dataesr/scientific-paragraphs-categorization huggingface dataset:
For detailed instructions on map zoom levels creation, see https://github. com/sysbio-curie/NaviCell. An additional way for map exploration is highlighting individual entities of the map and their neighbours. We developed a NaviCell function for selecting species of interest and its neighbours. This function allows step-wise enlarging of the neighbourhood coverage to understand the signalling propagation on the map, as shown for p53* molecule on the DNA repair map, in Figure  5D.
Visualization of omics data in the context of signalling network maps To make data visualization a straightforward and easy task, we developed a built-in toolbox for visualization and analysis of high-throughput data in the context of comprehensive signalling networks. The integrated NaviCell web-based toolbox allows importing and visualizing heterogeneous omics data on top of the maps and to perform simple functional data analysis. Standard common IDs in the annotation of proteins, genes (HUGO) and metabolites (CHEBI) allows the NaviCell data visualization functionality. NaviCell also computes aggregated values for sample groups and protein families. For visualization, the tool contains standard heatmaps, barplots and glyphs, as well as the map staining technique for displaying large-scale trends in the numerical values on top of the map.
The combination of these flexible features provides an opportunity to adjust the modes of visualization to the type of data and to acquire the most meaningful picture  (19). Extended documentation, a tutorial, a live example and a guide for data integration using NaviCell are provided at https://navi cell. curie. fr/pages/nav_web_service. html. To illustrate data visualization, the EMT regulation map from NaviCell collection (9) https://navicell. curie. fr/pages/ signalling_network_emt_regulation_description. html was used to analyse omics data from ovarian cancer patients  (45). The data files can be downloaded from https://navicell. curie. fr/pages/nav_web_service. html. Expression and mutational profiles of proliferative and mesenchymal ovarian cancer classes were compared. Expression data from the two groups of patients were visualized on the EMT regulation map using the map staining technique in NaviCell, showing average expression of each gene across the samples.
The mutation profiles for the same disease classes were visualized using glyphs. There is a clear difference in the expression pattern between the two classes, indicating a major activation of epithelial to mesenchymal transition (EMT) regulators in mesenchymal class (Figure  6B ), characterized by invasive clinical outcome, opposite to the proliferative class (Figure  6A ). A closer look at the EMT inducers ZEB1&2, SNAIL, CTNBB1, shows that they are largely mutated and demonstrate a low expression pattern in the proliferative, clinically, less invasive class (Figure  6C ), in opposite to the invasive mesenchymal class (Figure  6D ). The observation is consistent with the notion that in order to induce ETM and invasion, the cancer cell needs to be in low-proliferative status  (9). Similar molecular portraits of cancer can be automatically generated using NaviCom, which connects cBioPortal and NaviCell allowing visualization of different high-throughput data types simultaneously on a network map in one click  (22).
garuda-alliance. org), will facilitate the compatibility between tools and resources. The map construction and annotation procedures that we expose in this manuscript aim to integrate community efforts, which are in line with FAIR principles  (23). We are convinced that the application of standardized map construction procedures, providing access to the maps' content via web-based platforms, will increase the reuse of the existing maps, improving management and updating and will allow efficient generation of new high-quality signalling maps. Actually, the idea of exchangeable, re-usable maps or map modules lies in the basis of the Disease Community resource creation, where common mechanisms implicated in various diseases will be shared between disease-specific maps. Finally, standardly created maps provided in common formats will ensure their compatibility with various systems biology tools, facilitating their applications for data analysis and modelling.
Funding This work has been supported by the  COLOSYS  grant  ANR-15-CMED-0001-04 , provided by the  Agence Nationale de la Recherche  under the frame of ERACoSysMed-1, the  ERA-Net  for Systems Medicine in clinical research and medical practice and by  INSERM Plan Cancer  N  BIO2014-08 COMET grant  under  ITMO Cancer BioSys program. This work received support from  MASTODON program  by  CNRS  (project  APLIGOOGLE ).
TEN-HMS Study In this setting, the Trans-European Network-Home-Care Management System (TEN-HMS) study, conducted in 2005, was the first large study that analyzed the role of telemonitoring in selected HF patients  [8]. In this study, 426 patients (mean age of >70 years for more than 50% of the patients) were randomly assigned to telemonitoring, nurse telephone support, or standard care in a 2:2:1 ratio. Telemonitoring allowed for data transfer (of weight, blood pressure, and ECG data) to a central Web server via a conventional telephone line, and then to workstations at each investigator site via a secure intranet connection. Patients were invited to transfer data twice a day. Values greater or lower than the predefined limits were automatically sent to study nurses who could either directly advise the patient or, in more severe cases, inform the primary care physician.
Thus, several projects provide questionnaires or forms regarding HF signs and symptoms (Table  2 )  [27]  such as dyspnea, palpitations, edema, and fatigue corresponding to the acronym EPOF, namely, "Essoufflement, prise de Poids, oEdèmes, and Fatigue", as experienced by the patient  [38]. Several projects also include ECG monitoring and even video-conferencing  [36, 37]. A few of these telemedicine projects use machine learning in order to predict patient risks of acute HF or acute CHF decompensation  [39, 40]. These projects also include ICT and Web 2. 0 technologies. In this setting, the cloud-based software aggregates, cleans and analyzes patient data to identify patterns that may indicate potential risks and to provide predictive insights on healthcare outcomes, such as the software MyPredi TM (version 2) for the E-care project (see below)  [37, 41].
For these projects, several tools have been developed and used, such as artificial neural network (ANN) algorithms, data mining software and ontology  [40]. In this context, three clinical datasets are of particular interest: (1) patients' phenotypes, (2) patients' electronic medical records containing physicians' notes, laboratory test results, as well as other information on diseases, treatments, and epidemiology that may be of interest for association studies and predictive modeling on prognosis and drug responses, and (3) knowledge from literature, including rules on HF management  [41]. In addition to these tools, it must be emphasized that HF telemonitoring may use implantable invasive devices that send data either intermittently or continuously to the receiving physician (e. g. , automatic telemonitoring).
0)  [33, 43]. These latter projects have been developed in an effort to optimize home-monitoring of CHF patients. Regarding the patient phenotype, the system detects instances where there is a risk of cardiac decompensation and re-hospitalization ("personalized medicine"). In this context, MyPredi TM automatically generates indicators of a worsening of the patient's HF status ("predictive medicine")  [33, 43]. In this context, it must be underscored that the E-care telemonitoring solution (with MyPredi TM software) is currently being assessed for the "EC medical device" mark (proving its excellent quality), rather than just the simple "EC" mark, as is the case with numerous devices currently on the market. All of these projects or studies satisfy the conditions of telemedicine, as laid out in Article 36 de la Loi de Financement de la Sécurité Sociale (Article 36 of the Social Security Financing Act) [28-33,43].
SCAD Project In the SCAD ("Suivi Cardiologique à Distance") project, 90 patients were randomly selected from 2009 April to 2011 May (n = 45 for each group) (extracted data from the Thesis of the Faculty of Medicine from Caen, France and [29]). The population was elderly, with a mean age of 78 ± 6 years, mostly male (78%) and at high risk of re-hospitalization (mean brain natriuretic peptide (BNP) level of 1025 ± 950 pg/mL). At twelve months, 1040 days of hospitalization for acute HF were recorded. Monitoring by educational telemedicine significantly reduced the number of hospital days for acute HF: 590 days in the control group vs. 450 days in the telemedicine group (p = 0. 044). The criterion "death or hospitalization for acute HF" occurred less frequently in the telemedicine group, specifically 57.
PRADO-INCADO Project As discussed above, the E-care platform, with MyPredi TM AI software, appears capable of preventing hospitalization through early detection of CHF deterioration and the subsequent warning of the patient's caregivers, thus allowing them to intervene  [43]. In addition, the platform is capable of outlining the patient's care pathway, which is a major theme in medicine for government and authorities. It likewise provides a means for various healthcare professionals to communicate with each other, while facilitating access to medical resources. With this in mind, an enhanced version of the E-care platform and MyPredi TM AI is scheduled to be tested in CHF patients' homes, as part of the so-called PRADO-INCADO project. PRADO is a French program to support patients returning home after hospitalization, with PRADO-INCADO specifically targeting the post-hospitalization period of HF patients (Figure  6 )  [32].
Over a period of several months, 200 patients with NYHA Class II to IV HF will be followed-up by means of the PRADO organizational model developed by the national health insurance administration for HF patients (to date, n = 35)  [33]. For recruitment, the project will benefit from the HF care protocol that includes cardiologists, internists, emergency physicians and geriatricians from the Strasbourg University Hospital  [37, 43]. Here again, no draconian selection of patients will occur. Instead, patients will be gradually enrolled so that they are deemed representative of CHF subjects across France. Within this care protocol, the mean age of patients we are likely to enroll is 82 years  [36]. Both morbidity and mortality data from the 100 patients using the telemedicine solution will be compared to those of patients enrolled into the PRADO program (n = 100), as well as to those of patients eligible for neither the PRADO-INCADO project nor the PRADO program, representing the control group (n = 100).
Alerts indicating a deterioration of the patient's condition will be generated by AI (new software version of MyPredi TM adapted for the management of DM) and transmitted to the healthcare professionals in charge of the patient. The healthcare professional can thus anticipate decompensation and can initiate appropriate measures outside of an emergency setting. These innovative solutions derived from new technologies should be readily accepted by patients. Medical data can likewise be shared among health professionals, being part of a city-wide hospital network. Ultimately, an improvement in the patients' quality of life can be expected. DIABETe does not compete with Diabeo TM (version 1) or other expert systems aimed at optimizing the glycemic balance, which is per se the main objective of DM management  [44, 46].
A. =10h 0m 20. 7s, Dec. =2 • 35'17") with continuous frequency coverage between 30. 969 GHz and 39. 033 GHz. The GOODS-N data form a 57-pointing mosaic (center: R. A. =12h 36m 59s, Dec. =62 • 13'43. 5") with continuous coverage between 29. 981  GHz and 38. 001 GHz (Figs. 1, 2). The total on-source time was approximately 93 hrs in the COSMOS field and 122 hrs in the GOODS-N field. The frequency range targeted in this project covers CO(1-0) at z = 1. 95-2. 85 and CO(2-1) at z = 4. 91-6. 70, such that the space density of CO(2-1) line emitters is expected to be smaller than for CO(1-0) (Fig. 1 ; e. g. ,  Popping et al. 2014 Popping et al. , 2016)). Both the large redshift spacing and the expected redshift evolution of the space density of CO emitters lessens the severity of the redshift ambiguity in our survey compared to previous studies.
At 34 GHz the VLA primary beam can be described as a circular Gaussian with FWHM∼80 , so our pointing centers were optimized to achieve a sensitivity that is approximately uniform in the central regions of the mosaics by choosing a spacing of 55 (< 80 / √ 2) in a standard hexagonally packed mosaic  (Condon et al. 1998). During each observation, we targeted a set of 7 pointings in succession, alternating through phase calibration. We performed pointing scans at the beginning of each observation, with additional pointing observations throughout for observations longer than 2 hours. Most of the COS-MOS and GOODS-N data were taken in the D configuration of the VLA. Some of the observations, especially for the GOODS-N pointings, were fully or partially carried out in DnC configuration, in re-configuration from D to DnC (D→DnC) and in re-configuration from DnC to C (DnC→C).
COSMOS observations The dataset in the COSMOS field consists of 46 dynamically scheduled observations between 2013 January 26 and 2013 May 14, each about 3 hours in duration. Flux calibration was performed with reference to 3C286, and J1041+0610 was observed for phase and amplitude calibration. Three frequency tunings offset in steps of 12 MHz were adopted to cover the gaps between spectral windows and to obtain uninterrupted bandwidth.
GOODS-N observations The GOODS-N dataset consists of 90 observations between 2013 January 27 and 2014 September 27, each about 2 hours in duration. Pointing 6, which covers the 3 mm PdBI pointing of the CO deep field in  Decarli et al. (2014)  in the HDF-N, was observed both as part of the 1-7 pointing set, and in two additional, targeted observations to achieve better sensitivity. Pointing GN57 was observed for 3 hours (127 min on source) in D-array configuration on 18 December 2015, in order to follow up the most significant negative line feature in GN1-56 (see Appendix D for details). J1302+5748 was used for phase calibration, and the flux was calibrated by observing either 3C286 (in 7 observations) or in reference to the phase calibrator (in the remaining observations). An average phase calibrator flux at 34 GHz of S=0.
Calibrated data from each pointing were imaged separately without any CLEAN cycles, because the fields do not contain strong continuum or line sources (see Section 4). We imaged the total intensity (sum of the two polarizations) using natural weighting and choosing a pixel size of 0. 5 consistently in the two fields. The smallest adopted channel width is 4 MHz, equivalent to 35 km s -1 mid-band, which is less than the typical line width from galaxies. With this choice, our data cubes have ∼2000 channels after averaging polarizations. A crucial aspect of the imaging procedure, necessary for blind line searches, is to avoid any frequency regridding by inter-Figure  3. Measured rms noise, per pointing, in 4 MHz channels as a function of frequency, at the native spatial resolution (top), and after smoothing to a common beam size (bottom).
LINE SEARCH METHODS The main objective of this survey is to carry out a blind search for CO(1-0) and CO(2-1) emission lines in the COLDz dataset. No other bright lines are expected to contaminate the 30-39 GHz frequency range (Fig. 1 ). The line brightness sensitivity is approximately equal for the low and high redshift bins (corresponding to CO(1-0) and CO(2-1), respectively; Figure  4 ). We therefore expect a higher source density in the low redshift bin due to the expected evolution of the cosmic gas density  (Popping et al. 2014 (Popping et al. , 2016)). Hence, we will assume that all detected features correspond to CO(1-0) unless data at other wavelengths suggest that they belong to the higher redshift bin. In order to detect emission lines in our data cubes, we have implemented a previously published method (spread,  Decarli et al.
IDENTIFICATION AND STACKING OF GALAXIES WITH PREVIOUS SPECTROSCOPIC REDSHIFTS 5. 1. Identification and stacking of previous mid-J blind CO surveys We searched the GOODS-N dataset for low-J CO counterparts to the candidate mid-J CO detections from our previous CO blind survey in the HDF-N with the PdBI  (Decarli et al. 2014). We find a single match in our candidate list, which corresponds to CO(2-1) line emission from HDF850. 1. We systematically searched for every possible mid-J/low-J CO line combination that would place a low-J CO line in our surveyed volume (Table  4 ). Several of these possible mid-J/low-J CO line combinations are not the preferred line identifications by  Decarli et al. (2014). Therefore, our non-detections are consistent with their preferred redshift in those cases constraining or ruling out several alternative redshift solutions allowed by the PdBI data alone.
While the sensitivity of our deeper field (in COS-MOS) is within a factor of two of the sensitivity that was previously achieved by ASPECS through ALMA in a comparable redshift bin (after correcting for CO excitation), the volume that we could sample in that field is six times larger. Furthermore, the volume covered in both fields combined is >50 times as large as that covered by ASPECS-Pilot and > 60 times as large as that carried out in the HDF-N with the PdBI, given the > 60 times larger survey area (∼60 vs ∼1 arcmin 2 ). In this study, we have also significantly further developed the methods utilized to carry out blind searches for emission lines in interferometric datasets. In particular, we have generalized the Matched Filtering technique that is commonly used in the spectral dimension to identify spectral lines, to the regime of interferometric data cubes where sources may be spatially extended.
The significant detection of CO emission from galaxies in the stack suggests that our dataset is rich in additional signal, that is too faint to be reliably blindly identified at the current depth, but which can be mined through spectroscopic observations at other wavelengths. We have therefore demonstrated the power of stacking the CO signal from galaxies with spectroscopic redshifts, in order to fully take advantage of the information in CO deep field data. We have also developed statistical methods (presented in Appendix F) to evaluate the purity, completeness and recovered candidate properties with higher accuracy than previous techniques. This enables us to infer the best constraints to date on the CO(1-0) luminosity function at z ∼2-3 (Paper II). With this CO deep field study, we also further demonstrate that blind CO searches are sensitive to "optically dark", dust-obscured galaxies at very high redshift, such as GN10 and HDF850.
METHODS In this section we provide additional details of our line search methods in interferometric data cubes, which were used to carry out the blind line search presented in Section 4 of the main text. We first provide a more complete description of our method of choice, Matched Filtering in 3D 11  (MF3D, which extends Matched Filtering in the spectral dimension; i. e. , MF1D), and then we compare its performance to three alternative methods that we have also investigated.
We compute templates that are Gaussians in frequency and circular 2D Gaussians spatially (sizes given in Table  5 ). We then convolve the signal-to-noise cube with these templates by multiplication in Fourier space to produce multiple Matched-Filtered cubes, one for each template. The main difference between the traditional application of Matched Filtering in astronomical images and our application to interferometric data comes from the spatially correlated nature of the noise in interferometric images. In the case of un-correlated (i. e. , white) noise, the matched filter simply corresponds to the expected source shape and size, but correlated noise introduces deviations from this matching, as described in the following. The frequency width of a template approximately matches the line width that it selects, because the noise in different channels is uncorrelated.
This implies that the peak height corresponds to the total SNR of the candidate. For the 3D case, in particular for spatially extended templates, we need to account for the fact that the synthesized beam size results in small scale spatial noise correlations. While the calculation for the noise in the smoothed cube is close to the measured values (see Appendix B), we decide to measure the noise in the convolved cubes directly from the standard deviation of pixel values. Since our dataset is mostly free of signal, we estimate the noise in each Matched-Filtered cube in the COSMOS field simply by taking the standard deviation of the whole cube, and normalize by dividing each pixel by this value. In the case of the GOODS-N mosaic though, the beam size is not uniform across the mosaic, even after smoothing.
While both of these algorithms perform equally well in recovering all of our top line candidates, the simple 3D peak identification algorithm implemented in MF3D simultaneously utilizes the full 3D information. Based on all these considerations, we find that the best use of Duchamp in our data is achieved by manually adopting different frequency-width smoothing templates and combining the resulting signal-to-noise ratios, to select unresolved line candidates of different velocity widths. This procedure directly mimics our MF3D method, and therefore we do not adopt Duchamp for the COLDz survey data.
The reason why smoothing DnC data seems to necessarily produce slightly different beams than Dconfiguration data can be appreciated from a look at the Fourier Transform of a typical image from these pointings, effectively their uv coverage, in Fig. 11. Smoothing multiplies the uv-plane by a tapering Gaussian of the appropriate size, calculated from the size of the starting beam size and the target beam size. However, the Dconfiguration uv coverage, does not look the same as a Gaussian-tapered DnC-configuration uv coverage, hence the final beam is always going to be an imperfect match (unless both datasets are smoothed to a very large beam, at which point the initial shape of either uv-coverage does not matter).
We calibrated the data using casa v. 4. 5 using the VLA pipeline and minor manual flagging, and we imaged the visibilities using natural weighting. The data cube was produced with 1 MHz channels, corresponding to ∼13 km s -1 , which is small compared to the expected linewidth. The data cube has a beam size of 4. 4 ×3. 4 , and an rms noise of ∼0. 2 mJy beam -1 in 1 MHz-wide channels. We did not detect the lower frequency line (Fig. 13 ), and the new observations in pointing GN57 at the same frequency do not show any evidence for absorption at the same position and frequency (Fig. 12 ). We therefore rule out the presence of an absorption line, and we conclude that the original feature was simply due to noise. By excluding the possibility that the most significant negative feature may correspond to real absorption we strengthen our confidence in the assumption that all (or at least most) negative line features are due to noise, which is crucial for the purity assessment of our positive line candidates.