dataesr/scientific-paragraphs-categorization huggingface dataset:
These were comprised of 294 genes for the interactome, 330 genes for the regulome, and 113 genes after identification of meta-subnetworks. There are 32 genes found common between the 113 genes identified by HTS-Net and the 126 genes identified by  Chia (Fig 4A). Chia focused on transcriptional regulators PRDM14, NFRKB and YAP1. All were found present in high-scoring subnetworks. PRDM14 was found in 7 regulome subnetworks. The base regulome subnetwork related to PRDM14 has a score of 2. 412. NFRKB was detected in 48 interactome subnetworks, 3 regulome subnetworks and retained in 3 meta-subnetworks. The best NFRKB-related interactome subnetwork score was 3. 11. YAP1 was detected in 94 interactome subnetworks and 12 meta-subnetworks. The best YAP1-related subnetwork score was 3. 54. Thanks to the HTS-Net pipeline, we were able to identify other interesting genes from this dataset.
Network analysis of HCV host cofactor replication  (Tai et al. )  Two analyses were previously performed with this dataset: the original from Tai and colleagues and a network-based analysis from Gonzalez & Zimmer  [11] , which gives the opportunity to make comparisons and understand advantages and limitations of each approach. We performed an analysis with HTS-Net, as reported in this section. HTS-Net mapped 17,695 knockdowns to Entrez Gene identifiers, 12,906 genes to the interactome and 8,990 genes to the regulome. 141 genes were retained as significant both in interactome and regulome networks and were found in respectively 32 and 28 corresponding subnetworks, and 55 meta-subnetworks. Of the 141 genes identified with HTS-Net, 23 were common with the set published by Tai. All studies identified genes in the COPI coat.
Comparison of HTS-Net with existing algorithms In order to understand the differences and common points of HTS-Net with existing approaches, we performed a complete analysis of the Tai dataset with three approaches: analysis program by Gonzales & Zimmer  [11] , specifically designed for RNAi, the general-purpose network analysis program HotNet2  [20]  and the CARD suite  [21]. We specifically used the Tai dataset since the analysis by Gonzales & Zimmer's method was only available for this dataset. Lists of hits identified by each method are provided as supplementary material Excel file on the HTS-Net Web site and as S2 File. Key results of the comparison are reported in Table  2. Venn diagrams comparing the overlap among methods are shown Fig  7. HTS-Net. The running time was 20 minutes when running the parallelized code version on our local cluster.
; it was identified by Gonzalez and HTS-Net. As a TF, this could represent a valuable HCV target. CARD. Using the RNAiCut and Network analysis modules from CARD, we obtained 119 genes (on 3 subnetworks) for network of hits genes (score greater that 2. 786) and 493 genes with non hits (z-score lower than 1. 0). The system allows downloading networks of hits. We then proceeded with the functional analysis of the obtained genes for comparison with other methods. The running time was about 40 minutes for RNAiCut and a few seconds for Network analysis. The use of CARD on the Tai dataset revealed 19 genes already detected by Tai, and 61 genes were common with HTS-Net result (Fig  7B ). Among them was found NCOA6, a transcriptional coactivator that can interact with nuclear hormone receptors to enhance their transcriptional activator functions.
HotNet2. We performed a run after establishing influences matrices for HPRD networks plus 100 random influences matrices for statistical analysis, and computing heat matrix for the Tai dataset after appropriate data formatting. After run, HotNet2 returned 4 analyses with 4 different Delta values. Changing Delta had a large impact on the size and number of the subnetworks found. In order to obtain a reasonable subnetwork size, we chose delta = 0,0244, that yielded 36 subnetworks of size 5 or more. The running time was 40h 45min to generate influence matrices for HPRD network (this step is done only once) and 12min 32sec for the Hot-Net2 run itself, using 4 cores. There were only 4 genes in common between Hotnet2 and HTS-Net, while no genes initially detected by  Tai (Fig 7C). This can be due to the different metric, which was initially designed for mutations and that gives higher weight to network connections with the heat diffusion process.
Also, the program only included HPRD in its current distribution, which may be another limiting factor. HotNet2 makes possible to include new interactomes, but the computational cost of calculating influence matrices is prohibitive on our local setting. We examined results for the top-scoring subnetwork. It contains PPP1CB, which is an interesting hit, although not found by other programs, since it has been found in the first 40 up-regulated genes in HCV-related Hepatocellular carcinoma (HCC) samples analyzed by De Giorgi and colleagues  [51]. However, none of the strong hits initially identified by Tai were found.
Discussion We developed a new tool, HTS-Net, which uses a systems biology algorithm for the unbiased identification of potential candidates in RNAi screenings. HTS-Net replaces genes in their interaction and regulatory context to discover modules of interacting genes that are biologically relevant to the experiment. We demonstrated the usefulness of our approach on three biological applications: regulation of human embryonic stem cells  (Chia et al. , 2010)   [39]  differentiation of breast cancer stem cells  [38] , and identification of human host-HCV regulators  (Tai et al. , 2009). [37]  Chia et al. built an RNAi screen for the identification of hESC identity. Our approach allowed identifying a number of genes that were not previously found by Chia. Among them, we identified PRC2 as master regulator of hESC differentiation.
Wolf et al. built an RNAi screening to identify key regulators of breast cancer stem cells (CSC) behavior. The re-analysis with HTS-Net allowed identifying the role of the autophagy regulator ATGA4 in the maintenance of the CSC subpopulation. While HTS-Net did not identify ATG4A to have a role in CSC behavior, we were able to identify ATG4B and its interacting neighbors. The Tai analysis dealt with the identification of human host proteins that interact with and support HCV replication. We compared results of the original publication and a re-analysis of these data by Gonzalez. All studies identified genes in the COPI coat, but our method was specifically able to identify kinases such as PIP5K1A. The comparison with other reference programs on the Tai dataset shows that HTS-Net was able to retrieve a large number of hits identified in the original publication while identifying new hits not identified by other network approaches.
HTS-Net offers a more complete biological environment for RNAi hits by using regulatory data. To some extent, HTS-Net was closer to other network approaches than Tai (larger overlap). This is due to general similarities in network analysis that are able to include hits having lower scores but higher connectivities. HTS-Net had closer results with CARD and Gonzales & Zimmer, while HotNet2 gave distant results, which may be due to the fact that this is not a method specifically designed for RNAi analysis. In terms of hardware, HTS-Net is provided under source code and cluster implementation, or can be used as a web service, requiring no installation but with a longer running time. CARD is a web-only service, while HotNet2 requires local installation. Gonzales & Zimmer did not provide web service or source code.
Conclusion We are proposing the pipeline HTS-Net, a network-based RNAi analysis pipeline, for the analysis of RNAi screening data. The pipeline is available from our Mobyle server, and was tested with three datasets and compared with other networks-based approaches, some specifically designed for RNAi screening analyses (CARD)  [21] , Gonzales & Zimmer's approach  [11] , and other for general-purpose analysis (HotNet2)  [20]. Our approach showed many advantages when compared with other methods. It has similar (Gonzales) or more (CARD, Hotnet2) genes found in common with the Tai analysis, which means that fundamental biological information is retrieved, while keeping the ability of discovering new hits of interest. The production of exhaustive reports allows causal exploration of gene networks, since regulatory data is included in the pipeline, feature not proposed by other software (with the exception of HotNet2).
Running time stays reasonable on a web server, and fast execution can be provided when needed by local installation and run on a Linux server or cluster. The HTS-Net algorithm avoids defining a hard cutoff on individual genes, allows bypassing the limitations of traditional analyses and unravels key components for the studied systems, such as master genes regulators. Our approach is a first answer to deciphering the regulatory complexity of studied systems in RNAi screenings. Possible improvements include taking into account the interaction quality when building subnetworks, by introducing weighted vertices reflecting the nature of the interactions and the biological evidence that supports it. As genome-wide experimental setups such as the one presented here are becoming commonplace, tools such as HTS-Net will be more and more useful to carry advanced analytics.
Methods Aperture production. The magnetized needle has been prepared out of a 60 nm thick nickel film by focused ion beam (FIB) and then placed across a 5 mm aperture, also milled by FIB out of a beam-opaque platinum film. After the preparation, the phase shift is measured through electron holography (see Supplementary Note 3) and the needle thickness is further refined through FIB, to best approximate the desired phase shift. The accuracy in determining the phase shift is limited by the This corresponds to studying the intensity of the dipolar peak (red peak in Fig. 3d ) for a variety of different beam positions, as schematically shown in a. For all the imperfections of these early experiments, there is remarkable agreement between the simulated (b,d) and experimental data (c,e). If the probe is parallel to the rod, the map shows a strong peak in the centre, with two satellite lobes on the side, for a probe oriented orthogonally to the particle, the centre shows a strong dip with four satellite lobes.
Then, a 40 nm-thick layer of Al was deposited on the sample using thermal evaporation (ME300, Plassys). Finally, lift-off has been accomplished by immersing the sample in acetone unveiling the Al structures on the membranes. The width of the nanorods, approximately uniform, is between 40 and 50 nm. Spectroscopic experiments and data treatment. The spectroscopic experiments have been performed on a FEI Titan 3 TEM, outfitted with a Wien filter monochromator, an aberration-corrected illumination system and a Gatan Enfinium electron spectrometer. To reach the low angles required by these experiments, we operated the microscope in low magnification mode, switching almost completely off the objective lens. The spectra in Fig. 3  have been acquired employing an acceleration voltage of 120 kV and a semi-collection angle of B20 mrad, whereas the maps in Fig.
3  have been acquired with a high tension of 60 kV and a semi-collection angle of about 10 mrad. In both cases the illumination semi-convergence angle is B20 mrad. The spectra presented in Fig. 3  have been treated by normalizing them and then subtracting reference zero loss peaks. The zero-loss peaks have been recorded immediately before the spectrum acquisition, with the corresponding aperture and by placing the beam over a hole in the sample to eliminate any spectral feature corresponding to the sample. Numerical simulations. The simulation code was developed on top of the freely available package MNPBEM, a boundary element method simulation code. First, eigenmodes were numerically computed using MNPBEM  19, 20. Once we obtained the eigenmodes and spectral functions, we integrated equation (2) using for C i;? a numerically simulated probe profile.
Acknowledgements We thank  F. J. Garcı ´a de Abajo  and  D. M. Ugarte  for interesting and fruitful discussion. This work was supported by funding from the  European Research Council  under the  7th Framework Program  ( FP7) ERC Starting Grant 278510 VORTEX. Financial support from the  European Union  under the  Framework 7 program  under a contract for an  Integrated Infrastructure Initiative  (Reference number  312483 ESTEEM2 ) is also gratefully acknowledged. Aluminum nanostructures were fabricated using the Nanomat nanofabrication facility.
The role of PZMs in ASD risk is unknown and we therefore explored the contribution of this type of variation to ASD. PZMs are efficiently detected by candidate gene sequencing panels, given their deep sequencing coverage. However, PZMs present in greater than 25-30% of cells (or 15% alternate allele fraction (AAF)) can be detected with reasonable sensitivity using WES  8. We re-called WES data from 5,947 trios, adding 282 newly sequenced trios, from the Autism Sequencing Consortium and Simons Simplex Collection, and using a custom pipeline, we resequenced PZMs detected from WES data using 3 resequencing technologies, providing a systematic evaluation of PZM's contribution to ASD risk.
Excess of de novo mutations with low AAFs We analyzed de novo mutations in WES data from 5,947 families, which included 4,032 ASD trios and 1,918 quads that also have unaffected siblings (Supplementary Tables  1 2 3 )  4, 5. The vast majority of samples (96%) were derived from whole blood DNA, and a negligible fraction from lymphoblastoid cells (3%) and primary saliva (1%). We included all samples derived from various tissue types, but removed outlier samples with a large number of de novo or mosaic mutations from our analyses (see Methods). We increased specificity for likely pathogenic mutations by filtering out variants that are present in control exomes, resulting in modestly lower rates of de novo mutations than previously called (4,846 in   total). Of these, a substantial portion (23%) showed low AAFs of ≤40% (Fig.
Detection of PZMs from WES and secondary resequencing Given our initial observations that some de novo mutations might be PZMs, we developed a pipeline to quantitatively categorize PZMs with high or low confidence in our cohort of 5,947 ASD families (see Methods). Of 4,846 total de novo mutations (which we define as Group A, Fig. 1B ), 1,113 were candidate PZMs (23%, Group B), defined as having an AAF that was ≤80% of the modal AAFs, which ranged from 40-50%. Of these Group B mutations, 468 were interpreted as high-confidence PZMs (9. 7%, Group C) because they showed statistically significant deviation from the modal AAFs. We compared the 4,846 de novo mutations in Group A from our study with previous studies reporting these datasets  4, 5  , and found that 1,297 of the de novo mutations (26.
An excess of recurrent PZMs in genes found in probands implicate these genes in ASD In probands, 27/735 genes (3. 7%) showed recurrent non-synonymous PZMs, versus 2/322 genes (0. 62%) with recurrent non-synonymous PZMs in siblings, representing a 6. 1-fold excess of genes with recurrent non-synonymous PZMs in the probands (95% CI = [1. 52,53. 2], Fisher's Exact Test P=0. 0035, permutation P = 0. 0037). This strongly suggests that some of these genes with recurrent non-synonymous PZMs are relevant for ASD risk. Given our finding that some genes with recurrent non-synonymous PZMs are likely to confer risk for ASD, we focused on these genes containing recurrent non-synonymous PZMs. We obtained a background set of 84,448 variants that are privately inherited (i. e. , variants that are not found in our controls, consisting of parents and siblings, as well as in control databases such as the Exome Variant Server, but were inherited from a parent in an affected or unaffected offspring; see Methods and Supplementary Table  13 ).
We detected and confirmed the three missense mutations in SMARCA4 in the three probands with ASD (p. P143A with AAF 21%, p. I184T with AAF 33%, p. P109L with AAF 36%, Fig. 4A ), all predicted to be deleterious using PolyPhen2 and SIFT  17, 18. The p. P143A mutation had a CADD score of 19. 81, while the p. I184T and p. P109L mutations had CADD scores of ≥20 (26. 4 and 34 respectively). The p. P109L mutation was previously reported as a somatic mutation in a lung carcinoma sample from the COSMIC database (COSM710132)  25. CloneSeq on blood-derived DNA for these three individuals with the SMARCA4 mutations confirmed two of the mutations as likely PZMs (p. P143A: 45 alternate out of 164 total colonies, binomial P = 4. 9×10 -9 , and p. I184T: 39 alternate out of 118 total colonies, binomial P = 1.
4% in 2,388 families with ASD  29. Furthermore, the size of our dataset has allowed us to explore and confirm the role of PZMs in conferring risk to ASD, analyze the mutational characteristics of PZMs, and begin to use them to study the spatio-temporal distribution of PZMs in ASD. Our analysis also revealed striking enrichment of PZMs within genes that are clinically relevant to ASD, including the bona fide ASD risk gene SCN2A. The identification of recurrent non-synonymous PZMs in a small set of genes in ASD probands also provides strong evidence for the clinical importance of PZMs. The finding that LoF and missense PZMs in critical exons in ASD probands showed enrichment in amygdala expression is intriguing since the amygdala plays key roles in emotional and social responses  30  , such as conditioned fear.
PZM detection pipeline applied on the ASC and SSC datasets We first performed joint-calling of the raw files from the previously published and new exomes, in order to obtain standardized datasets for our analyses. Next, we developed a stringent pipeline to call autosomal de novo point mutations from our jointly-called exomes, i. e. mutations that are strictly present in the probands or siblings but are not found in both parents. We refer to all de novo mutations as Group A, whereas de novo point mutations with AAF equal to or less than 80% of the modal AAF for each cohort are defined as candidate PZMs called "Group B". Mutations in Group B where the deviation from the modal AAF was statistically significant (binomial P≤1×10 -4 ) formed "Group C", the group most likely to be PZMs. The AAF was calculated using: number of alternate reads/(total number of reference + alternate reads).
Quantitative PCR for assaying copy number variants For 36 PZMs in Group C where there are copy number variants in the Database of Genomic Variants (http://dgv. tcag. ca/dgv/app/home) that are within 2kb of the PZMs, we selected predesigned primers from ThermoFisher that assay the copy number variants. The DNA samples used for these quantitative PCR assays were extracted from whole blood samples from the Simons Simplex Collection, and the quantitative PCR assays were performed by the Biopolymers core facility at Harvard Medical School. The reference assay used was AGO1.
Acknowledgments We are grateful to all the families who participated in the research, including the  Simons Foundation Autism Research Initiative (SFARI) Simplex Collection (SSC) , the  Autism Sequencing Consortium (ASC)  and  Autism Speaks. We acknowledge the clinicians and organizations that contributed to samples used in this study, including the ASC and SSC principal investigators, the coordinators and staff at the  ASC  and SSC sites for the recruitment and comprehensive assessment of simplex families, and the  ASC, SFARI  and  NDAR  staff for facilitating access to the datasets. We also thank the 3 anonymous reviewers for their critical suggestions, which have helped to improve the work significantly. This work was supported by a grant from the  Simons Foundation  ( 178093 , C. A. W.
Data processing and annotation The Autism Sequencing Consortium (ASC) has performed joint calling of the variants in the 5,947 trios from the ASC and the Simons Simplex Collection (SSC) whose exome sequences have been previously published  4, 5. The variants were called using two versions of the GATK  41  (the Unified Genotyper and the Haplotype Caller), and annotated using SnpEff versions 2. 0. 5 and 3. 5  42. To remove exomes with inheritance errors, as well as potential artifactual mosaic mutations induced by cell passaging, we removed outlier exomes that had more than 2 PZMs or more than 5 de novo mutations from downstream analyses.
Quantitative evaluation tools measuring the performance of the method along those different dimensions are also provided. The resulting meshes can be used as input and validation for biomechanical simulations. Availability: DRACO-STEM is supplied as a package of the open-source multi-platform plant modeling library OpenAlea (http://openalea. github. io/) implemented in Python, and is freely distributed on GitHub (https://github. com/VirtualPlants/draco-stem) along with guidelines for installation and use.
Implementation and Visualization The DRACO-STEM algorithms, as well as the data structure used to represent cellular complexes are supplied as packages of the open-source plant modeling library OpenAlea  (Pradal et al. , 2008 (Pradal et al. , , 2009)). This library is designed as a middleware providing tools and components to build dynamic systems implementing plant models. It is developed in Python language and is available on the GitHub software sharing platform. OpenAlea comes with a multi-paradigm development platform called OpenAleaLab which allows modelers to integrate different sources into a unique customizable environment  (Coste et al. , 2014). In particular, specific component related to cell tissue modeling have been developed into a particular instance of the development environment (TissueLab) to provide easy visualization and manipulation of cell tissue structures.
This way, cellular complexes representing the tissue can be visualized in interaction with tissue images, along with other useful visual components (plots, interactive console, code editor). Figure  12  shows an example of the integration in the OpenAleaLab platform. Using the DRACO-STEM algorithms within the OpenAlea framework is rather simple as it is integrated to work on the same data structures and with the same philosophy as the rest of the library. A specific class named DracoMesh is used to perform the optimization and dualization and is instantiated using an image as argument. Then three main functions applied to this object reflect the different steps of the process (Delaunay complex creation, adjacency complex optimization and dual reconstruction) and allow to manipulate parameter values.
Mesh Generation Results We applied our evaluation method to triangular tissue meshes generated from a dataset of segmented tissue images. This dataset was composed of two time-series of a floral meristem at early developmental stages, containing respectively 3 and 10 time points. Each image contains in average around 1,000 cells, and offers a good diversity of surface curvature, and quite variable cell shapes. Using different meshing techniques and evaluating the resulting structures using the same quantitative criteria provided us with extensive measures of the pros and cons of each method, and is the best way to assess which constitutes the best compromise. The methods we evaluated correspond to those implemented in the library we propose, and all constitute ways to reconstruct a topologically accurate tissue reconstruction: • Interfaces of Delaunay refinement tetrahedra (IDRA): mesh obtained by converting the image into a coarse tetrahedral mesh using Delaunay refinement  (Shewchuk, 1998) , and keeping only the triangles belonging to tetrahedra of different labels.