dataesr/scientific-paragraphs-categorization huggingface dataset:
Homologies with known proteins were searched using Uniprot database. Homologies were described as partial when the coverage and length of the amino acid sequence did not exceed 60% of the homologous protein sequence. of pathogenicity island ϕNM2, and between an autolysin amidase domain and isdB, a gene encoding a hemoglobin receptor required for heme iron utilization  43. NCTC8325 is characterized by the presence of srn_9342 (also located at the 3′end of an autolysin amidase domain) and srn_9345/srn_9346, whereas a three-CDS insertion occurred where Newman has srn_9343/srn_9344. In CA-347, srn_9342-srn_9344 organization is conserved, but the last two sRNA genes are lacking. Interestingly, Mu50 and JH1 have two copies of srn_9342 (Fig. 5 ). In Mu50, the first copy is retrieved as a single srn upstream of fhuD (and therefore dissociated from an autolysin), and an organization similar to that of NCTC8325 is also present elsewhere in the genome.
While no ORF was identified on the positive strand of the srn_9342-9346 locus, analysis of the minus strand revealed a small ORF within the srn_9343 sequence (Table  5 ). It contains a start codon located downstream of a putative RBS and therefore potentially code for a 33-amino acid peptide. This putative peptide sequence was predicted and annotated as hypothetical protein in NCBI database only in three S. aureus strains and three phages (Fig. 6A ), with a predicted molecular weight of 3. 9 kDa. To assess whether it is produced by S. aureus, we cloned srn_9343 under the control of its own promoter by including up to 250 nt upstream in translational fusion with a 3xFLAG (adding a 2. 9-kDa epitope at its C-terminus). Western blot analysis did not reveal any translation of the fusion peptide in cell extracts or in the Newman supernatant (Fig.
Discussion The last decade has led to the discovery of most S. aureus sRNAs, although their functions remain unknown in most cases  22. This exponential rise in the identification of sRNAs expressed by this major pathogen was enabled by the wide availability and affordability of next-generation sequencing technologies. So far, studies devoted to the detection of novel sRNAs have focused on a few strains, and the results were recently compiled into a dedicated database  22. Additionally, GenBank files that include sRNAs are available for three S. aureus strains  21. It was necessary to clarify the count of already published sRNAs, so simple identifiers were provided for all of   6 ) under the pVeg promoter. This is compared to empty vector (pCN35) and to PepA1 from Sayed et al. 66     them in addition to their gene coordinates.
This resulted in the identification of 48 loci for which RNA-Seq data allowed read clustering. Although DETR'PROK identified sRNA content differences between the Newman and the Newman Δsrn_3610_ sprc strains, differential expression analysis revealed no statistical differences between the two. Previous studies underlined the high probability of identifying repeated sequences, or 5′ or 3′ UTRs  11, 14, 22. To avoid this issue, we assigned specific parameters to DETR'PROK, used several cut-offs, systematically used BLAST, and viewed the read mappings to determine whether sRNA candidates seemed to be independent transcriptional units. In addition, misannotated genes were removed during a final manual cull (see File S2) to avoid the identification of putative artefacts. Although we cannot exclude the removal of some true sRNAs, by being safe the combination of these measures narrowed down the candidate set to 17 IGRs, and these were subsequently confirmed by northern blot and/or RT-PCR.
None of them were previously reported in the SRD  22  or BSRD databases 50 devoted respectively to Staphylococcus and bacterial sRNA. A careful study of the 17 transcribed IGRs began with MiSeq, which led to the discovery of five additional sRNAs. This was completed with RACE-mapping, which characterized the 24 independent transcriptional units or primary transcripts. According to Rfam and Riboswitch finder  51, 52  , the 24 novel sRNAs do not present the sequence and structural characteristics of riboswitches. An in-depth study of IGR_1141544 revealed the presence of three TSSs on the plus strand, and two sRNAs transcribed from the minus strand at the same locus as Srn_9344 and Srn_9345. Such transcripts are common in bacteria with the discovery of natural antisense transcription  40, 53, 54.
Interestingly, our data indicated that many of these novel sRNAs are downregulated under oxidative stress. This can be opposed to the observation made in N315 during the identification of the Teg sRNAs  11  , implying that each sRNA responds to its own set of physico-chemical modifications. In conclusion, including the 24 novel sRNA coordinates in the SRD will enable subsequent RNA-Seq studies using various experimental conditions to monitor their physiological role. Global transcriptome approaches are necessary for an exhaustive characterization of the S. aureus RNome, and this step is essential before trying to understand its overarching role and function within the bacterium and especially during infection. Our work shows that the search for novel sRNAs in well-studied bacteria is valuable, and that this can be facilitated through setting conditions that mimic the in vivo environment, by using different RNA-Seq approaches, or by studying strains deleted for major transcription factors to start characterizing the complex connections between sRNAs and proteins in this major human pathogen.
Acknowledgements J. B. and T. M. are recipients of a fellowship from the  Direction Générale pour l' Armement  and the  Conseil Régional de Bretagne. M. S. was supported in part by the region Bretagne grants  SAD SARS 8254  and  SARS_2 9181  (to Y. A. ). Y. A. was supported by a  Marie Curie International Incoming Fellowship  (project  621959-SarHyb ) within the 7 th European Community Framework Programme. Other source of funding:  Institut National de la Santé et de la Recherche Médicale ; University of Rennes 1 and The  French National Research Agency (sRNA-FIT  to B. F. ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We are very grateful to the "plate-forme  Génomique Santé "  Biogenouest Génomique Biosit core facility  for their technical assistance and to the  CNRS-UPMC ABiMS bioinformatics platform  (http://abims.
configuration of fibres is more complex. The challenge participants had access to three-quarters of the dataset and their models were ranked on their ability to predict the remaining unseen quarter of the data. The challenge provided a unique opportunity for a quantitative comparison of diverse methods from multiple groups worldwide. The comparison of the challenge entries reveals interesting trends that could potentially ii. Which tissue features can be derived from the model? iii. What subset of those features can we estimate from limited acquisition time on a standard clinical scanner and what dataset best supports such estimates? The intuition gained from (i) is generalizable over a wide range of applications, while (ii) and (iii) are highly dependent on the MRI study design and the available hardware.
Therefore, our challenge focuses on question (i), as an understanding of (i) is necessary to inform (ii) and (iii). To that end, we acquire the richest possible dataset using the most powerful hardware available and the most motivated subject available (UF). Specifically, we use the Connectome scanner,  51  which is unique among human scanners in having 300 mT/m gradients, rather than 40 mT/m as is typical of state-of-the-art human scanners. Preclinical work by Dyrby et al. 13  highlights the benefits of such strong gradients and the first results from the Connectome scanner  42, [52] [53] [54]  are now starting to verify those findings. This kind of model comparison, based on prediction error, is a common and crucial part of the development of any statistical model-based estimation applications.
Burnham and Anderson  55  explain how and why such comparisons should be performed to reject models that are theoretically plausible but not supported by the data. To that end, we used a uniquely rich dataset acquired on the Connectome system  42  composed of around 5000 points in q space with, for each shell, a unique combination of gradient strength, diffusion time, pulse width and echo time. This offers the opportunity for the comparison of the many different types of models within a common framework, over a very wide range of measurement space. Using this rich dataset, we organized the White Matter Modeling challenge, held during the 2015 International Symposium on Biomedical Imaging (ISBI) in New York. The goal of the challenge was to evaluate and compare the models in two different tissue configurations that are common in the brain: (1) a WM region of interest where fibres are relatively straight and parallel, specifically the genu of the corpus callosum; and (2) a region in which the fibre configuration is more complex, specifically the fornix.
Challenge participants had access to three-quarters of each whole dataset; the participating models were evaluated on how well they predicted the remaining 'unseen' part of the data. As announced before the challenge, the final ranking was based exclusively on the performance on the genu data. In this article, however, we include results from both the genu and the fornix. The article is organized as follows. We first describe in section 2 the experimental protocol, data post-processing and preparation of the training and testing data for the challenge. We then present the methods for ranking the models and tabulate the various models involved in the competition succinctly. We report the challenge results in section 3 and discuss these results in section 4; a more detailed description of the models follows in the Appendix.
Post-processing All post-processing was performed using Software Library (FSL). 56 The DW images were corrected for eddy current distortions separately for each combination of  and Δ using FSL's Eddy module (www. fmrib. ox. ac. uk/fsl/eddy) with its default settings. The images were then co-registered using FSL's Fnirt package. As the 48 shells were acquired across a wide range of TEs, over two days, we chose to proceed in two steps. First, within each quarter of the dataset (different day, different ) we registered all the b = 0 images together. We then applied these transformations to their intermediary DW images, using a trilinear resampling interpolation. The second stage involved co-registering the four different quarters. To help the co-registration, especially between the two days images that required some through-plane adjustment as well, we omitted areas of considerable eddy-current distortions by reducing the number of slices from 20 to 5 (i.
Training and testing data The data for this work originated from two regions of interest (ROIs), each containing 6 voxels (see Figure  1 ). The first ROI was selected in the middle of the genu in the corpus callosum, where the fibres are mostly straight and coherent. The second ROI's fibre configuration is more complex: it lies in the body of fornix, where two bundles of fibres bend and bifurcate. The dataset was split into two parts: the training dataset and the testing dataset. The training dataset was fully available for the challenge participants. The testing dataset was retained by one of the organizers (UF). The DW signal of the training dataset (36 shells, with acquisition parameters shown in black in Table  1 ) was provided together with the gradient scheme on the challenge website (http://cmic.
cs. ucl. ac. uk/wmmchallenge/). This data was used by the participants to estimate their DW-MRI model parameters. The signal attenuation in the testing dataset (12 shells, with acquisition parameters shown in red in Table  1 ) was kept unseen. It contained one shell, chosen at random, from each TE-specific set of four shells (i. e of the same combination of  and Δ). The challenge participants were then asked to predict the signal for the corresponding gradient scheme. They were free to use as much or as little of the training data provided as they wished to predict the signal of the test dataset for the six voxels in each ROI. Figure  2  shows the DW signal attenuation for each shell in the genu dataset, with stars in the legend indicating which shells were left out for testing. In this plot, a small number of data appear as 'outliers' (two such data are shown with arrows in the bottom-left subplot of Figure  2 ).
RESULTS Figure  4  shows the averaged prediction error in each ROI (top subplot is for the genu, bottom subplot is for the fornix) and the corresponding overall ranking of the participating models in the challenge. The first six models in the genu ranking performed similarly, each higher ranked model marginally improving on the prediction error. The prediction error clearly increased at a higher rate for the subsequent models. In the fornix dataset, the prediction error was higher than in the genu. For both datasets, the first six models were the same, albeit permuted. Most of the models performed Summary of the various diffusion models evaluated. Tissue models are models that include an explicit description of the underlying tissue microstructure with a multi-compartment approach. In contrast, signal models focus on describing the DW signal attenuation without explicitly describing the underlying tissue and instead correspond to a 'signal processing' approach  bilities; some models perform better within a given b-value range but are penalized more in another.
Main conclusions The first insight is on the type of model used. Signal models do not necessarily outrank tissue models; indeed, using our dataset, models of the signal (Alipoor, Sakaie, Fick, Rivera) ranked on average lower than models of the tissues, despite their theoretical ability to offer more flexibility in describing the raw signal. This is quite surprising, as the current perception within the field is that, generally, we can capture the signal variation much better through a functional description of the signal (signal models) rather than via a biophysical model of the tissue (tissue models). The former generally consist of bases of arbitrary complexity, whereas the latter are generally very parsimonious models that rely on extremely crude descriptions of tissue (e. g. white matter as parallel impermeable cylinders).
Limitations and future directions Although this challenge provides several new insights into the choice of model and fitting procedure for diffusion-based quantitative imaging tools, it has a number of limitations that future challenges might be designed to address. One limitation of the study is that we use a very rich acquisition FIGURE 8 Fornix signal for the group consisting of the best 7 from 14 models. We show only four (of twelve) representative shells; these are shown by blue stars, while red circles denote model-predicted data. The best models are listed first. The x-axis is the cosine of the angle between G and n protocol that is not representative of common or clinical acquisition protocols. In particular, we cover a very wide range of b values and the data acquisition (protocol) we use consists of many TEs, unlike many other multi-shell diffusion datasets that use a fixed TE.
As stated in the Introduction, our intention is to sample the measurement space as widely as possible to support the most informative models possible. Varying the TE makes it possible to probe compartment-specific T 2 (the decay of which Ferizi et al. 42  finds to be monoexponential at the voxel level), an investigation that would be impossible with a single TE. However, the good performance of DIAMOND also shows that a model with fixed  and Δ can still capture the signal variation in multi-TE datasets and that, while the majority of the full data was ignored in each of the reconstructions, its prediction error compared favourably with other techniques. We use the unique human Connectome scanner  51  to acquire a dataset with gradients of up to 300mT/m, which is not readily available in most current MR machines.
Future work can take this further, by selecting unseen shells outside the min-max range of experimental parameters. This is likely to penalize more complex models that overfit the data even more strongly. We did not take into account the computational demand of each model, and this might limit the generalization of the results. Models that use bootstrapping generally have a higher computational burden and may not be feasible for large datasets, e. g. whole brain coverage. The dataset used in this challenge is specific to one subject who underwent a long-duration acquisition, which adds to the question of generalizability. The subsequent preprocessing of the data is also a factor to bear in mind: the registration of two 4h datasets, across such a broad range of echo times, poses its own challenges for certain non-homogenous regions in the brain, such as the fornix (compared with, for example, the relatively large genu).
CONCLUSION Challenges such as this have great value in bringing the community together and provide an unbiased comparison of wide-ranging solutions to key data-processing problems. They raise new insights and ideas, motivating more directed future studies. The data are publicly available for others to use, with more details of the dataset given on the Challenge website at http://cmic. cs. ucl. ac. uk/wmmchallenge/. On this website, an up-to-date ranking of the models will be available, where additional models can be added after the publication of the article and where the community will be able to evaluate further the impact of noise correction, compartment-specific T 2 estimation, inter-class model assumptions, e. g. tissue versus signal models, or indeed intra-class model assumptions, e.
One isotropic component was always included. The model was first fitted to half of the diffusion-weighted data (randomly selected), after which outliers were rejected (> 2. 5). Thereafter a second fit was performed. Both fit steps assumed Gaussian noise and utilized the 'lsqcurvefit' function in Matlab. The procedure was repeated 100 times for different randomly generated models. To prepare for submission of the results, only the models that best predicted the hidden half of the data were selected, after which the median of the selected predictions was used for the final prediction.
We used elements of the SFM implemented in the dipy software library  80  and the EN implemented in scikit-learn. 81 In addition, to account for differences in SNR, we implemented a weighted least-squares strategy, whereby each signal's contribution to the fit was weighted by its TE, as well as the gradient strength used. EN has two tuning parameters, determining (1) the ratio of L1-to-L2 regularization and (2) the weight of the regularization relative to the least-squares fit to the signal. To find the proper values of these parameters, we employed k-fold cross-validation,  43  leaving out one shell of measurement in each iteration for cross-validation. We determined that the tuning parameters with the lowest Least Squares Error (LSE)  18  provide an almost-even balance of L1 and L2 penalty with weak overall regularization.
Because of the combination of a dense sampling grid (362 points distributed on the sphere) and multiple restriction kernels (45 per sampling point), the maximal number of parameters for the model is approximately 16 300, more than the number of data points. However, because regularization is employed, the effective number of parameters is much smaller, resulting in an active set of approximately 20 regressors. 82 We have made the code to reproduce our results fully available at https://arokem. github. io/ISBI2015.
A. 2. 2 Sakaie-Tatsuoka-Ghosh (Cleveland, USA): an empirical approach As the extent of q space in the dataset is unusually comprehensive, we chose a simple, generic approach to gain intuition. Visual inspection suggested use of a restricted and hindered component, each with angular variation: S i = A TE i (fR i + (1 -f) exp(-b i D i )) (A8) where S i is the predicted signal for a signal acquired with TE i , b i. A TE i is the median signal at a given TE with no diffusion weighting. Fitted parameters are f, the volume fraction of R i , the restricted component, and D i , the diffusivity. R i and D i are modelled as spherical harmonics with real, antipodal symmetry  91  with maximum degree 4. The model has 31 fit parameters for each voxel. Data were fitted using using a non-linear least-squares algorithm (lsqcurvefit, MATLAB).
The parameters of the models were estimated using the training dataset: the b values using the equation by Stejskal and Tanner 88  and the S 0 values as the median of the gradient-free signals with equal echo time per voxel. The initial basis comprises 33 PDDs distributed in the unitary sphere. The bounds  {0,1} ∈ [1, 39] × 10 -4 and  {2,3} ∈ [1, 9] × 10 -4 mm 2 ∕s and the LASSO regularization parameter (equals 1. 7) was tuned by hand such as to provide the minimum error. The best multi-tensorial model for both algorithms was used for each voxel to predict the corresponding unseen data.
ACKNOWLEDGEMENTS Research reported in this manuscript was supported by the following organizations. EPSRC supported this work through grants  EP/G007748 ,  EP/L022680/1 ,  EP/I027084/01 ,  EP/M020533/1  and  EP/N018702/1. U. Ferizi  is also supported by the  National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)  of the  National Institute of Health (NIH)  under award numbers  R21AR066897  and  R01AR067789. B. Scherrer was supported in part by  NIHR01 NS079788 ,  R01 EB019483 ,  R01 EB018988  and  BCH TRP Pilot  and  BCH CTREC K-to-R Merit Award. M. Nilsson  is supported by the  Swedish Strategic Research (SSF)  Grant  AM13-0090. R. Deriche  and  R. Fick  were supported in part by the  European Research Council (ERC)  under the  Horizon 2020 research and innovation program  ( ERC Advanced Grant  agreement No 694665 : CoBCoM).
Women diagnosed with cutaneous melanoma have a survival advantage compared to men, which has been hypothesized to be due to difference in behavior and/or biology (sex hormones). It remains controversial whether this advantage is dependent on age or stage of disease. We sought to compare melanomaspecific survival between females in pre, peri, and postmenopausal age groups to males in the same age group, adjusting for stage of disease. This is a retrospective population-based cohort study using the Surveillance, Epidemiology, and End Results (SEER) database. Patients diagnosed from 1 January 1992 through 31 January 2011 with primary invasive cutaneous melanoma were included in our cohort. Melanoma-specific survival was the main outcome studied. Of the 106,511 subjects that were included, 45% were female.
Estrogen may also play a role as some reports suggest the female survival advantage is abrogated in postmenopausal females as estrogen levels decline  [16, 17]. While some investigators have hypothesized that sex influences only local tumor invasion  [18] , others believe that the effect is limited to lymphatic or hematogenous metastasis  [19, 20]. However, studies in patients with metastatic disease are limited and have thus far yielded conflicting results  [21] [22] [23]. Thus, we elected to investigate the relationship between sex and survival by utilizing the Surveillance, Epidemiology, and End Results (SEER) database. Specifically, our aim was to delineate whether the survival advantage in women is restricted to early stage malignant melanoma, as previously reported, or whether it may persist in advanced or metastatic disease.