dataesr/scientific-paragraphs-categorization huggingface dataset:
36-0. 38) compared to the whole CCC (Ho = 0. 33), and the Ho density distribution from accessions in each collection follow similar patterns to the CCC and were contained within it (Figure  6A  and Table  3 ). Therefore, we propose a 10 percent core collection for research and breeding in AGROSAVIA. The 10 percent core collection is a suitable size that captures the total diversity of the CCC and has a manageable number of accessions for research and breeding projects. The sample proportion in the three genetic groups (CCC_Group_A, CCC_Group_B1, and CCC_Group_B2) was 24 percent, 53 percent, and 24 percent for all genotyped samples, and 15 percent, 52 percent, and 33 percent for the 10 percent core collection. Finally, we used the available phenotypic data of three agronomical traits (ATW, NTP, and TTY) for 846 genotyped accessions of the CCC to validate the representation of phenotypic variation in the core and mini-core collections.
Part of the curation process is keeping all the information related to the germplasm organized by attributes or categories. The database should contain information on common names, taxonomic classification based on different authors (Hawkes, Spooner), biological status (wild, native/landrace, and breeding \research germplasm), and ethnobotanical classification (Chauchas and Guatas). Regarding the different taxonomic classifications, it is important to maintain the information of Hawkes' classification, which facilitates the management and use of germplasm in the conservation and breeding programs. Even though the classification based on  Spooner et al. (2007)  that regroups four former species (S. phureja, S. tuberosum subsp. andigenum, S. stenotomum, and S. chaucha) into a single species is well supported, a suite of morphological descriptors, length of plant cycle, photoperiod adaptation, and cytoplasm type support their differentiation  (Hawkes, 1990; Spooner et al.
Future perspectives for CCC management This study successfully genotyped 90. 9 percent of the clonal collection of the CCC of potatoes and a representation of released varieties. This result is an outstanding achievement considering this collection's size (2,499 wild and cultivated potato accessions) and its importance for the region. Moreover, we are interested that this collection become in a regional reference. Therefore, we would recommend aligning the CCC conservation strategy with the ten actions suggested for improving the global-scale conservation of potato resources, as proposed by several institutions and organizations  (Nagel et al. , 2022). Some of the actions that can be taken after this study include finding duplicates, clarifying the possible accession admixture exposed in this study, determining the ploidy using the chloroplast count in guard cells, verifying the taxonomy and biological status for accessions in the curation process, completing the digitalization of documentation and centralization of all historical and new evaluation data in the GRIN-Global platform, prioritize future evaluations in field experiments and data collection using the core collection, and completing the evaluation of core accessions that have missing information from previous biotic and abiotic evaluations.
This collection's passport data, morphological characterization, and genetic profiling can be used to compare the in vitro and field collection to verify the identity of germplasm. Ellis et al. (2018)  showed that some accession admixing could happen over time in the genebanks. Therefore, through an identity verification process, the collection can be revised, documented, and organized to correct errors and mitigate future mistakes by digitalizing data and implementing barcoding and quality management standards developed for genebanks. The genotyping data generated in this study can also be compared with other collections, such as CIP's collection, genotyped using the same technology. This comparison could serve to identify signatures of selection, materials with potential use in breeding, duplicity, genetic gaps, homologate accessions between the collections, and assess the extent to which CCC diversity is representative of the world's largest collection of native potatoes.
Acknowledgments We especially thank  L. P. Delgadillo  for supporting DNA extractions for a part of the potato accessions used in this study. Thanks to  B. Coronel  and  R. T. Torres  for collecting leaves from some accessions of the CCC in the field. We also thank  R. T. Torres  for locating and organizing all the CCC passport data using the oldest books, systematized by hand. We would also like to thank  M. S. Ceroń  for sharing the studies about the ploidy for the CCC. We thank the  Molecular Laboratory at C. I. Tibaitatáof AGROSAVIA  for their support in genotyping the CCC accessions. Finally, we thank the former curator,  Ivan Valbuena , for his dedication to the CCC that motivated this work and  Olga Spellman ,  Science Writer , Editor and  Coordinator of the Alliance Science Writing Service  ( Alliance of Bioversity International and CIAT ) for English and copy editing of this manuscript.
Introduction Evolutions in portable devices introduce to us new technologies to enhance user experience and customer satisfaction, through communication, graphics, sensing, and processing improvements. One notable aspect, such as recent smartphone designs, is introducing modules which can be used to enable and enhance indoor localization capabilities. In particular, ultrawideband (UWB) technology is resurfacing among leading industries and manufacturers, and being exploited for location awareness and security. Several flagship mobile products, from iPhone 11 Pro-and Galaxy Note 20 Ultra, supports UWB communication capabilities, which can be utilized in the future for indoor location-based services such as smarter car keys  [1] , indoor navigation  [2] , and various augmented reality(AR) applications  [3].
LUVI incorporates various modules for intelligent VA generation control and calculation, maintaining overall system performance while reducing complexity. The main module is based on lightweight VA selection. Our preliminary study analyzed various factors such as VA number and placement with respect position estimation accuracy  [6]. We showed that eliminating certain VAs in the TDoA that contain redundant information does not affect accuracy but reduces calculation complexity. Note that, we named it as a VA selection algorithm based on position clustering. Although effective, the eventual system could benefit further from other factors, e. g. the VA selection algorithm was only beneficial for mobile devices, whereas energy consumption remained constant since tags continue to consume communication energy for VA creation.
Therefore, in our LUVI, we introduce a new module for an adaptive ranging module which intelligently decides whether a ranging attempt should be made or not. The decision is made locally by the mobile device to temporarily halt ranging, e. g. if the current estimation is already accurate and no further estimation is required, or an NLOS environment is detected and further estimation is a waste of Fig. 1. Virtual anchor based relative positioning system. resource. LUVI was fully implemented and tested using an actual testbed and evaluated under several practical scenarios. Performance evaluation confirmed that LUVI was robust to NLOS and reduces average error by up to 30% while dramatically reducing computational complexity. The remainder of this paper is organized as follows. Section 2 describes three key technologies for the LUVI, and Section 3 describes LUVI and key components in detail.
Details of LUVI Fig. 3  shows the relative positioning system with LUVI designed to operate within a mobile device that performs position estimation using UWB tags already deployed in the target area, but without depending on any infrastructure or pre-learning. Thus, LUVI receives input from UWB ranging and VIO, and provides relative coordinates to the stationary tags. Calculated relative coordinates from LUVI are then used by location services, e. g. rendering location labelled data on AR application. LUVI comprises five main modules: relative position estimator; VA generator, database manager, and selector; and adaptive ranging that includes NLOS identification.
Virtual anchor generator The VA generator module creates a VA by recording the mobile device displacement from the VIO when obtaining the UWB ranging value. VAs in a 2D coordinate system M originating from where the VIO session started can be structured as V = {(t, x, y, r i )|(x, y) ∈ M, i ∈ Tags}, ( 1 ) where x and y are the device displacement in M at timestamp t. Measured distance r i at timestamp t is obtained by ranging with tag i. The system generates a VA at predetermined time periods and stores it in the database vector V. This sampling period can be adjusted for different network situations, which we discuss in more detail in the adaptive ranging algorithm. Errors that occur in VIO, such as drift, will affect the final estimation by causing drift in the position of the affected VAs.
Virtual anchor database manager Generated VAs are stored in the VA database. The relative position estimator module can estimate the tag's position (x,y) with 3 or more VAs in the database. If the position of tag j needs to be estimated, then V j can be extracted from V as Fig. 3. A Diagram of LUVI. V j = {V ′ ∈ V|i = j}, (2) which can be considered as a query result for tag j in the database. The proposed LUVI requires storing sufficiently large number of VAs to cope with errors that may occur in ranging. However, high VA sampling rate can occupy too much memory, and increase computational complexity. VAs created a long time ago may also deteriorate positioning accuracy due to error accumulation in VIO  [35]. Therefore, implementing the VA database as an appropriately sized circular buffer can not only limit the database size, but can also address the error accumulation problem.
Virtual anchor selector The number of VAs stored in the database steadily increases as the system continues to operate. Using many VAs to estimate the position improves position estimation accuracy, but also increases computational complexity for relative position estimation because computational complexity for position estimator is O(n 3 ), where n is the number of VAs in the database. This not only increases device's energy consumption, but also affects real-time relative position calculation. The detailed empirical analysis for this issue from our preliminary work  [6]  showed that 100 VAs can cause 70-100 ms delay per calculation. Another insight from that earlier study was that accuracy increased with increasing VA count for the position estimator, benefits more if the VAs are situated at various angles around the tag to overcomes geometric dilution of precision (GDOP)  [36].
Adaptive ranging manager The adaptive ranging manager (ARM) minimizes unnecessary ranging by adjusting the ranging period according to the surrounding environment and situation, i. e. , it adaptively controls the system sampling rate. The proposed LUVI can maintain estimated relative position in the system despite the lower sampling rate using VIO. The ARM is activated after the LUVI position estimator achieves a certain accuracy level. The adaptive ranging algorithm determines whether the position estimate converges from the NLOS identification and position estimation, evaluates ranging quality through periodic NLOS identification after activation, and selectively stores the results in the VA database. Therefore, NLOS identification is a very important LUVI component. Advantages from implementing ARM includes reduced communication resource by reducing ranging, consequently reducing UWB battery consumption and improving operation time; and minimizing accuracy impacts from large error-causing obstacles such as concrete walls.
-B. Choi et al. first assume a constant k, i. e. , the number of VAs to be selected from VA database V. Constant k also represents the target number of clusters to be created in the map. Map size is defined as the VA minimum and maximum coordinates furthest from the tag, but within transmission distance. Therefore, we create a virtual rectangular map with the tag in its center, and then divide the map into k clusters. However, each cluster may not have the same number of VAs due to location estimator movement and VA placement. Several rules are required for the proposed clustering process, 1 Clusters should be equal size, and 2 Clusters should be uniformly distributed in the map; to ensure the selected VAs have uniform distribution among themselves, eliminating proximity or LOS redundancy.
The tag pair does the ranging, and the mobile device does the LUVI by synthesizing the VIO tracking values and ranging values from the ARCore and tag respectively. Our application is developed powered by ARCore  [37] , which is a popular AR platform by Google. It embeds VIO to track the motion of a device in the real world and estimate its pose. So, we utilized the VIO embedded in ARCore to implement LUVI. At the time of our research, there were no open APIs available to enable low-level UWB communication control on current commercial smartphones. Therefore, we built the testbed shown in Fig. 5  using Decawave's DWM1001 module  [38]. When the tags on each side are ranging, the mobile-device side tag send its ranging value to the mobile device over BLE in real time. Also, the mobile device can also turn ranging on and off or control the frequency of the ranging by BLE.
Virtual anchor selection algorithm The VA selection algorithm was evaluated by analyzing the VA data collected in the experiments. We conducted 10 experiments with random mobility in both environments (open space and office). Then extracted k virtual anchors for accuracy and time complexity analysis, Fig. 5. LUVI testbed. from LUVI database for each experiment, according to the VA selection and number. We also evaluated the random method, selecting k VAs in a completely random manner. Figs. 8  and 9  show the total number of VAs generated in one experiment, the VAs finally selected by the selection algorithm, and the estimated positions during an experiment. The VAs shown in the figure is generated on the trajectory of the mobile device, and LUVI's VA selection algorithm selects VAs in various locations.
Automation of tRNA flanking sequence assignment The unique structure of the tRNA neochromosome necessitated a computer assisted design approach to complement the design process. The bioinformatics algorithm and associated scripts (Fig. S1A ) were used to streamline the assignment of tRNA genes to respective flanking sequences recovered from A. gossypii and E. cymbalariae. Sixteen tRNA arrays (Fig. 1A ), consisting of concatenated tRNA cassettes, housed tRNA genes from each of the sixteen respective S. cerevisiae chromosomes were re-organized so that relative tRNA gene orientation was unidirectional for each array. The unidirectionality was deemed potentially important to assure that the directions of replication forks and RNAPIII were always congruent. Furthermore, tRNA genes in tRNA arrays were aggregated by chromosome in a modular manner to both facilitate the construction of the tRNA neochromosome itself, and additionally, complement the loss of tRNA genes on individual synthetic chromosomes as part of the Sc2.
Automating flanking sequence assignment to tRNA genes An algorithm (Fig. S1A ) and associated scripts (https://github. com/cailabuom/neochromosome) were used to assign tRNA genes to respective flanking sequences recovered from the A. gossypii and E. cymbalariae genome. These were based on custom Python scripts with BioPython modules. S. cerevisiae tRNA gene sequences (lacking introns), and A. gossypii/E. cymbalariae 5' and 3' flanking sequences were recovered from Genbank files downloaded from the NCBI genome database (accession numbers may be found in Tab. S8), with metadata exported to comma separated value (. csv) files. All tRNA gene anticodons were determined (i. e. independently verified) by 'calling' a program compiled from the source code of tRNAscan-SE  (Schattner et al. , 2005). We developed an algorithm to preferentially map tRNA genes to flanking sequence based on the following design parameters: (1) tRNA genes of S.
cymbalariae. Some tRNA genes were discarded from the available pool altogether. Following tRNA gene/flanking sequence assignment, tRNA cassettes were concatenated into tRNA arrays and generated as Genbank output files. Table  S3  details a full list of each S. cerevisiae tRNA gene and its associated assigned flanking sequence. Supplementary Data S1 includes the output file generated from scripts, including annotated tRNA arrays and metadata on tRNA genes and their associated flanking sequences. tRNA gene flanking sequences were also obtained from the yeast, Kluyveromyces lactis, but were not used in our final design.
Universal homologous region The Universal Homologous Region (UHR) was designed using a Perl script to produce a sequence of randomly-generated DNA 500 bp in length. This sequence was subjected to BLAST (https://blast. ncbi. nlm. nih. gov/Blast. cgi) against the S. cerevisiae genome, with no significant homology found. This DNA sequence is listed in Table  S8. The UHR was synthesized de novo from overlapping oligonucleotides using the overlap-extension PCR method from the Build-A-Genome course  (Cooper et al. , 2012).
The presence of 500 bp homologous sequences on the three fragments facilitated recombination-mediated integration of the chrX tRNA array and the repair of the inversion to generate the final tRNA neochromosome (YCy2508) (cf. Fig. 2B ). Single colonies were selected on SC-His-Met + 5-FOA with 2% galactose agar plates. Candidates were screened with primers to detect the correct homologous recombination borders as well as the missing tRNA cassettes of the chrX tRNA array before full PCR Tag analysis, size verification via PFGE and short read sequencing was performed resulting in the final strain YCy2508. The raw reads of YCy2508 are deposited at NCBI BioProject PRJNA351844 (SAMN30265401).
tRNA neochromosome stability assay Overnight cultures of haploid BY4741, BY4742 and diploid MATa/a and MATa/α containing the tRNA neochromosome (YCy2837, YCy2838, YCy2508 and YCy2844) were inoculated as biological triplicates into 20 mL SC-His to a starting OD600 of 0. 1. Every 24 h, cultures were back diluted to OD600 0. 1 and glycerol stocks were prepared over a time-course of seven days corresponding to approximately 50 generations. On the seventh day, dilution series were plated on SC-His media and four randomly selected candidates per strain were tested with a subset of the PCR Tag primers via colony PCR. DNA of 2 mL overnight culture was extracted with the MasterPure™ Yeast DNA Purification Kit (Lucigen) and whole PCR Tag analysis was performed on population level to test the general presence of all tRNAs within the population (data not shown).
The remaining procedure was performed according to the manufacturer's guidelines. DNA quality was accessed via gel-electrophoresis, NanoDrop™ 2000 spectrophotometer and Qubit 4 fluorometer using the dsDNA BR reagents. Library preparation was performed using the SQK-LSK109 and the Native Barcoding kits (EXP-NDB104 and EXP-NDB114) according to the manufacturing guidelines. The only alteration was to increase the DNA mass 5-fold to match the required starting material molarity, DNA is approx. 50 kb in size based on our initial PFGE analysis (data not shown). Sequencing was performed on a MinION Mk1B device (Oxford Nanopore Technologies) using flow cell chemistry R9. 4. 1 (FLO-MIN106D). Base calling was performed using Guppy and minimap2 was used for sequence alignments. Samtools and IGV were used for data analysis.
Tecan growth curve assay Growth curve analysis was performed in a Tecan F200 plate reader in 96-well flat bottom plates (Corning) with 150 µL of medium inoculated to a starting OD600 of 0. 05. Plates were sealed with TopSeal-A PLUS (Perkin Elmer). The following kinetic parameters were run with a varying number of cycles (289 cycles for approx. 24 hrs at 30°C): 2:23 min orbital shaking (1. 5 mm amplitude), 2:23 min linear shaking (2 mm amplitude) followed by OD measurement at 595 nm. Data was analyzed and visualized with custom R scripts and the R package Growthcurver  (Sprouffske and Wagner, 2016).
8 μg of rRNA-depleted RNA. sRNAs below 200 nt were subsequently removed using the Ambion MirVana miRNA Isolation kit (according to manufacturer's instructions), resulting in around 300 ng of RNA. RNAs were checked for successful rRNA depletion and sRNA depletion using an Agilent Bioanalyzer RNA 6000 Nano. Illumina sequencing ready libraries were prepared using NEBNext Small RNA Library Prep Set for Illumina following the manufacturer's instructions and checked on a Bioanalyzer DNA 1000. Illumina sequencing was performed on an Illumina NextSeq 500. tRNA quantification and differential expression testing Illumina sequencing data were quality controlled with FastQC v0. 10. 1 (de Sena Brandine and Smith, 2019). Sequencing adapters were trimmed with cutadapt v1. 15  (Kechin et al. , 2017). tRNAs were quantified at the transcript-level with Salmon v1.
2. 1  (Patro et al. , 2017)  using both pre-tRNA and mature tRNA transcript models. tRNAs were detected de novo using tRNAscan-SE v. 2. 0  (Lowe and Chan, 2016)  on the tRNA neochromosome reference sequence. pre-tRNAs were modified with 10 nucleotide additional leader and trailer sequences based on the genomic coordinates. tRNAs derived from synthetic and native chromosomes were aggregated in a single reference transcriptome. The identical reference transcriptome was used to quantify each sample, independent of neochromosome presence, allowing for determination of misquantification rates on the neochromosome. Additional small RNA species were downloaded from Ensembl. Salmon was run with both sequence and position bias modelling enabled and in paired end mode. Differential expression testing was performed with Sleuth v0.
(E) RPC40, in contrast, produced a recovery in growth phenotype for both the circular and linear tRNA neochromosome strain. The control strain (BY4743 + pRS413) shows two types of colony sizes. Sequencing of two big and two small clones by nanopore sequencing revealed the small clones have a low abundance of mitochondrial DNA in comparison to the large clones (data not shown). and YCy2670 ), respectively, normalized to the homozygous diploid control strain (YCy2649). KEGG and GO analysis globally show a similar pattern to that of transcriptomics. However, an additional increase in membrane-associated proteins and endoplasmic reticulum can be observed. Up-regulated and down-regulated features are labeled in red and green, respectively. and E. cymbalariae. The flow chart formed the basis of programming scripts used to generate the tRNA arrays of the tRNA neochromosome.
Analysis confirms that the final tRNA neochromosome strain is MATa. Taking the results together, as well as the ability of the strains to mate with MATα-strains (data not shown), suggest a whole-genome duplication resulting in a MATa/a genotype. All tested strains are haploid. 100,000 total events were detected for each sample and each histogram consists of minimum 50,000 events. Y-axis displays number of events and x-axis intensity. (C) Structural variations in the tRNA neochromosome detected by Nanopore Sequencing of the parental strains and evolved isolates. (D) Nanopore Sequencing revealed an aneuploidy for chrVIII in the parental strain and the evolved strain containing >70% of the tRNA neochromosome content while the aneuploidy is lost in the strain only containing 30% of the tRNA neochromosome content.