dataesr/scientific-paragraphs-categorization huggingface dataset:
This is the first time that this model is applied in Parkinson's disease and we therefore briefly describe the model below. The NeuroFlexor software program was used to analyze resistance to stretch (in Newton) with specific time point values identified before stretch (P0), in the early phase of stretch (P1), and at the end of fast stretch (P2) (see Figure  1 ). For the slow trials force values were extracted at the end of the stretch. Points on the resistance profile are identified automatically for use in the model. These force points were used to estimate the passive (inertia, viscosity, elasticity) and active (neural) components.
Data Processing and Statistical Analysis. Data were organized in more versus less affected hand. In the PD group, the most affected hand was defined according to clinical rating of rigidity and hand motor function according to UPDRS. In the control group, the most affected hand was defined as the nondominant hand  [30]. The first trials from slow and fast stretches were excluded from the analysis in order to avoid bias from startle reflexes and mechanical hysteresis  [21]. NC, VC, and EC values in Newton were noted for each fast stretch (nine), which was analyzed in set with the remaining four slow stretches. For analysis of resting tension P0 values, reflecting force applied before onset of stretch, were noted for each trial (Figure  1 ). The total resistance was also calculated (= NC + EC + VC) for each trial.
Results MERMAID design and operational principles. The MERMAID uses a popular Autonomous Profiling Explorer float (Teledyne Webb Research), also employed by oceanographers in the Argo 8 program. The float is equipped with a Rafos II (Benthos) hydrophone sensitive down to 0. 1 Hz, dedicated electronics, and software for signal analysis. Signal transmission by Iridium satellite is two way and allows for modification of filtering and detection parameters, as well as of the operating depth (typically between 700 and 2,000 m). The full control over the recording strategy offered by the two-way communication makes the MERMAID a highly flexible recording instrument and contrasts it drastically with the OBSs and moored hydrophones. The MERMAID drifts passively, typically with a speed of several km per day, until an earthquake signal is detected.
Adding 5 years of data expected from an array of 1,000 MERMAIDs yields good resolution at all depths for most of the area covered by the ocean except for the Southeastern Atlantic, where the synthetic MERMAID coverage is less. Even though each measurement is at a unique location and we added errors with a s. d. of 0. 4 s, the least-squares inversion clearly reproduces the anomalies almost to their full input amplitude of 5%. The results of the first deployments have motivated us to start developing an even more powerful float that has a longer lifetime, and will be able to accommodate up to eight different sensors for multidisciplinary monitoring of the oceans, such as highfrequency biological sounds (marine mammal vocalizations)  13  , meteorological sounds triggered by rainfall  14  and biogeochemical data.
Though we are unable to reprogram at distance the discrimination algorithm stored in the MERMAIDprocessing board, two-way communication via Iridium allows us to manipulate digital filters and adapt acceptance parameters in the algorithm (for the satellite communication, we are using the RUDICS service that allows the transmission of large amounts of data at a reasonable cost). This allowed us to improve the detection of seismic signals in the Indian Ocean after first noise samples were obtained. We can also adapt the length of the seismogram and the degree of its compression during transmission. The data presented here were sampled at 40 Hz, but transmitted (after wavelet compression) at either 20 or 5 Hz. The pre-and posttrigger windows are adaptable, but generally have a length of 100 s each, giving 200s long seismograms.
During initial tests with longer windows, we have not observed any arrivals that could reliably be identified as teleseismic shear waves converted into acoustic waves at the ocean bottom. Adaptations for the Indian Ocean. MERMAID's software allows numerical filtering of the recorded signal before running the STA/LTA algorithm (even though the transmitted seismograms remain unfiltered). To counter the effect of much higher noise at low frequencies, the high-pass corner frequency of this filter was raised from 0. 1 to 1. 0 Hz. This resulted in fewer false triggers, though the ability to discriminate between T waves (the waves caught in the sound fixing and ranging channel and thus travelling mostly in the water) and P waves was affected. The efficiency of the discrimination algorithm was restored by modifying its parameters.
For nearby events we assumed that all events of magnitude M45. 5 and M46 are recorded if they occur within a distance of o15°and between 15°and 30°, respectively. These regional events have their ray paths mostly in the upper mantle and contribute little to lower-mantle resolution. Comparison with the ISC earthquake catalogue gives 102,080 and 341,607 delay times expected to be recorded by a network of 300 and 1,000 MERMAIDs, respectively. To estimate the parallel output of the land-based stations, we used the ISC database 17 of delay times over the same period; as a result, 1,567,829 observed seismic delay times were obtained (only events of a magnitude of 5 and higher were used with conventional selection criteria to assure data quality). Although this number is significantly larger than the number of events expected from the MERMAID network (a longer time period for ISC data would have resulted in even more data but would not fill the gaps in the oceans), the ray coverage in the oceanic domain depends very strongly on the MERMAID presence (Fig.
This study describes the functioning of the model and presents the model's calibration and evaluation. SuMoToRI was calibrated and evaluated with independent datasets from greenhouse experiments under contrasting S supply conditions. It is run with a small number of parameters with generic values, except in the case of the radiation use efficiency, which was shown to be modulated by S supply. The model gave satisfying predictions of the dynamics of growth, S allocation between compartments and S partitioning, such as the mobile-S fraction in the leaves, which is an indicator of the remobilization potential toward growing sinks. The mechanistic features of SuMoToRI provide a process-based framework that has enabled the description of the S remobilizing process in a species characterized by senescence during the vegetative phase.
Plant Cultivation Two experiments were performed from January until July in 2011 for Experiment 1 and in 2013 for Experiment 2. Data from Experiments 1 and 2 were used for model calibration and model evaluation, respectively. Plants of cv. Yudal were grown in a greenhouse at the UMR 950 EVA in Caen, France (49 • 10 N, 0 • 21 W) for 40 days (Experiment 1) and 38 days (Experiment 2) in the greenhouse under natural day/light conditions, and put into a climatic chamber maintained at 5 • C for vernalization for 3 weeks (Experiment 1) and 4 weeks (Experiment 2) with a photoperiod of 16h (day) and 8h (night). Plants were then transferred into individual pots (mix of 2:3 perlite and 1:3 vermiculite) distributed randomly in the greenhouse at a plant density of 40 plants m -2 (Experiment 1) and 30 plants m -2 (Experiment 2), until the onset of pod formation.
(2015). Measurements of SO 4 2-were performed only for Experiment 1. Therefore, SO 4 2-contents in Experiment 2 were estimated from the time courses of the S:SO 4 2-ratio, which were determined for data from Experiment 1. The environmental variables necessary to run the model, i. e. , daily PARi and maximum and minimum temperatures were recorded with line quantum sensors (LI-191, Eurosep Instruments) set at the top of the canopy and temperature probes (105T Campbell, Campbell Scientific Ltd. , Leicestershire, UK).
Model Implementation and Statistical Assessment of Performance The model was implemented with the R language environment for statistical computing, version 2. 9. 1 (R Development Core aQS and bQS are the parameters of the S uptake function. Their values are adjusted for each experiment (input variables). Team, 2009). The predictive quality of the model was evaluated with the root mean square error (RMSE, in the same unit as the variable), which provides the mean difference between n predicted and observed values and, the index of agreement  (d, Willmott, 1982) , which indicates the degree of agreement between simulated values with their corresponding observed values. A low RMSE and a d value close to one unit are targeted when evaluating the performance of model simulations. The RMSE was calculated as follows: RMSE = n i=1 (S i -O i ) 2 /n where S i and O i are the simulated and observed values at time i, and n is the number of observations.
Model Calibration Model calibration was performed with the dataset from Experiment 1. Parameter estimations were acquired by fitting to observations for each of the model's functions from the end of vernalization (Supplemental Figure  S1  and Table  S1 ). Tables  3  and 4  give the calibrated parameter values and initialization states for both S conditions. Adjusted parameters in HS and LS conditions were not significantly different except for the radiation use efficiencies (RUEs) (Table  3 ). Indeed, distinct RUE values were used according to the S condition. As illustrated in Figure  2 , the RUE value that corresponds to the slope of the relationship TDW vs. accumulated PAR abs was significantly lower in LS than in HS (P < 0. 05). The critical S demand for BL cannot be applied to low biomass.
Perspectives of Model Development In the light of this work, two avenues for the future development of SuMoToRI can be foreseen. First, in the present version, SuMoToRI does not include predicted S uptake from the soil. Other rapeseed models have developed a soil module to quantify N fluxes transferred from soil to the roots based on (i) the balance-sheet method  (Rémy and Hébert, 1977; Machet et al. , 1990)  to predict net N soil mineralization combined with a prediction of nitrate leaching  (Burns, 1976) , N from FL  (Dejoux, 1999)  and a coefficient of effective uptake as in Azodyn-Rape  (Jeuffroy et al. , 2003)  or (ii) the functioning of nitrate transporters and root sizes as in CERES-Rape  (Gabrielle et al. , 1998a)  and in  Malagoli et al. (2004). These approaches should be further investigated to consider the S mineralization processes in the soil  (Suhardi, 1992)  and the features of sulfate transporters  (Takahashi et al.
ACKNOWLEDGMENTS The authors thank the  BrACySol (Center of biological resources in INRA Rennes, France)  for providing the seeds. They also thank  M. Bodereau ,  J. Pichon ,  J. Bonnefoy ,  P. Beauclair ,  A. -F. Ameline , and  C. Orieux  for their technical assistance;  J. -B. Cliquet  for helpful reading of the manuscript;  Laurence Cantrill  for his assistance in editing. We are most grateful to the  PLATIN' (Plateau d'Isotopie de Normandie) core facility  for the element analysis used in this study. This work was financially supported by the  Environment and Agronomy division of INRA  and by the  Regional Council of Lower Normandy , the  Regional Council of Brittany , the  European Regional Development Fund (ERDF)  and  Timac Agro International  through the  SERAPIS  project.
This paper describes a two-fold approach carried out in collaboration between IFPEN and the French ADEME with the aim to evaluate a Plug-in Hybrid vehicle according to its conditions of use. The vehicle considered, an Opel Ampera, has been widely tested on a chassis dyno and simulated using IFPEN LMS. IMAGINE. Lab AMESim® platform. The software principles and its validation on different specific cases, thanks to experimental results, are described. The PHEV simulation tool is used to evaluate the influence of two European standard procedures and the influence of their parameters on the weighed CO 2 emission.
As a consequence the evaluation and forecast of PHEVs true capabilities in Green House Gas (GHG) emissions and local nuisances reduction in a day to day basis become very complex. With the aim to get a better understanding of PHEVs potential, IFPEN, with the support of the French Environment & Energy Management Agency (ADEME), has set up a research program associating experimental analysis together with system simulation of PHEVs In the frame of this program two cost shared projects, CONSOVEx and SIMULVEx have been carried out (see chap. 2). The paper will present and discuss results obtained for different types of use of the vehicle, according to the Normalized European Test Procedure (R101) and to the new Worldwide harmonized Light vehicles Test Procedures (WLTP). In this last case, the weighting between the CD and CS operations of the drivetrain will also be taken into account through statistics expressed by a ratio called Utility Factor (UF).
Projects CONSOVEx and SIMULVEx The aim of these two cost-shared programs is to evaluate energy consumption of EVs and GHG emissions of PHEVs according to their conditions of use, i. e. : • Driving schedules and auxiliaries use for EVs ; • Driving schedules, procedures and distance between charge for PHEVs. Due to the high number of cases to be considered and to the high cost of experiments, the two programs included both experimental and simulation phases. The experimental phases were carried out on IFP Group chassis dyno with the purpose to generate data for system analysis and comprehension together with software validation. Once validated, the software will enable to fully investigate the drivetrain behaviour in order to set up the already described energy and GHG analysis but also to prepare the next generation of drivetrains using optimized components, architectures and control.
Battery The battery model implemented in Amesim is based on a simple circuit model composed of a voltage source and a resistance, both functions of the battery state of charge (SOC). From the main characteristics of the battery (chemistry, P/E ratio…), the Open Circuit Voltage (OCV) and the internal resistance are obtained using an IFPEN in-house software  [Petit 2014 ]. Due to the lack of battery data a final adjustment is made to fit with the experimental results. Figure  6  shows the OCV in function of the SOC for the fitted and raw results.
We also highlighted the fact that the simulation has to be carried out on a system level but relies on specific in-house built tools dedicated to components sizing and data generation (EMTool for electric machines, dedicated tools for batteries sizing and optimized energy management). The software validation indicates that the drivetrain operating modes are well reproduced and that the error in the energy flow in the drivetrain do not exceed -/+4%. The evaluation of PHEVs fuel, electricity consumption and CO 2 emission at vehicle level is very complex because the result has to be expressed as a weighted value computed with data corresponding to charge depleting and charge sustaining operations. The weighting can be done through a constant value (25 km assumed CS operation in the R101 procedure) or through a set of ratios between CD and total distances between charges (Utility Factor -UF).
Motivation: High-throughput sequencing technologies provide access to an increasing number of bacterial genomes. Today, many analyses involve the comparison of biological properties among many strains of a given species, or among species of a particular genus. Tools that can help the microbiologist with these tasks become increasingly important. Results: Insyght is a comparative visualization tool whose core features combine a synchronized navigation across genomic data of multiple organisms with a versatile interoperability between complementary views. In this work, we have greatly increased the scope of the Insyght public dataset by including 2688 complete bacterial genomes available in Ensembl thus vastly improving its phylogenetic coverage. We also report the development of a virtual machine that allows users to easily set up and customize their own local Insyght server.
In  Lacroix et al. (2014) , we focused mainly on Insyght visualization capabilities and functionalities but the underlying database contained, at that time, only 400 complete bacterial genomes. Here, we have processed the 2688 complete bacterial genomes available in Ensembl Bacteria when we started this work (June 2014) to provide a more comprehensive coverage of the microbial diversity. We have also developed a virtual machine that allows users to easily set up their own local Insyght server to analyze their genomic data privately.
Pipeline The database is populated by a pipeline of Perl scripts that (i) process the genome files, (ii) run the BLASTp jobs on a supercomputer, (iii) parse the results and (iv) execute the program that identify the syntenies between the >3. 5 million pairs of genomes. Two protein coding genes in distinct genomes are considered orthologous if they give rise to a bi-directional best hit (BDBH) when comparing the corresponding proteomes and if the sequence alignment includes more than 50% of the length of the shortest proteins with an e-value less than 0. 01. Two proteins for which the e-value of the sequence alignment is less than 0. 01 and that do not fall into the 'ortholog' category are considered homologous but are not displayed unless they belong to a synteny. Syntenies are delineated with a dynamic programming algorithm that determines the highest scoring paths among all the possible gapped chains of collinear homologues.
Public dataset We inserted in the database the 2688 complete genomes of Ensembl Bacteria available early 2014, carried out the cross comparisons of their proteomes with BLASTp, and computed the synteny regions for all >3. 5 million pairs of genomes. We report $2. 4 billion orthologs; on average, their alignments have a coverage of $90%, an identity of $43%, a score of $241 and an e-value of $2e À 05. We report $1. 85 billion singleton orthologs and $140 million syntenies that comprise $550 million orthologs and $190 million non-BDBH homologs. On average, alignments of non-BDBH homologs have a coverage of $76%, an identity of $35%, a score of $145 and an e-value of $8e À 05. BLASTp jobs generated 1. 2 TB of raw, compressed data (tabular format). The database size is 3. 5 TB, the largest table having $5.
Virtual machine We provide a virtual machine (VM) to allow for users' private analysis in a local environment. Users can customize which genomes to compare, the blast parameters, etc. The VM is based on the Ubuntu OS and is pre-configured with all the software (Perl scripts, relational database, web application) and dependencies  (PostgreSQL, tomcat, etc. ) that are required to set up a private Insyght server. The '. ova' image file ($3. 3 GB) can be imported into a virtualization management environment (e. g. VirtualBox). Documentation on how to run the pipeline and manage the data is provided. Since our publication in 2014, the pipeline provided in the VM has been revamped: it is now $2. 5Â faster, suitable for draft genomes (many contigs), and allows analysis of different genomes with identical taxonomic identifiers.
Conclusion In this note, we report the successful integration and availability of a non-trivial amount of complete genomes in Insyght. Nevertheless, it will be increasingly difficult for us to process the huge number of newly sequenced genomes, in particular with the advent of 3 rd generation sequencing technologies that are capable of providing completely assembled genomes. To tackle this issue, we provide a customizable version of Insyght via the virtual machine described above that will allow the user to import part of our public database and to add a set of private genomes, either complete or in draft. In practice, we plan to restrict the public dataset to a single 'best' representative for each group of closely related genomes and to allow users to extract only species in a particular taxonomic node (of course limiting this possibility to 'manageable' amounts of transferred data).
IDA-an intelligent driver assistant IDA is a smart parking app that builds on the park guidance information systems 1  (PGI). However, IDA extends common PGI systems by offering suggestions based on parking fee or proximity to destination. Additionally, IDA' makes a new contribution to the existing parking systems by adding two novel features. These are: 1. Use of natural language: the interaction between the application and the driver is done using speech dialogues. This feature complies with current traffic regulations in Singapore enabling drivers to safely use the app while driving. 2. Ability to react to changes in the car park occupancy -unlike E-parking systems which reserve lots for a nominal fee, IDA takes a more sustainable approach: after a parking decision is taken IDA keeps monitoring the car park availability following a once-per-minute check-up routine.
If the number of lots drops to critical level, i. e. less than 20, IDA redirects the driver to another parking place. In this way, drivers save costs and parking resources are optimal allocated -parking lots are not kept empty for reservations while drivers with no reservation are struggling to find an empty spot driving around and increasing the traffic. Furthermore, the app includes several interconnected modules responsible for:  Collecting and managing parking availability -the data is obtained from the Singapore Land Transport Authority (LTA); it is periodically updated and stored on a data base server  Processing speech and dialogues, as well as generating responses in natural language using a text-to-speech (TTS) module.  Assigning parking lots on a floor map -currently, the assignments are only simulated; this feature will be implemented in the second project milestone  GPS and google maps interfacing which enables the application to detect the driver location and calculate the nearest car park distance  Keeping track of parking details -the app can send on request the parking details per SMS.
Strain FF2 T exhibited a 97. 47% 16S rRNA sequence identity with Diaminobutyricimonas aerilata (GenBank accession number JQ639052), the phylogenetically closest bacterial species with a validly published name (Fig. 1 ). The value was lower than the 98. 8% 16S rRNA gene sequence threshold recommended by Meier-Kolthoff et al. [17]  to delineate a new species within phylum Actinobacteria without carrying out DNA-DNA hybridization. Different growth temperatures (25°C, 30°C, 37°C, 45°C and 56°C) were tested. Growth occurred between 37°C and 45°C, but optimal growth was observed at 37°C after 48 hours' incubation in aerobic conditions. The colonies were 0. 8 mm in diameter and exhibited a light yellow colour on agar enriched with 5% sheep's blood (bioMérieux). Growth of the strain was tested under anaerobic and microaerophilic conditions using GENbag anaer and GENbag microaer systems, respectively (bioMérieux) and under aerobic conditions, with or without 5% CO 2.
MALDI-TOF protein analysis was carried out as previously described  [18, 19]  using a Microflex LT (Brüker Daltonics,  Briefly, a score 2. 000 with a species with a validly published name provided allows identification at the species level; a score of 1. 700 and <2. 000 allows identification at the genus level; and a score of <1. 700 does not allow any identification. A score ranging between 1. 3 and 1. 4 was obtained for strain FF2 T and did not allow any identification. We added the spectrum from strain FF2 T to our database (Fig. 4 ). Finally, the gel view showed spectral differences with other members of the family Microbacteriaceae (Fig. 5 ).