dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2014) , which may reflect a decrease in recruitment to these frontal regions. Indeed, frontal regions are the first regions to see their neuroanatomy impacted by aging  (Dennis & Cabeza, 2008). These results allow us to understand this concept at the network communication dynamics levels. Further investigation of investigation transfer during task completion will be necessary to specify its associations with the direct implementation of cognitive processes. Several methodological considerations must be discussed regarding the reported results. First, the investigation of resting-state activity prevents in part the direct investigation of the neural bases of cognitive processes, which may explain the small number of associations with cognition. This could also reflect the fact that the Cam-CAN database does not include tasks directly testing executive functions.
D) Amino acid mutations in the spike gene of the BA. 4 and BA. 5 lineages. Mutations that differ from BA. 2 are denoted in red, including the wild-type amino acid at position Q493 (denoted by the red *). Before the emergence of BA. 4 and BA. 5, F486V had been observed only in 54 of 10 million publicly available genome sequences in GISAID (https://covspectrum. org/explore/World/AllSamples/AllTimes/variants?aaMutations=S%3AF486V&). Selection analyses focusing on ratios of non-synonymous and synonymous substitution rates at individual codons have indicated that, since December 2020 S:486 has been evolving under strong negative selection favouring the F state at this site (i. e. , the amino acid that is found in Wuhan-Hu-1) (Extended Data Fig. 2 ). Although rare, the F486L mutation has been observed in some viral lineages found infecting minks and in human cases linked to mink farms and has been shown to directly enhance entry into cells expressing mink/ferret ACE2  16.
When binding to hACE2, spike amino acid F486 interacts with hACE2 residues L79, M82, and Y83, which collectively comprise a hotspot for ACE2 differences between mammalian species  17, 18. Mutations at F486 are associated with a reduction in neutralising activity by class 1 (and some class 2) neutralising antibodies and by polyclonal sera  [13] [14] [15]. Deep mutational scanning suggests that F486 is a key site for escape of vaccine-and infection-elicited RBD-targeted antibodies, including those still able to neutralize Omicron/BA. 1 (https://jbloomlab. github. io/SARS2_RBD_Ab_escape_maps/escape-calc/)  19. The S:69-70del means BA. 4 and BA. 5 can again be presumptively identified (against a background of BA. 2 infection) using the proxy marker of S-gene target failure (SGTF) with the TaqPath™ COVID-19 qPCR assay (Thermo Fisher Scientific, Waltham, MA, USA).
Midnight protocol For Oxford Nanopore sequencing, the Midnight primer kit was used as described previously54. cDNA synthesis was performed on the extracted RNA using the LunaScript RT mastermix (New England BioLabs) followed by gene-specific multiplex PCR using the Midnight primer pools, which produce 1,200 bp amplicons that overlap to cover the 30 kb SARS-CoV-2 genome. Amplicons from each pool were pooled and used neat for barcoding with the Oxford Nanopore Rapid Barcoding kit according to the manufacturer's protocol. Barcoded samples were pooled and bead-purified. After the bead clean-up, the library was loaded on a prepared R9. 4. 1 flowcell. A GridION X5 or MinION sequencing run was initiated using MinKNOW software with the base-call setting switched off.
In some instances, mutations were confirmed visually with. bam files using Geneious v. 2020. 1. 2 (Biomatters). The reference genome used throughout the assembly process was NC_045512. 2 (numbering equivalent to MN908947. 3). Raw reads from the Illumina COVIDSeq protocol were assembled using the Exatype NGS SARS-CoV-2 pipeline v. 1. 6. 1 (https://sars-cov-2. exatype. com/). This pipeline performs quality control on reads and then maps the reads to a reference using Examap. The reference genome used throughout the assembly process was NC_045512. 2 (accession number: MN908947. 3). Several of the initial Ion Torrent genomes contained a number of frameshifts, which caused unknown variant calls. Manual inspection revealed that these were probably sequencing errors resulting in mis-assembled regions (probably due to the known error profile of Ion Torrent sequencers)57.
To resolve this, the raw reads from the IonTorrent platform were assembled using the SARSCoV2 RECoVERY (Reconstruction of Coronavirus Genomes & Rapid Analysis) pipeline implemented in the Galaxy instance ARIES (https://aries. iss. it). This pipeline fixed the observed frameshifts, confirming that they were artefacts of mis-assembly; this subsequently resolved the variant calls. The Exatype and RECoVERY pipelines each produce a consensus sequence for each sample. These consensus sequences were manually inspected and polished using Aliview v. 1. 27 (http://ormbunkar. se/aliview/). All of the sequences passing internal quality control were deposited in GISAID (https://www. gisaid. org/), and the GISAID accession identifiers are included as part of Extended Data Table  1.
Phylogenetic analysis We initially analysed genomes from South Africa against the global reference dataset using a custom pipeline based on a local version of NextStrain (https://github. com/nextstrain/ncov)  28. The pipeline contains several Python scripts that manage the analysis workflow. It performs an alignment of genomes in NextAlign  29  , phylogenetic tree inference in IQ-Tree V1. 6. 9  30  , tree dating and ancestral state construction and annotation (https://github. com/nextstrain/ncov). The initial phylogenetic analysis enabled us to identify clusters corresponding to the BA. 4 (n=120) and BA. 5 (n=51) lineages. We extracted these clusters and constructed a preliminary maximum-likelihood tree with a subset of BA. 2 sequences (n=52) in IQ-tree. We inspected this maximum-likelihood tree in TempEst v.
1. 5. 3  31  for the presence of a temporal or molecular clock signal. Linear regression of root-to-tip genetic distances against sampling dates indicated that the SARS-CoV-2 sequences evolved in a relatively strong clock-like manner (correlation coefficient = 0. 6, R 2 = 0. 4) (Extended Data Fig. 4 ). We then estimated time-calibrated phylogenies using the Bayesian software package BEAST v. 1. 10. 4  32. For this analysis, we used the strict molecular clock model, the HKY+I+G, nucleotide substitution model and the exponential growth coalescent model  33. We computed Markov chain Monte Carlo (MCMC) in duplicate runs of 20 million states each, sampling every 2,000 steps. Convergence of MCMC chains was checked using Tracer v. 1. 7. 1  34. Maximum clade credibility trees were summarized from the MCMC samples using TreeAnnotator after discarding 10% as burn-in.
Phylogeographic analysis To model phylogenetic diffusion of the new cluster across the country, we used a flexible relaxed random walk diffusion model that accommodates branch-specific variation in rates of dispersal with a Cauchy distribution  37. For each sequence, latitude and longitude were attributed to the most precise district or provincial information available and linked to the diagnostic sample. As described in 'Phylogenetic analysis', MCMC chains were run in duplicate for 10 million generations and sampled every 1,000 steps, with convergence assessed using Tracer v. 1. 7. 1. Maximum clade credibility trees were summarized using TreeAnnotator after discarding 10% as burn-in. We used the R package seraphim  38  to extract and map spatiotemporal information embedded in posterior trees.
Lineage classification We used a previously proposed dynamic lineage classification method  39  from the 'Phylogenetic Assignment of Named Global Outbreak Lineages' (pangolin) software suite v4. 0. 6 with the --Usher option (https://github. com/cov-lineages/pangolin)  40. This is aimed at identifying the most epidemiologically important lineages of SARS-CoV-2 at the time of analysis, enabling researchers to monitor the epidemic in a particular geographic region. A lineage is a linear chain of viruses in a phylogenetic tree showing connection from the ancestor to the last descendant. Variant refers to a genetically distinct virus with different mutations to other viruses.
Selection analysis To identify which (if any) of the observed mutations in the spike protein was most likely to increase viral fitness, we used the natural selection analysis of SARS-CoV-2 pipeline (https://observablehq. com/@spond/revised-sars-cov-2-analytics-page). This pipeline examines the entire global SARS-CoV-2 nucleotide sequence dataset for evidence of: (i) polymorphisms having arisen in multiple epidemiologically unlinked lineages that have statistical support for non-neutral evolution (mixed effects model of evolution) 41 , (ii) sites at which these polymorphisms have support for a greater-than-expected ratio of nonsynonymous-tosynonymous nucleotide substitution rates on internal branches of the phylogenetic tree (fixedeffects likelihood)  42  and (iii) whether these polymorphisms have increased in frequency in the regions of the world in which they have occurred.
Data Availability Statement All of the SARS-CoV-2 genomes generated and presented in this manuscript are publicly accessible through the GISAID platform (https://www. gisaid. org/). The GISAID accession identifiers of the sequences analysed in this study are provided as part of Supplementary Table  S1. Other raw data for this study are provided as a supplementary dataset at https://github. com/krisp-kwazulu-natal/SARSCoV2_South_Africa_Omicron_BA4_BA5. The reference SARS-CoV-2 genome (MN908947. 3) was downloaded from the NCBI database (https://www. ncbi. nlm. nih. gov/).
Epidemiological dynamics We analysed daily cases of SARS-CoV-2 in South Africa up to 25 April 2022 from publicly released data provided by the National Department of Health and the National Institute for Communicable Diseases. This was accessible through the repository of the Data Science for Social Impact Research Group at the University of Pretoria (https://github. com/dsfsi/covid19za)  25, 26. The National Department of Health releases daily updates on the number of confirmed new cases, deaths and recoveries, with a breakdown by province.
For example, hippocampus and lateral ventricle volumes can be used as early biomarkers of Alzheimer's disease. At this scale, also, cortical and subcortical gray matter (sGM) structures are of special interest for the neuroimaging community. Classic neuroimaging tools such as the wellknown FSL package  (Jenkinson et al. , 2012)  or Freesurfer  (Fischl et al. , 2002)  have been widely used over the last 2 decades. More recently, multi-atlas label fusion segmentation techniques have been extensively applied, thanks to their ability to combine multiple atlas information minimizing mislabeling due to inaccurate registrations  (Coupé et al. , 2011; Wang and Yushkevich, 2013; Manjón et al. , 2014; Romero et al. , 2015). However, segmentation of the whole brain into a large number of structures is still a very challenging problem even for modern multi-atlas based methods  (Wang and Yushkevich, 2013; Cardoso et al.
, 2019) , or more recently the Assemblynet method  (Coupé et al. , 2020). The aim of this study is to present a new software pipeline for whole brain analysis that we have called vol2Brain. It is based on an optimized multi-atlas label fusion scheme that has a reduced execution time, thanks to the use of our fast collaborative patch-matching approach, which has been specifically designed to deal with both normal appearing and lesioned brains (a feature that most of preceding methods ignored). This pipeline automatically provides volumetric brain information at different scales in a very simple manner through a web-based service not requiring any installation or technical requirements in a similar manner as previously done by our volBrain platform that since 2015 has processed more than 360,000 brains online worldwide.
Dataset Description In our proposed method, we used an improved version of the full Neuromorphometrics dataset (http://www. neuromorphometrics. com), which consists of 114 manually segmented brain MR volumes corresponding to subjects with ages covering almost the full lifespan (from 5 to 96 years). Dense neuroanatomical manual labeling of MRI brain scans was performed at Neuromorphometrics, Inc. , following the methods described in the study by  Caviness et al. (1999). The original MRI scans were obtained from the following sources: (1) the Open Access Series of Imaging Studies (OASIS) project website (http://www. oasis-brains. org/) (N = 30), (2) the Child and Adolescent NeuroDevelopment Initiative (CANDI) Neuroimaging Access Point (http://www. nitrc. org/ projects/candi_share) (N = 13), (3) the Alzheimer's Disease Neuroimaging Initiative (ADNI) project website (http://adni.
loni. usc. edu/data-samples/access-data/) (N = 30), (4) the McConnell Brain Imaging Center (http://www. bic. mni. mcgill. ca/ ServicesAtlases/Colin27Highres/) (N = 1), and (5) the 20Repeats dataset (http://www. oasis-brains. org/) (N = 40). Before manual labeling, all the images were preprocessed with an automated bias field inhomogeneity correction  (Arnold et al. , 2001)  and geometrically normalized using three anatomical landmarks [anterior commissure (AC), posterior commissure (PC), and mid-sagittal point]. The scans were reoriented and resliced so that anatomical labeling could be done in coronal planes that follow the AC-PC axis. The manual outlining was performed using an in-house software called the NVM and the exact specification of each region of interest is defined in (1) Neuromorphometrics' General Segmentation Protocol (http:// neuromorphometrics.
Image Resolution, Orientation, and Size After checking each individual file, we found that they had different acquisition orientations (coronal, sagittal, and axial). They also have different resolutions (1 × 1 × 1, 0. 95 × 0. 93 × 1. 2, 1. 26 × 1. 24 × 12, etc. ) and different volume sizes  (256 × 256 × 307, 256 × 256 × 299, 256 × 256 × 160, etc. ). To standardize them, we registered all image and corresponding label files to the MNI152 space using ANTS software, which resulted in a homogeneous dataset with axial orientation, 1 × 1 × 1 mm 3 voxel resolution, and a volume size of 181 × 217 × 181 voxels. We also checked the image quality and we removed 14 cases from the original dataset that presented strong image artifacts and severe blurring effects. This resulted in a final dataset of 100 cases.
Labeling Errors Once the dataset had a homogeneous number of labels, we inspected them to check their quality. After inspecting the dataset visually, we found that the boundaries of all the structures in sagittal and axial planes were very irregular. This is probably due to the fact that the original manual delineation was performed in the coronal plane. However, one of the main problems we found was the fact that cortical gray matter (cGM) was severely overestimated, and correspondingly, the CSF and WM were underestimated. This fact has been already highlighted by other researchers  (Huo et al. , 2017)  who pointed out this problem in the context of cortical thickness estimation. The same problem arises in the cerebellum, although it is a bit less pronounced. To solve this problem  (Huo et al.
, 2017) , an automatic fusion of the original GM/WM maps was used, and partial volume maps were generated by the TOADS method  (Bazin and Pham, 2008)  to correct the cortical labels. In this study, we have followed a different approach based on the original manual segmentation and the intensity information. First, we combined all the 135 labels into seven different classes (CSF, cGM, cerebral white matter (cWM), sGM, cerebellar gray matter (ceGM), cerebellar white matter (ceWM), and brain stem (BS)]. External CSF was not labeled in the Neuromorphometrics dataset, so we added it using volBrain  (Manjón and Coupé, 2016)  (we copied CSF label to those pixels that had label 0 in the original label file). Then, the median value of cGM and cWM was estimated and used to generate the partial volume maps using a linear mixing model  (Manjón et al.
, 2008). Voxels in the cGM and cWM interface were relabeled according to their partial volume content (e. g. , a cGM voxel with a cWM partial volume coefficient bigger than its corresponding cGM partial volume coefficient was relabeled as cWM). The same process was repeated for the CSF/cGM interface, the ceGM/ceWM interface, and the ceGM/CSF interface. To ensure the regularity of the new label maps, each partial volume map was regularized using a non-local means filter  (Coupé et al. , 2018). Finally, each case was visually revised and small labeling errors were manually corrected using the ITK-SNAP software. Most of the corrections were related with cGM in the upper part of the brain, and misclassifications of WM lesions were termed as cGM and CSF-related corrections. Figure  1  shows an example of the cGM/cWM tissue maps before and after the correction.
LesionBrain Dataset One of the main goals of the proposed pipeline was to make it robust to the presence of WM lesions that normally are misclassified as gray matter (GM) in pathological brains. To this end, we included not only healthy cases but also subjects with WM lesions in our library. Specifically, 32 of the 100 cases of the previously described Neuromorphometrics dataset had WM visible lesions with a lesion load ranging from moderate to severe. We are aware that WM lesions can appear anywhere in the brain, but it is also known that they have a priori probability to be located in the periventricular areas among others  (Coupé et al. , 2018). We found though that the number of cases with lesions on the dataset was not enough to capture the diversity of WM lesion distribution, so we decided to expand the dataset using a manually labeled MS dataset.
We previously used this dataset to develop a MS segmentation method  (Coupé et al. , 2018). This dataset is composed of 43 patients with MS who underwent 3T 3D-T1w MPRAGE and 3D-Fluid-Attenuated Inversion Recovery (FLAIR) MRI. We used only the T1 images, as this is the input modality of our proposed pipeline. To further increase the size of the dataset, we included the left-right flipped version of the images and labels resulting in an extended dataset of 86 cases.
Vol2Brain Pipeline Description The vol2Brain pipeline is a set of image processing tasks dedicated to improve the quality of the input data and to set them into a specific geometric and intensity space, to segment the different structures and to generate useful volumetric information (refer to Figure  3  for a general overview). The vol2Brain pipeline is based on the following steps: FIGURE 3 | vol2Brain pipeline scheme. In the first row, the preprocessing for any new subject is presented. In the second row, the results of the ICC extraction, structure, and tissue segmentations jointly with the cortical thickness estimation are presented. Finally, in the third row, the volumetric information is extracted and presented.
Preprocessing We have used the same preprocessing steps as those described in the volBrain pipeline  (Manjón and Coupé, 2016) , as it has been demonstrated to be very robust (based in our experience processing more than 360,000 subjects worldwide). This preprocessing consists of the following steps. To improve the image quality, first, the raw image is denoised using the Spatially Adaptive Non-Local Means (SANLM) filter  (Manjón et al. , 2010)  and inhomogeneity is corrected using the N4 method  (Tustison et al. , 2010). The resulting image is then affinely registered to the Montreal Neurological Institute (MNI) space using the ANTS software  (Avants et al. , 2008). The image in the MNI space has a size of 181 × 217 × 181 voxels with 1 mm 3 voxel resolution. Then, we used an inhomogeneity correction based on SPM8  (Ashburner and Friston, 2005)  toolbox, as this model-based method has proven to be quite robust once the data are located at the MNI space.
Report Generation The output produced by the vol2Brain pipeline consists in a pdf and csv files. These files summarize the volumes and asymmetry ratios estimated from the images. If the user provides sex and age of the submitted subject, population-based normal volumes and asymmetry bounds for all structures are added for reference purposes. These normality bounds were automatically estimated from the IXI dataset (https://brain-development. org/ixi-dataset/), which contains almost 600 normal subjects covering most of the adult lifespan. We are aware that one of the most important sources of variability is the use of different scanners to build the normative values (although the use of our preprocessing reduces this variability). In the near future, we will extend the dataset to have a larger and more representative sample of the population as we already did for the volBrain pipeline  (Coupé et al.
EXPERIMENTS AND RESULTS In this section, some experimental results are shown to highlight the accuracy and reproducibility of the proposed pipeline. A leave-two-out procedure was performed for the 100 subjects of the library (i. e. , excluding the case to be segmented and its mirrored version). In the dataset, there are 19 cases that were scanned and labeled twice for the purpose of reproducibility estimation. In this case, a leavefour-out procedure was applied to avoid any problem (i. e. , excluding the case to be segmented and its mirrored version of the two acquisitions of the same subject). To measure the segmentation quality, the dice index  (Zijdenbos et al. , 1994)  was computed by comparing the manual segmentations with the segmentations obtained with our method. A visual example of the automatic segmentation results is shown in Figure  4.
Results Since presenting dice results of the 135 labels would be impractical, we have decided to show the average results for cortical and non-cortical labels as done in previous studies  (Wang and Yushkevich, 2013). In Table  1 , the results of the proposed method are shown with and without the corrective learning step (PEC) to show the impact that this postprocessing has in the final results (it improved the results in all the cases). To further explore the results, we separated them by dataset, as it is well-known that results within the same dataset are normally better than across the datasets. This allows to explore the generalization capabilities of the proposed method. Results are summarized in Table  2. As can be seen, results of the OASIS dataset were the best among the datasets. This makes perfect sense, as precisely, this dataset is the largest.
CANDI dataset showed the worst results. This dataset had the worst image quality, which somehow explains these results. One of the objectives of the proposed method was to be able to deal with images with white mater lesions. This is fundamental, as if we do not take into account those regions, they are normally misclassified as a cGM or sGM (which also affects the cortical thickness estimation)  (Dadar et al. , 2021). The results of WM lesion segmentation are summarized in Table  3  (left and right lesions were considered together). We separated the results by lesion volume, as it is well-known that small lesions are more difficult to segment than the big ones  (Manjón et al. , 2018). Once the full brain is segmented into 135 labels, those labels are grouped together to provide information at different anatomical scales.