dataesr/scientific-paragraphs-categorization huggingface dataset:
V. PERFORMANCE ANALYSIS ON THE CLUSTERING TECHNIQUE To evaluate the performance of our designed system, simulation models are developed using three different software platforms: 1. OMNeT++ (Objective Modular Network Testbed in C++  [17] ,  [18] ) is shown in Figure  11  with video results available online  [19]. In this simulation 2D analysis of cluster, formation is conducted. Blue color doted and the diagonal line  represents the path of UAV while blue circle shows its communication range. The small red colored circles are the node which was selected as CH in the previous iteration. 2. The snapshot of our MATLAB simulation is provided in Figure  12  with a complete video available online  [20]. The 3D analysis is conducted in this simulation. A 100 × 100 meter plane bed is taken for sensor nodes deployment.
The data transmission by the CH to the UAV uses a higher frequency (Wi-Fi) and causes the largest energy depletion. The developed sink traverses the network to collect data for the static sensor nodes. In this scenario, three types of data collection are possible: 1. DDC (Direct Data Collection) UAV  [27]  2. NADC (Network Assisted Data Collection)  [28]  3. ADAM (our designed UAV controlled protocol) DDC, the UAV visits each sensor node individually. For NADC, the sensor nodes make a cluster to which the UAV visits only the CH to collect the data, but of the UAV is defined and controlled by the ground network topology. In our proposed system, ADAM, the UAV moves freely, guides the ground sensor nodes in forming clusters, and help in the selection of the CH. To evaluate the performance of the system, we created a simulation model for each type with different simulation runs to study the variation of the parameters.
B. DEAD NODES INVESTIGATION IN MATLAB The results of our simulations on dead nodes in each UAV round with respect to different routing schemes are shown in Figure  17. Each scheme is represented as a different color curve with the higher performance being observed in the lower curve. The red and purple lines show the performance of our proposed ADAM protocol. We test the ADAM system against two cases with a fixed height and an adoptive height. In the first case, although the UAV flies at a constant height (the UAV is not reducing the height to collect data) even then the proposed system performs well due to duel frequency use, no flooding of information, no periodic updates, and improved CH selection. The red curve shows the results of this scenario. In the second case, the performance of the proposed system is tested with the adaptive height scenario.
VI. PERFORMANCE ANALYSIS ON THE LOCALIZATION SYSTEM The proposed localisation system reviewed by simulation in MATLAB. A 433 MHz single antenna is mounted over the UAV that moves with a constant speed of 20 m/s. Two target nodes emitting narrowband signals a(θ) are placed at ground  level with the azimuth angles of 20 0 and -60 0. The elevation angle is taken to be 0 for all nodes. The SA-UAV locates the target nodes. The parameters used in simulation are listed in table  3. As shown in Figure  22 , the AoA of these two target nodes are measured accurately (20 0 and -60 0 ) by the UAV using our proposed system. The directions of both targets are measured with and without our proposed system. The black curve shows the estimation by MUSIC without our modification,  which does not work well. The red curve represents a successful MUSIC process after incorporating our developed modules, including the calibration and rectification.
VIII. CONCLUSION Our focus in this study is to harness IoT and UAVs in conventional agriculture to produce quality and quantity crops with optimum resource utilisation. The challenge was to develop an IoT based system that can resist in harsh weather, able to work with no or limited infrastructure, integrates heterogeneous sensors to collect the required data. To cope with these constraints, we developed ADAM system, a UAV controlled routing protocol for data collection. The formation of dynamic clusters from heterogeneous sensor nodes, a Bayesian-based cluster head selection, and a special localisation system are the key features of our design. An important advantage of this scheme is that the UAV can move independently to scan the desired area and harvest only selected IoT nodes. Another benefit of this protocol is that it preserves the maximum node energy by shifting most of the working load onto the UAV that uses very low power frequency for localisation and collection of data at a reasonable height.
The recent release of genomic sequences for 3000 rice varieties provides access to the genetic diversity at species level for this crop. We take advantage of this resource to unravel some features of the retrotranspositional landscape of rice. We develop software TRACK-POSON specifically for the detection of transposable elements insertion polymorphisms (TIPs) from large datasets. We apply this tool to 32 families of retrotransposons and identify more than 50,000 TIPs in the 3000 rice genomes. Most polymorphisms are found at very low frequency, suggesting that they may have occurred recently in agro. A genome-wide association study shows that these activations in rice may be triggered by external stimuli, rather than by the alteration of genetic factors involved in transposable element silencing pathways.
This offers the opportunity to study genome dynamics at intra-specific level and at an unprecedented scale, although this requires the development of bioinformatic tools that are suitable for the analysis of very large datasets. Conceptually, the detection of TIPs from populations based on Illumina data is straightforward, although prone to high false discovery rate (FDR) for large genomes (several 100 Mbp) due to the small reads size  24. The methods commonly used are pairedend mapping (PEM) and split reads. For the first method, all paired reads are mapped onto a reference genome and discordant pairs (i. e. , both reads from the same "amplicon" mapping at different locations) should correspond to structural variants and declared as TIPs if one end matches a known TE. The second method consists in identifying reads for which one part maps onto the reference genome sequence while the other matches a TE  [25] [26] [27].
The main limitation of these approaches (besides FDR) is that the mapping step of all paired reads onto a reference genome is computationally intensive. Therefore, we developed TRACKPOSON software for the efficient detection of TIPs of known TEs in large datasets. Using this new tool, we successfully characterized TIPs among 3000 rice genomes for 32 retrotransposons families. We chose this sample as being representative of the 300 families found in the rice genome in terms of superfamilies, copy number and transpositional activity  16, [28] [29] [30] [31]  , thus unraveling the retrotranspositional landscape of a plant genome at the species level. Using this data, we tentatively looked for genetic factors that may trigger transposition in agro in rice and conclude that for most TE families, such activation likely originated from external stimuli, rather than from a mutation within a genetic factor involved in the control of transposition.
Results TRACKPOSON is a tool for the detection of TIPs in gene pools. The new strategy we developed consists in first mapping all reads of a given accession onto each TE family represented by a single consensus sequence (as opposed to mapping them onto the complete genome, as in the case of conventional PEM procedures) and then mapping the unmapped paired reads onto the rice reference genome, split into 10 kb windows (Fig. 1 ). In this regard, TRACKPOSON is not designed for a full characterization of all structural variations, but rather as a fast tool to unravel the transpositional activity of known TE families from very large genomic dataset. We first tested TRACKPOSON on a rice mutant for which TIPs had been previously characterized by using our previous PEM-based software  39  and wet-lab validated by PCR amplification and sequencing (Supplementary Figure  1 ).
This considerably improved the sensitivity of detection (Supplementary Figure  2 ), with little risk that it increased the false-positive detection rate because the probability that two chimeric amplicons match the exact same 10 kb window as the one previously detected in another accession should be very low. In order to check for the performance of this method, we resequenced one rice accession (Mutha Samba::IRGC 49924-2) that was originally sequenced in the 3000 genome collection at a 8. 3× depth, i. e. , among the lowest coverage in the dataset. We used Nanopore long-read sequencing technology (Minion device). One flowcell was used and produced 4. 77 Gbp of sequence (11× genome coverage). We manually checked for the presence of TIPs of Hopi, Tos17, and Karma, from the reads thus generated  40.
We chose these three families because they are representative of the diversity of the retrotransposons found in the reference rice genome, i. e. , a low (Tos17) and a high (Hopi) copy-number LTR-retrotransposon families and a moderately repeated LINE family (Karma). We should however emphasize that TRACKPOSON could only unravel the transpositional activity of TE families identified from the reference genome sequence and therefore that we could not test the efficiency of this software on a retrotransposon family with genomic features that are very different from Hopi, Tos17, or Karma, in case such family exists in one of the 3000 rice genomes represented in the dataset. TRACKPOSON identified 501, 8, and 9 insertions of Hopi, Tos17, and Karma, in Mutha Samba cultivar, respectively. Hundred percent of the insertions of both Tos17 and Karma identified by TRACKPOSON were validated.
In the case of Hopi, 473 insertions were validated. These results show that the specificity of TRACKPOSON is ~94. 5%. The rate of false negatives (sensitivity) was also estimated. While there was a 100% overlap between the insertions detected by TRACKPOSON and those detected using Nanopore reads for both Tos17 and Karma, which is indicative of 100% sensitivity for these two families, we detected a total of of 581 Hopi insertions from the Nanopore dataset, which corresponds to a drop of sensitivity to 81% for this family. However, the mappability of a complex genome such as that of rice with small reads obtained on Illumina platforms is expected to not be 100% because of the presence of repeats that impede unambiguous mapping at unique sites  41. In the case of rice, the mappability of 100 bp windows at a 1e -20 threshold (i.
e. , that used for the second step of the detection, see Methods section) was estimated to be 63. 5%, on average. This estimation fits with previous estimates of the repeat content of Japonica rice genome (i. e. , ~40%  15  ). We determined the mappability of the regions where we detected Hopi insertions with Nanopore reads. We found an average of 58% for all insertions. However, the insertions found only in the nanopore data (and not detected by TRACKPOSON) have a significant lower mappability of 42%. This may explain the lower sensitivity of our software for this particular family. Retrotranspositional landscape of Asian rice. TRACKPOSON was used to detect TIPs for 32 retrotransposon families in the 3000 rice genomes dataset. The number of complete elements for these families in the reference rice genome is given in Table  1.
We chose a sample of families that are representative of the diversity found in this accession in terms of number of repeats, from very moderately (e. g. , Tos17 with two complete copies in the reference genome) to highly repeated families (e. g. , Houba with 150 complete copies). Therefore, we consider that the features revealed by this sample of retrotransposons should be representative of the complete retrotranspositional landscape of rice genome shaped by the whole 300 LTR-retrotransposon families  16. In total, we identified 53,262 and 47,007 TIPs for the 3000 genomes and the 1067 traditional varieties included in the dataset, respectively (Table  1 ). Each family showed some variation of copy number among the 3000 rice accessions (Fig. 2a ). Overall, the total number of insertions when all families were considered varied from 3324 to 12,380 with an average of 6225.
However, in rice, like in the other model species Arabidopsis thaliana, transposition is strictly controlled by several epigenetic pathways  44  , which raises the question of whether transposition is triggered in agro by mutations in key genes a The transpositional history of each TE families was based on the histogram in Fig. 2  b Numbers after "±" denote standard deviation involved in these pathways in cultivated populations or, alternatively, by external stimuli such as biotic or abiotic stress that may modify the epigenetic landscape of the genome, mimicking the impediment of these pathways. We tested both hypotheses by using a genome-wide association study (GWAS), similar to that of Quadrana et al. 45. Using the single-nucleotide polymorphism (SNP) dataset generated in the framework of the 3000 rice genome project  46  , we sought associations between these markers and the number of copies (taken here as a phenotype) for each retrotransposon family (Fig.
4  and Supplementary Figures  3 4 5 6 7 8 9 10 11 12 13 14 ). Significant association peaks were found for the 32 families, although the clearest results were obtained for the 12 less repeated families (e. g. , Tos17, Fig. 4a ). For the others, e. g. Rire2 or Rire3, the peaks were too numerous. For the 12 less repeated families, a total of 26 significant peaks were found (Supplementary Figures 3-14). Twenty fell into a region with a copy of the element identified using our TRACKPOSON software. The remaining five peaks (Fam86x2, Fam89, Fam124, Karma, and Houba) did not fall within a locus harboring a copy of the retrotransposon. However, the mappability in these regions was <60% because of the presence of repeats, which suggests that some insertions may not have been detected with TRACKPOSON.
The split of the Indica/Aus lineages appears to be more recent with a median at 98. 6% (~540,000 years ago). Combined with the median age of the Aus-specific insertions (see above), our results suggest that the split between the progenitors of Indica and Aus may have occurred between ~230,000 and ~540,000 years ago. However the median date of insertions that are common between Japonica and Aus (seventh group) appears to be older (96. 4%, translating into a date of 1. 4 Mya) than the median date of insertions that are common between Japonica and Indica (estimated above at 800,000 years ago). The much smaller sample of Aus varieties in the 1067 traditional cultivar's dataset (84 accessions) may explain this discrepency. Alternatively, it could originate from demographic history of the populations of the rice progenitor O.
Discussion Several softwares designed for TIPs detection are currently available  26, 27. However, all require as a first step to map all sequencing reads onto a reference genome sequence, using Burrows-Wheeler-based algorithms. This mapping step is computationally intensive and thus the computation time necessary to analyze a large dataset (i. e. , >1000 genomes) is too long. The availability of large datasets is now only a reality for a few plant species such as rice and A. thaliana, but this will certainly change in the near future. In this report, we show that TRACKPOSON, the new software we developed for identifying TIPs, is efficient for the analysis of thousands of genomes. It produces few false positives, as evidenced by wet-lab validation (Supplementary Figure  1  and Supplementary Tables  1  and 2 ).
False negatives remain an issue with low genome coverage data; however, the two-pass procedure (see Results and Methods sections) can considerably limit the risk of missing TIPs, as confirmed by the sequencing of one accession with long-read technology (Nanopore). However, unlike the softwares cited above, TRACKPOSON is not designed for the exhaustive characterization of all structural variations caused by transposition, but rather for the complete and efficient characterization of the transpositional activity of known families among thousands genomes. The 53,262 TIPs we identified in the 3000 rice genomes for 32 retrotransposon families were found at low frequency among rice accessions, similarly to what was observed for A. thaliana  45, 50. Most insertions are private or shared by two accessions, which suggests that they occurred after rice domestication and therefore in agro.
The advent of large genomic datasets opens new perspectives in the exploitation of genetic resources for most crops. For rice, the 3000 genomes are now being routinely used for gene discovery. However, most studies rely on the use of SNP data, while large InDels and in particular those caused by the activity of TEs remain mostly unexploited. In this study, we show that TEs are at the origin of a large extent of structural variations found genomewide and arise at a rapid rate. Hence, these mobile elements contribute significantly to the genomic diversity of rice. We also show that most of this diversity originated after domestication, therefore in rice fields and wherever rice is grown, further suggesting that the whole rice gene pool may be a reservoir of structural variants. Whether such TE-driven genomic changes are phenotypically relevant and could therefore have been selected for as a source of adaptation for rice remains to be demonstrated, although evidence that TIPs are causal to phenotypes of agronomic interest have been reported for other crops.
Methods TRACKPOSON pipeline. The TRACKPOSON pipeline is freely available at http:// gamay. univ-perp. fr/~Panaudlab/TRACKPOSON. tar. gz. It is designed to detect TIPs in large datasets (>1000 genomes), using an existing TE database. The first step consists in mapping paired reads of genomic data in fastq format onto indexed consensus sequence of each TE family. Bowtie2 (v. 2. 2. 0) in very-sensitive mode  51  is used in this first step. The Sam file thus obtained is parsed using the flag value as a criterion as follows: only the read pairs for which one read mapped against the TE were kept. The next step consists of mapping the unmapped paired read onto the rice reference genome sequence (Nipponbare rice genome IRGSP1. 0) using blastn (v. 2. 2. 31+)  52  with an e value threshold of 1e-20.
fr/ ~Panaudlab/TRACKPOSON_Results. tar. gz. The wall time for running the software on the 3000 genomes ranged from a few hours to several days on a cluster of 88 cores, depending on the level of repetition of the family, confirming that this new strategy considerably improved the speed of detection. Mappability. As mentioned in the paragraph above, only reads mapping unequivocally at a unique position onto the reference genome sequence were kept for positioning the TIPs. This obviously reduced the mappable genome to only unique sequences, which are estimated to be ~60% of the rice genome  15. Each 10 kb window was sliced into 100 bp sequences-the repeat level of which was estimated using blastn against the reference genome with the same parameters as used in the mapping step of TRACKPOSON.
Acknowledgements This work was supported by a  CNRS/Région Languedoc Roussillon research grant  and by the  University of Perpignan  via domitia. This work was publicly funded through a  CNRS/Région Languedoc Roussillon research grant , by the  University of Perpignan  via domitia and  ANR  (the  French National Research Agency)  under the " Investissements d'avenir " programme with the reference  ANR-10-LABX-001-01 Labex Agro  and coordinated by  Agropolis Fondation. The work was also supported by an  Academia Sinica Thematic  Project  AS-TP-107-L02  in Taiwan. The authors thank  Marie Mirouze ,  Scott A. Jackson ,  Josep Casacuberta , and  Joris Bertrand  for their useful comments on the manuscript.
Data availability The Nanopore sequencing data is available on NCBI under the BioProject ID PRJNA507708. The previously published 3000 rice genome raw sequencing data are available from GigaScience Database (https://doi. org/10. 5524/200001)  23. A reporting summary for this Article is available as a Supplementary Information file. The source data underlying Fig. 2 , Fig. 3a-c , Fig. 5 , Supplementary Figure  1 , Supplementary Figure  2 , and Supplementary Table  3  are provided as a Source Data file.
Pilot Testing The pilot was a 3-arm, individually randomized, controlled study in an online sample of adults with overweight and obesity comparing (1) Top Tips only app, (2) Top Tips plus app (including an additional tip on dealing with tempting foods), and (3) no intervention control group (waiting list). The active intervention period was 3 months, and follow-up data were collected at the end of this period. The study was approved by the University College London Ethics Committee (study ID: 5766/003). It also followed the Consort extension for pilot studies (Multimedia Appendix 5).
A research website was also set up to provide interested participants with additional information about the study. Interested participants were invited to fill out an online survey where they were screened for eligibility. Recruitment took place over 2 months, from the beginning of January to the beginning of March 2017. Eligible participants who gave informed consent and completed the baseline questionnaire were individually randomized to 1 of the 3 group conditions: (1) Top Tips only app, (2) Top Tips plus app, and (3) waiting list. Randomization was performed using the Minimpy software (Sourceforge)  [27]  and was stratified by gender, age, and BMI classification.
Measures Automated data from Top Tips only and Top Tips plus users were collected over the 3-month intervention period to assess usage patterns. This included data on the number of log-ins, pages viewed, plans made, and the total time spent on the apps in minutes. Information was also collected on the number of times that weight was logged and each tip was achieved. Information was collected from the Top Tips plus users on the number of times they resisted food cravings, number of times they did not resist, and number of times they did not have food cravings. The online questionnaires at baseline and follow-up collected information on sociodemographics, self-regulation, anthropometrics, and dietary behaviors. Participants were asked to report their gender, age, ethnicity, marital status, education, and employment status.
Descriptive analyses were used to assess the relationships between overall app usage and changes in eating self-regulatory skills, weight, and target behaviors over 3 months. For this analysis, the levels of change in self-regulatory skills, weight, and target behaviors were categorized into 2 groups using ranked percentiles: (1) percentile <75 represented medium-to-small changes and (2) percentile ≥75 represented large changes. Rank percentiles were used to categorize data into low and high as the data were skewed. Users' feedback on their experience using the app was analyzed using thematic analysis  [33]. This method identifies and reports patterns (themes) within data. All quantitative analyses were undertaken using SPSS version 24. 0 (SPSS Inc).
Acknowledgments Researchers involved in this study were supported by  Cancer Research UK (CRUK) ,  Yorkshire Cancer Research (YCR) , and  Coordination for the Improvement of Higher Education Personnel (CAPES/Brazil). The views expressed in this paper are those of the authors and not necessarily those of CRUK, YCR, and  CAPES/Brazil. The app development was partially funded by the charity  Weight Concern  and partially funded by  White October , a  Web design and development company.