dataesr/scientific-paragraphs-categorization huggingface dataset:
To overcome the difficulty of SNP discovery in O. foetida genome as a non-model and tetraploid plant, we utilized three different informatics pipelines, namely UNEAK, pyRAD and Stacks. This study showed that genetic differentiation occurred in the Tunisian O. foetida emphasizing the isolation by distance effect. However, no strong population clustering was detected in this work basing on the three data sets and clustering methods used. The present study shed the light on the current distribution and the genetic variation situation of the fetid broomrape in Tunisia, highlighting the importance of understanding the evolution of this parasite and its genetic background. This will aid in developing efficient strategies to control this parasite and its expansion in Tunisia and worldwide.
Bioinformatic Analysis The SNP calling was a challenging task due to the absence of a reference genome and the tetraploidy of O. foetida. We used three different software or workflows for comparison; (1) The Universal Network-Enabled Analysis Kit (UNEAK) implemented on TASSEL v3. 0 software characterized by its ability to resolve the paralog, repeat issues and to analyze ploidy organisms  (Lu et al. , 2013) , (2) Stacks, which distinguishes between sequence polymorphism and sequencing errors by the inclusion of a maximum likelihood statistical model  (Catchen et al. , 2013) , and (3) pyRAD  (Eaton, 2014) , which allows a better identification of homology across highly divergent samples by the incorporation of indel variation  (Qi et al. , 2015). We started our analysis by trimming raw reads with TRIMMOMATIC v.
0. 32 software (using the commands LEADING:19, TRAILING:19, SLIDINGWINDOW:30:20, AVGQUAL:20 and MINLEN:51). For the UNEAK pipeline, Tag counts were generated from FastQ files with the FastqToTagCountPlugin, then were merged with the MergeMultipleTagCountPlugin, the minimum count of a tag that must be present was defined to five. Tag pair was identified for SNP calling with UTagCountToTagPairPlugin and the error tolerance rate was specified as 0. 03. Then, UTagPairToTBTPlugi and UTBTToMapInfoPlugin were applied before generating the HapMap file with UMapInfoToHapMapPlugin by fixing minimum and maximum allele frequency on mnMAF = 0. 05 and -mxMAF = 0. 5,-mnC = 0. 5 and -mxC = 1. For Stacks, we set the minimum number of identical reads required to create a stack (m = 3), the nucleotide mismatches between loci within a single individual (M = 2), and the mismatches between loci when building the catalog (n = 1).
The 20 independent runs with 50000 burn-in and 100000 MCMCs were performed for each K value from 1 to 10 on a Linux workstation. Secondly, TESS3 provides ancestry coefficients estimations by combining matrix factorization and spatial statistical methods  (Caye et al. , 2016). It was developed in order to improve the inference of admixture proportions when they are variable across space  (Durand et al. , 2009). We applied a geno format file to run TESS3 in the Linux R environment according to the following parameters; K = 1:18, method = "projected. ls", ploidy = 2, max. iteration = 1000, rep = 20, keep = "best. " The visualization of barplots was performed using the R package "Pophelper" v2. 3. 0  (Francis, 2017 ) in R v3. 5. 3 (R Core Team, 2019). To complement the fastSTRUCTURE and TESS3 analysis, we carried out a discriminant analysis of principal components (DAPC), which does not assume any population models using the "adegenet" v2.
1. 1  (Jombart and Ahmed, 2011)  package in R v3. 5. 3 (R Core Team, 2019). Additionally, we performed the principal component analysis (PCA) using the "adegenet" v2. 1. 1  (Jombart and Ahmed, 2011)  package in R v3. 5. 3 (R Core Team, 2019), to extract the synthetic variables in order to represent major genetic variations. Genetic diversity and genetic variance were estimated within and among the 18 collected populations. We used "hierfstat" v0. 04-22 package  (De Meeûs and Goudet, 2007)  in the R v3. 5. 3 (R Core Team, 2019) to determine genetic diversity parameters including the allelic richness (Ar), the observed heterozygosity (Ho), the expected heterozygosity (He), the inbreeding coefficient (F IS ) and the fixation index (F ST ). The analysis of molecular variance (AMOVA) was carried out to examine patterns of genetic variation and to estimate variance components for the 18 collected populations using "poppr" v2.
8. 2  (Kamvar et al. , 2014)  package in R v3. 5. 3 (R Core Team, 2019) (using poppr. amova function). Furthermore, we tested the isolation by distance (IBD) by performing a Mantel test for correlation between Edwards' distances and Euclidean geographic distances for the 18 collected populations with "adegenet" v2. 1. 1  (Jombart and Ahmed, 2011)  package in R v3. 5. 3 (R Core Team, 2019) (using mantel. randtest function) by conducting 999 permutations for significance assessment.
Population Structure According to our surveys conducted in several regions known as natural habitats for Orobanche spp. , we detected the presence of O. foetida populations only in the north of Tunisia as mentioned in Figure  1. To investigate population structure of this fetid broomrape collected from the north of the country, we applied three different clustering methods including model-based methods (TESS3 and fastSTRUCTURE) and non-model-based method (DAPC) to the three genetic data sets generated by pyRAD, Stacks and UNEAK pipelines. For a better comparison among the clustering patterns, we superimposed the pie charts of the average membership probabilities of each population (estimated Q values) on geographic maps (Figure  2 ). We defined the parameter K, which describes the number of subpopulations that make up the total population  (Verity and Nichols, 2016) , as the minimum values of Ks that can capture biologically interpretable genetic structure  (Pritchard et al.
Moreover, we recognized a slight divergence among the assessed groups. However, we observed an overlapping especially among the three groups representing populations from the north-west using the three data sets. Additionally, we calculated the pairwise F ST among the four groups in order to investigate the genetic distance among them. As expected, the highest F ST values were observed between Group 1 (north-east) and the other Groups (north-west) using the three datasets (Table  1 ). Whereas, the lowest F ST values were detected firstly between Group 2 and Group 3 using Stacks data set (F ST = 0. 0078), secondly, between Group 2 and Group 4 using pyRAD (F ST = 0. 0109) and UNEAK (F ST = 0. 0080) data sets as shown in Table  1. Overall, we detected a low F ST values among the four potential groups using the three data sets.
Comparison of RADseq Pipelines Used for SNP Calling In the present study, we adopted a de novo assembly approach due to the absence of a reference genome, but the choice of the pipeline remains a relevant decision. Indeed, pipelines differ in the applied algorithms and the possibility to change some parameters. Besides for several pipelines, some intermediate files are not accessible; these files are temporary and deleted in the end of the analysis, which does not allow to track the evolution of the workflow and to detect some possible issues. For that purpose, we used three different software pipelines UNEAK, pyRAD and Stacks, which generate different numbers of SNP as expected. The number of the detected SNPs varied among the three data sets. Indeed, Stacks generated the highest number (10,755 SNPs) followed by pyRAD (1,785 SNPs), however, UNEAK detected only 206 SNPs.
This last result could be explained by the ability of UNEAK to eliminate paralogs, which are highly present in polyploidy species. Differences noticed in our results when applying different pipelines for the same dataset are also reported in various studies. In fact, the work of  Torkamaneh et al. (2016)  on the comparison of seven pipelines and two sequencing technologies, reported that UNEAK generated 82% more SNPs than Stacks. Moreover,  Qi et al. (2015)  found differences in phylogenetic patterns using pipelines requiring or not a reference genome (Stacks, TASSEL, UNEAK and pyRAD). Additionally, they obtained higher number of SNPs (25,000 to 54,000 SNPs) for reference genome pipelines than without a reference genome (13,000 to 24,000) which emphasizes the importance of a whole genome sequencing.
II. RELATED WORK In this section, we detail the related work by presenting different PET solutions from the literature. As illustrated in Figure  1 , this includes anonymization, pseudonymization, homomorphic encryption, differential privacy, data summarization, and decentralized learning. We describe the benefits and limitations of each PET solution. We further present a few prototypes that utilize blockchain technology to improve user privacy. Anonymization and pseudonymization techniques are used to remove personal information from the dataset allowing data owners to remain unidentifiable  [6]. Anonymization is completely irreversible whereas pseudonymization allows the data owner to be re-identified by using some additional information. The three most common techniques are K-anonymization  [4] , L-diversity  [7]  and T-closeness  [8].
K-anonymity is achieved using suppression and generalization methods until each row of the dataset becomes identical to at least k -1 other rows. L-diversity is based on k-anonymity to address homogeneity and background knowledge attacks  [9] , whereas T-closeness  [8]  reduces attribute disclosure by decreasing the granularity of the data. These techniques are being widely adopted to achieve user privacy. However, they consistently reduce the originality of the information by decreasing the accuracy and precision. This highly affects the utility of the data. Higher levels of anonymization enhance privacy but make data unproductive and ineffective. Homomorphic encryption allows procedures to be performed on encrypted information, guaranteeing the same output as if the operations were performed on the clear data.
This is done by adding statistical noise to each user's data before it is shared  [11]. The aggregate information from the dataset can then be extracted by deducting the noise from it. However, information regarding a particular individual cannot be derived. Three major strategies are used in differential privacy: Laplace, Exponential, and Gaussian  [12]. The Laplace and Gaussian strategies are typically utilized for numerical datasets, while exponential techniques are used for non-numerical datasets. Differential privacy is considered to be an effective method to gather aggregate user information while preserving the privacy of individuals. However, privacy is achieved at the expense of data accuracy. The amount of noise added highly impacts the precision and accuracy of data. The trade-off between data precision and privacy is controlled using an operator referred to as epsilon  [13].
III. BLOCKCHAIN OVERVIEW Blockchain technology has recently attained a lot of attention as it is used in various intuitive applications. Blockchain is a distributed database maintaining a scalable list of data records. Each block of information carries transaction data, a timestamp and an encrypted hash value of the previously linked block  [25]. Linking these blocks using the hash function makes the chain of data grow in the database. The concept came up in 2008 by Satoshi Nakamoto, who devised the first blockchain database by solving the double-spending problem using the Hashcash method. Blockchain was further adopted as a technique to implement the cryptocurrency bitcoin  [15]. In bitcoin, blocks serve as a public ledger that keeps all the financial transactions. Recently, blockchain has been integrated with other business domains and fields of application to enhance their security and privacy.
D. WHY BLOCKCHAIN The revolution of blockchain technology has recently reached efficient and successful integration with different applications. This success comes from fundamental features that we summarise here as follows: • Decentralization: Blockchain distributes information to all nodes available on its network instead of keeping the information in a central entity. This makes it more secure, as no single entity can take control of the network and tamper with information. • Immutability: Data inside a blockchain cannot be altered since there are many copies of data on different nodes, which makes tampering almost impossible. Furthermore, the use of cryptography does not allow anyone to intrude and alter the data saved on the blockchain. Any modification of data needs to be agreed upon by a majority of nodes.
• Integrity, Authenticity, and Non-Repudiation: When a transaction is made the data is guaranteed against any modifications by verifying the content against its transmitted hash. The user's private key is used to digitally sign the transaction, which assures the authenticity and non-repudiation of each message. • Auditability and Traceability: Each transaction is recorded in a verifiable and permanent manner. In a blockchain, all operations are traceable and available to each participant. These features facilitate accountability and audit.
K E D and store it over the smartcontract along with the data identifier. The system model of PETchain is presented in Figure  4. There are two kinds of messages over PETchain: API calls, and contract transactions. The API messages are used by entities to interact with each other, whereas contract transactions are sent to the smartcontract over the Ethereum network. To completely understand how the PETchain system model works we describe the several steps involved: 1) Publish smartcontract: The user defines its access control over its smartcontract. The smartcontract contains the list of service providers authorized to utilize user data. 2) Store data: The user encrypts its data using E D and uploads it over the distributed storage system. 3) Update data identifier: The user updates the data identifier of the uploaded data over its smartcontract.
In the following step, the data file is digitally signed by the user. The user uses the private key P eth r of thee Ethereum's EOA to sign the data file. The public key P eth u can then be used to verify that the data file belongs to the same user that is registered on Ethereum and owns the smartcontract. The hash of the digitally signed data file is taken using an SHA 256 algorithm. The hash acts as the data identifier. The user then uploads the data over IPFS in the form of an IPFS object that consists of the data field and link field. The data field consists of a data file whereas the link consists of the data identifier. IPFS data field can only accommodate 256 Kb of data. Therefore, if the data file is larger than 256 KB, it is broken down into several IPFS objects and stored over different peers in the network.
C. TRUSTED EXECUTION ENVIRONMENT In PETchain, a trusted execution environment (TEE) is used to process user's data. Users can implement their TEE or use a third party trusted executor. A TEE is a processing environment that allows the authenticity, integrity, and confidentiality of its data and code. The basic building blocks of TEE are presented in Figure  6. TEE allows service providers to process the user's encrypted data in a secure environment without having access to the data. The trusted executor has access to both user's data and the service provider's code. However, the architecture of TEE makes it unfeasible for anyone to have a copy of the data. Once the code is executed over the data the service provider is informed about the results of the execution. In PETchain, we choose Graphe-neOS 3 which is an open-source, security-hardened GrapheneOS implemented over Intel SGX hardware provides adequate performance.
D. PETchain SMARTCONTRACT We describe now the PETchain smartcontract and its functions. The PETchain smartcontract is written and compiled in solidity. The contract is deployed and owned by the user. When the contract is deployed the user's EOA address is recorded as the owner of the smartcontract. The smartcontract is then used to store and retrieve data identifiers. The variables used in the smartcontract to store information include i) data_id: contains data identifier, ii) data_key: contains encrypted key for the data, iii) SP_address: contains the address of the service provider, iv) TE_url: contains the URL of the trusted executor, and v) Status: indicates whether the service provider is trusted or not. We use mapping value type in solidity to relate and store information due to its lower computational cost compared to arrays  [34].
6 The genesis consists of a simple JSON file that contains the configuration parameters and thresholds. Listing 1 shows a sample genesis file that consists of all necessary parameters required to set up a PoA blockchain. The algorithm used for PoA consensus in Ethereum is Clique. The chainid for a Clique network can be any number other than 1,2,3,4,42 and 62 which are associated with the main and test networks. The difficulty field is set to zero in clique as no mining is required. The gasLimit field is used to set the blockgas-limit whereas the Epoch Period is for configuring the block-time. The extraData field is used to add the addresses of accounts that are selected as sealers at blockchain initialization. We used to initialize a node in each virtual machine. Each node is configured with the same genesis block.
To initialize the blockchain the enode address of nodes are provided. Enode address is a URL that consists of the 512 bit public key and the IP address of nodes. This allows nodes to discover their peers. After initializing the blockchain we 4 https://github. com/ethereum/go-ethereum/wiki/geth 5 https://github. com/ibrahimtariqjaved/PETchain 6 https://github. com/puppeth?q=&type=&language Listing 1. JSON genesis. used Remix  7  and Metamask 8  wallet to deploy PETchain contracts. The EOA generated using geth were imported to the Metamask to deploy smartcontract. The remix is linked to Metamask using injected web3 provider whereas the Metamask wallet is connected to our clique network using RPC URL.
VI. CONCLUSION In this paper, we proposed a novel privacy enhancing technology based on blockchain to manage and exploit user data. The proposed technique aims to address user privacy holistically. In our approach, users can define their access control policy by deploying their smartcontract. Users upload encrypted data to IPFS and store its hash to their smartcontract. The authorized service providers can access the hash but can only decrypt and process the data in an isolated execution environment. This allows service providers to process user data without acquiring nor misusing the data. To the best of our knowledge, this is the first-ever implementation of PET using blockchain technology that holistically addresses privacy. In our approach, the users select a trusted execution environment where their data can be processed in a privacy enabled manner.
Moreover, they can store their data independently in a distributed manner using IPFS. The proposed solution has been implemented on a consortium blockchain due to its privacy, cost, and performance advantages over the public blockchain. The performance of PETchain is analyzed using the Ethereum platform. It was observed that a high TPS can be achieved by having low block-time and a high gas-limit. However, lower block-time was observed to have a high amount of lost blocks, decreasing the performance of the blockchain. Therefore, a bock-time of 10 is proposed that allows a TPS of around 60. Moreover, a lower number of sealers is recommended, to reduce propagation delay in the network. For our future work, we aim to analyze and improve PETchain by checking its compatibility with GDPR.
Tree structural and biomass growth studies mainly focus on the shoot compartment. Tree roots usually have to be taken apart due to the difficulties involved in measuring and observing this compartment, particularly root growth. In the context of climate change, the study of tree structural plasticity has become crucial and both shoot and root systems need to be considered simultaneously as they play a joint role in adapting traits to climate change (water availability for roots and light or carbon availability for shoots). We developed a botanically accurate whole-plant model and its simulator (RoCoCau) with a linkable external module (TOY) to represent shoot and root compartment dependencies and hence tree structural plasticity in different air and soil environments. This paper describes a new deep neural network calibration trained on simulated data sets computed from a set of more than 360 000 random TOY parameter values and random climate values.
e. architectural units;  Barthélémy and Caraglio 2007). Both models produce botanically accurate shapes obtained by calibrating their parameters using measurements taken on real plants. The models were tested and validated on many plant species  (Barczi et al. 2008 (Barczi et al. , 2018)). The simulator of RoCoCau combines the AmapSim and DigR simulators in a single program in which all shoot and root meristems are synchronously simulated with a software architecture that allows external models that can interact with the default simulation kernel to be plugged in  (Barczi et al. 2008).
e. structural categories and their relative organization). For RoCoCau settings and for TOY settings, it is also up to the modeller to define the content of each of the six functional categories in terms of structure. The flexibility of this approach makes it possible simulate a wide range of architectures considering both structure and function. Figure  1  shows a simple example of a shoot/root architectural unit and a shoot growth loop. In the rest of this paper, it is important to clearly distinguish between the structural axis categories (a classification defined in RoCoCau) and the functional axis categories (a classification defined in TOY). The software implementation of RoCoCau enables dynamic linking of plug-ins that contain additional knowledge that will interact with the default RoCoCau algorithm.
Pre-processing the simulation of architecture development by RoCoCau, to be used as inputs for VoxNet Representing plant architectural development as a voxelized density map requires the definition of a unique pre-processing procedure that can transform a simulation by the RoCoCau software into a voxelized density map (Fig. 4 ). In this study, the simulations provided by the RoCoCau software are performed in 10 time steps, representing 10 successive plant structures. To be compatible with the VoxNet network input format, the size of the 3D image containing the voxelized density map is fixed at 30 × 30 × 30 voxels. For each simulation, the pre-processing procedure relies on (i) the voxelization of the 10 successive structures of the RoCoCau simulation, and (ii) the segmentation of the 3D image into 10 timespecific zones to host these structures at 10 successive ages.
Testing the quality of the prediction by trained VoxNet In each training run, changes in the value of the training loss function (RMSE) were monitored to ensure the effective learning of VoxNet (Fig. 5B ). Same monitoring was also carried out on the validation data set to avoid overfitting. The quality of the training is tested on a test data set containing 400 test examples. For each example of the test data set, the input of the example, i. e. the voxelized density map is used as an input to the trained VoxNet. The trained VoxNet then delivers a prediction for the values of the chosen set of three hidden parameters it has been trained to predict, e. g. the death threshold, the branching threshold and the apical growth factor (see Table  1 ). Its prediction is then compared with the output of the example, containing the true values of the three hidden parameters.