dataesr/scientific-paragraphs-categorization huggingface dataset:
Publications The growth in the number of publications in our database correlates closely with the evolution of public science and technology policies in Portugal (Fig. 1 ). Until 1994, a period during which public investments in science and technology were quite limited, Portuguese oncologists published less than 50 articles per year. While initial attempts to establish a science and technology policy, influenced by the role played by the Organization for Economic Cooperation and Development (OECD), can be traced back to the 1960s, the creation in 1967 of JNICT-Junta Nacional de Investigaca ˜o Cientı ´fica e Tecnologica 4 did not lead to significant results (Goncalves 1996). In particular, the budget for science and technology remained largely inadequate  (Heitor et al. 2014). The democratic constitution of 1976coincidentally the year of the first publications in our database-referred to the State's responsibility to promote science and technology to support the country's development (Goncalves 1996).
In the second half of the 80s, with Portugal joining the European Union in 1986, the government, spurred by the OECD recommendations and by pressures from Portuguese researchers to place science and technology on the agenda, launched an institutional reform and promised to increase public investment in research and development (Goncalves 1996). In the following years, governmental activity in the research domain focused mainly on obtaining additional European funding rather than designing and promoting innovative science policies (Goncalves 1996). In the beginning of the nineties, the program CIENCIA, co-funded by the Portuguese state and the European Union, was the main source of funding for research in Portugal  (Goncalves 1996). By this time, with the support of European Structural Funds, JNICT implemented programs supporting individual research fellowships and physical infrastructures  (Heitor et al.
Types of publications differ between clinical and non-clinical settings (universities, research centers and associate laboratories). Among the top twenty research institutions, hospitals published a higher proportion of meeting abstracts, with only one exception, the Hospital Centre of Porto, whereas publications of universities and associated laboratories consisted of more than 60% articles. Different publication types point to different kinds of research and reward systems in clinical vs. non-clinical research. A majority of publications in our database (57%) are articles, with a relatively large presence of meeting abstracts (27%), followed by reviews (7%), editorial material (3%), letters and proceedings (2%), and other types of publications (less than 1%). 8 We observe a general increase in editorial material and reviews, possibly indicating a growing visibility of oncologists working in Portugal, since this type of publications are frequently made upon a journal's invitation.
The creation of the Portuguese Oncology Institute (IPO) in Lisbon in 1923 is related to several societal, medical and political movements that led to its establishment as the official center for oncology in the country  (Raposo 2004) , with a clear mission to focus on research as an activity at least as important as care (IPO 2016). The IPO regional centers in Coimbra and Porto were created by the government only much later, i. e. in 1967 and 1974, respectively. This historical fact accounts for the prominent role of IPO Lisbon in the two first decades of our database. A second period, beginning in the 1990s, is associated to the aforementioned profound changes in the Portuguese science and technology system, namely the advent of a democratic regime and Portugal joining the European Union, which resulted in the design and consolidation of a public policy for science and technology and, in particular, in the creation of oncology research units and laboratories.
As we will see below, this thematic landscape is quite different from the general thematic network of Portuguese oncology, which means that collaborations focus on only a subset of specific topics. To analyze the temporal evolution of institutional collaborations we examined their transformation during four different periods  (1976-1999, 2000-2004, 2005-2009, 2010-2015)  (Fig. 4 ). The first period covers publications in our database from the initial ones in 1976 until the beginning of rapid growth in publications in 1999. Figure  4  shows both recent and longstanding institutional collaborations. While initially international collaborations were mostly confined to European institutions, more recently we see the emergence of collaborations with the USA, Brazil and Canada, a pattern that can be related to the development of science and technology following the admittance of Portugal to the European Union.
Thematics After investigating the presence of institutional collaborations, we proceed to the analysis of the content of those collaborations. To this end, we used the Natural Language Processing tools provided by the CorText platform to identify the terms occurring in titles, abstracts and keywords of the publications in our database. We extracted 98 terms and analyzed their distributional co-occurrences. The resulting semantic network (Fig. 7 ) shows the main themes in Portuguese oncology research from 1976 to 2015. Figure  7  features nine main thematic clusters, strongly focused on organ-centered research  (stomach, intestine, thyroid, uterus, lung, breast, prostate, and Fig. 7  Semantic network of the period 1976-2015. In this figure, each node represents a term and its size is proportional to the number of co-occurrences it features in the database.
To perform research and provide specialized cancer care to the general population was the guiding principle underlying the creation of the three IPO, especially the one from Lisbon. As previously mentioned, the profile of the research institutions doing oncology research in Portugal presents a balance between non-clinical and clinical institutions. Significantly, most clusters featured on maps of collaborative institutional networks include both kinds of institutions. This signals the relevance of translational research, that is, research based on the ''exchange of concepts and techniques between fundamental biology and medical practice''  (Cambrosio et al. 2006: 3140). To further investigate these patterns, we analyzed the research level of the top twenty journals 10 in our database (which account for nearly 20% of the publications in the database), using Lewison and Paraje (2004) metrics.
Our tool generates SPs based on the Enterprise Service Bus (ESB) Switchyard  [16]. Switchyard is a recent free software ESB that includes different technologies, such as the Service Component Architecture (SCA), HornetQ (a MOM implementation)  [15]   and Apache Camel  [14]. These technologies are integrated on demand in the generated SP depending on the features of AM SP. Our tool also relies on the FAMILIAR tool  [13]  to develop and manage the F M SP and AM SP (e. g. , to check that AM SP is in conformity with F M SP ). From the AM SP illustrated in Fig. 3 , our tool succeeds to automatically generate a valid and fully functional SP. This generated SP has been successfully deployed in the JBoss Java server without any further manual interventions. It should be noted that the SP developer can manually adapt the generated SP to his/her application requirements (e.
g. , defining the business logic of the SP). The generated SP is composed of 333 Java code instructions and five XMLs 2. These XMLs permit to configure the SOAP and MOM technologies and to configure the ESB Switchyard. The time required to derive the AM SP (see Fig. 3 ) and generate its corresponding SP is several seconds. By using the SOA traditional approach (i. e. , using the SOA traditional service contracts and relying on the tools offered by the ESB Switchyard), we require more than 20 minutes to develop the same SP and we need many manual interventions. Hence, we can say that using our F M SP (see Fig. 1 ) reduces the required effort and time to develop valid and fully functional SPs.
We then applied a knowledge extraction algorithm (Online Methods) on each individual map in order to estimate its evolutionary cohesiveness and to identify genes with anomalous, 'outlier' barcodes that might reflect unusual evolutionary pressures within the system. Here, we used the Local Outlier Factor (LOF)  10  , a powerful anomaly detection algorithm which is related to density-based clustering and is suitable for analyzing large-scale, multidimensional datasets where the underlying data distribution is unknown. The LOF method identified a total of 1147 outlier genes in 248 KEGG maps (lbgi. igbmc. fr/barcodes and Supplementary Fig. 2 ). The most cohesive pathways, i. e. those with the least outliers (Supplementary Fig. 3  and Supplementary Table  1 ), were typically involved in universal biological processes such as translation or cell growth/death, in line with previous observations  11.
Two examples are highlighted in Fig. 2d. First, the genes involved in both cell cycle and oocyte meiosis pathways are generally cohesive with the other genes in these pathways, but the crosstalk with the progesterone-mediated oocyte maturation pathway contains a higher proportion of outlier genes (Supplementary Table  2 ). In fact, cell cycle and oocyte meiosis pathways are conserved in most vertebrates, while the exact nature of oocyte maturation is more variable between species. A number of these functional specificities are highlighted by the EvoKEN outliers, such as the Myt1 gene coding for a cdc2-inhibitory kinase (PMYT1_HUMAN), which acts as a negative regulator of entry into mitosis during the cell cycle. Inspection of the Myt1 evolutionary barcode (Supplementary Fig. 5a ) indicates a more divergent sequence family than is typical for this conserved pathway.
The RIP1 evolutionary barcode (Supplementary Fig. 6a ) shows a typical sequence conservation in vertebrate evolution, but synteny is only observed in mammals and not in fishes for example where RIP1 plays a different role in TLR signaling  15. Other examples of outliers are provided in Supplementary Fig. 6. Unraveling the evolutionary history of these pathways and their crosstalk will be important in understanding how the immune system functions and in developing effective therapeutic and vaccine strategies. It is clear that more in-depth analysis, involving phylogenetic tree and ancestor reconstruction would be required to describe in detail the evolutionary events identified in these studies. The advantage of EvoKEN is that it provides an effective framework for investigating the evolution of large systems at different granularity levels from local network motifs to the cellular level, allowing the rapid identification of interesting patterns in a particular biological context.
Construction of EvoluCodes for the human proteome The evolutionary barcodes (EvoluCodes) used in this study were constructed as described in  8. Each protein-coding human gene is thus associated with one EvoluCode that is visualized as a 2D matrix. The columns of the matrix correspond to the studied organisms, which in this work consist of 17 vertebrates with almost complete genomes from the Ensembl 16 database (version 51). The rows of the matrix correspond to different evolutionary parameters (Table  1 ) that were extracted from multiple alignments  17, 18  , synteny analysis and orthology data  19. For each vertebrate organism, the most closely related homolog to the human reference gene was identified (based on percent residue identity) and 10 parameters were calculated.
Analysis of human pathway data and definition of cohesive/outlier genes We based our analysis on pathway data from the Kyoto Encyclopedia of Genes and Genomes (KEGG) knowledge base. We analyzed 248 human pathways with the help of the KEGG SOAP server (http://www. kegg. jp/kegg/soap/). A total of 5849 EvoluCodes could be mapped to the genes in these pathways. For each pathway, we then identified 'outlier' genes, i. e. genes with an unusual evolutionary history (EvoluCode) compared to the other genes in the pathway. We determined outliers using an anomaly detection algorithm called Local Outlier Factor (LOF)  22. The basic concept of LOF is the local density, where locality is given by k nearest neighbors. By comparing the local density of an evolutionary barcode to the local densities of its neighbors, we identify regions of similar density, as well as barcodes that have a substantially lower density than their neighbors.
These are considered to be outliers. The local density is estimated by the typical distance at which a barcode can be "reached" from its neighbors. First, the 2D matrix representing an EvoluCode, consisting of 10 rows and 17 columns, is redimensioned to a 1D vector of length, n=170. Then, if A and B are 2 EvoluCodes in Euclidean n-space, with A = (a 1 , a 2 ,. ,a n ) and B = (b 1 , b 2 ,. ,b n ), the distance between A and B is: 𝑑(𝐴, 𝐵) = √∑(𝑏 𝑖 -𝑎 𝑖 ) 2 𝑛 𝑖=1 The Euclidean distance between the EvoluCode A and its k nearest neighbors is denoted kdist(A) and the set of k nearest neighbors is N k (A). The reachability distance is then calculated as: ℎ_  (, ) = max{(), (, )} The local reachability density (lrd) of EvoluCode A is defined as: lrd k (A) = 1 / ( ∑ 𝑟𝑒𝑎𝑐ℎ𝑎𝑏𝑖𝑙𝑖𝑡𝑦_𝑑𝑖𝑠𝑡 𝑘 (𝐴,𝐵) 𝐵∈𝑁 𝑘 (𝐴) |𝑁 𝑘 (𝐴)| ) And the local reachability densities are then compared with those of the neighbors using: LOF k (A) = ( ∑ lrd (B) lrd (A) B∈N k (A) |N k (A)| ) The LOF score thus represents the cohesiveness of the EvoluCode associated with each gene in the context of its pathway.
The authors of the LOF algorithm consider that a score less than 1 indicates a clear inlier object, i. e. a cohesive barcode. Genes with a LOF score significantly greater than 1 are considered as outliers. However, the threshold determining a clear outlier depends on the dataset. Here, we defined the outlier threshold value as the upper quartile for the LOF scores of the EvoluCodes in the context of the 248 human pathways, which was 1. 037.
Analysis of metabolic pathways The KEGG database currently contains pathway data for 84 human KEGG metabolic pathways, where the nodes in the networks represent metabolites (substrates, intermediates and products). The edges between nodes represent reactions that are associated with one or more genes/proteins. For our experiment, we selected all pathways with more than 20 human genes, giving us an initial set of 20 pathways, containing a total of 875 different reactions, of which 671 reactions were associated with cohesive genes and 204 reactions with outlier genes. We defined 6 classes of local topological motifs within these pathways, based on node redundancy and connectivity (Table  2 ). We then determined the topological localization of all biological reactions associated with the outlier genes.
Construction of the cellular level evolutionary map We constructed a cellular level map, representing the evolutionary histories of the pathways in the KEGG database. For the 200 human KEGG pathways, we identified the genes shared by each pair of pathways. We then focused our analysis on the pathway pairs sharing at least 3 genes, representing 155 KEGG pathways, which describe mainly cellular processes and signal transductions. In the evolutionary map, each node represents a specific KEGG pathway and the edge joining 2 nodes represents the genes shared by the two pathways. The node diameter is proportional to the number of genes implicated in the pathway. Each node is assigned a color representing the homogeneity of the EvoluCodes associated with the genes. The cohesiveness of the pathway evolution is estimated based on LOF value dispersion, using the IQR (interquartile range, IQR = Q 3 -Q 1 ).
This work was supported by the  Decrypthon program , co-funded by  Association Française contre les Myopathies  (AFM,  14390-15392 ),  IBM  and  Centre National de la Recherche Scientifique (CNRS). We acknowledge financial support from the  ANR  (EvolHHuPro:  BLAN07-1-198915  and Puzzle-Fit:  09-PIRI-0018-02 ) and Institute funds from the  CNRS, INSERM , and the  Université de Strasbourg.
This parameter database represents the duplicability of a human gene compared to the other species co-ortholog number of co-orthologs in the vertebrate Ortholog/paralog species with respect to human. This database parameter reflects gene duplications in the non human lineage Synteny parameter indicating conservation of Synteny database intervals: Interval 1 represents values that are lower than what is generally observed for a specific parameter in a specific organism and is assigned a blue-to-green gradient  Interval 2 represents values that correspond to what is generally observed for a specific parameter in a specific organism and is assigned a green color  Interval 3 represents values that are higher than what is generally observed for a specific parameter in a specific organism and is assigned a green-to-red gradient.
This paper is organized as follows: In § 2 we describe our dataset from the ALMA archive, the new MUSE observations, the ancillary archival data and previous work. We describe the source detections and measurements from the ALMA and MUSE observations, the broad-band photometry based on archival data, and the analysis of the measurements in § 3. In § 4 we discuss possible scenarios explaining the absorption. A summary and the conclusions are given in § 5. We present additionally morpho-kinematic modelling and the fitting of the absorption lines in the quasar spectrum in the Appendix. Throughout the paper, we adopt an H 0 = 70 km s -1 Mpc -1 , Ω M = 0. 3, and Ω Λ = 0. 7 cosmology.
OBSERVATIONS AND DATA REDUCTION The aim of this work is to identify and characterize galaxies connected to the intervening quasar absorber with a particular focus on the molecular gas content of the host galaxies. To do this, we use ALMACAL, a novel wide and deep (sub-) millimetre survey utilizing calibrator observations from the ALMA archive  (Oteo et al. 2016). From our parent sample we have identified a particular absorber at z = 0. 633 towards the quasar J0423-0130 (04h23m15. 8s -01d20m33s, z = 0. 915). The absorber is a Lyman Limit System with an HI column density of log(N(HI)/ atoms cm -2 ) = 18. 54 +0. 07 -0. 10. We choose this source for our pilot study, because we detect two 12 CO emission lines in our ALMACAL data. We have obtained MUSE follow-up observations of the field. We show that combining these two datasets with other ancillary data yields a more complete picture of such systems.
ALMACAL The data used in this work are taken from the ALMACAL survey, a wide and deep survey utilizing the ALMA calibrator archive. The retrieval and calibration of the ALMA calibrator data are described in detail by  Oteo et al. (2016). Up until 1. July 2017, the quasar J0423-0130 was observed four times at frequencies corresponding to possible 12 CO emission lines at the absorber redshift. Two observations are available in Band 4 covering the frequency of the redshifted 12 CO(2-1) emission line and two observations in Band 6 covering the frequency of the redshifted 12 CO(3-2) emission line. The details of these observations are given in J0423A J0423B J0423C J0423D QSO Figure  1. Overview of the detected sources in the field of J0423-0130. The reconstructed white-light image from our MUSE observations is shown in grey-scales, the red and cyan contours show the 12 CO(2-1) and 12 CO(3-2) emission, respectively.
1. We carry out the calibration and data reduction using the Common Astronomy Software Applications (CASA) software package version 4. 7. We examine each individual dataset used for the emission line search first in the uv plane to apply further interactive flagging if necessary. We reduce the spectral resolution to a maximum of 15. 625 kHz. This results in a velocity resolution of 33 km s -1 (and 66 km s -1 after Hanning smoothing) for the Band 4 observations and a velocity resolution of 22 km s -1 (and 44 km s -1 after Hanning smoothing) for the Band 6 observations. The spectral resolution ensures that an emission line with a width of 300 km s -1 is covered by at least 5 and 7 channels in the respective bands. Before the imaging, we concatenate the two datasets in each band. For the image cubes and the continuum image, the imaging was done using the standard 'clean' algorithm.
63335  10 ± 1  5. 2 ± 0. 3  1. 2 ± 0. 1  610 ± 40  1. 2mm continuum optical image of the relative strengths and weaknesses of all possible source finding strategies is presented by  Popping et al. (2012). We chose the "Smooth + Clip Finder" since it offers the highest completeness and reliability for finding sources on a variety of scales. This is appropriate in our case since we do not know a priori whether our sources are spatially resolved. We use twelve smoothing kernels between 0. 7 and 4. 2 in the spatial dimension and between 66 km s -1 and 990 km s -1 for the Band 4 data cube and 44 km s -1 and 660 km s -1 for the Band 6 data cube in the spectral dimension. The detection threshold is 4σ for both data cubes. We require the detection to be within a range of ±2000 km s -1 relative to the absorber redshift, because we are targeting 12 CO emission at the absorber redshift.
35  (Villanueva et al. 2017). The projected impact parameter between the 12 CO detection and the quasar sight line is 133 kpc and the position of the 12 CO detection is aligned with that of one of the galaxies detected in the MUSE cube. We discuss this in more detail in Section 3. 2. Furthermore, we combine all Band 6 observations from ALMACAL to search for continuum emission from the CO-detected galaxy. Hence, we can compare the total farinfrared luminosity based SFR with the SFR based on the dust-uncorrected [OII] emission line flux. We show in Fig. 3  (left) the Band 6 continuum map with the 12 CO (2-1) and 12 CO (3-2) emission line contours overplotted. We exclude any contamination from the 12 CO(3-2) emission line by excluding the datasets covering the relevant frequency. The total flux density at 1.
Furthermore, we assume, that the minimum size of a galaxy is given by the seeing of 0. 7 , which corresponds to 4. 8 kpc at the absorber redshift and the FWHM of the line is at least 3 Å. The resulting [OII] flux would be 0. 3 × 10 -17 erg s -1 cm -2. This converts into a 3σ limiting SFR of 0. 2 M yr -1. Additionally, we determine the limiting SFR at the quasar position. Therefore, we extract a 2 × 2 wide cube at the position of the quasar with a width of 865 km s -1 centred on the expected [OII] emission line covering all the observed [OII] emission. The emission from the quasar is determined by a linear fit and removed from the data cube. The limiting SFR at the position of the quasar is calculated from the noise in the continuum subtracted cube and assuming the same emission properties as described above.
We use the Sersic profile as input for the 3D morpho-kinematic fit. We infer from this fit whether the kinematics are compatible with that observed in a rotating disk and how the position angles of the galaxies are related to each other and how the quasar absorption is oriented with respect to the major and minor axes of the galaxies. However, given the data quality we will not be able to detect warpdf in the disks. We determine the half-light radius, axis ratio, position angle (PA) and the Sersic index from the [OIII] pdfeudo-narrow-band image using Galfit. This is a twodimensional fitting algorithm extracting structural parameters from galaxy images. The algorithm models the light profile and is designed to fit multiple components. Here we perform a single component fit of the [OIII] pdfeudo-narrow band image.