dataesr/scientific-paragraphs-categorization huggingface dataset:
Following the same procedure adopted by our group  [10] , we also evaluated the TIS score in our GIST series, comparing the results with the TIS score distribution in tumor types collected in The Cancer Genome Atlas (TCGA) database. Interestingly, we found that SDH-deficient GISTs showed TIS scores closer to glioblastoma multiforme and kidney renal papillary cell carcinoma, while KIT-mutant GISTs placed near to breast cancer and pancreatic adenocarcinoma. For sake of clarity, we included in this analysis also the GISTs of our previous series  [10] , excluding the KIT-mutant GISTs and leaving the PDGFRAmutant GISTs. In strong agreement with our previous findings  [24] , PDGFRA-mutant GISTs are confirmed as the most immunogenic GIST molecular subgroup, showing a TIS score very similar to that of tumor types known to benefit from immunotherapy (such as lung cancer) (Figure  6 ).
Taken together, these observations indicate that the expression of EMT, hedgehog, and hypoxia pathways is strongly linked to the SDH complex deficiency, a feature shared with pheochromocytoma/paraganglioma, which could explain and support clinically relevant differences with other GIST subgroups, such as for the metastatic behavior. In this study, the immunological state of both groups of GISTs was evaluated. The results showed a significant absence of immune infiltrate in SDH-deficient patients, which indeed display a low abundance of tumor-infiltrating CD8+, M1 macrophages, NK cells, and dendritic cells. Moreover, the EIIS signature in SDH-deficient GISTs is lower than in KIT-mutant GISTs. By comparing the TIS score in our GIST series with the TIS score distribution in several tumor types (collected in TCGA database), we can see that the SDH-deficient TIS score is closer to that of glioblastoma multiforme and kidney renal papillary cell carcinoma, emerging as the lowest with respect to other GIST molecular subgroups (both KIT-mutant and PDGFRA-mutant) that showed a TIS score more similar to hot tumors (such as melanoma and lung cancer).
S/MIME S/MIME is an enhancement of the MIME standard  (Borenstein and Freed, 1993) , providing cryptographic security for e-mails based on MIME. Most popular e-mail clients such as MS Outlook, Mozilla Thunderbird or Apple Mail support S/MIME messages. S/MIME relies on the X. 509 public key infrastructure (PKI) to exchange and validate the public keys of other e-mail users. Users must obtain a valid X. 509 public key certificate signed by a trustworthy Certification Authority (CA). This digital certificate is then automatically embedded into all outgoing e-mails to distribute the public key of the sender. Thus, the e-mail software of users receiving an S/MIME-secured e-mail can extract the sender's certificate including the embedded public key. pEp pEp is based on Open PGP and its message format, but introduces several enhancements and new concepts to make e-mail encryption easier to use.
Moreover, we assumed that even if the technology is generally usable, its implementation within an MUA might make it difficult to use and vice-versa. Therefore, we have decided to ask the participants to test either two different implementations (i. e. MUA software) of a same technology (either PGP, S/MIME or pEp) or two different e-mail encryption technologies. In the second case, we asked the participants to test a pEp implementation and then to choose between a PGP and S/MIME implementation, in order to have a feedback regarding pEp and check if it meets its goal of simplifying the e-mail encryption process. The chosen MUAs are depicted in  • Android (pEp) • Outlook 2016 (S/MIME) Test #3 • Thunderbird (pEp) • Outlook 2016 (S/MIME) Test #4 • Android (pEp) • Outlook 2016 (S/MIME) Test #5 • Thunderbird (pEp) PGP + pEp • FlowCrypt (PGP) Test #6 • Thunderbird (pEp) • Maildroid (PGP) Test #7 • Android (pEp) • Outlook 2016 (PGP) Test #8 • Thunderbird (pEp) S/MIME • Thunderbird Test #9 • Outlook 2016 • Apple mail (iOS 12) Test #10 • Apple mail (MacOS desktop) PGP • Thunderbird Test #11 • FlowCrypt pEp • Thunderbird Test #12 • Android The user testing covered all the tasks necessary to encrypt emails, including installation (when required) and configuration of the encryption tools, and sending of a secure email.
Participants have given written consent to participate in the study. The user testing protocol started with a short interview of the participant to collect participant's demographic data (ex. , age, nationality, profession, etc. ), preferred mail user agent (MUA) or e-mail program used to read emails, and previous experience with cryptography (in general or e-mail encryption in particular). After the interview, we proceeded with the observation of the user using a MUA to send secure e-mails. The aim of the user testing was to focus on the observation of the complexity of the tasks related to a specific encryption technology when using familiar MUA software, rather than to challenge the users with an unknown MUA. Therefore, we proposed to the participants to use their preferred MUA and focus on the configuration and use of encryption features to send a secure e-mail.
As we shall see in Table  4 , five users tried PGP, nine users tried S/MIME, and 10 users tried pEp. The number of how often a certain configuration was tested varies from a single user to up to six users at most. Independently of the MUA and the encryption technology used, users were asked to complete three main tasks: configure the MUA, perform the key management and compose a secure e-mail. The key management task was required only for PGP, as this is done automatically for S/MIME (when requesting a certificate) and pEp (see Key Management From a User Perspective). Whilst the overall user goal (i. e. , sending a secure e-mail) is the same for all scenarios tested, the sub-tasks (i. e. , the sequence of steps required to perform the task) vary according to the encryption technology and its implementation in an MUA software.
Indeed, the number of sub-tasks might differ among the MUAs even when comparing the same encryption technology. This means that sub-tasks are dependent of a particular implementation of the encryption technology in a MUA software (example: in Thunderbird, Microsoft Outlook, etc. ). We cannot at this point compare different MUAs software but we can compare effectiveness with respect to how many steps are required to complete (sub-) tasks. For example, in Table  7 , regarding pEp, we can see that the task Configuration requires one single step (namely, "install pEp mail app") with Android, while it requires two steps when using Thunderbird. In our analysis, we also report the issues found with each MUA. Regarding the comparison of PGP, S/MIME and pEp, we can only address it with respect to the final goal of the user, which is to write a secure e-mail.
User Testing Results for PGP The Configuration task requires only one step (i. e. , sub-task) for Microsoft Outlook, Flowcrypt and Maildroid, whereas for Thunderbird three sub-tasks are necessary to complete this task. In addition, the sub-task Enforce usage of PGP in account settings had some usability issue. The Key management task requires only one sub-task with Flowcrypt, whereas in Microsoft Outlook and Thunderbird two sub-tasks (Generate a key pair/Import a key pair and Get recipient key) are necessary and even more sub-tasks 3) for Maildroid. In Maildroid, the generation of a key pair (related to the task Generate a key pair in Outlook and Thunderbird) requires two sub-tasks: Generate key pair externally via PC software or online service and Transfer externally generated key to mobile device, which is more tedious.
User Testing Results for S/MIME For the comparison between the MUA software in terms of sub-tasks, we can see, in Table  7  that Outlook, Thunderbird and Apple mail (MacOS) requires the same sub-tasks for the Configuration task. However, for Apple mail (iOS12) and Maildroid two more sub-task are required: Transfer pfx file to the mobile device and Activate S/MIME for Apple Mail (iOS12) and Install Cryptoplugin and Transfer pfx file to the mobile device for Maildroid. Moreover, the sub-task Unzip to get pfx file for Apple Mail (MacOS) and Transfer pfx file to the mobile device for both Apple Mail (iOS12) and Maildroid were more difficult for the users. Regarding the Mail composition task, all the MUAs have the same number of sub-tasks to complete this task. The participants faced several difficulties that are explained below, depending on the MUA used.
In addition, due to an unresolvable software bug in Outlook, a user could send encrypted e-mail only as reply to encrypted incoming e-mails and not initiate a new secure e-mail communication. Regarding mobile MUAs, the users encountered similar usability issues with PGP (the generation of a new PGP key pair has to be done externally, then the keys must be transferred to the mobile phone) and S/MIME (management of the certificate that could not be received and unzipped directly on the mobile phone). pEp showed to be the easiest technology to use, but unfortunately it is not (yet) compatible with all major platforms. As a consequence, we could not test it on Apple MacOS or Apple iOS platforms, which are used by a large fraction of e-mail users.
S/MIME particularly lacks implementations for webmail platforms. While pEp successfully simplifies and automates the key management process and uses less tech-savvy language in its user interfaces, this technology struggles with very low popularity (none of our participants ever used it prior to our study). Furthermore, only very few pEp implementations were available at the time of our study, and cross-platform support for Apple MacOS or Apple iOS was missing. Our results might be considered quite preliminary due to the small sample of participants during the user testing and the limited distribution of the online survey to a few Western European and North African countries. We want to stress that they allow us to uncover the "tip of the iceberg," exposing the complexity of tasks for using e-mail encryption mechanisms.
g. , biological status and geographic origin) are often incomplete and phenotypic evaluations are often limited to morphological and a few major agronomic traits; (iii) limited access to available information; the heterogeneous nature and non-standardized (biased) way of data collection and integration cause an under-use of the huge amount of existing information and data; databases are centralized, not interconnected and not designed to integrate data obtained by external users, strongly limiting the access to available information, which remains separated in genebank/project information systems; also, the available information is not easily accessible to users due to unfriendly searching and visualization tools  (Krajewski et al. , 2015). The optimal use of genetic resources represents a precondition to increase sustainability.
INCREASE GROUNDS The general objective of INCREASE is to improve the sustainable use of plant genetic resources in food and agriculture working with the four abovementioned important food legumes and promoting the conservation and use of all kinds of their genetic resources. The INCREASE goal will be achieved through the improvement of food-legume genetic resource data management and sharing, by development of optimized databases, data-management solutions and web-based searching and data visualization tools, based on data findability, accessibility, interoperability and reusability (FAIR) principles to facilitate, with much more useful and easily accessible information, the access of stakeholders to plant genetic resources  (Ghaffar et al. , 2019; Halewood et al. , 2018; K€ onig et al. , 2020; Neveu et al.
INCREASE will additionally enrich the information management of genebanks by facilitating the communication and coordination between different institutions and by promoting data collection and process management and data sharing solutions, in both a centralized and decentralized manner; the access to users will be facilitated through a participatory strategy and this will allow the design of innovative conservation plans, which can include decentralized management approaches. To reach these goals, INCREASE is planning to enhance the efficiency, standardization and speed of data handling and facilitate the integration of information produced by users that, presently, cannot be integrated in databases due to problems of data compatibility and cost accrued on data input, which consequently limit the availability of relevant data for (and from) stakeholders.
INCREASE Intelligent Collections In order to efficiently explore the plant genetic resources diversity, for each crop, INCREASE will assemble and curate Intelligent Collections (ICs, Intelligent as in able to memorize, learn, improve and evolve) as a set of nested core collections of different sizes that represent the entire diversity of each crop  (Cortinovis et al. , 2021). On the basis of what was developed during the project BEAN_ADAPT (ERA-CAPS 2nd call, 2014), for each crop, ICs will be developed, a set of nested core collections of genetically purified accessions (based on single homozygous genotypes, purified by one or two cycles of SSD) from a large sample of representative accessions from genebanks and from dynamic conservation on-farm. ICs will: (i) have a memory, as they are based on collections of derived inbred lines, with all genotypic and phenotypic information obtained in different experiments and by different actors referring to the same identical genotype; (ii) learn, from the analysis of the data integrated into the databases about the structure of genetic diversity and its relationship with phenotypic diversity and the environment; and (iii) improve and evolve, by correcting mistakes and making progressive adjustments of the sampling procedures according to the novel information that will be obtained.
Along with genotypic data, sub-cores of nested core collections will be phenotyped in evaluation activities carried out by different actors, both internal and external to the INCREASE project consortium. The genotypic information and related genomic predictions will constitute the links between phenotypically characterized and noncharacterized accessions, as a very powerful tool to exploit the whole collection of genebank accessions, as far as the genotyping data are available. Genome-wide association (GWA) analysis will be performed using conventional  (Mackay et al. , 2009; Ogura and Busch, 2015)  and innovative machine learning (ML) approaches based on AI. Marker-trait associations will be identified to facilitate the exploration of germplasm collections not phenotypically characterized.
, 2021; Kroc et al. , 2021). In addition, we will use smart phenotyping tools, based on image analysis for phenotypic data on traits of interest (e. g. , seeds, flower, pods and leaves). Image analysis based on deep learning will be used to analyze the images, allowing a deep evaluation of seed color, seed patterns, seed size and seed shape, in a precise and rapid manner. Furthermore, root traits will be recorded in the field ('shovelomics';  Trachsel et al. , 2011)  using a subset of the lines present in the MLFTs (which will include the domesticated genotypes of the H-CORE). A large-scale and comprehensive metabolic profiling across the four legume species and many genotypes across different environmental conditions on both leaf and seed tissues will be conducted. Considering the complexity of legume species metabolism, we will use different approaches to ensure metabolite annotation: (i) using an in-house library (with >3500 compounds) and (ii) using the accurate m/z peaks in full scan and MS2 produced by Orbitrap high-resolution mass spectrometry using a Q Exactive TM Focus Hybrid Q-Orbitrap (Thermo Scientific, Waltham, MA, USA) to match online databases (e.
g. , m/zCloud, Chemspider, GNPS)  (Aksenov et al. , 2021; Aron et al. , 2020)  for peak annotation. In addition, the MS and MS2 spectra will be compared with previously identified compounds and published articles in plants and specifically the targeted species. The metabolite output data will be reported and documented in Excel files following our recent recommendation standard  (Alseekh et al. , 2021a)  and will be available in the project respiratory database. In parallel to the metabolite profiling, we will perform transcriptomic analysis on leaf tissue of the same materials. The transcriptomic and metabolic analysis will provide essential information to be integrated with genomic data in order to understand the molecular mechanisms associated to phenotypic responses and G 9 E of plant genetic resources and to highlight quantitative loci underlying metabolic and transcriptomic variation via the performance of network analyses  (Beleggia et al.
In addition, we will evaluate the effects of water stress on plant and leaf photosynthesis. samples of H-CORE genotypes to develop pan-genomes of common bean and lentil and obtain new high-quality de novo whole genome sequences (i. e. , a contiguous, haplotype-resolved representation of the entire genome) for the four INCREASE species. The already available genotypic data of all the species (generated by the INCREASE consortium and already present in the public domain) will be exploited and integrated, including the reference genomes of all the species. High-quality whole genome assemblies and pangenomes (core and dispensable genes) will be developed and we will use several approaches to exploit the structure of the genetic diversity and identify the functional diversity that could have a major role in the conservation, adaptation and improvement of food legumes.
Based on the large amounts of data produced we will investigate the demography and selection shaping neutral genetic diversity and functional variation of crops; we intend also to identify the genetic structure that if unknown would result in failure in identification of selection signals and in the identification of spurious genotype-phenotype associations. We will undertake genome-wide scans to identify candidate polymorphisms (outliers) involved in local adaptation and rely on demographic scenarios inferred and ML algorithms to detect footprints of selection. In addition, we also intend to associate allele frequencies to environmental variation by taking latitude and longitude as proxies. During these field evaluations of the association panel, traits such as grain production will be taken as proxies for fitness.
, 2019; Wambugu et al. , 2018) ; (iii) identify unique germplasm that needs particular attention in conservation  (Gowda et al. , 2013) ; and (iv) strictly monitor the multiplication of accessions  (Mascher et al. , 2019). Finally, advanced molecular methods will be applied to assess the levels and values of heterogeneity in germplasm accessions and in populations conserved on-farm  (Frankin et al. , © 2021  The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd. , The Plant Journal, (2021), 108, 646-660 2020;  Gouda et al. , 2020). Genomics data will be used to sample highly variable sets of accessions for target genes associated with traits of interest. All results from the data analysis will be imported into the databases, with complementary visualization tools provided via user-friendly web applications  (Sanderson et al.
INCREASE will verify the potential of direct genotyping of heterogeneous accessions from pooled DNA samples, as a way of bypass SSD purification using R-CORE haplotype data as the reference dataset, inside an open and traceable datasharing policy. INCREASE will additionally tailor phenotyping approaches based on the needs of the users (i. e. , breeders, farmers, agri-food and non-food industries, gardeners, consumers), not only considering conventional traits, but also accounting for specific features of the value chain (e. g. , food quality, nutrition value) and of the target agroecosystem (e. g. , crop rotation, intercropping, biological treatments of crops, sustainable agriculture under the aggravating conditions of climate change), with the possibility to explore genotype 9 environment interactions for adaptation to different environmental conditions.
It is intended that this will not only encompass agronomic performance, but also include information concerning nutritional content and quality traits. Moreover, the combined use of transcriptomic and metabolomic approaches and their associated networks will allow identification of putative genes responsible for phenotypic plasticity  (Alseekh et al. , 2017 (Alseekh et al. , , 2021b;; Beleggia et al. , 2016; Liu et al. , 2021; Zhu et al. , 2018). Artificial intelligence, and in particular deep learning techniques based also on multispectral images, will support the overall information discovery process by collecting annotated datasets and modeling major phenotypic effects. INCREASE will obtain high-quality genotypic data anchored on pan-genome assemblies, with the possibility to exploit the structural variations of the molecular diversity of these food legumes.
This could be particularly important, as many structural variations have major phenotypic effects, such as disease R gene clusters, which can be difficult to characterize as they can have a highly repeated structure  (Chen at al. , 2018; Gao et al. , 2016; Richard et al. , 2018). In INCREASE a new data structure and web portal for data analysis will be designed within the modern Big Data Analytics concept and with a data sharing layer able to overcome the described limitation by providing different tools, for different users, but embedded within a powerful data architecture of huge computational power. The web portal will provide a framework for the integrated tools, allowing users to define their own subsets (collections) based on the data stored in the project's databases. It will provide features to navigate between and combine the different search results of the domains (phenomics, genomics, metabolomics, taxonomy and georeferences).
CONCLUDING REMARKS INCREASE plans to establish an open space for the efficient and effective conservation and use of food-legume genetic resources and will generate, analyze and integrate massive, novel and already available genotypic and phenotypic information by the development of improved databases and innovative visualization tools. Indeed, we will improve genebank standards for data management and develop a central data management infrastructure and an expert portal, which will connect plant genetic resources with precise and high-quality genotypic and phenotypic information along with widely homogenized passport data. This will facilitate plant genetic resource exploration and we are convinced that including research scientists, breeders, genebank curators, farmers, agri-food and non-food industries and consumers will dramatically boost the competitiveness of legumes in the European agriculture and food sector, having a major impact on economy and society as well as addressing the need for enhancing the production of plant protein crops in Europe.
PARTICIPATORY APPROACH AND OPEN SCIENCE INCREASE will enhance management and use of plant genetic resources by implementing a participatory approach to acquire relevant plant genetic resources and related information worldwide, to expand the quality and quantity of seed and information resources, along with the interests of European users. The project involves a wide range of stakeholders (R&D, value chain, scientific research, civil society, environmental non-governmental organizations (NGOs), schools, citizens) interacting with each other under the coordination of INCREASE. The aims are to create and work within a 'legume research community', genebanks, plant breeders, plant and crop scientists, farmers (in conventional and organic agriculture), gardeners, seed suppliers, food industry, database and computational experts, NGOs related to environmental conservation and associations of citizens that will contribute to the project and, at the same time, benefit from the data produced and from the outcomes reached, enhancing the management and use of genetic resources on these important food legumes and maximizing the project impact.