dataesr/scientific-paragraphs-categorization huggingface dataset:
C ancer is a leading cause of morbidity and mortal- ity in the developing world, with the majority of cancer deaths now occurring in low-and middle-income countries (LMICs). 1  In 2008, the World Health Organization (WHO) global cancer database (GLOBO-CAN) estimated that approximately 715 600 new cases of cancer occur annually in Africa. 2 However, despite this enormous burden of disease, the capacity for diagnosis and treatment of cancer in this region is limited. 3, 4 hile some cancers can be treated effectively with curative intent with systemic chemotherapy alone, the common epithelial cancers seen in both LMICs and high-income countries (HICs) all require surgical resection of early-stage disease if there is to be a chance for long-term cure, particularly breast, cervical, colorectal and prostate cancer.
Chemotherapy is delivered by internists and supervised by a network of visiting oncologists, with weekly remote consultation from US-based oncology specialists. Radiotherapy is performed in neighbouring Uganda. Social support includes health facility transportation, nutritional supplementation and additional support for poor patients, such as home construction. Palliative care is often provided at a district hospital closer to the patient's residence. Patients usually access the BCCOE through referral from other health facilities in Rwanda or from outside the country (primarily Burundi and the Democratic Republic of Congo). Clinical information on in-and out-patients is collected in paper-based medical files. From these, selected data are entered by a data officer into an electronic medical record (EMR), which can serve as a clinical database.
Source of data, data collection and analysis Variables extracted from the EMR included demographics; type and perceived stage of cancer (confirmed stage not available, perceived stage based on clinical assessment, indicating the clinician's perception of disease severity but not necessarily matched to a specific staging system); type of facility from which patient was referred; and 6-month outcomes-alive, dead, referred elsewhere for care or lost to follow-up (defined in this study as not attending scheduled clinic visits after 6 months). In patients alive at 6 months, data available on patient progress (improvement, no evidence of disease, progression of cancer) were extracted from the EMR. Incomplete and inconsistent data from the EMR were completed or verified from patient paper records.
Data on the method of diagnosis (histopathology or clinical) and medical treatment (surgical intervention, chemotherapy or combined surgery and chemotherapy) were extracted from the patient files into Excel® (Microsoft Corp, Redmond, WA, USA). Patient data were collected in November 2013. Analyses were completed using Stata v12 (Stata Corporation, College Station, TX, USA). Frequencies and proportions of patient characteristics at admission to BCCOE, patient management and patient 6-month outcomes are presented.
4%) underwent chemotherapy and/or radiotherapy without surgery. Ninety-nine (39. 6%) patients had no treatment. Of these, 51/99 (51. 5%) were referred to another facility for care, primarily due to advanced disease and the need for palliative care, eight died before treatment was started and the remainder had not started treatment during the study period. Presumed staging at presentation was only available for 99 patients, with surgery data available for 96. Of the 39 with presumed Stage 1/2, 24 (61. 5%) received surgery compared to 22/57 (38. 6%) with presumed Stage 3/4 (P = 0. 027). Of the 76 patients who underwent surgery at BCCOE, nearly 70% did so within 3 months of initial presentation. The median time from presentation to surgery was 21 days (interquartile range [IQR] 2-99). Surgical treatment in relation to different types of cancer and place of surgery is shown in Table  3.
Public Health Action Surgically resectable cancer care in Rwanda 131 ate. Breast cancer was the most common type of cancer operated on at our centre, while other forms of cancer, such as colorectal and head and neck cancer, were mainly operated on in other facilities. This reflects the inconsistent availability of surgical expertise to deal with these cancers at BCCOE. Six-month outcomes were generally good for those who remained in care, with few patients known to have died and only one patient lost to follow-up. The strengths of this study lie in the fact that all consecutive patients enrolled in care at BCCOE were included, and thus there was no special selection of patients. Due attention was also paid to internationally agreed recommendations for reporting on observational studies. 13 Limitations relate to the operational nature of this record review, with certain variables for which it would be important to have information not being available and data missing for other variables.
We believe that the missing data are a function of adopting new data collection tools and that they are probably missing randomly. Another limitation is that the results reflect the burden of cancer among patients presenting at BCCOE and do not necessarily reflect the general incidence of these cancers in the population or presentation and/or management at other sites. Several policy implications are derived from this study. First, many patients did not undergo surgery, possibly due to late presentation or to the limited surgical resources available at BCCOE. Surgical resources in Butaro are shared across all programmes, in-cluding BCCOE, and between 1 July 2012 and 30 June 2013, 1018 surgical procedures were completed, with an average of 2-3 per day. There are only two operating rooms, and at time of writing 73 people were on the waiting list for surgery.
Furthermore, and most importantly, these efforts must be coupled with a comprehensive screening programme that includes community education and training of primary health care staff, to ensure early presentation of patients, increasing the likelihood of long-term disease-free survival. Second, the data collection system needs to be strengthened. Variables essential for evaluating innovation and progress need to be decided on, and ultimately it is best to capture all information in an electronic database system. Current revisions and structuring of patient charts should encourage more complete and reliable data collection in the future. Ongoing monitoring and evaluation of these efforts is critical to know what is being accomplished, where gaps in care persist and how to design interventions to improve outcomes in the future.
The θ-2θ XRD measurements were performed in the range of 20°to 100°( in 2θ scale). Seven CdTe diffraction peaks were taken into account for the texture analysis: (111), (  220 ), (311), (400), (331), (422), and (531). The (511) diffraction peak was excluded from the texture analysis, as being superimposed with the (333) diffraction peak. The intensity of each CdTe diffraction peak was precisely determined by pseudo-Voigt fits, and their deconvolution with other SnO 2 or ZnO diffraction peaks was carefully achieved when required. The 00-041-1445, 00-036-1451, and 00-0150770 files of the International Center for Diffraction Data (ICDD) were used for SnO 2 , ZnO, and CdTe, respectively. Absorption measurements were performed with a UV-visible-NIR Perkin Elmer Lambda 950 spectrophotometer (Waltham, MA, USA).
r, θ, and z are the variables of the cylindrical coordinate system used. The optical databases were taken from  [20, 45, 46 , G Rey et al. , unpublished work] for ZnO, CdTe, CuSCN, and FTO, respectively. The 3D monochromatic generation rate was averaged over a circle perimeter following the procedure of  [47, 48]. G r; z; λ ð Þ¼ 1 2π Z 2 π θ ¼0 G r; θ; z; λ ð Þdθ ð2Þ Eventually, the 3D polychromatic generation rate was computed by weighting the 3D monochromatic generation rates with the solar irradiance spectrum (I AM1. 5G taken from  [49] ): G r; z ð Þ ¼ Z λ I AM 1 :5 g λ ð Þ I i nci d ent G r; z; λ ð Þd λ ; ð3Þ where I incident is the light intensity shining the ZnO/CdTe core-shell NW arrays from the FTO/glass substrate side.
Acknowledgements The authors are grateful to  B. Gayral ,  CEA-INAC, Grenoble, France , for his assistance in PL measurements. This work has been supported by the  Nanosciences Foundation of Grenoble  through the project  II-VI Photovoltaic and by Grenoble INP  with a  Bonus Qualité Recherche grant  through the project  CELESTE. This work has also been partially supported by the  Spanish Ministry  under contract  MAT2010-16116.
These specific signatures were then used as an input to mine and rank candidate genes, followed by filtration against skeletal muscle expression and association with known diseases. Signatures and identified candidate genes highlight both potential common pathological mechanisms and allelic disease groups. Recent discoveries of gene associations to diseases, like B3GALNT2, GMPPB and B3GNT1 to congenital muscular dystrophies, were prioritized in the ranked lists, suggesting a posteriori validation of our approach and predictions. We show an example of how the ranked lists can be used to help analyze high-throughput sequencing data to identify candidate genes, and highlight the best candidate genes matching genomic regions linked to myopathies without known causative genes. This strategy can be automatized to generate fresh candidate gene lists, which help cope with database annotation updates as new knowledge is incorporated.
Moreover, exome sequencing (ES) or genome sequencing (GS) aid in the discovery of new genes associated with various diseases  [11, 12]. There has recently been a surge in publications that use NGS to discover new genes associated with diseases, including myopathies  [13] [14] [15] [16] [17] [18] [19] [20]. The biggest challenge of NGS is to cope with the complexity of analyzing the massive amount of variants generated by the approach. Indeed, comparing two unrelated individuals may lead to about 3 million matching variants in their genomes or about 20,000 in their exomes, but only one of these variants can cause a monogenic disease. The resolution of this issue demands good filtering pipelines to exclude common or meaningless variants, based on the biochemical function of genome location as studied through the ENCODE project  [21] , and on relationships between human variations and phenotype as in ClinVar and in locus specific databases  [22, 23].
In addition, ranking systems can help prioritize validation of the most promising variants. It makes sense to focus on genes presumably implicated in the disease process via functional, structural or phenotypical links with known genes. One of the approaches to collect and compare these data is via in silico analysis using a multitude of open-access knowledge information sources. This approach has been recently done successfully for some disorders but not yet for myopathies  [24, 25]. Lists of candidate genes thus generated can be ranked and used to prioritize variants resulting from NGS analysis. Here, we propose ranked lists of candidate genes for individualized groups of inherited myopathies and related diseases that were obtained via data mining of online information databases. These lists can be coupled to NGS analyses pipelines to help filter and prioritize variants aiming at the discovery of novel genes.
Data-mining from online databases to address complex biological questions We used the data mining system Manteia  [28] , a public resource available online (manteia. igbmc. fr) that retrieves and combines data from freely available online data sources such as Ensembl, Reactome, OMIM, NCBI, Human Phenotype Ontology (HPO), Gene Ontology (GO), Mouse Genome Informatics, and InterPro. Manteia makes it possible to address complex biological questions by running several queries at the same time to mine and statistically analyze gene sets to highlight their annotation specificities compared to the rest of the genome. This study was conducted with Manteia version 2 with data downloaded in June 2013 from the different databases used in the system. Using Manteia's orthology module, we analyzed human gene sets and their mouse orthologs to find an enrichment on statistically significant terms within several annotation categories, including Gene Ontology (GO), Human Phenotype Ontology (HPO), Mammalian Phenotype Ontology (MPO), pathways (Reactome), protein motifs (Interpro) and interacting complexes (Reactome).
Additional filtering of ranked candidate genes using expression data and association with human diseases The ranked lists for each disease group include the known genes of the group, which were used to create the signatures, genes known to be associated with myopathies but implicated in other disease groups, and genes that are not listed in any disease group and thus represent potentially good candidate genes for myopathies. Among those, additional filtering was performed using tissue expression databases. Data from Illumina Body Map E-MAT513, established from mRNA-Seq of 16 human tissues, was downloaded for every gene, and genes with no expression in skeletal muscle or with an expression in skeletal muscle that was less than a cutoff of 10% of the maximum expression found in any other tissue were excluded.
The rationale behind this filtering is that if a gene is expressed in a tissue other than skeletal muscle at a much higher level, one expects such a gene to be implicated in disorders primarily involving that tissue. The 10% cutoff was empirically determined due to the fact that all genes already implicated in myopathies have skeletal muscle expression levels above this cutoff. To deal with missing expression data and eventual heterogeneity in Illumina Body Map's expression database, genes ruled out by the 10% threshold and candidate genes within the 100 first positions in the rankings for each disease group were double checked using expression data from the Genotype-Tissue Expression Project (GTEx)  [29]. For the Congenital Myasthenic Syndromes disease group, which includes diseases primarily related to neuromuscular junction protein defects, but also some peripheral nerve terminal protein defects, we decided to disregard the muscle expression filtering due to the fact that the implicated genes AGRN, CHAT, and CHRNE do not have significant skeletal muscle expression (they are instead expressed in the nerve terminal).
As expected, genes already known to be mutated in the various disease groups, which were used as the training set to create the mining signatures, appear at the top of the ranked lists of data mining. Considering congenital myopathies, out of the 22 genes chosen as the training set, 19 genes appeared in the data mining, while genes CCDC78, KBTBD13, and KLHL40 did not have annotation in the databases used at the time of this work. Thirteen of these genes were ranked within the first 100 genes, a coverage of 13/19 (68. 4%). The muscular dystrophy group had 31 out of 34 genes of its training set appearing in the data mining list, and of these 23 were found within the first 100 ranked genes (79. 3%). In the metabolic myopathy disease group, all 19 genes of the training set were ranked, and 18/19 were found within the top 100 genes in the rank (94.
Proposed candidate genes after filtration A number of candidate genes sharing disease group signatures with known myopathy genes are barely expressed in skeletal muscle or sometimes mutated in other diseases not affecting skeletal muscle. We thus added filtering steps based on tissue expression and known implication in diseases (see methods for details). Table  4  shows the top 8 ranked genes for each disease group after filtration on skeletal muscle expression and absence of link with diseases in OMIM (Online Mendelian Inheritance in Men, omim. org) database. Table  S2  lists the full ranked list of genes for each disease group without filtration, but annotated with skeletal muscle expression and OMIM diseases, and can be linked to NGS filtering pipelines to help prioritization of novel gene discovery, as shown in the discussion.
Gene clustering and ranking are dependent on database annotations Proposed genes in the final ranked list have gene scores with a major contribution of GO and IA terms, and eventual contribution of MPO terms. Thus, they represent genes that have mostly functional links with known myopathy genes (IA terms and GO term ontologies for biological processes and molecular functions), but also some degree of product colocalization in the muscle cell, as expected from matching cellular component-related GO terms. When available, data on altered skeletal muscle function in mouse models also tend to contribute to higher scores for proposed candidate genes. Database annotation can vary from one gene to another, as it is dependent on the history of research for each gene, including both the date when the gene was discovered and the amount of effort spent for its functional characterization.
In addition, animal models are generally phenotyped with a specific organ system in mind. To illustrate the effects of incomplete annotation, the genes TRAPPC11 and TNPO3, recently implicated in muscular dystrophies, were used as components of this disease group training set, but did not impact the results of the gene ranking due to their poor database annotation. Likewise, they were themselves not captured by the signature used for the Muscular Dystrophies disease group. TRAPPC11 does not appear in the ranking, as it was annotated with only two GO terms that are not significant for muscular dystrophies ("vesicle-mediated transport" and "Golgi apparatus"), it has no annotation for pathways or phenotypes, and its two protein motifs are unique in the genome. Annotation biases also account for higher placements of better-annotated genes that have some kind of overlap with myopathy genes.
Such is the case for motor neuron disease-associated genes, which give rise to human and mouse phenotypes that present some degree of phenotypic overlap with myopathies and tend to share many HPO or MPO terms with myopathy phenotypes. In the ranked list of muscular dystrophies, high scores with a predominant contribution of HPO terms were given to the genes SMN1, SMN2, ALS2, IGHMBP2 and AR. These genes are linked to different types of motor neuron disease, which ultimately manifest with muscle weakness and atrophy. In silico approaches need thus periodic revisits to adjust candidate lists based on association of new genes that impact training sets and discovery of new pathways or interactions that change corresponding database annotation, such as the recently published interactome of skeletal muscle proteins centered on proteins that cause limb-girdle muscular dystrophies  [47].
Conclusions The above integrated data mining approach was successfully used to retrieve both specific signatures for different myopathy groups and to uncover and rank interesting candidate genes for myopathies. Recent discoveries of gene implications that were correctly identified by the disease group's signature validated this approach. In silico approaches allow for systematic, but modifiable criteria to be used in generating ranked candidate lists and have the added benefit of automation, whereby such lists can be updated on the fly as new knowledge is incorporated in genomic databases. Signatures and candidate genes highlighted both potential common pathological mechanisms and overlap between several disease groups. In addition, the ranked candidate gene lists are helpful to prioritize functional validation of filtered variants from overwhelming NGS data.
This work was supported by  Institut National de la Santeet de la Recherche Medicale (INSERM) ,  Centre National de la Recherche Scientifique (CNRS) ,  Strasbourg University, Agence Nationale de la Recherche  ( ANR-11-BSV1-026 ),  Association Francaise contre les Myopathies  ( 15352 ),  Muscular Dystrophy Association  ( 186985 ), and  Myotubular Trust  (http://www. myotubulartrust. com/). OAN was supported by a fellowship from  CAPES Foundation, Ministry of Education of Brazil , process number  1286/51-2  (http://www. capes. gov. br/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Acknowledgments The authors wish to thank  John Clarke ,  Justin Deighan ,  Roger Yelle ,  Dave Brain  and two anonymous reviewers for valuable comments regarding the manuscript. This work was supported by  NASA Headquarters  under the MAVEN mission and the  NASA Earth and Space Science Fellowship Program- grant  11-Planet11F-0060. The Editor thanks two anonymous reviewers for their assistance in evaluating this paper. CHAFFIN ET AL. ©2013. The Authors.
Methods PuLsE was an international, multicenter, prospective, randomized, parallel-group, open-label, and long-term (2 years) effectiveness study (Fig. 1 ; ClinicalTrials. gov identifier: NCT00522418). The primary objective was to demonstrate superiority over time in health outcomes of BMP with adjunctive VNS therapy compared with BMP alone in patients with pharmacoresistant focal seizures. A total of 28 sites across Europe and Canada participated in the study. The design was dictated primarily by the need to ensure a relatively long duration of follow-up, in order to obtain a clinically meaningful assessment of long-term changes in HRQoL. This precluded on ethical grounds the use of a double-blind design, and required that the treating physicians be allowed to modify the regimen of AEDs as clinically indicated.
Statistical analyses The initial plan was to enroll 362 patients and to follow each patient for 2 years. The original statistical analysis plan included the intent-to-treat and per-protocol populations, but such analysis was not possible because of early study termination due to low enrollment rates, requiring revision of the statistical plan. Only patients with a baseline QOLIE-89 score and at least one postbaseline assessment were included in the statistical analysis of data from 3, 6, 9, and 12 months of follow-up. For longitudinal data collected at different visits postbaseline, we performed a mixed model repeated measures (MMRM) analysis of variance (ANOVA) using the SAS GLIMMIX (generalized linear mixed model) procedure to assess trend differences between the two treatment groups. The fitted model included fixed effects of treatment group (VNS + BMP vs.
Data from one study site (including 10 randomized patients) were removed from the analysis datasets, as inadvertently a centrally approved informed consent form was used without the additional mandatory approval of the site's local ethics committee. The remaining 112 randomized patients were included in the safety analyses. Of these, 96 (83%)including 48 patients allocated to VNS + BMP and 48 patients allocated to BMP-had baseline data and at least one post baseline follow-up QOLIE-89 assessment, and were thus included in the efficacy analyses. Sixty of these 96 patients had completed their 1-year follow-up visit by the time the study was terminated, including 55 patients with QOLIE-89 data available at each visit (assessments at 0, 3, 6, 9, and 12 months; 28 in the VNS + BMP group and 27 in the BMP group).
90 and 0. 13, respectively) (Table  2 ). Change from baseline values at follow-up time points are provided in Table  S4. A summary of changes in underlying AED treatment is provided in Table  S5. Both treatment groups had similar AED loads from baseline to follow-up time points. When change from baseline AED load was evaluated by MMRM analysis, a nonsignificant trend was observed between the groups (p = 0. 08) with a greater increase in the BMP group (means AE standard error (SE): 0. 18 AE 0. 05) than in the VNS + BMP group (means AE SE: 0. 06 AE 0. 05) (data not shown).