dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Many studies have reported the existence of intraspecific variations in the venom content of distinct Conus individuals belonging to the same species  [43] [44] [45] [46]. Reanalysis of the venom gland transcriptome of one Conus marmoreus individual revealed that ConoSorter was able to identify 72% of the annotated and complete known precursor conopeptides previously isolated in this species, and also led to the discovery of 158 new precursor conopeptides, 67. 1% of which were validated in a matched MS/MS dataset. Interestingly, we observed that the overall number of C. marmoreus precursor conopeptides found to date is comparable to the number Conus species can theoretically produce  [9]. Further investigation of the novel sequences identified by ConoSorter also allowed us to define 13 new superfamilies of conopeptides, which we have classified as H2, I4, M2, N2, O4, Q, R, U, W, X, Y2, Y3, and Z based on their intraspecific conservation rates and identity to established superfamilies.
Indeed, although conopeptide precursors are relatively short polypeptides, their average length is nonetheless ~70 amino acids (~210 nucleotides), there are those, including CalMKLL-1 and -2 conotoxin precursors from Conus californicus, that are 131 amino acids in length. The average length of a high quality RNA-seq read in this dataset was 317. 93 bases, indicating that failure to detect known conotoxins could be improved with longer reads. We suspect that further work in this field will take advantage of platforms offering up to 2 × 300 bp nucleotide reads, which not only allow for improved detection of conotoxins but may also facilitate de novo assembly of the Conus transcriptome.
Conclusions In this article we present ConoSorter, a high-throughput standalone program that implements regular expressions and pHMMs for large-scale identification and classification of precursor conopeptides into gene superfamilies and classes based on the ER signal, pro-, and mature conopeptide regions generated from raw next-generation transcriptomic or proteomic data. ConoSorter also generates a set of relevant additional information -frequency of protein sequences, length, number of cysteine residues, hydrophobicity rate of N-terminal region, similarity to known conopeptides -that allows the user to assess the reliability and relevance of the results and aids the identification of new conopeptide superfamilies and classes. When applied to ConoServer and UniProtKB/Swiss-Prot databases, ConoSorter is able to recognize 100% of known conotoxin superfamilies and classes with a minimum species specificity of 99%.
Percentage value between brackets following the superfamily name measures the intraspecific conservation of members populating this family (the "*" symbol means that only one sequence is part of the family). Bottom panel shows the 33 new precursor sequences spread over the 13 new superfamilies. Red rectangles enclose the signal sequences determined with SignalP 4. 0. The number following the precursor name is the precursor sequence frequency among the input data set. Bold partial sequences represent the peptide fragments retrieved by MS/MS sequencing of milked venom. of transcriptomic or proteomic data of conopeptide sequences, which could contribute to the acceleration of the discovery of new bioactive molecules.
Training set Query data are compared to known conopeptide sequences using regular expressions and pHMMs. The conopeptides sequences used to build these reference models were obtained from the latest updates of the ConoServer (27/05/2013) and UniProtKB (06/2013) databases. These databases contain complete or partial wild type precursor and mature toxin sequences, isolated either as conotoxin genes, transcripts, or proteins, as well as artificially synthesized peptides. For the training set, only full-length endogenous precursor and mature conopeptides isolated at the protein level, or sequences from a superfamily where all members were annotated at the gene/DNA level, were used. Synthetic constructs and patented sequences with undetermined amino acids have not been included in the training set.
The number of clusters containing sequences with the highest rate of similarity and/or groups with unique sequence for each superfamily and class are summarized in Table  2. The second sequence analysis approach implemented by ConoSorter is based on pHMMs. The sequence clusters described above used to create the regular expressions for each conopeptide superfamily / class have been used to build these models. Aligned clusters of sequences were converted to Stockholm format and the pHMMs were generated with hmmbuild from HMMER 3. 0 package  [52] [53] [54]. These conopeptide-specific profiles have then been concatenated to one single HMM database flat file, which has been subsequently compressed and indexed by using hmmpress from the same package.
Total scores for superfamilies and classes are then generated by simple addition of each region's score. Instances in which there is conflict in the identification between distinct regions of the same sequence (i. e. the signal, pro-and/or mature regions have matches to different superfamilies) are also identified. The group of sequences that did not return matches to known superfamilies and classes is submitted to a more flexible stochastic search using the conopeptide-specific pHMMs described above and hmmscan script from the HMMER 3. 0 package (hmmscan, like many other matching programs, use a default E-value threshold of 10). Total scores for superfamilies and classes are then calculated as the product of the E-values of the 3 independent regions. Two separate sets of results are thus obtained, one for those with clear similarity to known superfamilies and classes and one for those that are potentially novel, which are stored in tabulated files called "Regex.
Analysis of the mRNA pool isolated from Conus marmoreus venom gland The analysis of Conus marmoreus venom duct RNA sequencing data has recently been performed  [18]. Briefly, mRNAs were sequenced with a Roche 454 pyrosequencer, and corresponding conopeptide sequences were identified by a BLAST homology search. To confirm the existence of new conopeptides, peptides isolated from C. marmoreus milked venom were sequenced by MS and matched to the conopeptide transcripts. Here, we perform a reanalysis of this data using ConoSorter, as described above, with the addition of a number of computational steps to confidently identify novel conotoxin superfamilies and classes. Specifically, ConoSorter hits displaying matches only for the proand mature regions, as well as containing at least 60% hydrophobic amino acid in their N-terminal region were selected.
This cutoff was chosen based on an analysis of all ConoServer conopeptide sequences with unique and complete signal regions -644 in total, with a length and number of hydrophobic amino acids being 21. 28 and 15. 83 on average respectively (74. 56% hydrophobicity with a standard deviation σ=6. 59, and a minimum of 52. 00%). These selected sequences were submitted to SignalP 4. 0 in order to select sequences with a defined signal region  [55]. Using these signal peptides, and those from annotated superfamilies, we then built a similarity matrix to ascertain the minimal intraspecific and maximal interspecific identity rates within and between known superfamilies (Additional file 5: Table  S3 ). We submitted the isolated signal regions to the CD-hit clustering program by applying an identity cut-off of 75.
MINIMUM USER REQUIREMENTS Along the proposed literature 3D tele-immersive systems  [4] [5] , in this paper we demonstrate a cost-efficient, simple approach in order to reach a wide audience of average users and make 3D TI systems applicable in real-life situations. The importance of such an off-the-shelf approach generates a need to introduce consumer-grade devices to the pipeline, lifting the burden of time-consuming external software dependency installations. For the integrated demo, a Microsoft Kinect sensor is required. Furthermore, in order to allow communication of JavaScript client-based code with the Kinect skeleton tracking libraries, the ZigFu browser plug-in (available at http://zigfu. com/) is required. Installation of this plugin is a familiar process that takes no more than a couple of seconds, similar the installation of other web browser plugins, such as Adobe's Flash player.
AVATAR CONTROL VIA SKELETON TRACKING A second milestone for user immersion within the virtual scene alongside capturing and reconstruction, avatar embodiment allows users to puppeteer a virtual character created by an external authoring application. The process requires that the character mesh is parented to an articulated structure of control elements called bones. Bones can be viewed as oriented 3D line segments that connect transformable joints (such as a knee or shoulder). Avatar embodiment within the collaborative dance masterclass use case is achieved by allowing users to create avatars, rigged with a pre-defined 17-joints hierarchy (similar to the OpenNI joint tracking structure), as in  [7]. For each frame, the OpenNI skeleton-tracking module generates joint position and rotation data with respect to the Kinect camera's world coordinate system.
The local rotation quaternion lo q i is calculated from the axis i m and the eigen-angle i θ. The final rotation quaternion q i is calculated by multiplying the inverse rotation quaternion of the parent pa q with lo q i , i. e ( ) 1 lo pa q q q i i - = ⋅. Actual animation is computed by traversing the avatar joint hierarchy from its root and setting up the final skeleton pose. A set of joint influences and corresponding weights are applied onto each vertex of the mesh via skinning equations. The resulting mesh resembles the virtual character mimicking the user's pose, as can be seen in Fig. 3. The effect can also be applied with pre-recorded skeleton tracking data as input, allowing virtual characters to playback past recorded animations.
ON-LINE VIRTUAL DANCE EVALUATION In order to support collaborative dance features of the integrated 3DLife demo, and explore virtual environment feedback on immersed user activities, a variant of the automated dance evaluation algorithm described in  [8]  was integrated onto the demo application. In a nutshell, the employed algorithm makes use of quaternions to represent the user's joint position data relative to the skeletal hierarchy root joint (Torso). An instantaneous evaluation score is calculated for each frame, by computing the "all-joints" quaternionic correlation coefficient, within the temporal window of the last few frames. A web-oriented implementation of the algorithm was used to evaluate user real-time dance performance in comparison to a pre-recorded choreography uploaded by another user of the network.
More specifically, user data is placed within a FIFO queue, with a frame capacity of 150 T ∆ = frames (this corresponds to 5sec, considering the Kinect's frame rate 30fps). The same time window is applied for consideration of pre-recorded teacher skeleton data in the same time reference. Then: 1. The joint positions, relative to the Torso for both the teacher and student are calculated, by subtracting from each joint the Torso position. These positions are then represented as "pure" quaternions and stored in quaternionic matrices P( ; ) i t and Q( ; ) i t of sizes 17 T × ∆. 2. Additionally, the temporal mean quaternions P( ) i and Q( ) i are calculated and subtracted, to produce the zero- mean (along time) quaternionic matrices P( ; ) P( ; ) P( ) i t i t i = and Q( ; ) i t , respectively. 3.
Auto-stereoscopic rendering To further enhance modern 3D immersive experience, autostereoscopic rendering post-processing effects can be applied to the WebGL renderers, to support glasses-free 3D wherever a specialized display is available. To this end, a Three. js (http://mrdoob. github. com/three. js/) effect module for rendering WebGL 3D content to the Tridelity MP 4210 auto-stereoscopic display was implemented and integrated onto the demo. As the module applies the effect after the scene has been rendered, real-time performance of the rendering pipeline is not burdened.
In the present study, we evaluate the results obtained with the newly developed 18 O module for the iLOVECLIM coupled climate model, a derivative of the LOVECLIM model  (Goosse et al. , 2010). The difficulty does not reside presently in the advection of moisture which is computed explicitly but in the reduction of the atmosphere to three vertical layers, with a single moist layer  (Opsteegh et al. , 1998) , complicating the vertical fractionations along the precipitation path  (Roche, 2013). The equations and verification of implementation was achieved in the first part  (Roche, 2013). In the present manuscript, we present a comparison of fully coupled atmosphere-ocean-vegetation modelling results under preindustrial conditions with present-day δ 18 O data for the atmospheric and oceanic component.
Large-scale climatic to δ 18 O relationship To further test the applicability of the isotopic atmospheric component, we present in Figs. 2  and 4  two classical δ 18 O to climatic variable relationship: the first compares annual mean δ 18 O to annual mean temperature (the well-known Dansgaard relationship,  Dansgaard, 1964)  and the second the δ 18 O to annual mean precipitation relationship. Comparing the values obtained in the model (coloured points) with those from the GNIP dataset (grey dots), we see that the Dansgaard relationship does exist in our model (as already noted in Roche, 2013) albeit with a slightly lower slope. We interpret the lower slope as an underestimation of the fractionation towards lower temperatures/latitudes, originating from the use of the very simplified approximation of a single moist layer for the atmosphere in ECBilt  (Roche, 2013).
Annual mean near-surface δ 18 O Surface mean ocean δ 18 O results obtained (Fig. 8 ) are in very good agreement with data from the GISS database  (Schmidt et al. , 1999). The latitudinal gradients are faithfully reproduced with lower δ 18 O in high latitude regions and higher δ 18 O in tropical regions. We obtain as expected from data a lower δ 18 O around the equator the latter being a less evaporative region than the tropics, as seen already in the atmospheric part. The contrast between the evaporative zones and the mid-latitudes is well represented in the model. We observe nonetheless some notable discrepancies between the modelled distribution and data. One clearly apparent mismatch is the western Indian Ocean where we simulate much lower δ 18 O in near-surface ocean than observed in reality.
This bias is due to a shift of the African precipitation regions from the west to the east of the continent, leading to much less saline waters (and unrealistically depleted δ 18 O) in the western Indian Ocean. Another region where model and data do not agree is offshore California: the isotopic signal of the North Pacific depleted values does not penetrate as far south as observed. A similar pattern is observed in the GISS model  (Schmidt et al. , 2007) ; we do not have an explanation yet for such a disagreement. Figure  9  shows details of the North Atlantic and the Arctic Ocean region. We chose the region because of the strong gradients in δ 18 O occurring due to the different water masses. Our results show an excellent match between the model results and the near-surface ocean data from the GISS database.
Annual mean deep ocean δ 18 O Since we obtain satisfactory results at the surface of the oceans it is worth comparing our results to available data for the interior of the oceans. It shall be noted that representing the deep ocean δ 18 O is complicated since it depends strongly on the δ 18 O content at the location of deep water formation, a rather restricted area. A relatively small shift in δ 18 O may lead to a substantial drift in deep ocean δ 18 O with an opposite drift in surface waters δ 18 O. The cross sections presented are averaged over all the considered ocean basin, using a mask to define the ocean basins. The data points from the GISS database are all collapsed on the same cross section but not averaged. Thus, certain data points may represent values from specific locations that are not comparable to the mean.
Conclusions In the present manuscript, we have evaluated a pre-industrial control run against present-day observation of δ 18 O in precipitation and in the ocean waters. For the atmospheric part, we found that apart from central Antarctica, our model produces results in good accordance with what is known from the GNIP dataset, with some caveats due to the simplicity of the approach taken. If the general gradients (latitudinal, oceanic to continental) are well reproduced, the trends towards colder and drier are generally underestimated, indicating a too low fractionation in our single moist layer atmosphere. From the ocean perspective, we obtain a very good agreement between the simulated δ 18 O values in the oceans and the observed values as recorded in the GISS database. The slightly too low gradient found in the atmosphere is naturally also present in the oceanic part of our coupled model since the system is coupled and closed with respect to water and isotopic water content.
[11]  proposed a specialized approach using a context-sensitive Bayesian network for semantic inference of segmented scenes. The regions' remote sensing related semantic classes are inferred in a multistage process based on their spectral and textural characteristics as well as the semantics of adjacent regions. Another system presented by  [12]  is composed of a content-based multimodal Geospatial Information Retrieval and Indexing System (GeoIRIS) which includes automatic feature extraction, visual content mining from large-scale image databases, and high-dimensional database indexing for fast retrieval. Facing the difficulty of the of modeling of expert knowledge,  [13]  presented a clear list of the specific chal-lenges of the geoscience and geography fields for knowledge representation.
If the expert possesses a method to directly obtain good results, our approach is not interesting anymore. However, we carried out discussions with geographer experts and they have shown a strong interest in our method. Indeed, our method only uses simple knowledge and does not require direct examples, which are generally difficult to select in object-based classification. Moreover, a classifier can easily be created and used on different images without selecting new examples on each image. Using shapes and relationships between regions has been already explored (e. g. eCognition software). However, it is often difficult to link expert knowledge expressed in free speech and computer programs. In this paper, we explained how we used expert knowledge, and make it actionable to use for automatic image interpretation.
Background: Dementia is a frequent and devastating complication in Parkinson's disease (PD). There is an intensive search for biomarkers that may predict the progression from normal cognition (PD-NC) to dementia (PDD) in PD. Mild cognitive impairment in PD (PD-MCI) seems to represent a transitional state between PD-NC and PDD. Few studies have explored the structural changes that differentiate PD-NC from PD-MCI and PDD patients. Objectives and Methods: We aimed to analyze changes in cortical thickness on 3. 0T Magnetic Resonance Imaging (MRI) across stages of cognitive decline in a prospective sample of PD-NC (n = 26), PD-MCI (n = 26) and PDD (n = 20) patients, compared to a group of healthy subjects (HC) (n = 18). Cortical thickness measurements were made using the automatic software Freesurfer.
According to the diagnostic criteria for PDD proposed by the MDS  [4] , and published MDRS normative data  [24] , we classified PD patients as demented when total CDR score was $1 and impairment was present in at least two subscores of the MDRS. The diagnosis of PD-MCI was established when total CDR score = 0. 5 and only one MDRS subscore was impaired. PD-NC patients had total CDR score = 0 and all MDRS subscores were within the normal range. The score on the CDR was derived from a semistructured interview with the participant and a knowledgeable informant, following the version and scoring rules indicated by Morris et al. [25]  The interrater reliability  [26]  and validity  [27]  of the CDR have been established by correlation with neuropathologic features observed at autopsy. The CDR was administered by an experienced neurologist in cognitive defects in Parkinson's disease (JP), and two neuropsychologists (GLl and CG), who were blinded to the CDR classification, administered the Mattis DRS.
Data Acquisition MRI was obtained in all subjects using a 3. 0 Tesla Philips Achieva facility, and a dedicated protocol including 3D-MPRAGE of the whole brain (TR = 6. 7, TE = 3. 1, matrix 28862886170, voxel size 0. 88960. 88961. 2 mm). MR images were deidentified and saved in a dedicated repository, using the PICNIC web tool.
Statistical Analysis Clinical variables were also analysed using SPSS 17. 0. One-way ANOVA with post-hoc Bonferroni tests were used to test for between-group differences in demographical, clinical, and neuropsychological variables. In these analyses, statistical threshold was set at p,0. 05. Brain structural analysis. Statistical maps were generated using Freesurfer's QDEC 1. 4 (Query, Design, Estimate, Contrast) application. QDEC fits a general linear model (GLM) at each surface vertex to explain the data from all subjects in the study. The results were obtained with a FWHM (full-width/half max) of 10 mm. Significant differences were considered for clusters with a p-value ,0. 01, uncorrected. The discrimination threshold was reduced at uncorrected p,0. 001 to verify areas that distinguished cognitive groups.
Additionally, we suppose that the SAXS profiles were collected at several sample concentrations and X-ray exposure times. Both higher concentration and longer X-ray exposure times can provide more information at higher scattering angles. However, both conditions influence the resulting SAXS profile. At higher concentrations, particle-particle interactions can affect the slope of the very low angle part of the SAXS profile  (Glatter & Kratky, 1982). At longer exposures, radiation damage can perturb any region of the SAXS profile  (Kuwamoto et al. , 2004). To remove these unwanted effects it is thus necessary to merge datasets from different experimental conditions. It is the purpose of this method to show that it is possible to do so automatically with minimal user manipulation. In this article we present the method behind the SAXS Merge webserver, a tool presented by  Spill et al.
(2014)  which merges SAXS profiles in a robust, completely automated and statistically principled way. While the method was tested on SAXS datasets, it can also be applied for merging small-angle neutron scattering (SANS) datasets, because the basic equations and methods are similar for the two techniques  (Svergun, 2010). SAXS Merge consists of five steps: data clean-up, profile fitting using Gaussian processes, rescaling of each profile to a common reference, classification and detection of incompatible regions, and merging of the remaining data points. The resulting object is a probability distribution function describing the merged SAXS profile. Resulting data consist of the experimental points that are compatible with the distribution, a maximum posterior estimate of the SAXS profile across all experiments along with a credible interval, and estimates of a number of parameters such as the radius of gyration and the Porod exponent.