dataesr/scientific-paragraphs-categorization huggingface dataset:
They do so by creating and monitoring the flow of FTs that serve as access coins through dedicated IoMT interfaces, which they sell via an automatic broker. Potential buyers then interact directly with the broker to acquire these tokens that can then be redeemed for access to certain content. Although functional, this software could benefit from the workflow presented in this paper. Whilst this paper's innovation could support the MThing Smart Contracts (produced by the workflow shown in Figure  17 ) in the control of FTs (e. g. , by recording previous buyers), the main advantage it can provide resides in the minting and authentication of an NFT standing for the final access of the content, which can further be used as a Zero-Knowledge Proof (or ZKP)  [117]. In  [38] , the on chain functionalities are limited to the initial purchase of FTs.
  Technology association and flexibility: A key aspect of our architecture is the performance preservation of each of its parts. In fact, the architecture does not impose any extra constraints on any of the methods it accommodates. This allows, for instance, the identification method to be selected according to its format and robustness to various kinds of visual content attacks, inclusion of metadata or any other feature. This can be said with most technologies used in the workflow as not only are the database solution, App programming language, blockchain infrastructure, token standards are interchangeable, but the very input formats can also be modified to suit specific requirements (e. g. , to an audio-oriented use case). Note that current-day on chain environments lack precise semantic content identification that similarity-preserving fingerprints can bring thanks to this workflow.
The association of both provides a robust framework for the identification of semantic content, which can be put at the service of a wide array of scenarios thanks to the flexibility of the workflow. Yet, the advanced workflow is flexible in its expansion, enabling multifaceted management with simple additions and our solution is conceived, designed, and implemented to let the near duplicated content detection be substituted without breaking the workflow. If a use case did not need to worry itself with near-duplication and semantic content concerns, a hashing method could substitute the fingerprinting without affecting the nature of the processing chain. The Token Contract is the only element requiring any extra instantiation in the expansion of the architecture to support multiple databases and formats (cf.
This wide range of operations notably allows the tailoring of the asset and integration of complementary state-of-the-art solutions related for instance to royalties or brokering. We can also note the simplicity of using this solution. Once a qualified expert has set the architecture up, operators with no knowledge of databases and blockchain development can use it. The App serves as the sole interaction point for the operator, mostly through simple prompts. The workflow interrupts itself in case of database tampering, preventing the operator from knowingly or unknowingly validating malicious behaviour. Furthermore, when it comes to the interoperable aspect of the software and its components, although we do not deal with an Interoperability Mechanism (IM) as defined by  [119] , our solution showcases various interoperability features.
Moreover, the App can manage Smart Contracts  Technical challenges: The central presence of blockchain processing in this architecture brings native blockchain security mechanisms into the workflow. In our case, a Smart Contract acts a zero-trust third party which verifies the integrity of the database used to authenticate assets. As such, the workflow is as secure as the link between App and blockchain which lies in sound daily security practices and careful private key management. Yet, the presence of a blockchain also naturally brings other points of question, notably in terms of computational efficiency and energy consumption. In terms of computational efficiency, the bulk of the computational power required for our workflow is concentrated in the initial setup. After the setup and for routine use, the Smart Contract is only invoked at two specific moments, limiting the resource consumption of this process.
The first use of the Smart Contract is the greenlight function and does not constitute a transaction as it does not write any information on the blockchain. This call does not cost gas and is not limited by slow block rates. The second is the addition of a new entry in the database. This step is the initial setup brought to the scale of a single entry. A single line of code in the Smart Contract setting a Boolean variable to True is sufficient for this task. As was our aim, this design leaves the heavy lifting to the more efficient off chain technological bricks. Thus, we avoid the brunt of the main issue of on chain development, namely lengthy processing times. This resource consumption on the blockchain is measured via gas. Yet, gas costs are not only relative to specific blockchains but also to network congestion.
Moreover, the price of gas on the largest blockchains is also subject to the fluctuations of the financial market. The workflow presented in this paper minimizes these dependencies by only calling its blockchain elements (Smart and Token Contracts) in the event of the successful processing of a new verified entry. This limits post deployment gas spendings to a minimum. When it comes to the initial deployment, no strategies other than code optimization can curb down the cost of instantiating software on blockchains. For instance, the deployment of the Smart and Token Contracts of the Museum IPR use case we presented in Section V respectively used 15. 24% and 61. 45% of the default 4. 5M gas limit of our private EEA blockchain (i. e. , approx. 685,000 and 2. 7M). These values are made possible by the load-balancing features of the architecture, without which most fingerprints, even if they would be supported, would be prohibitively expensive to be stored on a blockchain.
VII. CONCLUSIONS web3 paradigms brought and continue to bring new highstake threats to multimedia content in terms of misuse (copying, IPR infringement, etc. ) but also carry the opportunity of more robust protection schemes. In this paper, we bring forward a flexible architecture that enables the backing of semantic information from given datasets in a blockchain-supported fashion and its subsequent usage through automated workflows. Specifically, this architecture makes the most out the mutually beneficial connection between off chain and on chain technologies to provide advanced multimedia identification and data integrity, respectively. Data authenticated as such is then made into blockchain assets that bear the trace of the process and can be distributed or serve as ZKPs. This solution can rely on a variety of web2 and web3 technologies and can be implemented standalone or noninvasively within already established workflows.
The App: The central piece of the architecture. It processes the multimedia content, communicates with the other blocks, and serves as the interface with the operator; details are presented in Section III. C. 1. • The Smart Contract: The on chain piece of code serving as an unfalsifiable integrity check for the off chain database; details are presented in Section III. C. 1. • The off chain database(s): The lightweight databases holding the hashes of the fingerprints for the recorded content. The advanced architecture is agnostic with respect to the database technology; details are presented in Section III. C. 1. • The Token prototype: A piece of code that uniformizes the input format of the information processed to create (or mint) the tokens; details are presented in Section III. C. 2. • The Token Contract(s): One or several Smart Contract(s) that establish(es) the capacities and limitations of the tokens while also generating them on demand in a permissioned fashion; details are presented in Section III.
Background: Single-stranded nucleic acids (ssNAs) have important biological roles and a high biotechnological potential linked to their ability to bind to numerous molecular targets. This depends on the different spatial conformations they can assume. The first level of ssNAs spatial organisation corresponds to their base pairs pattern, i. e. their secondary structure. Many computational tools have been developed to predict the ssNAs secondary structures, making the choice of the appropriate tool difficult, and an up-to-date guide on the limits and applicability of current secondary structure prediction tools is missing. Therefore, we performed a comparative study of the performances of 9 freely available tools (mfold, RNAfold, CentroidFold, CONTRAfold, MC-Fold, LinearFold, UFold, SPOT-RNA, and MXfold2) on a dataset of 538 ssNAs with known experimental secondary structure.
1b ), pseudoknots (Fig. 1c ), and multiple-ways junctions (Fig. 1d ), have been characterized. The knowledge of ssNA structures and functions benefits from the increase of the experimental data available in public databases, such as the Protein Data Bank and the Nucleic Acid Database. However, all the diversity of ssNAs structures has not been yet experimentally described, because the interest in this kind of molecules has arisen quite recently, and their structural characterization is hampered by their intrinsic high flexibility. SsNA structure prediction methods can be an interesting and powerful tool to help in the structural investigation of ssNAs in order to better understand the ssNAs functioning and to rationally design new ssNAs for therapeutic or diagnostic applications. So far, many algorithms have been developed for the prediction of the ssNAs secondary structures.
To do so, we retrieved 538 unique ssDNA and RNA sequences with known 3D structures, both free and in complex with proteins from public databases (namely PDB and NDB)  [13, 14] , and we extracted their secondary Table  1  SsNA secondary structure prediction tools included in the present study a Average run-time measured for sequences of 1000-1500 nucleotides structure using x3dna  [15]. To assess the performance of the selected prediction tools, we compared the predicted and the experimental ssNAs secondary structures using AptaMat as a metric. AptaMat is a secondary structure comparison metric we recently developed  [16]  able to provide a sensitive estimation of the impact of base pair variations between two structures. We showed that MFE-based approaches, such as RNAfold or mfold, still represent a good choice to predict ssNAs secondary structures.
ssNAs dataset For the benchmark of the selected ssNAs secondary structure prediction tools, we retrieved the sequences of ssNAs with available 3D structures from the Protein Data Bank  [14]  and the Nucleic Acid Database  [13]. We excluded the sequences containing non-natural nucleotides and/or G-quadruplexes and those in complex with a small molecule. The resulting dataset is made of 538 ssNAs, including 67 ssDNAs and 471 RNAs (Additional file 2). For each ssNA, we recovered the experimental secondary structure in the dot-bracket notation by using x3DNA-dssr  [15]  from its 3D structure.
The second MFE-based prediction tool included in the benchmark is RNAfold, which is part of the ViennaRNA tool suite  [5]. The main difference between RNAfold and mfold is the thermodynamics parameters applied to calculate MFE. In RNAfold, four energy models are included: (1) the Turner model of 1999  [18]  which works with a nearest-neighbour energy estimation at 37 • C; (2) the Turner model of 2004  [19] , which considers enthalpy changes to predict secondary structures at arbitrary temperatures; (3) the optimized Turner model by  Andronescu et al. 2007   [20] , which has been trained on an experimental dataset including structural and thermodynamics data; (4) the Mathews model (  2004 )  [19] , the only one developed explicitly for DNA sequences. In addition, RNAfold allows setting the same parameters as mfold, except for the choice of an energy threshold to output suboptimal foldings, which, in the ViennaRNA suite is handled by RNAsubopt.
Within MC-Fold, a pseudo-potential energy function is derived by using statistics from the PDB database on a library representing nucleotide relationships in structured RNAs, which includes all base pair types. Dynamic programming is then used to enumerate the obtained sub-optimal solutions. MC-Fold can handle H-type pseudoknots, by specifying the "pseudoknot" keyword when launching the simulation. We tested MC-fold with or without this option, in an attempt to better estimate the secondary structures of ssNAs with pseudoknots. We included in our benchmark another very recent tool, MXfold2  [8] , which estimates the most probable secondary structure by integrating ssNA folding scores learnt using a deep neural network with Turner's nearest-neighbor free energy parameters  [23]. In addition, we selected two deep learning-based tools which have the advantage of handling pseudoknots: SPOT-RNA  [10]  and UFold  [9].
Other prediction tools Several software implemented modifications to either MFE-based or knowledge-based algorithms for the ssNA secondary structure prediction. For example, LinearFold implements an algorithm applicable to both MFE-based (LinearFold-V) and alternative approaches, such as CONTRAfold, (LinearFold-C) to linearize the runtime of the dynamic programming (1) by iterating over the sequence from 5 ′ to 3 ′ incrementally tagging each nucleotide using the dot-bracket notation, and (2) by using the heuristic beam search method to prune the search space. In this benchmark, both LinearFold-C (default) and LinearFold-V were tested. CentroidFold  [6].
Comparison metrics To assess the accuracy of the selected tools, we compared the predicted secondary structure in the dot-bracket notation to the one retrieved from the experimental structure (i. e. the reference) for each sequence of the previously described dataset and each selected tool. For this purpose, we used AptaMat  [16]  as a metric, which we developed to specifically compare ssNAs secondary structures by representing them as matrices and making use of a metric built upon the Manhattan distance in the plane. The python code implementing AptaMat is publicly available at https:// github. com/ GEC-git/ AptaM at. git. We showed that AptaMat is able to determine the difference between secondary structures with the highest possible sensitivity. Indeed, it showed to be able to distinguish and correctly rank highly similar secondary structures, where other commonly used metrics (i.
Results and discussion The benchmark ssNAs dataset was built by retrieving all the ssNA sequences with available 3D structures from the Protein Data Bank  [14]  and the Nucleic Acid Database  [13] , excluding the sequences containing non-natural nucleotides and/or G-quadruplexes. Indeed, although recent works described promising methods to allow secondary structure prediction for nucleic acid analogues  [26] , the inclusion of non-natural nucleotides in our dataset may result in issues while predicting secondary structures as current software cannot directly handle unconventional nucleotides. G-quadruplexes were also discarded, since for these peculiar motifs recent studies have already shown the limits of the available prediction tools  [27, 28]. In addition, we excluded from the dataset the ssNAs experimentally characterized in complex with small molecules, because the interaction with the molecular target usually drastically alters the ssNAs base pairing  [29, 30].
However, we kept the ssNA sequences whose structure has been determined in complex with proteins: this might also affect, but only to a lower extent, the ssNA secondary structure  [31]. This choice allowed us to investigate how the prediction tools deal with this situation. The resulting dataset is one of the largest datasets containing structures retrieved from the PDB and NDB databases, and it is made of 538 ssNAs, including 67 ssDNAs and 471 RNAs (Additional file 2), with a length ranging from 7 to 1509 nucleotides. As compared to other datasets used in this kind of studies  [27, 32] , the one we built allows us to fully challenge the selected prediction tools, by testing their efficiency on two types of ssNAs (ssDNA and RNA), on a wide ssNAs length and structure complexity range, and on ssNAs whose structure can be affected by the surrounding environment.
e. far from the experimental) even when using the most appropriate tool. Therefore, the choice of a threshold allows us to provide easily interpretable results, together with taking into account small base pairs variations. Comparing the obtained results some differences can be observed in the percentage of good predictions ( Apta D ≤ 1. 5 , F1 score and MCC ≥ 0. 5 ) (Fig. 5 , Additional files 11 and 12). In particular, the F1 score indicates a higher percentage of good predictions as compared to both AptaMat and the MCC, probably because it is less accurate than the MCC, which gives a high score only if the prediction obtained good results in all the confusion matrix categories  [33]. However, the MCC sometimes fails in discriminating good from wrong predictions. The classification of some of the predictions for the ssNA with PDB code 6NOA (Additional file 13) offers an example of these issues.
These percentages increase to 79% and 83% , respectively, when we include the predic- tions with an Apta D ≤ 1. 5. Concerning RNAfold, we observed a slightly better accuracy for the prediction of RNA secondary structures against ssDNA structures. Indeed, it correctly predicts 48% and 40% of the RNA and ssDNA sequences, respectively. When predictions with an Apta D ≤ 1. 5 are included in the analysis, RNAfold showed a success percentage of 84% and 69% for RNA and ssDNA structures, respectively. It is important to emphasize that both mfold and RNAfold algorithms impose 3 nucleotides as the minimum hairpin loop size, while, in the considered dataset, we observed the presence of loops composed of 2 nucleotides (PDB ID: 1EZN, 1SNJ, 2N8A, 4ER8, 4F41, 4F43, 1RNG, 2L6I, 2B6G, 2JYM, 2ES5, 2PJP, 2UWM, 1EKZ, 2MTJ, 2M3Q, 6U8D, 4ZT0, 5VW1, and 5XBL).
As a consequence, these ssNAs cannot be exactly predicted by default. Moreover, both tools have some limits in correctly predicting base pairs involving the 5 ′ and 3 ′ ends. This issue might be considered negligible since the impact of terminal base pairs on global folding is limited. These two intrinsic limits of both mfold and RNAfold can explain ∼ 10% of the predicted ssNAs secondary structures showing 0 < Apta D ≤ 1. 5. In addition, mfold and RNAfold algorithms cannot predict pseudoknots: first, they do not make use of the extended dot-bracket notation, and, second, they do not contain the thermodynamics parameters for this kind of motif. This highly affects the estimation of the global accuracy of RNAfold and mfold since oligonucleotides with pseudoknots contribute to 14% of the dataset.
Another potential bias in the MFE-based algorithms is represented by the prediction of multiple-ways junctions, present in 17% of the ssNAs dataset. Indeed, mfold and RNAfold failed in correctly predicting the secondary structure of 43 and 46, respectively, out of 94 structures of the dataset with this structural organization. Most of these ssNAs structures have been experimentally resolved in complex with a protein or contain pseudoknots. Both situations might affect the secondary structure prediction. Indeed, the presence of pseudoknots might be responsible for the formation and stabilization of the multiple-ways junction, although we already pointed out the mfold and RNAfold failures in handling this kind of motif. Furthermore, as previously mentioned, the presence of a binding partner might influence the ssNA folding, potentially stabilizing a conformation that is metastable for the free ssNA.
For the same reason, in some cases (notably, PDB ID 2XXA, 1P5P, and 5TF6), both mfold and RNAfold predict the presence of multiple-ways junctions, while the experimental structure returns a unique hairpin/stem-loop as output. However, given the length of these 3 sequences (100 nucleotides on average) and the number of nucleotides involved in base pairs (36, 27 and 17 base pairs, respectively), the stability of the experimental structure in the free state is questionable.
The ssDNA structures with PDB ID 6IY5, 6FKE, 1ECU, 2VIC, and 2L5K are a striking example of this issue within the DNA model implemented in mfold (Fig. 4 ). RNAfold, on the contrary, does not show any difference in the percentage of identical predictions as a function of the used model. In this case, the absence of improvement might be due to penalties for terminal mismatches, or to difficulties linked to predicting the secondary structures of short oligonucleotides ( ≤ 15 nucleotides) using the Mathews DNA model. If we consider all the ssDNA secondary structures with an Apta D ≤ 1. 5 as well-pre- dicted, we could not observe any discrepancy between the DNA and RNA models, both giving ∼ 80% and 70% of good predictions with mfold and RNAfold, respectively (Fig. 2b ). Indeed, except for structures such as 2L5K, where the global structure is significantly modified, the above-mentioned penalties affect the prediction of a limited number of base pairs, poorly affecting the final predicted structure as compared to experiments (Fig.
Inclusion of suboptimal solutions It is known that a single-stranded oligonucleotide sequence can adopt distinct but similar and equiprobable conformations  [36, 37]. This is reflected by the alternative foldings found in some of the dataset structures obtained by NMR (PDB ID: 1M82, 1SCL, 1MFY, 1JO7, 5UZT, 2FEY, 2N6W), and it questions the default mfold and RNAfold prediction of one unique, namely the MFE, secondary structure. Therefore, since mfold and RNAfold can also generate suboptimal foldings, we extended the analysis of these two tools by allowing the generation of suboptimal structures, to verify if this might further improve the performances of the MFE-based algorithm in providing the expected secondary structure. As described in the "Materials and methods" section, we set the mfold and RNAfold parameters to retrieve all the possible suboptimal structures.
Nevertheless, since the number of the obtained suboptimal solutions was system-dependent, we decided to focus on the top 5 and 10 suboptimal solutions (Additional files 4 and 5). If we consider the whole dataset, the inclusion of suboptimal solutions in the analysis overall increased the percentage of identical or similar secondary structures compared to the reference. Nevertheless, we can observe that RNAfold predictions benefit at a higher extent from this as compared to the mfold. Indeed, using the default thermodynamics models, considering the top 5 or 10 suboptimal solutions provides an increase of ∼ 7% for mfold, while RNAfold correct predictions increase by ∼ 20% , leading to an excellent per- centage of correctly predicted structures of ∼ 65% (Fig. 2a , Additional files 4 and 5). Never- theless, this difference disappears if we include in the analysis the predicted structures with an Apta D ≤ 1.