dataesr/scientific-paragraphs-categorization huggingface dataset:
Confidence of gene calls Gene predictions were classified into levels of confidence based on the extent and quality of their alignments to transcripts, proteins, and genome alignments as described in Additional file 1: Table  S2. The alignment criteria were selected based on frequency distributions of sequence identity and coverage, as well as visual proofing via JBrowse. The characteristics of high-confidence and lowconfidence genes are distinctly different. The mean size of high-confidence genes is 3,280 bp, more than doubled compared with 1,526 bp for low-confidence genes. The high-confidence genes have an average of 5. 5 exons, again double when compared with 2. 7 exons per low-confidence gene (Table  4 ). The shorter gene length, combined with the observation that very few alternative splicing transcripts were identified in the low-confidence gene set (Table  4 ), suggested that some low-confidence genes may be potential gene fragments that often resulted from transposable element activity  [33, 34].
Comparison of Mt4. 0 annotation with prior version As published, Mt3. 5 (with the last annotation release Mt3. 5v5) contained 62,379 annotated gene loci with 14,309 additional predictions classified as transposable element-related. Mt3. 5 genes were curated by the IMGAG consortium using the EuGene pipeline  [35]. For Mt4. 0, all annotation work was carried out at JCVI. Thus we used both a different annotation pipeline and a different confidence classification system. This resulted in the prediction of 31,661 high confidence genes, 19,233 low confidence genes (total 50,894) together with 16,504 predictions classified as TE-related. This set of high and low confidence genes represents the official release of Mt4. 0 annotation set. A further set of 19,229 unsupported predictions that are only in silico predictions with minimal support from databases or other species are also available for download on the JCVI Medicago website, providing the most exhaustive set of gene loci that we have predicted.
JCVI Medicago website The JCVI Medicago website (http://www. jcvi. org/medicago) has been updated with the Mt4. 0 data and contains a number of bioinformatics utilities to search against the Medicago database. Five major services and resources are offered on the website: 1) BLAST service that allow searches against the genome and the proteome; 2) Genome browsers that allow interactive navigation of the genome, through both JBrowse and the previously deployed GBrowse; 3) Keyword and locus search for your favorite genes; 4) Gene information page that provide detailed information including functional searches and domain structures about every predicted gene locus; 5) Textpresso that provides access to related Medicago literature. The web interface for a selected set of tools is shown in Figure  7.
In addition to the query functionalities, we have also instantiated a Community Annotation Portal that extends the functionality of the original rice EuCAP  [40]  and also supports mutant and allele information previously developed by Frugoli et al. [41]. This allows researchers to edit gene structure, gene function and add mutant information in a user friendly interface. Researchers can become a "community annotator" and be able to edit gene function, gene symbol, associated publications and GenBank identifiers, assign mutant information, alleles and phenotype for any given genes. Through this interface, community members can provide their expertise to annotate or endorse their favorite genes and gene families as a complementary and long-term solution to our continuing Medicago genome curation efforts.
Availability of supporting data The Mt4. 0 assembly and associated gene models are available in GenBank under accession APNO00000000. The optical maps are available in the Genbank nucleotide database under accessions MAP_000013 to MAP_000020. Supplementary tables are included as an additional file:Additional file 1: Table  S1. Available RNAseq data used in Mt4. 0 for UTR and isoform instantiation. Additional file 1: Table  S2. Classification of genes into high and low confidence classes.
Study protocol Ten subjects without any history of cardiac disease (4 male/6 female, age 2768years, weight 6867 kg, heart rate 66611 bpm) were imaged on a clinical 1. 5T scanner (Achieva system, Philips Healthcare, Best, The Netherlands). The scanner was equipped with a gradient system delivering 40 mT/m maximum strength and 200 mT/m/ms maximum slew rate per physical gradient axis. A 5-channel cardiac array coil was used for signal detection. Written informed consent was obtained from each subject prior to imaging, and the study protocol was approved by the ethics committee of the canton of Zurich. Obtained informed consent included imaging as well as publication of anonymized data. Short-axis balanced steady state free precession cine data with a temporal resolution of 7 ms were obtained in the 2-chamber and short-axis planes of the left ventricle (LV) to identify the ventricular systolic (trigger delay: 277619 ms) and diastolic (trigger delay: 627685 ms) standstill periods.
Unipolar diffusion gradients were played out in 10 directions on a unitsphere  [50]  with a b-value of 500 s/mm 2. To reduce echo time, FID crushers necessary in STEAM were removed for b = 500 s/ mm 2 acquisitions, but kept for the b = 0 s/mm 2 acquisition. All eight signal averages of a diffusion weighted image were acquired within one breath hold. Different diffusion weightings were obtained in consecutive breath holds. The volunteers were allowed sufficient time to recover in-between breath holds to ensure consistent heart rates among the data series. A total of 11 breath holds of 14-16 s duration each were acquired per slice and two heart phases, resulting in a 15-18 min total net acquisition time for six slices at two heart phases.
The time course of the right stretch tensor was calculated from the deformation gradient field obtained from the tracked cubes as described by Hess et al. [54]. Stretch tensors were calculated relative to the systolic and the diastolic time points of diffusion imaging. The strain effect on the diffusion measurement is described by D observed ~1 D ð D 0 U t ð Þ {1 D true U t ð Þ {1 dt with U(t) being the time course of the right stretch tensor and D the duration of the R-R interval. In accordance with  [39]  the equation is expanded as: D observed ij ~1 D ð D 0 U {1 ik t ð ÞU {1 lj t ð Þ ! D true kl For validation purposes, additional DTI data were acquired during the systolic ''sweet-spot'' (trigger delay: 160 ms) in one of the volunteers and compared to data in end systole (trigger delay: 305 ms) and diastole (trigger delay: 620 ms).
Data analysis From the six slices pairs of two were grouped for the basal, medial and apical level and mean diffusivity (MD) and fractional anisotropy (FA) in systole and diastole were compared for each volunteer. Additionally, helix, transverse and sheet angles were calculated using projections of the first and third eigenvectors as described in Figure  2 (a-b )  [3]. To allow tracking the transmural course of the helix angle, a local anatomical basis was defined. To this end, the shape of the LV was mapped onto an ''ideal'' circular ring by means of coherent point drift mapping  [53]. This procedure allowed the definition of a locally normalized transmural position independent of variation of local thickness of the myocardium (Figure  2c ). The helix angle alignment was analyzed on a slice-by-slice basis.
Results Dual-phase cardiac DTI data was successfully acquired in all subjects. Total exam time including subject preparation was 1. 5-2 hours. The raw data images acquired at the systolic sweet spot, in peak systole and in diastole are presented in Figure  3. Data are shown for the ''b = 0'', the first three and the last diffusion-encoding direction as well as the average of all diffusion directions. Tagging data from the three orthogonally line-tagged stacks are given alongside. The temporally averaged stretch tensors as calculated from the tagging data allow radial, circumferential and longitudinal stretch components, which are presented as stretch maps, to be assessed. Figure  4a ,b shows the time course of the radial, circumferential and longitudinal stretch calculated from the right stretch tensors at the apical, medial and basal levels for systole and diastole.
The time points of acquisition of systolic and diastolic DTI data as well as the systolic sweet spot are indicated by vertical lines in Figure  4a, b. The transmural course of the helix angles and transverse and sheet angle histograms in systole (trigger delay: 305 ms) and diastole (trigger delay: 620 ms) (without and with strain correction) are shown for the medial level in Figure  4c, d. In addition, DTI data acquired in the systolic sweet spot (trigger delay: 160 ms) is overlaid. Strain correction results in changes in the transverse and sheet angle distributions in systole. Values obtained upon strain correction approach data acquired in the sweet spot. Differences in diastole are found to be smaller with and without strain correction. In Figure  5  systolic and diastolic tensor fields at a medial level with and without strain correction are compared.
More recently, DTI of the myocardium has been described at endsystole, where the heart reaches a quiescent or stand-still phase  [27, 41, 44]. With the present work, it has, however, demonstrated that DTI of the myocardium at end-systole is significantly influenced by strain. The effect of strain on helix angle measurements is small but its impact on measures of sheet architecture, such as sheet angle, is extremely large. Our results confirm those of Tseng and colleagues, who likewise showed that imaging away from the systolic/diastolic sweet spots produced small differences in helix angle but very large differences in sheet angle  [21, 22]. The validity of strain correction was verified by comparing data acquired in systole and diastole to data obtained in the systolic sweet spot, for which actual material strain equals the average strain across the cardiac cycle.
First, we can see that the Implicit estimator does not need to tune any hyper-parameters such as a and b in the EAP approach. We can also notice that the equality between the two estimators of variance obtained by Bayesian and Implicit approaches is established for a = -1 and b = 0, which is impossible since a and b (parameters of an Inverse Gamma distribution) have to be positive. This shows that the Implicit estimator does not correspond to a Bayesian one for specific prior values. To compare the performances of Implicit and Bayesian method, we start (as a first step) by simulation of data using Matlab software, and (in the second step) we validate our results by comparing them with those of the Bayesian method. To compare two statistical approaches and to appreciate in which measure the result will be more definite, we choose an indicator which is the Mean Squared Errors (MSE) between the estimator and the true value of the parameter.
Experimental protocol In order to evaluate the interest of using the Implicit approach for learning parameters in Conditional Gaussian Bayesian Networks and to measure the quality estimation, we have carried out repetitive experiments in several contexts. In these contexts, we are able to control several parameters such as the number of variables n (n = 10, 30, 50) and the size of generated datasets N (N = 100, 1. 000, 10. 000). The maximal cardinality K of our discrete variables is also controlled for Conditional Gaussian Bayesian Networks (K = 2, 3, 5). In such conditions, every dataset generation is iterated 10x10 times, with 10 randomly generated DAGs, and 10 random parameter values for each of these DAGs. Our goal is to compare the performance of two estimators working without any prior definition, i.
Evaluation criteria Accuracy evaluation of each method is estimated by the Kullback-Leibler (KL) divergence between the "original" distribution used for generating a given dataset and the "final" distribution obtained with parameter learning. For large numbers of variable configurations (greater than 10 5 ), a Markov Chain Monte Carlo (MCMC) approximation is used with 10 5 random configurations. Comparison of both methods is illustrated by plotting absolute values of KL obtained by the Implicit approach versus maximum likelihood for the same datasets. The fact that one method is better than the other can be observed with respect to the first diagonal (upper triangle : ML is better, versus lower triangle : implicit approach is better). In order to determine whether the observed differences are statistically significant, we use the Wilcoxon paired signed rank test, with a significance level equal to 0.
Results and interpretations Figure  2  proposes the KL divergence obtained by Implicit approach versus the Maximum Likelihood one for the same datasets, for n = 10. Similar results have been obtained for n = 30 and 50. Whatever the values of K (maximum cardinality of variables) and N (dataset size), the Implicit approach gives either similar or better results than the ML one. Both approaches coincide when N is high (N = 1000 and 10000, results in magenta and black) but also with a small sample size (N = 100) but only when the maximum cardinality is low (K = 2). When the maximum cardinality is high (K = 3 and K = 5) and the dataset size N is low, the Implicit  Comparison of KL divergence obtained by Implicit approach versus the method of maximum likelihood for the same datasets (upper triangle : ML is better, versus lower triangle : Implicit approach is better) with respect to dataset size (N = 100, 1.
The launch of the WorldView-2 (WV2) sensor in 2009 has been an incentive that greatly stimulates this study. This spaceborne sensor has doubled the spectral capabilities (notably a far red waveband) of all very high resolution counterparts whilst furnishing ecological information down to 0. 5 m. Based on the WV2 synergistic spectral dataset, the coastal habitats and bathymetry can now be mapped at the submeter scale over ecosystems showing clear waters, typically over coral reefscapes  (Collin and Planes, 2011; Collin and Hench, 2012). The shallow seascapes were thereby selected as the study cases by virtue of their current socio-ecological paramountcy. Coral reefscapes are regarded as the beacon seascape given the essential ecological services they provide and the increased number of abiotic and biotic factors threatening them  (Emanuel, 2005; Hoegh-Guldberg et al.
Encompassing the three algae habitats, a algae super habitat is created since the far-reaching studies demonstrated the ecological differentiation occurring between general coraland macroalgae-dominated reefs  (Bellwood et al. , 2004; Roff and Mumby, 2012). Given the substantial scope of the NDVI spectral index in major land-based ecological studies  (Myneni et al. , 1997; Pettorelli et al. , 2005; Piao et al. , 2009; Sturm, 2010; Zhao and Running, 2010)  and its facilitated use, the coral reef indices will be revealed in the form of spectral Normalized Difference Ratios (NDR), mathematically formulated as follows: NDRij = i -j i + j (1) where i, j ∈ {"coastal," blue, green, yellow, red, "red edge" twodimensional matrices} and i = j. An exploratory dataset of 30 NDR was computed as a result of the pair combinations of the six optical bands.
SPATIAL ANALYSIS The spatial mapping of the retained NDR is realized in applying Equation (1) at each pixel of the two island imageries. Resulting NDCRI = m X - ⎛ ⎜ ⎜ ⎜ ⎜ ⎝ A0e ⎛ ⎜ ⎝ -X-A1 A2 2 2 ⎞ ⎟ ⎠ + A3 + A4X + A5X 2 ⎞ ⎟ ⎟ ⎟ ⎟ ⎠ (2) in which NDCRI means Normalized Difference Coral Reef Index, X refers to retained NDR, m is a tunable constant to scale the NDR residuals, and A0, A1, A2, A3, A4, and A5 are the unknown parameters of the non-linear depth regression. The accuracy assessment of the processed spatial models is sourced from the statistical relationships manifesting between the georeferenced measurements and associated pixel values retrieved from the spatial models. Since the models do not need any training data, all 897 measurements are used in the accuracy assessment. Whilst the dataset of measurements appears in the form of classification, the models are made of continuous values, matching the purpose of this study.
CORAL REEF INDICES The nested bottom-up and top-down approaches provide insight into the dataset of NDR successfully responding to the constraints enacted by the logical sequences of designed indices (see Figure  6 ). The amount of successful NDR decreases with the diagnostic precision (accounting for the 10 successful NDR types across the 12 designed indices): 25 for three-habitat level, 17 for four-habitat level, 13 for five-habitat level, and 7 for seven-habitat level (Table  1 ). A substantial amount (n = 49 or 79%) of successful NDR include the "red edge" band. Combining the "red edge" band with the "coastal," blue and green bands satisfactorily respect the requirements related to the three-, four-, and fivehabitat levels, respectively, regardless the coral habitat. Although the NDR based on the "red edge" band and the yellow or the red bands adequately translate the indices up to the five-habitat level for all coral habitats, the "red edge"-yellow does not correctly translate the seven-habitat level overarched by the Brown coral.
CONTINUOUS SPATIAL MODELS The spatial modeling of ecological indicators has proven to be excessively helpful in achieving conservation purposes during the process of area selection. From planning to outreach, any conservation projects to date are to use maps. The spatiallyexplicit quantification of ecologically-relevant proxies proposes a visual support that may facilitate understanding to many stakeholders, irrespective of their technical skills. By virtue of its versatility compared to sensors, and its ease to build through any open-source Geographical Information System and/or Remote Sensing software, the NDCRI lends strong support to managers tasking with coral reef sustainability and ecologists delving into understanding coral reef regime shifts. Initiating the latter aspect, we design, deepen and apply the histoscape concept to the Tetiaroa Pacific atoll and Moorea volcanic island.
ACKNOWLEDGMENTS This study is part of the  CREM (Coral Reefscape Ecology and Mapping ) project, funded by the  Marie Curie   FP7-PEOPLE-2010-RG. The first author gratefully acknowledges the  French Agency of Marine Protected Areas  and the  University of Quebec in Rimouski  for the purchase of  Moorea 's imagery and the use of the handborne spectroradiometer, respectively. Groundbased hyperspectral measurements have been achieved with the CRIOBE's support, which has developed the coral nursery jointly with the  Total Foundation  for the Biodiversity,  Te Mana  o te Moana association and  Moorea Intercontinental Resort. We are indebted to CRIOBE staff for collecting 897 georeferenced photographs and water depths.
What the case shows is that the patentability of DNA that has been isolated and purified presumes an analogy with chemistry while the tests revolve the identification of gene sequences and expressions of a patient by comparison with information in a database  (Caulfield 2011 , Rodgers 2010 , see Carolan 2010). More specifically the difference between DNA as chemistry and as information was recognized by the judges. The original ruling by the judge of the District Court had invalidated the patents with the observation that the DNA involved was known through its 'information content, its conveyance of the genetic code'. Similarly the final ruling by the supreme court explains that Myriad's claims are not expressed in terms of chemical composition and did not create or alter the genetic information encoded in the genes or the genetic structure of the DNA a Accordingly Myriad's diagnostic tests are an illustration of the conceptual difficulty and work involved in turning DNA into a biological object that can be patented  (Vermeulen et al.
Its implication is that the legal focus on DNA as a compositions of matter is situated in the context of the convergence of the life sciences and informatics alongside the introduction of open source models. Both the informatic attributes that are considered when a patent application is evaluated and the need for cheap, efficient and high quality access to interactive databases and software reflect a transformation of the usage of DNA in the life sciences. The remainder of section one has as its objective to show that the introduction of open source models in the life sciences reflects the usage of informatic artifactslike software, databases, hardware and so onand related practices like downloading, copying and searching of information around the world via the Internet. What this demonstrates is that there is nothing "idealistic" about discussing open source in the life sciences: there are many examples when including those that are directly equivalent to informatics (open source software, databases and the computing requirements of data-driven research).
Similarly source code and information in databases are considered legally equivalent with written text, meaning that they can be copyrighted. However the limitations on the scope of DNA patenting, as in the Myriad case, do not necessarily lead to other kinds of intellectual property or related forms of ownership over knowledge. Another option has become a possibility in line with the response to the extension of intellectual properties to source code in the early eighties. Specifically, the Free Software Foundation (FSF) has aimed to counter the restrictions on the usage of source code that became possible by extending intellectual property to software development. Its founder Richard Stalllman started the FSF in the early eighties and most commentators point to his release of the source code that he had programmed himself, which he made available on the condition that those that used it would do the same.
The aim is to make software 'free' on the basis of four freedoms: the freedom to run the program, for any purpose the freedom to study how the program works and adapt it to your need; the freedom to redistribute copies so you can help your neighbor; the freedom to improve the program, and release your improvement to the public, so that the whole community benefits c. The GPL was the first of the many different kinds of open licenses that are by now used throughout informatics and in many other domains. The most widespread examples are the well-known Creative Commons licenses that are being used for text, images, audio, video productions, architectural design as well as for software and databases wherein information is stored on maps of genomes, proteomes and metabolomes and so forth. Such open licensing, however, is used in a more pragmatic sense that is closer to the connotations of the term 'open source'.
The databases with sequence information on the human genome were made publicly accessible for everyone without the condition that enforced the sharing of this information in other formats  (Sulston and Ferry 2002: 222). Therefore information on genes could be patented and this might restrict some types of usage of the information in a database. On the one hand, this is not limited to the human genome but counts for most of the incredible amounts of information in various sub-disciplines of the life sciences. For example there are 100 giga bytes of sequenced DNA compiled in the GOLD database alone and similar databases exist for the 100. 000 or so human proteins that make up the proteome as well as for spliciomics, metabolomics and so forth d. This is information that is available for further research but does not use open licensing.