dataesr/scientific-paragraphs-categorization huggingface dataset:
This would provide a high level of confidence in the underlying model application, allowing the evaluation to focus on the specific model application (in the way FOCUS models are used for the exposure). In the absence of standard software, it would still be of benefit to both the applicant and the assessor if an implementation of the model can be provided. One key benefit is that, should there be any concerns about the exposure profile(s) used; the assessor would have the opportunity to re-run the modelling with alternative profiles. Although this is considered to be beneficial, it is not considered to be essential, noting the difficulty in providing the implementations that can be used by non-TKTD modelling experts. In case executable implementation of the model is not made available to the assessor, at least the source code should be provided.
The documentation of the implementation should contain an overview of the source code files, and the version and necessary packages of the used programming environment. Testing of the implementation ('implementation verification') needs to be performed and documented for any model implementation Kappa-rule A core concept of DEB theory (see respective entry). It is assumed that a fixed fraction (kappa) of the mobilised energy is allocated to somatic maintenance and growth, while the rest is allocated to maturity maintenance, maturation and reproduction. Note that in standard DEB framework, the kappa-rule should not be affected by a toxic compound Multiplication factor (Originally introduced by  Ashauer et al. (2013)  as the 'margin of safety') -Factor applied to an entire specific exposure profiles leading to a certain effect level, e.
Toxicological (in)dependence If the 95% depuration time of maximum of the internal concentration is larger than the time of the next exposure pulse, the two pulses are assumed to be toxicologically dependent, if the next pulse comes only after the depuration time, pulses are assumed independent Uncertainty The term is used to describe the lack of knowledge about models, parameters, constants, data and beliefs. There are many sources of uncertainty, including the science underlying a model, uncertainty in model parameters and input data, observation error and code uncertainty. Additional studies and collecting more information allow error that stems from uncertainty to be minimised/ reduced (or eliminated). In contrast, variability (see definition) is irreducible but can be better characterised or represented with further studies Uncertainty analysis Investigation of the effects of lack of knowledge or potential errors on the model (e.
It assesses how well the model fits relevant data patterns and if the model provides predicted endpoint/output values with an acceptable error range for risk assessment. This last step is performed through the comparison of model or submodel outputs with independent empirical data or the data that were not used for parameter estimation (calibration) Verification (of the code/model implementation) Examination of the algorithms and numerical technique in the computer code to ascertain that they truly represent the conceptual model and that there are no inherent numerical problems with obtaining a solution. Refers to basic tests to show that the source code works as it should for selected cases.
• DEBtox-model. bug: the model code in JAGS; • DEBtox-raw-data. txt: the observed data as they were provided in their raw format; • DEBtox-get-param. R: the R code to get parameter estimates within a separate. txt file; • DEBtox-results. R: the R code to check convergence and get graphical results of the goodness- of-fit. This file also provides the R code for  Figures 3, 4 and 5 of Billoir et al. (2011). These files provide outputs in two folders: • MCMC with two MCMC objects: À DEBtox-MCMC-param. RData containing the joint posterior distribution of the model fit to the observed data; À DEBtox-MCMC-pred. RData containing the predictions. • FIGS with graphical results: À DEBtox-Cext-Cint. pdf: fitting results with exposure data (Figure  4  from Billoir et al. , 2011); À DEBtox-density. pdf: densities of marginal posterior distributions; À DEBtox-obs-pred-SGRDm.
1) ARM AND HAND MOTOR ACTIVITY The data collected for assessment of the patients arm and hand motor activity are presented following the action sequence when using a cup. Firstly, monitoring of the liquid level allows analysis of the quantity of liquid poured into the cup. Therapists can use this information to evaluate the accuracy and coordination of movements during filling, since pouring water into a cup poses a challenge for motor-deficient stroke patients. Secondly, the pressure applied by the patient on the cup is monitored to evaluate the motor ability of the hand. Sunderland et al. [32]  showed that the force applied during the grasping phase is an indicator of the recovery state of the hand function. Finger spasticity or finger motor disorder can be easily detected, and this can be merged with other information to assess motor disorders.
3) ORIENTATION AND TREMOR DETECTION SyMPATHy contains an embedded inertial measurement unit (IMU) that retrieves raw movement data. The nine-axis motion-tracking device used (Invensense MPU-9150) contains a three-axis accelerometer, a three-axis gyroscope and three-axis magnetometer. Data are sampled at 30 Hz so that LEDs are lit smoothly (Figure  1. c). Data from the IMU sensor are used to calculate the orientation of the cup. Tremors are computed from the accelerometer and gyroscope in order to provide translational and rotational tremor information (Section IV-B2).
B. DATA PROCESSING 1) ORIENTATION CALCULATION The orientation of the cup is calculated from the values returned by the IMU sensor. We used the RTIMULib-Arduino 1 framework from RichardsTech to retrieve IMU data. This library also allows us to calibrate the accelerometer and the magnetometer and to compensate for gyroscope drift. When taking each reading, the gyroscopes bias is calculated and integrated into the calculation of the values. Based on the values returned by the IMU, the RTQF fusion algorithm is used to compute the position of the cup. This algorithm is a simplified version of a Kalman filter, which avoids matrix inversions and other intensive calculations for the effective fusion of data. RTQF linearly extrapolates between the previous and current orientations of the IMU using the gyroscope measurements and the time between samples.
02, since this gives the best fusion results. If the gyroscope and the accelerometer measurements exceed their full-scale range of motion, the algorithm will take a few seconds to close the gap between the predicted state and the measured state. A low-pass filter is also applied to the signals to suppress noise. We set the cutoff frequencies of the low-pass filter to 20 Hz for the gyroscope and 21 Hz for the accelerometer, based on empirical measurements. Data signals over 20 Hz are unnecessary since stroke tremors have a frequency lower than 5 Hz  [33]. Based on the quaternion computed by the RTQF algorithm, which corresponds to the 3D orientation of the cup, we calculated the tilt angle of the cup, corresponding to the angle between the cup and the vertical; in other words, this is the angle expressed in a spherical coordinate frame.
C. HARDWARE IMPLEMENTATION The architecture of SyMPATHy is based on the Raspberry Pi Zero (RPi-Z). The RPi-Z is a tiny computer (65. 0 mm × 31. 0 mm × 5 mm) which contains a 1 GHz single-core CPU and 512 MB RAM. The RPi-Z is inexpensive ($5) and well suited for prototyping. Data from the IMU sensor are retrieved using I 2 C (inter-integrated circuit) which is a multi-master, multi-slave, single-ended serial computer bus. The serial peripheral interface (SPI) bus, which is a synchronous serial communication interface for short distance communication, is used to communicate with the NFC reader. Moreover, due to a lack of analog-to-digital converters (ADC) on the RPi-Z that can convert a continuous physical quantity (usually voltage) to a digital value, we added these to the RPi-Z to connect the liquid sensors and the force sensors.
For example, Bluetooth communication requires a short distance between the device and the hotspot, thus limiting the operational space. In addition, Bluetooth communication can cause a loss of data, making the cup less reliable for patient monitoring. Although Wi-Fi offers an extended range and a better data transfer rate, its main constraint in terms of real-time communication is its high power consumption, which poses an important challenge in the field of autonomous smart objects. To solve this issue, the first prototype of SyMPATHy logs data locally using separate files. A Wi-Fi dongle was added to the cup in order to enable communication with a remote computer. Since power consumption is the main issue, the saved data are transferred to the computer over Wi-Fi when SyMPATHy is plugged into a micro-USB connector.
1) MONITORING OF MOTOR ACTIVITY BY A SMART CUP All of the participants mentioned that the use of an ADL to continuously monitor the motor activities of stroke patients within the rehabilitation center and at home was relevant, reporting that health care professionals cannot collect continuous data after the patient leaves the medical center. All participants also appreciated the fact that SyMPATHy can be used in the same way as any other standard cup. However, all participants suggested reducing the size and weight of SyMPATHy in order to facilitate its usage by stroke patients.
B. INSIGHT ON THE VISUALIZATION TOOL In order to present the data collected by SyMPATHy to the therapists, we designed an accessible visualization tool that allows for easy collection, recording and visualization of data in real time using a tablet. This tool also allows for visualization of previous records on the tablet. We decided to divide the application interface into four sections corresponding to the orientation of the cup, the pressure during grasping, the liquid level and tremors. Line charts were used for the orientation of the cup, the pressure during grasping and the liquid level, since these can present time series information and are the most widely used visualization tool for temporal data. A bar chart was used for translational and rotational tremors, as these are single values computed at the end of the record.
Each bar of the tremor graph represents the amplitude of the tremor on the corresponding axis during recording. By clicking on the graph, the user can display the tremor frequency of the corresponding axis. The application interface is illustrated in Figure  3. After the first visualization tool was designed, a preliminary study was conducted with health care professionals to assess its usability interface and to collect feedback on possible improvements. Three health care professionals who participated in the previous study were involved in this study. The therapists were two females and one male aged between 24 and 35 (M = 27. 5, SD = 6. 3) who worked with stroke patients in the rehabilitation phase. We presented the tool to these therapists and discussed the interface and the way in which the data were displayed.
c: DATA TRANSMISSION Six participants (five ischemic stroke patients with hemiparesis and dysmetria, and one hemorrhagic stroke patient with hemiparesis) agreed to the transmission of data over the Internet to the rehabilitation center; they did not have concerns about privacy since SyMPATHy data were not judged to be private by these participants. Two participants, including the oldest one (86 y. o. ) did not want to transmit their data via the Internet. One of them preferred to record data locally, and brought the USB stick to the rehabilitation center.
E. CONCLUSION This preliminary study provides interesting results from an initial evaluation of the usability and acceptability of the SyMPATHy smart cup. Most of the patients (≈ 89%) involved in this study mentioned that the shape, size and weight of the cup were good and that the cup was easily graspable. The rim was too thick, according to ≈ 56% of the patients. Most of the participants (≈ 89%) reported that the cup would be useful during sessions, and ≈ 67% agreed that the cup would be useful at home during their everyday life. Finally, ≈ 67% of the patients had no concerns about data transmission. The preliminary feedback from the patients in terms of usability and acceptability are promising, and further experiments are required to complete these findings.
The results showed that SyMPATHy was a promising prototype but required several improvements. Based on these results, a new cup prototype was developed, involving the removal of the displays and the detection of the cups position. This resulted in a reduction in the size and weight of the cup. A mobile application for data collection and visualization was also created. Interviews with health care professionals regarding this visualization tool allowed us to collect feedback and improve the visualization interfaces. Finally, a preliminary study was carried out with patients to assess the usability and acceptability of SyMPATHy. The results showed that the majority of patients agreed to use this device during sessions at the rehabilitation center and at home, and the design of the cup did not pose fundamental challenges for the patients except for the rim of the cup, which needs to be made thinner.
Future work will address several issues. New technologies will be investigated to provide a pressure map for the entire surface of the cup, providing more detailed information about grasping pressure and hand and finger positions, as identified in the preliminary study. Furthermore, information on movements such as linear acceleration or translation amplitude could provide complementary data on these movements. Experiments with stroke patients and therapists during rehabilitation sessions and at home that will assess the acceptability and usability of the SyMPATHy cup in more detail are currently planned. We are also planning to investigate the effectiveness of the measurements performed by the cup compared to clinical outcomes that monitor the recovery such as dynamometers or ARAT procotol.
Further, we compared our simulated potential productivity to simulations from ORCHIDEE-GM  [43]. ORCHIDEE-GM  [18]  is a version of ORCHIDEE  [44]  that has been recently developed to explicitly represent grassland management (GM) such as mowing and livestock density. Grassland management was developed by implementing the management module from PaSim  [45, 46] , a grassland model developed initially for site applications, into ORCHIDEE. For the regional validation, we used the N application data derived from  [4]  and CAPRI (described above) as well as WATCH climate data  [47]. The simulations (starting 1901) were thereby spun up for 500 model years in order to achieve equilibrium in carbon pool sizes with respect to the long-term climate. The soil carbon pool size was solved analytically during spin-up in order to reduce computation time.
Existing datasets are often model derived and characterized by large gaps and uncertainties, particularly relating to spatial patterns and livestock manure  [63]. While fertilization scenario data exists for specific crop types  [37, 64] , more efforts need to be done for managed grasslands given their high importance in the landscape of Europe. Some attempts have been made by  [65]  which were further processed and used by  [37]. However, the projections developed in the latter study contain only the two classes "intensive" and "extensive" which is insufficient for further detailed use in ecosystem models. The importance of N application projections for crops has been recently stressed by  [11]. Since the pasture area is projected to decrease in Europe  [12]  accompanied with an intensification of livestock production systems, changes in land-use intensity for grasslands can be expected as well.
Funding: JHB and VL are funded by the  ERA-Net BiodivERsA , with the national funder FORMAS, part of the 2012 BiodivERsA call for research proposals via the  EC21C  project. NBO is supported by  Lund University Center for studies of Carbon Cycle and Climate Interactions (LUCCI)  and  Lund University and University of Gothenburg Biodiversity and Ecosystem Services in a Changing Climate (BECC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
However, a common feature and limitation of these approaches is that they perform the walks in a single network of relationships between genes and proteins. Doing so, these approaches ignore a rich variety of information on physical and functional relationships between biological macromolecules. Indeed, not only PPI are nowadays described on a large-scale: immuno-precipitation experiments followed by mass-spectrometry can inform on the in vivo molecular complexes  (Ruepp et al. , 2009) , pathways interaction data are cured and stored in dedicated databases such as Reactome  (Fabregat et al. , 2016)  and Kegg  (Kanehisa et al. , 2008). In addition, other functional interactions can be derived, for instance from transcriptomics expression data by constructing a coexpression network, or from gene ontology annotations  (Ashburner et al.
(2015). To this goal, we estimated the relevance of each phenotype based on its frequency in the HPO database, and used the relative information content (IC), defined as follows: IC(i) = -log(f i ) (12) where f i is the frequency of the phenotype i within our set of HPO diseases. The similarity between phenotypes i and j is then computed as: sim(i, j) = max t∈anc(i)∩anc(j) IC(t) (13) where anc(i) indicates the ancestors of the phenotype i in the ontology graph. Finally, the phenotype similarity between a pair of diseases D a and D b , corresponding to two sets of HPO phenotypes, is measured by the total IC of their shared phenotypes, as described in  Resnik (1999) : sim(D a , D b ) = 1 |D a | i∈Da max j∈D b (sim(i, j)) + 1 |D b | j∈D b max i∈Da (sim(j, i)) (14) The similarity score between all pairs of diseases is computed according to Eq.
Leave-one-out cross validation In order to evaluate the performances of the different RWR algorithms, we designed a Leave-One-Out Cross-Validation (LOOCV) strategy. We downloaded diseases and associated genes from OMIM  (Hamosh et al. , 2005)  (downloaded on December, 2016) and DisGeNET v4. 0  (Piñero et al. , 2016)  (associations with a score greater than or equal to 0. 15, downloaded on December, 2016). Only diseases associated to at least two genes are considered. Each gene is removed one-by-one and considered as the left-out gene. The remaining genes are used as seed(s) in the RWR algorithms. Depending on the RWR algorithms to be tested, different subsets of these disease and associated genes datasets are extracted as explained on the results sections. All the network nodes are then scored and ranked according to their proximity to the seed(s).
Leave-one-out cross-validations on monoplex, aggregated and multiplex networks For these networks, the seeds used in the RWR algorithms are the gene/protein nodes only. To maximize the size of the test set, we ran the LOOCV with gene-disease associations extracted from DisGeNET v4. 0  (Piñero et al. , 2016). The DisGeNET dataset contains 6 565 gene-disease associations, corresponding to 4 148 different diseases.
Leave-one-out cross-validation on heterogeneous and multiplexheterogeneous networks For these heterogeneous networks, the seeds used in the RWR algorithms are the gene/protein nodes, but also the disease nodes. Given that the nodes in the disease-disease network are OMIM diseases  (Hamosh et al. , 2005)  (material and methods), it is mandatory to use gene-disease associations from OMIM for the LOOCV. The OMIM dataset contains 4 996 gene-disease associations, corresponding to 4 188 different diseases. As in previous applications of the LOOCV, for every disease, each known disease-associated gene is left-out one by one. The remaining disease genes and the disease itself are used as seed nodes. It is to note that in order to simulate an unknown gene-disease association, we also removed the bipartite association linking the left-out gene and the disease of the current run.
This test set contains 273 diseases and 1 312 gene-disease associations. For every disease, each of its associated genes is iteratively left-out, and the remaining gene(s) are considered as seed(s) to run the algorithms. We then compared the ability of the different algorithms to retrieve the left-out gene. Results are displayed in Fig. 2. Focusing first on monoplex networks, the worst performance is observed for the classical RWR algorithm applied to the Co-expression network. It seems difficult to retrieve known disease-associated genes from a network built from correlations of mRNA expression data alone. The Pathway-derived network achieves the best performance among the monoplex networks, probably because pathways databases are usually built on established biological knowledge and curated.
DISCUSSION Physical and functional relationships between genes and proteins are diverse. They are identified or derived from various approaches, each having its own features, strengths and weaknesses. In this context, the integration of different sources of interaction, exploiting data pluralism, is expected to outperform current approaches dealing with single networks. Indeed, the combination of different large-scale interaction datasets increases the available biological information, and potentially reduce the bias and incompleteness of isolated sources  (Menche et al. , 2015). We and others also hypothesized that the multiplex framework, which retains information on the topology of the individual networks, would perform better as compared to the aggregation of the different interaction sources,  (Kurant and Thiran, 2006; Kivelä et al.
It is interesting to note that the direct interaction between the products of LMNA and ZMPSTE24 is missing in the databases we used to construct the multiplex network. However, the ZMPSTE24 node is retrieved through different trajectories in the random walk. Hence, the combination of multiple network sources in this case allow completing missing interaction data. We focused our applications on a multiplex network composed of a PPI, a Pathway and a Co-Expression network. Other biological networks could be collected or constructed from -omics data, and integrated into our multiplexheterogeneous framework. Functional interactions can be derived, for instance, by connecting genes annotated for the same Gene Ontology (GO) terms  (Ashburner et al. , 2000). It would also be valuable to include networks with transcription factors -targets genes, non-coding RNAs, as well as drug and therapeutic targets.
, 2014). Such a degree-biased random walk could be applied to the RWR-MH algorithm in the future. In addition, for the sake of simplicity, all the networks considered in this study are unweighted. Nevertheless, the extension to weighted networks is straightforward, as pointed out in the material and methods. The use of weighted networks could improve the prioritization results because we can assign larger transition probabilities to the most confident interactions or to the more similar diseases. For instance, STRING database stores scored proteinprotein interactions indicating its confidence based on the evidences  (Szklarczyk et al. , 2015). The edges in our Co-expression network are established based on threshold imposed on the value of the computed correlation coefficient. This coefficient can be included into the Co-expression network to favor the transitions between the proteins whose expressions are more correlated.