dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This research (in part) used computational resources of COMA and HA-PACS provided by  Multidisciplinary Cooperative Research Program in Center for Computational Sciences, University of Tsukuba, Japan. We thank the reviewer for the attention and time spent on our work, and respond to each of their comments in detail below: Reply: We have added a short explanation of the basics of the SLC glucose transport and how the process cycles through the various states in the Introduction, and now also emphasize the main changes arising from the pathogenic mutations in the Conclusion.
5 μm in corneal tissue. A dispersion compensation lens (LSM04DC, Thorlabs) is employed to compensate for the dispersion induced by the objective lens. Reference-and sample-arm signals are mixed by a 50:50 coupler (TW1064R3A2A, Thorlabs) and detected by a balanced detector. With future in vivo applications in mind, the power of the probe beam at the output (on the sample) was intentionally reduced to around 1 mW, to ensure light levels during imaging well below the maximum permissible exposure for ocular tissues. The signal-to-noiseratio at the imaging depth is >55 dB. The interferometric data is then processed by a data acquisition board (500 MSPS, 12-bit resolution) with a digitizer (embedded in the OCT engine from Axsun) before being transferred to a computer (Intel i7-9700F, Nvidia GeForce RTX 2060 SUPER, 16GB RAM) via a PCIe port by a frame grabber (PCIe-1433, National Instrument, Austin, Texas, United Statess) for further processing.
Workflow and Algorithm After obtaining three B-scan reference images along the slow axis and as the C-scan data is being obtained, each B-scan image in the volumetric data is processed using a custom algorithm, first developed in MATLAB as a testbed prototype, and then programmed in C++ for real-time processing. The algorithm, as summarized in the flowchart depicted in Fig. 2 , consists of two main threads, namely preprocessing and motion compensation, that are detailed in the following Secs. 2. 2. 1-2. 2. 3. Briefly, the preprocessing procedure is used to segment the surface boundary from the sample and fit to a higher-order polynomial curve. The motion-compensation procedure is based on this preprocessing result and uses it to deduce the sample motion by comparing it to the reference images.
Algorithm Efficiency Our algorithm was first developed in MATLAB as a testbed prototype, then implemented in C++ for real-time processing. To demonstrate its efficiency and real-time capabilities, we evaluated the time consumption of the algorithm during volumetric (C-scan) imaging. The tested C-scan image contains 512 B-scans and the size of each B-scan is 1024 × 1024. The mean value of time consumption for each B-scan is 7. 6 ms, including both preprocessing and motion compensation procedures, which is faster than our 100-Hz B-scan rate. Detailed time consumptions for the diverse processing steps are shown in Fig. 15. Approximately 36% of the 7. 6 ms are used for thresholding, 28% are used for various filter operations based on convolution, 26% are used for connect component analysis, 9% are used for matrix-related calculations, and the remaining 1% are used for other procedures.
Acknowledgments This work was supported in part by the  NIH National Eye Institute  (award  1R01EY032127-01 ), the  JHU Cohen Translational Engineering Fund , the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  ( SYNERGY grant  agreement no. 610110 ), and by the  French  state funds managed by the  Agence Nationale de la Recherche  within the  Programme Investissements d'Avenir, IHU FOReSIGHT  ( ANR-18-IAHU-0001 ). We gratefully acknowledge the contribution of  Cristina Georgeon  and  Marie Borderie  for acquisition and export of the clinical OCT images.
Method details OpenStreetMap creates and distributes free geographic crowdsourced data  [1]. Primary roads, highways and trunks can be obtained from OpenStreetMap to study the transport infrastructure of a country or a region. The data gives the x, y coordinates as a sequence of vertices of each road segment. However, the data is not perfect  [2]. Take, for example, the road infrastructure of Madagascar, a country in East Africa with more than 28 million inhabitants in the fourth-largest island in the world. The raw data consists of 5. 5 million vertices forming 164,540 road segments. A curved road is composed of a sequence of multiple vertices, so the length of a highway is approximated by the sum of all components, providing a reasonable estimate of road length. Thus, we can measure the physical distance between the two vertices as the length of that segment (using  [3 , 4]  ) and have a good approximation of the road distance between any two locations in the network.
After all, any interurban journey should start at any 26 agglomerations and finish at any other 25. Here, we present the steps we follow to start with the OpenStreetMap road infrastructure data, combine it with cities data and construct a connected and simplified network. The result is a network where most nodes are cities, and the remaining nodes are "transport nodes", required to describe the road infrastructure within a network consisting of road crossings and where the edges are existing road infrastructure. We connect cities and highways based on proximity. This means that if the ending of one road is sufficiently close to the beginning of another, we assume that it is possible to travel between them. Also, if a city is sufficiently close to a road, we assume that the people of that city can use that road.
Simplify the network by dissolving nodes We simplify the network by removing (dissolving) road nodes with a degree δ k = 2 connecting the same road type at both ends. We sum the lengths of the corresponding segments to form the new edge. We replace the road node with a longer edge. Each step drops one node and edge from the network without losing the details about distances. Also, we remove all loops, that is, edges that begin and end in the same node. Data from OpenStreetMap in Madagascar contains 5. 5 million vertices forming more than 160,0 0 0 road segments. After our procedure, we have obtained a network that describes the available infrastructure in Madagascar for intercity journeys. The network comprises 14 cities and 15 transport nodes, from which there is one road crossing and 14 additional nodes where the type of road switches ( Fig  2  ).
The network has 26 edges connecting it. For each edge, we estimate its road distance, so it is possible to detect the shortest path between all pairs of cities. The added segments represent less than 2% of the length of the roads in the country, but they enable us to obtain a connected network. The same procedure has been applied to data for continental African cities. The continental network has 7,361 nodes (2,162 are cities and 5,199 are transport nodes), and the network has 9,159 edges connecting its nodes. The network data is available at  [7]  and the code at  [8]. It can be used to model interurban mobility patterns  [9]  and clustering of urban agglomerations  [10]  , among many. Our procedure can also be used for other types of infrastructure, although the parameters C = 10 km and T = 24 km should be adjusted.
Investigation of the diversity of malaria parasite antigens can help prioritize and validate them as vaccine candidates and identify the most common variants for inclusion in vaccine formulations. Studies of vaccine candidates of the most virulent human malaria parasite, Plasmodium falciparum, have focused on a handful of well-known antigens, while several others have never been studied. Here we examine the global diversity and population structure of leading vaccine candidate antigens of P. falciparum using the MalariaGEN Pf3K (version 5. 1) resource, comprising more than 2600 genomes from 15 malaria endemic countries. A stringent variant calling pipeline was used to extract high quality antigen gene 'haplotypes' from the global dataset and a new R-package named VaxPack was used to streamline population genetic analyses.
In addition, a newly developed algorithm that enables spatial averaging of selection pressure on 3D protein structures was applied to the dataset. We analysed the genes encoding 23 leading and novel candidate malaria vaccine antigens including csp, trap, eba175, ama1, rh5, and CelTOS. Our analysis shows that current malaria vaccine formulations are based on rare haplotypes and thus may have limited efficacy against natural parasite populations. High levels of diversity with evidence of balancing selection was detected for most of the erythrocytic and pre-erythrocytic antigens. Measures of natural selection were then mapped to 3D protein structures to predict targets of functional antibodies. For some antigens, geographical variation in the intensity and distribution of these signals on the 3D structure suggests adaptation to different human host or mosquito vector populations.
Results WGS data from a total of 2661 P. falciparum isolates from 16 countries from the Asia-Pacific and Africa were processed to obtain gene sequences for population genetic analysis. The pipeline filters the dataset to remove high complexity infections as determined by F ws  [24]. This step aimed to ensure the correct assignment of variants to a single major clone within the sample (S2 Fig). We found a large proportion (greater than 30%) of field isolates from African regions predicted to have more than two clones, especially in Malawi and Ghana, likely reflecting high transmission intensity in those areas. However, the number of genomes in the final dataset (n = 1499) remained high, even after removing these samples. From these genomes, gene sequences for 23 antigens (Table  1 ) were obtained.
Tables A and B in S1 Text). For most antigens with multiple balancing selection hotspots such as AMA1, CSP (C-term), EBA175 (RII), CelTOS, the most common haplotypes were only remotely related to 3D7 vaccine haplotype (Table B in S1 Text). No 3D7 haplotypes were observed in the dataset for full length EBA175, MSP1 and SERA8 (Table A in S1 Text,  Fig 2). This suggests that a 3D7-based malaria vaccine may be effective against only a small proportion of natural parasite populations.
A spatially derived approach to Tajima's D (D � ) was applied to available 3D structures (predicted or experimentally defined) for a subset of parasite populations covering all major endemic regions, including Southeast Asia, Africa, and Oceania (PNG). Structures included well-studied antigens like CSP (C-terminal), TRAP (ectodomain), AMA1, EBA175 (RII), MSP1, RH5, CyRPA, SERA5, CelTOS, and MSP1-19. We observed moderate to high D � scores (1. 0-2. 0), high scores (2. 1-3. 0), and extremely high scores (> 3. 1) within most of these  In general, we found a dichotomous pattern of balancing selection on some antigens for countries in the Asia-Pacific versus African populations. The TRAP ectodomain (PDB Code: 4HQF. A) which is comprised of tandem von Willebrand factor A (VWA) and thrombospondin type I repeat (TSR) domains (AA residues: E41 -K240, 3D7 sequence) were included in our analyses  [41].
8-2. 0) were observed within most Asia-Pacific countries, but not African countries (D � scores < 0. 4) (Fig  6A ). The F2 domain makes most of the contact with glycan  [54]  and residues C476-C488 are a part of F2 β-finger domain that is also a target of inhibitory antibodies, R215 and R217  [54, 55]. The linker region was found to be conserved within all populations (Fig  6A ). However, the limited nucleotide diversity was observed amongst geographical regions. The cryo-EM structure of full length RH5 (PDB Code: 6MPV, chain B) was used in our analysis  [56]. Evidence of balancing selection was limited within African countries parasite populations based on the spatially derived Tajima's D � scores. In contrast, within Asia-Pacific countries, moderate to high D � scores (1. 5-2. 0) were consistently observed at the Basigin (BSG or CD147) binding sites and appear to be under balancing selection (AA residues: S189 -D207, 3D7 sequence) (Fig  6B ).
Discussion Here we have conducted a systematic meta-population genetic analysis of 23 malaria vaccine antigens using a global dataset of P. falciparum WGS (after filtration for high complexity countries. Sample sizes: Malawi (n = 142), Ghana (n = 249), Cambodia (n = 433), and PNG (n = 112). c. Spatially derived Tajima's D (D � ) calculations for populations from Asia-Pacific and African regions for CSP (C-terminal) with incorporation of protein structural information using 15 Å window. The crystal structure of the thrombospondin receptor (TSR) domain of CSP  [58]  (PDB code: 3VDJ, chain A, AA residues: Y306-H376, 3D7 sequence), which consists of Th2R and Th3R (T-cell epitopes) was used  [59]. Structure was coloured according to D � scores mapped to each residue with undefined D � were shown in white.
In addition, the incorporation of sequence-based metrics such as Tajima's D into protein structures enhances biological understanding, as evidenced by the finding of unique patterns of immune selection in different geographic areas. However, the dataset mostly includes single census of clinical samples from different collection timepoints, which prevent the exploration of patterns under different clinical presentations and changes in transmission. Further investigations could be done using sequencing datasets from longitudinal cohorts in association with epidemiological parameters to identify specific polymorphisms that enable the parasite to evade host immune responses. Nevertheless, in silico findings from our study support in vitro and in vivo studies and might uncover novel targets of host immunity.
Processing and analysis of whole genome sequence data P. falciparum read alignments from all 2668 samples were mapped to an indexed 3D7 reference genome using uBAM files as input. We followed the standard best practice from Genome Analysis Toolkit (GATK) version 4. 0. 12. 0 implemented in nextflow (https://github. com/gatkworkflows/gatk4-germline-snps-indels) with some minor changes as follows: (i) running the pipeline twice-the first without base quality score recalibration (BQSR) and (ii) using the pass variants from the first run for BQSR and hard filtering of variants instead of variant quality score recalibration (VQSR) as there is no external variant call set available. Variants were determined using GATK's haplotypeCaller to generate haploid genotype calls (P falciparum blood stages are haploid) and all isolates were joint genotyped using gvcf files.
Variants that had been processed with the GATK pipeline were functionally annotated with SnpEff for genomic variant annotations and functional effect prediction (version 4. 3T)  [100]. We restricted our analyses to single nucleotide polymorphisms (SNPs) from the 14 nuclear chromosomes only, excluding indels and variants from hypervariable regions, as they evolve by different mechanisms to SNPs and have much less impact on antigen genes (S1 Fig). To obtain a highquality variant dataset for downstream analyses, we further developed stringent variant filters using GATK's SelectVariants and VariantFiltration modules such as read coverage statistics, relative distribution of reads  [101]  and multiplicity of infection (MOI) (S1 Fig). Variants were removed if they met at least one of the following filtering criteria: QD < 20.
0, MQ < 50. 0, MQRankSum < -2 (if annotated), ReadPosRankSum (less than -4 or greater than 4 if annotated), SOR > 1. Further filtration was performed based on read depth, and missingness (S1 Fig). We estimated the clonal proportions for each sample using R package, moimix (available at https://github. com/bahlolab/moimix), a robust alternative (B)-allele frequency-based estimation. We only kept data from samples with single infections (MOI = 1) or two infections (MOI = 2; i. e. Fws > 0. 80)  [24]. MOI = 2 isolates were treated as diploid genomes permitting heterozygous genotype calls (S2 Fig). Multi-allelic sites were also permitted in our bioinformatics framework (S1 Fig). All samples that had F ws greater than 0. 90 were treated as MOI = 1. For MOI = 2 samples, phasing of haplotype was performed using read-depth and B-allele frequency (S1 Fig).
Thus, the final dataset contained high quality data from 948 MOI = 1 and 551 MOI = 2 isolates. Nucleotides that did not have clear major or minor alleles were classified as 'undefined' bases (S1 Fig). Variants within gene coding regions were extracted in fastA format using a custom R-script (https://github. com/myonaung/Naung-et-al-2021). After processing the sequence data, identifying high quality variants and removing complex infections, gene sequences were extracted and compiled as a fastA formatted file.
Processing of antigen sequences Antigens were selected based on their inclusion as vaccine candidates in the WHO Rainbow Table  [20]  or whether they had been previously identified as novel candidates based on previous literature  [6]. Details of regions analysed, and other details are summarised in Table  1. The raw fastA output each gene was further edited using customized python script (https:// github. com/myonaung/Naung-et-al-2021) by removing sequences that contain undefined or low-quality bases and sequences arising from minor clones in the co-existence of different genotypes (phased by read depth (S1 Fig) ). FastA formatted files of respective genes can be found in https://github. com/myonaung/Naung-et-al-2021/tree/master/Raw_FASTA. As Illumina sequencing is subject to some miscalled bases, singleton variants (i.
e. minor alleles seen only once in the entire dataset) were assumed to be artefacts and converted back to reference alleles to prevent false positive variants. The presence of at least two isolates with the same minor allele was considered independent validation of the existence of these alleles in natural parasite populations. Apart from removing singletons, no other minor allele frequency threshold was applied to the dataset. In the case of multiple clones being identified (0. 90 � Fws > 0. 80), only the defined major alleles were included for further analyses. Samples collected from the same country were combined and assumed to have been sampled from a single population.
Population genetic analyses Population genetic parameters were calculated using a customized in-house R package (source code available at https://github. com/BarryLab01/vaxpack). This program takes aligned multi-fastA files excluding intronic regions as input, alongside reference sequence of the same length. As measure of diversity we defined the polymorphisms for each antigen by the number of polymorphic sites (S), the number of synonymous (dS) SNPs, number of nonsynonymous (dN) SNPs, dN-based haplotypes, the Nei's nucleotide diversity (π) calculated as p ¼ X ij x i x j p ij ð1Þ where x i and x j are the respective frequencies of the i th and j th sequences, π ij is the number of nucleotide differences per nucleotide site between i th and j th sequences  [92]. The haplotype diversity (Hd) was defined as Hd ¼ ½n=ðn À 1Þ�½ð1 À Sðf i Þ 2 Þ� ð2Þ where n is the sample size and f is the frequency of the i th allele.
Immune mediated selection due to parasite adaptations to evade antibody recognition of dominant epitopes on a particular antigen is indicated by the presence of balancing selection. 1  to investigate the global range of diversity of each antigen as well as the frequency of reference (3D7) haplotypes (frequently used in vaccine development), while the population specific dataset was used to investigate the range and distribution of diversity for countries (23 antigens). RON2 (PF3D7_1452000) was excluded from analyses as no polymorphism was found. The full-length antigen genes as well as functionally important domains were included in the country level. For each analysed antigen gene, we investigated relationships amongst haplotypes by constructing haplotype networks using the Templeton, Crandall, and Sing (TCS) method PopArt version 1.
1 program  [30, 107]. It is given by the following equation  [30, 107] : RSA ¼ ASA=ASA MAXðGlyÀ XÀ AlaÞ � 100% ð3Þ RSA values for structured regions with known PDB or homology-modelled structures were calculated with DSSP  [31]  using maximum allowed solvent accessibilities of residues from Tien et al. 2013  [107]. Therefore, RSA represents the ratio of parts of a biomolecule exposed to solvent or accessible surface area (ASA) of a given residue observed in the three-dimensional state over maximum surface area of a residue with potential exposure to solvent (ASA MAX ) within an extended tripeptide flanked with either glycine or alanine residue. The data was stored in SQLite database using RSQlite package on RStudio. Selection pressure may apply to residues that are non-continuous on the linear sequence but proximal in 3D space.
3D structure-based and surface accessibility analysis For each antigen included in Table  1 , the 3D structure for full length or specific domains was first derived from the Protein Data Bank (PDB) from the Research Collaboratory for Structural Bioinformatics (RCSB) website (www. rcsb. org). For each antigen that does not have experimentally determined or complete structure, the possibility of comparative structure modelling was determined based on the distribution of intrinsic disordered regions available at https:// plasmosip. burnet. edu. au/submission [28]. Except for AMA1, which was modelled manually, template-based models of 3D7 reference strain for these antigens were predicted using Mod-Pipe, an automated software pipeline that utilises the program MODELLER  [108]  for the generation of comparative proteins based on previous studies  [16].
We used a manually modelled structure of AMA1, which includes full domains I-III based on a combination of P. falciparum and P. vivax templates according to previously published work  [27]. The modelled structures were assumed to be reliable if the ModPipe Quality Score (MPQS) is greater than 1. 1 This included for antigens-EBA175 (RII), MSP1  19  , and CelTOS which is 45% sequence identity to P. vivax CelTOS structure (PDB Code: 5TSZ)  [61]. These structure models are accessible via ModBase (https://modbase. compbio. ucsf. edu/) [108]. The BiostructMap Python package available at https://github. com/andrewguy/biostructmap was used to calculate nucleotide diversity (π), and Tajima's D with consideration of spatial information using a 3D sliding window with a radius of 15 Å (D � = spatially derived D).
Sample sizes: Malawi (n = 135), Ghana (n = 223), Cambodia (n = 431), and PNG (n = 111). d. Tajima's D (D � ) calculation for geographic area or countries from the Asia-Pacific and African regions for MSP1 19 with incorporation of protein structural information using 15 Å window. The structured region of MSP1 (MSP1-19, AA residue: N1607-S1699) is based on the ModPipe homology model using template (PDB code: 1OB1) [60]. The structure was coloured according to D � scores mapped to each residue with undefined D � were shown in white. The circle indicates variable balancing selection hotspots between Asia-Pacific and African countries. Sample sizes: Malawi (n = 101), Ghana (n = 183), Cambodia (n = 270), and PNG (n = 72). https://doi. org/10. 1371/journal. pcbi. 1009801. g006.
[104,105]e selection, Tajima's D (D) statistics were calculated[102,103]. Under neutral conditions, Tajima's D values are expected to be approximately zero[104,105], positive D values indicate an excess of intermediate frequency polymorphisms most likely due to balancing (immune) selection, and negative values indicate purifying (directional) selection. However, measurement of Tajima's D across genetically differentiated populations with varying allele frequencies (i. e. due to population structure) can produce false positive signals of balancing selection. Thus, all Tajima's D analyses were calculated separately for each country. Analyses were first carried out with the complete global dataset (n = 1499 samples) for all antigens from Table.