dataesr/scientific-paragraphs-categorization huggingface dataset:
Within the lycophyte and bryophyte species, no type II, III, IV nor VIII nsLTPs were identified. In the same way, no type III, VIII, IX or XI were identified within gymnosperm species. Ten out of the 16 moss P. patens nsLTPs were type IX, the other 6 remained un-typed, and the only liverwort Marchantia polymorpha nsLTP was a type VI. The 34 S. moellendorfii sequences were mainly types V and VI (15 and 7, respectively) and seven nsLTPs belonged to the new type XI. The P. patens and S. moellendorfii nsLTPs formed independent branches or were located at the same branch as type V in Wang's phylogenetic tree  (Wang et al. , 2012)  and were included in type D in Edstam's classification  (Edstam et al. , 2011). However, Edstam's type D included rice and Arabidopsis type IV, V and VI nsLTPs. Edstam's type G nsLTPs, which corresponded to GPI-anchored LTPs and types J and K , which did not fit our molecular mass criteria or contain more than one 8CM motif were not included in our dataset.
cacao nsLtp genes are organized in clusters, as observed in the rice, Arabidopsis and sorghum genomes  (Boutrot et al. , 2008; Wang et al. , 2012). Apart from nine sequences that were classified in the new type XI, all other T. cacao nsLTPs were classified within the previously identified types and belonged mainly to type I (14 sequences), type VI (7 sequences) and type V (6 sequences). It is worth noting that all the nsLTPs identified as allergens (IgE binding) were type I, except one type II nsLTP (545_BRACM). The 501_MEDTR nsLTP was also suggested to play a role in the root nodulation process  (Pii et al. , 2009; Pii et al. , 2013). Lipid signaling (lysophosphatidylcholine) has been reported to be involved in symbiosis  (Bucher et al. , 2009). This analysis was the most extensive so far and confirmed most of Boutrot's classification, but complements it due to a larger dataset and a more detailed phylogeny analysis.
In the following sections, we list type I nsLTP-specific residues whose differential conservation was supported by structural or experimental data. First, Gly37, which was specifically conserved in type I nsLTPs, allowed very tight contact of helix 1 and helix 2, which were connected by the disulfide bridge Cys17-Cys34. The closest backbone distance between position 13 of helix 1 and position 37 of helix 2 was 3. 34 Å in a type I nsLTP structure (PDB code 1mid) while it was 6. 45 Å in a type II nsLTP structure (PDB code 1tuk). These increased helix distances closed the ligand tunnel, which was opened in type I nsLTPs between helix 1 and helix 3, and created two distinct cavities separated by a septum in type II nsLTPs  (Hoh et al. , 2005). Larger distances between helix 1 and helix 2 were predicted in all nsLTP sequences where Gly37 was mutated into larger residues (i.
NsLTP structure-function relationship Dealing with big datasets can be cumbersome and requires a very efficient interface. To address this challenge, we developed InTreeGreat, a Javascript/PHP interface, compatible with every standard web navigator. It is able to display and explore any tree and to deal with branch and leaf coloring, branch lengths, branch support (or any other branch labels), and can aggregate heterogeneous data (annotations, expression profiles, etc. ). Figure  6  shows how InTreeGreat can be used to display phylogenic trees together with various types of annotations. Among the annotated nsLTPs (433 out of 797), we focused on those that had been reported for their role in plant defense and/or resistance against pathogens (bacteria and/or fungi). To simplify, we shall hereafter refer to them as "defense nsLTPs" in the present discussion.
By investigating structural similarities between the 31 identified defense nsLTPs in our annotated dataset, we attempted to identify key amino acid residues that may bestow their functional properties on these proteins. Looking at the distribution of the defense nsLTPs in our structural classification (Figure  6 ) we observed that they were predominantly found in the type I part of the tree (28 proteins), with only 3 defense nsLTPs with a type II (85, 151, 501 -UniProtKB -P82900: Non-specific lipidtransfer protein 2G, Q8W453: Putative lipid-transfer protein DIR1, O24101: Lipid transfer protein). We therefore preferred to focus on the Type-1 fold nsLTPs and study the structural trace(s) inside this important subfamily of nsLTPs. The cluster containing all Type-1 fold defense nsLTPs corresponded to the whole type I part of the tree (402 members).
This phylogenetic analysis was conducted from a clearly defined dataset: sequences were selected using unambiguous parameters optimizing the quality of the output tree, also considering our 3D structural integration objective. Although GPI-Anchored LTP could have been included in this study, their incomplete homology with other LTPs and the lack of any experimental 3D structure, convinced us not to include them. Thanks to this choice, alignment quality was preserved, and a better-quality 3D structural model are used. This analysis allowed us to classify unambiguously all 798 sequences in the main two nsLTP families, complementing and reinforcing the former classification by Boutrot  (Boutrot et al. 2008). C) The production of more than 600 3D structural models and the collection of numerous functional annotations enabled progress to be made in the study of structure-function relationships of nsLTPs.
CONCLUSIONS Plant non-specific Lipid Transfer Proteins constitute a complex family of proteins whose biological functions are far from well understood. However, it has become clear for years that they are of increasing interest for agronomical and nutritional issues. Experimental approaches are irreplaceable for accessing their inner functional mechanisms. However, such methods are expensive and time-consuming. Furthermore, they produce a large amount of heterogeneous data. For all these reasons, resorting to bioinformatics methods has long become necessary to organize and analyze existing data, and/or model and hypothesize new data. This paper presented a new methodology based on the combination of either classical or original bioinformatics approaches, using various computational tools to extract information and suggest new hypotheses from a large pool of experimental data about the plant nsLTP superfamily of proteins.
ACKNOWLEDGEMENTS This work was supported by the  CIRAD -UMR AGAP HPC Data Centre of the South Green Bioinformatics platform  (http://www. southgreen. fr/)The authors are thankful to  Dr. Franck Molina  for his key role at the beginning of this project and all the fruitful and friendly discussions we are thankful to  Peter Biggins  for the careful and critical review of this manuscript.
On each channel, the atomic amount of data exchanged by the connected actors is called a token, and all write and read operations are measured in tokens. An actor produces (resp. consumes) a certain number of tokens on a channel when it writes (resp. reads) the corresponding amount of data. With this policy, the graph can be assimilated to a Kahn Process Network (KPN)  [13]. In a KPN, the communications are determinate, but in general it is not possible to decide if the sizes of the channels can be bounded for an unbounded execution of the system. Synchronous and Asynchronous Constraints. In practice, sensors and actuators have a fixed sampling rate, and the production of each data sample occurs at that specified frequency. To model these constraints, we propose to label some actors with frequencies, corresponding to the real-life constraint.
Tool Support for Liveness Checking DIVERSITY is a customizable model analysis tool based on symbolic execution, available in the Eclipse Formal Modeling Project  [17]. DIVERSITY provides a pivot language called xLIA (eXecutable Language for Interaction and Architecture) introducing a set of communication and execution primitives allowing one to encode a wide class of dynamic model semantics  [2, 9] , Communicating STS  [1] , and abstractions of hybrid systems  [15]. In this work, we use it to analyze Polygraph models, to check their liveness in a similar way to that defined by a scheduler as per Definition 10. The root entity in an xLIA model is a so-called system. A system is an executable entity that can be atomic (state-machine) or compositional or hierarchical. A Polygraph model translated to xLIA is a system where the actors are state-machines with input/output ports associated with the ends of the channels.
Regarding state machine semantics, all the states are pseudo-states, except idle which is stable. This means that any fired transition must be completed until returning to the idle state. The else transition will be evaluated if there is no possible reception. The xLIA language allows a fine-grained definition of an execution model for the actors of a polygraph. Some instructions associate a sequence of actors to fire with each tick of a clock. When attempting to fire a timed actor, only one firing is triggered if possible, and when attempting the same for other actors, as many firings as possible are triggered. Hence, the timed actors are only fired at the expected tick, and cause a deadlock result if it's not possible. For the other actors, a counter limits their number of firings to their coordinate in the minimal repetition vector, as required by Theorem 2.
At this stage, since both PARAM and PRISM failed to verify our simplest model because of its complexity, we considered using a different approach based on Parametric Statistical Model Checking. For this purpose, we developed a prototype tool  5. In this context, our model was expressed as a python program using real-valued variables both for the position of the UAV and for the probabilistic parameters. It appears that PSMC is particularly efficient in this context, and was able to verify our model (by performing more than 20k simulations) in less than 1 minute. We therefore chose to pursue our experimentation using this prototype tool and refined versions of our model. In the second version of the model, depicted in Figure  7b , we allowed deviations to also occur along the x-axis. This is not problematic when considering a straight line flight plan, but could become important as soon as the flight plan is curved (as the one in Figure  1 ).
In another initiative  [70] , the access control of social network data has been designed to preserve privacy and trust. While the access control method has been implemented within social network data privacy and sharing, the access control mechanism can be leveraged for sharing the health data as an overlay layer on top of blockchain. A novel access control method of IoT data has been illustrated in  [71]  where the access control policies have been deployed as a smart contract within a blockchain. The framework also deploys a judge smart contract to monitor the behavior of IoT nodes based on the real-time data available from IoT nodes. Based on the smart contract policy and the types of data, the judge contract can either accept or punish the misbehavior of the IoT node. This will allow the fairness and validity of the IoT data, thereby adding explainability and reasoning to the IoT data.
An interesting access control mechanism to support critical health-related events has been proposed in  [72]. The system takes into account the dynamic contextual data available from a subject's surrounding IoT data and maps that event into the management of critical situations. The authors have also introduced an ontology to map each dynamic contextual role with the associated access control policies. Although much advancement has taken place in the OT domain, sensor-based activity recognition, and data security, in-home QoL-enhancing OT interventions for cancer care treatment have yet to interweave the much-needed OT within the treatment plan. In this paper, we contribute toward bridging the gap by proposing a framework that takes advantage of technological advancements in IoT and blockchain-based data analytics to support OT-based QoL data as a complement to cancer care treatment.
Software Components Figure  4  shows the high-level software components of the proposed framework. The QoL Data Manager is responsible for capturing the multisensory OT data and sharing with both live and off-line processing. One of its components, Live Data Manager, is responsible for live tracking the activities of daily living corresponding to OT, while the OT Session Recorder works while an active OT session takes place. The Cyber-Physical QoL Intelligence component houses all those components that process and intelligently recognize different OT actions. The Health-Related QoL (HRQoL) Rendering engine Sensors 2019,  19, 5258  receives the streams of different sensory media, parses each stream, and based on the therapy, renders it to appropriate User Interfaces. The User Interfaces that require spatiotemporal processing, such as Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) Serious Games  [92] , go through the spatiotemporal analyzer for adding OT game logic.
Software Components Figure  4  shows the high-level software components of the proposed framework. The QoL Data Manager is responsible for capturing the multisensory OT data and sharing with both live and offline processing. One of its components, Live Data Manager, is responsible for live tracking the activities of daily living corresponding to OT, while the OT Session Recorder works while an active OT session takes place. The Cyber-Physical QoL Intelligence component houses all those components that process and intelligently recognize different OT actions. The Health-Related QoL (HRQoL) Rendering engine receives the streams of different sensory media, parses each stream, and based on the therapy, renders it to appropriate User Interfaces. The User Interfaces that require spatiotemporal processing, such as Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) Serious Games  [92] , go through the spatiotemporal analyzer for adding OT game logic.
The Sensors 2019, 19, 5258 14 of 30 patient intends to share a subset of historical as well as the QoL dataset available from his private blockchain, which is termed here as Q 1. Hence, the system creates a smart contract to facilitate this transaction, which uses the public key of the patient (Pu A ) to provide the decryption function Decrypt for access to the sharable dataset Q 1. Once the smart contract has accessed the dataset Q 1 , it requests the public key of the oncologist (Pu B ) to encrypt the QoL dataset Q 1 and send a notification to the oncologist to visualize Q 1. The oncologist can now decrypt the Q 1 dataset by using his/her private key (PK B ) from his/her digital wallet. After visualizing the QoL OT data, the therapist can decide the functional status of the patient and adjust the treatment.
Each gesture is interpreted as a specific OT action where each action has a QoL monitoring metric mapped to it. The gestures corresponding to QoL actions can be customized. When a cancer patient makes certain gestures in the OT environment, specific QoL data is collected. In our proposed research, off-the-shelf sensor devices such as Microsoft Kinect, the Leap Motion controller and the Myo EMG device from Thalmic labs, radar sensors, EEG, and other medical IoT hardware and serious games-based software-based frameworks have been investigated  [99] [100] [101] [102] [103]. In our proposed system, we have combined smart home control with OT in a mixed reality environment using natural user gestures, which can be tailored for cancer patients' QoL monitoring.
Examples of these physical activities are opening a door using a knob rotating gesture, dimming a light using a particular gesture, speeding up a fan using a gesture, etc. When a patient performs OT such as turning the thermostat of the air-conditioner, the actual movement of the thermostat will take place on the smart switch connected to the network, and the patient will be able to see that on his terminal screen, too. Then, the OT movements are mapped to certain QoL measurement metrics or a questionnaire, depending on the therapy profile of a patient. Similarly, if the OT includes turning on a light switch, the light will turn on if the patient manages to perform the therapy correctly. In all cases, the user will be able to see the percentage of success of performing the therapy. The data generated by this OT session will give an occupational therapist as well as a physiotherapist insight into the improvement and effectiveness of the physiotherapy.
Finally, using our novel clinical data analytics, a therapist can visualize a live statistical analysis of the kinetic and kinematic motions and metrics and decide the quality of improvement of a particular patient clinically. The framework detects the following gestures that help in defining and measuring occupational therapy-related activities: Figure  20  shows our developed non-invasive, quality of life-supporting ambient intelligent sensory board, which will allow different aspects of patient's data to be captured. With the help of analytics and an AI model for each type of captured sensory data through the DApps, the data are securely stored at the patient's edge network. We have tested an EEG device to capture somatosensory data, an EMG device to capture muscle tone, gesture-tracking sensors to capture kinematic and kinetic data, a pupil tracker for gaze tracking, medical IoT devices for vital information collection, and smart home IoT devices to collect user ambient information.
We have tested an EEG device to capture somatosensory data, an EMG device to capture muscle tone, gesture-tracking sensors to capture kinematic and kinetic data, a pupil tracker for gaze tracking, medical IoT devices for vital information collection, and smart home IoT devices to collect user ambient information. Figure  20  shows the ambient intelligent environment surrounding a cancer patient, employing a set of IoT sensors to be able to capture quality of life-related data, digesting the captured data ubiquitously, saving it to the nearby edge blockchain or decentralized big data repository, using deep learning AI techniques to find phenomena of interest, and applying monitoring analytics to share the right data with the community of interest. While the transactions are stored in the blockchain, the EMR data will be stored in the off-chain, while the hashes of the off-chain will be stored in the block for further references.
Blockchain Performance Testing We have tested the blockchain machine farm with a physical setup consisting of 4 Core, 8 threads, and 16 GB of RAM. The IoT data-sending rate was maintained at 300 transactions per second (tps). The following table shows the latency metadata of the test. Table  1  shows the summary of the system benchmark test, in which the maximum latency is 4. 32 seconds, the minimum latency is 1 second,.
Blockchain Performance Testing We have tested the blockchain machine farm with a physical setup consisting of 4 Core, 8 threads, and 16 GB of RAM. The IoT data-sending rate was maintained at 300 transactions per second (tps). The following table shows the latency metadata of the test. Table  1  shows the summary of the system benchmark test, in which the maximum latency is 4. 32 seconds, the minimum latency is 1 second, and the average latency is 2. 9 seconds for a benchmark test of 266 transactions per second. The latency is because the high throughput IoT data needs to be captured, parsed, and then sent to the private, permissioned blockchain nodes, which approve the transactions and add the InterPlanetary File System (IPFS) hash to the blockchain. 1  assumes different blockchain and control nodes with a certificate authority that are part of the blockchain and off-chain clients.
the bivalve Pinctada margaritifera has the capacity to produce the most varied and colourful pearls in the world. Colour expression in the inner shell is under combined genetic and environmental control and is correlated with the colour of pearls produced when the same individual is used as a graft donor. one major limitation when studying colour phenotypes is grader subjectivity, which leads to inconsistent colour qualification and quantification. Through the use of HSV (Hue Saturation Value) colour space, we created an R package named 'ImaginR' to characterise inner shell colour variations in P. margaritifera. Using a machine-learning protocol with a training dataset, ImaginR was able to reassign individual oysters and pearls to predefined human-based phenotype categories. We then tested the package on samples obtained in an experiment testing the effects of donor conditioning depth on the colour of the donor inner shell and colour of the pearls harvested from recipients following grafting and 20 months of culture in situ.
To select the colour area, the free GNU Image Manipulation Program (version 2. 8. 22) was used (selection with lasso, copy, paste like image, export as. jpeg). R software v 3. 2. 3 (R foundation for Statistical Computing) was used to develop and run an image analysis package, which we named ImaginR. For the remainder of the analysis, the working directory is set and the folder containing all the pictures is placed inside it. After loading the ImaginR (V2) package, the only function needed to run the analysis is called OutPutResult(). Simply by typing OutPutResult(), R will then recognize the picture folder and automatically perform the analysis. ImaginR will import all pictures with the. jpeg extension and list the file names into an R object. Thus, the picture's name (which corresponds to one sample) is stocked into this object.
The analysis will therefore be made on a picture-by-picture basis. The picture is imported through the load. image() function of the ImaginR package  57. Then, each pixel is given an RGB (Red Green Blue channels) coding, which will be converted into hex triplet code (all conversions are realized with the grDevices package  58  ) and compared to a white hex triplet database. The white hex triplet values of the pictures are thus deleted in order to remove any information derived from the background of the picture. The remaining pixels are converted back into an RGB matrix, and an average is calculated for each channel (R, G and B). The average colour of the chromatic zone of each sample is then obtained. The hex triplet code is also calculated from this average. The average RGB is converted into HSV code with the rgb2hsv() function from the grDevices package.
Thus, for each image, the hue (H), saturation (S) and brightness (V) provide a synthesis of the colour status. The hue variable is compared to a reference hue range that delimits colours to classify the sample into a known phenotype. The reference hue range was delimited with a machine learning procedure using pictures of individuals with the coloured peripheral zones of the two valves extracted as described above. These ImaginR database individuals were obtained from a colour breeding selection programme at SCA Regahiga Pearls (Mangareva island, Gambier archipelago, French Polynesia). Approximately 200 individuals of each colour phenotype with a size between 10 and 12 cm were produced. Among these, five individuals per colour phenotype group were selected for their particularly colourful phenotypes and according to colour characteristics sought by pearl farmers.