dataesr/scientific-paragraphs-categorization huggingface dataset:
Estimating residue conservation -variability scores BALCONY is the R package that calculates entropy scores for protein conservation studies. The following different metrics are available: 1. Landgraf -a metric applying the evolutionary information from substitution matrices  [12] ; This score allows to weigh sequences and uses Gonnet substitution matrix, which provides asymmetrical values for residue substitutions. 2. Cumulative relative entropy -this metric takes into account the evolutionary relationship between sequences measured by pairwise distances  [13]. 3. Real-valued Evolutionary Trace (RET)the score which combines the evolutionary trace score and entropy measure  [14]. This score cumulates information about variability in subgroups created by dividing the evolutionary tree of sequences present in MSA.
Our normalized metric (E score ) is calculated with equations: P i ¼ max p i ð Þ n i P norm i ¼ P i max P ð Þ E score ¼ -ln P norm i À Á max -ln P norm i À Á À Á where: P i = amino acids frequency on i th position where gaps are included; n i = amino acids count on i th position where gaps are excluded. For more information about properties of scores and their comparison see  [14, [18] [19] [20]. Other measures, if available in R environment, (for example one available in package 'entropy'  [8] ) can be easily incorporated into BALCONY workflow. Users can use variability/conservation/entropy data of their choice and run BALCONY functions to process it. Analysis with BALCONY can be performed in standard or group mode. In the latter case, additional information about particular amino acid's properties (i.
Other types of analyses, like identification of functional peptides, are better suited for the non-entropy metrics  [21]. The BALCONY package implements different variability scores to satisfy multiple purposes of residues variability studies. In fact, an MSA should be seen as a sample (often not perfect and reliable) of related sequences that aims at estimation of the actual composition of amino acids at any given peptide site. Notably, as a substantial basis for the downstream analyses the MSA reliability is of particular concern. In order to avoid taxonomic bias, which may strongly influence results, BALCONY allows to use sequence weighting in most of the implemented variability scores. Weighting is available in all methods using Henikoff and Henikoff position based method  [22]  but cumulative relative entropy.
Use case To provide an example of BALCONY functionality we present a short, yet informative study. The data used to perform the analysis presented below regarding both MSA of homologous sequences to human soluble epoxide hydrolase and example of structural data are provided with the package as R dataset. We focus on the analysis of amino acids building tunnels in human soluble epoxide hydrolase (sEH). Information about tunnels and tunnel lining residues was obtained with CAVER 3. 02 software  [25]. Tunnels were calculated against 50 ns MD simulation, appropriate protein structure was obtained from PDB (PDB ID 4JNC)  [26]. The alignment was prepared from sequences available in the UniProt database  [27]. The technical details concerning analysis are available in BALCONY manual available in Additional file 2; MSA and tunnels data are included in the BALCONY package.
The alignment data in FASTA format was used in the downstream analysis. All protein isoforms in the set of aligned sequences were deleted. The threshold for the consensus calculation was set in order to select the most frequent amino acids at position but with minimum frequency over 30%, as a common rule is that sequences are homologous if they are more than 30% identical. UniProt id for 4JNC was combined with that of PDB id in order to create a mapping library. Then the consensus sequence was calculated. Amino acids classified in MSA as outliers, whose percentage of similarity between consensus and aligned sequence was small, were excluded from the analysis. In order to apply the representative group of amino acids, the consensus sequence similarity for grouped alignment was calculated. For each amino acid and grouped amino acid variation for each aligned position was calculated.
The reference sequence was found from the previously created mapping library. This approach allows for convenient analysis of different protein variants without interfering with the MSA. The entropy of each of the multiple sequence aligned position was calculated. Among all available scores in the R BALCONY package (Kabat, Shannon, Schneider, Landgraf, E score , Cumulative Relative Entropy, Real-valued Evolutionary Trace) were applied for comparison of their characteristics. It is due to the user which entropy score is applied for the analysis, as there is no rigorous mathematical test for judging a conservation measure. Choice of the proper conservation score may be based on the following criteria: mathematical properties, amino acid frequency, stereochemical properties, number of gaps, sequence weighting  [15].
Conclusions BALCONY facilitates the reading, processing and visualization of evolutionary conservation data originating from an MSA. Importantly, BALCONY can be easily integrated with other R packages that provide a coherent data flow in downstream and upstream analyses of biological sequences. The distinctive feature of the package is the convenience in which dispersed residues in the protein sequence can be studied. Some regions of protein structure require particular investigation in terms of evolutionary variability. They might be interesting from the viewpoint of enzyme design, providing information about the evolution of residues important for protein properties (e. g. thermostability, selectivity, activity) and suggesting hotspots for amino acids substitutions. Not only the hotspots for mutagenesis are obvious, but also substitutions that enhance, degrade, or are potentially neutral to particular features.
It is worth adding that the input file facilitates linkage of calculated entropy/variability scores to other features of analysed amino acids. User has absolute freedom with decision of the source of the additional data, it can be taken from other bioinformatics tools, computational tools, but also can be taken from crystal structures information (e. g. βfactors), other experimental measurements. To provide an example of BALCONY usage, we have performed an evolutionary analysis of residues building the main tunnel of human soluble epoxide hydrolase. As the additional feature, the occurrence of particular event during molecular dynamic simulations was used. The example is available in the BALCONY manual file, together with a detailed installation guide and package description.
Time-aware NED To test the importance of time-awareness for NED, we use two settings. We enhance a basic NED system and a state-of-the-art system by enriching both with temporal signatures and contexts. diaNED-1, as basic NED system, uses a mention-entity prior reflecting entity prominence and a keyphrase-based language model for the similarity of mention and entity contexts (as suggested by  Hoffart et al. (2011) ). These components are cast into edge weights for a graph over which the final disambiguation is computed. Hyper-parameters for the relative influence of the two components are tuned using an SVM. We added the temporal dimension to the feature set and retrained the model accordingly to get new feature weights. diaNED-2 based on  Yamada et al. (2016) : This is a learning-to-rank-based model.
A Diachronic NED Data Set Datasets for NED evaluation contain articles published within a short period. Consequently, all mentions share a temporal context making it difficult to evaluate temporal variability. CoNLL-AIDA  (Hoffart et al. , 2011)  are newswire articles from 1996, TAC 2010 (Ji et al. , 2010) news and forum articles from  2004 -2007 , and Microposts-2014 (Cano et al. , 2014)  tweets from 2011. To account for this limitation, we create a new diachronic benchmark containing documents with heterogeneous temporal context. As in Microposts-2014, we limit documents to single sentences and headlines from HistoryNet. com (HN) and The New York Times corpus (NYT). For the annotation process, we followed the entity annotation guidelines, which have been used for annotating CoNLL-AIDA  (Hoffart et al.
, 2011). HN is an online resource of world history with information on popular historical topics. Its section Today in History contains short texts on what happened on a specific day with a total of 7,061 facts/events (excluding born today). Using Stanford NER  (Finkel et al. , 2005) , we extracted 13,773 entity mentions and randomly selected 350 of them. We annotated all entity mentions in respective sentences with their Wikipedia ids. After removing NER errors and out-of-KB entities, the dataset contains 865 gold entity mentions in 334 sentences. Examples are: "Conrad II claims the throne in France" from 1032 or "The Old Pretender, son of James III dies" from 1766. NYT contains more than 1. 5 million documents published between 1987 and 2007. After apply-ing the same procedure, the dataset contains 368 manually annotated mentions in 290 news headlines.
Evaluation To evaluate the importance of temporal information in NED, we focus in our analysis on the newly created diaNED corpus. As standard NED datasets CoNLL-AIDA and TAC 2010 contain only articles published within a short period of time, they are not suited for evaluating timeaware NED (cf. Section 4), and experiments on these datasets showed no significant differences between using diaNED-1 and diaNED-2 with or without their time-awareness features. Note that the temporal contexts in the HN sentences and the NYT headlines of the diaNED corpus are part of the metadata. Thus, to ensure a fair comparison among all systems, we added the temporal contexts in the form of year information to all documents to allow the non-time-aware systems to exploit the temporal context in case the respective year number occurs as part of the entities' textual context.
Conclusions and Ongoing Work We proposed the first NED method with explicit consideration of temporal background. As demon-strated in our experiments, this time-awareness improves NED quality over diachronic texts that span long time periods. The diaNED dataset and the temporal signatures of entities are publicly available. 5 urrently, we integrate a strategy for handling out-of-KB entities to determine how temporal affinity may help in the nil detection problem. Furthermore, we plan large-scale experiments with distant supervision data which will also allow to evaluate the effectiveness of considering temporal expressions in the context of the entity mentions as further temporal context information. Finally, using a multilingual temporal tagger  (Strötgen and Gertz, 2015) , the value of time for NED could be studied for further languages.
Introduction The recent introduction of leadless pacing technologies in real-world practice has been successful, demonstrating a high procedural success rate (99. 6%) with a low rate of major complications (1. 51%) through 30 days postimplant, and reaffirming the positive results observed in the investigational trial. 1, 2 Existing leadless pacing systems, however, are designed as single-chamber pacemakers, limiting their potential use to 14%-32% of all pacemaker implantations. 3, 4 ] [7]  Micra TPS (Medtronic, Minneapolis, MN) is a transcatheter pacing system that is directly implanted in the right ventricle and provides rate response via a 3-axis accelerometer (ACC). Custom software was developed to detect atrial contraction using the ACC, enabling AV synchronous pacing in this single-chamber ventricular leadless pacemaker.
The detailed study design and primary results have been previously reported. 8 e report the case of an 86-year-old man who was implanted with a Micra TPS after repeated episodes of complete heart block and who was included in the MARVEL study. The patient had no relevant medical history except frequent episodes of paroxysmal atrial fibrillation (PAF). A baseline 12-lead electrocardiogram immediately prior to AV algorithm software download showed SR at 38 beats/ min with first-degree AV block and complete right bundle branch block. During data collection, the patient developed an episode of PAF. We report the behavior of the VDD pacing system during SR and PAF. In SR, all the ACC signals were recognized, and the algorithm appropriately tracked the atrial contraction (Figure  1A ). The investigational protocol included a 30-minute resting period where AV synchrony was assessed.
Accelerometer; Atrial contraction; AV block; AV synchronous pacing; Leadless pacemaker; Paroxysmal atrial fibrillation (Heart Rhythm Case Reports 2018;4:561-563)Dr Garweg proctors for Medtronic. Mr Sheldon is an employee and shareholder of Medtronic. Dr Chinitz receives compensation for services from Abbott, Biosense Webster, Pfizer, Biotronik, and Medtronic; and receives fellowship support from Biotronik, Boston Scientific, and Medtronic. Dr Ritter receives compensation for services from Medtronic. Dr Steinwender receives compensation for services from Biotronik and Medtronic; and is on the speaker's bureau for Abbott, Biotronik, Boston Scientific, and Medtronic. The MARVEL study was funded by Medtronic (ClinicalTrials. gov number: NCT03157297). Address reprint requests and correspondence: Dr Christophe Garweg, Department of Cardiovascular Sciences, Herestraat 49, 3000 Leuven, Belgium.
KEYWORDS Accelerometer; Atrial contraction; AV block; AV synchronous pacing; Leadless pacemaker; Paroxysmal atrial fibrillation (Heart Rhythm Case Reports 2018;4:561-563)Dr Garweg proctors for Medtronic. Mr Sheldon is an employee and shareholder of Medtronic. Dr Chinitz receives compensation for services from Abbott, Biosense Webster, Pfizer, Biotronik, and Medtronic; and receives fellowship support from Biotronik, Boston Scientific, and Medtronic. Dr Ritter receives compensation for services from Medtronic. Dr Steinwender receives compensation for services from Biotronik and Medtronic; and is on the speaker's bureau for Abbott, Biotronik, Boston Scientific, and Medtronic. The MARVEL study was funded by Medtronic (ClinicalTrials. gov number: NCT03157297). Address reprint requests and correspondence: Dr Christophe Garweg, Department of Cardiovascular Sciences, Herestraat 49, 3000 Leuven, Belgium.
Developing effective strategies for reducing the atmospheric pollutant concentrations below regulatory threshold levels requires identifying the main origins/sources of air pollution. This can be achieved by implementing so called source apportionment methods in atmospheric dispersion models. This study presents the results of a source apportionment module implemented in the SIRANE urban air-quality model. This module uses the tagged species approach and includes two methods, named SA-NO and SA-NOX, in order to evaluate the sources' contributions to the NO 2 concentrations in air. We also present results of a data assimilation method, named SALS, that uses the source apportionment estimates to improve the accuracy of the SIRANE model results. The source apportionment module and the assimilation method have been tested on a real case study (the urban agglomeration of Lyon, France, for the year 2008) focusing on the NO 2 emissions and concentrations.
Here, we present the results of a data assimilation method named Source Apportionment Least Square (SALS), developed for the SIRANE urban air quality model  [69, 70]. In what follows, we first present the SIRANE model and the chemical scheme implemented in it (Sections 2. 1 and 2. 2). Secondly, we introduce the principles of the source apportionment modules and the data assimilation technique adopted (Section 2. 3). Finally, we show the results of a real case study (Section 3), the urban Lyon agglomeration, for the year 2008.
Source Apportionment Module When activating the source apportionment module in the SIRANE model, the sources emit both classical species (e. g. , NO) and tagged species (e. g. , NO g for the source g). In simulating their dispersion in the atmosphere, as a first step, these are both treated as inert species. The role of chemical reactions is then taken into account in a second step of the simulation. The concentration of the species s is denoted as c d s at the end of the dispersion phase and as c s after the chemical reactions phase. Similarly, the concentration of the species s tagged from the source g is denoted c d s,g , after the dispersion phase, and c s,g , at the end of the chemical reactions.
Case Study-The Lyon Urban Agglomeration We present an application of the source apportionment module (with the SA-NO and the SA-NOX models) to evaluate the source contribution to NO 2 concentration on the Lyon urban agglomeration (approximately 1. 4 million people) , for the year 2008. The case study is the same as that used for an extensive analysis of the performances of the SIRANE model, recently presented by Soulhac et al. [72]. We consider the contributions by three typologies of pollutant sources, namely (i) traffic, (ii) industrial sources, and (iii) miscellaneous distributed sources (all other sources not included in the two previous categories, mainly domestic heating), as well as that due to the background concentration (Figure  1a ), i. e. , related to all sources that are placed outside the studied domain.
Conclusions A source apportionment module, using the tagged species approach, has been developed for the SIRANE model in order to estimate the contribution to air pollution of different typologies of pollutant sources. This module includes two methods, named SA-NO and SA-NOX, to evaluate the sources' contributions to NO 2 concentrations. This module has been applied to evaluate the traffic, miscellaneous distributed sources including residential-tertiary sector, industrial sources and background concentration contribution to NO 2 concentration on the Lyon agglomeration in 2008. Overall, the NO 2 contributions evaluated with the SA-NO and SA-NOX models are similar. The contribution of the industrial sources to the NO 2 annual mean concentrations on the Lyon agglomeration is negligible compared to the other emissions' sectors.
Management of increasingly complex, multi-vector, district energy systems, operated by separate stakeholders, including prosumers, is a vital challenge to overcome in a fragmented energy landscape. The complex value chain involved forms a cognitive virtual network with the shared objective to reduce energy consumption, greenhouse gas emissions and maximise human comfort. This paper will aim to illustrate the PENTAGON platform for integrated management of key stakeholder data to produce automatic, holistic and pre-emptive decisions that ensure near-optimal management of a district energy system. The PENTAGON platform architecture consists of five key modules including Smart Connector to interface with existing District Energy Management Systems (DEMS), a time series database, a prediction module, a multi-vector optimisation module and a module ensuring the electric grid stability.
This has enabled the development of complex collaborative systems inspired by human cognition, such as artificial neural networks, fuzzy logic, semantic web technologies, etc. Our buildings and energy systems are equipped with a large number of heterogeneous devices, which mostly come from different vendors and use different communication protocols. To provide holistic management of energy systems, that are capable of tackling underlying heterogeneity, classification and description of different information within the decentralised energy systems and consumers are needed  [3]. Semantic web technologies can improve the interoperability while reducing heterogeneity. These technologies (e. g. , ontologies) can facilitate rapid exploration of information, i. e. , it becomes easier to retrieve, correlate and integrate data by knowledge discovery, associating meaning to data and providing interrelationships between modelled entities.
The proposed energy platform leverages semantic modelling to allow interoperability of different data sources and provides an additional level of robustness to energy management of multi-vector systems. The rest of the paper is organised as follow. Section 2 details energy management platform along with its components; Smart Connector, prediction module, multi-vector optimiser and Smart Operation. In Section 3, we describe ICT architecture and interaction between different modules. Pilot site implementations are discussed in Section 4 and Conclusions are presented at the end of the paper.
Definition of the Platform • Smart Connector Smart Connector acts as a translator, and collects data at every time step from the DEMS and organises it in a predefined structure. This data is then read and treated by other PENTAGON components. The Smart Connector also translates the optimal control actions (i. e. , set-points calculated by the multi-vector optimiser) so that they can be interpreted and applied by the DEMS. The Multi-vector optimiser will always send an optimised solution over the next 24 hours, however the Smart Connector will only communicate the next time step (15 minutes) of the solution and will store the remaining 24h solutions until the new control actions are available. • Prediction module Predictive analytics play an important role in the management of multi-vector energy systems and serve as a core component of smart thermal and electrical grids.
The PENTAGON prediction module will manage the prediction models and will forecast different variables. The variables of interest will include thermal and electrical energy demands and energy production from renewable energy sources. To train prediction models, different input features (e. g. , outdoor weather conditions, time-related information, building occupancy, etc. ) are tested to improve their performance. • Multi-vector optimiser Holistic management of decentralised district energy systems is the key to their success. Multi-vector energy networks (e. g. , heat, gas and electricity) that were previously independently controlled are now required to be managed and controlled in an integrated manner as they have become more coupled (e. g. output of one systems is the input of second system).
The PENTAGON multi-vector optimiser is responsible for computing control actions and set-points to minimise a defined objective function (e. g. minimise energy consumption/cost, increase occupants' comfort, etc. ). The optimiser will exploit available flexibility within the district while satisfying the energy demand of the buildings. The optimiser will also consider the specific characteristics of conversion technologies to minimise the overall energy cost of the district while satisfying the systems' and networks' constraints. Internal simplified models will be used to effectively evaluate potential optimal solutions, dispatch, and define operational constraints. The computed power flow and set-point values will then be send to the Smart Operation and District Sever. • Smart Operation Smart Operation is a proprietary software tool from Tractebel that is used for the optimisation of an electrical distribution network in the presence of distributed energy resources (DERs) such as photovoltaic (PV) systems, wind turbines, electric vehicles (EVs), battery storage systems, etc.
Communication between ICT Components In the context of utility control infrastructures, European standardisation bodies recommend service-oriented architecture (SOA)  [4] , to facilitate the integration of: • data services; • functional logic services; • and business logic services. In an SOA, software functionalities are packaged in autonomous self-contained modules that operate independently from each other's state and context  [5]. The PENTAGON model component uses the interface provided by the district server to fetch the data measured in the district that are required for prediction. SOAs allow for complex yet flexible systems, individual components can evolve independently, assuming changes in interface specifications are kept to a minimum. The most common technique to implement interoperable SOA are web services.