dataesr/scientific-paragraphs-categorization huggingface dataset:
CONCLUSION AND OUTLOOK The Uniclust databases provide functionally homogeneous clusters of sequences at three clustering depths (90%, 50% and 30% sequence identity), sets of representative sequences, MSAs of clusters, and annotations of all sequences with Pfam, SCOP, and PDB matches. The Uniclust and Uniboost MSAs are also offered as databases for HHblits, the most sensitive method for remote protein homology detection, and the provision of regular updates to these databases resolves a sore deficiency of HHblits, which was limited by very irregular and rare database updates. The MSAs in Uniboost might also prove to be a useful resource for (deep) machine-learning applications, which benefit from training on massive amounts of labeled and annotated sequence profiles. The clustering with our MMseqs2 software currently takes around five days on 10 × 16 cores, which is sustainable for the next five to ten years due to near-ideal scalability of MMseqs2.
(The S öding lab's cluster has 640 cores at this time. ) But we are also actively developing both MM-seqs2 and HHblits to achieve even higher speeds and sensitivities. We expect considerable improvements in the near future in the sensitivity with which we will detect and annotate structural domains in Uniclust/UniProtKB sequences using HHblits. Similarly, extending MMseqs2 to profileprofile searches will improve the sensitivity for building the Uniboost MSAs, which again will impact the sensitivity of the domain annotations. The Uniclust server facilitates profiting from the Uniclust databases and deep HHblits domain annotations. We hope that they will become a widely used resource for protein sequence analysis.
(24)aining Uniclust multiple sequence alignments for all clusters in a3m format, generated with Clustal Omega(24), and additional support files for use with legacy HH-suite version 2 and current version 3. • uniclust yyyy mm annotation. tar. gz: archive containing three files with Pfam, SCOP, and PDB annotations, each formatted as tab-separated lists with nine columns: (1,2) identifiers for query and target, (3-5, 6-8) domain start and end-position and total sequence length for both UniProt and database sequence, (9) HHblits E-value.
yyyy mm hhsuite. tar. gz: archive (24)aining Uniclust multiple sequence alignments for all clusters in a3m format, generated with Clustal Omega(24), and additional support files for use with legacy HH-suite version 2 and current version 3. • uniclust yyyy mm annotation. tar. gz: archive containing three files with Pfam, SCOP, and PDB annotations, each formatted as tab-separated lists with nine columns: (1,2) identifiers for query and target, (3-5, 6-8) domain start and end-position and total sequence length for both UniProt and database sequence, (9) HHblits E-value. • uniboost## yyyy mm. tar. gz: Uniboost database files in compressed A3M alignment format, with additional sup- port files for HH-suite version 3. • uniclust30.
Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels.
This is not the case for Pléiades-HR. Our experiments show the feasibility to compute shadow masks from PHR sequence but they also show that the algorithm is not stable. If we push the analysis further, we can see that the atmospheric corrections computed with our in-house method and applied to PHR sequences are not satisfying in the NIR channel. More precisely, the time series for the NIR reflectance does not appear to be stable, contrary to what we observe with the Landsat 8 NIR reflectance sequences. This may be due to the parameterization of the 6S software used in our work. In the future, we will use another tool, namely the optical calibration tool available in the Orfeo Tool Box 3  , to produce Pléiades-HR TOC images. The first tests carried out in that sense show real improvements.
However, lime treated soil external erosion resistance is still to be quantified in the field for proper designing and dimensioning of lime treated soil external erosion protection or spillways. With this purpose, an experimental earthfill dike has been built along the river Vidourle (France) in July 2015, in the frame of the French R&D program "DigueELITE". This 50 m long and 3,5 m high dike is made of lime treated silt and is provided with sensors (succion, water content and temperature) and piezometer in order to be monitored. It will also be tested against surface erosion. The final objective of this R&D program is to provide guidelines for designing innovative overflow resistant earthfill dikes. This article describes the performance reached by lime treated soils and associated design requirements and application; the experimental dike construction and lessons learned; the monitoring program; the dike design perspectives opened by soil treatment.
Important works in laboratory and full-scale, were used to quantify the performance of lime treated soils in hydraulic structures such as stability, watertightness, internal erosion  [10, 11, 12]. These results are taken into account by the CMD Committee (P) of ICOLD in the ongoing drafting of a bulletin dedicated to Cemented Soil Dams (CSD). DOI: 10. 1051/ , 6 E3S Web of Conferences e3sconf/201 FLOODrisk 2016 -3 rd European Conference on Flood Risk Management However, the resistance to external erosion is still to be quantified in the field for the proper design of protection against surface erosion. For this purpose, an experimental earthfill dike made of lime treated silt has been built in July 2015 along the river Vidourle (France) in Aimargues, in the frame of the French R&D program "DigueELITE".
Therefore an experimental dike has been built within the French R&D program DigueELITE about lime treated soils in hydraulic works. This dike will be tested against external erosion by applying steady artificial overflow. The experimental device itself is innovative and being developed within the DigueELITE program. This experiment is also the opportunity to validate the construction methodology of lime treated earthfill for hydraulic works, which differs from usual methodology applied in soil treatment for other infrastructure. Methodology applied is described below. The experimental dike will furthermore enrich the lime treated soil characteristic database with visual follow-up, permeability and internal erosion tests,… The experimental dike is integrated in the rehabilitation works of the dike network along the river Vidourle.
Ni and coworkers  [25]  describe a novel application to identify carbonylated lipid peroxidation products, which may be useful as biomarkers of a range of diseases including atherosclerosis, fatty liver disease, and reperfusion injury, using LipidXplorer software. One problem with new bioinformatic tools that limits their uptake is that they may be written in a variety of languages. Giacomoni and coworkers  [26]  borrow an approach from genomics to develop a web-based workflow for LC-MS-based metabolomics that links software from a variety of sources. Workflow4Metabolomics provides a virtual research environment built upon the Galaxy environment  [27]  linking together open source tools that allow users to completely process their data without extensive experience of the software or tools.
The outputs produced by the generation task are: • Test suite definition (TSD) -the test suite definition is an XML document that represents the abstract interaction paths that are the results of the symbolic execution of the collection of PSMs, starting from first test input classes. • Test suite(s) (TSs) -a test suite is a collection of test cases; each test case is a partially ordered collection of instantiated messages (input, oracles) that instantiates a TSD interaction path. The preprocessing phase performs consistency checking across all input artifacts, binds the service and mock PSMs through wires and build a parallel state machine which combines all individual PSMs. The test case generation leverages model checking techniques provided by the TLA+ framework  [12]  that "implements" the well-known TLA formal specification language based on temporal logic.
First, we translate the PSMs into the PlusCal, a TLA+ companion algorithm language supported by the TLA+ framework (Translation activity in Figure  1 ). The model obtained in PlusCal is then compiled into the TLA+ core language (Compilation activity in Figure  1 ). TLA+ is backed by the TLC model checker to exhaustively check correctness properties across all possible executions of the system and by the TLAPS proof system that relies on SMT (Satisfiability-Modulo Theory) solvers for checking TLA+ proofs. Through assertions, execution traces that match some criteria -for instance where messages of some specific types, or containing some specific values, are exchanged -are requested to the proof system (Model checking activity in Figure  1 ). Hence, the TLC model checker achieves the generation of the execution traces by temporal logic constraint propagation.
710 The next morning we arrived at the border post at 7:30. Navigli 2007), hereafter LEXSUB. Ten sentences for the target lemma post. n 3 are shown in Table  1 , with the corresponding sentence ids (s#) in the LEXSUB data set and the target token underlined. In LEXSUB, human annotators saw a target lemma in a given sentence context and were asked to provide one or more substitutes for the lemma in that context. There were 10 instances for each lemma, and the lemmas were manually selected by the task organizers. The cross-lingual lexical substitution task  (Mihalcea, Sinha, and McCarthy 2010)  (CLLS) is similar, except that whereas in LEXSUB both the original sentence and the substitutes were in English, CLLS used Spanish substitutes. For both tasks, multiple annotators provided substitutes for each target instance.
In comparing Umid versus Uiaa, we see that Umid seems to be generally easier to predict, as it has more models with a significant F test. Comparing the CLLS and LEXSUB substitutions, we see that the use of LEXSUB leads to much better predictions than CLLS. Most strikingly, in predicting Uiaa no model achieves significance using CLLS. We have commented on this issue before: The reason for this effect is that many lemmas in CLLS have only one component and are therefore excluded from the intra-clust clusterability estimation. Clusterability in practice. As this round of experiments used the raw clusterability figures to predict partitionability, rather than their rank, it points the way to using clusterability in practice: Given a lemma, collect instance data (for example paraphrases, translations, or vectors).
The intra-clust metrics are calculated for both LEXSUB and CLLS independently whereas the inter-clust metrics (pF and V) compare the two independent clustering solutions with each other. fire. v is more clusterable as can be seen by the clusters over the LEXSUB and CLLS data (Table  11 ), which denote a clear sense distinction, and by the Uiaa and Umid from the Usim gold standard. The measures WPR, VR, V, and pF are all higher for the more clusterable fire. v compared with solid. a, whereas SEP is lower as anticipated. The two lemmas were selected as examples with the same number of COMPS to allow for a comparison of the values. The overlap measure nc s is higher for solid. a as anticipated. 26 ote that for the highly clusterable lemma fire. v there are no substitutes in common in the two groupings with either the LEXSUB or CLLS data because there is no substitute overlap in the sentences, which results in the COMPS and CLIQUES solutions being equivalent, whereas for solid.
This may be due to their reliance on CLLS, which generally produces weaker results compared to LEXSUB. Our baseline, which measures the amount of overlap in overlapping clustering solutions, shows consistently weaker performance than the intra-clust measures. A variant of the inter-clust measures we would like to explore is a comparison of results from different clustering algorithms. Because more clusterable data is computationally easier to cluster  (Ackerman and Ben-David 2009a) , we assume more clusterable data should produce closer results across different algorithms operating on the same input data. We plan to test this empirically in future. Clusterability metrics should be useful in planning annotation projects (and estimating their costs) as well as for determining the appropriate lexical representation for a lemma.
The corresponding extension of the Python interface of NEST (PyNEST) allows the user to set up their own structural plasticity experiments for large scale networks. The rest of this work is divided into three major parts. The first describes the major elements of the structural plasticity algorithm and the set of tests that were designed in order to measure the performance of an implementation of this algorithm. We also present some use cases for the structural plasticity framework. In the second part we provide the results of the technical implementation in NEST and describe how the design matches the memory and speed requirements for large scale simulations. We also present results for the use cases described in the previous section. In the third part, we discuss the results of the implementation and performance tests.
Only physical cores were used, no simultaneous multithreading was enabled. A hybrid optimization approach was chosen, in which MPI is used for communication between nodes and OpenMP for intra node communication. All measurements were performed on the JUROPATEST cluster, which provides up to 70 nodes (T-Platforms V210s Blades), each with 2 × Intel(R) Xeon(R) CPU E5 -2695 v3 (Haswell) with 14-core processors (2. 30 GHz) and 128 GB DDR memory, running with Scientific Linux release 6. 5 (Carbon).
Here we used the original model of  Potjans and Diesmann (2014)  and enable the structural plasticity after an initial stabilization period of 30 s. Simulations were performed on JUROPATEST (70 nodes with 2 × 14-core processors Intel(R) Xeon(R) CPU E5 -2695 v3 (Haswell) at 2. 30 GHz and 128 GB DDR memory, running with Scientific Linux release 6. 5) and JURECA (with 260 compute nodes with Intel Xeon E5 -2680 v3 Haswell CPUs with 2 × 12 cores per CPU, 128 GB of RAM per node and running on CentOS 7 Linux distribution).
Implementation of the Structural Plasticity Model into NEST The implementation of the structural plasticity algorithm described in this work is based on the version 2. 8 of the NEST software  Eppler et al. (2015). In accordance with the original formalization described in Section 2. 1, the algorithm consists of three repeating parts which can be visualized in a general form in Figure  2  and described as follows: In (1) the number of synaptic elements is calculated depending on the electrical activity of the neuron. These calculations are optimized using MPI and OpenMP. In (2) the structural plasticity manager gathers the number of synaptic elements per neuron using MPI directives and in (3) creates or deletes synapses to update the connections between neurons using MPI and OpenMP. 1. Update in electrical activity and intracellular calcium concentration.
The framework can be further extended by the user to incorporate more complex element growth dynamics models. An example of such curves is shown in Figure  1 , where independent dynamics for each type of element in a network of 8 populations (see use case on cortical microcircuit) have been defined. Synaptic elements are used as a discrete value, the actual number of available synaptic elements is an integer truncated from the float variable used to represent them. The Archiving_Node class now incorporates a map data structure to store the synaptic elements. The method Archiving_Node::update_synaptic_element takes care of updating the number of each of the synaptic elements in the map using the value of the calcium concentration at the time of the call and the corresponding growth curve. 3.
The number of available synaptic elements is transmitted globaly and the synaptic plasticity manager takes care of forming new synapses or deleting existing ones based on this information. The update of electrical activity and of the number of synaptic elements is performed by every individual neuron and therefore benefits from the parallel framework already implemented in NEST. Indeed, the NEST software has already demonstrated its high scaling properties on supercomputer, including the JUQUEEN system  (Helias et al. , 2012; Kunkel et al. , 2014). Finally, the Python interface of NEST (PyNEST) was extended to allow users to easily set up the structural plasticity parameters. It is important to highlight that the user can enable structural plasticity inside the simulation and then disable it when the network has achieved a desired connectivity pattern or activity level.
The annotation of genomes from NGS platforms needs to be automated and fully integrated. However, maintaining consistency and accuracy in genome annotation is a challenging problem because millions of protein database entries are not assigned reliable functions. This shortcoming limits the knowledge that can be extracted from genomes and metabolic models. Launched in 2005, the MicroScope platform (http://www. genoscope. cns. fr/agc/microscope) is an integrative resource that supports systematic and efficient revision of microbial genome annotation, data management and comparative analysis. Effective comparative analysis requires a consistent and complete view of biological data, and therefore, support for reviewing the quality of functional annotation is critical. MicroScope allows users to analyze microbial (meta)genomes together with post-genomic experiment results if any (i.
INTRODUCTION In the era of high-throughput sequencing technologies, deciphering genome sequence of an organism or an environmental sample that contains multiple organisms is a routine task. Indeed, genomics is entering in the 'Big data' era  (1) , and understanding the biological meanings encoded in genomes remains a challenging task. To address the need for 'quick' microbial genome annotation, several automatic pipelines have been developed. These include the RAST  (2)  and PATRIC  (3)  web servers that provide genome annotation services in under a day, or the command line software Prokka (4) that produces similar results in a few hours and which is useful when throughput and privacy issues are critical. For metagenome analysis, MG-RAST  (5)  and EBI metagenomics  (6)  offer similar services on shotgun sequencing reads.
Last year, the NCBI has released a new version of their Prokaryotic Genome Annotation Pipeline capable of analyzing more than 2000 prokaryotic genomes per day  (7). Today, the vast majority of genome sequences receives only fully-automatic annotation, mainly based on sequence similarity, i. e. a process which can lead to the introduction and propagation of poor annotation and errors  (8). Resources such as HAMAP  (9)  and NCBI RefSeq  (10)  have been developed with the aim of gradually increasing the quality and completeness of microbial genome annotation. However, curation efforts remain restricted to large and widespread protein families and these resources can-D518 Nucleic Acids Research, 2017, Vol. 45, Database issue not replace expert curations by specialist biologists. Thus, several systems have been developed to generate automatic annotations and provide graphical facilities for subsequent manual review of the predictions (i.
e. only part of the >70 000 bacterial genomes available in GenBank/EMBL databanks today are integrated into the MicroScope platform). For more than 10 years, several analysis tools and functionalities have been developed to ease the automatic and expert annotation process  (20)  and to systematically reconstruct and curate complete metabolic networks from genome annotations  (21). After a short overview of the Mi-croScope system, this paper focuses on some major novelties since 2013: improvements of the Web interface for the submission of genomic data and integration of original tools and pipelines for the computation of pan-genomes and the prediction of biosynthetic gene clusters. Statistics about the MicroScope database growth content and the Mi-croScope users are also presented together with some selected use cases leading to information sharing and publications.
Knowledge database: PkGDB (Prokaryotic Genome DataBase) The results of these analysis tools, together with the primary data used as inputs, are stored in specific relational tables within PkGDB: a database based on the open source MySQL relational database management system. The PkGDB architecture supports integration of automatic and human-curated functional annotations and records a history of all the modifications. Technical improvements were made to reduce the PkGDB loading time of new results generated by the various pipelines. The InnoDB table engine is now used to directly insert new data in production, minimizing performance and concurrency issues. This improvement has resulted in a decrease of genome integration time in MicroScope (by a factor of 2 to 10 depending on the size of the table to modify).
Indeed, inserts were previously made by a costly table copy/modification/reindexation procedure on a dedicated server. Metabolic pathway predictions of the MicroScope genomes are stored in the MicroCyc resource (http://www. genoscope. cns. fr/agc/microcyc) which gathers instances of Pathway Genome DataBases (PGDBs) of the BioCyc system  (23). The MicroCyc web server is connected to the MaGe graphical interfaces (Figure  1 ). To ease the comparison of metabolic networks from several organisms, relational tables have been designed in PkGDB to store information of these MicroCyc PGDBs, together with the KEGG metabolic pathways and modules  (24). The size of PkGDB is today 500 GB for databanks and genome data, and 22TB for the computational results. The input (meta-)genome sequences or short reads (DNA-seq or RNA-seq) are submitted via a dedicated Web interface and subsequently processed through several automatic analysis pipelines made up of homemade and third party computational tools.
Those latter are organized in a workflow management system including syntactic, functional and relational annotation pipelines, analysis of gene expression based on RNA-seq data and Variant Discovery relying on DNA-seq data. The results of these automatic workflows are stored and organized in various interconnected relational databases. The central database, called PkGDB (Prokaryotic Genome DataBase), stores all the results from the genome annotation pipelines while MicroCyc and UNSEEN (Unified Nextgen SEquENcing data knowledge base) store genome-scale metabolic networks that have been reconstructed using the PathoLogic algorithm and the results from the processing of short read data, respectively. MicroScope users can browse sequences, annotations and analysis results through dedicated Web graphical interfaces directly connected to the databases.