dataesr/scientific-paragraphs-categorization huggingface dataset:
(43). These processing steps have been integrated in an in-house workflow to perform semi-automatic QC of MRI data. This set of QC functionalities was written as an extension to ExploreASL (43) called ExploreQC (39). The semi-automated QC procedure was based on two steps: feature estimation and visualization. Image quality features were computed from five image feature domains: motion, noise, inhomogeneity, asymmetry, and descriptives. The visualization module consists of an interactive dashboard with violin and scatter plots for observing variation between and within sites, respectively. Individual scans can be visually inspected by selecting their data points on the scatter plots, allowing to visualize the scans themselves together with the QC features (Figure  4 ). The toolbox in freely available online (https://github.
Clinical data harmonization Different strategies were used for the harmonization of the clinical data in the AMYPAD trials, mostly determined by the design of each study. The prospective nature of the DPMS allowed for the implementation of harmonized strategies already from the beginning of the project, which were executed during the whole data collections. However, the PNHS dataset is composed by the combination of prospective and historical data from multiple sources, these limited the capacity to define harmonize methods during data collection, as most data was already obtained, and required a more thoroughly process of harmonization across the different data sources. The AMYPAD DPMS clinical dataset includes baseline and follow-up variables concerning sociodemographic, clinical, and cognitive features of 840 memory clinic patients.
These data were prospectively collected locally by the teams of the 8 AMYPAD DPMS recruiting memory clinics using electronic case report forms (developed by IXICO) and, therefore, following harmonized procedures defined in advance during the early phases of the study. Then, after data collection, the AMYPAD DPMS dataset had a final quality-checked by the sponsor team (University of Geneva). Meanwhile, the AMYPAD PNHS clinical dataset is a combination of prospective and historical data from 17 European sites. Due to the variety of sources and data formats present across the Parent Cohorts, the data curation process in PNHS deals with multiple challenges. Among these obstacles, the most notables are the use of different data models, measurements, and cognitive questionnaires. Therefore, it was decided to perform a comprehensive process of data curation based on the work of the Data Curation Network (https:// datacurationnetwork.
org) which developed a standardized set of CURATED steps (Check, Understand, Request, Augment, Transform, Evaluate, and Document). This process resulted in the largest European dataset phenotyping longitudinally individuals at risk of AD-related progression, which currently consists of ∼3,350 subjects, ∼1,600 of those with a baseline amyloid PET and about 940 of them having at least one follow-up PET acquisition. The dataset currently contains 9,740 observations (visits) and 614 variables, grouped into (68) "concepts" and (  13 ) "domains, " such as demographics, family history, genetics, vital signs, medical history, neuropsychological questionnaires, lifestyle, CSF, PET and MRI. While current dataset has been developed using its own data model, tailored to the needs of the project, the AMYPAD PNHS has been selected to work with the European Health Data & Evidence Network (EHDEN) in the adoption of the OMOP data model.
This will allow for the systematic analysis of the PNHS database, using a harmonize format as well as a common presentation of terminologies, vocabularies and coding schemes (EHDEN has received funding from the IMI 2 Join Undertaking under the grant agreement No 806968). All this process of data handling has been performed in close collaboration with the ARIDHIA team, where their expertise in data science has played a major role supporting data integration, harmonization and storage.
Availability of software A couple of open-source software packages dedicated to PET imaging in dementia have been developed: NiftyPET for neuro-image reconstruction with basic analyses, and NiftyPAD for dynamic PET analyses. NiftyPET (https://niftypet. readthedocs. io/) is an opensource software solution for standalone and high-throughput PET image reconstruction, manipulation, processing and analysis with high quantitative accuracy and precision (Figure  5 )  (44). One of its key applications is brain imaging in dementia using amyloid and tau tracers. The key computational routines are written in CUDA C for fast and efficient execution on NVIDIA GPU devices. The routines are then embedded in Python C extensions to be readily available for high-level manipulation of PET data in Python. Using NiftyPET, it has been possible to accurately assess the precision of MR-PET image registration, critical for accurate quantification of amyloid PET data  (45).
Data access The AMYPAD PNHS dataset is hosted in the Alzheimer's Disease Data Initiative (ADDI) Workbench, with the first private data release made in November 2021 (Figure  6 ). Thanks to a 5-year partnership between the AMYPAD consortium and ADDI, the PNHS dataset will remain available to the research community beyond the project duration, with the first public release planned by the end of the first quarter of 2023. Those researchers interested in using the AMYPAD PNHS data can request access to the imaging, clinical, and biomarker data for scientific research investigation and/or educational activities. The application can be performed via the FAIR Data Service of the Alzheimer's Disease Data Initiative (ADDI). In this platform, the user will indicate if the request includes only access to the clinical data or also to the neuroimaging data, the data domains, and the type of data (i.
This process will take up to 2 months (1 month for the assessment of the DPC and 1 month for the Parent Cohort). The results for the data access request will be sent to the researcher via the FAIR platform, and approved application will receive access to retrieve the data in the AD Workbench. In case that neuroimaging data was also requested, information to access the XNAT will be also provided via the FAIR platform (more details in the next section).
Image data access and XNAT Imaging data from all sites have been collected by IXICO and have undergone quality control and betweensite harmonization. Image data are disseminated by the Amsterdam UMC using an XNAT system (www. xnat. org), an open-source medical image server that allows control of multi-user access and storage of clinical non-imaging data. The image data that is made available, adheres to the PET-BIDS standard (https://bids-specification. readthedocs. io) (47), which ensures transparency of the image provenance and processing history, and enables open and reproducible science. Together with the EPAD project, the AMYPAD group is working in conjunction with the Alzheimers Disease Data Initiative (https://www. alzheimersdata. org), which will ensure the availability of the main clinical databases for these projects, and support sharing of the imaging data as facilitated by Amsterdam UMC.
1A ). As shown in Fig. 1C , 28 gaps in 12X. v0 were filled in PN_T2T, the largest gap being 16 951 bp in the 1-Mb syntenic region on chromosome 18 (Fig. 1C ). Orientation errors in 12X. v0 were also corrected, such as inversions and translocations compared with PN_T2T (Fig. 1A , Supplementary Data Fig. S3 ). For example, two large inversions, which were located surrounding the centromere of chromosome 3 and at the ends of chromosome 5, with the length of 4. 9 and 1. 9 Mb, were observed between two versions of the assembly, respectively (Fig. 1A  and B      detected with structural domains (77. 4%, Supplementary Data Table  S2 ). Based on the species-specific pan-TE database constructed by RepeatModeler2, the repeats were detected with the pipeline shown in Fig. 2A. Finally, 66. 47% of our gap-free grape genome was marked as repetitive sequences (Fig.
1D ). As a comparison, 62. 47% of the repetitive sequences were identified in the 12X. v0 genome using the same pipeline (Supplementary Data Table  S3 ). Among the repeats predicted in the PN_T2T genome, the largest portion comprises TEs (63. 90%), with a total length of 316 Mb (59. 96% and 292 Mb in 12X. v0). The TEs mainly consisted of the long terminal repeat (LTR) type (47. 54%), predominantly Gypsy (20. 22%) and Copia (19. 67%) elements. In total, we detected 276 rDNA sequences, representing 0. 019% of the genome.
5A ). On chromosome 18 (25-36 Mb), there were 1 237 genes enriched in domains mainly including Integrase domain, C-JID domain, NB-ARC domain, Leucine rich repeat, Multicopper oxidase, Reverse transcriptase, Terpene synthase, and TIR. Our results show that many of the strongly enriched structural domains are part of the structural domains of plant disease resistance genes (R genes), including NB-ARC, TIR, and structures identified by the Colis database. We analyzed the domain architecture of our 41 064 PN_T2T PCGs and identified 3 381 possible R genes. Collectively, these R genes and gene clusters in grapes highlight a tremendous opportunity for exploring plant defense mechanisms.
Hotspots of heterozygosity in a nearly homozygous genotype The current plant used to build the grapevine reference genome originated from the 'Helfensteiner' cultivar selfed for nine generations, which resulted in a 99. 8% homozygous genome (Supplementary Data Fig. S2A-D ). The remaining heterozygous sites are still of interest as they could represent hotspots of required heterozygosity, with lethal consequences if found in the homozygous state. Thus, we collected Illumina resequencing reads for four clones of PN40024 maintained in different international laboratories. Interestingly, the heterozygous SNPs and structure variants (SVs) were enriched in specific regions when mapped to PN_T2T. In total, we found 208 330 heterozygous SNPs shared by the four samples, and 35 886 SNPs specific to one to three samples.
Sample collection and genome sequencing PN40024 is a line that belongs to one of the near-homozygous lines originally derived from the 'Helfensteiner' cultivar  [31]  by successive selfing steps, estimated to be close to 97% homozygosity as tested by SSR markers  [32]. We got this inbred material from INRAE under a Material Transfer Agreement (MTA) and transplanted it in the greenhouse belonging to AGIS (Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China) for subsequent experiments. Young leaves and ovules from PN40024 were f lash-frozen in liquid nitrogen. Genomic DNA and RNA were isolated using the DNeasy Plant Mini Kit (Qiagen) following the manufacturer's instructions. For PacBio HiFi sequencing, two single-molecule real-time cells were sequenced on a PacBio Sequel II platform, and a total of 21 Gb of HiFi reads was generated using CCS (https://github.
Telomere-to-telomere genome assembly Initially, the PN40024 genome was assembled by incorporating PacBio single-molecule real-time long-read sequences. Reads generated by the PacBio Sequel II platform were self-corrected, trimmed, and assembled by hifiasm, using default parameters (https://github. com/chhylp123/hifiasm) [43]. The initial output of hifiasm (v. 0. 13) yielded the p_ctg draft assembly. Genome heterozygosity was estimated using a k-mer-based approach by GenomeScope 2. 0  [69] ; it was estimated to be close to 99. 8% homozygosity (Supplementary Data Fig. S2A-D ). Then, homologybased scaffolds were generated with MUMmer (v. 4. 0. 0)  [70]  'scaffold', using the 12X. v0 reference genome (Supplementary Data Fig. S3 ). By applying MUMmer tools, we ordered and oriented the contig-level assemblies into 19 chromosomes, and joined the adjacent contigs to generate a scaffold with 100 N.
Annotation of genes and transposable elements We have used a self-developed method for genome annotation. The putative genes were first searched for by using transcripts and Uni-Prot as evidence. A preliminary gene model was then built for the putative genes and further search was performed using AUGUSTUS (v. 3. 4. 0)  [72]. All the found putative genes fragments were then filtered, including genes involving duplicated regions, genes with coding sequence lengths shorter than 90 and genes not supported by any evidence. We attempted to complement missing genes and the complete genes were subjected to alternative splicing analyses. Finally, all the results were examined by hidden Markov models downloaded from the Pfam database to obtain the final gene models. Interproscan (v. 5. 56-89. 0) [73]  was used for function annotation for our assembly, and Pfam (v.
34. 0)  [74]  and Coils (v. 2. 2. 1)  [75]  were used for the identification of structural domains (https://github. com/unavailable-2374/ Genome-Wide-Annotation-Pipeline). The primary repeat analysis is outlined in Fig. 2A  and began with the construction of a pan-Vitis database of repeat families by RepeatModeler (open-2. 0. 3)  [76]  and a series of scripts, which was then applied with RepeatMasker (open-4. 1. 2). For building this pan-Vitis repeat database we downloaded 17 Vitis genomes from NCBI, then used RepeatModeler2 to identify TE families. After that, we got 17 consensus fasta files of TE families and by removing the single-copy and failed annotations we aggregated these files. We used NCBI-BLAST+2. 9. 0  [77]  to remove some redundant sequences (-i 80%, -l 80%). Next, we got the final file of repeat identity, then used deepTE  [78]  with the Plant model to classify the unclassified repeat elements.
Genome comparison between different versions of the grapevine reference genome To compare previous versions of the grapevine genome with PN_T2T, we aligned the genomes using minimap2 and indexed the alignment BAM file using SAMtools (minimap2 -ax asm5 -t 4 -eqx A. fa B. fa | samtools sort -O BAM -> A_B. bam, samtools index A_B. bam). Next, to detect structural variations between genomes, we needed to find synteny and structural rearrangements between the genomes. For this, we used SyRI (syri -c A_B. bam -r A. fa -q B. fa -F B -prefix A_B). Finally, Plotsr was used to generate the graph (plotsr -sr A_Bsyri. out -sr B_Csyri. out -sr C_Dsyri. out -genomes genomes. txt -o output_plot. pdf, https://github. com/ schneebergerlab/plotsr). MUMmer (v. 4. 0. 0) was used to compare the 12X. v0 genome with the reference genome PN_T2T using whole-genome alignments  [70].
First, we aligned the two genome sequences using nucmer (nucmer -mum) and then filtered oneto-one alignments with a minimum alignment length of 10 000 bp (delta-filter -i 95 -l 10 000). SAMtools (v. 1. 7) was used to extract the sequence of chromosome 18 (25. 0-26. 0 Mb) in 12X. v0 and align the sequence in PN_T2T. The gap information was detected with a python script (getgaps. py) and finally we used LINKVIEW2 (https://github. com/ YangJianshun/LINKVIEW2) to visualize the alignment results.
Identification of telomeres and centromeres The telomere repeat units were explored by using the TIDK (v. 0. 2. 0) (https://github. com/tolkit/telomeric-identifier) with options tidk explore -f genome. fa -minimum 5 -maximum 12 -o tidk_explore -t 2 -log -dir telomere_find -extension TSV. Then the whole genome was searched using the following parameters: tidk search -f genome. fa -s TTTAGGG -o tidk_search -dir telomere_find. Finally, we completed the rapid statistics of telomeres based on the TIDK plot and used the R script to visualize the telomere peak. For centromere annotation, TRF (v. 4. 09)  [79]  was used to finish tandem repeat annotation with the parameters trf genome. fa 2 7 7 80 10 50 500 -f -d -m, and then we merged the results of annotation by using TRF2GFF (https://github. com/Adamtaranto/TRF2 GFF).
To complete the data statistics and visualization, we used information extracted by using the awk command in the Linux system and analyzed the results in IGV (v. 2. 12. 3)  [80]. We used four softwares to show more details about the centromeric region: Iqtree (v. 2. 1. 4-beta) [81]  was used to achieve the phylogenetic tree (options: -m GTR + I + G -bb 1000 -bnni -alrt 1000); itol (v. 6)  [82]  was used to visualize the phylogenetic tree; GeneDoc (v. 2. 7. 0) (https://github. com/karlnicholas/genedoc) was used to achieve multiple sequence alignment; and R script was used to plot the data statistics and typeset details. To detect the functions of the genes captured in the centromeric regions, we downloaded the protein sequence library of Swiss-Prot (2022/08/30, https://ftp. ncbi. nlm. nih. gov/blast/db/ FASTA/) for a local blast.
After this, we extracted all the protein sequences of PN_T2T blasted by diamond (v. 2. 0. 15) (parameter: -k 1 -e 0. 00001, https://github. com/python-diamond/Diamond). We further uploaded the Swiss-Prot ID to DAVID (https://david. ncifcrf. gov/tools. jsp) and completed GO enrichment and annotation. Finally, data visualization was completed by our R scripts.
Identification of gene clusters To define the clustered genes in the reference genome, protein sequences were extracted using gffread and then filtered by evalue <1e-5 and similarity >30% using BLASTP for all-versus-all alignments. The filtered alignment results were combined with functional annotations to filter out alignment results that did not share the same structural domains. Finally, we determined the presence of gene clusters by identifying three consecutive identical Pfam accessions (https://www. ebi. ac. uk/interpro/entry/ pfam/#table), using such Pfam accessions as seeds, and going up and down 30 genes to find genes with the same Pfam accessions. In total, 377 gene clusters were found (Supplementary Data Table  S8 ).
Acknowledgements This work was supported by the  National Natural Science Fund for Excellent Young Scientists Fund Program (Overseas)  to Y. Z. , the  National Key Research and Development Program of China  (grant  2019YFA0906200 ), the  Agricultural Science and Technology Innovation Program  ( CAAS-ZDRW202101 ), the  Shenzhen Science and Technology Program  (grant  KQTD2016113010482651 ), and the  BMBF-funded de. NBI Cloud  within the  German Network for Bioinformatics Infrastructure (de. NBI). We thank  Bianca Frommer ,  Marie Lahaye ,  David Navarro-Payá ,  Marcela K. Tello-Ruiz , and  Kapeel Chougule  for their help in analyzing the RNA-seq data and in running the gene annotation pipeline. This study is also based upon work from  COST Action CA17111 INTEGRAPE  and from the  COST   Innovators Grant GRAPEDIA  ( IG17111 ), supported by  COST (European Cooperation in Science and Technology).
Data availability All PacBio sequence data have been deposited in the NCBI Sequence Read Archive under project number PRJNA882193 and the National Genomics Data Center (NGDC) Genome Sequence Archive (GSA) (https://ngdc. cncb. ac. cn/gsa/), with BioProject number PRJCA012093. The assembly and annotation as well as the sequences of centromeres and heterozygous regions have been deposited in zenodo: https://zenodo. org/record/7751391#. ZBgVmcJBy3A. The assembly and its annotation will be also hosted in the GRAPEDIA portal (https://grapedia. org/).
eu/ (accessed on 19 January 2023)). This project proposes a lightweight software stack and synergetic meta-orchestration framework for the next-generation compute continuum ranging from the IoT to the remote cloud, which perfectly matches the needs of SAR operations. The paper is organized as follows. Section 2 reports the related work on the main related technologies in cloud computing, sensor networks, the Internet of Things (IoT), robotic applications and cloud robotics. Section 3 describes the reference use case for SAR operations, with its requirements. Section 4 describes the proposed solutions based on the approach followed in the NEPHELE project. Section 5 concludes the paper.
Nonetheless, it is still an actively researched problem in robotics, so much so that the robotics research community is only now working towards designing benchmarks and mechanisms to compare different SLAM implementations. In recent years, there has been particularly intense research into VSLAM (visual SLAM)  [71]. Specifically, the focus is on using primarily visual (camera) sensors to allow robots to track and keep local maps of their relative positions also in indoor environments (where GPS-based navigation fails). Extracting key features from images, the robot can determine where it is in the local environment by comparing features to a database of images taken of the environment during prior passes by the robot. As this latter procedure quickly becomes the bottleneck for its applicability, solutions are being explored to offload the processing to the cloud  [72]  and to the edge  [56].
Nephele Project as Enabler for SAR Operations NEPHELE (NEPHELE project website: https://nephele-project. eu/ (accessed on 19 January 2023)) is a research and innovation action (RIA) project funded by the Horizon Europe program under the topic "Future European platforms for the Edge: Meta Operating Systems" for the duration of three years (September 2022-August 2025). Its vision is to enable the efficient, reliable and secure end-to-end orchestration of hyper-distributed applications over a programmable infrastructure that is spanning across the compute continuum from IoT to edge to cloud. In doing this, it aims at removing the existing openness and interoperability barriers in the convergence of IoT technologies against cloud and edge computing orchestration platforms and introducing automation and decentralized intelligence mechanisms powered by 5G and distributed AI technologies.
To reach this overall objective, the NEPHELE project aims to introduce two core innovations, namely: 1. An IoT and edge computing software stack for leveraging the virtualization of IoT devices at the edge part of the infrastructure and supporting openness and interoperability aspects in a device-independent way. Through this software stack, the management of a wide range of IoT devices and platforms can be realized in a unified way, avoiding the usage of middleware platforms, whereas edge computing functionalities can be offered on demand to efficiently support IoT applications' operations. The concept of the virtual object (VO) is introduced, where the VO is considered the virtual counterpart of an IoT device. The VO significantly extends the notion of a digital twin as it provides a set of abstractions for managing any type of IoT device through a virtualized instance while augmenting the supported functionalities through the hosting of a multi-layer software stack, called a virtual object stack (VOStack).
These devices are ready to be used with some basic software components running and connected to a local network, e. g. , a 5G access point. As for the software components already deployed on the robots, we foresee the ROS environment correctly set up, with some basic ROS components already running. The sensors can be either pre-deployed in the area or carried by firefighter personnel. When a SAR mission is started, the Service Consumer connects to the NEPHELE HDA repository and looks for the HDA to deploy and provides the input data that are required. Then, the following operations will have to be initiated by the NEPHELE platform (see Figure  2  for reference of the building blocks): We foresee a service consumer (e. g. , a firefighter brigade) owning a set of physical devices (robots, drones and sensors).