dataesr/scientific-paragraphs-categorization huggingface dataset:
The presence of degeneration or necrosis may alter the microstructure of perilesional tissue, leading to problems in the estimation of fiber orientations, especially in areas with presence of crossing fibers, which can, in turn, induce artefacts in track reconstructions  (Ciccarelli et al. , 2006; Schonberg et al. , 2006; Møller et al. , 2007; Dell'Acqua, et al. , 2010). The use of light anesthesia to avoid movements during the MRI acquisition may have influenced the functional activations as the rate of failure can be as high as 21% with propofol and an auditory task  (Bernal et al. , 2012 ; note, however, that we were able to reconstruct canonical ICA networks for each patient; Fig. 1B ). The DWI data from the NIH database were acquired with different scanning parameters which may impact the comparison with the data obtained in our group patients.
In summary, we report the first 3D reconstructions of the dorsal and ventral language pathways together with fine-grained assessments of cognitive and language functions in a homogeneous group of four-year-old children who had a left PAIS. We provide evidence for an association between the structural and functional (although to a lesser extent) brain reorganization patterns toward the right hemisphere and language production in pre-school children with congenital lesions. We speculate that interhemispheric plasticity through structural and functional hyperconnectivity mechanisms might be crucial for positive outcomes following early lesions.
Dual Simulation Optimization: Case Study To address the case study we use the specic framework shown in Figure  3  derived from the generic one shown in Figure  1. This framework integrates simulation with optimization, where a DES model interfaces with an inner BIP optimizer, and where an outer ACO optimizer evaluates dierent scenarios, with each ant representing weights (i. e. , scenario) for each objective that is simulated. The BIP model is described in Section 5. 2 while the ACO algorithm is described in Section 5. 3. In this case study, simulation is used to predict the system status (i. e. , variables such as WIP distribu-Figure  3 : DualSimOpt for case study. tion and throughput) at given points in time when operator assignment is required. Operator assignment is performed with a multi-objective BIP model which is called by the simulation model and obtaining from it all updated data required to make assignment decisions.
Simulation Model The simulation model of the assembly line can be used to eciently solve the operator assignment problem and assist the line supervisor in making re-assignment decisions that can minimize the impact of operators or machines unavailability on productivity and maximize throughput. ManPy was used to model the assembly line  (Dagkakis et al. , 2015). ManPy is an Open Source (OS) simulation library (http: //www. manpy-simulation. org/) written in Python, that uses the SimPy software (http://simpy. readthedocs. org/) to implement a process-based simulation worldview. The simulation model of the case study is presented in  Dagkakis et al. (2014). As the simulation model is deterministic replications are not required. Realistic initial WIP levels are set at the start of the simulation model.
e. , see Obj 2 ). Although the binary mathematical model can work independently of the simulation model, its integration within the simulation module enables the generation of good assignment schedules for an entire A custom ManPy object built called SkilledOperatorRouter was developed for the needs of the case. This checks the state of the system and if assignment of operators is required it invokes the binary mathematical model sending all the information it needs about the state, i. e. , the available machines and operators, the previous assignments and the weights. There are three cases when the assignment is triggered as the model runs (see Figure  4 ): At given intervals: This simulates the model, where the line supervisor regularly checks the state of the system and decides if an assignment is needed.
Acknowledgements The research leading to these results was supported from funding from the  European Union Seventh Framework Programme  ( FP7-2012-NMP-ICT-FoF)  under grant agreement no:  314364 , the  Electronic Component Systems for European Leadership Joint Undertaking  which receives support from the  European Union 's  Horizon 2020 research and Innovation Program  [737459, 2017] (Productive 4. 0, produc-tive40. eu) and the nancial support of  Science Foundation Ireland (SFI)  [ 16/RC/3918 , co-funded by the  European Regional Development Fund ].
SCFA Analysis of Caecal Samples SCFA (acetate, propionate, and butyrate) content was determined by gas chromatography (Nelson 1020, Perkin-Elmer, St Quentin en Yvelines, France). The samples were extracted with water (wt g/vol), centrifuged at 17,000 × g for 10 min, and the supernatant collected. The proteins were precipitated using a phosphotungstic acid saturated solution. A volume of 0. 1 mL of the supernatant was analyzed using a gas-liquid chromatograph (Autosystem XL; Perkin Elmer, Saint-Quentin-en-Yvelines, France). All samples were analyzed in duplicate. The data was collected and peaks integrated using Turbochromv6 software (Perkin Elmer, Courtaboeuf, France).
The quality of the run was checked internally using PhiX, and then each pair-end sequence was assigned to its sample with the help of the previously integrated index. Each pairend sequence was assembled using Flash software  (Magoc and Salzberg, 2011)  using at least a 10 bp-overlap between the forward and reverse sequences, allowing 10% of mismatch. The lack of contamination was checked with a negative control during the PCR (water as template). The quality of the stitching procedure was checked using four bacterial samples that are run routinely in the sequencing facility in parallel to the current samples.
16S rDNA Gene Sequences and Statistical Analysis Sequences were first analyzed using the FROGS pipeline to obtain the OTU (Operational Taxonomic Units or phylotypes) abundance table. The successive steps involved de-noising and clustering of the sequences into OTUs using SWARM; chimera removal using VSEARCH; taxonomic affiliation for each OTU using both RDP Classifier and NCBI Blast+ on Silva SSU 119 and 123  (Escudie et al. , 2017). Statistical analyses were performed using "R" language and environment version 3. 2. 3. β-diversity (UniFrac and weighted UniFrac dissimilarity), α-diversity measurements and analysis of the differences in OTUs between samples were performed using the add-on package "Phyloseq"  (McMurdie and Holmes, 2013). Differences in the microbial communities between control and treated groups were evaluated using constrained analysis of principal coordinates and permutational multivariate ANOVA.
Effect of Supplementation With SymbioIntest R on the Whole Gut Microbiota Community Three weeks after the transfer of donor microbiota, the mice were split into two groups, one supplemented daily with 10 mg of SymbioIntest R and the other with water as a negative control. Four weeks after the start of supplementation, the mice were killed and the caecal contents were recovered to determine the impact of supplementation on the gut microbiota structure. PCoA presented in Figure  3  are based on UniFrac distance metrics. This data shows that the gut microbiota of mice inoculated with fecal samples of D1 and D2 cluster according to the treatment. Therefore, this reveals distinct differences in bacterial communities between control mice and mice supplemented with SymbioIntest R for these groups (Figures  3A, B ).
SCFA Analysis We further measured the acetate, propionate and butyrate concentrations in the caecal samples of the control and treated group. We found that the treatment did not alter the caecal acetate concentration of any mice (Supplementary Figure  4 ). The propionate concentration was higher in the supplementfed mice than in the control mice for two groups of mice: those that had been inoculated with the gut microbiota of D2 and D3 (Supplementary Figure  5 ). The caecal butyrate concentration was significantly higher in mice supplemented with SymbioIntest R than in control mice for those inoculated with the gut microbiota of D1, D2, and D3, but not D6 (Figure  5 ). However, in contrast to what was found at the caecal level, when we measured the butyrate concentration in the fecal samples, we did not find any difference between the supplement-treated and control group mice, irrespective of the donor used (data not shown).
Therefore, it is possible to guarantee the accurate calibration of a portion of the raw data collected at the right moment because raw data with a strong linearity can be well calibrated by a simple linear regression (SLR), which is the main characteristic that distinguishes the EAC from other calibration methods. As a result, the EAC mitigates the interaction between raw data with a weak linearity and raw data with a strong one. In addition to minimizing error drift, the coefficient of the calibration algorithm is continually updated as a function of weather conditions. Therefore, an Environment-Adaptive Calibration (EAC) system is developed to provide calibrated results in near real time, in which EAC algorithm is implemented based on a tuned Raptor IoT cloud platform. The main contributions of this paper are described in three aspects: 1.
An Environment-Adaptive Calibration (EAC) algorithm/software was proposed for long-term and real-time air quality monitoring. 2. An IoT Cloud-based Environment-Adaptive Calibration (EAC) hardware platform has been developed by tuning our Raptor platform. 3. A performance evaluation was carried out to validate the performance of the EAC system. The remainder of this paper is organized as follows: Section II summarizes the state-of-the-art of the calibration techniques dedicated to low-cost gas sensors. Section III presents the details of the IoT Cloud-based Environment-Adaptive Calibration (EAC) hardware platform which is based on the Raptor platform. In Section IV, a long-term data measurement campaign is carried out based on the EAC platform. Through a deep analysis of the long-term field test data, a new calibration method named the EAC algorithm is developed in section V.
The performance evaluation meets the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement  [25]. Cross et al. demonstrated a field-based calibration technique that combines co-located measurements with a high-dimensional model representation (HDMR) of the interferences to measure air pollutants such as CO, NO, NO 2 , O 3 , and CO 2. The work shows that interference effects derived from the variable ambient gas concentration mix, and environmental conditions change over three seasons  [24]. Spinelle et al. broadly investigated the performance of several classical calibration methods for low-cost sensors. The performances of simple/multiple linear regression and supervised-learning techniques were compared  [22] ,  [23]. Different low-cost gas sensors were used to monitor O 3 , NO 2 , CO 2 , CO and nitrogen monoxide (NO).
D. REMOTE LINUX SERVER To test and evaluate the performance of the Raptor platform, all of the sensory data and the QoS of Raptor platform must be analyzed. Thus, we implement a remote server with the following functionalities: -Remote server connection through LAN -Data and error log file -Sensory raw data display -Sensory calibrated data display -Remote Raptor node reconfiguration. This remote server is based on AWS 'Amazon Web Services'. Because of the data and error log file saved on the remote server, the following performances of the Raptor platform may be carried out: -Sensor calibrations -GUI: wireless sensor node management, data display, etc. -API for data upload and display -Smartphone QR code reading for displaying data. Therefore, the above software is developed on the remote server.
IV. DATA MEASUREMENT AND ANALYSIS A. DATA MEASUREMENT CAMPAIGN A long-term measurement campaign was performed from 2nd Jan. 2017 to 4th Aug. 2017 on the Lecoq Garden located in Clermont-Ferrand city, France (45. 771193N, 3. 087498W). The raw and reference data were collected hourly from the EAC platform and air quality monitoring station. The node with ID N75, which is Exemplar, is equipped on the pipe that is responsible for collecting air to the standard (referencegrade) equipment. The nodes with ID N71 and N72 are deployed beside N75 to be Followers as shown in Figure  5. The measurement takes approximately 8 months and spans from winter to summer. The air temperature, relative air humidity and wind speed of a part of datasets are plotted in Figure  6. It can be noted that the air temperature and the air relative humidity evolve in opposite directions.
B. DATA ANALYSIS The measurement campaign focuses on the influence of meteorological conditions (i. e. , air temperature, relative air humidity and wind speed) on the linear relationship between the reference data (measurement of the air quality monitoring station) and raw data (measurement of sensors). The NO 2 measurement is used in this study because its raw data are more sensitive to meteorological conditions. The scatter plot of the dataset with all meteorological conditions is composed using the air quality reference station data (ordinate) and the raw sensor measurement data (abscissa) as shown in Figure  7. We observe that there are many points with a weak linearity in the lower left part of the figure. After many experiments, we found that there are indeed strong linear characteristics of low-cost Alphasense electrochemical sensors under certain meteorological conditions.
VI. PERFORMANCE EVALUATION A. REFERENCE CALIBRATION METHODS Simple and multiple linear regressions are chosen as reference methods. In fact, the EAC method also uses simple or multiple linear regressions as a function of specific meteorological conditions. The cloud server processes the data of the air quality monitoring station and the raw data of Exemplar node. Because of the Raptor IoT cloud framework, the cloud server can send calibration parameters in near real time to the Followers nodes deployed in the vicinity of Exemplar. Here, measurement data obtained from Exemplar in seven weeks are used for training. Thereafter, the calibration parameters are used for the raw data of Followers measured in three consecutive weeks. The calibration time interval is set to one week.
2) MULTIPLE LINEAR REGRESSION (MLR) Multiple linear regression is an extension of simple linear regression. Instead of just a single scalar value x, we have a vectorx i = x 1 ,. , x p for every data point I, and we have n observation data points. y is the estimate value of each data point as a linear function of the different x variables: y = β 0 + β 1 X 1 + β 2 X 2 +. + β p X p + ε. (8) For n data points, the equation is:     y 1. y n     =   1 x 11. x 1p. 1 x n1. x np       β 0. β p     +     ε 1. ε n     (9) or Y = X β + ε. (10) In the multiple linear regression model, the ordinary least squares (OLS) estimator of β is: β = X T X -1 X T Y (11) In this paper we use the Scilab program  [51]  to find the optimal estimate β. Thus, the calibrate sensor data is: Ỹ = X β + ε.
This work was supported by in part by the  H2020   CAPTOR  Project, in part by the  Provincial Science and Technology Pillar Program of HUBEI  under 2017AAA027, in part by the  Outstanding Youth S&T Innovation Team Plan for Universities of Hubei Province  under Grant  T201610 , and in part by the  Fundamental Research Funds for the Central Universities CHD  under Grant  300102249106 , Grant  300102248305 , and Grant  300102249302.
Implementation The above formulation for surface and volume growth has been implemented in a finite element framework. An in-house C++ code based on the deal. II  [34]  open source finite element library is used to implement the model of surface and volume growth depicted in Figure  5. Key highlights of this computational implementation are the ability to handle growing meshes (to model the growth of the reference configuration in a Lagrangian setting) and related dynamic updates to the solution data structures (global vectors and matrices). Simulations presented in this work use hexahedral elements with a linear/quadratic Lagrange basis, and one to four layers of elements through the thickness. The code base is publicly available as a GitHub repository  [35]. The attachment of the mantle to ω tt k and its extension up to ω 0t k+1 is implemented by extending the finite element mesh by one or more rows of elements as shown in Fig 5.
Introduction The era of nonribosomal peptides (NRPs) started with the brilliance of Fleming to analyze the unexpected ability of a filamentous Penicillium strain to impair bacterial growth. These observations led to the discovery of the penicillin antibiotic, a NRP derived compound which was not identified as such at that time  (Fleming, 1929). Indeed, the biosynthesis mechanism of this type of peptides, named thiotemplate mechanism, was elucidated only 42 years later with the description of the Non Ribosomal Peptide Synthetase (NRPS) of the gramicidin antibiotic from B. subtilis  (Lipmann et al. , 1971). Since then, there has been growing interest in the study of this kind of compounds. As a result, NORINE, the first database entirely dedicated to NRPs was established in 2007, starting with 700 NRPs entries  (Caboche et al.
Recent advances in genome sequencing and annotation From the mid-2000s, Second Generation Sequencing (SGS) technologies, also named Next Generation Sequencing (including Illumina and Ion Torrent technologies), revolutionized genomics and many other biological fields, by offering low-cost genome sequencing  (Loman et al. , 2012). These developments generated an exponential growth in the genome sequences available in public databases (more than 190,000 prokaryotic genome assemblies available on NCBI databases as of March 2019, https://www. ncbi. nlm. nih. gov/genome/browse/) (Tatusova et al. , 2014). SGS technologies are based on the principle of a high-throughput method, with a short read length up to a few hundred bases (e. g. , 250 bp for Illumina HiSeq), and usually a low error rate. The drawbacks of short read genome assemblies are related to low contiguity and poor gene completeness  (Heather and Chain, 2016; Koren and Phillippy, 2015).
, 2016). This long read length may present a real advantage in the case of BGC studies, allowing for obtaining complete sequences, and therefore the entire set of involved genes. Yet, in contrast to SGS, TGS displays a lower throughput and a higher error rate  (Rhoads and Au, 2015). Several software packages, such as Flye  (Kolmogorov et al. , 2019)  and Canu  (Koren et al. , 2017) , provide efficient methods for TGS data assembly. When the read depth coverage is not sufficient, hybrid strategies taking advantage of the low error rate of SGS can be used for polishing TGS reads or contigs, allowing for obtaining a high-quality genome assembly  (Bashir et al. , 2012). For instance, SPAdes  (Antipov et al. , 2016)  offers hybrid assemblies, while Pilon  (Walker et al. , 2014)  allows the user to polish TGS assemblies.
Bioinformatics tools dedicated to NRPS study Publicly available bioinformatics tools for annotating NRPS started to be devised a decade ago  (Blin et al. , 2017a) , since then, a large number of software packages and methodologies for genome mining analyses has been released (Fig. 2 ). The Secondary Metabolite Bioinformatics Portal represents a very useful online resource (http://www. secondarymetabolites. org/) facilitating the exploration of these numerous software packages. It gathers information about the majority of genome mining and annotation tools, as well as databases applied to secondary metabolism investigation  (Weber and Kim, 2016). The portion of these tools implying specific functionalities for NRPS and closely related PKS is mostly constituted with generic algorithms applicable for both bacteria and fungi, even if the training of their algorithm was mainly based on bacterial data.
For instance, NRPS annotation: NP. searcher  (Li et al. , 2009) , ClustScan  (Starcevic et al. , 2008) , and antiSMASH  (Blin et al. , 2017a)  are generic tools for the overall annotation of NRPS. The latter is currently the most comprehensive and upto-date pipeline for BGC analysis, providing a great deal of available functionalities for refining NRPS annotations  (Blin et al. , 2017a; Weber and Kim, 2016). Among the small portion of tools dedicated to Fungi, SMURF  (Khaldi et al. , 2010)  is also a well-known software performing the basic identification of putative synthase and auxiliary enzyme genes. It is also noteworthy that antiSMASH possesses a version for fungal data (i. e. , fungiSMASH -http://fungismash. secondarymetabolites. org)  (Blin et al. , 2017c)  which adapts the internal gene predictor for an eukaryotic one (GlimmerHMM).
, 2007). This method was integrated and further developed in NRPSsp (http://www. nrpssp. com/) and NRPS-PKS-substrate-predictor (http://www. cmbi. ru. nl/NRPS-PKS-substrate-predictor/)  ( Khayatt et al. , 2013; Prieto et al. , 2012). Another method, based on a Support Vector Machine approach, is exploited in NRPSpredictor2. This software package provides predictions according to the physicochemical properties of the 34 residues in a radius of 8 angstrom around the PheA binding pocket  (Rausch et al. , 2005; Röttig et al. , 2011). Substrate prediction obtained through the above-mentioned methods are usually accurate when dealing with bacterial data but are still not entirely reliable with fungal data. Three reasons can justify these limitations regarding substrate prediction : (1) low sequence conservation between A-domains (10-40% identity)  (Agüero-Chapin et al.
Linking NRPS to related clusters or known products Once the NRPS architecture is elucidated thanks to the above described bioinformatics tools, it is important to determine if the compound is already described and associated to a characterized NRP (Fig. 2 ). A lot of useful tools are available to perform this dereplication steps, but only few of them will be described thereafter. The standard approach for analyzing BGC is to use the Basic Local Alignment Search Tool (BLAST) search program hosted on the NCBI website (http:// www. ncbi. nlm. nih. gov/blast). This application enables to perform a sequence similarity search relative for instance to sequences listed in the "nucleotide" (BLASTn) or the "non-redundant protein sequences" (BLASTp) collections of the NCBI  (McGinnis and Madden, 2004; NCBI Resource Coordinators, 2016).
Alternatively, it is possible to query dedicated databases for BGCs, NRPSs and their products. Regarding BGC pathways, ClusterMine360 is a PKS/NRPS database mainly updated by crowdsourcing, which offers a two-way approach where nearly 1000 gene clusters can be browsed directly from a cluster list or following a search from more than 200 compound families  (Conway and Boddy, 2013). The antiSMASH database is another relevant resource, encompassing currently a collection of antiSMASH results for almost 25,000 genomes -but mainly of bacterial origin -and offering the possibility to build precise queries  (Blin et al. , 2017b). Multi-GeneBlast tool enables identifying homologs of multigene modules, such as NRPSs, on the basis of similarity and synteny criteria  (Medema et al. , 2013). MultiGeneBlast is also integrated in antiSMASH, where it suggests some potentially related clusters (or the identical cluster itself) taken from different BGC databases of varying levels of curation.