dataesr/scientific-paragraphs-categorization huggingface dataset:
Briefly, they mainly consist of a van der Waals potential (vdW), an empirical term for attractive and repulsive polar interactions, and an optional desolvation potential that combines a weighted solvent accessible surface area approach  [17]  with a rapid probabilistic approximation to the calculation of solvent accessible surface areas  [18]  for computational efficiency. The vdW term can be calculated during docking, or precalculated and stored on grid files by the ancillary program rbcalcgrid; this increases computational performance. Two distinct scoring functions have been optimized using a binding affinity validation set (described in the Manual). The default scoring function (SF3) uses the repulsive polar term but not the desolvation term, while the solvation scoring function (SF5) does the opposite.
Benchmarking The performance of rDock was compared with that of Glide (version 57111  [19] ) and AutoDock Vina  [20]  for database enrichment and binding mode prediction for various test sets. As detailed in Supporting Information Text S1, all receptors, docking cavities and ligands were prepared in the same manner and running parameters modified to ensure exhaustive sampling by all programs. Protein-ligand binding mode predictions. The CCDC-Astex Diverse Set of 85 diverse protein-ligand complexes was selected for comparing binding mode prediction  [21]. The results, represented by percentage of correct predictions (ligand RMSD below 2 A ˚) can be seen in Table  2. rDock calculations converge after 20-50 GA runs (Figure  S2 ; convergence also discussed below). The predicted binding mode is correct in approximately 80% of cases for rDock and Vina, while Glide's performance is close to 70%.
The optimal protocol is specific for each particular system and parameter-set, but can be identified with a purpose-built script (see Manual). Guided docking. Usually, VS applications exploit existing information to optimize the cavity definition (e. g. choice of protein conformation, displaceable water molecules) and to bias the docking protocol with empirical restraints (e. g. pharmacophoric points, shape similarity). This is an essential step common to all successful docking-based VS undertakings  [3, 27]. For this reason, we have compared the outcome of VS on Hsp90, a DUD system for which we have developed and used optimal docking protocols  [7, 8, 28]. The cavity includes 2 interstitial water molecules and two pharmacophoric points. As shown in Table  5  and Figures  S8  and S9 , all VS performance metrics improve significantly, particularly those related to early enrichment (logAUC, EF1%).
Availability and future directions The program is released under the Lesser General Public License and the source code, scripts, manuals, and test sets are available at http://rdock. sourceforge. net/. The current version has prototype code to sample fully the degrees of freedom and occupancy of interstitial water molecules, as previously described for GOLD  [29] , or to dock simultaneously to an ensemble of receptor coordinates to simulate receptor flexibility in an efficient way. These features require further validation. Future develop-.
The rDock program was developed from 1998-2006 by the software team at  RiboTargets (subsequently Vernalis (R&D) Ltd ). Maintenance, benchmarkings and subsequent developments have been funded by the  Wellcome Trust  ( C2D2  initiative; York U. ) and the  Spanish Ministerio de Economı ´a y Competitividad  ( SAF2009-08811& SAF2012-33481 ; Barcelona U. ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
The most important benefit of Cloud Computing is that organizations no longer need to expend capital up-front for hardware and software purchases. Indeed, all services are provided on a pay-peruse basis. The cloud services market is forecast to grow, and numerous providers offer database as a service (DBaaS). Nevertheless, as the number of DBaaS' offerings increases, it becomes difficult to compare various offerings through checking of a documentation ads-oriented. In this paper, we propose and describe DBaaS-Expert -a framework which helps a user to choose the right DBaaS Cloud Provider among DBaaS' offerings. The core components of DBaaS-Expert is first an ontology which captures cloud data management systems services concepts, and second a ranking core which scores each DBaaS offer in terms of criteria.
Introduction Cloud computing has emerged as a new paradigm, which allows enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction  [1]. Cloud providers typically publish their service description, pricing policies and Service-Level-Agreement (SLA) rules on their websites in various formats. A data management system is one of the applications that are deployed in the cloud, the service is denoted as database-as-a-service (DBaaS). Nevertheless, as the number of DBaaS offerings increases, with different cost plans and different services, it becomes necessary to be able to automate the ranking of DBaaS offerings along a company needs.
Therefore, a company should have detailed knowledge of the offerings of cloud providers that can meet its operational needs. This is not obvious, since expertise in cloud computing is required but is lacking. We propose DBaaS-Expert -a framework addressing the selection of the most suitable cloud-based data management system. In order to develop this framework, first we conducted a thorough DBaaS offerings review. Second, we propose a list of dimensions, which describe DBaaS offerings and an ontology for DBaaS. Third, we compute the ranking values of database service candidates based on user requirements. In our work, we perform the ranking following a known method of multi-criteria decision-making (MCDM): Analytic Hierarchy Process (AHP). To the best of our knowledge, there is no existing work which addressed the problem of selection of the right DBaaS offer.
Related Work The existence of multiple options and features of cloud services, makes the selection of the appropriate cloud provider very difficult and challenging. Several works have proposed to automate cloud service selection for IaaS and PaaS models. Next, we overview related work and highlight our contribution. Some reviewed papers are based on benchmarking  [2, 3]. Most of them propose revolving well known TPC benchmarks into benchmarks for data management systems' assessment in the cloud. Curino et al. [4]  propose OLTP-Bench, an open-source framework for benchmarking on-line transaction processing (OLTP) and web workloads. Other reviewed papers  [5] [6] [7]  adopted a different approach based on the proposal of a meta-data model for the description of Cloud Service Providers (CSP) offerings.
Among ontology-based papers one cite Zhang et al. [7]. They implemented CloudRecommender -a system for infrastructure services (IaaS) selection. They propose a Cloud Computing Ontology to facilitate the discovery of IaaS services categorized into functional services and QoS data. Variability modeling is used to understand and define commonalities and variabilities in software product lines and to support product derivation. For instance, Wittern et al. [6]  adopt feature modeling to capture aspects and configurations of cloud services. Quinton et al. [5]  address services selection from multiple CSPs using Hybrid Modeling. Indeed, within a multi-cloud configuration, a CSP A is selected for hosting the database, while a CSP B is selected for hosting the application. They use feature models to describe cloud systems configurations.
Proposed Framework DBaaS-Expert is a framework for cloud-based data management system selection. It helps to find relevant database criteria that meet users' requirements. The main component of DBaaS-Expert is the DBaaS ontology. The latter is detailed in Section 4. The main functionalities of DBaaS-Expert are ensured by the following modules: application module, mapping module and ranking module. These modules are part of the logical architecture of DBaaS-Expert framework depicted in Figure  1:  -Application Module. Users communicate with DBaaS-Expert via the application module. This module allows users to enter their business requirements. In order to enable the matching between user requirements and the ontology, the application module presents the DBaaS dimensions (as defined in the ontology).
5584) and Criterion C k is the least important criterion. Assessment of DBaaS' Offerings. First of all, the assessment of DBaaSs' offerings in terms of criteria is performed by experts. Assessment of DBaaSs' offerings in terms of criteria may be done by two ways, (i) pairwise comparisons as for criteria: assessments are chosen in a scale of 1 to 9; or (ii) direct assignment through metering of each criterion. The latter is challenging. Indeed, criteria have different types of data (cost: $, transaction throughput: Tps, feature: bool) and    different considerations (cost: lower is better, throughput: greater is better,. ). For the sake of simplicity, DBaaS offers are assessed in terms of criteria along pairwise comparisons. Table  2  illustrates DBaaS offerings assessment along two criteria through pairwise comparisons.
Conversely, a wealth of software tools exist for creating on-screen visualizations  [7, 8] , but do not support the creation of physical objects. In addition to the fundamental differences between physical objects and graphical content (e. g, physical objects are solid rather than flat, and they are subject to physical laws such as gravity), fabrication machines have unique constraints that differ from those of computer displays and 2D printers. Building physical visualizations currently requires complex workflows, a heterogeneous set of tools, and expertise in both data visualization and fabrication. The lack of proper tools ultimately limits a designer's abilities to create physical visualizations of larger-scale data, to reuse an existing physical design with new datasets, or to rapidly iterate through possible design variations.
MakerVis Walkthrough We built a first prototype -MakerVis -that provides rudimentary support for an integrated design workflow (see screenshot Figure  8 ). We illustrate it with a scenario inspired from observations of design sessions with real users, where non-experts created physical visualizations of personal data (see Figures  7, 9 ,10, and 11). Tom wants to visualize his commit activity in a code repository containing several research projects. The process of creating a physical visualization has three stages: 1) designing the visualization and creating digital design files with MakerVis, 2) customizing and fabricating the generated design files, and 3) assembling and finishing the final result.
Customize and Fabricate Tom prefers to exchange the regular timeline that was provided by his data with more personally meaningful time landmarks, such as timestamps for project deadlines. While this is currently not supported in MakerVis, the created design files are in a standard vector file format (svg) and can currently still be relabelled using vector editing software. Once he finishes customizing the design file, he sends the file to a laser cutter for fabrication (Fig. 2 ).
Assemble and Finish After fabrication, Tom collects his fabricated parts (Fig. 3 ) from the laser cutter. MakerVis provides suggestions on how to further customize or finish parts after fabrication. Tom would like to increase the contrast of the engraved gridlines and chooses to try MakerVis' suggestion of tracing of lines with a felt pen (Fig. 5 ). Finally, he uses the assembly instructions provided by MakerVis to put together his physical visualization (Fig. 7 ). The number of possible physical visualizations is enormous (see  [1] ). MakerVis currently supports a set of pragmatic visualizations: line charts (Figure  7 , bar charts (Figure  10 , prism maps (Figure  9 ) and scatterplots (Figure  11 ). However, the software can be easily extended to support a range of visualization types, fabrication techniques, and corresponding materials.
Introduction Advances in functional imaging along with molecularly targeted probes and contrast agents are providing feature-rich data sets from multiple sources before and following treatment that allow for greater detail in the characterization and interrogation of the cancer disease process  [1]. However, in spite of the increased toolbox of techniques for evaluating a patient's tumor response, the clinical standard assessment tool for measuring tumor treatment response is the Response Evaluation Criteria in Solid Tumors (RECIST)  [2]. The RECIST clinical response metric is extensively used on anatomic tumor images acquired by computed tomography (CT) and magnetic resonance imaging (MRI) scans wherein linear measurements of the longest diameter of a tumor (or sum from multiple tumors) are obtained to compute an objective endpoint.
Clinical Trial Data These studies had institutional review board approval, and informed consent was obtained from all patients. The University of Michigan QIN research effort relies on the use of image data obtained from several multicenter prospective clinical trials including a Cancer Research UK-sponsored trial (Neo COMICE) entitled "Establishing the Efficacy of Advanced Semi-automated Functional MR Imaging in the Early Prediction of Response of Locally Advanced Breast Cancer to NAC", the USA-based I-Spy 2 trial entitled "An adaptive breast cancer trial design in the setting of NAC", and SWOG-S0800 substudy trial focused on diffusion-weighted imaging (DWI) as well as data collected at the University of Michigan. The SWOG-S0800 trial enrolled women with Her-2 negative inflammatory breast carcinoma or Her-2 negative locally advanced breast carcinoma.
In addition, patients who were accrued as part of the SWOG-S0800 underwent test-retest examinations to provide image data required for evaluating the overall accuracy or variability of the PRM approach. Data presented in this current research effort were derived from the Neo COMICE and SWOG-S0800 trials. For the SWOG-S0800 trial, MR data was acquired on Achieva and Ingenia 3T Philips MRI systems using 7-channel and 16-channel dedicated breast coils. Nominal sequence parameters were as follows: three-axis, diffusion-weighted, single-shot single spin-echo, echo-planar imaging; TR = 4000 to 5000 ms; TE minimum = 55 to 80 ms; SENSE = 2 or 3; b values = 0, 100, 600 s/mm 2 ; bilateral axial scans with FOV of 35 to 40 cm adjusted to patient size; SPAIR fat suppression; slice thickness = 4 mm; acquisition matrix of 160 to 184; number of averages adjusted for a 4-to 5-minute scan.
The challenge with this scenario is that user-identified points also have a noise spectrum, sometimes large. However, such analysis can still yield valuable, quantifiable insight into the power of registration solutions  [47]. Shared human image data sets are also becoming more readily available with projects like the QIN. The emerging paradigm of shared research image databases provides a valuable paradigm for making test data accessible and sharing new computational approaches to problems. Overall, routine clinical use of the PRM biomarker response metric will likely require solid correlation with clinical outcome metrics such as histopathology for residual disease assessment. Change detection using PRM is quantitative and the level of variation that indicates change must be verified for each scenario, particularly when measuring interval change in the absence of personalized test-retest data.
The University of Michigan QIN research team has partnered with Imbio, LLC, a company that is focused on development and migration of PRM to a web-based platform to provide ease of access to automatic processing and report generation. The Imbio cloud-based approach will implement robust software tools with minimal user interaction and automated quality control to ensure fidelity of analysis. Development of this tool would make PRM accessible across multiple sites, thus providing a robust image registration algorithm for PRM analysis as a shareable tool to the overall research community.
Results Plant DNA and landscape changes. For the first run, which included nine samples, we obtained 3,151,601 reads that represented 68,137 unique sequences. After the different filtering steps, as described in the Methods, the number of sequences was reduced to 2,140,829 (428 unique sequences). For the second run, which included 35 samples, 5,575,081 reads (386,880 unique sequences) were obtained after high-throughput sequencing. This number was further reduced to 4,059,103 after filtering (909 unique sequences). Plant contaminations, mainly from a plant sequence from the Lamiales order (42% of total read numbers after filtering), were detected in the control samples of the second run. Overall, a large dataset of plant taxa is obtained. For this study, we focused on Pinus sp. and Alnus sp.
The resulting supernatant was recovered and used as starting material in the extraction steps that were carried out using a commercial kit (NucleoSpin Soil; Macherey-Nagel, Du ¨ren, Germany) by skipping the lysis step and following the manufacturer's instructions  16. The DNA extract was eluted in 100 ml of SE buffer. DNA amplification and high-throughput sequencing. We targeted two taxonomic groups as follows: mammals and plants. Mammalian DNA was amplified using a new primer pair (MamP007F, 5 0 -CGAGAAGACCCTATGGAGCT-3 0 ; MamP007R, 5 0 -CCGAGGTCRCCCCAACC-3 0 ), which led to the amplification of a 60-to 84-bp fragment of the mitochondrial 16S gene. These primers were designed using the ecoPrimers program  43  (http://www. grenoble. prabi. fr/trac/ecoPrimers). Plant DNA was amplified using universal primers (g, 5 0 -GGGCAATCCTGAG CCAA-3 0 ; and h, 5 0 -CCATTGAGTCTCTGCACCTATC-3 0 ) that targeted the P6 loop region of the chloroplast trnL (UAA) intron  44.
For both primer pairs, the PCR mixture was denatured at 95 °C for 10 min, followed by 45 cycles of 30 s at 95 °C and 30 s at 50 °C (no elongation step). The PCR products were then purified and mixed together (equivolume mixes) before sequencing, which was performed using an Illumina HiSeq 2000 platform (2 Â 100-bp, pairedend reads). Two different runs were performed as follows: the first run included 9 samples (called 'RTM') to test the method; and the second run included 35 samples (called 'ANT') to improve the resolution of our record. This second run also integrated the control samples. Sequence analysis and taxa assignment. Sequence data were analysed using the OBITOOLS software (http://www. grenoble. prabi. fr/trac/OBITools). First, the direct and reverse reads that corresponded to the same DNA fragment were aligned and merged using the SolexaPairEnd program, with the quality being taken into account when merging the two reads.
The ngsfilter program was then used to assign sequences to the relevant sample based on the associated primers and tags. Only sequences containing perfect tags and primers with a maximum of three errors were considered. We used the obiuniq program to merge strictly identical sequences and kept the information about the original samples they originated from. Then, the sequences were filtered using the obigrep program according to their length as follows: sequences shorter than 50 bp for the MamP007 and 10 bp for the gh primers were removed; and sequences that had an occurrence lower than 100 over the entire dataset were removed. Because we used short markers, clustering sequences at a high level of similarity, as is usually performed with this type of data  47  , was not suitable because a single difference in the nucleotide sequence often indicated different species.
The obiclean program  48  was developed to detect artifactual sequences that contained amplification and/or sequencing errors. For each PCR product, the program creates networks that connect sequences with one indel or substitution difference. Two sequences are linked if the occurrence of the less-frequent sequence is less than 20% of that of the other sequence. A status is then assigned to each sequence as follows: 'head' (most common sequence of a network), 'singleton' (sequence without a variant) or 'internal' (sequences that are neither 'head' nor 'singleton' and that most likely correspond to amplification or sequencing errors). Only sequences that were more often 'head' or 'singleton' than 'internal' in the global dataset were kept for the following steps. Finally, the sequences were assigned to the relevant taxa using the ecoTag program, which finds highly similar sequences in a suitable database (created by in silico PCR from Genbank with the ecoPCR program  49  ).
Each sequence was then assigned to unique taxa. If no complete match between a unique sequence in the database and the query sequence was found, the allotted taxa corresponded to the last common node in the NCBI taxonomic tree of all the taxids that annotated the sequences and matched the query sequence  50. Only sequences corresponding to the Viridiplantae phylum when using plant primers and to the Mammalia class when using the mammalian primers were saved for the following steps. Another filtering step was performed to disentangle sequences that were genuinely released by organisms from the lake catchment from potential sporadic contamination. A sequence was considered genuine (with a high confidence level) at a given time if it was found at least two times among the eight PCR replicates and with a minimum of five read numbers.
Figure3| Effects of pasturing activity and climate on erosion over 3,000 years. We present a comparison between data about human occupation and activities from the archaeological survey (a: red squares representing charcoal dating from the stone-dwelling remnants), lake sediment DNA (b: Bos sp. DNA, c: Ovis sp. DNA), the reconstruction of local erosion dynamic (d: Lake Anterne erosion indicator) and a proxy of climatic conditions in the western part of the Alps (e: Aletsch Glacier fluctuations 27  ). The question marks on the curve mean that the reconstruction of the position of the glacier terminal point is uncertain. Diamonds represent sediment samples that were taken in cores for metabarcoding analyis. Open diamonds show samples where no DNA sequences were found. Coloured diamonds show DNA sequences that were present in only one replicate.
Acknowledgements We thank the  French National Research Agency  for funding the fieldwork and sedimentological measurements through the  Pygmalion research program  ( ANR BLAN07-2_204489 ). Archaeological investigations were carried out, thanks to the  Pygmalion program  and grants from the  Haute Savoie Service of Archaeology  under the responsibility of Joe ¨l Serralongue, who we would also like to thank. The  Universitede Savoie  and  Joseph Fourier  as well as the CNRS jointly funded  C. Giguet-Covex 's salary and DNA measurements in the framework of the  DiPEE Chambery-Grenoble (Retromont program ) and the  MetaBar research program  ( ANR 11 BSV7 020 01 ). We would also like to thank  ASTERS , the manager of the  Haute Savoie natural reserves , for constant help since the onset of the palaeoenvironmental studies on Lake Anterne.
The currently existing mobile mapping systems equipped with active 3D sensors allow to acquire the environment with high sampling rates at high vehicle velocities. While providing an effective solution for environment sensing over large scale distances, such acquisition provides only a discrete representation of the geometry. Thus, a continuous map of the underlying surface must be built. Mobile acquisition introduces several constraints for the state-of-the-art surface reconstruction algorithms. Smoothing becomes a difficult task for recovering sharp depth features while avoiding mesh shrinkage. In addition, interpolation-based techniques are not suitable for noisy datasets acquired by Mobile Laser Scanning (MLS) systems. Furthermore, scalability is a major concern for enabling real-time rendering over large scale distances while preserving geometric details.