dataesr/scientific-paragraphs-categorization huggingface dataset:
Several plant biology laboratories from our research organization have implemented SeedUSoon in recent years, and it is now available for broader distribution (under the protection of a proprietary license agreement). The design of this software is intended to help others optimize the tracking of their biological material. Ultimately, SeedUSoon will contribute to a facilitated and improved exchange of information to accompany seed exchange between laboratories. supporting the development of SeedUSoon, and Brandon Loveall from IMPROVENCE for English proofreading of the software interface and manuscript. We also thank the creators of the FigureJ plug-in for their helpful figure preparation tool  (Mutterer and Zinck, 2013).
However, as already stated, it is not possible to compute the instantaneous firing rate during spiking network simulations, so we decided to employ the same spike integration technique for the fusimotor activation of chain fibers. Another possible approach to spike integration is to employ α-shaped responses. This leads to similar results; however, such a spike integration technique cannot be implemented with the SpiNNaker APIs, as they currently only support exponential synapses. We then employed a model selection procedure to select the maximum impulse response r and decay time τ for the spike integration mechanism (Equation  10 ). In the original model, the activation level of fusimotor activation under constant γmotoneurons activity rose and stabilized at a certain maximum value. Therefore, we created a dataset of these maximum values, using the Hill-type activations, for different frequencies of motoneurons firing rates: {10, 50, 75, 100, 150}spikes/s.
Then we performed a model selection procedure on all the different combinations of values for r and τ ranging in 0. 01 ≤ r ≤ 0. 4 and in 100 ms ≤ τ ≤ 500 ms, with discrete sampling. To evaluate the parameters, we simulated the spike integration at a given frequency for 3 s, and we computed the percentage error of the obtained maximum activation levels compared with those in the dataset (magnitude error). As it reported in  Mileusnic et al. (2006) , fusimotor activation reaches 90% of the maximum value in 343 ms for f dynamic and in 471 ms for f static. To match this property, we also evaluated the obtained percentage at these specific points in time and computed the discrepancies with 90% (shape error). During the parameter identification procedure, input for the spike integration was simulated by generating spikes at fixed intervals, which produced a regular oscillation in the computed activations.
To limit the effect of these oscillations on the selection procedure, errors were computed on a smoothed activation. For each parameter combination, we summed up the magnitude and shape errors, and we averaged them on the different frequencies of the dataset. At the end of the procedure, we found that these values had the smallest average percentage error (7%): r dynamic = 0. 08, τ dynamic = 310 ms r static = 0. 09, τ static = 425 ms These values provide accurate results for the fusimotor activity ranging from 30 spikes/s to 150 spikes/s, while they tend to make the spike integration mechanism underestimate f for lower stimulation frequencies and to overestimate it for higher ones. For the static activation level of the chain fiber, we scaled f static , relative to bag 2 , by a factor equal to the mean scaling factor between the maximum levels of the original model at the same stimulation frequencies of the dataset.
RESULTS To assess the effectiveness of the proposed model, we performed several experimental trials. In particular, we validated the NEST and SpiNNaker implementations against a MATLAB Simulink simulation, comparable with the one performed in  Mileusnic et al. (2006). Then, we employed the model to translate sensory information from different simulated and physical robotics systems, proving the generality of the approach. In all the experiments, we did not change the parameter values from the one reported in  Mileusnic et al. (2006) , which are optimized on cat soleous muscle recordings. However, the same anatomical structure exists in all mammals; therefore, because of the accurate representation of the anatomy of the model by changing the parameter values one could in principle reproduce the different properties of the various muscles.
Validation As the first step, we developed a MATLAB Simulink model of Equations (  1 )-(  11 ), whose results were directly, albeit empirically, comparable with those reported in  Mileusnic et al. (2006). Then, we compared the results, in terms of afferent rates, of the two different implementations, with the Simulink reference by executing the same tasks in terms of fiber stretch and fusimotor stimulations. After the execution, the spike trains were recorded, and the rate was computed by sorting them into bins of fixed time intervals (30 ms) and counting them to compute the average rate for the bin. In particular, we simulated a simple stretch, with the fiber length remaining constant at 0. 95L 0 for 1. 1 s, stretching at 0. 11L 0 /s for 1. 1 s, and then remaining constant at 1. 08L 0 for the final 1.
Because the link system has the same structure as the previous case, we could translate encoder values for such a joint using Equations (  17 )-(  20 ) by changing the kinematic parameters to match the iCub kinematics (Figure  8 ). To actually send the translated encoder values to SpiNNaker, as well as to retrieve live spiking activity from the simulation, a proper real-time data exchange middleware was developed in C++. The joint speed cannot be directly retrieved from the motor, so it was necessary to compute it. To test the robustness of the spindle model against input noise, a simple single-step differentiation was employed. The neck roll joint was then moved in a sinusoidal fashion, with a peak-to-peak amplitude of 30 degrees and a frequency of 0. 5Hz, but the maximum stretch was maintained for 1 s on every side, by keeping the head still when it reached the maximum range of motion, resulting in a motion with a period of 4 s.
The neuromorphic implementation guarantees real-time performance and scalability for real robotic applications. Most humanoid robots have no more than 50 degrees of freedom, and if we consider all of them as actuated by a pair of muscles and that the population relative to one muscle can be simulated on a single SpiNNaker core, the whole simulation of the afferent fibers for all muscles will not occupy more than 12% of the processing capability of a SpiNN-5 board. From a neuroscientific point of view, the model can be used to further explore action-perception loops for reflexes and voluntary movements. One of the possible extensions of the proposed model can be based on the integration of the modulation of different responses by γ-drive. In a previous work  Grandjean and Maier (2014) , supported by experimental data from a cat during passive sinusoidal stretches, the authors proposed a computational model able to predict type Ia and IIa muscle spindle activity as a function of the time-varying γ-drive.
While pausing and restarting may not be an issue in many synchronized closed-loop applications such as the one presented in this work, it actually slows down the overall neural simulation. Possible solutions to overcome this problem include modifying the implementation to make enable it to receive data through a socket or employing synchronization frameworks capable of injecting information while the simulation is running, such as MUSIC  (Djurfeldt et al. , 2010). The SpiNNaker implementation is limited in the accuracy of the computation because of the fixed-point arithmetic and in the number of models that can be simulated on a single core. This could force the user to split the population relative to a muscle if a biologically accurate number of spindles is employed, with a consequent duplication of the code for the live injection of length and stretch information.
MASK, the new ARIA initiative, focusses on: (1) the implementation of multi-sectoral care pathways; (2) deploying emerging technologies; (3) with real world data; (4) to provide individualized and predictive medicine;  (5)  in patients with rhinitis and asthma multi-morbidity;  (6)  to be used by a multi-disciplinary group or by patients themselves (self-care) using the AIRWAYS ICPs algorithm (Fig. 1 );  (7)  across the life cycle  [15, 16]. An App (Android and iOS)  [16]  has been developed and is associated with an inter-operable tablet for physicians and other healthcare professionals (HCPs)  [17]. An elegant and simple common language, the Visual Analogue Scale (VAS), is used to assess and manage AR  [18, 19]. It is currently being combined with allergen and pollution exposure using various methods including Google Trends  [20, 21].
(b) Implementation AIRWAYS ICPs has developed a strategy for lowresource settings based on existing WHO initiatives such as the WHO Package of Essential Non-communicable disease (PEN  [28] ) or validated primary management strategies in low-and middle-income countries (LMICs)  [29]. The first ARIA workshop report (1999) already had a specific goal to reach patients in LMICs  [6]. The expertise of GARD for the deployment of GPs in LMICs is used to address the equity dimensions of MASK. Gender is also considered in ARIA. The intervention has a comprehensive approach to health promotion addressing all relevant determinants (e. g. social determinants) and using different strategies (e. g. setting approach).
A care pathway from the patient to the health care professional has been built. It is currently being combined with Google Trends to assess pollen seasons  [20, 21] , pollen levels and pollution data. The application is freely available in 17 languages from the Apple App store (iOS) and Google Play Store (Android) in 22 countries (translated and back-translated, culturally adapted and legally compliant). Due to the simplicity of the tool, it can be used in developed and many developing countries (if a smart phone is available). A pilot study in 5000 users across 20 countries has been analysed. A simple questionnaire administered by cell phones has enabled the identification of phenotypic differences between a priori defined rhinitis groups. The results of the study suggested novel concepts and research questions in AR that cannot be identified using classical methods  [56].
A cross-sectional study evaluated the impact of uncontrolled rhinitis assessed by VAS on work productivity using cell phone data collection. It also compared the impact of asthma, rhinitis and conjunctivitis on work  [34]. In users with uncontrolled rhinitis, approximately 90% had some work impairment and over 50% had severe work impairment. This pilot study provided not only proof-of-concept for data on the work impairment collected with the app but also data on the app itself, especially the distribution of responses for the VAS. This supports the interpretation that persons with rhinitis report both the presence and the absence MASK activities are being implemented by a group of 450 members in 70 countries. All stakeholders needed for the implementation of an action plan at the national and local levels actively participate.
Evaluation However, a sufficient amount of data is needed and results are expected in 2018. Box 11: There is a defined and appropriate evaluation framework assessing structure, process and outcomes. The use of validated tools and/or the results of evaluation are linked to actions to reshape the implementation accordingly and/or the intervention is assessed for efficiency (cost vs. outcome) MASK data are available using a real-time database and results are regularly published. Some 2016 data are already in press  [34, 56]. The results of 2016 have induced a change in some of the questions of the App and in the re-analysis of data using a novel approach, which suits observational studies better. In randomized controlled trials (RCTs), each subject is randomly assigned to a treated or control group, whereas observational studies examine the possible effect of a treatment on subjects where the investigator has no control over the experiment and cannot randomize the allocation of subjects  [62].
The pilot study allowed us to show that MASK can accurately assess work productivity  [34]. In the Twinning, work productivity will be assessed in several settings including the Northern Ireland NHS, North of England and Valencienne, France hospitals. EQ-5D is one of the MASK tools and will make it possible to assess the cost-effectiveness of interventions. The results of ARIA are clear, but the results of MASK can only be assessed and its impact understood when a sufficient number of users will have been monitored. Interim data from pilot studies are encouraging  [26, 34, 56]. They show in 11,300 users (June 17) that the phenotype of AR can be assessed and some features such as work productivity can be appraised. A real-time database is available and the statistical analysis of the data is in place, allowing for a few preliminary reports to be published or in preparation already.
It follows the EIP-AHA recommendations on a 5-step framework:  [1]  what to scale up: (1-1) databases of Good Practices, (1-2) assessment of viability of the scaling up of Good Practices, (1-3) classification of Good Practices for local replication; and  [2]  how to scale up: (2-1) facilitating partnerships for scaling up, (2-2) implementation of key success factors and lessons learnt, including emerging technologies for individualised and predictive medicine. Scaling-up will take place within and beyond Europe with GARD  [25, 30, 31, 45, 92]. MASK is implemented in 22 countries (17 languages). We aim to include five more countries in 2017. Strengthening the WHO NCD Action Plan AIRWAYS ICPs is a GARD demonstration project (WH0). It is in line with the WHO NCD Action Plan since it aims to reduce the preventable and avoidable burden of morbidity, mortality and disability by means of multi-sectoral collaboration and cooperation at national, regional and global levels.
checklist. However, one ear, nose and throat (ENT) expert from Belgium and one Allergy-Public health expert from the UK found that many items were not partly or not in line with the checklist. Overall, from 75 to 94% of items were found to be in line with the CHRO-DIS checklist. Comments are provided for a percentage of agreement < 75%. Box 2 At this stage there is no change in MASK incurred by the SurveyMonkey. There will be a revision of the product at the end of 2017 taking the comments into consideration. Box 8 Since the SurveyMonkey, ethical committee has been granted (Kohln-Bohn Region) and the MHRA and the ethics committee have indicated that the App is not a medical device. Box 12 More data have been evaluated and there is a strong consistency of results. Box 13 At this stage there is no change in MASK incurred by the SurveyMonkey.
Offsets between the position of the dark matter central peak and the peak of the luminous matter are difficult to explain with a standard ΛCDM but are naturally produced for reasonable values of the dark matter cross-section  (Rocha et al. 2013). A difference of this nature has been claimed recently based on detailed lensing data in the center of a cluster that contains 4 bright member galaxies  (Massey et al. 2015). In the case of the Hubble frontier field clusters, it is interesting that our free form analysis of MACS0146 also indicates a possible offset between the lensing based centroids of the brightest galaxies and their luminous stellar centroids. These differences are subtle and it will be important to look at a larger sample and the model dependencies, and systematic uncertainties, in detail to support any claim of new physics.
In this paper, we explore the cluster A370 (z = 0. 375, Mahdavi & Geller 2001) using our free-form code WSLAP+  (Diego et al. 2005a (Diego et al. , 2007 (Diego et al. , 2016;; Sendra et al. 2014). Our algorithm does not rely on assumptions on the distribution of the dark matter and seems to perform equally well in clusters that are more or less symmetric as clusters that present a complex morphology. A370, the final cluster to be completed in the HFF program (in September 2016), contains two very large elliptical galaxies with extended cD light profiles separated by roughly 190 kpc. The HFF data reveals that long radial arcs point towards each of these galaxies (although not quite precisely) suggesting that A370 is a double cluster with two overlapping cores that are clearly in the process of merging.
2015; Zitrin et al. 2015). To date, ∼ 12 multiply lensed galaxies have been reliably identified  (Johnson et al. 2014; Richard et al. 2014 ) in the redshift range 0. 8 < z < 6. We can also make use of the recent redshift catalogs com-piled by Grism Lens-Amplified Survey from Space 2   (Schmidt et al. 2014; Treu et al. 2015)  or GLASS hereafter, to examine and in some cases update redshifts adopted in earlier work. GLASS data is useful for both goals, confirming the cluster members and identifying new multiply lensed images. The data from the HFF and GLASS programs are complemented with X-ray data from Chandra to trace the hot baryonic component in the core of the cluster. The free-form nature of our method means we can objectively determine the relative distances of any set of multiple images with respect to any other, providing geometric estimates of the source distance for comparison with the independently determined photometric redshifts.
Section 5 discusses the reference (or driver) model and alternative models that are presented in the paper. Results based on the driver model applied to new HFF and GLASS data and a discussion of the new systems that we are able to uncover using the driver model are presented in section 6. In section 7 we compare our results with simulations from the MUSIC project 3. Our results are discussed in section 8 and we conclude in section 9. Throughout the paper we assume a cosmological model with ΩM = 0. 3, Λ = 0. 7, h = 70 km/s/Mpc. For this model, 1 = 5. 16 kpc at the distance of the cluster (z = 0. 375). In all images (except when noted otherwise) we adopt the standard convention where north is up and east is left. 2 HFF, GLASS, AND X-RAY DATA 2. 1 HFF. We use the reduced public imaging data obtained from the ACS and WFC3 Hubble instruments, retrieved from the Mikulski Archive for Space Telescope 4  (MAST).
For the optical data (filters: F435W, F606W and F814W), we used the recently released data that includes the first 76 orbits of HFF data on this cluster (ID 14038, PI. J. Lotz) plus 6 orbits from previous programs (ID 11507, P. I K. Noll and ID 11591, P. I J-P. Kneib). For the IR data, we used data collected in the HFF program ( 2 orbits in the filter F140W, ID 14038, PI. J. Lotz) as well as previous programs in the filters F105W (  1  F160W (3 orbits, ID 11591, P. I J-P. Kneib and ID 14216 P. I R. Kirshner) totaling 92 orbits in all six bands. From the original files, we produce two sets of colour images by combining the optical and IR bands. The first set is based on the original data while in the second set we apply a high-pass filter to reduce the diffuse emission from member galaxies and a low-pass filter to increase the signal-to-noise ratio of small compact faint objects.
GLASS. We make use of the spectroscopic redshifts of (multiply) lensed sources behind A370 as well as cluster members, including the publicly available GLASS (ID 13459, P. I T. Treu (Schmidt et al. 2014; Treu et al. 2015)  v001 redshift catalog available at https://archive. stsci. edu/prepds/ glass/. The GLASS data include HST grism spectroscopy in the WFC3 G102 and G141 grisms at 10 and 4 orbits depth, respectively, to obtain comparable 1σ flux limits of 5 × 10 -18 erg/s/cm 2 per position angle over the full wavelength range of the two grisms (0. 8-1. 7µm). Before each grism exposure, short pre-imaging in F105W and F140W was obtained for optimal alignment and extraction. These  data are included in the imaging mosaics described above. The GLASS v001 redshift catalog of A370 was generated by careful vetting and visual inspection of the GLASS grism spectra of emission line sources and objects with a continuum H-band magnitude brighter than 23.
As opposed to the majority of the GLASS v001 redshift catalogs, the A370 redshifts were determined without any photometric prior, as the HFF data were incomplete at the time the A370 redshift catalog was assembled. Each redshift was assigned a quality (Qz)from 1 (worst) to 4 (best) corresponding to a redshift determined based on tentative low-S/N spectral features, and multiple high-S/N emission lines, respectively. For detailed general information on the GLASS v001 redshift catalogs see  Treu et al. (2015). The improved photometric information presented in the current study, has led to improved spectroscopic redshift estimates for a few objects as described in Section 4 and 5. The GLASS NIR footprint is shown in Fig. 1  and the redshift distribution of GLASS sources is shown in Fig. 2. The peak in the redshift distribution at z≈ 1 is discussed in section 8.
Chandra. To explore the dynamical state of A370, we also produce an X-ray image using public Chandra data. In particular, we used data from the ACIS-I and ACIS-S instruments with the Obs ID 7715 and Obs ID 555 (PI. Garmire) totaling 85 ks. The X-ray data is smoothed using the code ASMOOTH  (Ebeling, White & Rangarajan 2006). A false color image from the HFF imaging overlaid contours of the smoothed Xray data is shown in figure  1. The distribution of X-rays seem to follow a smooth distribution with no obvious peak at the centre although some sub-structure may be present near the centre that shows some correspondence with the position of the BCGs and may become more clear with deeper planned X-ray data.
LENSING DATA For the lensing data we follow the multiple-image system identifications from  Richard et al. (2010 Richard et al. ( , 2014) )  and  Johnson et al. (2014)  that include between 9 and 12 multiply lensed systems (see compilation in Table  A1  in the appendix). From these papers we also adopt the numbering scheme (except for system 10 that is redefined in this work) as well as the spectroscopic redshifts. Some of the redshifts are updated with the recent estimates from GLASS (see section 2). We redefine image 8. 3 in  Richard et al. (2010 Richard et al. ( , 2014)) ;  Johnson et al. (2014)  which now is part of our new system 22. We find a new candidate for the third counterimage of system 8 (8. 3) that is hidden behind one arm in a spiral galaxy that is itself lensed by a prominent member galaxy.
380 with Qz = 2) of a bright foreground galaxy with a reliable photo-z of zmean = 0. 168. The low redshift of this galaxy means it is intrinsically of relatively low luminosity and so unimportant for the lens model. The northern BCG in the cluster has a lower redshift in GLASS (z = 0. 32) but with a low quality flag (Qz = 2) due to a lack of emission lines. Independent photometric redshifts with two different codes BPZ  (Benítez 2000)  and EAZY  (Brammer, van Dokkum & Coppi 2008)  result in z = 0. 385 and z = 0. 36 respectively, in agreement with being a cluster member. Chandra data also supports this hypothesis since a small X-ray peak is found at the position of this galaxy suggesting a local gas density enhancement/gas cooling (of the intracluster gas) around this galaxy. Ng (in Eq. 4) is the number of deflection fields (from cluster members) that we consider.