dataesr/scientific-paragraphs-categorization huggingface dataset:
Criteria for Inclusion and Exclusion of Allergens in COMPARE The PRP evaluation criteria are consistent with those widely adopted for inclusion of sequences in other allergen databases  (8, 44, 45). The minimum criterion adopted for inclusion of a candidate is peer-reviewed evidence of IgE binding, either in the published literature or other peer-reviewed documentation. This criterion is highly conservative, with no requirement for demonstration of IgE functionality (e. g. , cross-linking for degranulation of IgE-bearing cells). The evaluation of evidence for IgE binding includes consideration of the experimental approach, the quality of sera, and characterization of the IgE binding itself. Ideally, IgE binding is demonstrated to a purified full-length recombinant version of the allergen. In case evidence of IgE binding is based on purified natural protein, the peer-review panel gives special attention to the evidence provided with respect to purity on a case-bycase basis.
Similarly, if evidence of IgE binding is based on proteomic approaches combining IgE 1D-or 2D-blots and mass spectrometry, single bands or spots should be discrete enough and each contain sequence evidence from one protein only. Peptides (10 amino acids in length or above) explicitly identified by mass spectrometry and which sequences are included in the supporting literature are accepted as individual entries. As a result, a single protein may be represented in the database by several peptide fragments, listed as individual entries. Table  2  summarizes the inclusion and exclusion criteria, as well as considerations for the scientific review decision process, defined by the COMPARE PRP. Table  3  is a concise overview of the criteria used to declare an allergen in the COMPARE database. Candidate sequences rejected by the panel for lack of evidence can be revisited in subsequent years if new evidence becomes available.
Information regarding all candidate allergens submitted to the PRP each year, whether ultimately "accepted" or "rejected, " are retained in internal records for tracking purposes. With the release of COMPARE 2019, the reviewers' decisions and comments about all "accepted" or "rejected" candidates were made publicly available as part of the COMPARE transparency documentation (see the "Database" page and "Documentation" tab at http://www. comparedatabase. org). In summary, Figure  1  provides a description of the COMPARE peer review cycle and workflow applied at each annual update. Note that since COMPARE started building upon the existing AllergenOnline v. 16 database, allergens with an entry date of 2016 or prior years were not subjected to the COMPARE peer review process.
Maintaining Data Currency and Accuracy A sequence database that is used to identify potential hazards related to food safety is of maximum utility if it contains the most recent allergens. Therefore, COMPARE maintains its utility via an annual screening of protein sequence databases (NCBI Protein, UniProtKB) and other sources (as described in sections 2. 4 and 2. 5). It is expected that the COMPARE process will continue to evolve each year, to adapt to the changes in sequencing technology and informatics/annotation trends observed in a previous update cycle. An annual update process facilitates confidence in communicating the presence of new allergens in a timely fashion, while still allowing for a thorough, high-quality review process. The established cyclic annual workflow supports a January (of the next new year) release of the updated database with sequence culling from public databases conducted early enough in the year (typically from May to May).
IMPLEMENTATION AND RESULTS To date, the COMPARE process as described above, with some limited modifications, has been implemented to produce the first release of the database (COMPARE 2017) and four subsequent annual updates (COMPARE 2018-2021 data releases; Table  1 ). The web interface presents the database main fields in a searchable table view with the following fields for each allergen sequence entry: Species (of the source organism); Common Name, Description (of the allergen sequence), IUIS Name, Accession, Length, Year Adopted (the year of inclusion in the database-note that for sequences marked "2016" or years prior, this corresponds to the year listed for entry of the sequence in AllergenOnline, the foundational data from which COMPARE developed, as marked in AllergenOnline v. 16 ), and a "VIEW" clickable box leading to a window with the individual data for each sequence (the actual sequence, and supporting scientific publications with evidence of allergenicity).
Supplementary Table  2   Along with the continuous database update and process improvement, sequence comparison tools associated with the database were implemented to provide bioinformatics analysis of proteins for potential allergenicity. Currently, there are three sequence comparison approaches accessible to the public: (1) FASTA search of the whole protein sequence as a query against the up-to-date COMPARE database. The search parameters such as E-value can be set by the user. The output alignments can be downloaded for further evaluation; (2) FASTA searches  (default search parameters) of each sequentially overlapping 80aa fragment (sliding window) from a query protein against the upto-date COMPARE database using the default search parameters of FASTA. Any alignment >35% identity over ≥ 80 aa will be displayed as a hit that needs further evaluation based on the guideline of FAO/WHO  (30)  and CODEX Alimentarius Commission  (28, 43) ; (3) A FASTA search (default search parameters) of the whole protein sequence as a query against the up-to-date COMPARE database for the exact match of eight contiguous amino acids with the allergens in the database, although this approach adds little value to the risk assessment of proteins for potential allergenicity due to high false positive rates  (47) (48) (49).
DISCUSSION AND STRATEGIES FOR HARMONIZATION Reliable resources to assess the allergenic potential of novel proteins expressed in foods are essential to confidently make safety determinations and provide robust resources with public accessibility. The internationally recognized standard for safety assessment of foods derived from biotechnology is the Codex Alimentarius Commission  (43)  and recommends that novel proteins expressed in foods be analyzed for any amino acid sequence similarities to known allergens. Using a comparative sequence-level approach for the initial scan of potential allergens allows for a reproducible and fully documented process to establish a protein allergen database. Clinical relevance of allergen sequences by a panel of scientific experts in allergology provides validation that entries added in COMPARE are known allergens, allowing the database to be a reliable resource.
Learnings From 5 Years of COMPARE The results portrayed in Figure  2  (quantitative outcomes from the screening and review processes for COMPARE 2017) illustrate how important it is to implement an automated sorting algorithm to sort through the vast amount of "noise" retrieved from protein sequence database screenings, when searching for any protein related to "allerg * " (55,641 hit sequences) or when using keywords very specific to allergens such as profilins, tropomyosins, and so on (15,704 hit sequences). The analysis of the results of the second-year process suggests that the noise increases year by year: the combined retrieved numbers for "allerg * " and specific allergen keywords in the initial screening were close to 118,000 hits for COMPARE 2018, almost double that for COMPARE 2017 (data not shown).
In other words, the requirement of an exact match between the candidate sequence being evaluated and the sequence(s) tested in the associated literature was not met in these specific instances (specific examples can be found in the COMPARE Database webpage, by clicking on the "Documentation" hyperlink, and then opening the "Transparency file"). Additionally, the standardized key-word search of literature databases has proven to be a valuable complementary source of valid candidates and resulted in some instances in additional allergens being picked up by COMPARE that were not listed in AllergenOnline of the same year's update. Examples of this include accessions ADV71357. 1, NP_001036878. 1, NP_001138311. 1, XP_392204. 2, which are not found in AllergenOnline at the time of these writings.
Strategies for Harmonization Given the scientific and public health value of a curated allergen database, opportunities to continually enhance its utility are paramount. As such, several improvements have been identified and have been completed or are under active development. These activities (further described in the paragraphs below) include: (1) retrospective historic screenings of the years prior to 2016 to identify possible extra allergens that may not have been included in AllergenOnline v. 16 , and (2) curation of the COMPARE database to include all available WHO/IUIS recognized allergen designations.
Applying Current COMPARE Approaches to Historical Datasets As described in this article, COMPARE builds on the foundational sequence list developed over a 10-year period by the University of Nebraska FARRP, which publishes AllergenOnline  (8). The list of sequences from AllergenOnline v16 were imported "as is" and were not evaluated by COMPARE PRP. Recently, a screening of historic datasets with records dated from 2016 and years prior was undertaken by applying the COMPARE process to harmonize the database content to up-to-date COMPARE processes (since from the beginning, starting in 2016, the process always consisted in screenings of the previous "1 year" time-window). In this process, the decision was taken to not remove sequences uploaded into COMPARE 2017 from the foundational sequence list from AllergenOnline v.
16, but only to complement the database with additional sequences identified using the COMPARE algorithm applied retrospectively and reviewed by PRP. Therefore, the data from AllergenOnline v. 16 has remained intact over the years, as imported for the built of COMPARE 2017. This choice was made to remain conservative and to avoid confusion amongst users. Overall, based on the high-throughput COMPARE screening process, and after PRP review, the historic screenings contributed to 57 new sequences added in COMPARE 2020, a substantial part of them not included in the current version of AllergenOnline. The supporting articles associated with these sequences are dated from 2016 or before, and it is unclear why they were not part of AllergenOnline v16. Sequences from the historic screenings are labeled as "2020H" or "2020H_MS" in the field "year adopted" of the database, as well as in column B of the "COMPARE-2020 Transparency file" (available under the "Documentation" tab of the COMPARE database website), to distinguish them from entries resulting from the regular annual screening process, labeled "2020".
Curating Allergen Designations in COMPARE to Nomenclature Standards Standardized allergen naming conventions, as set by the WHO/IUIS Allergen Nomenclature Sub-Committee, help reduce the use of non-standard allergen names and descriptions and are a key aspect of understanding related proteins across species  (6). Unfortunately, not all sequence records of allergens comply with IUIS standards: allergens identified in older research have been named according to various non-standard conventions; many allergen sequences identified in more recent years often include irrelevant automated annotations from sequence records. For these reasons, it is important to undertake a curation of allergen designations to generate meaningful description lines to the eyes of allergy experts and allergen database users, based on official IUIS allergen designations when available, as opposed to automatically adopting description lines from source protein records (generally from the NCBI Protein database or UniProtKB).
Such curation has been started in COMPARE 2019 (for all new entries added in 2019) based on a defined stepwise priority approach described in the "COMPARE 2019 Documentation File" (available at http://db. comparedatabase. org/docs/COMPARE-2019-Documentation-2019-01-17. pdf?v= 20190117), and has continued in future iterations of COMPARE. The COMPARE management team expects the adoption of allergen names to current standards, as set by the WHO/IUIS Allergen Nomenclature Sub-Committee (http://www. allergen. org), to be an ongoing quality improvement process that will occur in parallel with the curation of the "Description" fields, by the experts in the PRP. The goal will be to alleviate confusion over multiple names and make clearer the link between the publications that support a protein's inclusion in COMPARE as an allergen and sequences themselves.
CONCLUSIONS The COMPARE database provides a rigorous, transparent, and annually updated resource for screening potential allergens. The data-driven and clinical research-based screening processes allow for comprehensive and clearly documented sequence identification and evaluation by an independent panel of experts. The process has flexibility to account for changes in technology, variability in terminology, and annotation trends by incorporating the observations and learnings of a given cycle into the following build cycle, reflecting ongoing continuous improvement. With oversight and coordination by HESI, additional enhancements to COMPARE are anticipated to provide added utility to meet contemporary safety assessment and research needs into the future. In addition, the bioinformatics analytical capability through COMPASS provides tools to the public for the risk assessment of potential protein allergenicity.
COMPARE database year-by-year evolution of the process development and resulting end-products. Historical screenings"-a plan to undertake a historic screening by applying the COMPARE process to sequence records dated from 2016 and previous years was completed. This project was undertaken to harmonize the database content to up-to-date COMPARE processes, given that the COMPARE process was developed in 2016 and applied only to a "1-year" time window every year since then. "Database audit"-this process was conducted by an external Bioinformatics services provider to analyze the current content of the COMPARE database as a quality control measure.
describes the number of candidate allergens identified through the various sources and the final number of allergens accepted in COMPARE 2018, from each category. The 2018 COMPARE database included 68 allergen sequences added to the 1,970 allergens constituting COMPARE 2017 (2,038 total). The COMPARE 2019 evaluation process results are presented in Supplementary there are 2,081 sequences in COMPARE 2019. The COMPARE 2020 evaluation process results are presented in Supplementary.
ACKNOWLEDGMENTS The authors gratefully acknowledge the input from the government, academic, and industry scientists of the  HESI Protein Allergens, Toxins and Bioinformatics (PATB) committee  for their contributions to this work, as well as the  Joint Institute for Food Safety and Nutrition  (established between the  FDA  and the  University of Maryland ) for their partnership in developing and maintaining the COMPARE allergen database The authors also thank former  HESI  staff  Brianna Farr  and  Oscar Bermudez  for their support at various stages of the project, as well as  Christina West  for the manuscript review and editorial support.
in COMPARE, including in cases where a sequence is listed in IUIS but a publication is not yet available (e. g. , Der p 37, Der f 26 and Per a 13). These entries are accepted by IUIS based on information provided in direct communication between the submitters of the allergen and the IUIS committee. JIFSAN collaborates in the COMPARE process by facilitating (among other program components) the procurement of the literature associated with the candidate allergens and administrating a custom-built web-based portal where the candidate-specific information from all sources is integrated for peer review by an independent panel of international experts. COMPARE 2021 January 29, 2021 2,348 In relation to other databases, for example, this may also explain differences between COMPARE and AllergenOnline, discussed in section 4.
1. Examples of entries in COMPARE not found in IUIS or AllergenOnline include QCI56569. 1, Q9YGJ8. 2, and CAA58223. 1. The identification and procurement of primary Peer Review Process Candidate allergens and associated primary literature data are provided to the COMPARE PRP. The scientific literature review COMPARE, COMprehensive Protein Allergen REsource; IUIS, World Health Organization/International Union of Immunological Societies Allergen Nomenclature Database; NCBI, National Center for Biotechnology Information. a The first version of COMPARE was built from the pre-existing AllergenOnline v. 16 database (1,956 entries; http://www. allergenonline. org), to which 14 new entries approved by the COMPARE peer review panel were added. applied scientific literature that relates to the sequences identified as new "allergen candidates" is the second step in the COMPARE process, after the bioinformatics screenings and parallel direct literature search (Appendix-1 in Supplementary Materials).
The second metrics does not take into account the robust cells, therefore we have that S 1 L = E 1 /(2 n • (r • s)) = 21/(2 4 • (5 • 5)) = 21/400. 3) Benchmarks and Simulations: The defect simulations have been run on a machine with two Intel Xeon E5-2683 for a total of 64 CPUs and 756 GByte of main memory, running Linux CentOS 7. The benchmarks functions are expressed in PLA form and are taken from a subset of LGSynth93  [47]. Each output of a function is implemented as a separate Boolean function for total of about 1000 functions. The software used for simulations is written in C++. We used ESPRESSO  [23]  to implement the method described in  [6] , and a collection of Python scripts for computing minimum-area lattices by transformation to a series of SAT problems, to simulate the results reported in  [17].
3D printing electronics could be a promising candidate for comparing the crossbar types with actual values. As a future work, a fully automated software tool for the synthesis methodology is planned to be developed. It will take the target function, the used technology, and the performance specifications (area, delay and/or power consumption) as input and return the optimized crossbar-arrays structure as an output. This software tool, to be developed, will benefit from the Text optimization algorithms introduced in this study. It will also produce the mapping using technology specification data. The tool is expected to be updated based on established experimental results and novel algorithms. Another direction could be the study of transient faults on lattices, so that it can generate complete defect tolerance analysis.
The work is primarily done when he was with  Istanbul Technical University ; he is currently affiliated with University of Virginia This work is part of a project that has received funding from the  European Union 's  H2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement # 691178 , as well as supported by the  TUBITAK-Career  project # 113E760  and  TUBITAK-2501  project # 218E068. Copyright (c) 2020  IEEE. Personal use of this material is permitted. However,.
The Blockchain Blockchain is a technology for storing and transmitting information transparently, securely, and operating without a central control body  [19]. Blockchains are databases that contain a history of all exchanges between its users since its creation. The database is secure and distributed-it is also known as distributed ledger technology. It is shared by its users without an intermediary, allowing anyone-with permission if it is private-to evaluate the validity of the chain. As its name suggests, it is presented as a series of "blocks" containing data. The blocks are linked together by identifiers, a block contains its own identifier and that of the block before, yielding the "chain" aspect of the blockchain  [19]. Blockchain was for many years a technology known only to a few insiders focusing on technical dimensions and characteristics.
• Sharding: Sharding, is Ethereum's solution to scalability. Sharding, or partitioning, is the solution considered by the Ethereum developers to remedy the problem (congestion characterized by longer confirmation times and higher transaction fees). This technique is used for databases: it partitions the data horizontally in order to distribute the storage in memory. For Ethereum sharding would consist of paralleling both the storage and the execution of autonomous contracts without losing security. In conclusion, the main characteristics of blockchains can be summarized to include four major dimensions that help contribute to overall blockchain capabilities and environment as discussed in the next section:.
Example crowdshipping services with reverse logistics possibilities located in India include companies such as FreightBazaar. com, Trucksuvidha. com, and Lorryguru. com [43]. In designing transportation networks, especially with respect to environmental sustainability performance, the use of eco-design-also called design for the environment-for disassembly, reuse, remanufacturing, and recycling is common  [44] [45] [46]. Eco-design information can be gathered through the blockchain tracking capabilities. Tracking data would include transportation activities and characteristics of transportation vehicles along reverse logistics activities. An example would include design and management of containers based on long term data and vehicle routing when designing products and materials for end-of-life disassembly or collection.
The first aim of the PI paradigm is to make supply chains efficient, effective, and sustainable (see e. g. ,  [69] ). For example, there could be a focus on optimizing operations occurring in a Rail-Road PI-Hub cross-docking terminal. Energy consumption is also an example of environmental concern of PI-conveyors used to transfer PI-containers from trains to outbound trucks in a multimodal setting. PI can be compared to the classical cross-dock hubs, as an example of recent sustainability-oriented PI research. Interestingly there is no detailed and explicit study dedicated to the application of PI in the reverse logistics context-providing a direction of this nexus supported by blockchain. There are some studies that do initiate this link. For example the Ethereum blockchain and smart contracts can be used to implement a shareable and secured tracking system for hyperconnected logistics, A simulation using the AnyLogic software tool can offer insights on the monitoring of properties depicting shipment lifecycle constraints through a stream of blockchain log events processed by BeepBeep 3, an open source stream processing engine.
In  [10] , a rigid free-rotating sail was considered as a method of withstanding harsh weather conditions while maintaining sailing performance. They designed the rig using a Vortex Lattice Method combined with a Velocity Prediction Program to maximize aerodynamic performance. Furthermore, in  [11] , the authors described the Roboat ASV based on the commercially available Laerling 3. 75 m sailboat type. The model focused on safety and stability. In  [12] , the boat used a mono-hull with a passive keel and large foam thickness to ensure that the boat maintained buoyancy if flooded. In comparison to previous work, in this paper, the mechanical design is specifically intended to optimize the sailboat's robustness, and considers the extended mission duration for a transatlantic crossing from west to east.