dataesr/scientific-paragraphs-categorization huggingface dataset:
1 assembly. In the ITGB4 sequence (GenBank: KP025765) the mutation is positioned at 30,204-30,207 bp of the ITGB4 genomic sequence. In the predicted CDS, the deletion is located at 4,412-4,415 bp (c. 4412_4415del). The GQ value for the deletion was 99 for the control and 35 for the case, and the DP value was 180 and 11, respectively. This mutation is located on sheep ITGB4 predicted exon 33 and leads to a shift in the open reading frame of the ITGB4 coding sequence and a stop codon at position 1,473 (Fig  3 ). This mutation could lead to a truncated protein of 1,472 amino acids  (1, 470  amino acids of normal integrin β4 followed by two missense amino acids and a premature termination codon) in contrast to the 1,751-amino-acid length of the normal integrin β4 protein. Based on the deleterious effect on the composition and structure of the resulting protein, this 4-bp deletion was identified as the putative causal mutation for JEB in sheep.
These genotypes completely agreed with the autosomal recessive mode of Mendelian inheritance inferred for JEB in Churra sheep. The discovery of this mutation has allowed screening of affected flocks to identify phenotypically normal individuals carrying the 4-bp deletion to avoid at-risk mattings. Simple and rapid JEB genotyping methods like fragment length analysis in capillary electrophoresis sequencer and restriction fragment length polymorphism analysis are proposed (S2 Fig). The implementation of this gene-assisted selection approach will solve the problem of JEB in flocks of Churra sheep. De novo BAC clone sequencing allowed us to obtain the complete ITGB4 gene sequence (GenBank KP025765). The previous ITGB4 ovine gene sequence (ENSOARG00000009764) was missing 16. 6% of the bases when compared with KP025765 (S1 Fig).
The RNA-seq approach enables the generation of extensive transcriptome information in the absence of a wellannotated genome. In the present study, the transcriptome information was essential in the annotation of the ITGB4 gene sequence (GenBank: KP025765). The sheep ITGB4 CDS obtained from the KP025765 sequence is distributed into 38 exons and encodes a predicted protein of 1751 amino acids. The transcript available in Ensembl (ENSOART00000010637) has 42 exons and encodes a predicted protein of 1835 residues. The percentage of identity of the ovine integrin β4 protein with that of other species, such as cow (DAA18168. 1) or human (AAI43743. 1), is higher for the integrin β4 protein obtained from KP025765 (92% and 89%, respectively) than for the sequence obtained from the ENSOART00000010637 transcript (86% and 82%, respectively), which suggests that the ITGB4 sequence obtained in this study is more reliable than the available sequence from the ovine genome assembly (Oar_v3.
Isolation of the ovine ITGB4 gene ITGB4 was isolated from three sheep CHORI-243 bacterial artificial chromosome (BAC) libraries  [50]. The selected BAC clones predicted to span the region in which the ITGB4 gene is located were CH243-344E15, CH243-306J7 and CH243-504K6. The BACMAX DNA Purification Kit (Epicentre) was used for purification of the DNA. Ion Torrent PGM data were generated from the three BAC libraries constructed from 500 ng of DNA quantitated using the Qubit Fluorometer 2. 0 (Life Technologies, Grand Island, NY). The fragment libraries were constructed using the Ion Xpress Plus Fragment Library kit based on Ion Shear chemistry according to the user guide. The barcode and adaptor ligation were performed per the manufacturer's protocol using the Ion Xpress Barcode Adapters 1-16 Kit.
The quantitation and size distribution of the fragments were analyzed on an Agilent Bioanalyzer using the High Sensitivity Kit (Agilent). The template preparation and emulsion PCR were performed using the Ion PGM Template OT2 200 Kit (Life Technologies) according to the manufacturer's instructions. The template product then was quantitated using the Ion Sphere Quality Control Kit on at Qubit Fluorometer 2. 0. OneTouch ES was used for the enrichment of the Ion Sphere Particles (ISP) template products. Finally, the samples were loaded onto a 316 chip V2 and sequenced using the Ion PGM 200 Sequencing Kit V2 (Life Technologies). The reads were collected using Ion Torrent Suite v4. 0, which also sorts the data according to the barcodes. The Torrent Suite software scores the quality of the reads by assigning Q20 scores according to Ion Torrent's quality scoring computation.
MIRA (version 3. 4. 0) was used for de novo assembly of all of reads from each of the three BACs. The mean coverage was 120X for each BAC. The Torrent Mapping Alignment Program (TMAP) was used for alignment of all of the reads from each of the three BACs against the reference (Chr 19 Cow sequence; chromosome: UMD3. 1:19:1:64057457:1). Both, the BAC DNA sequencing and the de novo assembly were performed at AC-gen Reading Life (Valladolid, Spain).
The Ovis aries genome, the annotation file and the reference variant file were downloaded from the Ensembl database (http://www. ensembl. org/index. html). The quality of the reads generated was assessed using the program FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). The paired ends for each sample were aligned to Oar v3. 1 using STAR  [36]. For the ITGB4 gene prediction, Augustus software was used  [37, 38]. Once the alignment to the reference genome was performed, Samtools  [51]  was used to sort, index and extract the IDs of the reads aligned within the ITGB4 region from the bam files (OAR11: 54,830,000-54,880,000). Seqtk tool (https://github. com/lh3/seqtk) was used with the ID list from the bam file and the initial fastq file to create a new fastq file that contained only the reads that align within the ITGB4 region.
The new fastq files were mapped against the ITGB4 sequence obtained from the BAC clones using STAR. Cufflinks  [52]  was used to assemble the bam files and to create an exonpart hints file for Augustus. The putative coding transcript for the ITGB4 BAC clone sequence was identified with Augustus by providing the ITGB4 sequence, exon boundaries and an integrin β4 protein profile and allowing the program to predict exactly one gene. For variant detection, GATK was used  [39]. The GATK analysis was performed on the STAR alignments following the RNA-seq workflow recommended in the best-practices section of the webpage for the GATK software  [53, 54]. Prior to the variant analysis, the PCR duplicates from the bam files were removed using Picard tools (http://picard. sourceforge. net/). Then, an indel realignment and a base quality score recalibration were performed using the Ovis aries vcf file downloaded from Ensembl as a reference.
5 μM, 0. 25 M betaine and 0. 75 μl of AmpliTaq Gold polymerase (5 U/ml) (Applied Biosystems). The PCR conditions were 5 min at 96°C; 40 cycles of 30 sec at 96°C, 45 sec at 58°C, and 40 sec at 72°C; and then a final extension of 10 min at 72°C. The amplicons were purified using Illustra Exonuclease I and alkaline phosphatase (Illustra ExoProStar 1-Step, GE Healthcare Life Sciences) treatment and were dideoxy-sequenced in both directions using the Big Dye Terminator Cycle Sequencing Kit, v3. 1 (Applied Biosystems) using the same primers used for the fragment amplification. After sequencing on an ABI Prism 3130 automatic sequencer (Applied Biosystems), the sequence data were analyzed using SeqScape v2. 5 (Applied Biosystems).
The use of modern sensor technology to monitor honeybee colony status is growing in popularity  [23] [24] [25]. There are already devices, such as electronic scales for measuring temporal hive weight changes, but the increase in hive weight can be due to increased pollen and nectar collection by foragers even in times of colony stress, including the presence of diseases  [26]. It is therefore of interest to develop new non-invasive methods that can further contribute to assessing colony physiological status. Our approach is to use accelerometers inserted in the central frame of the hives to measure vibrational amplitudes in order to provide information on bee population, activity, and development. To validate vibrational data, visual inspection data of the colonies, particularly the brood development, are reported for correlation.
Apiaries One 'Langstroth' and 19 'Dadant' hives were set up in a line over approximately 40 meters length in Avignon, France, in March 2014, with permission from INRA (owner of the site). They were managed using standard beekeeping practices and continuously monitored for vibrations until November 2014 apart from short power cuts. Only data from this apiary were used for the numerical discrimination exercise. Two hives ('British Standard National') were set up in Nottingham, UK, in March 2013, with permission from NTU (owner of the site), and were continuously monitored for vibrations for 13 months apart from short power cuts. The population status of the colonies in Avignon was evaluated seven times, from March to October, using the ColEval  [27]  method. This method allows estimation of the percentage of each frame having brood, honey and pollen, as well as the number of worker bees in the colony  [27].
Vibrational measurements Accelerometers (100mV/g in France and 1000mV/g in UK, Brüel and Kjaer (Naerum, Denmark) ref: 4705) were pressed into the honey comb of the centre of the central frame of all monitored hives, to monitor vibrations transverse to the plane of the comb. Small amounts of molten wax were poured on the sensors to avoid direct exposure of metallic parts. Spectra with a bandwidth of 5500Hz and a resolution of 3. 125 Hz were averaged for 3 minutes and stored on a hard disc with Brüel and Kjaer's PULSE software, on a computer set to reboot itself every day at midnight. Data were stored on files covering one day of data (tailored PULSE macros supplied by Brüel and Kjaer). Although occasional breaks in data logging occurred during power cuts, recordings resumed automatically upon reestablishment of power.
Four of the twenty colonies in France were additionally monitored with accelerometers secured, externally, to the middle front face of the brood box (S4 to S7 Figs). One of the colonies in the UK was measured with eight accelerometers embedded in the centre of eight contiguous frames. Longer spectral averaging was explored by using successive averaged spectra. Histograms were obtained with a bin width sometimes tailored to the vibrational average strength of the colony under consideration, and ranging from 0. 1x10 -7 m/s -2 to 0. 4x10 -7 m/s -2. Analysis was undertaken using code written in the Matlab1 core (at Nottingham Trent University, NTU), with the additional use of the 'statistics toolbox' for Generalised Linear Modelling, using the glmfit function. We used the DFA algorithm implemented in Matlab1 with a routine made available to us by Professor Roy Goodacre (Manchester University, UK).
Correlation between vibrational measurements and visually assessed frame condition The brood, pollen and honey levels visually assessed on most frames exhibit remarkable agreement on either side of the frames. As there is only one sensor in a given frame and it is in the middle, we assume that it is affected equally by each side and the ColEval data were therefore Pollen levels do not seem to substantially affect the maximum amplitude. Brood and honey levels both contribute to reduce the measured signal and the effect of linearly combining them, together with pollen to further improve the correlation, are also shown in The contribution to signal damping from the honey levels is the highest even though, on these central frames, honey is seldom greater than 20% of the frame area and is often located at the periphery.
contiguous subwords of the input sequences, our approach considers spaced words, i. e. words containing wildcard or don't care characters at positions defined by a predefined pattern P. This is similar as in the spaced-seeds approach that is used in database searching  [23]. As in existing alignment-free methods, we compared the (relative) frequencies of these spaced words using standard distance measures  [24]. In  [25] , we extended this approach by using whole sets P = {P 1 ,. , P m } of patterns and calculating the spaced-word frequencies with respect to all patterns in P. In this multiple-pattern approach, the distance between two sequences is defined as the average of the distances based on the individual patterns P i ∈ P, see also  [26]. 'Spaced words' have been proposed simultaneously by Onodera and Shibuya for protein classification  [27]  and by  Ghandi et al.
From top to bottom, the applied methods were: 1. spaced words with the single-pattern approach and the Jensen-Shannon distance (squares) and the distance d N defined in equation (  4 ) in this paper (circles), 2. the multiple-pattern version of Spaced Words using sets P of m = 100 patterns with the same distance functions, 3. distances calculated with K r  [37] , 4. with kmacs  [47]  and ACS  [30]  and 5. with Co-phylog  [38]. values calculated by the program k mismatch average common substring (kmacs) that we previously developed  [47]  are roughly linear to the real distances d for values of up to around d = 0. 3. From around d = 0. 5 on, the curve becomes flat. With k = 30 mismatches, the performance of kmacs was better than with k = 0, in which case kmacs corresponds to the Average Common Substring (ACS) approach  [30].
In addition, we used the competing approaches FFP  [32] , CVTree  [48] , K r  [37] , kmacs  [47] , ACS  [30]  and Co-phylog  [38]. For some of these methods, program parameters need to be defined, e. g. a predefined word length or the number of allowed mismatches. For these methods we tested various parameters and used the best performing values. With each method, we calculated a distance matrix for the input sequences, and we compared this matrix to a reference distance matrix that we calculated with the program Dnadist from the PHYLIP package  [49]  based on a reference multiple alignment. For comparison with the reference matrix, we used a software program based on the Mantel test  [50]  that was also used by Didier et al. [51]. Figure  2  shows the results of this comparison. As can be seen, our new distance measure d RC , applied to multiple spaced-word frequencies, produced distance matrices close to the reference matrix and outperformed the Jenson-Shannon distance for all pattern lengths that we tested.
Acknowledgements We would like to thank  Marcus Boden ,  Sebastian Lindner ,  Alec Guyomard  and  Claudine Devauchelle  for help with the program evaluation and  Gilles Didier  for help with the software to compare distance matrices. We thank  Matteo Comin ,  Ruth Kantorovitz ,  Saurabh Sinha  and an unknown WABI reviewer for pointing out an an error regarding the covariance of spaced-word matches in the previous version of this manuscript that was published at WABI 2014.
Bayesian classifier The objective of segmentation is to assign a label or class C i , i 2 {1,. , N}, to each pixel location x in an image. In this work, we consider mono-channel segmentation of EUV images into N = 3 classes: CH, AR, and QS. Generalization to multichannel acquisition is discussed in Section 6. Our supervised method requires an expert to supply, at training time, a rough segmentation of one or more preprocessed images into the three classes. Assuming properly calibrated data, the classifier will segment new images and maximize the posterior probability P(C i |I(x)), that is, the probability that intensity I observed at pixel x of the dataset belongs to the class C i. Bayes' theorem states that this posterior probability is proportional to the likelihood p(I(x)|C i ) of observing pixel I(x) when class C i is assumed multiplied by the prior probability p(C i ) of being labelled C i : pðC i jI x ð ÞÞ $ pðI x ð ÞjC i ÞP C i ð Þ: Assuming a uniform P(C i ) leads to a Maximum Likelihood (ML) classifier (Sect.
More properties may easily be incorporated in this framework under the Naive Bayes assumption. Examples include intensities in other wavelengths, magnetogram measurements, and optical flow velocities. Morphological and geometrical properties such as the area or properties describing the shape  (Reiss et al. 2014)  of connected components belonging to AR or CH classes could also be included. In this paper, we show the performance of a MAP estimator given the intensity I(x) in one EUV channel as well as the heliographic latitude. We explain below how to estimate p(C i ) and p(L(x)|C i ) from a dataset of images.
Area coverage and latitude distribution estimation Given a dataset of images to be segmented, the parameters of the distributions p(C i ) and p(L(x)|C i ) can be estimated using an EM procedure  (Dempster et al. 1977)  as follows: 1. Start with uniform p(C i ) and p(L(x)|C i ). 2. Compute MAP segmentations using this p(C i ) and p(L(x)|C i ) on the dataset (Maximization step) 3. Recalculate p(C i ) and p(L(x)|C i ) from these MAP segmentations on the dataset (Expectation step) 4. Repeat steps 2 and 3 until the values converge. One or both of these likelihood functions could be prescribed by an expert, or built manually. It is for example well known that CHs appear preferentially at high latitudes  (Lowder et al. 2014 ) and that ARs appear in two bands on both sides of the equator  (Yeates et al.
2008). Hence one could manually build p(L(x)|C i ) and simplify the above EM procedure by computing only p(C i ). In this EM-scheme, the probability distributions p(C i ) and p(L(x)|C i ) are assumed to be stationary (i. e. not time-dependent) during the period covered by the dataset. More precisely we are computing an average distribution over that time period. As p(C i ) and p(L(x)|C i ) evolve over the solar cycle, care must be taken to estimate these distributions on appropriate time periods. When an extended time period is covered, a sliding window approach should be considered in order to handle the different regimes present in a solar cycle. The above procedure can also integrate information on morphological or geometrical properties. Estimating the corresponding distributions would require a first segmentation to obtain connected components of AR and CH.
Dataset and training masks Since May 2010, the Atmospheric Imaging Assembly (AIA;  Lemen et al. 2012 ) on board the Solar Dynamics Observatory (SDO) mission delivers 4096 • 4096 pixel images of the solar corona in 10 bandpasses. Since the structures we are interested in are large scale, and in order to reduce the computational and storage burdens, we use the level 1. 5 calibrated and rebinned 1024 • 1024 pixel synoptic 2  images 3. Those synoptic AIA images are a proxy for the images to be produced by the SUVI instrument on board the next generation of GOES-R satellites. SUVI will return 1024 • 1024 images in six EUV channels at least once every minute. Those channels are similar or close to the ones of AIA. To demonstrate the properties of our algorithm we consider two datasets of SDO-AIA 19.
3 nm images: A four year Dataset spanning October 1st, 2010 up to and including September 30th, 2014 at a cadence of one image per day. A three month Dataset going from January 1st, 2011 up to and including March 31st, 2011 at a higher cadence of one image per hour. When properly preprocessed as described in Section 3. 2, statistics of a series of images are comparable. The four year dataset covers roughly the ascending part of Solar Cycle 24, where ARs are relatively well defined and isolated, and CHs may extend up to low latitudes. Extending the analysis on other time periods, such as solar minima or maxima, would require training masks for these different regimes too, and to process the data using for example a sliding window approach. The remaining part of this section presents a noise analysis of EUV images (Sect.
Preprocessing The SDO-AIA synoptic images are already calibrated to level 1. 5 by the instrument team. Our analysis uses the four year dataset, for which the intensity in each image is normalized to [0, 1] as follows: 1. Perform limb brightness correction (see Sect. 3. 2. 1) 2. Rescale to [0, 1] by dividing by the 99. 9th percentile of the intensity values of one of the images (in our case the first one) and clipping the resulting value to the interval [0, 1]. This keeps rare extreme values from skewing the scaling of the images.
e. F I (q) = median h I(q, h). Even though the median over all angles is taken, F I (q) will usually not be smooth due to the presence of ARs. Therefore the median of all image radial profiles is taken and used as the final radial profile to perform the limb brightness correction: F(q) = median I F I (q). The radial profile F(q) for the four year dataset is shown in Figure  1  in blue. The bump located at about 0. 4 solar radii is due to the AR bands and is unwanted. To remove it, we consider a smaller dataset where relatively few ARs were present as proposed in  Kraaikamp & Verbeeck (2015)  and compute the radial intensity profile F(q) on this restricted dataset. We used the period from October 2010 to March 2011. The resulting radial intensity profile is shown in Figure  1  in green and is used as the final profile for the limb brightness correction.
Training masks For the results shown in Section 4, we use as a first training mask a manually segmented preprocessed image from our four year dataset. Figure  2a  shows the image taken on April 23, 2013 at 00:00:07 UT together with the AR, QS, and CH boundaries that were prescribed manually. Such masks can easily be created by a standard image manipulation program that features layered editing, for example the free and open source program GIMP. Care must be taken to minimize overlap between masks for different classes. Indeed, although the proposed method can in theory handle such overlap, in practice they will translate into an increased overlap between intensity distribution of various classes, leading to increased classification uncertainty for the range of intensities in the overlapping region.
Conversely, it is also important to avoid big gaps between the masks of neighboring classes to ensure that the decision boundary found through Eq. (  4 ) is as desired. For example, adequate AR segmentation requires to provide masks not only for the ARs themselves, but also for the QS that is close to the desired AR boundaries. The SPoCA-suite  (Verbeeck et al. 2014 ) provides the SPoCA-AR and SPoCA-CH modules which are integrated into the SDO Event Detection System (EDS). Every 4 h, the EDS generates and uploads the SPoCA entries into the AR and CH catalogs of the HEK or Heliophysics Events Knowledgebase  (Hurlburt et al. 2012)  For evaluation purposes we used as a second training set the AR and CH masks computed for the EDS modules, see Figure  2b. The pixels that were classified as neither AR nor CH were used as the QS class.
Maximum A Posteriori segmentation This section discusses the MAP segmentation obtained on the dataset described above. 2a ), and the SPoCA segmented training mask that selects only the AR core (Fig. 2b ). Each class distribution is roughly unimodal with only a small amount of overlap in the transition zones between CH and QS, and between QS and AR. In the manually segmented image, we also observe some overlap between the dark CH and bright AR classes. This is most likely due to errors in the manual segmentation, e. g. bright points embedded in CHs that were misclassified as CH. The SPoCA training mask being computed with an automated threshold-based segmentation technique, produces a smaller overlap between the AR and QS classes. A small overlap is nevertheless still present due to post-processing, e.