dataesr/scientific-paragraphs-categorization huggingface dataset:
MtDNA sequencing analysis A mitochondrial DNA analysis was performed for two genes: CO1  [40]  and ND4  [41]. The 270 DNA samples were amplified and sequenced with the primers previously published. Amplified fragments were purified with the MinElute PCR Purification kit (Qiagen) and sequenced using BigDye Terminator v3. 1 Cycle Sequencing kit (Applied Biosystems) on a Genetic Analyser 3130xl (Applied Biosystems). Sequences were analyzed using Staden Package (MRC Cambridge, England), nucleotide sequences were aligned with BioEdit  [42]. The haplotype numbers were assigned in reference to the published Ae. aegypti sequence from Cambodia for CO1 (GenBank accession No. JQ926688) and for ND4 (GenBank accession No. JQ926722). The nucleotide diversity (π), the Tajima  [43] , the Fu and Li  [44]  and Fu  [45]  tests were computed by DNASP v5  [46]  to determine the neutrality of the populations.
The phylogenetic networks based on CO1 and ND4 sequences were constructed using a reduced-median algorithm  [47]  as implemented in the Network program  [48]. MRBAYES 3. 1. 2 software  [49]  was used to make a CO1-ND4 combined analysis using sequences obtained in this study and retrieved from GenBank. Four Markov chains were run for 2,000,000 generations with a 25% burn-in. The tree was drawn with FigTree v1. 4. 2 (Institute of Evolutionary Biology, University of Edinburgh). A Principle Coordinate Analysis (PCoA) of the mtDNA sequences was realized with DARwin software  [50].
Microsatellites analysis Genetic variability of the samples. The 11 microsatellite loci allowed the identification of genotypes from the 270 Ae. aegypti adult mosquitoes sampled from the nine sites. The presence of a null allele was suspected at loci AG2 and 145TAAA1, which were therefore excluded from the analysis, leading to a dataset of 9 loci. A total of 57 alleles was observed for all samples (Table  2 ). The allelic richness was determined as 6 for the whole population ranging from 4 alleles for locus AC2 to 9 alleles for locus AG5. Regarding the allelic richness, no significant difference was observed among the different sampling sites or the different island countries.
The genetic structure in the Pacific region indicates a western, central and eastern differentiation between the Ae. aegypti samples. Previous studies reported that the vector competence of Ae. aegypti for dengue virus is linked to the mosquito genetic factor and to the dengue virus strain  [72, 73]. Thus characterization of vector competence for arboviruses in Pacific island mosquitoes is also an important issue deserving investigation. Community for the help in coordination and data access. We thank Nicolas Pocquet for scientific discussion on Ae. aegypti. We thank Evelyne Henriot, Yves Mermoud, José-Louis Barbançon for sharing information regarding the past colonization of New Caledonia. Sequencing and genotyping were performed on "La Plateforme du Vivant", Noumea, New Caledonia.
Acknowledgments We thank  Olivia O'Connor  for her valuable suggestions during the project. We thank  Subdivision Santé des Australes  ( Patricia Anania  and  Mathias Ellacott ) and for assistance in mosquito sampling in Tubuai, the  Heads of Units Drs Mike Kama, Eric Rafai (Fiji) ,  Dr Reynold 'Ofanoa  (Tonga) and all the Environmental Health Inspectors who participated in the field collection and laboratory rearing, particularly  Mrs Kelera Oli  and  Alisi Tokailagi ,  Mr Waqa Tikoisuva  and  Kareleli Voi  (Fiji) and  Mr Uatesoni Tu'Angalu  and  Siaola Mahe  (Tonga). We thank  Mrs Sela 'Akolo Fa 'u for revision of the manuscript. We are grateful to Secretariat of the Pacific.
Measurement temperatures ranged from 6 K to 20 K and the energy and momentum resolutions were ∼ 15 meV/0. 02 Å-1 and 2 meV/0. 003 Å-1 for synchrotron and laser ARPES experiments, respectively. Electronic structure calculations were done using the VASP software package  [34]  with PAW pseudopotentials  [35]  that include spin-orbit coupling. The PBE functional  [36]  was used in the exchange-correlation potential. A 16×10×4 Γ-centered k-point mesh was used for Brillouin zone sampling, and the energy cutoff was set to 450 eV. The Wannier-based projected tight-binding models  [37] [38] [39]  capturing all the d-states of Mo and pstates of Te were used to analyze the surface density of states. Surface spectra were calculated by the software package Wannier tools  [40] , which is based on the iterative Green's function  [41].
ACKNOWLEDGMENTS We gratefully acknowledge discussions with  G. Autés ,  A. Bernevig ,  R. Cerny ,  A. Morpurgo ,  C. Renner. This work was supported by the  Swiss National Science Foundation  ( 200021-146995 ,  200021-153405 ,  PZ00P2-161327 ). Q. S. W. and A. A. S. acknowledge funding from  Microsoft Research  and the  Swiss National Science Foundation  through the National Competence Centers in Research MARVEL and QSIT. Q. S. W. was also supported by the  National Natural Science Foundation of China  ( 11404024 ). The authors acknowledge  Diamond Light Source  for time on beamline I05 under proposal  SI12404.
Wearable Activity Trackers To assess acceptability of commercially available wearable activity trackers, we began by identifying all devices available to Canadian consumers as of November 1, 2013. We identified and reviewed 4 devices by Fitbit, 2 devices by Jawbone, and 1 device each from Withings, Misfit, and Nike. The research team selected 4 devices for testing purpose, with each device representing a different feature available with activity trackers (Table  1 ). All 4 devices used an accelerometer to assess steps (pedometer). We chose the Fitbit Zip because it was inexpensive, could be clipped to clothing, and allowed users to track activity on a simple interactive screen. We selected the Jawbone Up 24 as it could be worn on the wrist and could collect data on sleep quality. We selected the Misfit Shine because it could be worn on the wrist or clothing, was waterproof, and could double as a watch.
Data Collection and Analysis Prior to testing any devices, a researcher measured each participant's weight, height, resting heart rate, and blood pressure. Participants completed a paper-based questionnaire on demographics and computer experience. Participants self-reported their physical activity using the validated Short Form International Physical Activity Questionnaire, which assesses the duration and frequency of walking, moderate activity, and vigorous activity among adults  [61]. High physical activity was defined as either a minimum of 1500 metabolic equivalent of task (MET) minutes/week of vigorous activity or a minimum of 3000 MET minutes/week. Moderate physical activity was defined as a minimum of 600 MET minutes/week, 3 or more days of vigorous activity of at least 20 minutes/day or 5 or more days of moderate activity or walking of at least 30 minutes/day.
Individuals who did not meet the criteria for moderate or high activity were classified as having "low activity. " After testing each device, participants completed a questionnaire to describe how they used the device, how satisfied they were with the device, whether or not they would purchase a device, and then rated the devices with a 17-item questionnaire developed using the TAM, which assesses the domains of external variables, perceived usefulness, perceived ease of use, attitude toward using, behavioral intention to use, and actual system use (Table  3 )  [62] [63] [64] [65]. After testing all 5 devices, participants completed a final debrief questionnaire where they ranked the devices according to preference and indicated whether they planned to purchase a particular device. Demographic and clinical information was summarized using descriptive statistics using SPSS (version 22; IBM Corporation, New York, NY, USA).
The final dataset used in the thematic analysis included participants' written notes, researcher observations, and the recorded and transcribed focus group discussions. The TAM identifies two key beliefs as the primary reason for behaviors that encourage computer and technology acceptance: perceived usefulness and perceived ease of use, with the idea being that if people think that using a technology will make their life easier or think a technology is easy to use, then they will be more likely to adopt the technology  [69]. In particular, TAM identifies that using technology, both new and established, is associated with behavioral intention, specifically that people form intentions to use technologies when they have a positive feeling about the technology in question. TAM identified the following areas that can influence adoption: external variables, perceived usefulness (U), perceived ease of use (e), attitude toward using (A), behavioral intention to use (BI), and actual system use.
2. The likelihood for a trigger set with detection statistics {x j |j = 1,. , M } is (Loredo & Wasserman 1995; Farr et al. 2015) L ({x j |j = 1,. , M } |Λ 1 , Λ 0 ) =    M j=1 [Λ 1 p 1 (x j ) + Λ 0 p 0 (x j )]    exp [-Λ 1 -Λ 0 ]. (3) See the Supplement for a derivation of this likelihood function for our Poisson mixture model. In each pipeline, the shape of the astrophysical trigger distribution p 1 (x) is, to a very great extent, universal (Schutz 2011; Chen & Holz 2014); that is, it does not depend on the properties of the source (see Supplement). It is this remarkable property that motivates this approach to our analysis. In principle the data from the LIGO detector contain much more information than can be summarised by a trigger with detection statistic x. For example, to obtain information about the source associated to a trigger (if any) we can follow it up with a separate PE analysis  (Veitch et al.
For example, the UK's BBSRC has recently established a collaborative network to foster the best in animal welfare research which involves social science and humanities scholars. Other examples of work which tie in to the agenda we describe here including work on data-driven biology and the 3Rs (BBSRC), the bioeconomy (Horizon 2010), big data and health innovation (ESRC). Together, these initiatives confirm the value of multi-disciplinary conversations which are increasingly central to research  [49]. As we now discuss, the four themes listed above provide a broad framework for formulating research priorities and new programmes of research. First, there is an important set of questions which reflect the changing international landscapes of animal research. Research priorities here include understanding how international changes in biological research, open data and open access, legislation on the sourcing and use of animals, and understandings of sentience may alter the regulation and practice of animal research.
It also emphasizes the need to foster dialogue about the diversity of practices across sites, to help identify and share best practice, and to understand what enables or constrains multidisciplinary communication and collaboration, without collapsing one discipline into another. The ongoing nature of social, economic and cultural change means it is unlikely there will be a simple or final answer to the research questions generated in this collaborative agenda-setting process. For experimental scientists, working to generate data and reduce uncertainty, the open and reflexive nature of questioning and explanation in the humanities and social sciences can be challenging. Nevertheless, this was not the dominant experience in this exercise. The collaborative process and publication demonstrates the shared commitment to communication and research across disciplinary divides.
The 36-month-long DIDRO project seeks to bring existing developments on remote sensing by drone on the scope of the project: dikes surveys as routine inspection or in relation to a flood crisis. Drone offers a new complementary means of surveying which can map broad areas efficiently while being more flexible and easier to operate than other airborne means. The system shall consist in a drone vector, in dedicated sensors (such as LiDAR, visible, near-infrared and thermal infrared optics), in data processing models with analytics specific to dikes surveying and finally in a GIS capitalizing all appropriate data to dikes managers.
The implementation of those new regulations has highlighted, besides the disparity of resources, a lack of staff. To better structure the dike managers and to help dikes managers coping with all the challenges under their responsibility, France Digues association was created in 2013. Dikes inspection methods nowadays require significant human and financial resources. DIDRO project (acronym for Dikes surveying by drones) aims at developing a solution of dikes survey by instrumented drone. The 36month-long DIDRO project shall create a comprehensive service: a drone vector (any brand, any model thanks to the versatility of the sensors), dedicated sensors (such as LiDAR, visible, near-infrared, thermal infrared optics), appropriate processing softwares and finally a GIS database capitalizing all appropriate measurements to dikes managers.
The underlying technologies of the DIDRO project will indeed evolve from a Technology Readiness level (TRL) 4 to TRL 7. The system consists in a drone vector (compliant with a wide range of models, thanks to the versatility of the sensors), of dedicated sensors (such as LiDAR, visible, near-infrared and thermal infrared optics), of appropriate processing software and finally of a GIS database DIDRO shall lead to a large-scale inspection tool which improves the detection rate of disorders and provides a large array of remote-sensed measurements. It shall thus ease the identification of those disorders. Its multidisciplinary approach is based on a multi-sensor imaging instrumentation (LiDAR, Visible and Near Infrared (VNIR), thermal Infrared (IR), geophysical sensors) and associated analysis of multi-spectral signatures.
Data Collection Outcome Measures Baseline data on participants was collected using a structured questionnaire. Information on demographics, healthcare training, humanitarian response activities, ultrasound and RA experience was gathered. Knowledge assessment exams were scored with a single best answer for each question and participants were assigned a percent correct for both the pre-test and post-test. During observed simulation exams for the RA techniques, each rater provided a critical action completion score ranging from zero to sixteen based on eight assessment parameters. Accrued data was de-identified and entered into a password-protected database that was accessible only by study personnel. The primary outcome measures were the change in knowledge attainment based on mean pre-and post-test scores on the standardized exams and demonstration of technical competency in performing the LGFICB and USGFNB RA techniques.
Statistical Analysis Statistical analyses were performed using STATA 13. 0 (StataCorp LP, College Station, USA). Characteristics of the cohort were evaluated using descriptive methods. Categorical variables were explored using frequencies with percentages, and continuous variables were analyzed using medians with corresponding interquartile ranges (IQR) or means with associated 95% confidence intervals (95% CI). Given the small sample size knowledge attainment data was analyzed using Shapiro-Wilks tests and frequency distributions to evaluate for normality. The results were found to satisfy criteria for normality and as such, one sample paired t-tests were used for the primary outcome of change in mean knowledge scores. A p value less than 0. 05 was considered significant in primary outcome assessments.
Small RNA sequencing and mapping We obtained small RNAs reads derived from 49 (HiSeq) and 22 (SOLiD) libraries. SOLiD samples were prepared from platysma, masseter and gluteus medius muscles, heart, liver, cartilage, bone and blood of 35 horses. MiRNA from the platysma and masseter muscles, heart and liver were extracted from 15 healthy horses of different ages and breeds (collected at the slaughterhouse). The HiSeq samples included serum RNA samples from 44 Warmbloods horses and five ponies (four Shetland and one Welsh pony). More details are given in Additional file 1: Table  S1. After quality filtering for low quality reads and trimming for adaptors sequences (see Methods for details), HiSeq data from serum consisted of a total of 12 M reads on average with mean per sequence quality of 38 in the Phred scale  [18]  per library.
Novel equine miRNA discovery Two complementary algorithms were used to identify miRNAs in equine tissues: i) miRDeep2; and ii) miRdentify. Briefly, we used equine known mature and hairpin structure and human mature sequences as guidance (miRBase v. 2); and minimum miRDeep score of 1 (-t Horse -b 1). Among 781 known equine miRNAs expressed in our dataset (10 more mature and hairpin combinations identified by mirDeep2 compared to miRBase), from 131 to 256 were expressed per library (average = 183 (27 %)). The number of known miRNAs per library identified by miRdentify ranged from 14 to 164 (average = 56). A total of 2,493 regions in the equine genome expressed in the dataset met the miRDeep2 criteria and thus were considered as novel candidate miRNAs by miRDeep2, whereas only seven met the criteria of miRdentify.
Equine miRNA expression For the known and discovered putative miRNAs, we investigated their expression levels in the small RNA libraries. A total of 2,093 unique combination of mature and hairpin sequences were expressed in our dataset (one mature miRNA may derive from more than one hairpin, also one or two mature miRNA derive from a single hairpin structure  [4] ). The mean expression of trimmed means of M values (TMM) of known (miRBase, release 21) and novel miRNAs ranged from 0 to 140,700 count per million (cpm), with an average of 0. 64 cpm per miRNA. We then removed lowly expressed miRNAs (<1 cpm in 90 % samples), as they seem to play minor roles in gene expression regulation  [23] [24] [25]. We detected 965 distinct mature miRNAs (280 known and 685 novel) expressed from 951 unique hairpin sequences (268 known and 683 novel) by combining the results from the two approaches.
Warmbloods After removing lowly expressed miRNAs, our dataset consisted of 683 novel miRNAs (Fig. 1 ), from which 49 % were transcribed from the sense strand, accordingly to known equine miRNAs that are annotated equally on both strands (miRBase, release 21). The highest number of novel miRNAs was identified on the largest chromosome one, but also 40 novel miRNAs were identified on unplaced chromosomes (Fig. 4 ). The unplaced chromosomes are often neglected, as it is the least annotated region of the horse genome. A future EquCab3 reference genome is believed to decrease the number of unplaced scaffolds and improve the quality of the reference genome. Interestingly, we identified a putative novel miRNA encoded by the mitochondrial genome that has been previously described in horse  [32] , however, the mitomiR was identified only in one heart sample and was excluded from further analysis.
Conclusions Our sRNA-seq data and analyses of expression patterns in 9 different tissues presented a global view of tissue distribution of known and novel miRNAs and the relation to their chromosomal locations. We identified 683 novel equine miRNAs expressed in seven solid tissues, blood and serum. Additionally, our approach evidenced that such data supported identification of specific miRNAs as markers of functions related to breeds or disease tissues. For instance, we showed an increased expression of circulating serum miR-122 and miR-200 in ponies together with the predicted miRNA target genes that are required in the control of energy metabolism. The increased expression of eca-miR-483 in ponies relative to Warmblood suggested an increased predisposition to metabolic diseases in ponies.
Briefly, blood was collected from jugular vein into sodiumheparin-containing vacutainers  [54]. Within 1-6 h serum was separated from cells via centrifugation 1800 g for 20 min. Serum was then transferred to new tubes and stored in -80 °C until RNA extraction (up to 4 years). In order to verify the level of haemolysis in serum samples we used VersaMax ELISA Microplate Reader (Molecular Devices) and SoftMax Pro software (version 3. 1. 2). The absorbance in 200 μl of serum was measured at 414 nm wavelength, which is the length of maximum absorbance for haemoglobin  [55, 56]. The RNA was extracted from 2 ml of serum using miRNeasy serum/plasma kit as described in  [20]. The concentration of RNA extracted was measured with Qubit RNA High Sensitivity kit and ranged from 2 to 428 ng/μl (20 ng/μl on average).
For a prediction of potential horse specific RNAs, a criterion of miRDeep score of 1 was adopted as a cut-off point. Therefore, all precursors with total miRDeep scores above the cut-off point were considered as putative miRNAs. The miRDeep2 tool allows for guided search for novel miRNA with an optional set of miRNAs from a related species. As the miRNA annotations in horse genome are relatively poor, we used the most comprehensive set of human miRNAs as guidance. Known equine miRNAs were obtained from the miRBase database, release 21  [13]. In addition, human known miRNAs from miRBase were used as "related species" set of miRNAs. The putative miRNA identified by the miRDeep2 program or miRdentify that did not show a significant match to miRBase were regarded as novel miRNAs. The miRdentify tool applies 10 additional parameters: heterogeneity at both 5' and 3' ends (low heterogeneity is expected), 5' and 3' arm overhang (two nucleotide 3' overhangs at both ends), duplex and flanking sequence minimal free energy (MFE; lower MFE is desirable), nucleotide and structural entropy (higher degree of nucleotide entropy and lower structural complexity of the hairpin are desirable), tailing (high level of tailing with adenosine or uridine); and multimap factors (reads with more than four genomic hits are discarded)  [17].
Differential expression analysis of serum samples The MDS plots, which measure the differences between samples and project it into two dimensions, were through the "MDSplot" function of edgeR package. The estimated raw counts of each miRNA from miRDeep2 output were used for the differential expression analysis using edgeR package. To avoid inclusion of miRNAs containing too few reads, as recommended by authors of the package, only mature miRNAs with the expression > 10 cpm in 90 % of the samples were kept for the analysis. In order to discover biologically important changes in expression, the TMM normalization method  [66]  of edgeR package was applied ("calcNormFactors" function) and the dispersions were calculated based on the quantile-adjusted conditional maximum likelihood ("estimateDisp" function).
Acknowledgements The authors would like to thank all participating horse owners and veterinarians for their support of this study. We would like to thank also our colleagues involved in the sample collection and experimental part of the experiment:  Simone Lanz ,  Helen Rettmer ,  Lucia Unger , and  Nathalie Fouché. We also appreciate the support of the  Next Generation Sequencing Platform of the University of Bern  and the Vital-IT high-performance computing centre of the  Swiss Institute of Bioinformatics  (http://www. vital-it. ch/). We are grateful to  Laurence Wimel  from the  IFCE experimental station  for its help.
Parsing-based approaches  (Yih et al. , 2014; Berant et al. , 2013; Berant and Liang, 2014; Reddy et al. , 2014)  answer factoid questions by learning a structured representation for the sentences, called logical form. This logical form is then used to query the knowledge base and retrieve the answer. IR-based approaches try to identify the best possible match between the knowledge base and the question  (Bordes et al. , 2014a; Bordes et al. , 2014b; Yao and Van Durme, 2014; Dong et al. , 2015). In this work, we focus on the second approach, using embedding models, mainly because it is robust to invalid syntax and can exploit information of the answer. We focus on the Wikianswers  (Fader et al. , 2013)  dataset constructed for Reverb. On Wikianswers, the underlying semantics is very simple (just one single triple).