dataesr/scientific-paragraphs-categorization huggingface dataset:
The contribution to the dataset by AFRIMED partners through personal data is also listed in Table  S2. The Kuntze and G. elegans  (Sauvageau)  Molinari & Guiry. These species were assumed as a rather uniform ecological entity pertaining all to the shallow rocky shores (see  Fabbrizzi et al. , 2020). In the second step, each type of forest was split in two restoration features according to the level of Habitat Richness (HR) surrounding the forest. The HR data layer was assembled using the model data on the distribution of the following Mediterranean species/habitat: Posidonia oceanica meadows, bioconstructions (coralligenous formations and maërl beds), essential fish habitats (nursery and spawning grounds), and deep-sea habitats  (Martin et al. , 2014; Boero et al. , 2016). We preferred data derived from models to the raw ones since spatial information on marine species and habitats are largely incomplete.
The cost of a PU decreases in relation to its proximity to the following facilities: i) International, National and Regional MPAs. Information about their distribution across the Mediterranean Sea were retrieved from MAPAMED database (https://medpan. org/main_ activities/mapamed/); ii) Ports (World Port Index, 2014 -htt ps://maps. princeton. edu/catalog/sde-columbia-worldports2014: this dataset was derived from the 23rd Edition of the World Port Index prepared and published by the United States National Imagery and Mapping Agency); iii) Diving facilities (https://www. google. com/m aps/d/viewer?hl=en&gl=US&ved=0CFMQjwU&ei=SOzqTIfn CJGQyQWB7KXcDg&ie=UTF8&oe=UTF8&msa=0&mid=1WLySIl MSbCBLJ0TtucPh43BOgcw&ll=36. 648535204425414% 2C13. 872461034103644&z=5); iv) Marine Stations (MARS network, https://www.
Final recommendations Despite the focus of the study was on the identification of criteria for macroalgal forests restoration, our intention was also to improve the spatial planning of future restoration efforts across marine habitats. Setting binding targets should be science-based and data-driven to ensure the effectiveness of restoration actions, as their cost in the marine environment is usually very high. More specifically: • The collection of new information about current and historical species distribution, especially in data-poor regions, is critical for better understanding the drivers of changes, optimizing the identification of restoration sites. Our dataset underrepresented the southern and eastern Mediterranean biasing the spatial prioritization analysis. In addition, suitable sites were identified pooling different fucalean species together.
dataset expresses the number of Regressing forests and Extinct forests occurring in each PU. The species considered for the identification of the restoration features were: C. compressa (Esper) Gerloff & Nizamuddin, C. foeniculacea (Linnaeus) Greville, C. humilis Schousboe ex Kützing, E. amentacea (C. Agardh) Molinari & Guiry, E. brachycarpa (J. Agardh) Molinari & Guiry, E. crinita (Duby) Molinari & Guiry, E. mediterranea (Sauvageau) Molinari & Guiry, G. barbata (Stackhouse).
2. Description macrosyntR is an R package that features 5 functions covering all actions needed to go from input files to the drawing of Oxford Grids. A stable version is available on CRAN (https://CRAN. R-project. org/package=macrosyntR) and the developmental version is hosted on github at : https://github. com/SamiLhll/macrosyntR 2. 1. Input DATA : The data required are a table of orthologous genes, that is a two column table containing sequence names of pairs of orthologous genes in the two species, and one BED file (https://www. ensembl. org/info/website/upload/bed. html) per species to specify gene order, each composed of the three mandatory fields Chrom, ChromStart, ChromEnd and the additional field seqName. The sequence names of the two files must match with sequence names of the first and second column of the table of orthologs.
2. 5. Example using public datasets : To demonstrate the use of the package, we compare two publicly available datasets. We selected the gene predictions of the lancelet Branchiostoma floridae  (Simakov et al. , 2020)  and the deep-sea tubeworm Paraescarpia echinospica  (Sun et al. , 2021). Orthologs were calculated as reciprocal best hits using Diamond Blast  (Buchfink et al. , 2021)  called through rbhXpress  (El Hilali, 2022). Two R command lines are then enough to draw an Oxford Grid (Figure  1A ). The default order is the chromosomal alphabetical order. We can improve the interpretability by using the clustering method (Figure  1B ) and track the chromosomal fusions that have shaped the karyotypes of these species.
In two other examples, the porting of a Cholesky factorization  [22]  and a plasma simulation  [19]  had to sequentialize MPI communications, potentially at the expense of performance, as the standard was not providing any guarantees on suspending tasks when synchronizing on MPI requests. A Lack of Interoperability These issues lead the community to develop interoperability interfaces between OpenMP and MPI. The Task-Aware MPI (TAMPI)  [20]  library was proposed as an extra layer above MPI, to overlap MPI request synchronization with useful computation through task-switches. This library must be used by programmers adapting their code to replace MPI with TAMPI calls. MPICH+BOLT  [5, 12]  through Argobots, or MPC  [16] , proposed to automate this interoperability through the threading library, so that OpenMP tasks can perform blocking MPI calls and suspend seamlessly.
Regarding (a), creating two dependent tasks does not provide more parallelism in this case. Our evaluation section 4. 1 shows that our proposal can remove some task management costs by following the C sequential order of execution, and synchronizing at some point in the execution without spawning a new task. On (b), OpenMP memory model does not mention anything specific on tasks with a detach clause. When a C variable is declared as such, we believe the memory model falls back to "[. ] the programmer must use synchronization that ensures that the lifetime of the variable does not end before completion of the explicit task region sharing it. Any other access by one task to the private variables of another task results in unspecified behavior ". In practice, the code Listing 1. 1 retrieved from  [18, 20]  will execute as follows: the variable req is pushed onto the executing thread stack (line 4), the asynchronous operation starts (line 5), a completion callback is registered into the MPI runtime (line 6) and the task returns (line 7).
The thread may then schedule other tasks until the callback is raised, potentially erasing the req variable onto its stack that has been passed by address earlier. The MPI_Detach proposal  [18]  tackles this risk by dereferencing and copying the MPI_Request pointer 8. Note that MPI_Request copy is not standardized, even though Open MPI, MPICH or MPC-MPI represents MPI_Request as integers so copies are achieved seamlessly. The MPI_Continue proposal  [21]  tackles this risk by consuming the MPI_Request setting it to MPI_REQUEST_NULL on return. Finally, for (c), the programmer has to create a continuation task explicitly and remove the synchronization call (MPI_Wait). On the other hand, runtime interoperability requiring no user code modification is possible and has been proposed in  [12, 16] : as illustrated on Listing 1.
omp_fulfill_event(event) The omp_fulfill_event(event) routine can be extended as follows: -Constraints on Arguments A program that calls this routine on an event that was already fulfilled is non-conforming. A program that calls this routine with an event handle that was not created by the detach clause, or not returned by the omp_task_continuation_event routine, is non-conforming. The constraint on argument now also allows the task implicit continuation event to be passed to the routine. If the multiple clauses restriction on the taskwait construct were to be relieved as proposed, the omp_fulfill_event(event) could also be extended as follows: -Execution Model Events The task-fulfill event occurs in a thread that executes an omp_fulfill_event region before the event is fulfilled if the OpenMP event object was created by a detach clause on a task or returned by the omp_task_continuation_event routine.
If suspending deep into the task call-stack, the task can be preempted for later resume on any thread without blocking onto its current thread. Every approach has its own limits. With GCC, scheduling flexibility is poor and may even lead to deadlocks when composing with other asynchronous programming models such as MPI, with threads spinning onto the same blocking task. With LLVM, scheduling points and variables must be fully known when compiling the task routine, which cannot be achieved for C Variable-Length Array (VLA) or Linux alloca routine leading to undefined behaviors  10. With MPC-OMP, annotated tasks execution context may be created unnecessarily if the task never actually suspends; and statically fixing the stack size is an issue: too small, execution may stack-overflow; too big, unnecessary memory usage could impact performances.
In  [2] , authors explore automated verification of stack size requirements for C programs at compile-time, that could complement MPC-OMP fixed-size stacks. As a taskwait extension, our proposal integrates well into LLVM CPS tasking: the compiler could generate a continuation upon detecting a taskwait detach(event) construct on a task' outter-most scope. In other cases, stacking tasks frame on top of each other as done currently by LLVM/MPC restrict scheduling decision possibilities. OpenMP as a Low-Level Parallel Runtime OpenMP is used as a low-level backend runtime for intra-node parallelization of higher-level programming models such as Kokkos  [24] , PGAS (XcalableMP  [15] ), or Domain Specific Languages/Abstraction (DSL/DSA) ( Devito  [11, 13] , Nablab  [10] ). They provide a higher abstraction that enables the user to write its code relieving programmers from low-level parallel implementation details.
In the specific case of DSL using MLIR  [9]  compilers, OpenMP is easily targetable, and merging/suspending tasks can be achieved. Our contribution could be used by these programming models whenever a task must suspend. Suspending Tasks in Rust The Rust programming language  [8]  supports asynchronous programming which, much like OpenMP, allows sequential-looking code to be run concurrently. A report of the Rust programming language in 2017 showed interest in CPS tasking as it is a lightweight solution that does not require per-tasks stack  11. An experimental implementation was made the same year  12  to suspend tasks using async/await syntax. The authors mention that "[Rust] coroutines are translated to state machines internally by the compiler " which is similar to the LLVM untied tasks implementation.
CEN6 scaffolding and assembly A fraction of the ultralong nanopore reads (>160 kb) was used to create scaffolds covering most of the assembled region. The scaffolding process was initiated by identifying "seed" nanopore reads, which contained sequences of genetic markers located in the nonrecombining region of linkage group LGII in the pea high-density genetic map  [22]. A portion of these marker sequences were also detected on metaphase chromosomes with the highly sensitive FISH protocol, which was used to determine their exact physical location (S1 Fig). Additional physically localized seed reads were derived from the edges of the arrays of satellite repeats, FabTR-48, -49, and -50, which were previously shown to be specific to CEN6  [6, 20]. Next, the seed reads were extended in both 5' and 3' directions by searching the database of ultralong reads using BLASTN  [39]  and mini-map2  [40]  for similarities with their 60 kb terminal regions.
Assembly annotation Annotation of repetitive sequences was performed using a combination of different tools available on the RepeatExplorer Galaxy Server (https://repeatexplorer-elixir. cerit-sc. cz/). Transposable element sequences encoding conserved protein domains were identified based on their similarities to the REXdb database  [45]  using DANTE (https://github. com/kavonrtep/dante). Full-length LTR-retrotransposon sequences were annotated using the DANTE_LTR tool (https://github. com/kavonrtep/dante_ltr), which combines the results of DANTE with similarity-and structure-based identification of LTR-retrotransposon signatures such as LTRs, primer binding sites (PBSs), and target site duplications (TSDs). The identified full-length LTR-retrotransposons were also used to create a reference database for similarity-based annotation of repeats in the assembly.
The database was also enriched with consensus sequences of repeats obtained from the RepeatExplorer analysis of Fabeae genomes  [46]  and with a collection of Fabeae satDNA sequences compiled on the basis of our previous studies  [20, 25, 46]. In parallel with similarity-based detection, tandem repeats were identified, based on their genomic organization, with Tandem Repeats Finder ver. 4. 09  [47]  using the parameters "2 5 7 80 10 500 2000". The output of the search was parsed and converted to GFF format using TRAP  [48]. Gene annotation was performed by launching FINDER  [49]  on the CEN6 assembly supplemented with unscaffolded HiFi contigs representative of the remaining parts of the genome. Briefly, 30 RNA-seq libraries  [50, 51]  were mapped to the assembly by STAR, and assembled with psi-class  [52].
Next, the mapped data were processed by braker2  [53]  to perform a de novo annotation of genes. To improve the quality of annotation, Ryūtō  [54]  was run twice on the mapping results, once for the stranded library and the second time for the unstranded library. The results of Ryūtō and psi-class were combined using Mikado  [55]  to obtain a highquality (HQ) annotation dataset. A low-quality (LQ) dataset was built by filtering braker2 results as follows. First, genes overlapping a repeat annotation were removed. Then, only the genes with at least one hit in the eggNOG protein database were retained. Functional annotation of these genes was performed using TRAPID with the PLAZA Dicots 4. 0 database.
The resulting reads were quality-filtered and trimmed using Trimmomatic  [56]  (minimum allowed length = 100 nt), yielding 122-211 million reads per sample, which were mapped onto the assembly using Bowtie 2 version 2. 4. 2  [57] , with options -p 64 -U. Subsequent analysis was performed on full output from Bowtie2 program and on output where all multimapped reads were filtered out. Filtering of multimapped reads was performed using Sambamba version 0. 8. 1  [58]  with options "-F [XS] = = null and not unmapped and not duplicate". Regions with statistically significant ChIP/Input enrichment ratio were identified by comparing ChIP and Input mapped reads using the epic2 program  [59] , with the parameter "-bin-size 200". Alternative identification of enrichment was performed using MACS2  [60]  version 2.
Methylation analysis Cytosine methylation was analyzed in all three contexts (CG, CHG, and CHH) by detecting the frequency of 5-methyl cytosine (5mC) in nanopore reads, which were aligned to the CEN6 assembly using DeepSignal-plant ver. 0. 1. 4 [24]  with the model "model. dp2. CNN. arabnrice2-1_120m_R9. 4plus_tem. bn13_sn16. both_bilstm. epoch6. ckpt". Prior to the analysis, nanopore reads were rebasecalled using the latest version of Guppy (ver. 6. 0. 1) and resquiggled using Tombo ver. 1. 5. 1. Methylation frequencies were calculated for each cytosine position in the assembly, based on the number of methylated and methyl-free cytosines detected in the aligned nanopore reads. The methylation analysis pipeline was run on a Linux server equipped with 126 GB RAM, 24 CPUs, and the NVIDIA GeForce GTX 3060 graphics card.
Acknowledgments We thank  V. Tetourova  ´and  J. La  ´talovafor technical assistance, and  D. Bera  ´nkovaand  E. Hřibova ( Institute of Experimental Botany, Olomouc, Czech Republic ) for help with labeling myTags  Custom Immortal Probe. We also thank the  ELIXIR CZ Research Infrastructure Project  ( LM2018131 ) for providing computing and data-storage facilities.
IoT has a number of further applications in the food sector, such as in food production, logistics, resource and waste management, safety, quality, and traceability  (Jagtap, Duong, et al. , 2021). The term "blockchain" refers to a distributed database of all digital transactions or events that can be verified at any time in the future  (Antonucci et al. , 2019). A chain is created by encrypting transaction data in chronological sequence so that it can maintain unalterable records. The blockchain architecture model is composed of six levels: data, network, consensus, incentive, contract and application  (Zhang et al. , 2022c). Current food supply chains, particularly those connected to large distribution platforms, have a significant number of participants dispersed along the chain, resulting in poor information exchange and potentially unreliable data among participants  (Wang et al.
Commercially-available traceability systems in the F&V supply chain often do not cover the entire length of the supply chain nor rely on open and transparent interoperability standards  (Tagarakis et al. , 2021). To address this, these authors developed a novel IoT-based system, namely AgroTRACE, to monitor the quality of F&V across the entire food supply chain and consequently improve traceability. Indeed, various IoT technologies were implemented at different stages, starting from the field level to the consumer and back to the field, bringing added value to fresh produce supply chains. Further examples include work by  Li et al. (2021) , who proposed a low-cost IoT quality traceability identification code for F&Vs using IPv6-based Linux server and HBuilder development platform. Zhao et al.
(2015)  combined IoT with database and RFID technology to collect information such as humidity, air temperature and humidity, illumination and CO 2 concentration, and store such data, to have more information about the production environment of vegetables and consequently improve their traceability. Zeng et al. (2021)  combined IoT, QR code traceability technology and cloud Big Data to enhance traceability of Golden Pears. Several of such IoT devices and applications have already reached the market. For instance, Hugh Lowe Farms use IoT to monitor the growing conditions of their strawberries and track their delivery (Hortidaily, 2022). DiMuto uses video of agricultural products before packaging is sealed, digitise it using IoT with QR and bar codes, and consolidates documentation associated with transactions (PYMNTS, 2022).
Blockchain An efficient traceability system must include quantitative and qualitative information about the final food product and its origin  (Demestichas et al. , 2020). The body of research material on the direct or indirect relationship between blockchain and traceability in the agrifood chain (either alone or in combination with other 4. 0 technologies) has increased dramatically in recent years. A search of the Scopus database (performed on of 24 September 2022 including the terms blockchain and traceability and food or agriculture in the title, abstract or keywords) yields a total of 270 publications from 2018 (articles, reviews, and book chapters in English). However, there are not many specific studies on the use of blockchain for traceability in the fruit and vegetable supply chain, as only 15 papers have been found to date.
The traceability model enables the design of a consensus mechanism and a blockchain smart contract model. Recently,  Ayed et al. (2022)  discussed how different DNA traceability techniques in combination with other 4. 0 technologies, such as blockchain, IoT and AI can offer a reliable method to certify and authenticate healthy food products, in particular for olive oil. For the case of extra virgin olive oil, Conti, (2022) proposed a traceability system that uses currently available and low-cost digital technologies, with the possibility to be integrated into a database for public authority controls and that allows the consumer to easily carry product information using their NFC-enabled smartphone. The low cost and ease of use of the proposed system allows it to be adopted for small and micro farms, products with regional characteristics and organic production.
Soil To enhance better cultivation practices for retaining soil fertility. To expedite the collection, analysis and sharing of data  (Hou et al. , 2020)  To uncover hidden patterns from soil datasets and obtain the necessary information for identifying soil conditions, such as nutrients, pH levels and soil moisture  (Finger et al. , 2019; Kolipaka, 2020)  Provide more detailed insights into the data characteristics of soil and support farmers in their crop yield predictions and decisions  (Rajeswari & Suthendran, 2019)  To find out fertilizer recommendation classes on behalf of existing soil nutrition composition  (Garg et al. , 2019)  Water To increase water use efficiency (Ciruela-Lorenzo et al. , 2020) To assess annual agricultural conditions, set annual agricultural production plans, and ensure the efficient use of water and the prevention of land degradation  (Zhang & Huisingh, 2018)  To assist in water audits and policy formulations.
, 2019)  To eliminate waste by utilizing consumer complaints made in retail stores or rely on social media data  (Mishra & Singh, 2018)  To develop a Big Data cloud-computing framework to assist farmers in measuring their carbon footprint in a cost-effective manner  (Singh et al. , 2018 )  (Jakku et al. , 2022)  (continued on next page) pesticides, among others. Such data is crucial to ensure food safety and compliance with food standards. Additionally, IoT combined with databases could meet and satisfy consumers' needs for information about product storage and transportation conditions, such as relative humidity, lightness, and CO 2 concentrations. In recent years, blockchain has been one of research hottest topics due to its promising potential to reduce supply chain risks, ensure safety, and prevent fraud.
The Saturn-mass exoplanet WASP-39b has been the subject of extensive efforts to determine its atmospheric properties using transmission spectroscopy  [1] [2] [3] [4]. However, these efforts have been hampered by modelling degeneracies between composition and cloud properties that are caused by limited data quality  [5] [6] [7] [8] [9]. Here we present the transmission spectrum of WASP-39b obtained using the Single-Object Slitless Spectroscopy (SOSS) mode of the Near Infrared Imager and Slitless Spectrograph (NIRISS) instrument on the JWST. This spectrum spans 0. 6-2. 8 μm in wavelength and shows several water-absorption bands, the potassium resonance doublet and signatures of clouds. The precision and broad wavelength coverage of NIRISS/SOSS allows us to break model degeneracies between cloud properties and the atmospheric composition of WASP-39b, favouring a heavy-element enhancement ('metallicity') of about 10-30 times the solar value, a sub-solar carbon-to-oxygen (C/O) ratio and a solar-to-super-solar potassium-to-oxygen (K/O) ratio.
In the SOSS mode, the spectra are spread across more than 20 pixels in the cross-dispersion direction by means of a cylindrical defocusing lens (see Extended Data Fig. 1 ), thus allowing longer integration times and reducing the impact of pixel-level differences in the detector response. However, this defocus results in the physical overlap of both orders on the detector. The time-series observation was composed of 537 integrations of 49. 4 s (nine groups per integration), corresponding to a duty cycle of 89%. We extracted the stellar spectra from the time-series observations using six different pipelines to test the impact of differences in spectral order tracing, 1/f noise correction, background removal and spectrum extraction methodology (see Methods and Extended Data Figs. 2  and 3 ). We created spectrophotometric light curves for each pipeline (Fig.