dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability The publicly available data that we reanalysed here were generated by Wetzels et al. 22  , Neubauer et al. 23  , and Mann et al. 13. The metagenomic sequencing data from the rumen papillae samples are available on the NCBI SRA under the accession number PRJNAXXXX. The genomes are available on NCBI under the accession numbers PRJNAXXXX.
Given the scarcity of information about the components of the chaulmoogra oil biosynthetic pathway, identifying genes/transcripts encoding enzymes involved in synthesizing these fatty acids may provide valuable information for future studies. The discovery of genes encoding enzymes involved in plants specialized metabolism (such as the synthesis of chaulmoogra oil) represents a unique challenge. Complex enzymatic networks can produce several byproducts instead of a single compound  [7]. An additional complication is that different plant species vary considerably in their oil content and fatty acid composition  [8]. This is particularly true of the pathway of cyclopentenic acids, where there are few studies. Additionally, chaulmoogra oil-producing species do not have genomic information available in public databases, making studies related to omics difficult.
Quality Control and Assembly The Trimmomatic v. 0. 32 software (https://github. com/timflutre/trimmomatic, accessed on 18 October 2022) was used to verify the quality of the reads. We used a Phred score of Q > 20 as a threshold to trim low-quality bases from the ends of the reads. Highquality reads were used to assemble a de novo transcriptome using the Trinity v. 2. 1. 1 software (https://github. com/trinityrnaseq/trinityrnaseq, accessed on 18 October 2022) following an analysis pipeline (Supplementary Figure  S1 ).
Quality of Transcripts, Complete Transcripts, and Super Transcripts The transcripts' quality and completeness were assessed using the Benchmarking Universal Single-Copy Orthologs (BUSCO 3. 1. 0) (https://github. com/openpaul/busco, accessed on 18 October 2022), based on ortholog groups from Arabidopsis thaliana and Solanaceae. The Busco software was chosen because it allowed us to detect the natural variation of conserved genes within clades, based on evolutionarily informed expectations of gene content of near-universal single-copy orthologs for a variety of eukaryotic clades. The indexing and alignment of transcripts was carried out using Bowtie2 2. 2. 5 (https://github. com/BenLangmead/bowtie, accessed on 18 October 2022). The trimmed transcripts of each library were aligned against the indexed reference and subsequently analyzed with RSEM (http://deweylab.
github. io/RSEM/, accessed on 18 October 2022) to estimate the expression values normalized by transcripts per million (TPM) in each RNA-Seq library. The TransDecoder software 5. 5. 0 (https://github. com/TransDecoder/TransDecoder/, accessed on 18 October 2022) was used to identify candidate coding regions for starting and ending transcript sequences. For this purpose, the Basic Local Alignment Search Tool (BLAST) was used versus the Uniref and Pfam 31. 0 databases (http://pfam. xfam. org, accessed on 18 October 2022). This allowed predicting complete sequences of isoforms selected by TransDecoder, as well as protein sequences. Pfam is an extensive collection of protein families, represented by multi-sequence alignments, using hidden Markov chains (HMMs). In this sense, only complete sequences (i) with start and end sequence transcription, (ii) showing high similarity with cured sequences from the Uniref database, and (iii) with functional domains based on the statistical alignment of Pfam were considered.
The Trinity software (https://github. com/trinityrnaseq/trinityrnaseq, accessed on 18 October 2022) was used to reconstruct the primary transcript sequence and obtain super transcripts. This analysis recognizes unique and common sequence regions between isoforms and merges these isoforms into a single linear sequence. The super transcripts are useful in the context of de novo free genomes, since they yield a gene sequence similar to what can be obtained when sequencing the genome.
Functional Annotation The functional annotation of the C. brasiliensis transcript was generated with the Egg-NOG mapper database using HMM to obtain the EggNOG mapper 4. 5 orthology data (Supplementary Figure  S1 ). For all transcripts, the clusters of orthologous groups (COGs) and archaeal clusters of orthologous genes (arCOG) were assigned  [9]. The sequences of C. brasiliensis proteins were also annotated based on the InterProScan databases (http://www. ebi. ac. uk/InterProScan/ accessed on 18 October 2022) and the Pannzer 2 tool (protein annotation using Z-score)  [10]. These analyses recovered the EC protein codes, which were used in the KEGG mapper  [11]  for the identification of lipid pathways. Annotations with ARGOT_PPV greater than 0. 35 were selected from the results obtained with Pannzer 2.
Phylogenetic Analysis The 18S ribosomal sequence of C. brasiliensis was identified by searching for putative homologs of the 18S A. thaliana sequence. The 18S transcription of C. brasiliensis was performed against the Non-Redundant Database (NR) of the National Center for Biotechnology Information (NCBI) to obtain the 18S data of species used in the phylogeny construction. Redundant sequences were excluded and at least one copy of each genus was maintained. Multiple nucleotide alignment (ClustalW) of 79 sequences was performed using the Mega X 10. 1 software  [12]. The model test was performed using MEGA X 10. 1. Genetic relationships between accessions were calculated using a distance matrix obtained by the Tamura-Nei nucleotide substitution model  [13]. The identified taxa were grouped based on the maximum likelihood method.
Analysis of Orthologous Gene Families The ARALIP website platform was used to select gene families related to lipids in A. thaliana (http://aralip. plantbiology. msu. edu/pathways/pathways accessed on 18 October 2022). Searches for the term "fatty acids" were carried out in the Phytozome database v. 12 (https://phytozome. jgi. doe. gov/ accessed on 18 October 2022), including searches for gene sequences related to lipid metabolism in A. thaliana, Glycine max, and Populus trichocarpa. BLASTp was used to compare the lipid families of the model species against the C. brasiliensis protein database. The BLASTp result was used to further analyze gene orthology in OrthoMCL v. 1. 4 [14]  with the standard inflation parameter I = 1. 5. OrthoMCL was used to obtain the best hits and explore similarity measures to ortholog and paralog groups.
Heatmap A heatmap was constructed with all the lipids identified in all libraries using the Or-thoMCL, Pannzer 2, EggNOG mapper, KEGG Mapper, and InterProScan. A heatmap was constructed to visualize the expression of the C. brasiliensis lipid transcripts identified in the different tissues with the aid of the "ComplexHeatmap" package (https://github. com/jokergoo/ComplexHeatmap accessed on 18 October 2022) in the R version 4. 0. 1 environment (R Core Team). Thereafter, the values of TPM (transcripts per million) were transformed into z-scores with the function "scale". Euclidean distance and grouping with complete linkage were used to add the dendrogram to the heatmap. From the functional annotation analyses, an ontology gene enrichment (GO) analysis was performed for each heatmap cluster, using the BINGO  [15].
Construction of the Lipid Metabolism Model The following tools were used to build the lipid metabolism model: (i) the OrthoMCL software to identify the orthologous lipid gene families; (ii) the Mercator4 v5. 0 software (https://www. plabipd. de/portal/mercator4, accessed on 18 October 2022) to map the unigene file previously identified, obtaining a text file with one or more BINs per protein; (iii) the MapMan program to visualize the expression and meta-analysis data, to annotate the plant omics data  [16] , to correlate each unigene to its expression level, and to identify the lipid metabolism pathways (X4. 2 Lipid metabolism R2. 0); (iv) Pannzer 2  [10]  and lnter-ProScan (http://www. ebi. ac. uk/InterProScan/ accessed on 18 October 2022) for protein annotation (focusing on keywords such as fatty, lipid, and desat to contemplate the largest gene number from the lipid pathway; (v) the Kegg Mapper platform  [11]  to rescue the protein EC (Enzyme Commission Numbers), to automatically assign KO identifiers (KEGG Orthology; https://www.
kegg. jp/ or https://www. genome. jp/kegg/ accessed on 18 October 2022) and to map the described lipid metabolism pathways (biosynthesis of fatty acids and unsaturated fatty acids, and elongation of fatty acids); and (vi) the "Com-plexHeatmap" package (https://github. com/jokergoo/ComplexHeatmap, accessed on 18 October 2022) in R version 4. 0. 1 to generate a heatmap from the rescued EC. For heatmap generation, the TPM values were transformed to z-scores with the "scale" function, while for clustering, the Euclidean distance and clustering with complete linkage function were used. The lipid pathways and the expression data were manually associated in a general scheme.
RNA Sequencing: The First Transcriptomic Information of C. brasiliensis The Illumina MiSeq sequencing (2 × 250 bp) of C. brasiliensis generated about 10. 4 million paired-end reads (Phred > 20) for 24 distinct cDNA tissue libraries. The assembly of reads from the 24 libraries of vegetative and reproductive tissues generated a total of 263,562 unigenes, with an average length of 340 base pairs (bp). The average length of the transcripts varied by 554. 34 bp, with N50 length of 637 bp and the GC content of 42. 68% (Supplementary Table  S2 ). The GC content was 41. 37% and N50 totalized 696,928 bp. We used TransDecoder to reduce the number of total transcripts and the number of unigenes, but to increase the average N50 length (Supplementary Table  S2 ). The total number of initial unigenes was 263,562 and after treatment with the TransDecoder, the total number of unigenes was 12,908.
The shorter sequences may lack a characterized protein domain or may be too short to show sequence matches, resulting in false-negative results. Because genomic and transcriptomic information is currently lacking for C. brasiliensis in databases, these cases of no hits can be considered putative novel protein sequences. The metabolic network analysis of C. brasiliensis revealed 148 pathways, but the combined global map showed 742 metabolic pathways (ko01100) and 334 secondary metabolite biosynthesis enzymes (ko01110). This result reveals that C. brasiliensis can be considered a plant rich in secondary metabolites. Due to the commercial importance of the oil in C. brasiliensis, our study focused on "lipid metabolism", for which 13 pathways were identified (Figure  2 ). A large number of sequences involved in lipid synthesis and metabolism was predicted, as well as the biochemical pathways involved in the synthesis of chaulmoogra oil.
This is the first transcriptome study of C. brasiliensis, providing basic information for biotechnological applications of great use for the species, which will help understand chaulmoogra oil biosynthesis. The dataset developed in this study expands the database of C. brasiliensis. These resources can contribute to the discovery of new genes in developing seeds, which reinforces the importance of the present study. The basic information produced can be applied in future biotechnological approaches providing a booter to obtain improved varieties via genetic engineering, as well as for the development of molecular markers. Finally, the set of identified unigenes can also contribute to the future annotation and assembly of the complete genome sequence of C. brasiliensis. To our knowledge, this is one of the most complete and extensive efforts to annotate lipid genes from tropical non-timber trees.
Acknowledgments: The authors thank  Fernando Santana  for technical support and authorization of plant material collections in cocoa farms in the Camamu region,  Bahia, Brazil , and to members of the  BIOPREA group  ( Caio Argolo ,  Ceslaine Barbosa ,  Dalma Brito ,  Edson Mário ,  Jonathan Mucherino ,  Patrícia Gonzalez ,  Raner Santana  and  Valter Magalhães ) for their scientific discussions. The authors thank  Annette Fahrenkrog ,  Bárbara Müller ,  Leandro Neves  and  Christopher Dervinis , who contributed to the transcriptome.
Study design The COVID-19 pandemic presents a continuously changing epidemiological landscape; therefore, the study designs is adaptable to accommodate unpredictable circumstances. The project will generally use a mixture of methods tailored to each of the project components. For example, (i) stakeholder engagement will use qualitative and quantitative approaches to generate data. This will be done on a representative number along the hierarchy of the stakeholders in a cross-sectional study, (ii) develop a DCT tool with stakeholder feedback for contextualisation and (iii) a longitudinal study to test the DCT tool with truck drivers will capture data using the mobile application.
Sample size Formative research study: We will recruit a diverse sample of truck drivers from any of the six study sites (figure  3A ). Other stakeholders will be purposively selected to ensure diverse experience and expertise. We estimate that a Open access maximum of 30 participants will contribute to threefocus group discussions (FGDs), with another 6-12 individuals taking part in the key informant interviews (KII), while the self-administered questionnaire-based survey will generate data from approximately 300 respondents. The sample size estimation for examining knowledge attitudes and practices related to COVID-19 among haulage drivers and allied stakeholders will be done based on the methodology published by this group. 16 uantitative study: We shall conduct a survey regarding stakeholder input on the mobile application's utility, functionality and privacy.
We will employ a human-centred design approachan iterative process of idea generation, prototyping, user-testing and piloting that involves end-users in the development process  23  (figure  3B ). 24 The application will be developed to embody values that maximise user anonymity, flexibility and minimal data storage and battery life. Third objective will be to test the utility of the DCT tool among haulage truck drivers. This will be achieved through truck drivers' interaction with the tool. Once the mobile application is downloaded and installed by the driver, it will (a) ask for consent from the driver to access the global positioning system (GPS) and (b) request for information on regular haulage routes such as departure and destination. In this regard, the mobile app will have a feature that a user can choose to allow its activation whenever movement relative to the road network is detected that is, 'auto-activation when on road network'.
Finally, (c) the application will then generate a universally unique identifier (UUID) or 'digital token' as a pseudonym and deposit it into the secure, centralised database. Once processes (1) to (2) as shown in (figures 4 and 5) are completed, the app will launch and run in the background. As an option, the app will also request the user for their metadata (age, sex, district of residence and phone contact), which will also be stored encrypted on the phone. The three pieces of information that is, UUID, sampled GPS and metadata are kept encrypted separately. It is noteworthy that the regional haulage truck drivers will be required to have a negative test valid for 14 days, although a driver can cross the border 2-4 times a month. 17 OVID-19 testing: COVID-19 testing and reporting at POEs, as shown by steps 3-5 in figure  5 , will be conducted by public health officials.
Open access Africa driver's number used as laboratory sample ID. The laboratory will then post the results into the centralised results dispatch system (RDS) managed by the Ministry of Health. The scanned QR-codes will generate a list of ID's, which our system will use to retrieve results from the RDS. Here, the DCT system will do the following: 1. Retrieve and notify the driver of their results. 2. If the result is positive, the driver will receive a notification and the current guidance by the ministry of health as a reminder. 3. Public health will then use the tracking data to create a 3-day risk map for cases which will be available as a web interface accessible to the public health surveillance team to support decision making. 4. Once notified, the case and contacts are required to quarantine based on the guideline, adherence will be assessed using the inherent geo-fencing on road network.
Since a transmission event is associated with a location linked to communities on the road network, over time, public health can use the PPT to predict/estimate the risk for the communities liked to the road network. Notably, we estimate that asymptomatic and presymptomatic cases will account for 15% and 52% of the notified positive cases, respectively,  25  which are associated with a lower secondary transmission risk. Such estimates will be contextualised using our empirical data and used to evaluate the tool's effectiveness for contact tracing, that is, the sensitivity and proportion of expected false positive contact notification. Fourth objective will be to develop models for mapping risk, infer transmission and estimate a haulage attribution: The data collected by the mobile application (DCT) will be analysed as follows: first, we will use machine learning methods to compute the probability of drivers testing positive as a function of characteristics of their movements (eg, their route, duration of the journey, the departure time, vehicle type (~fuelling frequency) estimated traffic and contact structure).
Determining a probabilistic output will enable the public health officials to prioritise who to test (risk-based diagnostics), limiting the number of contacts between infected drivers and individuals of the community. Note that since this process will need a training data set, the first 4 months will be to generate this data set (~160 000 trips). Therefore, the utility of this approach for this analysis will be after 6 months of data collection. Second, the movements of trucks along the road networks will form a large network where nodes such as villages/towns/cities and edges are movements of haulage vehicles. This network will then be analysed to evaluate which communities are key in the structure of the network, their potential for disease transmission and infection as well as their potential as candidates for placing efficient testing facility outposts.
These stakeholder dialogues will follow the same approach as described for the KAP above. Formative research data analysis will be done using interpretative description approaches,  30  such as those used in ethnography and naturalistic inquiry to analyse data arising from the KAP survey, KII and FGDs targeting the stakeholders. These approaches allow us to examine the practical implications of DCT on the routine activities of the truck drivers in the context of the existing body of knowledge in an iterative analysis, while exploring challenges and opportunities to using DCT technologies. We shall use qualitative analysis software, such as Nvivo to code and interrogate our data.
DATA COLLECTION, PROTECTION AND MANAGEMENT The team will take the following steps to secure the mobile app and THEA-C19 server: 1. Use transport layer security for all communication between the mobile app and THEA-C19 server. There are extra security layers embedded which are not disclosed here to ensure that the utility of the tools is tamperproof. The contact tracing component will primarily use the collected time-stamped GPS data, which will be strictly limited to the road network, and are securely collected and transmitted to a server. The data collected will be stored in a relational database and will be retrievable in a number of formats including (comma separated values)CSV for analysis. The database will be replicated online to provide redundancy and ensure uninterrupted operation.
In addition, daily backups will be taken in the event that we need to restore the data which will be kept for the duration of the project. Use of the data will only be triggered by a case; it is this case's risk profile that informs the contact tracing and notification via the mobile application. Otherwise, all data will be kept separate. Diagnostic data will be generated and posted to a secure public health centralised system also referred to as the RDS. The stakeholder engagement component will generate qualitative data, collected as part of the KAP analysis linked to COVID-19, ethics and legal aspects of technology deployment. It will consist of audiorecordings, observational notes, transcribed texts and still photography. Participants will be required to consent before downloading or installing the app.
ETHICS AND DISSEMINATION In Uganda, ethics approval was obtained from School of Public Health Higher Degrees, Research and Ethics Committee at Makerere University (approval number SPH-2021-35) and Uganda National Council for Science and Technology (approval number HS156ES). At the University of Edinburgh, study approval was granted by the Human Ethics and Research Committee at the Easter Bush Campus (HERC-538). We will obtain informed consent for participation in interviews, FGDs, still photography and videography. Data from interviews, questionnaires, GPS, photos, videos and notes will be anonymised. Open access from individual results provided conveniently without financial cost. However, since research participants will not be provided any therapeutic interventions, consent forms clearly state that no financial benefits shall be gained as a result of individual participation in this research study, however haulage companies may ultimately benefit monetarily by reducing their employees' exposure to the virus thereby limiting sickness-related absences while maintaining operations.
We will provide all participating organisations with a report of the main findings as soon as the analyses have been completed and independently verified by at least two referees who are not part of the study team. We will actively disseminate the results of this study through publications and presentations. At National level, the study is nested within the Ministry of Heath, with study outputs directly feeding into the public health responses via incident command teams. At the regional level, study findings will be presented at regional haulage sector meetings facilitated by the NLP in Uganda. Internationally, we will actively disseminate the study findings through peer-reviewed publications, pre-existing partnerships and our organisation websites such as https:// project-thea. org/ and Github account for the open source code https://github.
This work has received ethics approval from the  School of Public Health Higher Degrees, Research and Ethics Committee at Makerere for Science and Technology. This work will be disseminated through peerreviewed publications, our websites https://project-thea. org/ and  Github  for the open source code https://github. com/project-thea/. by Funding This study is funded by the  National Institute of Health Research/Medical Research Council-GECO  award, ref  MR/V034952/1.
2. Add rate limiting to the application program interface(API) service to prevent denial of service attacks and ensure availability at all times. Open access 3. an access log for all system accesses. 4. Sanitise and validate all data submitted to the server to prevent cross-site scripting and SQL injection at- tacks. 5. Use the latest version of the frameworks, runtimes and database software to ensure the latest security patches are in place. Actively monitor common vulnerabilities and exposures(CVE) disclosures for all software pack- ages used in order to respond to any reported vulner- abilities quickly. by copyright. http://bmjopen. bmj. com/ on September 5, 2022 at University of Edinburgh. Protected.