dataesr/scientific-paragraphs-categorization huggingface dataset:
Secondary antibodies included the following: Alexa Fluorconjugated goat anti-mouse IgG (A11029/A21424; Life Technologies), antirabbit IgG (1:2000; A11034/A21429; Life Technologies), N-histofine peroxidase anti-rabbit or rat (T414341F/T414311F; Microm Microtech), and HRP-conjugated goat anti-mouse or rabbit (1:2500; SC-2004/ Image analysis of Iba1, Gfap, and S100 stainings. For each experiment, tissues were all prepared and analyzed together: dissection, fixation, sectioning, labeling, and imaging. Analysis was performed using the Fiji software. For Iba1 surface quantification, HRP signal was extracted (color deconvolution; vectors: hematoxylin and eosin, diaminobenzidine) from background (rolling, 10). After thresholding (maxEntropy), images were converted to masks, and particles were analyzed (size, 10 to infinity; circularity, 0.
The five most intense ions per survey scan were selected for collision-induced dissociation fragmentation, and the resulting fragments were analyzed in the linear trap (LTQ). Target ions already selected for MS/MS were dynamically excluded for 180 s. Data were acquired using the Xcalibur software (version 2. 2), and the resulting spectra were then analyzed via the Mascot Software (version 2. 3) created with Proteome Discoverer (version 1. 4; Thermo Fisher Scientific) using the SwissProt M. musculus database. Carbamidomethylation of cysteines, oxidation of methionine, and protein N-terminal acetylation were set as variable modifications for all Mascot searches. Specificity of trypsin digestion was set for cleavage after Lys or Arg except before Pro, and two missed trypsin cleavage sites were allowed.
AK2 deficiency impairs the proliferation and differentiation of the granulocyte lineage. In order to evaluate the impact of AK2 deficiency on myeloid differentiation, CD34+ progenitors transduced with the shAK2 or shCont lentiviral vector were sorted and the CD34+GFP+ cells were cultured either on methylcellulose or in the presence of G-CSF or macrophage colony-stimulating factor (M-CSF). In a colonyforming unit (CFU) assay, fewer CFUs were detected in the shAK2 condition than in the shCont conditiondemonstrating the low clonogenic capacity of AK2-deficient progenitors (Figure  3a ). Similar inhibition was observed with shAK2 #1 (Supplementary Figure  S1E ). These data also show that AK2 expression is required during the very first stages of hematopoietic differentiation. To measure granulocyte differentiation, cells were cultured in suspension in the presence of G-CSF.
We observed slightly greater depolarization of the mitochondrial membrane depolarization in AK2-deficient cells than in controls (Figure  4c ), although the difference was not statistically significant. In contrast to neutrophil differentiation, monocyte differentiation was not affected by AK2 knockdown (Supplementary Figure  S5 ). The impact of AK2 downregulation in the HL60 cell line fits well with the data reported for primary cells during myeloid differentiation. Accordingly, this is a valid model for better understanding AK2's function during the differentiation process and, in particular, establishing whether the absence of AK2 can deregulate mitochondrial metabolism. During HL60 neutrophil differentiation, we first evaluated cell metabolism by measuring lactate and pyruvate production and glucose consumption in the culture supernatant.
Monoclonal antibodies against CD4 (OKT4), CD7 (M-T701), CD8 (RPA-T8), CD10 (97C5), CD11b (D12), CD14 (M5E2), CD24 (ML5), CD34 (8G12), CD56 (B159), mouse IgM, mouse IgG1k, mouse IgG2a, mouse IgG2b isotype controls, annexin V and 7-aminoactinomycin D (7AAD) were obtained from BD Biosciences (San José, CA, USA). CD15 (80H5) and mouse IgM control antibodies were purchased from Beckman Coulter (San Diego, CA, USA). Murine β1-integrin (CD29, HMβ1-1) was from Biolegend (San Diego, CA, USA). Sytox Blue was obtained from Life Technologies (Carlsbad, CA, USA). The lineage cocktail has been described elsewhere. 27 After staining, cells were analyzed on a MACSQuant analyzer, with gating on viable, 7AAD-negative or Sytox Blue-negative cells. The data were processed using FlowJo software (Treestar, Ashland, OR, USA).
Phosphorylating respiration was defined as routine respiration minus oligomycin-insensitive respiration. Maximum electron transfer capacity was defined as the maximum respiratory capacity in the presence of FCCP. Transcriptome analysis. Total RNA was isolated from sorted GFP+ progenitors after 3 days of culture on OP9-hDelta1 cells, using the RNeasy Plus Micro kit (Qiagen, Dusseldorf, Germany). Two-round amplification was performed with the ExpressArt C&E mRNA amplification nano kit (AmpTec, Hamburg, Germany). Microarray experiments were performed on Human Genome 433 plus 2. 0 GeneChips, according to standard Affymetrix protocols. The arrays were read with a confocal laser (GeneChip Scanner 3000 7G, Affymetrix, Santa Clara, CA, USA). The CEL files were generated using GeneChip Command Console (AGCC) software (Affymetrix).
Gene expression levels were normalized using the GC-RMA algorithm and flags were computed using MAS5 algorithm. Quality assessment of the chips was performed with an affyQCReport R package (Affymetrix). Intergroup comparisons were performed using a paired Student's t test. Cluster analysis was performed by hierarchical clustering using the Spearman correlation similarity measure and the average linkage algorithm. Gene set enrichment analysis was performed with gene set enrichment analysis software using the Reactome pathways derived from the Molecular Signatures Database. The microarray data have been deposited at ArrayExpress (EBI) with the accession number E-MTAB-2680. Statistical analysis. For all analyses, three or more independent experiments were performed. Data are reported as the mean ± standard error of the mean (S.
Acknowledgements. This study was funded by  INSERM , the  French Ministry for Research  and has received a state subsidy managed by the  National Research Agency  under the  'Investments for the Future' program  (reference:  ANR-01-A0-IAHU ) and  ERC  funding (reference:  PIDIMMUN 249816 ). We are grateful to  Agnes Rotig ,  Vahid Asnafi ,  Geneviève Courtois ,  Frédéric Rieux-Laucat  and  Bruno Cassinat  for fruitful discussions. We thank  Olivier Pellé  (from the flow cytometry facility at  U1163 ) for assistance with cell sorting and  Sandrine Lenoir ,  Justine Trimouillas  and  Erika Muth  for technical assistance. We would like to thank the  Saint-Louis Hospital (Paris, France)  for providing cord blood samples. We also thank the  ProfileXpert genomic platform (Lyon)  for performing the Affymetrix array analysis.
69 0. 035 Glucose metabolism -1. 6 0. 068 Respiratory electron transport ATP synthesis -1. 6 0. 068 Others Kinesins -1. 74 0. 021 Steroid hormones -1. 72 0. 028 Amine-derived hormones -1. 72 0. 026 mRNA processing -1. 67 0. 043 DNA repair -1. 64 0. 051 MHC class II antigen presentation -1. 64 0. 05 Processing of capped intron containing pre -1. 64 0. 048 mRNA Amine compound SLC transporters -1. 62 0. 057 Amino acid transport across the plasma -1. 62 0. 058 membrane Abbreviations: FDR: false discovery rate; NES, normalized enrichment score. Gene set enrichment analysis with the REACTOME gene sets from the Molecular Signatures database.
Data The analyses in this study are based on a combined dataset from two studies, the US based ADNI database (http://adni. loni. usc. edu) [9]  and the European AddNeuroMed study  [10]. The ADNI was launched in 2003 by the National Institute on Aging (NIA), the National Institute of Biomedical Imaging and Bioengineering (NIBIB), the Food and Drug Administration (FDA), private pharmaceutical companies and non-profit organizations, as a $60 million, 5-year public-private partnership. The primary goal of ADNI has been to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early AD. Determination of sensitive and specific markers of very early AD progression is intended to aid researchers and clinicians to develop new treatments and monitor their effectiveness, as well as lessen the time and cost of clinical trials.
For up-to-date information, see http://www. adni-info. org/. The AddNeuroMed study is a European multicentre private-partnership project -a pilot for the FP6 Innovative Medicine Initiative. The main aim of AddNeuroMed study is to find biomarkers for AD, in particular for early diagnosis of AD. We integrated brain imaging and common SNP data from the ADNI and AddNeuroMed studies.
Subjects and samples We started with 1,198 subjects, 818 from the ADNI and 380 from the AddNeuroMed studies. Genotype data was available for each subject on a common set of 551,342 SNPs in the merged dataset. ADNI data for 818 individuals are publicly available from the ADNI database (http://adni. loni. usc. edu/) which includes brain imaging data (1. 5T MRI scans), genotype data based on Illumina platform (Illumina, San Diego, CA), and demographic and neuropsychological information. More details of this data are available in the supplementary materials of Furney et al. [7]. AddNeuroMed is a Europe-wide multi-center study with six data collection centers: University of Kuopio (Finland), University of Perugia (Italy), Aristotle University of Thessaloniki (Greece), King's College London (United Kingdom), and University of Toulouse (France).
Genotyping and QC of genetic data Illumina HumanHap610-Quad BeadChip platform, containing 620,901 markers, was used for genotyping all samples. A description of the genotyping procedure of the ADNI samples can be found in Shen and colleagues  [5]. Genotyping of the AddNeuroMed samples was carried out at the CNG (Centre National de Génotypage, France) as described previously by Furney and colleagues (See Supplementary Materials)  [7]. ADNI and AddNeuroMed genotype data were merged using PLINK  [11]. After repairing strand-flipping and removing any ambiguous SNPs, the merged data consisted of 1,198 subjects and a common set of 551,342 SNPs. We then applied the following quality control (QC) steps to filter out subjects and SNPs based on: (i) Genotype call rate, (ii) Minor allele frequency (MAF), (iii) Hardy-Weinberg Equilibrium (HWE) test, (iv) Gender mismatches, (v) Genetic relatedness, (vi) Autosomal heterozygosity, and (vii) Divergent ancestry (population structure).
We excluded SNPs and subjects with call rate <98%, MAF <1%, and failing Hardy-Weinberg Equilibrium (HWE) test at p < 0. 001. We also excluded individuals whose clinical gender does not match with the genetically inferred gender, those who are genetically related, and individuals with unusually high or low autosomal heterozygosity score. For genetic relatedness, we calculated the Identical-by-descent (IBD) score and dropped one individual (lowest SNP call rate) of each pair with IBD >0. 1875, which is the half-way point between 2nd and 3rd degree relatives. For filtering based on autosomal heterozygosity, we excluded subjects with unusually high or low (outside 4SD about mean) heterozygosity score (F) which can be obtained from PLINK  [11]  software using the -het flag. For filtering divergent ancestry outliers we combined our study samples with the HapMap3 data, and excluded outliers based on the first two principal components (PCs), calculated using the EIGENSOFT package (http://genetics.
Single-SNP imaging QTLs We initially considered the commonly used single-SNP analyses where we test SNP × Disease interaction for every SNP separately considering a multiple linear regression model of the form: E(y) = β 0 + β 1 SNP + β 2 Disease + β 3 Age +β 4 Sex + β 5 APOEε4 + k j=1 β pj PC j +β 6 SNP × Disease ( 1 ) where y is the quantitative phenotype measure, ␤ 1 and ␤ 6 are the SNP effect and SNP × Disease interaction effect, respectively. PCj is the j-th principal component to take account of population structure or any other omitted confounders (j = 1,. ,k). The value of k is equal to the number of statistically significant PCs based on the Tracy-Widom distribution  [19, 20]  implemented in the EIGENSOFT package (http://genetics. med. harvard. edu/reich/Reich Lab/ Software. html).
The EIGENSOFT package combines functionality from population genetics methods  [21]  and EIGENSTRAT stratification correction method  [22]. We also adjusted for Age, Sex, and APOE4 genotype. Although APOE4 is a wellknown genetic risk factor for AD, some studies (e. g. , de-Almada  [23] ; Berge et al. [24] ) suggested protective effect of APOE2 and APOE3 genotype markers. To identify possible (protective) effects of APOE2 and APOE3 markers, we tested Disease × APOE2 and Disease×APOE3 interaction effects within the single-SNP imaging QTLs models (1) controlling for the effects of Age, Sex, APOE4 genotype, and population structure (via the principal components). In model (  1 ), the disease variable is treated as quantitative (0 = Control, 1 = MCI, 2 = AD). The quantitative coding of the disease variable automatically imposes the effect homogeneity assumption, that is, it assumes that (MCI versus Control) effect is the same as (AD versus Control) effect.
Previously associated AD GWAS findings A considerable number of genetic markers for AD have been reported in the literature based on imaging-QTL studies as well as various case-control GWAS. The AlzGene database  [28]  (http://www. alzgene. org) ranked those genes in order of meta-analysis p-values and assessed for their epidemiological credibility. The top 10 genes with meta-analysis p-values < 0. 00001   are: APOE, BIN1, CLU, ABCA7, CR1, PICALM, MS4A6A, CD33, MS4A4E, and CD2AP. Although the current study did not identify any marker from the top 10 genes of the AlzGene, the study replicated two previous hits from the literature, namely ARPP-21 and DISC1 originally identified in our previous work  [7]  and  [30] , respectively (see full Hyperlasso results-Supplementary document). In a recent meta-analysisof74,046subjects,Lambertetal.
CONCLUSIONS We identified several novel markers associated with AD through our one-SNP-at-a-time and simultaneous multi-SNP Hyperlasso analysis, one of which was replicated in both analyses. The SNP rs1256429 (p = 8. 7 × 10 -10 , intronic to HOMER2) was found to be significantly associated with AD in one-SNP-at-atime analysis. This SNP was also one of the top hits in the Hyperlasso analysis. The other SNPs that passed our stringent Hyperlasso penalty (to ensure statistical significance at the genome-wide level; p = 1 × 10 -8 ) and the additional filtering threshold (␤sd<-0. 15) of the penalized regression coefficients are: rs2724509 (flanking EOMES), rs2829841 (intronic to JAM2) and rs10770042 (in the coding region of WEE1). A limitation of the current study is that, due to the unavailability of similar additional datasets, we were unable to replicate our findings in an independent validation study.
An obvious future direction of research in this area would be to validate the findings in a replication study using an independent validation dataset. Collecting brain imaging and genetic data on big enough samples is challenging due to high cost of such studies. However, imaging-genetics studies may provide realistic opportunity in finding the unidentified genetic markers of AD in the near future when such studies become more affordable due to technological advances. Nevertheless, the discussions of previous literature relevant to those genes in the previous section support our belief that the markers identified in this study are novel additions to the existing list of genetic variants associated with AD which can be validated in future replicated studies. We have also been we have been able to replicate some earlier findings and have been able to suggest the genetic basis for some genes that have been previously identified as being linked to AD and cognitive performance such as the genes ARPP-21  [7, 44] , JAM2  [44]  and DISC1  [43].
ACKNOWLEDGMENTS This study was supported by  InnoMed , ( Innovative Medicines in Europe ) an  Integrated  Project funded by the  European Union  of the  Sixth Framework program  priority  FP6-2004-LIFESCIHEALTH-5 ,  Life Sciences, Genomics and Biotechnology for Health. Thanks to the  Strategic Research Programme in Neuroscience at Karolinska Institutet  (StratNeuro). The authors are also grateful for funding from the  National Institute of Health Research (NIHR) Biomedical Research Centre for Mental Health  at the  South London and Maudsley NHS Foundation Trust and King's College London, Institute of Psychiatry , the  National Institute of Health Research (NIHR) Biomedical Research Unit  for Dementia at the  South London and Maudsley NHS Foundation Trust and King's College London, Institute of Psychiatry and Alzheimer's Research UK.
Data collection We used a video camera system to observe and record two types of files; snapshots of each quadrant of each tank every 10 seconds and snapshots of a quadrant where any movement was detected to a maximum of two motion-activated snapshots per second. Because of the continuous movement of guinea pigs in quadrant 1, we only used snapshots from the other 3 quadrants where activation of snapshots was solely related to triatomine movement. Because triatomines are nocturnal, we used red-light bulbs between dusk and dawn to provide illumination for the cameras. To confirm the lack of disturbance of red light in the dispersal of T. infestans we conducted a pilot with three different light colors: white, green, and red. We illuminated a 2-square-foot-area tank containing 20 T. infestans and observed their behavior.
We first considered a simple Poisson regression. Second, we considered a hierarchical Poisson regression with random intercept to account for different counts in the refuge at the time of intervention. Finally, to allow random variation of the rate of emigration, or insect count decline in the primary refuge, between repetitions, we used a hierarchical Poisson regression with random intercept and slope. We also included as regressors the median daily temperature and the median daily relative humidity. The fit of the alternative models to the data was compared with Akaike's Information Criterion (AIC). We plotted the observed number of triatomine insects in each quadrant and boxes over time. We also plotted the observed number of triatomine insects in the box close to the guinea pig cage as a function of time and the predicted number obtained from the hierarchical Poisson regression with random intercept, the model with lowest AIC.
Acknowledgments We thank our laboratory and data team in Arequipa. We gratefully acknowledge the following organizations that work with us in the control of Chagas disease in Arequipa:  Ministerio de Salud del Peru(MINSA) , the  Direccion General de Salud de las Personas (DGSP) , the  Estrategia Sanitaria Nacional de Prevencion y Control de Enfermedades Metaxenicas y Otras Transmitidas por Vectores (ESNPCEMOTVS) , the  Direccion General de Salud Ambiental (DIGESA) , the  Gobierno Regional de Arequipa , the  Gerencia Regional de Salud de Arequipa (GRSA).
Both mummies had been previously subjected to conventional X-rays in 1966  [34, 35]  and Multidectector Computed Tomography (MDTC) in 2001  [29, 36]. The integral 2001 CT scan dataset of images is not publicly accessible. Therefore, direct comparisons between the CT scan images and current X-ray imaging couldn't be performed. The previous CT report suggested that both corpses underwent a shorter thanatological treatment, presumably compared to royal individuals of the period, but still allowed all inner organs-brain included-to have been preserved reasonably well  [29]. Nevertheless, there was no meaningful attempt to fully explain the nature and significance of this 'shorter procedure' with respect to the possible practical and religious significance of this unusual type of mummification.
Acknowledgements This work is supported by the  Laboratoire de Cristallographie, Departement de Physique, UniversiteMentouri-Constantine, Algeria , and the  UMR 6226 CNRS-UniversiteRennes 1 'Sciences Chimiques de Rennes', France. We would also like to thank  Mr F. Saidi , Engineer at the  UniversiteMentouri-Constantine , for assistance with the data collection.
8 mL of RNase to a final concentration of 50 mg mL 21. Samples were stored on ice for 15 min. The relative fluorescence of 1000 nuclei per sample was measured using a Partec GmbH PAII flow cytometer (Mu ¨nster, Germany) fitted with a 100 W mercury arc lamp. The resulting histograms were analysed with the FlowMax software (v. 2. 0, Partec GmbH) and the nuclear DNA contents estimated with the following formula: 2C-target ¼ (target fluorescence peak/standard fluorescence peak) × 2C-standard.
Statistical analyses on morphometric data For each plant collected, we measured the following morphological traits: length of root, width of root at the top and at midpoint, length from the top of the root to the first branching node of the shoot and length from the top of the root to the tip of the main shoot. Counts of buds, inflorescences, secondary stems and tertiary stems were also recorded. Statistical analyses were performed on samples collected from Cannon Hill Common and Cooling Common as these both had sufficient numbers of T. pratensis, T. porrifolius and hybrids for site to be treated as a random effect. Each trait was investigated for normality with parametric and non-parametric tests of homogeneity of variances with Bartlett tests and Fligner -Killeen tests. The number of tertiary stems and the number of buds per plant were square-root transformed to meet assumptions of normality.
Linear mixed effect (LME) models were fitted by residual maximum likelihood (REML) with the function lmer in the lme4 package  (Bates et al. 2014). Each trait was fitted individually as a response variable, with plant group treated as a fixed effect and site treated as a random effect. An LME model with all nine traits simultaneously as response variables was also fitted to test for an overall significant difference among the three groups (i. e. T. pratensis, T. porrifolius and hybrids). Statistical analyses were implemented in R (R Development Core Team 2008). After morphological data were collected, whole plants from each sampling site were pressed and dried using standard herbarium-sized blots and folders.
, 2007 (Mavrodiev et al. , 2015)) , and all sequences were compared on a site-by-site basis. The strategy of amplification of ITS, ETS, ADH and plastid loci followed that described in  Mavrodiev et al. (2008b Mavrodiev et al. ( , 2012)) , including the listed primers. The ADH locus was amplified using the primer pair: ADH_F and ADH_R from  Mavrodiev et al. (2012). Maximum likelihood (ML) analyses of the ITS and ETS datasets were conducted separately using PhyML v. 3. 1  (Guindon et al. 2010)  following the strategy described in  Mavrodiev et al. (2014)  using sequence data from  Mavrodiev et al. (2008b Mavrodiev et al. ( , 2012)). All sequenced samples of T. porrifolius and T. pratensis were included in the phylogenetic analyses to check their identification. For hybrid plants, we compared all sequences on a site-by-site basis and presented all results in the format of Tables (see for example  Mavrodiev et al.
DNA sequence analyses The five hybrids for which we obtained ADH sequences all showed the presence of double peaks at sites that differentiate the parents, corresponding to the bases present in both T. porrifolius and T. pratensis plants (Table  2 ): this is consistent with an F 1 hybrid status of these plants. For their plastid loci, the hybrids only had haplotypes found in T. pratensis (Table  2 ), which indicates that T. pratensis is the maternal parent of all five of the hybrids analysed. Plant 3447, which had T. porrifolius morphology, showed two unusual base variants in its ADH sequence that were not found in any other plants analysed. Phylogenetic analyses of ITS and ETS sequence data showed that in almost full agreement with previous results (e. g. Mavrodiev et al. 2005 Mavrodiev et al.