dataesr/scientific-paragraphs-categorization huggingface dataset:
Imaging For CT simulation, patients were in supine position with the arms above the head using an armrest. Data were acquired with a 3-mm slice in free breathing. No four dimensional (4D) acquisition was performed because 4D CT scan was not implemented in our department at the date of this study. For FDG-PET/CT, a 4D acquisition was performed in radiotherapy treatment position, 50 min after intravenously injection of 3. 7 MBq/Kg of [ 18 F]-FDG, on a PET/CT integrated system (Discovery RX, General Electric Medical System®, Milwaukee, WI) using the Real-time Position Management (RPM) device (Varian®) and the "Motion Free" software (General Electric Medical System®). The respiratory cycle was rebinned in six phases.
Target volumes definition For the target volume definition, CT simulation and PET/ CT were registered using a rigid algorithm. The GTV CT was delineated manually on CT simulation using a parenchymal window (-1000 and +200 Hounsfield Units). Patients 1 and 3 presented a non-mobile lesion (located in the upper lobe or attached to the chest), therefore a 5 mm isotropic margin was added to GTV to create PTV CT. For patient 2, a 8 mm isotropic margin was added. The Biological Target Volume (BTV) was defined on 4D-PET and corresponded to the sum of the six BTV delineated on the six respiratory-gated PET images. BTV was delineated on each PET image using the fuzzy locally adaptive Bayesian (FLAB) method  (9) , which is an automatic segmentation method. This statistic algorithm combined with a fuzzy measure is particularly adapted to noisy and blurry PET images, and has been validated on both simulated and clinical datasets for accuracy and robustness.
Dose prescription and treatment planning For each patient, two RapidArc plans (Varian Medical System®) were calculated by the same radiophysicist using the Eclipse treatment planning software (Helios, Varian Medical System®) with the Analytic Anisotropic Algorithm v. 10. For the first plan, the objective was to deliver 48 Gy in PTV CT in 4 fractions of 12 Gy. For the second plan, the objective was to deliver 48 Gy in PTV CT and to guide hotspots in BTV with SIB. Both plans adopted the following ROSEL recommendations: 95% of the PTV CT had to receive at least 100% of the prescribed dose and 99% of the PTV CT at least 90% of the prescribed dose. The maximum dose within the PTV CT (Dmax PTV CT ) had to be lower than 125% of the prescribed dose. Additional constraints included the D2cm (maximal dose at 2 cm of the PTV CT ), the R50% and R100% (ratios between PTV CT and the 50% and 100% isodoses, respectively) allowing to verify the rapid fall-off of the dose outside the PTV CT.
Sequencing was performed on Illumina's HiSeq2000 using 100 bp paired-end reads. Sequence alignment and variant calling was performed against the reference human genome (UCSC hg19) using bwa and the Genome Analysis Toolkit. Polymorphisms found in the public databases and in 50 in-house exomes of controls were excluded. Secondarily, mutations identified in SQSTM1 gene were validated by Sanger sequencing according to previously described methods  [6]. The entire coding sequence was also sequenced in 350 French age-matched healthy controls.
Molecular analyses A heterozygous mutation, c. 714 716delGAA, p. Lys238del, was identified in the exon 5 of SQSTM1 gene in the three affected siblings (007, 008, 009). The mutation was validated by the Sanger method in the three patients (Fig. 1 ). The Lys238 residue is conserved across species. The mutation was previously identified as a disease-causing mutation in ALS patients  [4]  and was predicted to be deleterious by SIFT/PROVEAN in silico software (score: -11. 7). Furthermore, this mutation was absent from 350 French controls, from 6,503 individuals of the 'exome variant server' (http://evs. gs. washington. edu/EVS/), dbSNP (http://www. ncbi. nlm. nih. gov/projects/SNP/) and 1000Genomes (http://www. 1000genomes. org/) databases, thus strongly supporting its deleterious effect.
ACKNOWLEDGMENTS This work was funded by the  Neuromics FP7  contract  E12009DD  (to A. B. ), the  France Alzheimer Association  contract  R12091DD  (to A. B. ), 'The  Programme Hospitalier de Recherche Clinique' (PHRC)  (to I. L. B. ), and from the program " Investissements d'avenir "  ANR-10-IAIHU-06. This work was also funded in part, by  Alzheimer's Research UK , by an anonymous donor, by the  Wellcome Trust / MRC Joint Call in Neurodegeneration award  ( WT089698 ) to the  UK Parkinson's Disease Consortium  whose members are from the  University College London/ Institute of Neurology , the  University of Sheffield , and the  MRC Protein phosphorylation Unit at the University of Dundee , Scotland, and by a fellowship from  Alzheimer's Research UK  to  Dr Guerreiro. We thank  Ms. Lydia Guennec ,  Isabelle Lagroua ,  Sylvie Forlani  and  Christelle Dussert  (DNA and cell bank of  CRICM, Hôpital de la Salpêtrière, Paris ) for their excellent technical assistance.
For example, the set of all protein kinases is often selected for screening on the basis of the lower cost of the corresponding small interfering RNA (siRNA) libraries. This situation leads to a small number of genes being functionally overannotated, such as oncogenes and protein kinases, whereas most of the human genome contains genes whose functions remain to be characterized. In a few cases, candidate genes are identified after extensive literature and database searches by association with genes already implicated in the process under consideration. Given the multiplicity of data sources for gene similarity and the fact that links between genes can be indirect, candidate gene selection is a difficult task. An efficient data-mining method to select genes relevant to a biological process of interest would allow investigators to focus experimental screens on the most likely candidates.
Because most biological data can be viewed as weighted undirected graphs with genes as nodes and "interactions" or "functional links" as edges, kernels on graph nodes represent a natural measure of similarity between genes. Previous applications of the kernel concept have generally focused on kernels with free parameters (e. g. , radial basis kernels, the GeneMANIA method (Table  1 ), which integrates data sources first by computing an average graph before applying a single kernel function (see Supplemental Information). Although these tests showed that most kernels in principle represent gene functional similarity well, they are likely to give an overoptimistic view of the prediction performance due to the interdependence between the Panther pathways and source databases. For example, defining pathway genes in Panther relies on functional information from the same literature used to establish protein interactions and GO databases.
To avoid circularity in biological databases, we tested whether we could predict hits from human genome-wide RNAi screens published after the establishment of the source data. As example sources of phenotypic functional gene relations, we used the outcome of the MitoCheck genome-wide RNAi screen  (Neumann et al. , 2010) , and von Neumann diffusion kernels (see Materials and Methods). To ensure that the kernels captured relevant relationships between genes, we first examined which kernel on which data source gave the best performance in retrieving known functional relationships between genes as defined in the Panther pathways database  (Mi et al. , 2005). Using a few pathway genes as query, we ranked each gene in the genome by the sum of its similarities to the query genes and then counted how many genes from the whole pathway were found above different thresholds (see Materials and Methods).
We found that the commute time (CT) kernel gave the best overall performance for all data sources (Figure  2A  and Supplemental Figure  S1 ) and that combining the best kernels for each data source for data integration further improved function retrieval (Table  1  and Supplemental Figure  S2 ). This approach only slightly outperformed FIGURE 1: Overview of the data integration, gene selection, and experimental testing procedure. (A) Each data source is viewed as an undirected weighted graph whose adjacency matrix is used to derive a kernel matrix representing similarities between genes. Data integration is performed by averaging kernels from different data sources. Genes are ranked by the sum of their similarities to a list of query genes representing a biological process of interest. (B) Genes from the top of the ranked list are targeted by RNA interference, and the resulting phenotypes are captured by automated microscopy of live cells, followed by computational image analysis.
Combined kernels are powerful predictors of gene function The interdependent nature of biological databases can lead to a good performance of computational methods in information retrieval but makes it difficult to assess performance for predicting new genes for biological functions. To test the kernel performance, we therefore tested new gene function predictions more stringently using data from genome-scale RNAi screens that were not included in our data sources. We could show that the top-ranked kernel-predicted genes are significantly enriched in the expected phenotypes for all five phenotypes queried with example genes (mitosis defect, cytokinesis defect, increased cell motility, DNA damage response, and NF-κ B activation). Nevertheless, many of the top kernel-predicted genes did not score as hits in the screens examined.
It should also be noted that genes not represented in the source data are not accessible to the method. To be able to select completely uncharacterized genes, genome-wide experimental data sets or ab initio (e. g. , sequence-derived) data would have to be included. However, our preliminary tests of genome-wide microarray data and sequence-based predicted interactions led us to exclude these data sets for making predictions because of poor performance. Although the kernel combination approach slightly but consistently outperformed GeneMANIA, we note that it is difficult to demonstrate that any approach is the best possible without extensive experimental validation of all alternative methods. Nevertheless, the success rate of our predictions represents a fivefold increase over genome-wide screening, which in this context makes graphderived, kernel-based gene ranking of practical value.
This method looks for and scores protein pairs in a given target genome (e. g. , human) found as fused (co-occurring) domain architectures in homologues from genomes of other species  (Reid et al. , 2010). The CODA method was run against all sequences in the human proteome using CATH and Pfam protein domain annotations, and both (CODAcath and pfam) were combined into one single data set  (Morilla et al. , 2010). CODA predictions were benchmarked using the BP data set as positive examples and randomizations of this data set as negative examples. The CODA data set used in this work was formed by CODA-predicted interactions at a cut-off precision of at least 80%.
Data sources of gene interactions BP: GO similarities across biological processes were calculated using the Ensembl56 GO assignments, computed as root term frequency/frequency of the most informative common ancestor term and discarding pairs with score less than some threshold to remove unspecific connections through high-level GO terms. Here the threshold is arbitrarily set as the information content of the term "chromosome condensation. " Similarities between genes were calculated using the maximum GO similarity between them. HIPPO: iRefIndex  (Razick et al. , 2008 ; accessed 29 June 2010) binary interactions from other organisms mapped to human using Ensembl orthology information. Edge weights are set to 1 over the product of the number of human orthologues of each interaction partner to reflect confidence of association.
In this scheme, interactions whose partners both have a unique orthologue in human get a weight of 1. MEMP: Gene coexpression network using absolute Pearson correlation and rank aggregation across many data sets. All-against-all coexpression was calculated from 764 public data sets and aggregated using the MEM tool with default parameters  (Adler et al. , 2009). Probe sets were mapped to Ensembl 56, and ambiguous probe sets were removed. In case of multiple probe sets mapping to the same Ensembl ID, median score was used. To construct the graph, edge weights were taken as negative log of the best corrected p value associated with each edge. PI: compilation of physical protein-protein interactions from the following databases: IntAct, MINT, MIPS, STRING, BIOGRID, DIP, HPRD, and Reactome (accessed 11 October 2010).
Each protein was assigned to an Ensembl56 gene using Ensembl56's external references if the gene was not already identified by an Ensembl ID in the source database. We noticed that some genes considered common contaminants in pull-down experiments analyzed by mass spectrometry (e. g. , UBC) have a high number of interactors. In an attempt to reduce nonspecific interactions, eight genes with >300 interaction partners were removed: ENSG00000078369 (GNB1), ENSG00000150991 (UBC), ENSG00000170027 (YWHAG), ENSG00000164924 (YWHAZ), ENSG00000141510 (TP53), ENSG00000197122 (SRC), ENSG00000146648 (EGFR), ENSG00000177885 (GRB2), and ENSG00000127928 (GNGT1). TM: An interaction graph was generated using the iHOP natural language processing protocol  (Hoffmann and Valencia, 2004, 2005). Genes were identified in abstracts in September 2010 using iHOP by mapping to their HGNC names.
Screen image analysis Images were processed using the CellCognition software  (Held et al. , 2010). Only the H2B-mCherry channel was used. After segmentation, a training set was assembled by manually assigning ∼1800 nuclei from negative control wells to one of the following 10 classes based on the morphological aspect of the nuclei: interphase, early prophase, late prophase, prometaphase, metaphase, early anaphase, late anaphase, apoptosis, artifact, and out of focus. Early prophase was defined as the first visually detectable changes in the texture of chromatin after interphase, and late prophase was defined as the first appearance of clearly condensed chromatin before prometaphase. This late-prophase definition corresponds to events preceding complete disassembly of the nuclear envelope as judged by the LMNA-eGFP marker.
Chromatin volume quantification A fully automated computational pipeline to derive chromatin volume from confocal image stacks was implemented in Matlab and is described below. Intensity decay with increasing distance from the coverslip surface was modeled as an exponential function of distance from the surface  (Kervrann et al. , 2004). Intensity-corrected stacks were interpolated to have an isotropic resolution along xy and z to provide greater flexibility in 3D image analysis. A 3D Gaussian filter was then applied on the interpolated stacks to reduce the effects of noise. Large variations in chromatin compaction in different mitotic phases lead to highly variable intensity/brightness of the chromatin area, which can cause undersegmentation or oversegmentation, depending on the mitotic phase.
ACKNOWLEDGMENTS This work was supported by grants from the  European Commission, Experimental Network for Functional Integration  (Contract  LSHG-CT-2005-518254 ),  EU-FP7-Systems Microscopy NoE  (Grant Agreement  258068 ), and  EU-FP7-MitoSys  (Grant Agreement 241548). I. M. was funded by  SAF2012-33110  and  CTS-486  ( Spanish Ministry of Economy and Competitiveness ,  Andalusian Government  and  Fondos Europeos de Desarrollo Regional). The  Centre for Biomedical Research on Rare Diseases  is an initiative of the  Carlos III Health Institute. J. M. F. was funded by the  National Bioinformatics Institute  ( BIO2007-666855 ), a project of the  Spanish Ministry of Economy and Competitiveness. M. J. R. was funded by the  Alexander von Humboldt Foundation. B. P. was funded by the  German Research Foundation Priority Programme   1384.
This repression was restored by TH treatment in hypothyroid mice. By in vivo ChIP, we showed that LXR is recruited to the Trh promoter region in the presence of T 3 but not in its absence. In contrast, RXR is recruited to the same region in the absence of T 3. There is no simultaneous presence of RXR and LXR on Trh promoter region, suggesting that the presence of a receptor excludes the other. The interactions between the two pathways were further confirmed by qPCR, showing that T 3 treatment of newborn mice induced hypothalamic regulation of a number of key LXR target genes implicated in metabolism and inflammation. Thus, our data provide insight into molecular pathways involved in central metabolic regulation, showing for the first time a central crosstalk between TR and LXR pathways.
1 mM (pH 7. 4) at 280uC. To quantify mRNAs in hypothalami, 1 mg of total RNA was reverse-transcribed using the High Capacity cDNA Reverse Transcription kit (Applied Biosystems). Selected LXR target genes were: Stearoyl-CoA desaturase 1 (Scd1), Peroxisome proliferatoractivated receptor a (Ppara), ATP-binding cassette (ABC) transporter (Abcg1), Lipoprotein lipase (Lpl), Sterol regulatory element binding transcription factor 1 (Srebp1), Liver X receptors (Lxra and Lxrb), ATP-binding cassette sub-family A (Abca1), Peroxisome proliferator-activated receptor c (Pparc) and Mlxipl (Chrebp, Carbohydrate response element binding protein). We tested also the Brain-derived neurotrophic factor (Bdnf) that could have a link with LXR signalling, and genes related to inflammation: Tumor necrosis factor-alpha (Tnfa), Interleukin-1 alpha (Il- The threshold cycle (CT) of each target product was determined and kept constant for all data analysis, and DCT between target and endogenous control (Gapdh) was calculated.
Statistical Analysis For in vivo gene transfer, non-parametric permutation test (Cytel Studio software) was used to assess statistical significance. Each experiment was carried out with n$10, repeated at least three times providing the same results. Differences were consid-ered significant at p,0. 05 with *, p,0. 05; **, p,0. 01; ***, p, 0. 001. QPCR results: Statistical analysis for qPCR data compared DDCT values using non-parametric ANOVA, followed by a permutation test to compare control and treated groups. Independent experiments (7#n#8) were repeated two times providing similar results and data were pooled.
0025) (Fig. 2 ) but not that of Mc4r promoter (data not shown). For the same purpose, we treated dams with T 4 at 12 mg/ml in the drinking water for 24 h before giving birth, and pups (d1) were co-transfected in the hypothalamus by TRH-f. luc (0. 2 mg/pup)/MC4R-r. luc (0. 8 mg/ pup) simultaneously with GW3965 at 10 26 M. Hypothalami were recovered 24 h later and Firefly and Renilla luciferases activities measured. As shown in Fig. 3A  (representative experiment), T 4 treatment of hypothyroid dams restored the ligand-dependent repression of Trh (p: 0. 0145) but not of MC4R (data not shown). These effects were also observed when the T 4 treatment began 48 h before birth (Fig. 3B , p: 0. 0094).
Acknowledgments We thank  S. Decherf  ( CNRS UMR 7221, Paris ) for advice on experimental techniques;  J. Terrien  ( CNRS UMR 7221, Paris ) for critical reading of the manuscript and  G. Morvan  ( CNRS UMR 7221, Paris ) for help with statistics. F. Uridat  and  S. Sosinsky  provided excellent animal care.
Background: Scientific publication is commonly used to communicate research findings and in most academic/research settings, to evaluate the potential of a researcher and for recruitment and promotion. It has also been said that researchers have the duty to make public, the findings of their research. As a result, researchers are encouraged to share their research findings with the scientific world through peer review publications. In this study, we looked at the characteristics and publication rate of theses that documented studies on human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome in Cameroon. Materials and Methods: To check if a thesis resulted in a publication, we searched: A database of publications on HIV in Cameroon, African Journals Online, PubMed and Google scholar.
We also checked if the thesis was written following the structure of most scientific articles (introduction/background, materials and methods, results, discussion and references). To check if a thesis resulted in a corresponding scientific publication (journal article, conference presentation, book or book chapter) we searched: A database of publications on HIV/ AIDS in Cameroon,  [3]  African Journals Online (www. ajol. info), PubMed (www. ncbi. nlm. nih. gov/pubmed) and Google scholar (scholar. google. com). The database has a listing of publications on HIV/AIDS in Cameroon (journal articles, conference proceedings/presentations, books, book chapters) listed on a number of bibliographic databases and had been created as previously described. [13] The search was carried out using the name of the student and or thesis supervisor as the search term.
We next measured the kinase activity of GFP-MPK10 NM and GFP-MPK10 mutant proteins purified from promastigotes. The results presented in Figure  4A  show that GFP-MPK10 NM undergoes weak auto-phosphorylation (exposure 3 h), but catalyzes robust MBP phosphorylation (exposure 1 h). It is interesting to note that although the auto-phosphorylation signals are much weaker than the MBP phosphorylation signals, they are similarly regulated. No MBP phosphorylation could be detected with the GFP-MPK10-K51A, -T190A, -Y192F or -T190A_Y192F mutants. These data demonstrate that, similarly to other eukaryotic MAPKs, T190 and Y192 are essential for MPK10 activity, as their mutation considerably reduced the activity of the kinase. In vitro kinase assay. GFP-MPK10 NM fusion protein was purified using anti-GFP antibody from logarithmic (Log) and stationary (Sta) growth phase promastigotes (Pro) and after 12 h, 24 h, 36 h, 48 h, 72 h, 96 h and 120 h of axenic amastigote (Ax.
6% of cell death (Figure  4B , lower panel right), from which they recovered slower than the other mutants (Figure  4B , lower panel left). This dominant negative effect, observed during the first 48 h after pH and temperature shift, corresponds to the period where MPK10 is the most active. Altogether, these data indicate that MPK10 could be important only transiently during differentiation. Moreover, these findings show that not only T190 phosphorylation is essential for the catalytic activity of MPK10 in promastigotes but also for axenic amastigote viability. Strikingly, increased cell survival can be restored in the GFP-MPK10-T190A mutant by Y192F mutation. We then measured the kinase activity of the purified mutant proteins from parasites at 48 h of differentiation, when the percentage of cell death was the highest, and at 96 h after differentiation, when parasites have recovered.
1 mM okadaic acid, 10 mM NaF, 10 mM o-phenanthroline, EDTA-free protease inhibitors (Roche)). Parasite pellets were snap-frozen in liquid nitrogen and stored at 280uC. Plasmids pXG-GFPK10. For transgenic expression of GFP-MPK10 fusion protein, the gene encoding the L. major MAPK homologue LmaMPK10 (accession number CAJ02415) was PCR amplified from 50 ng of genomic DNA of L. major FVI using primers 59-ACCAGATCTCCACCATGCAGGCCAAGGGC-39 (forward) and 59-GCGAGATCTTCACGACGGGGCCGGCGC-39 (reverse) and cloned into pGEM-T (Promega) to obtain pGEMT-MPK10. This plasmid was subsequently digested by BglII and subcloned into pXG-GFP+2 to generate pXG-GFP-MPK10 (kindly provided by S. Beverley, Washington University Medical School, St. Louis, MO, USA). The various MPK10 mutants described in the results section were obtained by site direct mutagenesis of pGEMT-MPK10 using the QuickChange II XL Site-Directed Mutagenesis kit (Stratagene) prior to subcloning into pXG-GFP2+.