dataesr/scientific-paragraphs-categorization huggingface dataset:
Focal retinal perfusion defects reflected by localized absence or collapse of the capillaries could be easily identified as areas of non-perfusion in OCTA, and diffused retinal perfusion decrease was usually detected by comparing with age-matched case-control groups. Therefore, normative databases played an important role in differentiating and grading the vascular abnormalities, especially for the diffused capillary defects. A few studies have reported the normative OCTA database in the macula or parapapillary regions  [10] [11] [12] ; however, developing a normative database in 12×12 mm is more challenging because of several obstacles, including perfusion density heterogeneity among macular, parapapillary to peripheral regions, and uneven illumination resulted from optical vignetting. Here for the first time, we developed a normative database for retinal perfusion density using 12×12 mm OCTA.
Images were acquired on a swept-source OCT (SS-OCT) prototype (PlexElite 9000, Zeiss Meditec, Dublin, CA, USA). Large vessels were automatically isolated using a validated U-Net, and the entire database was divided into 4 age groups: < 30 years old, 30-49 years old, 50-69 years old and >69 years old. We also demonstrated the usefulness of the normative database in quantifying the retinal perfusion change in eyes with primary open-angle glaucoma (POAG) and NPDR.
OCTA protocol This is a prospective study. Normal subjects were recruited from three cohorts (PIONEER: The PopulatION HEath and Eye Disease PRofile in Elderly Singaporeans; SIENA: the Singapore Imaging Eye Network; and REMODEL: Response of the Myopcardium to Hpertrophic Conditions in the Adult Population) from Singapore National Eye Center (SNEC) outpatient clinics during the period of January 2018 to January 2020. Inclusion criteria for the healthy subjects were no evidence of hypertension or ocular pathologies, including glaucoma, DR, and age-related macular degeneration. Another two groups of participants (n = 20) were included to evaluate the normative database: POAG participants were recruited from the SIENA protocol, and NPDR participants were recruited from Diabetes study in Nephropathy And other Microvascular complications protocol.
These studies were approved by the SingHealth Centralized Institutional Review Board and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from every participant. OCTA images were taken using a prototype SS-OCT system operated in 1050 nm region and 100 nm bandwidth. The system operated at a speed of 100,000 A-scans/second, and the axial and lateral resolutions in tissue were 6. 3µm and 20µm, respectively. A 12×12 mm scanning protocol centered at the fovea was applied, and each volume consisted of 500 A-scans and 500 B-scans. Each B-scan was repeated twice for generating OCTA images using an optical microangiography algorithm  [13]. Motion related artifacts were minimized by an integrated line scanning ophthalmoscope (LSO) eye tracker during data acquisition.
5). The diagram of the U-Net and the detailed parameters are shown in Fig. 1. Large vessels were manually delineated by two graders, where the overlap was regarded as the ground truth. The annotation of large vessels is based on whether the tubular structure of the vessel can be recognized. We trained the U-Net over 80 12×12 mm OCTA images from a separate database using a custom made IoU (Intersection over Union) loss function as shown in Eqn. (1) , where y pred was the predicted binarized matrix, y true was the ground-truth, and i,j were the coordinate indicators. L IoU = 1 - ∑︁ i,j ∈N y pred i,j y true i,j ∑︁ i,j ∈N y pred i,j + y true i. j -y pred i,j y true i,j (1) A five-fold cross-validation method was used to evaluate the architecture, the best of which was applied to segment the large vessels  [15].
Perfusion density heatmap A custom MATLAB (MathWorks Inc, MA, USA) algorithm was developed to generate a map representing microvascular perfusion density in SCP, DCP, and total retinal vasculature of healthy adults. It comprised the following steps. Firstly, to compensate for decreased illuminance at the peripheral regions of the 12×12 mm OCTA, we divided the OCTA image by a rotationally symmetric Gaussian lowpass filter (sigma = 9 mm). A sliding window scheme (window size: 3 × 3 mm; sliding step: 0. 375 mm in both directions) was used to segment the capillaries and compensate for the residual uneven illuminance. In each window, the threshold was empirically set as 40% of the mean intensity of large vessels, and the threshold was applied to all superficial, deep, and full retinal angiograms.
(D)). A binarized perfusion map with large vessel selectivity is shown in Fig. 2(E ). Red and green colors represented capillaries and large vessels, respectively. Secondly, to calculate the capillary perfusion density, the values of large vessel regions were set to Not a Number (NaN). Lastly, the perfusion map was calculated as the convolution between a normalized kernel (a disk-shaped structuring element, radius = 0. 164 mm) and the binarized capillary images, omitting NaN values. The perfusion map was converted to a jet color scale heatmap as shown in Fig. 2 (F). The correlation between perfusion density and SSI were fit in a linear function, and perfusion density from individual map was adjusted based on its SSI and the average SSI over the entire database, as shown in Fig. 2 (H). To register individual maps, centers of the fovea and optic nerve head (ONH) were automatically identified.
Clinical application of the normative database in various eye diseases The normative database could provide an intuitive observation of microvascular changes in a variety of eye diseases. A direct defect map could inform the ophthalmologists with the loci and severities of microvascular alterations, in a wide field of view. Next, we applied the normative datasets on two types of common eye diseases, namely, POAG and NPDR. Two representative eyes from the same male patient (age: 37 years old) illustrated the identification and characterization of the glaucomatous damage in microvasculature using the normative database. The visual field tests showed early arcuate  [37]  loss along with the nerve fiber bundles in both superior and inferior hemifields (Fig. 8(A) ) in the right eye, and in superior hemifields (Fig.
Traditional 6×6 mm macular or ONH scans can only visualize part of the injured area, and provide an incomprehensive characterization of the glaucomatous damage. Our data confirmed the value of OCT scans in glaucoma diagnosis, and microvascular changes could provide information of early nutrients supply insufficiency to the neurons. Whether microvascular based diagnosis overperforms RNFL thickness based diagnosis remains unknown. The hypothesis that combining wide-field microvascular and morphological information increases the diagnostic power of early-stage glaucoma detection needs further investigation with larger clinical datasets. Two representative eyes from two male patients (age: 64 & 72 years old) illustrated the usefulness of the normative database in moderate NPDR. Standard 35-degree fundus views showed clear evidence of hemorrhages (Fig.
Comparing with the age-matched normative database could simultaneously identify the loci and severities of the capillary dropout, especially in DCP. In the first case, some focal capillary defects and diffuse capillary change were detected in the peripheral and macular regions, respectively. In the macular region, DCP was less affected than SVP, while perfusion in both slabs were reduced in the retinal periphery (Fig. 8 (L) and 8(O)). In the second case, despite focal capillary defects, the overall retinal perfusion was significantly lower than the normal distribution, on both SCP and DCP (Fig. 8 (S) and 8(V)). Again, further investigation using larger clinical datasets will be needed for the clinical validation of our approach. These capillary defect maps have the potential to accurately quantify capillary drop out on consecutive visits to provide an additional measure for monitoring of peripheral vascular perfusion in eyes not only with early diabetic disease but also the therapeutic response of eyes undergoing treatment for proliferative diabetic retinopathy or diabetic macular edema.
Lastly, a total of 10 POAG eyes and 10 NPDR eyes were included to show their deviations for the normal distributions. The perfusion density in the entire 12×12 mm FOV, as well as the central 6×6 mm and the remaining square annulus, were evaluated, summarized in Table  2. As expected, significant differences in capillary perfusion were detected between the diseased eyes and the normal subjects, while the DCP were less affected. The sensitivity and specificity of using the normative database to classify POAG or NPDR needs to be done in larger datasets. One limitation of this study is to use a commercially available prototype (PlexElite 9000) while OCTA images can be affected by multiple factors, including sampling frequency, optical resolution, and processing algorithms. Without knowing the parameters and algorithms, comparing OCTA.
Conclusions In this study, we developed a normative database for 12×12 mm OCTA. We used a U-Net based deep learning algorithm to accurately segment the large blood vessels; the capillaries were consequently segmented by a moving window scheme. A total of 195 eyes were included and divided into 4 age groups: < 30 (N=24), 30-49 years old (N = 28), 50-69 (N=109) and >69 (N=34). Eyes with POAG and NPDR were used to demonstrate the usefulness of the normative database. Further investigations include evaluating the normative database on diagnosing pathological eyes in larger datasets. Disclosures. The authors declare that there are no conflicts of interest related to this article.
, 1995). Various studies have also inferred the evolutionary and epidemiological history of the globally spread subtypes  (Verbeeck et al. , 2006; Njouom et al. , 2007; Magiorkinis et al. , 2009; Zehender et al. , 2013; Choudhary et al. , 2014; McNaughton et al. , 2015; Paraskevis et al. , 2019). Most of these studies have used a limited number of samples and have focused on specific genomic regions (such as Core, E1/E2, and NS5A/NS5B) and/or on the epidemiological history of a restricted geographic region. Generating virus whole genomes and host genome-wide genotyping data for 507 patients located in the UK, Canada, USA, Australia, and New Zealand from the BOSON is the name of the clinical trial. This name is not a shortened version of any specific phrase. Gilead which sponsored this trial was using names like "FISSION", "FUSION" and "POSITRON" for their sofosbuvir trials BOSON cohort  (Foster et al.
Molecular clock signal of HCV-3a sequences Our 507 HCV-3a whole genomes from the BOSON study lack a molecular clock signal (Supplementary Fig. S1 ) as they were collected during a short interval in the 2013-14 period  (Foster et al. , 2015). To resolve this, we compiled 42 previously published HCV-3a whole genomes from public databases, the earliest of which was sampled in Canada in 1991. To explore the temporal signal in the updated data set, we estimated a maximum likelihood (ML) tree from whole genomes and calculated the correlation between root-to-tip genetic distance and sampling dates of the sequences using TempEst (Supplementary Fig. S1 )  (Rambaut et al. , 2016). We estimated a substitution rate of 2. 13 × 10 -3 (95 per cent CI: 1. 61 × 10 -3 to 2. 64 × 10 -3 ) per site per year, which is consistent with the previous estimate for HCV-3a (1.
Enrichment of South Asian ancestry individuals among HCV-3a-infected individuals from the UK To determine the host ancestry of the patients in our study, we estimated principal components (PCs) of the host genome-wide genotyping data for the BOSON cohort and projected them onto the genetic PCs of the 1000 Genomes Project where the ancestries are known (The 1000 Genomes Project Consortium 2015). We used these projections to validate and adjust the self-reported host ancestry of the patients (Fig. 1 , Supplementary Fig. S2 , see Section 4) and to distinguish between South Asian and East Asian ancestry, which both were self-reported as Asian in our data set. For BOSON hosts with HCV-3a infection where genetic data were not available, host ancestries were designated as their self-reported ancestries (nineteen individuals).
The host ancestries of HCV-3a sequences downloaded from public databases (where no host genetic data were available) were assigned based on the majority ethnic group of the countries where the samples came from. We observed that the majority of the patients had white European ancestry (N = 433, 80 per cent), and South Asians were the second largest group (N = 69, 13 per cent). The other host ancestries present in this data set were Admixed Americans (N = 12), East Asians (N = 6), Africans (N = 4), and other ethnic groups (N = 16). Although the patients in the BOSON cohort were recruited from the UK (N = 211), the USA (N = 65), Canada (N = 64), Australia (N = 113), and New Zealand (N = 35) where the majority of the population are of white European ancestry, we observed an enrichment of patients of South Asian ancestry in this cohort, especially in the UK where South Asian ancestry patients made 19 per cent of the HCV-3a infections (41/211, P-value = 1.
This hypothesis would predict a higher viral genetic diversity among South Asian hosts regardless of the country they come from and for these isolates to coalesce near the root of the tree and nest the isolates from individuals with other ancestries, both of which are supported by the data. Additionally, studies have shown a 5-fold increased risk of HCV infection for South Asians born outside of the UK over the UK-born South Asians  (Harris et al. , 2012). To infer the locations of infections for South Asian ancestry hosts living in the West, we conducted a structured coalescent analysis on host ancestries to infer their location of infection. For virus sequences obtained from public databases (where we do not have host genetic information), the host ancestry is assigned as the majority ethnic group in the sampling countries.
, 2009). Since IDU is shown to be the main transmission route for HCV genotype 3  (Harder et al. , 2004; Pybus et al. , 2005) , we suspect that these events indeed facilitated the spread of the virus during this period and could have been intensified by blood transfusion and unsafe therapeutic injections  (Magiorkinis et al. , 2009). In this study, we used the largest HCV-3a whole-genome data set to date and combined it with the host genetic data to reconstruct the virus epidemic history. Using the host genetic data, we discovered that almost all of the viruses from South Asian ancestry hosts cluster together near the root of the tree irrespective of which country the host came from. It also allowed us to distinguish between viruses from South Asian ancestry hosts acquired in South Asia and the country of sampling.
Materials Five hundred and seven HCV-3a whole-genome sequences were obtained from the BOSON clinical trial study  (Foster et al. , 2015). Due to the lack of temporal signal in the BOSON data, we downloaded previously published HCV-3a sequences from the Los Alamos HCV database  (Kuiken et al. , 2005). The following criteria were used to filter the sequences: 1. The sequence has to be categorized as HCV-3a. 2. The sequence has to cover the coding region of the genome. 3. The sequence data have to contain a sampling date. 4. When there are multiple samples from the same patient at different time points, we only include the earliest sample. 5. When there are multiple samples from the same patient at the same time point, we randomly select one sample to include in our data set. This resulted in forty-eight sequences that were added to our data set (Supplementary Table  S1 ).
, 2005; Lamoury et al. , 2015). We then investigated the aligned sequences by looking at their ML tree as constructed by FastTree  (Price, Dehal, and Arkin 2010). We observed that six pairs of sequences on the tips of the tree were very closely related (Supplementary Fig. S3 ). Upon further investigation, we found that for all pairs, both pairs were from the same country. Furthermore, all these samples were downloaded from Los Alamos database, had little description available in GenBank, and were submitted by the same authors (Supplementary Table  S3 ). This raised the possibility that these pairs of closely related samples were obtained from the same individuals. To be conservative, we randomly discarded one sample in each of these closely related pairs. As a result, six more sequences were removed from our data set and the final dataset consists of 498 sequences from the BOSON cohort and 42 sequences downloaded from Los Alamos database.
Host ancestry designation We downloaded the unimputed 1000 Genomes Project phase 3 genetic data (The 1000 Genomes Project Consortium 2015). Using Plink (v1. 9)  (Chang et al. , 2015) , the Single nucleotide polymorphisms (SNPs) in the 1000 Genomes Project were pruned by linkage disequilibrium on all autosomes, and the same set of SNPs were also pruned from the host genetic data from the BOSON cohort. PCs were then estimated for both 1000 Genomes Projects and BOSON respectively using Plink (v1. 9)  (Chang et al. , 2015). We then projected the PCs of the BOSON cohort onto the PCs of the 1000 Genomes Project. Next, we trained a random forest classifier as implemented by R package randomForest  (Liaw and Wiener 2002)  on the top 20 PCs from the 1000 Genome Project. We used the trained model to predict the host ancestry for the BOSON cohort.
We used a threshold of 60 per cent for the random forest score to assign ancestries in the BOSON cohort. For individuals where the random forest scores were less than 60 per cent, the 'Other/Unknown' ancestry was assigned. For BOSON hosts where genetic data were unavailable (N = 19), we used the following scheme for host ancestry designation: we inferred hosts with selfreported ethnicity of 'Asian' (N = 3) to be of South Asian ancestries as most self-reporting Asians in BOSON are of South Asian ancestries (84 per cent) according to the result of PCA; we inferred hosts self-reporting as 'White' to be of European ancestries (N = 15); and we designated hosts with other self-reported ethnicities as 'Other/Unknown' ancestry. For sequences that were downloaded from Los Alamos HCV database, the host genetic information was not available.
Phylogenetic analysis The molecular clock and Bayesian coalescent-based phylogeny of HCV were estimated using BEAST (v1. 10. 4)  (Suchard et al. , 2018) , which infers rooted and time-scaled phylogenies via an MCMC algorithm. Because of the large sample size in our data set, we took a few steps to ensure that the MCMC computation could be finished within a reasonable time. We first used FastTree  (Price, Dehal, and Arkin 2010)  to build an ML tree and then used Tem-pEst  (Rambaut et al. , 2016)  to root the tree. TempEst searches for the most likely root by finding the point in the tree that maximizes the likelihood of the tree given the sampling dates of the tips. The rooted ML tree was then used with the R package TreeDater  (Volz and Frost 2017)  to estimate a time-scaled tree. The resulting tree was then used as the starting point for the MCMC analysis conducted by BEAST.
, 2012)  was chosen for specifying the moves conducted by MCMC. Finally, the length of the MCMC chain was set to 100 million. MCMC analysis by BEAST was then run twice, and the result was combined. Tracer (v1. 7. 1)  (Rambaut et al. , 2018)  was used to visually inspect the results of the MCMC analysis to ensure good mixing. Besides Bayesian coalescent-based phylogeny, we also used an ML approach to estimate the phylogeny of HCV in our results. RAxML (v8. 2. 12)  (Stamatakis 2014)  was used to estimate an ML tree that was then rooted with TempEst  (Rambaut et al. , 2016). The tree was then dated by treeDater  (Volz and Frost 2017). The BEAST2  (Bouckaert et al. , 2014)  package MASCOT  (Müller, Rasmussen, and Stadler 2018)  was used for the phylogeographical analysis, where the phylogeny was fixed, and population sizes and migration rates between populations were set to be identical across all populations.
We implemented the closure problem for computing the dispersion tensor within the open-source platform OpenFOAM Ò. The resulting program called dispersionEvaluationFoam is available online (https://github. com/csoulain/dispersion EvaluationFoam) and benefits from all OpenFOAM features including High-Performance Computing and unstructured grids to process 3D images of rock samples  [19]. The paper is organized as follows. In Section 2, we describe the acquisition of the 3D images of a sandpack, the computation of the velocity profile, and the numerical method to determine the dispersion tensor. In Section 3, we first verify the accuracy of our numerical framework by simulating cases for which analytical solution exists, and we then compute the dispersion tensor of sandpack imaged using X-ray microtomography.
We verify that our OpenFOAM code for evaluating dispersion tensor predicts Taylor-Aris law. Considering that a cross-section is a Representative Elementary Volume of an infinite tube, the equations introduced in Section 2. 4 also lead to Taylor-Aris dispersion. Indeed, in cylindrical coordinates (e r , e h , e x ), the closure variable simplifies to B = B x (r)e x (because of invariance by rotation and translation along the tube axis) and equations (  7 )-(  9 ) write, ṽx ¼ D A 1 r o or r oB x or ; ð11Þ oB x or r¼R ¼ 0; ð12Þ Z R 0 rB x r ð Þdr ¼ 0: ð13Þ The integration of equations (  11 )-(  13 ) gives, B x r ð Þ ¼ hv x iR 2 4D A r R 2 À 1 2 r R 4 À 1 3 : ð14Þ Finally, using equations (  6 ), the dispersion tensor is D Ã xx D A ¼ 1 À 2 R 2 D A Z R 0 rṽ x B x r ð Þdr ¼ 1 þ hv x i 2 R 2 48D 2 A ; ð15Þ which corresponds to Taylor-Aris formula.
Conclusion We computed the longitudinal dispersion of a sandpack using pore-scale simulations. Three-dimensional images of rock samples were obtained using microtomography imaging, segmented into fluid and solid phase, and gridded. The determination of the hydrodynamic dispersion tensor relies on the closure problem proposed by Carbonell and Whitaker  [30]. We implemented their approach into a modern and open-source platform. Our numerical development, called dispersionEvaluationFoam, is based on the scientific library OpenFOAM for solving partial differential equations using the finite-volume method. dispersionE-valuationFoam is available publicly on our GitHub depository (https://github. com/csoulain/dispersionEvalu-ationFoam). Due to the versatility of OpenFOAM, our model handles 3D simulations, massively parallel calculations, and unstructured grids.
We verify the predictive aspect of dispersionEval-uationFoam by assessing the hydrodynamic dispersion in an infinite cylinder. The results are in very good agreement with Taylor-Aris theory. Then, we evaluate the tortuosity and the longitudinal dispersion of a sandpack. We found a dependency in hydrodynamic conditions that scales Pe 1. 25  in agreement with data published in the literature. The methodology proposed in this paper is generic and can be applied to any kind of rock. Importantly, the tools and the package we developed to perform our simulations are open-source, and we expect that our developments will be an important contribution to the flow and transport in porous media community. Fig. 6. Longitudinal dispersion in a sandpack obtained numerically using dispersionEvaluationFoam.
Acknowledgments. The research leading to these results has received funding from the  French Agency for Research (Agence Nationale de la Recherche, ANR)  through the labex Voltaire  ANR-10-LABX-100-01  and the grant FraMatI  ANR-19-CE05-0002. It has also received financial support from the  CNRS  through the  MITI interdisciplinary programs  and the  Region Centre-Val de Loire  (Contribution 201500103985). The authors benefitted from the use of the cluster at the  Centre de Calcul Scientifique en région Centre-Val de Loire.