dataesr/scientific-paragraphs-categorization huggingface dataset:
Ancillary equipment The detector is assembled in a holder that provides mechanical stability and hosts the sensors, the FEM cards, power supply electronics, high voltage bias supply, and the ancillary trigger electronics. The sensors are located in an enclosure flushed with dry air or nitrogen in order to prevent excessive moisture. The humidity level of the enclosure and the temperature of the hybrids are monitored through appropriate diagnostic sensors located on the carrier printed circuit board. The data of the diagnostic sensors are passed to the control software through the FEM cards that receive them through I2C bus channels. If either the temperature or humidity exceeds preset values the detector is automatically shut down. A comprehensive trigger system enables the users to synchronize the detector with external events or to synchronize external equipment with the frames being acquired.
Merlin Merlin is a readout system for up to four Medipix3 ASICs based on the National Instruments (NI) PXIe chassis and embedded controller and an FPGA card  (Horswell 2011; Plackett 2013). The FPGA card is connected to the detector head (up to 4 ASICs mounted on a chip board and an appropriate enclosure) through an adapter card that is plugged into the  FPGA card and through a cable up to 10 m long. The adapter card was developed at DLS as well as the Merlin software and firmware written in the LabVIEW graphical programming language. The ASICs on the detector head are powered up through the connecting cable and the voltages are supplied by the PXIe chassis through an extra module developed at DLS and plugged into the PXIe chassis. The sensor is also biased through the connecting cable and the high voltage bias supply is provided by the aforementioned extra module that is controlled by the Merlin software through the PXIe bus.
A TCP/IP interface was added to enable the integration into the standard control and data acquisition system of the beam lines of DLS. The TCP/IP interface enables also the system to be controlled over network connections from simple clients written in Matlab, LabVIEW, Python, or other languages. The Merlin system has been commercialized by Quantum Detectors for use at synchrotron experiments and more recently for electron microscopy experiments. To date 29 systems have been sold worldwide to organizations other than DLS.
Life-History Traits We used four LHTs in this study. Adult body mass (as a proxy for body mass, 16 missing values), maximum recorded lifespan (ML, as a proxy for longevity, 19 missing values), and female age of sexual maturity (ASM, 26 missing values) were obtained from the AnAge database (de Magalhaes and Costa 2009). Estimates about generation time were calculated from maximum longevity and age at maturity following a method detailed by UICN  (Pacifici et al. 2013 ): s ¼ ML Â 0:29 þ ASM: In the case of great apes, and most notably for Humans, these estimates appear to be too high (48. 5 years for Homo, 24. 7 for Pan, 23. 8 for Gorilla, and 24. 1 for Pongo). For these four species, direct estimates of the generation time  (29, 24, 19, and 25 years, respectively)  were taken from  Besenbacher et al.
Estimation of Polymorphism (p S and p N =p S ) The estimates of the synonymous nucleotide diversity p S and the ratio of nonsynonymous over synonymous diversity p N =p S and p S of ten primate species were calculated on the sequence data from  Perry et al. (2012) , such as reanalyzed by  Romiguier et al. (2014)  and  Figuet et al. (2016). We matched these polymorphism data for the three species Pan troglodytes, Propithecus vereauxi coquereli, and Eulemur mongoz, to Pan paniscus, Propithecus verreauxi, and Eulemur rufus, respectively, from the Perelman et al. multiple sequence alignment. A first series of analyses were conducted using the estimates of p N =p S and p S reported by  Romiguier et al. (2014). Alternatively, and in order to avoid the artifactual correlations induced between p S and p N =p S by shared data sampling error, the method of  Romiguier et al.
(2014)  was adapted so as to estimate p S and p N =p S on different subset of the sites, using the hypergeometric method  (James et al. 2017). Specifically, starting from the original fasta file containing the 8 variants for each contig of a given species, coding sites were randomly partitioned into two subsets, with equal probability independently for each site, and the p S and p N =p S statistics were computed on each subset using the dNdSpiNpiS software program (available at https://kimura. univ-montp2. fr/calcul/softwares. html), with the same options as those used in  Romiguier et al. (2014). Finally, the p S estimate from the first half was combined with the p N =p S estimate obtained on the second half. The results presented in the main document are all based on this hypergeometric method.
MCMC and Postanalysis Two independent chains were run under each model configuration. Convergence of the chains was first checked visually (a burnin of approximately 1,000 points, out of 6,000, was taken for the phenomenological model, and of 100 out of 3,100 for the mechanistic model) and quantified using Coevol's program Tracecomp (effective sample size greater than 500 and maximum discrepancy smaller than 0. 10 across all pairs of runs and across all statistics, except for the age of the root of the tree, which typically has a lower effective sample size). We used the posterior median as the point estimate. The statistical support for correlations is assessed in terms of the posterior probability of a positive or a negative correlation. The slope is estimated for each covariance matrix sampled from the distribution, which then gives a sample from the marginal posterior distribution over the slope.
Acknowledgments We wish to thank  Emeric Figuet ,  Jonathan Romiguier , and  Nicolas Galtier  for sharing polymorphism data (PopPhyl project) and for their help in rerunning the scripts for analyzing them,  Emmanuel Douzery  and  Frederic Delsuc  for editing the multiple sequence alignment of Perelman et al. to make it codon-compliant, and  Nicolas Galtier ,  Laurent Duret , and  Thibault Latrille  for their input on this work and their comments on the manuscript. This work was funded by  French National Research Agency  (Grant No. ANR-15-CE12-0010-01/DASIRE ). Phylogenetic analyses were conducted using the computing facilities of the  CC LBBE/PRABI.
Data Availability Coevol  (Lartillot and Poujol 2011)  is an open source program available on github: https://github. com/bayesiancook/coevol. All models and data used here, along with scripts to rerun the entire analysis, are accessible from this repo. Data and scripts for computing p S and p N =p S are available from figshare (https://www. doi. org/10. 6084/m9. figshare. 14784888. v1).
ACSN (https://acsn. curie. fr) is a web-based resource of multi-scale biological maps depicting molecular processes in cancer cell and tumor microenvironment. The core of the Atlas is a set of interconnected cancer-related signaling and metabolic network maps. Molecular mechanisms are depicted on the maps at the level of biochemical interactions, forming a large seamless network. The Atlas is a "geographic-like" interactive "world map" of molecular interactions leading the hallmarks of cancer as described by Hanahan and Weinberg. The Atlas is created using the systems biology standards and therefore is amenable for computational analysis. The maps of ACSN are organized in a hierarchical manner and decomposed into functional modules with meaningful network layout. Navigation of the ACSN is intuitive thanks to the Google Maps-like features in the NaviCell web platform.
Particularly, the exploration of the Atlas is simplified due to the semantic zooming feature, allowing the user to visualize the seamless Atlas and individual maps from the collection at different levels of details. The resource includes tools for visualization and analysis of molecular data in the context of signaling network maps. In this chapter we present how a disease-specific resource such as the Atlas of Cancer Signaling Network (ACSN) can be useful to study and interpret molecular perturbations in cancer, among others, explaining drug resistance, suggesting intervention points, finding phenotype shifts through disease progression, explaining susceptibility to a particular type of cancer and studying disease comorbidities.
Such quantitative rather than qualitative network changes, compared with the normal cell signaling, could be studied in the context of comprehensive signaling networks by analyzing experimental data obtained from tumor samples, patient-derived xenografts, cancer-related cell lines or animal models. This approach helps to understand the interplay between molecular mechanisms in cancer and to decipher how gene and protein interactions govern the hallmarks of cancer  (Hanahan and Weinberg, 2011)  in specific settings. Despite the existence of a large variety of pathway databases and resources  (Chowdhury and Sarkar, 2015) , only few of them are cancer-specific, and none of these resources depict the processes with enough granularity. In addition, pathway browsing interfaces are becoming more important for cancer researchers and clinicians however, further improvements are required.
Therefore, we constructed a resource, the Atlas of Cancer Signaling Network (ACSN, http://acsn. curie. fr), a web-based multi-scale resource of signaling network maps depicting molecular processes in the cancer cell and tumor microenvironment. These signaling network maps are organized by themes, each describing a major signaling mechanism, and are bound together in the form of a global map that we call "Atlas". The construction and update of ACSN involves manual curation and mining of the literature in cellular and molecular biology, together with the contribution of experts in those fields. The cell signaling mechanisms are depicted using the CellDesigner tool  (Kitano et al. , 2005)  at the level of biochemical interactions, assembling a large seamless network. The ACSN is applicable for data visualization, pathway definitions, and structural analyses, suitable for systems medicine  (Kuperstein et al.
, 2015). The cell signaling mechanisms are depicted in great detail, creating together a map of molecular interactions, presented as a global 'geographic-like' representation. ACSN has a hierarchical structure; each map is divided into functional modules, corresponding mainly to canonical signaling pathways. Currently the Atlas is being extended with additional maps depicting other important processes in the cancer cell as well as the components of the tumor microenvironment. An additional level of complexity will be added to the Atlas in the near future, representing different types of cells surrounding the tumor, and their interplay, to enable modelling of complex phenotypes. The navigation and website availability of ACSN is possible thanks to NaviCell web platform. NaviCell (https://navicell.
curie. fr) is a web-based environment empowered by Google Maps  (Kuperstein et al. , 2013). The maps generated with CellDesigner can be integrated in NaviCell, and the navigation features such as scrolling, zooming, markers, callout windows and zoom bar, are adopted from the Google Maps interface. The NaviCell web service can be used to import and visualize expression data (for molecular entities such as mRNAs, proteins, and miRNAs), gene copy-number data, mutation data and simple gene lists, such as small interfering RNA (siRNA)-based screening hit list. In a more elaborated situation, a weighted gene list can be visualized such as the distribution of mutation frequencies observed in multiple cancers. NaviCom (https://navicom. curie. fr) is a platform to generate interactive network based molecular portraits using high-throughput datasets.
Additionally, NaviCom connects with the cBioPortal database (www. cbioportal. org) (Cerami et al. , 2014)  and NaviCell web service, and it allows to display various high-throughput data types simultaneously on the network maps in a user-friendly way  (Dorel et al. , 2017). ACSN is a unique resource of cancer signaling knowledge, with a vast amount of information embedded and organized. Together with NaviCell, it is optimized for integration and visualization of cancer molecular profiles generated by highthroughput technologies, drug screening data or synthetic interactions studies. The integration and analysis of these data in the context of ACSN may help to better understand the biological relevance of results, guiding scientific hypotheses and suggesting potential therapeutic interventions for cancer patients.
Data visualization, network and pathway definitions, structural analysis and large genomic studies are just few of the available applications of ACSN; however, we will use some examples from those fields to demonstrate the ACSN's reach. This chapter is organized into four sections, dedicated to applications of ACSN in systems medicine. We present here how a disease-specific resource like ACSN can help to study and interpret molecular perturbations in cancer, among others, explaining drug resistance, suggesting intervention points, finding phenotype shifts through disease progression, explaining susceptibility to a particular type of cancer and studying disease comorbidities.
Triple negative breast cancer (TNBC) cell lines were studied for their sensitivity to AsiDNA, the derivative of Dbait DNA repair inhibitor, and Olaparib, the PARP inhibitor. Different TNBC cell lines show a wide distribution of response/resistance to these drugs, despite the fact that these cell lines are related to the same disease type. Integrative analyses of omics data (mRNA expression, copy number variations and mutational profiles) from these cell lines were performed, retrieving non-overlapping unique gene sets robustly correlated with resistance to each one of the drugs. Enrichment analysis of the omics data in the context of ACSN maps highlighted dysregulated functional modules across ACSN, associated with resistance to each one of the drugs allowing establishing drug resistance network-based molecular portraits.
, 2013; Ou et al. , 2012; Sánchez-Valle et al. , 2017). Since cancer has been described as a disease where cell survival is abnormally maintained, evading programmed cell death, the comparative study of three lung cancer and AD datasets was performed. Enrichment analysis of RCD map functional modules using gene expression data was performed using the ROMA algorithm  (Martignetti et al. , 2016) ; deregulated molecular functions as well as the main players were compared. In the AD data, the glucose metabolism, mitochondrial genes, as well as the oxidative phosphorylation and TCA cycle modules were significantly over-dispersed in the three studies, whereas in the lung cancer data ER stress was the only over-dispersed module across the three studies. Nevertheless, we found a downward trend in modules related to mitochondrial function in AD cases compared to lung cancer.
ACSN as a source of informative signatures of cancer ACSN can be used in various computational biology studies as a source of pathway definitions in terms of the sets of genes composing them or the structure of the connections between pathway members. ACSN and other pathway databases have been assessed regarding the value of pathway gene sets for downstream analyses, for example in functional enrichment studies  (Cantini et al. , 2018). A formalism of informative signatures, using an unsupervised approach to prioritize gene sets capable of defining a natural ranking of samples, has been developed for this purpose  (Cantini et al. , 2018) , and pathway definitions, both from knowledge-based databases like ACSN and from data-driven approaches like MSigDB (C4 computational gene sets,  (Liberzon et al.
, 2015)  were assessed for their chance to obtain significant enrichment scores in transcriptomic studies in cancer research. With this aim, a large corpus of data composed of 8991 transcriptomically profiled tumor samples for 32 cancer types from the Cancer Genome Atlas (TCGA, https://portal. gdc. cancer. gov/) was analyzed with 12096 definitions of pathways from various pathway databases, including ACSN. Then the pathway definitions were ranked in terms of the variance explained by the genes in the pathway, called eigen-genes, (overdispersion) and the spectral gap between the first and the second pathway eigene values (overcoordination). (Martignetti et al. , 2016). In a series of computational experiments, it was demonstrated that ACSN pathways have a higher chance to obtain significant enrichment scores when comparing typical groups of tumoral transcriptomes i.
ACSN as a source of network for finding correlation between gene profiles Definitions of pathways from ACSN have been used also for demonstrating the utility of several computational methods. The Cytoscape plugin DeDaL (Data-Driven network Layout) provides an algorithm for constructing datadriven 2D network layouts, which simultaneously reflects the structure of the network of interactions and the proximity between genes in the metrics defined by the omics data. This approach is useful for example, for finding correlations between gene profiles in a cohort of patients  (Czerwinska et al. , 2015). An example of such data-driven network layout for one of the pathways existing in ACSN, the Fanconi module of DNA repair map, is shown in Figure  4B. (Czerwinska et al. , 2015). Colors here visualize the t-test value for comparison the gene expression profile in basal vs non-basal breast cancers (green for downregulated in basal subtype and red for upregulated).
Complex stage-specific interventions in the MAPK pathway to disrupt proliferative signaling in bladder cancer Structural analysis of biological networks might be useful to find the most optimal SL gene combinations, possibly more than one pair, which might help to remove tumor cells more effectively  (Acencio et al. , 2013; Huang et al. , 2014; Zeng et al. , 2014). The MAPK signaling network is coordinated with various processes implicated in cell survival and part of the cell survival map in ACSN (https://acsn. curie. fr/navicell/maps/survival/master/index. html). The strategy of the following study consists of (i) identification of tumor stage-specific active functional modules, i. e. sets of MAPK signaling network components that are transcriptionally deregulated in bladder cancer  (Grieco et al.
, 2013)  compared with controls, and (ii) computation of intervention sets of MAPK map components, whose disruption blocks all the proliferative paths fostered by the identified active functional modules in bladder cancer (Inna Kuperstein et al. , 2015a) (Figure  6A ). Differential gene expression levels were computed, using transcriptomics data of five different bladder cancer stages. Regions of the map having high density of differentially expressed genes/proteins were identified and scored by GSEA (Gene Set Enrichment Analysis)  (Subramanian et al. , 2005) , being the highly scoring ones assumed to likely indicate sources of proliferative signals in the tumor. If paths exist in the map from the components belonging to any strongly activated network region to the node 'Proliferation', then presumably the region contributes to the activation of cell division (Figure  6A ).
Using minimal cut sets (computed using the OCSANA algorithm  (Vera-Licona et al. , 2013a)  in the Cytoscape plugin BiNoM  (Zinovyev et al. , 2008) , we removed sets of proteins leading to blockage of the identified proliferative paths. In the less invasive Ta stage, two significantly activated functional modules were identified (Figure  6B ; stages Ta). One contains AKT serine/threonine kinase (AKT) from the phosphatidylinositol-4,5-bisphosphate 3-kinase (PI3K) pathway that has been shown to be deeply involved in bladder cancer  (Askham et al. , 2010). Inhibitors targeting this protein have been recently developed  (Ching and Hansel, 2010)  and showed promising results. The second module contains the mitogen-activated protein kinase 7 (MAP3K7) protein, an upstream activator of mitogen-activated protein kinase 14 (p38) and mitogen-activated protein kinase 8 (JNK), which is activated by three stimuli: the TNF receptor superfamily member 1 (TNFR1), the Interleukin 1 Receptor Type 1 (IL1R1) and the Transforming Growth Factor Beta Receptor (TGFbR), frequently up-regulated pathways in Ta tumors.
OCSANA results suggested that the de-phosphorylation of both p38 and MAP3K7 can block the proliferative effects of MAP3K7-dependent functional module. In the analyzed data-set, the FGFR3 gene was found to be strongly expressed in the stage Ta, less in the stage T1, whereas it has low expression in invasive stages. Strikingly, the best intervention for Ta tumors consists in the disruption of both p38 and a downstream target of PI3K, AKT, that was identified as the most contributing gene with highest differential expression level in the path for the studied dataset (Figure  6B ). In the most invasive T2, T3 and T4 stages of bladder cancer, three up-regulated functional modules, with high expression of receptor tyrosine kinase/extracellular signal-regulated kinase (RTK/ERK) signaling components (Figure  6B ) were detected.
The OCSANA results point to the de-phosphorylation of both ERK and RAS as best interventions to block proliferation, which is consistent with current developments of RAS-and ERK inhibiting drugs for several cancer types  (Cakir and Grossman, 2009). The analysis suggested different interventions depending on the tumor stage. Network analysis using ACSN and OCSANA can be performed for individual patient tumor profiles, leading to personalized treatment recommendations  (Grieco, 2013). The optimal hitting set lists from these gene elements of MAPK network, which on removal cuts all the paths from the corresponding activated modules to the proliferation phenotype, were calculated using the OCSANA algorithm, and intervention sets for each stage of bladder cancer were suggested. (Adapted from  Grieco, 2013).
MCSs were evaluated for each patient (TCGA ovarian cancer dataset) using genomic, expression and mutation data integration and correlation with patient's resistance/sensitivity to Cisplatin. The top-correlated MCSs were ranked according for the mutation status of each gene. Those MCSs that were enriched with genes harboring inactivating mutations were suggested as best intervention combinations to restore sensitivity to Cisplatin by inhibiting the remaining 'active' genes in the set. This approach is relevant for complementing genotoxic chemotherapy by targeting specifically cancer cells and exploiting certain defects in the DNA repair machinery in each patient.
If the children had not been exposed to ionizing radiation, they would not have developed a cancer, so the exposure variable is of primary importance. The genetic factors compound of the disease is (only) a modulating factor. ACSN is a useful resource to study the DNA repair pathway, which is subdivided in 10 modules. Usually, to gain power in a GWAS study, pooling data from Multi-center studies is done. When pooling data from different origins, in addition to genetic heterogeneity due to different LD patterns, possible heterogeneity in exposure might decrease the power to detect true causal mechanisms in the development of the disease. Ideally, cancer association study design should consider exposure to environmental carcinogens, and always match controls to cases with relevant exposition.