dataesr/scientific-paragraphs-categorization huggingface dataset:
It is out of the scope of this study to validate these genes, but we would like to emphasize that, because these genes are duplicated, or overlap with other genes, they have been removed from the standard analysis. Concerning time, featureCounts is the fastest tool, taking 8-11min per sample; mmquant is second with 21-29 min (+1-3 min if the reads are not sorted); htseq-count, written in Python, takes 4h15min-5h29min. mmquant is slower than featureCounts because it has to store (and look up) all the reads that have been mapped several times. We obtained this results allocating one thread per BAM file, but featureCounts can be further accelerated by allocating more than one thread per input file, whereas mmquant and htseq-count cannot. To confirm these results, we used several differential RNA-Seq datasets available from the Gene Expression Omnibus (GEO)  [14] , summarized in Table  1.
Availability and requirements • Project name: mmquant • Project home page: https://bitbucket. org/mzytnicki/ multi-mapping-counter and https://toolshed. g2. bx. psu. edu/repository?repository_id=93e0efd7b8426c9c for in the Galaxy Tool Shed • Operating system(s): Linux • Programming language: C++ • Other requirements: C++11 or higher, and zlib • License: Lesser General Public License 3. 0.
Subjects and Data Acquisition In order to create the neonatal brain template, 7 participants (4 girls, 3 boys) were selected from MR images acquired as part of our previously published study  [8]. Newborns were imaged with a General Electric 1. 5 T MR scanner. The structural 3D volumetric T1-weighted imaging sequence was acquired with the following parameters: TR = 10. 1ms, TE = 2. 208ms and TI = 500ms. Each 3D volume was constructed from a sequence of slices of 512×512 pixels with 220 mm field of view resulting in a voxel size of 0. 47×0. 47×0. 7mm 3. Non-axial images were reoriented to the axial plane and all images were re-sliced to 0. 47×0. 47×0. 47mm 3 isotropic voxels. The neonatal MR template proposed in  [8]  was used as stereotaxic space to create the CT template. 26 newborn CT images (39-42 weeks GA) were selected from the large database acquired over recent years at Amiens University Hospital, France, many of which have been described in  [46].
CT Template Creation Development of a neonatal bimodal MR-CT template is comprised of two crucial steps: 1) development of an effective two-step intensity-based registration method to align neonatal CT data with the MR template, 2) construction of a neonatal CT template using an advanced groupwise registration and template building method. The selected MR template for this study is the one presented in  [8]. Because the only alternative template constructed in  [13]  presents excessive variations, especially in extracranial parts (http://cmrm. med. jhmi. edu/). The selected template contains the whole neonatal head and matches with the MNI stereotaxic space. An overview of the proposed methodology is shown in Fig  1. Data preprocessing. In order to obtain accurate registration results, CT images were manually rotated and translated in order to be almost aligned with the 'GRAMFC_T 39-42 ' neonatal MR brain template  [8]  using 3D Slicer software  [47].
The groupwise registration method proposed by Avants et al. [58]  has been integrated into the template construction function by ANTs software. It provides an optimal unbiased template with respect to the input data by using a symmetric diffeomorphic approach and by proposing an unbiased optimization strategy that accounts for appearance and shape variation. This symmetric groupwise normalization (SyGN) method applies SyN energy terms to measure image similarity and minimize diffeomorphism lengths. SyGN has been shown to be an effective tool for template building  [58]. To construct the template, a script named "antsMultivariateTemplateConstruction. sh" using cross-correlations  [59]  as a similarity measure was run on the CT'' images. The number of iterations was set to 4 by default, however as the images were in good alignment with each other, setting the iteration limit to 2 was sufficient for the convergence and creation of a reliable template.
The similarity of the normalized images with the template was then evaluated by MI similarity measure. The average MI values for cross-validation using 10 test images is reported in Table  1. Comparison of the MI values in the first and second columns of Table  1  demonstrates the superiority of the proposed method used for template creation and its capacity to be used as a template. To study the performance of the templates in normalized images, the similarity between normalized images was also examined. For this purpose, forty five different pairs were created from ten test images and each image of each pair was registered to the proposed CT template (GRAMFC_CT 39-42 ) and to the CT template proposed by Jafarian et al. (CT J ) using the same registration parameters. By assuming that I i and I j are a pair of images (I i ,I j ), following registration of these images to the GRAMFC_CT 39-42 and CT J templates to obtain ðI 0 i ; I 0 j Þ GRAMFC CT 39À 42 and ðI 0 i ; I 0 j Þ CT J , respectively, cross-correlation was then used to measure the similarity between the two data sets, i.
Conclusion We present an approach for construction of a bimodal MR-CT head template for newborns with 39-42 weeks GA. Regardless of the neonatal MR template chosen, a two-step intensitybased method was proposed for inter-subject inter-modality registration and a groupwise registration approach was applied to create a CT template. The constructed CT template is the first neonatal template in a standard stereotaxic space similar to MNI which could be used for spatial normalization in CT modality. The CT template was built using intensity-transformed CT data and presented as an intensity-transformed CT template. All evaluations showed that the intensity-transformed CT template is more reliable than the Hounsfield unit template for bimodal MR-CT template and image normalization using popular open source methods.
Examples include the simulations of  Bentivegna & Bruni (2016) ;  Giblin et al. (2016) ;  Mertens et al. (2016)  that solve the full GR evolution in a fluid approximation and focus on aspects of cos-mological backreaction  (Buchert & Räsänen 2012; Buchert et al. 2013) , a topic we shall not investigate here. To our knowledge, these codes also do not incorporate radiation and are thus not suitable for the present study. N-body methods have many advantages if one wants to model the evolution of large-scale structure especially in the non-linear regime, in particular since they can handle aspects such as mergers and virialization to sufficient accuracy. Based on a weak-field expansion of GR,  Adamek et al. (2016a,b)  have developed the cosmological N-body code gevolution. Although effects of early radiation have not been incorporated in the first release, we present here novel results of the most recent version 1.
COSIRA (N-body gauge) The code COSIRA (COsmological SImulations with RAdiation) has been recently introduced as the first hybrid code incorporating relativistic corrections to matter trajectories in cosmological simulations  (Brandbyge et al. 2017). COSIRA is essentially a modified version of the Newtonian N-body code GADGET-2, a code which is originally designed to only evolve matter particles. In COSIRA, the N-body code is interfaced with a modified version of the Einstein-Boltzmann solver CLASS. In this way the gravitational effect of evolving radiation perturbations on the matter N-body particles can be taken into account. Computations are performed in the Nb gauge, and in that gauge the impact of GR can be combined into an effective GR density perturbation δGR defined in the next paragraph.
This GR density is added to the CDM density and passed to the Poisson solver of GADGET-2. This modifies the long-range particle-mesh part of the force whereas the short-range tree part of the force is left unchanged. In detail, we can write for the combined force potential in the geodesic equation (  19 ), that -φ + γ = -φsim -φGR , (29a) with ∇ 2 φsim = 4πGa 2 ρcdm δsim , (29b) ∇ 2 φGR = 4πGa 2 ρcdm δGR , (29c) where ρcdm δGR ≡ α =cdm ραδα -(4πGa 2 ) -1 ∇ 2 γ, and δsim is the non-linear CDM density contrast obtained from the N-body simulation. We note that in this way COSIRA includes the linear effect from GR and radiation that is passed from the Einstein-Boltzmann code to the N-body code, but there is no feedback in the opposite direction. Such a feedback would occur as a nonlinear correction that goes beyond the linear scheme employed in the Einstein-Boltzmann code.
Once δGR has been incorporated in GADGET-2, the code evolves matter in full non-linearity whilst being in accordance with leading order GR. This also implies that the N-body output of COSIRA should be interpreted on the Nb gauge space-time. As mentioned above, the Nb gauge is so far only defined to the leading order in GR. As a consequence, COSIRA neglects some second-order GR corrections that gevolution does take into account. This applies for instance to the second-order anisotropic stress that contributes to χ in the non-linear regime as we shall discuss in section 4. 5. et al. (2016a,b)  introduced gevolution, the first cosmological N-body code that is based entirely on a weak-field expansion of GR. The two main differences to the traditional Newtonian method, which in some sense is also a weak-field limit of GR, are the following:.
However, a fixed lattice has advantages for parallelization, and gevolution is therefore typically an order of magnitude faster than an adaptive code for the same problem size. Furthermore, it is straightforward to add a linear source term for which a transfer function can be computed using an Einstein-Boltzmann solver. The most recent version 1. 1 of gevolution, presented here for the first time, can be linked directly with the Einstein-Boltzmann code CLASS such that the relevant transfer functions can be computed on the fly. At each time step a realization of the linear density field of radiation (or any other linear source such as e. g. light neutrinos) is prepared and added to the N-body (matter) source to obtain the right-hand side of equation (  13 ). The same is done for the anisotropic stress that sources equation (  12 ).
SUMMARY AND CONCLUSIONS We have presented a detailed study that investigates the effect of early radiation perturbations on the matter power spectrum at various times. We have tested three methods that incorporate radiation, i. e. , by the use of (1) the hybrid N-body code COSIRA, (2) the relativistic N-body code gevolution, and (3) the framework of Newtonian motion gauges. The last method does not require a modification of the Newtonian N-body approach itself and includes, as a limit, the commonly applied method to use modified initial data obtained through 'backscaling'. In more detail, the hybrid code COSIRA modifies the N-body TreePM-code GADGET-2 and couples it to the linear Einstein-Boltzmann code CLASS. Radiation perturbations and GR effects are incorporated to the leading order, and the N-body output is given in Nb gauge.
ACKNOWLEDGEMENTS We thank  Vincent Desjacques  for useful discussions. This work was supported by a grant from the  Swiss National Supercomputing Centre (CSCS)  under project  ID s710. CF is supported by the  Wallonia-Brussels Federation  grant  ARC11/15-040  and the  Belgian Federal Office for Science, Technical & Cultural Affairs  through the  Interuniversity Attraction Pole   P7/37. CR is supported by the  DFG  through the  SFB-Transregio TRR33  " The Dark Universe ". JB, TT and SH acknowledge support from the  Villum Foundation. This is a pre-copyedited, author-produced version of an article accepted for publication in Mon. Not. Roy. Astron. Soc. following peer review. The version of record, Mon. Not. Roy. Astron. Soc. 470 (2017)  303-313 , is available online at: http://dx. doi. org/10.
The advent of MPS entailed the development of a great number of bioinformatic tools to analyse the highdimensional data generated  [8]. Academic and commercial tools have been proposed, the latter being often academic software programs with pleasant interfaces and parameters adapted to specific sequencing technologies. These bioinformatic tools, called pipelines, determine the positions of mutations in a patient's sequence upon comparison with a reference sequence. The two main steps in the majority of pipelines are: read alignment on a reference sequence (e. g. , Bowtie  [9] , MAQ,  [10] , BWA  [11] , or SOAP  [12] ) and variant calling (e. g. , GATK  [13] , SAMtools  [14] , or FreeBayes  [15] ). Any pipeline may be used to analyse MPS data; however, choosing between pipelines is very difficult and requires objective comparisons.
e. , same reference and alternative proposition in VCF files). To estimate parameter θ s , we created a dummy variable that takes value 1 when the response variable corresponds to "identity" variants (0 otherwise). The same data structuring was used to analyse the results of the pipelines knowing the gold standard results but, here, only the positions sequenced by Sanger method and identified as variants were considered to estimate the sensitivity and, similarly, only the positions sequenced by Sanger and identified as non-variants were considered to estimate the specificity. All analyses were carried out with R software. Log-linear models were fitted with glm function using a Poisson distribution; these models included the adequate dummy variables. The mixed models that correspond to the finally retained models were fitted with glmer function of lme4 package with Poisson distribution.
Data description The MPS sequencing covered 41 genes over 390339 base-pairs per patient. For each patient, the MPS sequencing provided a list of variants obtained by BWA-GATK and another list obtained by TMAP-NextGENe®. Each list included nearly 2000 variants of which 300 SNVs (Table  1 ). In our comparisons with Sanger sequencing, we considered only the genes sequenced by both Sanger and MPS; i. e. , 1 to 3 genes (1085 to 16570 base-pairs) per patient. In this case, the number of variants decreased to an average of 25, of which an average of three SNVs per patient. Depending on the number of sequenced genes, the Sanger sequencing list included 0 to 9 variants.
2. Experimental likelihoods reconstructed from event rates, where applicable, including: Monte Carlo simulation of LHC observables, kinematical acceptancebased event rates for LEP sparticle searches, indirect DM search likelihoods computed at the level of single events, and direct DM search limits based on sophisticated simulation of the relevant experiments. 3. The use of the GAMBIT hierarchical model database and statistical framework, for an advanced treatment of uncertainties from dominant SM nuisance parameters across different observables, and a consistent theoretical and statistical treatment of all likelihoods. 4. The use of Diver  [169] , a new scanner based on differential evolution, which improves sampling performance compared to techniques used previously, and allows us to more accurately locate the highlikelihood regions.
See text for details. our adopted priors and sampling methodology. In Sec. 3, we then give a brief summary of the observables and likelihoods that we employ. We present our main results in Sec. 4  and their implications for future searches for the MSSM in Sec. 5, and conclude in Sec. 6. All GAMBIT input files, generated likelihood samples and best-fit benchmarks for this paper are publicly available online through Zenodo  [170].
We find a preference for light, Higgsino-dominated neutralinos, with m χ0 1 750 GeV at 68% CL and m χ0 1 2. 5 TeV at 95% CL. We have shown that stop/sbottom co-annihilation models lie just out of reach of current LHC searches, with stops and sbottoms as light at 500 GeV. This makes the prospects for probing at least some such models at the LHC in the near future quite promising. Both direct and indirect searches for dark matter place significant constraints on the allowed parameter ranges in the MSSM7, and the next generation of these experiments will probe large parts of the highest-likelihood areas of its parameter space. The current study is essentially a starting point for detailed, modular scans of supersymmetric models defined at the weak scale with GAMBIT. GAMBIT's hierarchical model database already contains many generalisations of the MSSM7, which would themselves make very interesting targets for global analyses similar to this one.
Acknowledgements We thank  Andrew Fowlie ,  Tomás Gonzalo ,  Julia Harz ,  Sebastian Hoof ,  Felix Kahlhoefer ,  James McKay ,  Roberto Ruiz ,  Roberto Trotta  and  Sebastian Wild  for useful discussions, and  Lucien Boland ,  Sean Crosby  and  Goncalo Borges  of the  Australian Centre of Excellence for Particle Physics at the Terascale  for computing assistance and resources. We warmly thank the  Casa Matemáticas Oaxaca , affiliated with the  Banff International Research Station , for hospitality whilst part of this work was completed, and the staff at  Cyfronet , for their always helpful supercomputing support. GAMBIT has been supported by  STFC (UK ;  ST/K00414X/1 ,  ST/P000762/1 ), the  Royal Society (UK ;  UF110191 ), Glasgow University (UK; Leadership Fellowship), the  Research Council of Norway  ( FRIPRO 230546/F20 ), NOTUR (Norway;  NN9284K ), the  Knut and Alice Wallenberg Foundation (Sweden; Wallenberg Academy Fellowship) , the  Swedish Research Council  ( 621-2014-5772 ), the  Australian Research Council  ( CE110001004 ,  FT130100018 ,  FT140100244 ,  FT160100274 ),  The University of Sydney (Australia ;  IRCA-G162448 ),  PLGrid Infrastructure (Poland) ,  Red Española de Supercomputación  (Spain;  FI-2016-1-0021 ),  Polish National Science Center (Sonata   UMO-2015/17/D/ST2/03532 ), the  Swiss National Science Foundation  ( PP00P2-144674 ), the  European Commission Horizon   2020 Marie Skłodowska-Curie actions  ( H2020-MSCA-RISE-2015-691164 ), the  ERA-CAN+ Twinning Program  ( EU & Canada ), the  Netherlands Organisation for Scientific Research  ( NWO-Vidi 680-47-532 ), the  National Science Foundation (USA ;  DGE-1339067 ), the  FRQNT (Québec)  and  NSERC/The Canadian Tri-Agencies Research Councils  ( BPDF-424460-2012 ).
1c, d ), we sought a new method to better understand potential SCNA drivers. Design of the HAPTRIG SCNA analysis tool. We developed a computational tool to identify pathways significantly disrupted by SCNAs in the highly noisy genetic background of OV tumours. The program was designed to analyse diverse genetic backgrounds which all yield at least one similar phenotype (Fig. 2a ). Many biological pathways have multiple bottleneck  7  or regulatory points  11  , any of which can equivalently affect pathway phenotype  12. While Gene Set Enrichment Analysis (GSEA) also looks at multiple genes within a pathway to determine statistical significance at the cohort level  13  , we designed our tool to incorporate two additional pieces of information to better characterize genetic disturbance of pathway biology: protein-protein interactions (to prioritize genes that modulate other genes within the same pathway) and haploinsufficiency data (to prioritize genes that are known to affect biology when only a single gene copy is altered).
3 ). We release the code for HAPTRIG as Supplementary Software 1, and provide example input data sets as Supplementary Data 5. Autophagy has long been implicated in tumour development and may have dual roles: loss of autophagy genes including BECN1 leads to early oncogenesis in mouse models  17, 18  ; however, KRAS mutant cancers are addicted to elevated autophagy  19. Interestingly, most proteostasis pathways in our pan-pathway analysis were enriched for deletions, including ER stress, ubiquitin-mediated proteolysis and the lysosome, although the peroxisome pathway was enriched for amplifications. Haploinsufficiency in model organism screens is associated with an inability to form adequately proportioned proteinquality control complexes  20  , suggesting single allele SCNAs disrupt these pathways.
Discussion The HAPTRIG tool represents an initial haploinsufficiency network-based analysis program that can be applied genome L A P Ub ER q < 10 -30 q < 10 -10 q < 0. 05 q < 10 -30 q < 10 -10 q < 0. 05 wide for any cancer. Sequencing of mutations has identified potentially targetable genes in minorities of OV patients  34, 36. However, given the excessive (two-third of the genome) SCNAs present in OV (Fig. 1a-d ), we undertook a strategy to identify pathways that are uniquely and perhaps unexpectedly disrupted by SCNAs. Our permutation strategy enabled identification of significant pathways despite a potentially passenger-filled SCNA landscape. Critically, aside from merely identifying known altered genetics such as suppression of the p53 pathway, enhancement of the focal adhesion pathway and disruption of homologous recombination repair pathways  37  , our top hits are not currently considered to be canonical OV driver pathways.
Yet, using in vivo and in vitro models, we validated that autophagy was suppressed in OV and moreover that by targeting this suppression by drugs that disrupt proteostasis we achieved remarkable tumour remission independent of platinum resistance. Given the strong autophagy phenotypes we found in OV, it is curious why the autophagy pathway has not been emphasized in prior integrative analysis publications. Previous publications have supported the finding that OV is deficient in DNA repair pathways, dysregulated in cell cycle control and often overexpress MYC and ERBB2 (Supplementary Table  4 ). HAPTRIG confirms these disruptions in KEGG pathways and in MSigDB (Molecular Signature Database) Hallmark pathways. Interestingly, GSEA  13  of copy-number data also highly ranks these pathways and autophagy, albeit at a lower rank than HAPTRIG.
In addition, we have provided a free web-tool (https://delaney. shinyapps. io/HAPTRIG_Single_Module_Beta/) to allow the community to easily perform a HAPTRIG analysis on 21 cancer types with 187 unique KEGG pathways. We suggest that a roadmap of targetable genetic changes in tumours need not be limited to mutations, and HAPTRIG may therefore reveal additional targetable pathways across cancer types. COAST therapy should be clinically tested in OV, given its strong effects, minimal toxicity  24  , and genetic rationale. Methods HAPTRIG analysis construction. HAPTRIG proteostasis networks were built from the KEGG pathways autophagy (hsa04140), Lysosome (hsa04142), endoplasmic reticulum processing (hsa04141), ubiquitin-mediated proteolysis (hsa04120), peroxisome (hsa04146) and the p53 (hsa04115) pathway.
The KEGG autophagy pathway was further curated using current knowledge by adding MAP1LC3B, encoding the protein most commonly used to define autophagosomes  25. We used protein-protein interactions (PPIs) from the BioGRID curated database  14  to connect input pathway genes. For the pan-pathway analysis and in quality control networks, all human KEGG pathways were used. The full list of 187 KEGG pathways tested is included in Supplementary Data 1. We obtained copy-number data (N ¼ 579 tumours for OV) from the UCSC cancer genome Browser  46  , using copy-number calls from the GISTIC2. 0 algorithm  47. For the 2009 OV data sets  16  , log 2 segmented copy-number data were used, since the array used was not a SNP6 array. There were 102 serous tumours and 11 endometrioid tumours. To incorporate information regarding dose sensitivity of genes into our network scores, orthologous data sets were used.