dataesr/scientific-paragraphs-categorization huggingface dataset:
ΔΔCt values for genes were examined using Ct values generated by StepOnePlus software (Applied Biosystems). Cell recovery from 2D cell culture. RNA from cells was isolated using TRIzol reagent. Typically, 6-well plates were used for the experiments, but the volumes were adjusted accordingly to different plate sizes. First, the medium was aspirated from the plate and cells were lysed using 1 ml of TRIzol reagent per well, which was incubated with the sample for 5 min at room temperature. Then, 200 μl of chloroform was added, and the sample was vortexed and incubated for 15 min at room temperature before centrifuging at 4 °C for 15 min at 12 000 rpm. 150 μl of the colourless top phase (containing DNA) was transferred to a new tube. Next, RNA was precipitated by adding 500 μl of isopropanol. The sample was vortexed and incubated at room temperature for 10 min, then centrifuged at 12 000 rpm for 10 min at 4 °C to obtain a pellet, which was then washed with 1 ml of 75% ethanol.
Live-cell imaging and image analysis An in-house image analysis programme to pre-process the time-lapse images and create a database to automatically classify cell interactions with the regional microenvironments was used. This novel program utilizes several open-source applications, such as FIJI and CellProfiler (FIJI: https:// imagej. net/Fiji; Cell Profiler: https://cellprofiler. org), and merges them with focus stack (z-stack) image analysis to enable cell tracking and segmentation. The experiments used brightfield (or transmission) images and green fluorescence protein (GFP) images. The intensity of GFP expression of the z-stack images acquired during the time-lapse experiments was projected onto one x-y plane at each time point; thus, the projected cell area, that is, the 2D projection of the 3D cell shape, was obtained at each time point.
Cell speed, velocity and polarization were measured and analysed using the image-assisted microfluidic platform. The parameters were calculated for 3D cancer cell culture in the presence of a microvessel or in a reference systemwithout endothelial lining. Cancer cell migration velocity (measured between two consecutive time points) was quantified. The direction of the cancer cell migration was quantified by velocity components in the x-direction (perpendicular to the endothelium-collagen matrix barrier) and y-direction (along the channel direction). The presence of asymmetric shape features in cancer cells with respect to the position of the cell nucleus was described by using the cell polarization parameter. The position of the cell centre of mass was calculated by the CellProfiler software, weighting the x and y coordinates of the pixels within the projected cell shape with their GFP intensity; a computational error of about 20% was made due to misrecognition of the projected cell shape profile during automatic segmentation.
The diffusion experiments were performed in the environmentally controlled chamber (set at 37 °C, 5% CO 2 ) of a fluorescent microscope (Leica, CTR6500). The dextran solution was perfused inside the microvessel channel, and images were acquired every 30 s over 30 min. Beyond 30 min, the geometry of the device (e. g. , pillars and the limited size of the gel chamber) broke down the perfect sink condition assumed in the calculation. Obtained fluorescent images were analysed using open-source software (FIJI).
Statistical methodology Data were analysed using GraphPad Prism software and its built-in tests, or RStudio. Data were tested for normality with Shapiro-Wilk test and subsequently analysed with suitable parametric or non-parametric tests. In order to assess significance, unpaired or paired t-test or ANOVA with Tukey's post hoc test was used, depending on the experiment in question. The following asterisk rating system for p-value was used: *p ≤ 0. 05, **p ≤ 0. 01.
The velocity components perpendicular to the ECM-endothelium barrier (x-axis as shown in Fig. 5a )) and along the ECM chamber were analysed (Fig. 5c )). Cancer cells in the device with a microvessel were shown to move towards the channel interface direction more (i. e. with more negative x-velocity component) compared to the microvessel-free control (Fig. 5c )). But there was no difference detected in the y-direction (Fig. 5d )). Comparing the data among the three microvessel types, a slight enhancement in directed migration was observed for the cancer cells facing the hCMEC/D3 vessels. Cancer cell shape axial asymmetries were also investigated during cellular migration inside the collagen I matrix. Brain tumour cells in the presence of hCMEC/D3 microvessel were characterized by the highest median of polarization parameter, as a scalar quantity (Fig.
Acknowledgements This work was supported by the  Engineering and Physical Sciences Research Council (EPSRC ,  EP/S009000/1 ),  European Research Council  ( ERC-StG 758865 ,  ERC  advanced grant  REP-789054-1 ),  National Institute for Health Research (NIHR) ,  Cancer Research UK (CRUK) ,  Academy of Medical Sciences , and  Cambridge NIHR BRC. M. G. was a recipient of the  CRUK Multidisciplinary Studentship. H. B. was a recipient of the  CRUK Innovator Award. The GBM cell lines were obtained from the  Glioma Cellular Genetics Resource  (www. gcgr. org. uk) funded by  Cancer Research UK. We thank  Prof. Steve Pollard  in facilitating the materials transfer. We would also like to thank  Charles Cullen  for his assistance in establishing the gravity-driven flow for the experiments and  Prof. Anne Ridley  for advice on the endothelial cell culture.
We found only one enhancer (hs737) with nominal significance in the discovery (p = 0. 0172), replication (p = 2. 5 × 10 -3 ), and combined dataset (p = 1. 1 × 10 -4 ). Each individual with a DNV in hs737 had shared phenotypes including being male, intact cognitive function, and hypotonia or motor delay. Our in vitro assessment of the DNVs showed they all reduce enhancer activity in a neuronal cell line. By epigenomic analyses, we found that hs737 is brain-specific and targets the transcription factor gene EBF3 in human fetal brain. EBF3 is genome-wide significant for coding DNVs in NDDs (missense p = 8. 12 × 10 -35 , loss-of-function p = 2. 26 × 10 -13 ) and is widely expressed in the body. Through characterization of promoters bound by EBF3 in neuronal cells, we saw enrichment for binding to NDD genes (p = 7.
Autism is a complex neurodevelopmental disorder with a heritability of ~80%  [17]. Large copy number variants  [18] [19] [20] [21]  and coding DNVs contribute to ~30% of cases with higher enrichment in females with autism and those with intellectual disability  [12-16, 22, 23]. Recently, we and others have identified an overall enrichment of de novo  [8] [9] [10] [11] 24]  or paternally inherited variants  [25]  within the regulatory sequence of individuals with autism. However, these studies have mostly assessed this aggregation of genetic burden across a large panel of pooled regulatory elements. To begin to parse out the underlying biology of autism DNVs in individual regulatory regions, we turned to VISTA, which is a database of functionally characterized developmental enhancers  [26] [27] [28].
These enhancers were identified based on multiple strategies including sequence conservation and epigenetic signatures. Each enhancer has been tested in transgenic mouse assays providing information on the spatial-temporal dynamics of their activity during mammalian development. We adapted the fitDNM model  [29]  that was previously used to test for excess DNV load in coding regions so that it would work in noncoding regions. We then applied our updated version of the fitDNM model to VISTA enhancers with known ability to drive expression in the embryonic brain. Application of this test in 2671 families with autism (n = 9831 individuals) revealed one VISTA enhancer (named hs737) with nominal significance for excess of DNVs in autism in our discovery cohort (516 families), replication cohort (2155 families), and the combined dataset.
0172, replication p = 2. 5 × 10 -3 ) and in the combined dataset (p = 1. 1 × 10 -4 ). The hs737 enhancer drives expression in the midbrain and hindbrain at embryonic day 11. 5 (E11. 5) [8]. In each individual with a DNV in hs737, we also assessed the rest of the genome for other potentially relevant genomic variants that could be contributing to autism in each of the individuals (Supplemental Table  S3 , S4). Individual 13396. p1 had no other large de novo CNVs or coding de novo SNVs/indels. Individual 12975. p1 had three total de novo missense variants with one in each of the following genes CHD6, FAM129B, and KCNC1 and also had a 1. 6-Mbp de novo deletion at 11q24. 1 containing the following genes BLID, BSX, C11orf63, CRTAM, SORL1, and UBASH3B. Individual 11257. p1 had two de novo missense variants in each of the following genes RUVBL1 and VKORC1L1.
We specifically assessed RNAseq data at E11. 5 since that is the timepoint at which lacZ reporter expression was tested and observed in the VISTA enhancer database for the hs737 enhancer (http://enhancer. lbl. gov/cgi-bin/imagedb3. pl?form=presentation&show= 1&experiment_id=737&organism_id=1). Next, we turned to human RNAseq data from Brainspan (http://brainspan. org/rnaseq/search/index. html), which contains expression profiles from various timepoints ranging from early in development (8 postconception weeks [pcw]) to adulthood (40 years) and examines many regions of the brain. We wanted to determine what transcription factor may be binding to the enhancer. As we describe below, the target gene of hs737 is EBF3. We hypothesized that the transcription factor that is binding may be correlated in expression with EBF3.
Dosage sensitivity of hs737 in the human genome To further assess the impact of enhancer hs737 on NDDs, we measured the effect of its dosage on the NDD phenotypes. We hypothesize that if heterozygous point Table  1  VISTA enhancers with an excess of de novo mutation based on fitDNM analysis mutations in hs737 alter phenotype strongly, the enhancer would be dosage sensitive and show copy number variation (CNV) only in individuals with an NDD. To test this, we first applied two approaches to assess CNVs in hs737. We assessed the morbidity map  [35, 36]  database containing 29,085 individuals with neurodevelopmental disorders (NDDs) and 19,584 controls. In particular, we looked at the window analysis in Coe et al. [35]  across the genome to identify the window containing hs737. This enhancer resided in a genomic window with an excess of deletions (case counts = 27, control counts = 0, p = 9.
Combining our WGS parental data, 1000 genomes project data, and morbidity map, there are no deletions or duplications in this enhancer in 28,128 non-NDD individuals (56,256 alleles). We also surveyed the gnomAD  [40]  database (v2. 1) and observed no CNVs in the 10,847 individuals contained there (n = 21,694 alleles). To avoid possible double counting between gno-mAD and other datasets, we do not present the aggregate data. Taken together, these results suggest that in addition to point mutations, CNVs involving hs737 may play a significant role in NDDs.
Epigenetic characteristics of hs737 Enhancers have well-characterized epigenetic signatures that are predictive of their activity in specific biological contexts. Thus, to examine the activity of hs737 in its native genomic context, we took advantage of available epigenomic datasets from relevant human samples. We found that hs737 has several hallmarks of neuronal enhancer activity in humans including H3K27ac enrichment in fetal brain tissue  [41] , DNaseI hypersensitive sites (DHS) indicative of chromatin accessibility in CNS tissues  [8, 24] , and conserved transcription factor binding sites (TFBS)  [8]  (HMR conserved transcription factor binding sites track in the UCSC Genome Browser  [42] ) (Supplemental Figure  S2 ). Hs737 was assessed for reporter activity in mouse embryos at E11. 5 in the VISTA database.
Gene target of hs737 To determine the potential gene target of the hs737 enhancer, we first looked at all of the genes residing within the same topologically associating domain (TAD) (hg38, chr10:128151746-130191746)  [46]. We focused on these genes since they are the most likely to be the targets of this enhancer. The genes included C10orf143, CTAG Fig. 2  Copy number variation over hs737. Displays the counts for both deletions and duplications over hs737 in individuals with neurodevelopmental disorders and controls E7P, EBF3, GLRX3, LINC01163, LOC728327, MGMT, and MIR4297; the only gene in this region that is constrained in the human population with a gnomAD  [3]  o/ e value for loss-of-function SNVs of 0. 03 is EBF3. To examine physical interactions between hs737/mo-hs737 and genes in this region, we analyzed two separate highresolution Hi-C datasets examining the 3D architecture of the genome throughout mouse neuronal differentiation and human fetal corticogenesis.
The mouse dataset was generated in a differentiation course from mouse embryonic stem cells (ESCs) to neural progenitor cells (NPCs) and then to cortical-like neurons (CNs)  [47]  with Hi-C at each of these major cell transition stages. We found that the Ebf3 promoter makes strong contacts across a large region encompassing mouse hs737 (mo-hs737) and that these interactions become stronger during differentiation to NPCs and CNs (Fig. 4B ). We used HiCCUPS  [48]  to call loops at each stage and found that in CNs there is a ~1. 3-Mbp loop that brings mo-hs737 into close proximity with the Ebf3 promoter (loop anchors chr7:136,050,000-136,075,000 and chr7:137,300, 000-137,325,000). We did not observe loops between mo-hs737 and any other genes on the chromosome. Next, analyzing the human corticogenesis data which was generated by dissecting three fetal brain samples into cortical plate (CP) and germinal zone (GZ) layers  [49] , we created chromatin contact maps using Juicer  [48]  for each dissection layer and called loops again using HiCCUPS  [48].
This is also the gene for the Mendelian phenotype hypotonia, ataxia, and delayed development syndrome (HADDS  [53] ) and has been characterized in detail in a set of ten patients  [54]. Searching the GTEx database, we find that EBF3 is widely expressed throughout the body and in the brain (Fig. 6A ) and similarly find in the Human Protein Atlas that EBF3 is detected in many tissues including the brain (Fig. 6B ), whereas hs737 displays much more restricted activity only being active throughout the midbrain and hindbrain (Fig. 6C ).
Statistical assessment of DNVs A list of VISTA enhancers driving the expression of their target genes in the brain was downloaded from the VISTA enhancer browser  [27]. DNVs were annotated to each enhancer using bedtools  [64]. We modified the fitDNM statistical approach (https://github. com/ TNTurnerLab/fitDNM)  [29] , a method to assess the excess mutational load of DNVs using variant-specific mutation rates calculated based on local sequence context, now applied to noncoding variants in the VISTA brain enhancers.
Copy number assessment of hs737 To test copy number variant enrichment in morbidity map  [35] , we downloaded Supplementary Dataset 1 from Coe et al. [35]  and identified the window in the genome containing hs737. We report in this paper the case counts, control counts, and p-values for deletions and duplications in this window. The QuicK-mer2  [37]  (https://github. com/KiddLab/ QuicK-mer2) workflow was run on WGS data to generate copy number estimates, in 1-kbp windows across the genome, in each individual. Briefly, this method utilizes a kmer-based approach to perform copy number estimation. After running QuicK-mer2, we utilized the bedtools  [64]  map function to calculate the average copy number across the copy number windows covering hs737 (b38, chr10:128,568,604-128,569,741). If the copy number was less than 1.
3, we called it as a deletion and if it was greater than 2. 7, we called it as a duplication. QuicK-mer2 was run on the autism families in this study and also the high-coverage 1000 genomes project data available as described at http://ftp. 1000genomes. ebi. ac. uk/vol1/ftp/data_collections/1000G_2504_high_ coverage/. To assess structural variation in gnomAD v2. 1  [40] , we queried for our enhancer region on hg19  (10-130366868-130368005)  and also available at this link https://gnomad. broadinstitute. org/region/10-13036686 8-130368005?dataset=gnomad_sv_r2_1. There were "No variants found" in this region.
Statistical testing of coding DNVs in EBF3 DNV data was collected from two recent papers on NDDs  [50, 51]. After overlapping samples between the two studies were removed, there were a total of 37,692 sequenced parent-child trios. To test for enrichment of coding DNVs that were loss-of-function or missense, we applied the chimpanzee-human  [50]  and denovolyzeR  [52]  models as previously described  [50, 65].
Mouse ENCODE chromatin state and interaction tracks Chromatin state and interaction data from mouse developmental timepoints were assessed in the ENCODE Regulation, ENC+EPD Enhc-Gene, ENCODE cCREs, and EPDnew Promoters tracks in the mm10 genome browser at UCSC  [42] [43] [44] [45]. ChIP sequencing assessment of data from Harms et al. [55]  We downloaded ChIP sequencing data from Harms et al. [55]  at the following GEO link https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE90682. To identify the promoter locations in the human genome, we looked at sequence 5 kbp upstream of the transcription start site using the Table Browser feature of the UCSC Genome Browser  [42]. We then used bedtools  [64]  intersect to identify which ChIP peaks overlapped with promoters in the human genome. To determine which NDD genes were bound at their promoter, we pulled the genome-wide significant gene lists from Coe et al.
[50]  and Kaplanis et al. [51]  and compared to our EBF3 bound promoter list. Ontology enrichment was performed using the Database for Annotation, Visualization and Integrated Discovery tool version 6. 8 (https://david. ncifcrf. gov/) [66]. RNA sequencing at mouse embryonic day 11. 5 from ENCODE Mouse E11. 5 forebrain RNAseq data was downloaded from https://www. encodeproject. org/files/ENCFF465 SNB/@@download/ENCFF465SNB. tsv, mouse E11. 5 midbrain RNAseq data was downloaded from https:// w w w. e n c o d e p r o j e c t. o r g / f i l e s / E N C F F 3 5 9 Z O A / @@download/ENCFF359ZOA. tsv, and mouse E11. 5 hindbrain data was downloaded from https://www. encodeproject. org/files/ENCFF750FTK/@@download/ ENCFF750FTK. tsv. For each file, we retained all Ensembl gene identifiers and annotated them to HGNCapproved identifiers using biomart (https://m.
fitDNM results for de novo mutations in VISTA enhancers driving brain expression. Table  S3. Other novo SNVs/indels seen in individuals with hs737 enhancer mutations. Table  S4. Other copy number variation in individuals with hs737 de novo mutations. Table  S5. Statistical significance calculations for the luciferase assays. Table  S6. Expression of transcription factors potentially binding at variant locations in the hs737 enhancers. Additional file 3: Table  S7 : should have the label "CNVs over hs737 in the morbidity map dataset. Additional file 4: Table  S8.
Funding This work was supported by grants from the  National Institutes of Health  ( R00MH117165  to T. N. T. ,  UM1HG008901  to M. C. Z. ). The Centers for Common Disease Genomics are funded by the  National Human Genome Research Institute  and the  National Heart, Lung, and Blood Institute  and the  GSP Coordinating Center  ( U24HG008956 ) contributed to cross-program scientific initiatives and provided logistical and general study coordination. Research conducted at the E. O. Lawrence Berkeley National Laboratory  was additionally supported by  NIH  grants ( R01HG003988 ) (to L. A. P. and D. E. D. ) and performed under a  Department of Energy Contract  ( DE-AC02-05CH11231 ),  University of California.
Availability of data and materials The DNV callset is available at SFARI Base accession: SFARI_SSC_WGS_2a, https://base. sfari. org/. We are grateful to all of the families at the participating SSC sites, as well as the principal investigators (A. Beaudet, R. Bernier, J. Constantino, E. Cook, E. Fombonne, D. Geschwind, R. Goin-Kochel, E. Hanson, D. Grice, A. Klin, D. Ledbetter, C. Lord, C. Martin, D. Martin, R. Maxim, J. Miles, O. Ousley, K. Pelphrey, B. Peterson, J. Piggot, C. Saulnier, M. State, W. Stone, J. Sutcliffe, C. Walsh, Z. Warren, and E. Wijsman). We appreciate obtaining access to phenotypic and genetic data on SFARI Base. Approved researchers can obtain the SSC population dataset described in this study (https://www. sfari. org/resource/simons-simplex-collection/) by applying at https://base.
Tomofastx is designed to conduct constrained single physics and joint physics inversions. The need for reproducible research  (Peng, 2011)  is facilitated by open-source code  (Gil et al. , 2016) , and thus we introduce and detail the different constraints implemented in Tomofast-x before providing a realistic synthetic application example using selected functionalities. We illustrate the use of Tomofast-x by performing a realistic synthetic study investigating several modelling sce-narios typically encountered by practitioners and provide information to get free access to the source code and to run it using the synthetic data shown in this paper. We perform single physics inversion of gravity data and study the influence of prior information using several amounts and types of constraints, and run joint inversion of gravity and magnetic data.
2. 5), and the degree of structural similarity between the models (for joint inversion or structurally constrained inversion). From a practical point of view, associated with the inversion algorithm is a user manual covering most functionalities and a reduced 2D Python notebook illustrating concepts (see Sect. 7 for more information) that can be used for testing or educational purposes. A summary of the inverse modelling workflow of Tomofast-x is shown in Fig. 1.
General design The implementation we present extends the original inversion platform "Tomofast"  (Martin et al. , 2013 (Martin et al. , , 2018)). Tomofast-x is an extended implementation proposed and modified by  Martin et al. (2018) ,  Giraud et al. (2019c, d) ,  Martin et al. (2020), and Ogarko et al. (2021a). Tomofast-x follows the object-oriented Fortran 2008 standard and utilises classes designed to account for the mathematics of the problem. This introduces enhanced modularity based on the implementation of specific modules that can be called depending on the type of inversion required. The utilisation of classes in Tomofast-x also eases the addition of new functionalities and permits the reduction of software complexity while maintaining flexibility. Our implementation uses an in-dexed hexahedral solid body mesh, giving the possibility to adapt the problem geometry, allowing the regularisation of the problem in the same fashion as  Wiik et al.
(2015)  or to perform overburden stripping. By default, the sensitivity matrix to geophysical measurements is stored in a sparse format (using the compressed sparse row format) to reduce memory consumption and for fast matrix vector multiplication. Attention has also been given to computational aspects. The only dependency of Tomofast-x is the message passing interface (MPI) libraries, which eases installation and usage. This allows optimal usage of multi-CPU systems regardless of the number of CPUs. Parallelisation is made on the model cells using a domain decomposition approach in space; i. e. the model is divided into nearly equal, non-overlapping contiguous parts distributed among the CPUs, hence enforcing minimum load imbalance. Consequently, the code is fully scalable as the maximum number of CPUs is not limited by the number of receivers or measured data points.