dataesr/scientific-paragraphs-categorization huggingface dataset:
L. major and P. falciparum protein sequences were downloaded from Trytrip (http://tritrypdb. org/) and PlasmoDB databases (http://plasmodb. org/), respectively. Each protein sequence was first transformed into an HMM by computing a multiple alignment of homologous sequences. This was done in two ways. The first approach (hereafter denoted as phylum specific) involves using only homologous sequences of species belonging to the same phylum as the target organism-for P. falciparum we use 6 Apicomplexa, and 6 Trypanosomatidae for L. major (see Methods). The second approach (phylum non-specific) involves using the HHBlits approach  [18]  on the whole Uniprot database. HHBlits proceeds in a Psiblast-like manner by iterative sequence searches (see Methods). Once all HMMs were built, they were compared to Pfam HMMs using the hhsearch procedure.
Domains selected this way are assigned as being certified. Three certification types can be considered. The first and most accurate one is to use already known Pfam domains of the protein to certify the presence of the potential domains. A complementary solution is to use the other known InterPro (i. e. non-Pfam) domains. This usually increases the number of certifications. However, because of the heterogeneity of the InterPro database, the certifications achieved this way may be of lower quality than those achieved with Pfam domains. These first two solutions certify domains solely in proteins in which at least one domain is already known. To overcome this limitation, a third solution is to certify the potential domain by another potential domain of the protein. With this solution, all pairs of potential domains of the protein are enumerated, and the two domains are certified if the pair belongs to the CDP list.
Newly predicted domains All new domain occurrences are available in Tables  S4  and S5 , and have been integrated in the EuPathDomains database (http:// www. atgc-montpellier. fr/EuPathDomains/), along with the GO annotations that can be deduced from all these new domains. EuPathDomains is a protein domain database dedicated to most of the eukaryotic pathogens present from the EuPathDB portal (http://eupathdb. org/eupathdb/). A survey of knowledge gained by the HHPRED+CODD approach can serve as a starting point for developing new hypotheses to gain further insight into the biological mechanisms of these parasites. Hence, we sought to analyze and characterize the specificities of newly discovered domains and their contribution to the understanding of parasite biological functions. We then tried to assess the functional relevance of these novel annotations based on the known and predicted properties of the corresponding protein in each parasite.
Such domains are indeed predicted for PF3D7_0933700, at the proper position, further suggesting that this protein is indeed the gamma subunit of TFIIA  [39, 40]. We also discovered a TFIIE_alpha domain in the protein PF3D7_1145800. The general transcription factor TFIIE has an essential role in eukaryotic transcription initiation together with RNA polymerase II and other general factors. We also identified a HTH_9 domain at the beginning of the protein. This protein is currently annotated ''conserved Plasmodium protein, unknown function'', but PlasmoDB reports GO annotations in agreement with our observation, in particular the GO:0005673 annotation ''transcription factor TFIIE complex''. Therefore this protein likely corresponds to the TFIIE_alpha peptide, as suggested by our study. Another interesting prediction was the discovery of the Auxin_resp domain (PF06507) of the very large P.
The CarD_CdnL_TRCF domain then adds a new function to the protein, putatively related to a repair mechanism during transcription. Indeed, TRCF (Transcription-Repair-Coupling Factor), for instance, is known to bind to UvrA, the DNA damage recognition protein, in order to increase strand repair during transcription  [47]. In trypanosomatids, the necessity to maintain an efficient repair mechanism is described in particular for the kinetoplastid DNA (kDNA), which is subjected to intensive endogenous oxidative damage. The efficiency of kDNA maintenance is thus a crucial mechanism to repair oxidative damage  [48]. The LmjF. 33. 2810 gene is annotated as a ''transcription elongation factor-like protein''. We were able to complement this annotation by the Med26 domain (PF08711). Med26, or TFIIS helical bundle-like domain, is present in the N-terminal part of the TFIIS protein.
A simple and efficient solution for this is to incorporate the domains occurrences already identified in the species into the Pfam seed alignment, and to train a new HMM on this alignment  [19]. All predictions along with the GO annotations that can be deduced have been integrated in the EuPathDomains database, a protein domain database dedicated to eukaryotic pathogens. Close analysis of some of the predictions involving Pfam domain families that were unknown in P. falciparum and L. major showed that the approach identifies key domains that were missing to date. For example this analysis revealed one of the missing enzymes involved in the first step of the shikimate pathway, an attractive drug target in P. falciparum because of its absence in mammalian cells. Importantly, several predictions reveal new domains in proteins currently devoid of any domain annotation.
HHPRED predictions We used HHPRED from the HH-suite 2. 0 in our experiments. First, each query protein sequence was used to build a multiple sequence alignment (MSA). This was done using two approaches, using either only the homologous proteins in close species, or every sequenced homologue, via the HHblits method  [18]. Phylum specific approach. For L. major, six species were included: four Leishmania species (L. major, L. infantum, L. braziliensis and L. mexicana) and two Trypanosoma species (Trypanosoma cruzi and Trypanosoma brucei) (http://tritrypdb. org/tritrypdb/). For P. falciparum, six species were analyzed: P. falciparum, P. vivax, P. yoelii, P. berghei, P. chabaudi and P. knowlesi. For each L. major and P. falciparum protein, we extracted its homologs in the set of selected closest species from the OrthoMCL database  [53].
The only difference is that we required a 20% minimal sequence identity threshold with the query sequence instead of the default 0%-threshold to ensure that only real homologues are included in the MSA. All generated MSAs were then transformed into an HMM profile using the hhmake procedure of HHPRED. hhmake was run with default parameters, except for the maximum sequence identity parameter that we set at 100 because the sequence identity is very high especially in Leishmania proteins. Each HMM was then compared with all HMMs of the Pfam database. We considered, for the following experiments, hits with e-values ranging from 0. 1 to 10. As explained in section Results, the experiments were done using local and global alignment modes, using Pfam 26 and Pfam 23 HMMs, respectively. From each alignment of a protein-HMM and a domain-HMM, the position of the potential domain on the query sequence was deduced.
MicroRNA array analysis Total RNA was extracted from chondrocytes in micropellet treated (or not) with IL-1β by using a miRvana isolation kit (Ambion, Carlsbad, CA, USA). MiRNA expression profiling was performed by using Miltenyi (Bergisch Gladbach, Germany) microarray facilities. Labeling and hybridization were performed in accordance with the protocol of the manufacturer. Raw data were normalized and additional data analysis was performed as described previously  [25]. Microarray data are available in the ArrayExpress database  [26]  under accession number E-MTAB-2229. Reverse transcription, microRNA reverse transcription, and quantitative polymerase chain reaction One microgram of Trizol-extracted total RNAs including microRNAs from the different samples were poly(A)tailed with poly(A) polymerase (NEB M0276L).
Incubation with Biotin-coupled secondary antibody, IgG (1:200; ABC kit Vector PK6100) was done for 1 hour at room temperature. Incubation with Avidin/Biotin complex (ABC kit Vector PK6100) was done for 30 minutes at room temperature. Immunolocalized antigens were detected by means of a DAB revelation kit (Sigma-Aldrich). Safranin-O staining was performed as described  [31]. Quantification of proliferating cell nuclear antigen (PCNA)positive or PCNA-negative hypertrophic chondrocytes was performed on four different sections of long bones of four mice of each genotype by using ImageJ software (D-0426) in accordance with the instructions of the manufacturer.
Acknowledgments We thank  P Canovas , of the  Service Chirugie Orthopédique , for OA cartilage samples ( CHU La Peyronie, Montpellier, France );  Marc Mathieu ,  Charles Lecellier , and  Manuel Serrano ,  Chantal Ripoll  and  Stephanie Venteo  for providing plasmids technical help, a mouse model and discussions. And the.
Patients and treatment From January 2000 to December 2010, all consecutive patients with histologically confirmed mccRCC who received systemic therapy in two main oncology treatment centers of our region (University Hospital and Polyclinique of Franche-Comté of Besançon) were included in the analysis. They were identified on the basis of BPC ® software (University Hospital, Besançon, France), a computerized physician order entry system. This software is capable of tracking injectable and oral chemotherapy and targeted therapy prescriptions according to the tumor. Baseline demographics, clinical history, laboratory findings, and treatments were retrospectively collected according to the medical records and BPC software. Two cohorts of patients were defined according to their systemic therapies: 1.
statistical analysis Between our two cohorts, qualitative and quantitative variables were respectively compared by Fisher's exact test or chi-square test and nonparametric Mann-Whitney test. Continuous variables were described by mean ± standard deviation and median (first quartile-third quartile), and qualitative variables by the size and percent rate. All tests were two-tailed and significant at an alpha threshold of 5% (P-value). Statistical analysis was performed with SAS ® software version 9. 3 (SAS Institute Inc, Cary, NC, USA).
11 However, the real impact of cytoreductive nephrectomy in this setting with targeted therapies available will be fully explored through the randomized Phase III CARMENA trial. In our study, one metastatic site was an independent predictor of short survival: liver metastasis (hazard ratio =2. 33 [1. 22-4. 45], P=0. 002). The negative impact of liver metastases on survival outcomes of patients treated with currently approved molecularly targeted agents has already been observed on another cohort of 2,027 patients from the International mRCC Database Consortium. 29 Furthermore, in both this International mRCC Database Consortium cohort and another from the Beuselinck et al study, bone metastases were found to have a negative impact on survival. 29, 30 In our study, bone metastases have not been related to OS.
A ntibiotics used in commercial agriculture can reach land through the application of animal manures. In order to assess whether exposure to environmentally relevant antibiotic concentrations promoted antibiotic resistance or had other impacts on soil microbial communities, a long-term field experiment was initiated in 1999 in London, Ontario, Canada. Plots cropped to soybeans have since received an annual spring application of a mixture of sulfamethazine, chlortetracycline, and tylosin, commonly used in swine production. Importantly, exposure to antibiotics resulted in a marked reduction in the persistence of sulfamethazine and tylosin  (1). Strain C448, identified as a Microbacterium sp. on the basis of 16S rRNA gene sequence analysis (Genbank accession number JQ804844), was isolated from plots treated with 10 mg/kg antibiotics on the basis of the ability to metabolize sulfamethazine  (1).
). Sequencing yielded 592,448 pairs of 150-bp high-quality filtered reads, totaling 177,734,400 bp, providing approximately 53-fold genome coverage. Using the Velvet 1. 2. 06 program with automatic expected coverage and coverage cutoff parameters and the setting k ϭ 31, we assembled the reads into a consensus sequence of 3. 29 Mbp (split into 268 contigs). The N 50 is 31,621 bp for this assembly, meaning that half the reported genome (3. 29 Mbp) occurs on contigs of 31,621 bp or larger, and the largest contig is 104,475 bp. Annotation of the C448 draft genomic sequence was performed using the BioProject facilities at the GenBank database (under the identifier B234, accession numbers PRJNA170195 and ID170195) and the Microbial Genome Annotation and Analysis Platform (MicroScope platform [http://www.
genoscope. cns. fr /agc/microscope/]) of the Genoscope (the French National Sequencing Center)  (5). The draft genome sequence of strain C448 consists of 3,273,510 bases, with a GC content of 67. 39%. The analysis of the C448 genome revealed that it contains numerous resistance genes, including sulI, encoding dihydropteroate synthase type 1. The int1 and parA genes, coding for an integrase and a resolvase, are located close to sul1, forming an island of resistance to sulfamethazine. Nucleotide sequence accession numbers. The draft sequence of Microbacterium sp. strain C448 has been deposited in the European Nucleotide Archive under the accession numbers HG779581 through HG779847 and CBVQ010000001 through CBVQ010000268. This paper is based on the analysis of the first version of this draft genome sequence.
Parity determination Age grading of An. gambiae s. l. was determined by parity analysis in samples from Senegal 2012 and from Burkina Faso 2013. A random batch of approximately 20 females from each village and each sampling day was dissected for their ovaries. Ovaries were dissected in water under a light microscope and allowed to dry. Parity rate was determined by observing the presence of coiled or uncoiled ovarian tracheoles  [15]. Data are available at Malaria Atlas Project  [13]. Mosquito species determination and Plasmodium spp. detection DNA was extracted with the Qiagen DNeasy kit and was used to identify the species in the An. gambiae complex  [16]. DNA from individual head + thoraxes were tested by Taqman polymerase chain reaction for Plasmodium spp. sporozoite detection  [17] , which used laboratoryconfirmed Plasmodium falciparum sporozoite-infected An.
Two types of analyses were performed: one to compare the difference in survival rate over time (in weeks) between treated and control villages and another to characterize the difference in survival rate over time (in weeks) between the treatment types. The first analysis was performed on all data obtained from Senegal and Burkina Faso only because there were no untreated villages during the sampling period in Liberia. This analysis included data from 3,140 An. gambiae s. l. and tested the influence of the site, village, species, collection, temperature and hygrometry fluctuations and time. The second analysis included data from 1,370 An. gambiae s. l. , collected from treated villages only (including Ngaisakoryah, Liberia) and collected after the date of drug distribution only, and tested the influence of site, treatment type, species, collection, temperature and hygrometry fluctuation and time.
The site variable was used as a random variable to account for the nested data structure, i. e. , the correlation between individuals from the same field site. For each analysis, the random structure was selected and compared with a generalized linear model with no random effect based on the lowest Akaike's information criterion (AIC). Statistical analyses were performed with Statistical Analysis Software (SAS Institute Inc. , Cary, NC, USA) using the GLIMMIX procedure. This procedure performs a type III hypothesis for the fixed effect variables and computes the F-statistic based on Satterthwaite's approximation. The maximal models included all the variables with interactions and were subsequently simplified following a step-by-step AIC-based procedure. Output of the minimal model was used to produce the local regression (LOESS procedure) with the computed 95% confidence intervals.
Sporozoite rate Variations in sporozoite rates over time were analysed only from data obtained in Senegal 2012, Liberia 2013 and Burkina Faso 2013 reported here. Analysis was performed using a generalized linear model with a binomial error structure (GLM procedure in SAS), due to the low number of field sites included. The effect of seven variables on mosquito sporozoite rate was analysed: site, village, species, collection as categorical variables and temperature and hygrometry fluctuations and time relative to MDA (in weeks) as numerical variables. The binary response variable was the status of Plasmodium infection in thoraces (i. e. , infected or uninfected). The model was subsequently simplified following a stepwise AIC-based procedure. Significance of the variable retained in the minimal model was determined using the type III test and normality of residuals was checked.
Acknowledgements We thank the local people in Senegal, Liberia and  Burkina Faso  for participating in this study, as well as the local authorities for their support. We are very grateful to the  IRSS  staff in Burkina Faso, to collaborators in Senegal and to the  LIBR  staff in Liberia for their technical assistance. We thank  Infoscitex Corp  for the use of their tent traps in our studies. We thank  Jon Seaman ,  Meg Gray ,  Timothy Burton , and  Wojtek Kuklinski  for their assistance in mosquito processing. We are very thankful to  Peter U Fischer  for discussion and comments. We acknowledge and are grateful for support from  National Institute of Allergy and Infectious Diseases  grants  R21-AI079528  and  R01-AI094349-01A1 ,  US Armed Forces Health Surveillance Center: Global Emerging Infections Surveillance Network , and  Grand Challenges Explorations  grant  51995  from the  Bill and Melinda Gates Foundation.
Microarray data Caco-2 cells were exposed to Nanobyk, Nanobyk DL, Nanobyk DA and pristine CeO 2 for 72 h at 21. 25 μg/ mL. Cells were also exposed to hydrogen peroxide as a positive control. Agilent Oligo microarrays spotted with 4 x 44,000 probes were hybridized in quadruplicates with RNAs originating from cells exposed to NPs, or from unexposed cells (for exact design details, see Methods). Additional file 3: Figure  S3  represents the scatter plots of the raw fluorescence intensities of genes filtered on the threshold intensity signal of the microarray experiments (n = 4). The scatter plot obtained for Nanobyk NPs versus control 1 was very similar to the control scatter plot representing the raw intensities of genes obtained from untreated cells from two different cultures (control 2 versus control 1) giving the false positive rate (5 genes).
Through two different toxicity assays (ATP and XTT), Caco-2 showed no loss of viability after exposure to up to 170 μg/mL over 72 h for Nanobyk 3810™ and pristine CeO 2 NPs. For degraded Nanobyk NPs, a slight effect (27% loss of viability) was observed for the highest concentration (170 μg/mL) after 72 h with the XTT test, but not with the ATP tests, for NB-DL and NB-DA. ATP and XTT tests are not based on the same principle. XTT is directly based on the enzymatic activity of succinate dehydrogenase, essential to mitochondria. We showed that this enzyme was down regulated in our dataset. ATP test measures the total amount of intracellular ATP that is not decreased in a so sensitive way. This explains why the sole XTT test is disturbed by those NPs. This last test tends to indicate that altered Nanobyk NPs induce a slight change in mitochondrial function.
Experiments were carried out in transmission mode on the ELETTRA synchrotron (Trieste, Italy) on the XAFS 11. 1 beam line  [33]. Before and after aging, the suspension was centrifuged (200,000 g) and freeze-dried. The powders were then diluted in PVP and pressed into thin pellets. The spectra were compiled from the merging of three scans, and the energy was calibrated using a standard reference CeO 2. XANES (X-ray absorption near-edge structure) data were obtained after performing standard procedures for pre-edge subtraction, and normalization using the IFEFFIT software package  [34].
We used Agilent microarrays with four independent genomes per chip (4 × 44,000 probes), thus 8 microarrays are sufficient to perform 32 hybridizations. In this experimental design, six analyses were conducted: a) control 2 versus control 1, as the experimental negative control, b) hydrogen-peroxide-exposed cells versus unexposed cells (named control 3), as the experimental positive control, and c) surface-untreated (pristine) CeO 2 -NP-exposed cells versus unexposed cells (named control 3), d) NB-exposed cells versus unexposed cells (named control 1), e) NB-DL exposed versus unexposed cells (named control 1) f ) NB-DA exposed versus unexposed cells (named control 2). For each analysis, eight raw fluorescence data files were obtained after scanning, which corresponded to four treated cells and four control cells.
All files (n = 32) were submitted to GeneSpring software GX11 (Agilent Technologies) for statistical analysis. Concerning the statistics methodology, we used a widespread method for determining the significance of the change in gene expression  [28, 35]. The raw data were first normalized using the percentile shift 75 normalization method. The normalized data were then filtered on the basis of spots present on 100% of the slides in one of two conditions (treated NPs or control). Only spots detected with at least 70% of their pixels above the threshold intensity signal (set to the median background plus two standard deviations) were selected. From the remaining spots, we selected those with fluorescence ratios (representing NP-exposed samples versus unexposed samples) greater than a 1. 5-fold-change cut-off, then we determined the statistical significance of the changes with a p-value ≤ 0.
Biological analysis Data were analyzed through the use of IPA (Ingenuity® Systems, www. ingenuity. com). Canonical pathways analysis identified the pathways from the IPA library of canonical pathways that were most significant to the data set. Molecules from the dataset that met the fold change cut-off of 1. 5 with p-value <0. 05, and were associated with a canonical pathway in the Ingenuity Knowledge Base, were considered for the analysis. The significance of the association between the dataset and the canonical pathway was measured in two ways: 1) a ratio of the number of molecules from the dataset that map to the pathway divided by the total number of molecules that map to the canonical pathway is displayed; 2) righttailed Fisher's exact test was used to calculate a p-value determining the probability that the association between genes in the dataset and the canonical pathway is explained by chance alone.
Acknowledgements The authors wish to thank the  Agence Nationale de la Recherche  for funding the  AgingNano&Troph  project ( ANR-08-CESA-001 ). Technical help for SEM studies was provided by  C. Dominici ,  CP2M  platform,  Université Aix-Marseille. We also thank  J. Courageot ,  Université Aix-Marseille  for fruitful discussions.
Availability of supporting data section The data sets supporting the results of this article are available as additional files (Additional file 4: Table  S1 ). The raw data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus (GEO) repository and are accessible through GEO Series accession number GSE60128 (http://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc=GSE60128).