dataesr/scientific-paragraphs-categorization huggingface dataset:
001. Final pH values were about 4. 6-4. 9 and 6. 0-7. 1 in YPD and YPGly, respectively. Short description of the strains used (For further information, see TableS1and Saccharomyces genome database at www. yeastgenome. org/): Wild-type W303 and BY-4742 are commonly used lab strains. WT-BY4742 is partially impaired in respiration as it is mutated in the HAP1 locus which encodes a major heme-responsive transcription factor that regulates O 2 -responsive genes. The RWT-BY4742 strain is restored for Hap1p (Gaisne et al. 1999). The yap1D and skn7D strains are hypersensitive to oxidative stress due to deletions of major transcription factors required for oxidative stress tolerance. The hem1D strain cannot synthesize heme and it is therefore unable to respire, devoid of catalase and cytochrome c peroxydase activities and impaired in lipid metabolism.
01; ***P < 0. 001. Final pH values were about 4. 6-4. 9 and 6. 0-7. 1 in YPD and YPGly, respectively. Short description of the strains used (For further information, see TableS1and Saccharomyces genome database at www. yeastgenome. org/): Wild-type W303 and BY-4742 are commonly used lab strains. WT-BY4742 is partially impaired in respiration as it is mutated in the HAP1 locus which encodes a major heme-responsive transcription factor that regulates O 2 -responsive genes. The RWT-BY4742 strain is restored for Hap1p (Gaisne et al. 1999). The yap1D and skn7D strains are hypersensitive to oxidative stress due to deletions of major transcription factors required for oxidative stress tolerance. The hem1D strain cannot synthesize heme and it is therefore unable to respire, devoid of catalase and cytochrome c peroxydase activities and impaired in lipid metabolism.
DNA was extracted from the exposed samples using the Nucleospin DNA extraction kit (Marcherey-Nagel GmbH & Co. KG, Du ¨ren, Germany). Each sample's DNA was suspended in 40 ml of elution buffer in a microfuge tube. The amount of DNA in each sample was quantified by comparison with that of a commercial standard DNA marker GelPilot plus 1kb (Qiagen, Valencia, CA, USA) in which the DNA amount is known. Aliquots (0. 89, 1. 33, and 3. 00 ng) of the standard DNA marker and 2 or 3 ml of the sample DNA were loaded on 1% agarose gels containing 30 ml of TE buffer and 0. 6 ml of ethidium bromide, and electrophoresis was conducted at 100 V for 5 min at room temperature. After electrophoresis, the gel was scanned with a Typhoon Trio Scanner (GE Healthcare, Piscataway, NJ, USA). Signal intensity was measured for each DNA band, and each sample's DNA amount was quantified using the software ImageQuant TL (GE Healthcare).
The membrane was washed again and thymine dimers were visualized with the Typhoon Trio Scanner, and the intensity of each dot was measured by the ImageQuant TL software. The pUC 19 plasmid DNA, which contained 5. 17610 4 thymine dimers per 1 Mb was used as standard, and thus the frequency of thymine dimer formation in each DNA sample was calculated comparing intensity of the samples to those of the standards. The pUC 19 plasmid DNA which contains 5. 17610 4 thymine dimers was used as molecular standard in this experiment.
Bioinformatics Tardigrades belong to the invertebrate superclade Ecdysozoa as do Caenorhabditis elegans and Drosophila melanogaster. A tardigrade photorepair gene has not been identified as of yet so the photorepair protein gene phrA from D. melanogaster was used (Accession: NM_078929) to search the R. varieornatus genome for homologous genes. BlastP  [15]  was performed with the amino acid sequence against the in house protein database predicted from R. varieornatus genome to search for homologues of the Drosophila melanogaster photorepair gene phrA. To search the homologous gene from H. dujardini, their EST sequences (http://www. ncbi. nlm. nih. gov/genomeprj/20353) were mapped to the genome of R. varieornatus by using Exogenerate program  [16]. Protein domains of the phrA genes were predicted using Pfam  [17] , and amino acid sequence alignment was performed using MAFFT version 6  [18].
Background: Most of the institutional and research information in the biomedical domain is available in the form of English text. Even in countries where English is an official language, such as the United States, language can be a barrier for accessing biomedical information for non-native speakers. Recent progress in machine translation suggests that this technique could help make English texts accessible to speakers of other languages. However, the lack of adequate specialized corpora needed to train statistical models currently limits the quality of automatic translations in the biomedical domain. Results: We show how a large-sized parallel corpus can automatically be obtained for the biomedical domain, using the MEDLINE database. The corpus generated in this work comprises article titles obtained from MEDLINE and abstract text automatically retrieved from journal websites, which substantially extends the corpora used in previous work.
Translation quality remains a major issue, especially when generic translation engines are used. For instance, Zeng-Treiter et al. [9]  found that Babel Fish translations  [12]  of medical record sentences from English into four target languages were incomprehensible and/or incorrect. Wu et al. [11]  show that using a specialized biomedical corpus to train a statistical machine translation system improves translation quality significantly. However, they note that large parallel corpora in the biomedical domain are not readily available, which limits the opportunities for quality automatic translations. In this paper, we present a method to obtain large parallel corpora in the biomedical domain relying on MEDLINE data. We apply this method to two language pairs, English/Spanish (EN/ ES) and English/French (EN/FR), and evaluate the quality of the resources extracted in two ways: first by direct analysis of the extracted data, and then by applying the data to train statistical machine translation models used to translate biomedical text.
Multi-lingual resources in MEDLINE Table  1  presents the availability of MEDLINE resources in languages other than English. It is interesting to note that the availability of abstract and DOI information is not always on par with the number of citations for a given language. In the Methods section, we describe a procedure to recover journal papers given the DOI code from selected publishers from MEDLINE citations that contain an English title and abstract but are not originally written in English. Using this approach, we built a Spanish/English parallel corpus (ENES) by processing 5,439 citations for articles in Spanish collected from the publishers' website and processed using a Python program based on regular expressions. We were able to extract data from 3,373 of those articles and obtain a 30,000 sentence corpus.
In the alignment, a large number of sentences are discarded. A large number of sentences are missed but we have preferred to have a more reliable set. Table  8  shows a case where sentence alignment was not successful. In this example, the Spanish sentences were not properly identified because of the lack of space after the full stop marking the end of the first sentence. In contrast, Table  9  shows a case where sentences were successfully aligned. Table  10  presents the statistics on the dataset for each language as well as the partition into different groups (training, tuning and testing) for the SMT experiments. For each language pair there are two sets of sentences, the set derived from the titles only and the set made of the abstract sentences we have extracted. The title set has  ABEN-Partial avulsion of the middle turbinate is an unusual complication of nasotracheal intubation, while minor nasal mucosal trauma is common.
Discussion Quality of parallel corpora obtained from MEDLINE Our manual evaluation of error causes (Table  5 ) shows that language inversion (i. e. labelling an extracted abstract in English ABEN and a French abstract ABFR) is a common error cause. This issue could be addressed by statistically checking that the distribution of extracted text matches that of the language label. Previous work also used URL matching and a comparison of extracted text length as criteria for correct extraction of bitexts  [18]. In our case, all texts can be found on the same webpage so that URL analysis is not necessary. However, text length can be useful to identify some erroneous extractions, for instance when keywords are extracted instead of a title. Interestingly, many cases of "suboptimal" extraction can still provide useful data for some applications such as Statistical Machine Translation, since sentences with lower hunalign scores do not degrade the translation performance (see Table  11 ).
Directions for future work Obtaining additional parallel corpora This work can be easily extended to other languages available from MEDLINE. Our experience in developing a tool to extract ENFR text and porting it for extracting ENES data from the same publishers shows that only little work is required. The data sets obtained in this work for the ENFR and ENES language pairs could also be extended by considering additional journals. The problem with this approach is the lack of standard formatting of the articles between journals. This implies developing custom analyzers, one for each journal. As presented in the introduction, there are other resources which could be used to increase the number of sentences in both languages. In the Results section, we have presented a table with language availability in MEDLINE.
Improving the results of SMT We find that abstract sentences are more complex compared to title sentences used in previous work. In addition, French sentences seem to be more difficult to translate compared to Spanish sentences. Spanish translations would benefit from having a larger set to improve the coverage of terms. Current work on out-of-domain data  [21]  confirms that this would be a path to explore. We would like to explore the use of Europarl corpus, or any corpora available, for this purpose. Another method for improving the quality of translations would be to use a domain-specific lexicon as this was found to be a useful resource for Serbian-English  [22]. For the biomedical domain, such lexicons could be obtained from the Unified Medical Language System® (UMLS®)  [23]. Finally, translation into English seems easier mainly due to the way articles are used and the less strong correlation between words for gender and number agreement.
Conclusion In this paper, we presented a method for obtaining large parallel corpora in the biomedical domain using the MEDLINE database. We show that the quality of the extracted bitexts is high. In turn, they can be used to train Statistical Machine Translation systems and produce the best results to date for machine translation in the biomedical domain. While further progress should be made by incorporating out-of-domain corpora and domain specific lexicons, we believe that this work paves the way for improved automatic translation in the biomedical domain.
Methods Obtaining parallel corpora from MEDLINE MEDLINE currently indexes about 4,000 journals in the biomedical domain. Although most of them publish articles in English, some also publish articles in other languages. In fact, 22% of the articles indexed in MEDLINE ) retrieved 601,464 citations, meaning that about 15% of the articles in a language other than English indexed in MEDLINE are in French. However, the detailed distribution of language in MEDLINE shows that English has been increasingly prevalent in recent years  [24]. For these articles, MEDLINE citations record the title and abstract in English if they exist, as well as the title in the original foreign language. However, abstracts in foreign languages are not recorded. In recent years, MEDLINE has also been recording DOI (Digital Object Identifier) information for the articles, so that MEDLINE citations can be linked to a webpage where the publishers make additional data available, such as foreign language abstracts.
Due to the heterogeneity of HTML formats, we focused on extracting the following information from two major publishers (EM Consulte (Elsevier) and Science Direct): English title (TIEN), French title (TIFR), Spanish title (TIES), English abstract (ABEN), French abstract (ABFR) and Spanish abstract (ABES). The only information missing from the MEDLINE citations is the foreign language abstracts (ABFR and ABES), but we also extracted information that was present in MEDLINE to use as an evaluation proxy for our approach. All publisher data was extracted using a regular-expression Python program, available under  [25].
Evaluation of the elements from the parallel corpora For a systematic evaluation of the extraction of the French and Spanish abstracts, we made the assumption that, for a given article, if the titles and English abstracts were correctly extracted, it was highly likely that the foreign language abstracts would also be extracted correctly. To assess the extraction of TIEN, TIFR and ABEN, we computed the edit distance between the MEDLINE information and what was extracted, relative to the average length of the strings. A manual evaluation was also conducted in order to assess the quality of the automatically extracted data. For each corpus, random samples of extracted data were reviewed by the authors (AJY, AN): 1. 100 articles with edit distance lower than 50 for all extracted fields in order to assess whether the extracted abstracts in French or Spanish were an adequate translation of the abstracts in English; 2.
Statistical machine translation models We have used Moses  [26]  as the toolkit for Statistical Machine Translation (SMT). Moses is a state-of-the-art open-source phrase based SMT. Experiments have been performed following the instructions described in the package  [26]. The support packages SRILM and GIZA++ have been installed. SRILM  [27]  is a package that performs n-gram language models extraction of trigrams. GIZA++  [28]  is a statistical machine translation toolkit that is used to train IBM Models 1-5 and an HMM word alignment model. The experiments with Moses involved three steps: training, tuning and testing. During the training step, Moses learns word-to-word translation and distortion models based on IBM Model 1-5. This model is used to build a phrase table and reordering model.
SMT corpus preparation We have two sources for sentences, the titles recovered from MEDLINE and the abstract text collected from the journals. Abstract text needs to be broken into sentences and aligned to the target/source language translation. Sentence splitting is performed with the Lingua::Sentences tool which has already been used in the context of the Europarl corpus, available in a large number of European languages. For each citation, sentences in each language pair (ENFR/ENES) are aligned using hunalign  [14] , that aligns bilingual text at the sentence level. Hunalign performs sentence alignment in two steps. First, hunalign uses Gale-Church sentence-length information and builds its own dictionary. Then, it performs a second pass using the dictionary and sentence-length information.
Acknowledgements This work was supported in part by the  Intramural Research Program  of the  National Institutes of Health, National Library of Medicine  and by an appointment of  A. Jimeno-Yepes  to the  NLM Research Participation Program  sponsored by the  National Library of Medicine  and administered by the  Oak Ridge Institute for Science and Education. The authors thank  Dr. Michael Filhol  for his editorial assistance. NICTA is funded by the  Australian Government  as represented by the  Department of Broadband, Communications  and the  Digital Economy  and the  Australian Research Council  through the  ICT Centre of Excellence program. The authors thank  Dr. Darmoni  for helpful discussions on the study.
The two other borders of Côte d'Ivoire that we examine are less clearly demarcated on the field  (Brownlie 1979). In surveys, the great majority of household heads belong to the Mande-Voltaic ethno-linguistic grouping. In particular, the boundary between Côte d'Ivoire and Mali lies across the Senoufo (Gur/Voltaic) area in its eastern part and the Malinke (Mande) area in its western part. The hazards of French conquest and of the wars against the Almami Samori Toure at the end of the 19th century reflected the boundaries' alignment within the French Empire and resulted in partitions of some former political entities (Kenedougou and Kong kingdoms). These borders were only stabilized after World War I. We also use French data for the colonial period (Huillery 2009) and explore differences in initial conditions between the border areas lying inside the French empire (Mali and Guinea).
1), which will be our preferred bandwidth everywhere. the location of regional cities (5,000 inhabitants or more) as of 1990; city names are reported only for cities over 30,000 inhabitants. The dashed line delineates the 75 km bandwidth around the boundaries. We then characterize the PSUs of household surveys by five geographical variables: rainfall, elevation, distance to the nearest river, population density, distance to the closest regional city, and distance to the capital city (for more details on variable sources and construction, see supplemental appendix S2). The geographical locations of cities with 5,000 inhabitants or more in the years  1960 and 1990  are drawn from the Africapolis database. 6 Even natural geography should not be too quickly considered to be predetermined before the drawing of the boundaries.
Survey data on development outcomes We gather a database composed of 15 multi-topic household surveys: seven for Côte d'Ivoire, six for Ghana, one for Mali, and one for Guinea. These surveys were implemented between 1986 and 1998 (table A. 2). We mainly use "income surveys", which correspond to the frame of Living Standard Measurement Surveys (LSMS), as designed by the World Bank in the 1980s. These surveys allow the measurement of consumption and income sources, and we code the geographical location of PSUs with precision using the locality name. We complement these income surveys with Demographic and Health Surveys (DHS), which do not measure consumption but record anthropometric data as well as housing conditions. All sample designs are two-stage and regionally stratified, and each PSU contains between 12 and 25 households.
We choose to analyze four different welfare outcomes: consumption per capita, children's height-for-age, access to electricity, and access to a man-made source of water. Despite the multi-topical nature of these surveys, very few other welfare outcomes are usable for comparison. First, we construct a household expenditure variable that includes all current expenditures, such as food, clothing, transportation, housing, and imputed rents, and expenditures for education. We only exclude overly infrequent or badly measured expenditures such as those in health, durable goods, and transfers. Except for Mali, we also compute the value of the consumption of own food production, which we add to household expenditures to obtain a total consumption variable. We use monthly data on the national consumer price index and express individual household consumption at 1988 or 1993 prices.
Comparisons of household consumption levels are made in US dollars at 1988 or 1993 exchange rates and prices. 7 We consider one important component of agricultural income, household production of the three main cash crops produced in the region: cocoa, coffee and cotton. We retain the value of output sold, either as directly reported by the households (Ghana and Guinea borders) or as physical quantities priced at the official and uniform producer price (Mali border; the Malian survey only records physical cotton output). We also extract data on wage incomes earned by household members. Second, height stature is available for children from six months to four years (59 months) of age, and only for 6-35 months in some DHS. We construct heightfor-age Z-scores using the World Health Organization standards  (WHO 2006)  and code children as stunted when the Z-score is below -2.
Data from Robert Bates, Karen Feree, James Habyarimana, Macartan Humphreys, and Smita Singh. 2001. "Economic Data (updated 2005)", http://hdl. handle. net/1902. 1/14978 UNF:5:8vHsoDT1Q8sUbiTZgDkctw== Murray Research Archive [Distributor] V1 [Version].
Validated biomarkers predictive of response/resistance to anthracyclines in breast cancer are currently lacking. The neoadjuvant Trial of Principle (TOP) study, in which patients with estrogen receptor (ER)-negative tumors were treated with anthracycline (epirubicin) monotherapy, was specifically designed to evaluate the predictive value of topoisomerase II-alpha (TOP2A) and develop a gene expression signature to identify those patients who do not benefit from anthracyclines. Here we describe in details the contents and quality controls for the gene expression and clinical data associated with the study published by Desmedt and colleagues in the Journal of Clinical Oncology in 2011 (Desmedt et al. , 2011). We also provide R code to easily access the data and perform the quality controls and basic analyses relevant to this dataset.
Study population and clinical data The neoadjuvant prospective TOP trial (NCT00162812) was conducted at different European hospitals and coordinated by the Institut Jules Bordet in Brussels, Belgium. One hundred and forty-nine patients were included in this trial. The inclusion criteria were the following: 1/histologically-confirmed breast cancer (either operable, locally advanced or inflammatory), 2/age ≤ 70 yrs, 3/female patient, 4/tumor size ≥ 2 cm at the ultrasound examination, 5/estrogen receptor (ER)-negative tumors, 6/multifocal and multicentric breast tumors are allowed if all foci are ERnegative (Table  1. ). It is reasonable to limit multifoci tumors to bifocal ones since a fixed and frozen samples should be obtained from each focus, 7/fixed and frozen samples from the primary tumor, obtained before treatment with epirubicin, must be available for evaluation of biological markers, 8/written consent before study registration, 9/performance status ≤ 1 (ECOG scale), 9/ANC ≥ 1500/ mm 3 , platelets ≥ 100,000/mm 3 , Hb ≥ 10 g/dl, total bilirubin and serum creatinine ≤ 1 N, GOT/GPT ≤ 1.
The pathological response assessment and the different TOP2A evaluations were carried out in a blinded fashion: pathological response determination, TOP2A gene, mRNA and protein analyses were conducted independently. All pathology reports were centrally reviewed and 17/19 pCRs were centrally reviewed at the Jules Bordet Institute. Two pCR cases could not be centrally reviewed since the participating center did not send the slides to the Jules Bordet Institute. The clinical data was collected, monitored and validated by the BrEAST Data Centre, Institut Jules Bordet. The anonymized clinical data were deposited in the Gene Expression Omnibus database (GEO;  [2] ) under accession number GSE16446.
Gene expression data One 5-μm tissue section (usually after ten 30-μm sections) of each biopsy was hematoxylin and eosin stained to monitor the tumor cell percentage of the tissue. Only specimens with more than 30% of tumor cells were included for further analysis. Isolation of RNA was performed using the Trizol method (Invitrogen) according to the manufacturer's instructions and purified using RNeasy mini-columns (Qiagen, Valencia, CA). The quality of the RNA obtained from each tumor sample was assessed based on the RNA profile generated by the bioanalyzer (Agilent Inc. ). RNA amplification, hybridization and image scanning were done according to standard Affymetrix protocols. We used the Affymetrix Human Genome U133-2. 0 plus GeneChip according to standard Affymetrix protocols. Microarray and sample annotation data were deposited in GEO under accession number GSE16446.