dataesr/scientific-paragraphs-categorization huggingface dataset:
Its panels are covered with a multilayer insulation (MLI) characterized by an IR emissivity of 0. 71 and a UV absorptivity of 0. 52, whereas the radiator is coated white with an IR emissivity of 0. 81 and UV absorptivity of 0. 25. The average spacecraft temperature in a 550 km SSO is taken to be 10 • C. The detector unit is modeled as an aluminum block connected to the radiator with a thermally conductive strand with a total resistance of 3. 2 K/W. Each of the four detectors and its proximity electronic circuitry consumes 0. 3 W. The incoming direct solar UV/VIS radiation, the reflected radiation from Earth's surface, and Earth's emitted IR radiation during a typical QKD orbital scenario with nighttime OGS encounter was calculated using Airbus' Thermica software  [45]. Taking further into account the different radiative and conductive heat fluxes between the satellite structure, the radiator, and the detector unit, the model developed allows us to calculate the minimum radiator surface area needed to maintain the detector module temperature below -30 • C.
Such data are crucial for future global scale quantum communication efforts. Miniaturization of CubeSat subsystems, such as those needed for quantum communication, will provide a boost to classical communication technologies and may lead to prototypes for future CubeSat space-qualified subsystems that one day may be available as COTS building blocks for other CubeSat missions. Although currently not a primary aim, launch of the NanoBob CubeSat in a slightly elliptic orbit will enable the investigation of the gravitational potential on entanglement. The finite speed of light and the description of gravity as space-time curvature are both manifestations of the role of locality in the theory of General Relativity. Quantum theory on the other hand is fundamentally non-local, as manifested by quantum entanglement.
Alternatively, the launch of two NanoBob satellites into different circular orbits may still present an economically attractive alternative to the use of an elliptical orbit, given that circular orbits see more and cheaper commercial launch opportunities. It may even be possible to combine data obtained by a single NanoBob satellite with those obtained in a future Space-QUEST experiment on board of the ISS. A limiting factor is likely the required much higher photon rate in order to achieve an adequate signal-to-noise ratio. Increasing the brightness of the source would benefit from larger non-linear crystals, which is already an active area of research. This in turn may require that the photon flux arriving at Alice be distributed over a large number of individual detectors-a costly exercise as it is estimated that roughly a hundred-fold higher photon flux is required.
Literature search We sought reviews of studies and articles describing validated tools that are currently available to assess DPC during a consultation. Our search covered five databases using four research equations (Fig  1 ). We restricted articles to those in English published after 1990 for which at least the title and abstract were available. In addition we searched English (NHS) and US (Agency for Healthcare Research and Quality, AHRQ) institutional databases. A manual search was also conducted using the bibliography of selected articles. Searches were last updated in August 2017. Two of the authors independently selected articles, then, together with a third expert, they reassessed the full text of those for which they had not initially concurred.
They were asked the series of generic questions on their perception of DPC during the consultation; the amount of information they had received, their adherence to treatment and/or the doctor's recommendations. Patient data and answers were anonymized before double entry in an Excel database. Statistical analysis. Statistical analysis was performed using Stata Version 13. 0 (Stata Corp, College Station, Texas) software for OSX. Categorical variables are expressed as numbers and percentages and continuous variables as the medians and interquartile range (IQR) [25th and 75th percentiles]. To evaluate our DPC score, the consistency of the items was assessed by Cronbach's alpha, giving an index between 0 and 1, followed by an unrotated Principal Component Analysis (PCA). The limitations of Cronbach's alpha have been debated  [31].
Clinical study Population. We included 189 patients. Of these patients, 12 were erroneously included (underage, hospitalization > 48 hours) and 21 were lost to follow-up (12%). Data from 156 patients were analyzed and 149 patients answered all questions. The patients were divided into two groups according to the diagnosis: ankle sprain: 83 patients (53. 2%) and infection: 73 patients (46. 8%) (Fig  3 ). Table  4  shows the characteristics of the patients and the median score of DPC-13 and DPC-15 questionnaires. The high level of response indicates the feasibility of use of the questionnaire. Internal and external validity of the DPC questionnaire. Cronbach's alpha was calculated for 149 of the 156 patients, because 7 patients did not answer all the questions. Alpha was calculated as 0. 89, indicating good internal consistency of the questionnaire (> 0.
Interestingly, this latter effect has also been seen in approximations to QCD which are di-rectly based on field theory (see, e. g. ,  [47] ). Notice also that we observe inverse catalysis at relatively low values of µ, where the sign problem is probably surmountable. Therefore we expect that it will be possible to check our results on the lattice. We do not dwell into the technicalities of our calculations in this Letter. Our calculations are based on the gravitational background described in  [29]  in detail. This background is obtained by solving Einstein's equations numerically, and it is necessarily complicated since it involves backreaction of the flavor branes. To facilitate our calculations and minimize the error and time we wrote a program in C++, which was in part based on the earlier Mathematica code  [48].
ACKNOWLEDGMENTS We are grateful to  G. Aarts ,  J. O. Andersen ,  A. Ballon-Bayona ,  I. Iatrakis ,  A. Schmitt , and  D. Zoakos  for discussions. This work was supported, in part by the  Netherlands Organisation for Scientific Research (NWO)  under VIDI grant  680-47-518 , and the  Delta Institute for Theoretical Physics (D-ITP ) funded by the  Dutch Ministry of Education, Culture and Science (OCW).
Sequencing and Variation in Potato Based on morphological data (Van den  Berg et al. , 1998)  and AFLP marker data  (Spooner et al. , 2005) , cultivated potato was proposed to have been domesticated from members of the wild species S. brevicaule complex in clade 4. To investigate the diversity of wild and cultivated potato, we selected 167 accessions of potato with a broad geographic distribution, including 146 wild potato accessions from clade 4 and 21 cultivated diploid potato accessions (Figure  1A ; Supplemental Table  1 ). We sequenced these accessions to an average of >123 genome coverage depth using Illumina HiSeq 4000 to generate 150 bp paired-end reads. The raw reads were subjected to a series of quality control procedures, and then the filtered high-quality reads were mapped back to the PGSC4.
03 potato genome  (Potato Genome Sequencing Consortium et al. , 2011). Based on comparisons to the reference genome, we identified 6 487 006 high-quality SNPs (Supplemental Table  2 ). To verify our SNPs, we used two broadly accepted SNP datasets (SolCAP 8303 Array Infinium SNPs and 69 011 Infinium High Confidence SNPs) that were generated by Sanger sequencing and Illumina sequencing of potato cultivars  (Hamilton et al. , 2011; Felcher et al. , 2012). Our candidate SNP dataset contained approximately 74% of these reported SNPs (Supplemental Figure  1 ), which indicates the high quality of the SNPs generated in this study. Of these 6 487 006 high-quality SNPs, most (57%) were located in intergenic regions and 16. 4% were located in coding sequences. Among the latter, there were 541 208 synonymous and 515 492 nonsynonymous SNPs; thus the ratio of nonsynonymous to synonymous substitutions in potato is 0.
4% (15 096) were located in coding sequences, among which 19. 5% (2937) were in-frame, 3 bp InDels (Supplemental Table  3 ). These genes with largeeffect mutations might be important in the functional evolution of potato genes. This dataset provides a set of molecular markers that could be used to identify agronomic traits in related genes and for variety breeding  (Hirsch et al. , 2013; Uitdewilligen et al. , 2013; Manrique-Carpintero et al. , 2015; Rak et al. , 2017).
, 2005)  with K changing progressively from 2 to 3, and dividing the wild accessions into two subgroups. All three analyses consistently showed that cultivated potatoes have a closer phylogenetic relationship to the northern wild subgroup than to the southern wild subgroup (Supplemental Figure  2 ). Based on these results, we propose that cultivated potatoes were domesticated from the members of the northern wild subgroup. To investigate the association between the subgroups and bioclimatic variables, we extracted a set of well-known bioclimatic variables from the WorldClim 2. 0 database, using data at 10 min resolution  (Fick and Hijmans, 2017). The northern and southern groups could be distinguished by isothermality and temperature differences in the environment (Supplemental Figure  3 ).
Identification of Gene Loss Events in Cultivated Potato To investigate the ancestral gene states in the wild species, we sequenced the ancestor of cultivated potato, S. candolleanum, to a depth of 683 genome coverage by sequencing 350 bp libraries using the Illumina HiSeq4000 platform. The reads were mapped to the PGSC4. 03 potato genome  (Potato Genome Sequencing Consortium et al. , 2011). We obtained 26 727 178 unmapped reads, which were subsequently assembled into 849 894 contigs. Of these contigs, 8994 contigs are >1000 bp and the max contig length was 39 803 bp (Supplemental Table  10 ; Supplemental Figure  5 ). These contigs were annotated using the IPR2GO, InterPro, KEGG, Swissprot, and TrEMBL databases. Of these contigs, 534 are >2 kb and could be annotated with information from at least one of the above databases.
Read Mapping, SNP/InDel Calling, and Annotation The clean reads were mapped back to the PGSC 4. 03 potato genome  (Potato Genome Sequencing Consortium et al. , 2011)  using Burrows-Wheeler Aligner (version 0. 7. 8)  (Li and Durbin, 2009) , which considers both the size and mismatch penalty. The alignment results were converted to BAM files using SAMtools software  (Li et al. , 2009a). The potential PCR duplicates were removed using the SAMtools command ''rmdup''. If multiple read pairs had identical external coordinates, we only retained the pair with the highest mapping quality. SNP calling at the population level was performed by using both GATK  (McKenna et al. , 2010)  and SAMtools  (Li et al. , 2009a). We chose the sites identified by both SAMtools and GATK for subsequent filtering. The SNPs supported by at least five mapped reads, RMS mapping quality R20, and Phred-scaled genotype quality R5 with less than 0.
2 missing data were considered high-quality SNPs, and these SNPs were used for subsequent analyses. Then, two broadly accepted SNP datasets were used to verify our SNPs: SolCAP 8303 Array Infinium SNPs and 69 011 Infinium High Confidence SNPs. SNPs in these two datasets were identified from transcriptome Sanger sequencing and Illumina sequencing of potato cultivars and can be downloaded from the following websites: SolCAP (http://solcap. msu. edu/potato_infinium. shtml) and PGSC Data Download (http://solanaceae. plantbiology. msu. edu/pgsc_download. shtml). InDel calling was similar to SNP calling, and only those %5 bp were taken into account. These SNPs/InDels were annotated based on comparisons to the reference genome using ANNOVAR  (Wang et al. , 2010) , an efficient software tool. Based on the genome annotation, SNPs/InDels were categorized as being located in exonic regions (overlapping with a coding exon), splicing sites (within 2 bp of a splicing junction), 5 0 UTRs and 3 0 UTRs, intronic regions (overlapping with an intron), upstream and downstream regions (within a 1 kb region upstream or downstream from the transcription start site), and intergenic regions.
Population Structure and Phylogenetic Analyses We performed a PCA of the 167 accessions to evaluate genetic structure using the software GCTA  (Yang et al. , 2011) , and used the Tracey-Widom test to determine the significance level of the eigenvector. We further constructed an NJ tree with 1000 bootstraps and S. chomatophilum Bitter as the outgroup using TreeBeST 1. 9. 2  (Vilella et al. , 2009). Moreover, we investigated population structure using the program frappe  (Tang et al. , 2005) , and the number of assumed genetic clusters K ranged from two to four, with 10 000 iterations for each run. To investigate the phylogenetic relationships between Solanum section Petota species, we included 167 wild and cultivated accessions of clade 4, 14 wild accessions from clade 1 + 2, 18 wild accessions from clade 3, and 2 non-tuber-bearing outgroup species.
We called SNPs from these 201 accessions using both SAMtools and GATK. Finally, 66 666 high-quality SNPs were used to construct the SVD quartets and ML trees. The filtered vcf file was converted to phylip format using TASSEL v 5. 0, and nexus format using a custom script vcf2nexus. pl (https://github. com/ HollyRuess/hrbi_perl). We ran two analyses: (1) ML with the program RAxML version 8. 0. 0  (Stamatakis, 2014) , using the GTR+G model and estimating individual alpha-shape parameters, GTR rates, and empirical base frequencies for each individual gene, the Lewis algorithm  (Lewis, 2001)  to correct for ascertainment bias, and running 1000 nonparametric bootstrap inferences, and (2) an SVD quartets analysis  (Chifman and Kubatko, 2014). In all analyses S. etuberosum and S. palustre were used as outgroups.
Identification of Gene Loss Events in Cultivated Potato To identify gene loss events in cultivated potato, we sequenced the genome of the progenitor of cultivated potato, S. candolleanum PI 568969, to a genome coverage of 683. The cleaned reads were mapped back to the PGSC 4. 03 potato genome using the same approaches mentioned above. The unmapped reads were subsequently assembled into contigs using SOAPdenovo  (Li et al. , 2010)  (default parameters). To obtain contigs with high confidence, contigs were filtered based on the following four criteria: (1) contigs <2 kb were excluded; (2) contigs were functionally annotated in at least one database; (3) redundant sequences were excluded based on a self-alignment approach with the depth >3; and (4) contigs that aligned with >30% coverage and >80% identity were not considered to be true novel sequences.
The remaining contigs were considered either true novel sequences or unassembled heterochromatic regions. To further evaluate the quality of these contigs, we performed additional statistics on the GC content of these contigs, the average read depth and coverage of these contigs, and the misassembly rate. The GC content of these contigs was 30. 82%, which is close to the GC content of the whole genome (34. 80%). These contigs were supported by the unmapped reads with 7. 393 average depth and 99. 98% 13coverage. Moreover, we used the contigs as a reference sequence and obtained 159 309 Het SNPs, which comprise 99. 43% of the total SNPs (160 218). (The Hom SNPs were considered misassemblies, while a higher ratio of Het SNPs were considered to reflect a more accurate assembly. ) The following databases were used to predict the gene structure of these contigs: Blast (http://blast.
ncbi. nlm. nih. gov/Blast. cgi), Genewise (http://www. ebi. ac. uk/$birney/wise2/), Augustus (http:// bioinf. uni-greifswald. de/augus-tus/) and EVienceModele (EVM, http:// evidencemodeler. sourceforge. net/). The following databases were used to annotate the function of these contigs: Swissprot (http://www. uniprot. org/), TrEMBL (http://www. uniprot. org/), KEGG (http://www. genome. jp/kegg/), and InterPro (https://www. ebi. ac. uk/interpro/). We subsequently chose 15 contigs to perform PCR amplification using DNA from S. candolleanum and cultivated potato as templates, respectively, and sequenced the PCR fragments to verify these contigs.
Climatic Associations to the Clades We explored the associations between climatic variables and groups of potatoes divided into the six clades discovered here (outgroup, clade 1 + 2, clade 3, clade 4 south, clade 4 north, cultivated), using a set of well-known bioclimatic variables. The nine accessions of S. verrucosum from Mexico were excluded to avoid confounding the assignment with the clade 4 southern group, leaving a total of 192 records. We extracted a set of 19 biogeographic variables and altitudes (20 variables in total), using the WorldClim 2. 0 database with data at 10 min resolution  (Fick and Hijmans, 2017). The bioclimatic variables were as follows: annual mean temperature (BIO1), mean diurnal range (mean of monthly [max temp-min temp]) (BIO2), isothermality (BIO2/BIO7) (3100) (BIO3), temperature seasonality (SD 3 100) (BIO4), max temperature of warmest month (BIO5), min temperature of coldest month (BIO6), temperature annual range (BIO5-BIO6) (BIO7), mean temperature of wettest quarter (BIO8), mean temperature of driest quarter (BIO9), mean temperature of warmest quarter (BIO10), mean temperature of coldest quarter (BIO11), annual precipitation (BIO12), precipitation of wettest month (BIO13), precipitation of driest month (BIO14), precipitation seasonality (coefficient of variation) (BIO15), precipitation of wettest quarter (BIO16), precipitation of driest quarter (BIO17), precipitation of warmest quarter (BIO18), precipitation of coldest quarter (BIO19), and altitude (BIO20).
We assigned the value for each variable to each accession using an R version 3. 3. 3 (Team, 2017)  script using the library raster  (Hijmans, 2016). Variables were then scaled to values between 0 and 1 before performing PCA. The results of the PCA were then used to identify the two or three most important variables in the first two principal components. To corroborate our results, we applied the randomForest  (Liaw and Wiener, 2002).
R package. URLs Solanum tuberosum genome, http://plants. ensembl. org/Solanum_ tuberosum/Info/Index; Solanum commersonii, https://www. ncbi. nlm. nih. gov/genome/?term= txid4109[orgn]; outgroup datasets, https://trace. ncbi. nlm. nih. gov/Traces/study/?acc=SRP0457 67; LASTZ (at the Miller Lab website), http://www. bx. psu. edu/miller_lab/; TreeBeST, http:// treesoft. sourceforge. net/treebest. shtml; frappe, http://med. stanford. edu/ tanglab/software/frappe. html; Ensembl, http://asia. ensembl. org/index. html; KEGG, http://www. genome. jp/keg/.
ACKNOWLEDGMENTS We thank  Jesse Schartner  and  Timothy Kazmierczak  at the  USDA ARS  for preparing and sending seeds from the  U. S. Germplasm System  and  David Gagneul ,  Eva Colleoni ,  Malika Chabi ,  Camille Carpentier  and  Marion Evronsart  at  UGSF-CNRS  for helping to harvest and freeze-dry the leaves. No conflicts of interest are declared.
Discussion Characterizing the social interactions between individuals as well as their behavioral reactions to the physical environment is a crucial step in our understanding of complex collective dynamics observed in many group-living species and their impact on individual fitness  [21, 24]. In the present work, we have analyzed the behavioral responses of a fish to the presence in its neighborhood of an obstacle and to a conspecific fish. In particular, we used the discrete decisions (kicks) of H. rhodostomus to control its heading during burst-and-coast swimming as a proxy to measure and model individual-level interactions. The large amount of data accumulated allowed us to disentangle and quantify the effects of these interactions on fish behavior with a high level of accuracy. We have quantified the spontaneous swimming behavior of a fish and modeled it by a kick dynamics with Gaussian distributed angle changes.
Experimental procedures and data collection The experimental tank (120×120 cm) was made of glass and was set on top of a box to isolate fish from vibrations. The setup, placed in a chamber made by four opaque white curtains, was surrounded by four LED light panels giving an isotropic lighting. Circular tanks (of radius R = 176, 250, and 353 mm) were set inside the experimental tank filled with 7 cm of water of controlled quality (50% of water purified by reverse osmosis and 50% of water treated by activated carbon) heated at 26. 69˚C (±1. 19˚C) (details in Table  1 ). Reflections of light due to the bottom of the experimental tank are avoided thanks to a white PVC layer. Each trial started by setting one or two fish randomly sampled from their breeding tank into a circular tank. Fish were let for 10 minutes to habituate before the start of the trial.
Data extraction and pre-processing Positions of fish on each frame have been tracked with the tracking software idTracker 2. 1  [5]. The idTracker output format gives fish identity and barycenter positions of individuals in the image (in pixels), where the latter needs to be converted into position in the experimental frame of reference (in millimeters). The intermediary Matlab files issued by the tracker store the background image, which is the information used to calculate wall positions and thus colliding distances more accurately). Also, the intermediary files give the area of the detected fish, which can be used to determine fish heading from shape detection, independently of the trajectories. The processing of the output and intermediary files is processed with a custom-built Matlab script, which is structured into several procedures: 1.
Segmented variables estimation This section aims to document each of these procedures. Detection of tank walls. From the tracking software idTracker, several files associated with one video are produced. In particular, a matrix is generated, with as many entries as the pixel resolution of the videos (1920×1080 pixels in our case), containing light intensity of each pixel, that is coded in a grayscale image of the video (Fig  8A ). The intersection of the bottom of the tank with the tank floor is shadowed (Fig  8A ). This shadow is manually enhanced to improve the detection of the tank walls. Before running the tracking on idTracker, the user has to define a mask in order to exclude areas where individuals (here fish) cannot be tracked (e. g. outside the tank) (Fig  8B ). This mask gives a raw circular estimation of the contour of the tank with radius R (outer circle) and a second raw circular estimation with arbitrary radius 0.
Conversion to metric frame of reference. A cubic spline estimation is computed to smooth the noisy signal of the radius of the detected tank (red line on Fig  8D ). The radius signal is repeated over 3 periods to avoid border effects, that is to make estimations at 0 and 2π connected. The Matlab package immoptibox (http://www2. imm. dtu. dk/projects/ immoptibox/) is used to estimate the cubic splines with function splinefit. Splines are piecewise-defined polynomial functions. Knots of the spline are chosen equally spaced. We take 30 knots on each period: the splinefit function estimates a local polynomial on each interval defined by two knots. The spline estimated over the second period (2π to 4π) is used for any subsequent calculations to ensure border continuity. The splineval function is used to find the radius of the tank wall corresponding to any angular position.