriptive statistics. Chi square test was used to compute p values for the primary outcome. Multiple logistic regression analysis was used to adjust for potential confounders. Duration of hospital stay was compared with ANOVA to adjust for confounders. p value of <0. 05 was considered significant. Results: 122/150 and 127/178 patients from centre 1 and 2 respectively were included for the final analysis.
Operational taxonomic units were pyrosequenced targeting 16S ribosomal RNA. Additionally, faecal samples were collected from age-, gender-and geography-matched healthy controls and processed as above. The microbial composition of the faecal samples was analysed using compositional data analysis. The relative abundance was visualised using ternary plots. Differences between healthy controls and patients, as well as between Italian and Dutch patients were tested using MANOVA. Paired baseline, inactive disease and flare samples were analysed using Hotelling's T 2 paired sample test. Association with various clinical parameters was tested using MANOVA. Finally, a random forest algorithm was used to distinguish Italian patients and healthy controls based on their microbiota composition. Results: Overall, 307 samples were available for analysis.