dataesr/scientific-paragraphs-categorization huggingface dataset:
The NCI-60 is a panel of 60 diverse human cancer cell lines used by the Developmental Therapeutics Program of the U. S. National Cancer Institute RRID:SCR_003057. Gene-centric RMA- normalized TRPV2 mRNA expression data in the Broad Institute and Novartis's Cancer Cell Line Encyclopedia (CCLE) larger cell panel was obtained through the CCLE website (https://portals. broadinstitute. org/ccle) (Barretina et al, 2012). To establish the dif- ferential plot of TRPV2 expression across all cancers plus the speci- fic analysis in SKCM compared to healthy tissue, we analyzed RNA sequencing expression data of tumor and normal samples from The Cancer Genome Atlas (TCGA) and the GTEx projects (through the GEPIA web server (http://gepia. cancer-pku. cn/index. html; Tang et al, 2017)) as well as from the GEO: GSE46517 dataset (Kabbarah et al, 2010; Data ref: Kwong et al, 2013).
Significance Statement The Human Brain Project (HBP) is a 10-year European large-scale scientific project that aims to simulate functions and structure of the human brain, bringing together several fields of neuroscience from fundamental to clinical research to develop new therapeutic strategies for the treatment of neurologic diseases. The HBP Partnering Projects (PPs) program has been one of the measures to open up the HBP and promote sharing of resources and knowledge among researchers. We report here some of the achievements made by the HBP Partnering Projects that participated in a dedicated workshop in September 2022 and provide future prospects of this program in regard to the EBRAINS Research Infrastructure (RI). HBP Consortium partners and 115 associated members (namely, partners that are not part of the HBP Consortium) and were categorized based on their research objectives in three main areas: medicine, neuroscience, and technology.
To assure continuation and improvement of tools and services developed by HBP scientists after the end of the HBP, a new platform, named EBRAINS, was created in 2019. A corresponding legal entity was established and a web portal was officially released in 2019, thus allowing accessibility for the larger neuroscience community  (Amunts et al. , 2022). The EBRAINS RI has facilitated significant breakthroughs, which include the following key services (1) the Atlases service, allowing users to navigate through the mouse, rat, monkey and human brain and access different types of data; (2) the Data & Knowledge service, permitting access to large collections of datasets, models and software; (3) the Simulation service, providing powerful tools to conduct and validate simulation studies ranging from cellular to network to whole brain levels.
EBRAINS is a European distributed, digital, open, stateof-the-art research infrastructure that fosters collaborative brain science, providing tools and services that will assist scientists in their research, by collecting, analyzing, sharing, and integrating brain data and with special focus on performing modeling and simulation of brain functions  (EBRAINS, 2023). EBRAINS has been included in the 2021 Roadmap of the European Strategy Forum on Research Infrastructures (ESFRI), having proved both scientific excellence and implementation rigor as a science infrastructure (ESFRI, November 2021). EBRAINS services are made by and for scientists, following the principle of collaborative science and being constantly updated. Depending on the type of support users need and the type of data they aspire to use, they can access the services through the EBRAINS platform at different levels (i.
By combining psychopharmacology, fMRI, PET imaging and computational modeling of behavioral responses with a model of striatal DA, the project demonstrated that interindividual variability in response to catecholaminergic drug depends on individual differences in baseline DA synthesis capacity  (Aarts et al. , 2021; Rostami Kandroodi et al. , 2021; van den Bosch et al. , 2022). Future directions include importing this 100 subjects database into the EBRAINS Knowledge Graph for its use in a model simulating dopamine drug effects on human cognition. For the Applied Brain Models session, Mehdi Khamassi (director at the Institute of Intelligent Systems and Robotics in Sorbonne University) presented his computational model of DA-based learning and exploration regulation. The model builds on the coordination of model-based (MB) and model-free (MF) reinforcement learning (RL), which was previously used to model rodent navigation  (Dollé et al.
The EBRAINS Atlases service was presented by Timo Dickscheid, focusing on the development of neuroinformatics solutions for online-accessible multimodal human brain atlas at cellular resolution, using deep learning methods for brain mapping and segmentation. The publicly accessible service includes software interfaces for accessing atlases, as well as tools and workflows for integrating data to the brain atlases and for their analysis. The multilevel human brain atlas has 390 subspaces and allows users to navigate via the 3D Interactive Atlas Viewer at nearly cellular level with the BigBrain model, with access to areal information about cells, fibers, receptors, and anatomic and functional connectivity. Future directions include increasing coverage of data features and datasets, integration of the multilevel connectome, and creation of a more complete NHP atlas.
The EBRAINS Data & Knowledge service was presented by Ingrid Reiten. This service has been curating data since 2018 with the goal of making it an available and useful service for the community. The Knowledge Graph combines meta-data ingestion pipelines, human user input, and quality assurance processes to ensure consistent, high-quality data for contributors and users. Through this process, neuroscientists can make their data, models, and software available according to the FAIR (Findable, Accessible, Interoperable, and Reusable) principle  (Wilkinson et al. , 2016)  and deliver data as open as possible and as closed as necessary. Various types of accessibility are offered, such as free access and access under embargo, controlled access, and restricted access. These services enable scientists to share, find, integrate, analyze, and simulate neuroscientific data.
The EBRAINS Simulation services were presented by Marmaduke Woodman. A special focus was given to the virtual brain  (Schirner et al. , 2022)  although many other mature services are available for simulation and modeling (e. g. , NEST, Elephant). These services enable testing mechanistic hypotheses and interpreting model driven data analysis. Several example user cases were presented including the TVB-NEST co-simulation software, an example of extensive association between tools accessible on the platform, bridging modeling to clinically relevant use (clinical trial started in 2019). Lena Oden presented how EBRAINS provides Interactive Computing e-Infrastructure combining federated high-performance computing (HPC), Cloud & Storage infrastructure for neuroscience research. Access is free of charge and granted to users through an excellence-based peer-review procedure and submission of a proposal through the new online application system JARDS.
Through the federated Fenix Authentication & Authorization Infrastructure, users have access to data repositories and scalable computing services. The advantage of using the service is that users have a single account to access all the services, and their proposal can include the application for resources at different HPC sites simultaneously. Amaryllis Raouzaiou presented how the HBP Technical Coordination coordinates technical aspects of the transition to the EBRAINS RI. This work includes the development of guidelines and documentation for EBRAINS, involvement in co-designing of processes and collaboration with component owners, creating a roadmap for the services implementation and fostering collaboration with various partners within the HBP. Further steps involve incorporation of more use cases through user feedback for scientific workflow optimization and address of any software maintenance issues.
Focus on primate brain specifics related partnering projects and EBRAINS services The first session of the PP meeting shed light on investigations pursued in NHP research, in healthy and pathologic conditions but also through cross-species comparisons of brain structure and function with humans and/or rodents. Nonhuman primate research is crucial to further our understanding of the link between structure and function of the primate-specific brain that include humans. Key aspects of human perception, cognition and behavior appear to depend on primate-specific specializations of the cerebral cortex, and this may explain why so many human neurologic and neuropsychiatric diseases are inadequately modeled in rodents (Izpisua  Belmonte et al. , 2015). In the HBP, several groups studied cross-species differences to enrich and validate models, datasets, or atlases over the course of the Project that are available at EBRAINS, and so did some of the Partnering Projects (see Fig.
2 ). One example is the integration of the Multilevel Macaque Brain Atlas, MEBRAINS, in EBRAINS, that was presented by Wim Vanduffel. A preliminary version of the atlas, using a population-based template MRI with a 0. 4 mm resolution, has been released and is designed to receive and integrate results from various domains of primate neuroscience research in a common space. For this to be achieved, interoperability is of paramount importance. The primate neuroscience community is formally invited to participate in the atlas implementation by feeding it with their results and it is ensured that they will receive support from the data curation team and also for development of new user services. NHPs have been and remain an essential model system to further understanding and modeling of primate-specific brain function, and the NHP Atlas holds great promise for centralizing the wide diversity of NHP research performed at multiple levels and scales.
Another example of knowledge and tools available in EBRAINS is related to the PRIMCORNET. This project, presented by Bjørg Kilavik and Sacha van Albada, combined experimental  (Gregoriou et al. , 2012; Confais et al. , 2020)  and modeling approaches to provide novel NHP electrophysiological datasets, validation tools and a multiarea cortical model available through EBRAINS  (Schmidt et al. , 2018) , paving the way to neurorobotics simulations. Macaque vision, presented by Simo Vanni, combined biological and computational approaches, notably EBRAINS tools (e. g. , the CxSystem2 software, accessible through the Knowledge Graph) for data-driven brain models and simulation, to create a physiologically plausible working model of early cortical vision in primates  (Hokkanen et al. , 2019; Vanni et al.
Focus on brain circuits and simulation related partnering projects and EBRAINS services Exploring and mapping brain circuits is critical to build efficient computational brain models. Most of the PPs involved in this field exploited tools offered by the EBRAINS RI that also served for running simulations to test working hypotheses. They also supported development of the EBRAINS tools and services through their work (see Fig. 3 ). During a dedicated session, HBP PPs presented their progress on developing methodologies and tools, and their acquisition of empirical data necessary for the building of digital brain circuits. MAPS, presented by Eleonora Centofante, investigated the changes in cellular activity (c-Fos) patterns induced by spatial learning in Rodent hippocampus  (Mastrorilli et al.
, 2022). To better understand brain dynamics during spatial learning, they developed a network model using the Fenix RI showing that training procedure can be decoded from hippocampal activity pattern. They plan to share their data with the EBRAINS platform. DOPAMAP, presented by Jee Hyun Kim, produced maps of dopamine receptors 1 and 2 in the developing mouse forebrain and provided a comprehensive collection of high-resolution microscopic images, registered to the Allen Mouse Brain Common Coordinate Framework and shared via the EBRAINS Knowledge Graph (see, e. g. , dopamine 1 receptor distribution). Data are available for download or direct inspection through an interactive webmicroscopy viewer. The DOPAMAP collection  (Bjerke et al. , 2022)  is an important resource for researchers, and also explores the use of brain atlases for the developing mouse brain.
Yet, beyond bringing significant progress in the fundamental understanding of the relationship between brain circuits, function and behavior  (Gregoriou et al. , 2012; Demertzi et al. , 2019; Froudist-Walsh et al. , 2021; Deco et al. , 2021; Schirner et al. , 2023) , PPs have directly contributed to the development of whole-brain models and digital platforms exploiting latest Artificial Intelligence and HPC technologies to create next-generation robots such as haptic telerobot hand (https://www. shadowrobot. com/). They also proposed personalized medicine services ranging from prospective screening and therapeutic intervention to clinical outcome monitoring, which have been substantiated by clinical trials (e. g. , NCT05159661, NCT04620707, NCT03643016, NCT02603640), innovative medicines initiatives (e.
Their involvement is essential in promoting the progress of brain science through the infrastructure. As emphasized by Jan Bjaalie and Steven Vermeulen, collaborative projects are now needed to build a strong infrastructure. The workshop concluded with Steven Vermeulen (EBRAINS), Paul Verschure (Donders Institute for Brain, Cognition and Behaviour), and Julien Vezoli (Ernst Strügmann Institute for Neuroscience in cooperation with the Max Planck Society) all emphasizing the importance of the Partnering Projects for the future development of EBRAINS. They envision EBRAINS to be a platform for collaboration, providing a common language, methods, and concepts to advance understanding of the brain. Finally, they discussed the need for EBRAINS to support projects from fundamental research to more applied levels for patients' benefit.
Julien Vezoli noted that Partnering Projects are not only contributing but, are also critical for the future development of EBRAINS. Paul Verschure believes that EBRAINS can be a fuel to bring communities together, creating a common language and methods, and advance understanding of the brain. Steven Vermeulen sees the start of a new normal with EBRAINS, supporting projects that need more than just services and tools but also a collaborative platform that we would build together. EBRAINS supports different types of projects and needs, ranging from individual researchers to research organizations  (Vermeulen, 2022) , offering advantages to researchers, research groups, consortia, research organizations and is continuously looking for new projects to continue development of its services. The future orientation of EBRAINS projects will be driven by a thematic focus on specific research questions (including fundamental research and applications toward health), the service orientation toward building tools, services and data resources, and the ambition of providing brain-inspired technologies  (Amunts et al.
This research was supported by the  EBRAINS research infrastructure , funded from the  European Union 's  Horizon 2020 Framework Program for Research and Innovation  under the  Specific Grant  Agreement No. 945539  ( Human Brain Project SGA3 ). Acknowledgments: The  Partnering Projects of the Human Brain Project represent  more than 70 interdisciplinary research projects from more than 20 countries worldwide. We would like to acknowledge all the HBP PPs for their scientific achievements and especially those present at the meeting for engaging discussions, the EBRAINS representatives for participating and for clarifying new avenues for future collaboration. We would like to thank all speakers for their active feedback on this manuscript. We warmly thank the  Donders Cognitive, Brain & Technology Summer school  ( DCBT 2022collaboration  with the  European School of Network Neuroscience euSNN, iNavigate , and NeuroTechEU enabling exchange with students and invited experts.
Statistical analysis Evaluation of significance between two groups was done employing Student t-test or Mann-Whitney test in the case of non-parametric data. Difference between three or more groups was tested by one-way analysis of variance (ANOVA) with Dunnet post-hoc test. Statistical analysis was done using GraphPad Prism 9 software. All the results are expressed as mean ± standard error of at least three independent experiments, and P < 0. 05 was considered statistically significant. References of figures: P < 0. 05 = *; P < 0. 01 = **; P < 0. 001 = ***; P < 0. 0001 = ****.
Funding and additional information This work was funded by grants from  INSERM, Institut National du Cancer (INCa ;  PLBio 2017 ,  2019 ,  2020 ),  Région Bretagne ,  Rennes Métropole ,  Fondation pour la recherche Médicale  (FRM;  DEQ20180339169 ),  EU   H2020 MSCA ITN-675448  (TRAINERS), la  Ligue Contre le Cancer Comités d'Ille-et-Vilaine ,  des Côtes d'Armor et du Morbihan  and  MSCA RISE-734749  (INSPIRED) to EC and MEFZ was supported by  NCI   CA136526. KB was funded by a grant from la  Ligue Contre le Cancer. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or INSERM.
Results In European ancestry samples, 14 genes were significantly associated (q < 0. 05) with BC. Of those, two genes, FMNL3 (P = 6. 11 × 10 -6 ) and AC058822. 1 (P = 1. 47 × 10 -4 ), represent new associations. High FMNL3 expression has previously been linked to poor prognosis in several other cancers. Meta-analysis of samples with diverse ancestry discovered further associations including established candidate genes ESR1 and CBLB. Furthermore, literature review and database query found further support for a biologically plausible link with cancer for genes CBLB, FMNL3, FGFR2, LSP1, MAP3K1, and SRGAP2C. Conclusions Using extended gene-based aggregation tests including coding and regulatory variation, we report identification of plausible target genes for previously identified single-marker associations with BC as well as the discovery of novel genes implicated in BC development.
Thus, extending genetic studies to diverse populations and groups is a necessary advance to gain a comprehensive understanding of genetic architectures of complex diseases. In this study, we extend the recently published gene aggregation method combining coding and regulatory variants  [9]  to large-scale whole-genome genotyping cohorts to uncover novel genes implicated in breast cancer development. We used data from the Breast Cancer Association Consortium (BCAC) which has been studied previously including GWAS  [10, 11] , candidate gene analysis  [12] , and polygenic risk score analysis  [13, 14]. We employ the following strategies to empower the discovery of novel gene-disease associations using data from BCAC: (1) aggregation of all coding and regulatory variants linked to a single gene, (2) effective utilization of low-frequency variants, (3) exploiting genetic diversity between different ancestral groups, and (4) restricting multiple testing burden to one statistical test per gene (~ 18,500).
Samples and genotype data We used data on 142,670 individuals from BCAC. Detailed description of recruitment criteria, sample demographics, genotyping quality control, and imputation of additional markers have been reported previously  [10, 15, 16]. In short, 83,471 breast cancer cases and 59,199 controls of diverse ancestry were recruited in 80 studies (see Fig. 1A , Additional file 1: Table  S1 ). For each study, country of origin, and case and control numbers can be found in Additional file 1: Table  S2. Samples were genotyped using the OncoArray (Illumina)  [17] , a custom SNP array enriched for cancer-associated genetic regions.
Quality control of genotype data Sample quality control based on genotype and imputation quality has been performed previously  [10]. In short, samples were genotyped on the custom OncoArray. Genotyped markers failing any of the following quality criteria were excluded: (i) call rate above 98% in all consortia, (ii) MAF < 1%, (iii) no significant deviation from Hardy-Weinberg Equilibrium (controls: P < 10 -7, cases: P < 10 -12). Markers were imputed in a two-stage approach using shapeit2 and impute2 (V2) and the October 2014 (version 3) release of the 1000 Genomes dataset as reference panel  [10]. The imputation was carried out for 5-Mb segments of the genome and for groups of 10,000 samples to reduce the computation burden. We included only low-frequency variants (minor allele frequency MAF < 0.
Selection of genetic elements Our previously developed analysis pipeline "mummy"  [9]  was used to identify coding and regulatory regions for individual genes and to prepare input data for robust rare variant SNPset association testing software MONSTER  [18]. Aggregation tests were performed for genes defined in GENCODE v25 and with at least three but not more than 5000 low-frequency variants. For each of these genes, we identified genetic elements that are likely to contain relevant functional or expression variation using the mummy wrapper. These include the exomes and untranslated regions (UTR) of the gene. We selected additional regulatory elements that have been shown to be enriched for complex trait associations  [19, 20] : promoter, enhancer, and transcription-factor-binding units if they could be linked to the gene.
These elements were identified from the Ensembl build 84 resource. The link of regulatory genetic elements to genes was either based on physical overlap with the coding region, e. g. , when an element was located within an intron of the gene, or physical overlap with significantly associated eQTLs for the specific gene (see Fig. 1B ). Thus, we included the three types of regulatory elements if there was evidence that they affect expression levels of the gene. This was based on eQTL data for all available cell types from GTEx version 6. For each gene, all the low-frequency variants in these selected genetic elements were extracted and formatted to the MONSTER required input and weighted using Phred-scaled EigenPC pathogenicity scores  [21]. EigenPC scores have been previously shown to offer the best balance between coding and noncoding variants for application in aggregation testing  [9].
Gene-based aggregation test MONSTER (Minimum P-value Optimized Nuisance parameter Score Test Extended to Relatives) was used to perform SNPset variant aggregation tests for the variants selected for each gene  [18]. MONSTER generalizes the SKAT-O algorithm to allow for testing of related samples and sample cohorts with underlying population structure using a mixed effects model. SKAT-O is a unified test that combines a variance component and a burden test. The original MONSTER code was adapted to allow for the inclusion of larger sample numbers. The adapted MONSTER code is available on github here: https:// github. com/ stef-muell er/ MONST ER. Samples were processed in 15 study groupings due to the computational demand. Groups were formed based on study origin and genetic ancestry of samples while ensuring balanced case and control numbers.