dataesr/scientific-paragraphs-categorization huggingface dataset:
If θ ( ( ) ) -( ( ) ) > ( ( ) ) + I P I P t t I P 0 , 0 , 0 , 0 5 j j j , the registration paradigm is switched and window W(t -1,I(t)) is registered to W(t -1,I(t -1)), yielding * - T t 1. Parametrization. Parameters for optimization, deformation resolution ( × 17 17) and multilevel scheme (2 levels) were kept constant. The window size was 50 mm in each dimension. Weight β is automatically determined before each registration as D D β = ( ) ( ) A B A B , / , SSD NGF. The parameters α and η (edge parameter of NGF) were manually chosen per scanner (ETH datasets: α η = = 10, 20, MED1: α η = = 5, 5, MED2: α η = = 100, 2). The threshold was θ = 0. 5 for ETH and MED2, and θ = 0. 75 for MED1.
This method was applied to 2D and 3D datasets. In the latter case it is in fact a 2. 5D method, as we are evaluating the motion in two orthogonal slices intersecting at the manual annotation. This yields two separate tracking results with redundant information on the intersection. The method is sensitive to out-of-plane-motion as any 2D tracking method, since no adjustment based on the combined information has yet been done. Another approach was proposed by this group and applied on 2D datasets, see the CLUST14 proceedings for details  (Lübke and Grozea 2014). It was based on patch-matching, maximizing NCC, random sampling, explicit masking to improve accuracy at the border, and GPU implementation. This approach was not included in this paper, as it was on average slower (84%) and had a similar mean accuracy (3% better) as the group's included method.
Run-time. The method was implemented using the Mevislab software (MeVis Medical Solutions, Bremen, Germany), Python, OpenCV and Numpy. The run-time was measured on a Intel Core i7-4770k with 32 GB RAM. The average run-time for all 2D points is 40 ms. As the 3D tracking is performed on two orthogonal slices, the average processing time per volume of 61 ms is slightly higher than in the 2D datasets but still below the 3D images frame-rate (real-time), except for the ICR data subset with temporal resolution of 24 Hz.
Run-time. With the semi-manual initialization, the algorithm performs very efficiently, with an average run-time of ± 1. 25 0. 84 ms per 2D frame and ± 46. 76 84. 50 ms per 3D volume. The core source code is written in C++ and integrated into MeVisLab (MeVis Medical Solutions, Bremen, Germany) for high level evaluation routines using Python scripts. The code was executed single threaded on a Windows 7 machine with an Intel Core i7-2600 CPU @ 3. 4 GHz and 32 GB RAM.
Assuming motion periodicity, two failure recovery strategies are employed. First, if the Bhattacharyya coefficient ρ drops below 0. 8, the found target position is discarded and tracking is repeated using the target search area defined in I(0). Second, if the target search area in the current frame is three times larger than the initial target's size, the search area and its position is reset to the one in the first frame. Run-time. The algorithm was implemented in MATLAB 2013b, and the experiments were conducted on a machine with an Intel i5-3320M processor at 2. 6 GHz clock speed and 8 GB RAM. Tracking speed using this hardware set up was approximately 33 ms.
Visual inspection of failure cases hint at problems due to accumulation of errors, disappearing structures at the image acquisition border, out-of-plane motion, substantial changes in image appearance or insufficient motion capture ranges. These might be the reasons of the high variance in the individual tracking performances across the sequences. Tracking run-times per frame range from 1. 8 ms to 84 ms for 2D and from 51 ms to about 100 ms for 3D. Processing times generally varied depending on the number of landmarks and the image appearance. Real-time performance was achieved by 4 methods for 2D and 3 for 3D landmarks (MEVIS + MED, MEVIS + FOKUS, PhR and TUM), assuming average acquisition times of 50 ms (20 Hz) and 125 ms (8 Hz). The remaining methods are not far off, indicating that further code optimization and hardware improvements are likely to provide real-time performance for the current dataset.
Acknowledgments ETH and MEVIS + MED acknowledge funding from the  European Union 's  7th Framework Program  ( FP7/2007-2013)  under grant agreement n°  611889  ( TRANS-FUSIMO ). The work of MEVIS and MEVIS + MED was supported by  Fraunhofer Internal Programs  under Grant n°  MAVO 823 287. M A Lediju Bell  acknowledges funding from the  UNCF/Merck Postdoctoral Science Research Fellowship. A Cifor thanks  Wellcome Trust/EPSRC Centre of Excellence  in Medical Engineering-Personalized Healthcare  WT088877/Z/09  and  Oxford EPSRC Impact Acceleration Account  for financial support. We would like to express our sincere appreciation to the authors whose contributions to this paper have required considerable commitment of time and effort. We also thank (in alphabetical order of surnames)  J Banerjee  from the  Biomedical Imaging Group, Erasmus MC, Rotterdam, The Netherlands ;  F Lindseth  and  S Vijayan  from  SINTEF Medical Technology, Trondheim, Norway ;  J A Schnabel  from the  Institute of Biomedical Engineering, University of Oxford, UK ; and their colleagues for providing data and annotations.
Image Quality and Anatomic Analysis HYPRFlow arterial and venous phase images and TOF images and DSA arterial and venous phase images were scored by 2 experienced neuroradiologists (with Ͼ25 years in practice) for image quality in the M2/M3 branches for all 3 modalities. Deep and superficial venous image quality was also scored for HYPRFlow and DSA. The examinations were presented by using a clinical PACS workstation. Source and MIP images were reviewed. The MR imaging research coordinator randomized the examinations, attended each review session, and entered the data into the score sheet. Images were scored from 1-4 (scale: 1, poor visualization; 2, visualized but not of diagnostic quality; 3, good visualization of diagnostic quality; 4, excellent visualization and of excellent diagnostic quality).
Our results show that transit time can be quantitated and that it is dramatically shortened compared with the contralateral analogous vessels. We further estimated the magnitude of the arteriovenous shunting by measuring the overlap integral for the largest artery supplying the AVM and compared this value with the analogous contralateral vessels. The analysis of contrast kinetics is augmented by the addition of flow encoding of the entire cerebrovascular system. Our flow analysis results demonstrate that flow measurements can be easily accomplished in primary arterial segments (A1, M1, and P1 segments) and major arterial branches supplying the AVM. Although we performed a limited flow analysis for this report, the HYPRFlow velocity data can be used to generate flow path lines,  13  estimate wall shear stress,  33  measure pulsatility, quantitate velocity/flow,  25  and identify pressure gradients.
However, signal loss due to blood products would fall under the general class of artifacts related to susceptibility phase dispersion and subsequent signal loss. Susceptibility-based signal loss was a major aspect of our analysis. The short TE of the PC-VIPR sequence reduces but does not eliminate signal loss due to susceptibility effects. Another limitation is the challenge related to detecting aneurysms within and remote from the nidus. Previous reports have shown that aneurysms can be accurately identified when a portion of the signal from the contrast-enhanced magnitude dataset (the contrast-enhanced 3D radial T1-weighted component of the phase-contrast acquisition) is combined with the flow images. 38 owever, this approach was not used in this investigation due to the large size of the datasets and the lack of automated processing tools.
The technology behind the cloud management layer is aligned with the NIST architecture and based on the Open Cloud Computing Interface specifications. In the MEDUSA context, technical requirements for computing, storage, network, and security resources have been identified for each MEDUSA application to be deployed. All requirements are then translated into machine-readable code that is used to provision the cloud resources. The components of the MEDUSA platform are hosted on the cloud through a security-aware, need-based provisioning process. By supporting on-demand hybrid and multi-cloud deployments, as well as monitoring, load balancing, and auto-scaling services through an agent embedded in each VM, the cloud management layer thus ensures a high resilience of the MEDUSA platform.
Security The security of the MEDUSA platform is currently mainly based in the use of digital certificates, which are used to authenticate MEDUSA applications (VMs), to secure the data exchanges through the network, and to provide strong authentication of MEDUSA users. The VMs containing the applications are deployed dynamically, and thus server certificates need to be created dynamically, during the deployment. A web service was developed to provide dynamic generation of server certificates for the different VMs in the MEDUSA platform. These server certificates must be created during the deployment of the VMs and there must be one certificate per application and VM (identified by the IP address). Regarding the user authentication, an authentication module is called when a user opens a MEDUSA session.
This module authenticates a user by checking the provided credentials against the user management component, which has access to a special internal directory containing the certificates used for strong authentication of MEDUSA users. The MEDUSA platform also uses robust image watermarking and fingerprinting methods to prevent and detect unauthorized modification and leaking of medical images by authorized users by. However, due to legal regulations, an important requirement when dealing with medical images is the capability reconstructing the original image data. Because of this, reversible or semantic-sensitive techniques for watermarking and fingerprinting can be used in the MEDUSA platform. These techniques enable to completely recover the original image data or at least the recovery of the regions of these images that are relevant for the user or application.
3D Segmentation Renderer The 3D Segmentation Renderer by Sopheon is responsible for displaying 3D segmentations generated by the MIPAs. This application was developed by using the WebGL library, which enables to render 3D graphics in the browser without installing additional software. Figure  3  shows the GUI of this application rendering the segmentation of brain tissue (in green and blue) and the segmentation of the infarcted region (in red).
Initial Evaluation As this is an on-going project, the discussion presented below is based upon an evaluation of the first fully-integrated prototype. The MSP integrates very heterogeneous applications, which run on different operational systems (Windows, Linux) and use different development technologies (Java, OpenCL, C#, C++). These applications are seamlessly available for the user from a single interface. Also, the deployment of the applications is transparently handled by the platform. This solution is provided in a smooth and transparent manner, hiding the complex details from the user. In the MEDUSA platform, the data and user input need to cross several software layers, which might introduce overheads and decrease performance. However, such poor performance was not noticed in the initial MSP prototype.
For instance, the Advanced Medical Image Processing application, which requires data exchange between different architectural components, was almost instantaneously ready for use without noticeable interaction delays. The MSP implements a complete acute stroke use case, which has been demonstrated live in various occasions. Impressions have been collected informally to assess the potential value of this prototype system. Table  1  compares the current stroke care situation in the Netherlands versus the stroke care that could be supported by the MEDUSA platform based on the functionalities currently present in the MSP. Because of its complexity, a detailed and quantitative evaluation of the MEDUSA platform involves several software components and requires a careful planning. The design of this evaluation was already defined in the first year of the project.
It is scheduled to take place during the last 6 months of the MEDUSA project (end of 2015). Concerning the image processing functionality, most of the MIPAs included in the MSP are too computationally expensive to be executed on a local machine according to the time constraints of an acute stroke patient. HPC capabilities delivered by cloud computing were crucial to improve the processing of these algorithms from hours to minutes, making them suitable for acute stroke care. For instance, the time to run the method used to reduce noise in CTP data was reduced from more than half an hour to less than 2 minutes  [28].
Discussion and Conclusion The development of the MEDUSA platform started in 2013. Back then, this kind of cloud-based solutions was not common. Today, however, there is a clear trend in the healthcare industry towards the usage of cloud computing, collaboration, and automated analyses of medical data. In addition, when dealing with processing of medical data constrained by the requirements of acute care situations, a lot of benefits can be derived from the use of cloud computing: scalability, pay-per-use model, high performance computing capabilities, remote access, etc. There are innumerous technical challenges for enabling the execution and communication of software components in a platform like MEDUSA. Regarding stroke care, the software components execute in different computing devices (CPUs, GPUs, etc.
) and based on different software platforms (web, Linux, Windows, etc. ). In the MEDUSA platform these challenges are tackled using SOA approach and a virtualized infrastructure. Because of the variety of application types, a uniform way of establishing communication between the MEDUSA applications has not been developed yet. Nevertheless, the direct communication between applications based on the exchange of well-defined file formats through WebSockets was demonstrated to be effective, without a negative impact in the development and integration of these applications. The current functionalities present in the MSP have the potential to improve several aspects of current stroke care. The MEDUSA platform is still under development. Thus, most of the components to implement security are still not completely integrated in the platform yet.
Acknowledgements This work has been funded by a  DGA (French National Defence Armaments Procurement Agency)  scholarship (ER) and by the  French Agence Nationale de la Recherche  (Grants  ANR-12-CORD-0030 ,  ANR-11-IDEX-0004-02 ) and by  Sorbonne-Universités   SU-15-R-PERSU-14 PERSU.
Microbial rhodopsins are a diverse group of photoactive transmembrane proteins found in all three domains of life and in viruses. Today, microbial rhodopsin research is a flourishing research field in which new understandings of rhodopsin diversity, function and evolution are contributing to broader microbiological and molecular knowledge. Here, we describe MicRhoDE, a comprehensive, high-quality and freely accessible database that facilitates analysis of the diversity and evolution of microbial rhodopsins. Rhodopsin sequences isolated from a vast array of marine and terrestrial environments were manually collected and curated. To each rhodopsin sequence are associated related metadata, including predicted spectral tuning of the protein, putative activity and function, taxonomy for sequences that can be linked to a 16S rRNA gene, sampling date and location, and supporting literature.
The database currently covers 7857 aligned sequences from more than 450 environmental samples or organisms. Based on a robust phylogenetic analysis, we introduce an operational classification system with multiple phylogenetic levels ranging from superclusters to species-level operational taxonomic units. An integrated pipeline for online sequence alignment and phylogenetic tree construction is also provided. With a user-friendly interface and integrated online bioinformatics tools, this unique resource should be highly valuable for upcoming studies of the biogeography, diversity, distribution and evolution of microbial rhodopsins.
Based on the analysis of marine and terrestrial metagenomic data, Finkel et al. (18)  suggested that microbial rhodopsins are the prominent phototrophic mechanism on Earth. However, more investigations are needed to understand the physiological functions and fitness benefits of these proteins and their actual role in microbial ecology and in energetic balance of ecosystems. In recent years, environmental genomics surveys have increasingly demonstrated the remarkable diversity of microbial rhodopsins in diverse aquatic and terrestrial environments  (8-10, 12, 13, 19-31). Most of these studies have been performed by using the proteorhodopsin gene as molecular marker. Analyses of microbial gene sequences that serve as markers are facilitated by the availability of annotated databases of aligned sequences.
Aligned sequences are required for diversity and phylogenetic analyses and for the design and evaluation of polymerase chain reaction (PCR) primers and probes. Group-specific PCR primers are used in quantitative real-time PCR for the quantification of gene copy numbers in the environment and for expression studies. Here, we present MicRhoDE, a comprehensive, highquality and freely accessible resource of nucleic acid sequences coding for microbial rhodopsins. The database and its associated description will be useful for studying the diversity, phylogeny and evolution of rhodopsincontaining microorganisms.
Data collection and curation The MicRhoDE database was initially constructed by extracting reference proteorhodopsin sequences from GenBank  (32) , Global Ocean Sampling (GOS) database obtained from the CAMERA website (http://camera. crbs. ucsd. edu/) and from the literature (Figure  1 ). This initial set was further complemented with other type-1 rhodopsins (actinorhodopsins, xanthorhodopsins, bacteriorhodopsins, halorhodopsins and sensory rhodopsins) and newly discovered types  (33, 34). To this initial set of sequences was added an original dataset (ProteoRhodopsin Global Diversity, PRGD) of marine proteorhodopsin genes obtained by Illumina sequencing of amplicons from diverse marine regions. The whole dataset was then used as a diversified seed to perform exhaustive similarity searches using BLAST in GenBank (as of March 2013) or GOS databases.
BLAST results were dereplicated and manually checked for quality. Finally, all reference nucleic acid sequences were manually curated and modified from their original deposits in GenBank and GOS databases, when necessary, to be all in the same open reading frame. Because all MicRhoDE sequences are also stored in GenBank and GOS databases, NCBI or JCVI record IDs are also available in MicRhoDE to keep track of the original data source. To date, 7857 type-1 rhodopsin sequences are stored in MicRhoDE, most of which (7193 sequences) represent proteorhodopsins. Although the majority of sequences are derived from environmental surveys of proteorhodopsin genes, the database also contains sequences obtained from a range of isolates or large genomic DNA fragments bearing a 16S rRNA gene copy. Among the 295 sequences whose taxonomic affiliation can be inferred from a 16S rRNA gene, 186 sequences come from cultivated organisms.
Since aligning 7857 nucleic acid sequences according to the secondary structure of the corresponding proteins is timeconsuming, the sequence dataset was split in two parts (Figure  1 ). The 3871 longest amino acid sequences (>100 amino acid residues) were aligned according to the protein secondary structure using MAFFT eINSi strategy  (36). The shorter ones were added to the robust alignment using MAFFT FFT strategy with the '-addfragments' option that conserves the original alignment. The 478 full-length type-1 rhodopsin sequences of the database, including 86 strains and 73 different species, were used to construct a robust backbone tree (Figure  1 ) by Bayesian inference (4 Markov Chain Monte Carlo chains of 150 million generations) using PhyloBayes software  (37). Shorter sequences were then sequentially inserted into the backbone tree by using the parsimony add option of the ARB software  (38).
Available metadata The associated metadata of each sequence such as the sampling date, location, biome of origin, oceanic province were extracted from the related literature, GenBank and GOS records, checked manually, and reconciled before importation in the database. For each sequence are also provided the position in the phylogenetic tree, its NCBI taxonomy when available, the type of rhodopsin, its predicted spectral tuning according to the amino acid residue at position 105 for proteorhodopsins  (15, 39)  (Supplementary Figure  S1 ). Putative activity and function according to the residues at position 97 and 108, respectively  (40)  and residue 101 for flavobacterial NQ rhodopsins (34) are also indicated. Altogether, the diversity of metadata associated to aligned and unaligned nucleic acid and protein sequences allows a variety of search options and data outputs.
MicRhoDE web interface MicRhoDE is a freely accessible public database (http:// micrhode. sb-roscoff. fr) implemented using the perl Catalyst web framework (http://www. catalystframework. org/) backed by a PostgreSQL database (http://www. post gresql. org/). MicRhoDE will be updated annually by adding new type-1 rhodopsin gene sequences. In addition to a short introduction to microbial rhodopsins, the homepage provides a brief description of the database content and clickable icons with direct links to the major utilities of the database, including database and sequence similarity searching, and phylogeny (Figure  3a ). MicRhoDE provides a powerful search module that accepts complex searches of given taxonomy, predicted protein features (such as activity, function and spectral tuning) and of a range of features (Figure  3b ).
Using a menu list, filters are also accessible for combining searches at the different cluster levels and features such as e. g. taxonomy, marine province of origin and predicted spectral tuning. Sequence similarity searches within the database are available using BLAST (version 2. 2. 26þ) submission form in the BLAST page (Figure  3c ). Metadata available in other public databases (accession ID, NCBI taxonomy, location, biome, marine province, date of isolation and related literature) and other restricted to MicRhoDE (rhodopsin affiliation according to phylogeny, rhodopsin type, predicted spectral tuning, putative activity and function) are optionally accessible in the data outputs of both search and BLAST modules (Figure  3d ). Available data outputs include visualization of results on a map (Figure  3e  and f ).
The phylogeny page provides three different items: (i) a software pipeline proposing the user to place its own sequences into the type-1 rhodopsin reference phylogenetic tree, (ii) a schematic representation of the reference phylogenetic tree, highlighting the classification in clusters and superclusters and (iii) a detailed reference phylogenetic tree, displayed using the Archaeopteryx phylogenetic tree viewer Java applet  (41). To place query amino acid sequences in the reference phylogeny, the user is redirected to a dedicated Galaxy instance  (42) (43) (44)  where the MicRhoDE workflow performs phylogenetic placement using Bayesian inference as implemented in the pplacer software  (45). The Galaxy instance (Figure  3g ) is available at http://webtools. sb-roscoff. fr/root?tool_id¼abims_micr hode_workflow.