dataesr/scientific-paragraphs-categorization huggingface dataset:
This kind of murmur is commonly observed in ventricular septal defect pathologies  [46]. Holosystolic murmur waves were detected, for example in the recordings of the patient with the identifier 49628. Early-systolic murmurs are also common in children, these murmurs happen immediately at the beginning of each systolic period and disappear shortly before the mid-systolic period. Usually such murmurs are innocent  [47]. Early-systolic murmur waves were observed, for example in the recordings of the patient with the identifier 49691. Note that in the provided dataset, the murmur waves can be easily extracted by using the segmentation data (S1 and S2 wave locations) provided for each heart beat. The majority of the detected murmurs have a Plateau shape, i. e. , their intensities are approximately constant over time.
Murmurs with such shapes can be associated to ventricular septal defect, a very common pathology in our database  [46]. Murmur waves with a Plateau shape were detected, for example in the recordings of the patient with the identifier 50159. Note that the diamond-shaped murmurs (18. 5%) are usually more prevalent within a pediatric population than murmurs with plateau shape (59. 8%)  [48]. The large amount of plateau murmurs observed in the dataset might be explained by the fact that digital filters can attenuate or modify the murmur shape. Murmur waves with a Diamond shape were observed, for example in the recordings of the patient with the identifier 50724. Some innocent murmurs are "musical" in quality (1. 3%, which is extremely a rare event)  [49]. A "harsh" murmur is described by a high-velocity blood flow from a higher to a lower pressure gradient  [50].
Regarding systolic murmurs, the Levine scale is a numeric score used to characterize the intensity or the loudness of a murmur  [40]. In the presented dataset, 27% of the systolic murmurs and 10% of the diastolic murmurs were grade III or greater, see Table  V. Murmur waves greater or equal than grade III were detected, for example in the patient with the identifier 50308. The analysis of the pitch is another important variable. The higher the pitch, the higher is the pressure gradient between the heart chambers. For example, aortic stenosis has a higher pitched murmur than a mitral stenosis. Furthermore, and in agreement with past observations, murmurs generated from a ventricular septal defect have usually a low pitch  [46]. We should note however that pitch analysis from auscultation is not trivial or easy to describe; since filtering effects on a listener's perception are not completely characterizable.
The location where the systolic, diastolic or systolicdiastolic murmurs were detected with the highest intensity is also an important feature to analyze. A damaged valve usually generates a murmur louder in its corresponding auscultation area. This is mainly due to the proximity and vicinity between the auscultation point and the damaged valve. For example, a murmur caused by aortic stenosis is often best heard at the upper sternal border, and usually on the right side  [23]. In this dataset, most of the murmurs are detected with a highest intensity in the pulmonary point, see Table  V. Note that in infants and children (up to three years old), the pulmonary and tricuspid spots partially overlap and, in most cases, a single recording auscultation location is used instead  [54]. Consequently, heart sounds collected from these locations in children can be very similar to each other.
Therefore it is not surprising that the pulmonary and tricuspid are the best auscultation locations to detect cardiac murmurs in our dataset. For example, the patient with the identifier 50052, murmur waves were detected more intensively in the aortic spot. The observed murmurs do radiate and spread homogeneously through the thorax (cf. Table  V ). Thus easily detected (almost equally) in many auscultation locations. As a final remark, the CirCor DigiScope dataset is by far the largest publicly available heart sound dataset (5282 recordings), including recordings collected from multiple auscultation locations on the body. Moreover, the majority of the heart sounds (215,780 in total) were manually segmented and their quality was assessed by two independent cardiac physiologists. Furthermore, the study resulted in a very detailed murmur characterization and classification database (including timing, pitch, grading, shape, quality, auscultation location, etc.
VII. CONCLUSIONS The CirCor DigiScope dataset represents a unique cohort from a pediatric population, and a pregnant population, with significant congenital and acquired cardiac diseases. The age distribution is also homogeneously distributed in our dataset, thus potentially paving the way to the design of robust decision support systems for different target populations, from neonates to adults. Given the rich annotations and characterizations provided with CirCor DigiScope dataset, the current work can be leveraged in various ways, including the design of multichannel systems for multi-site PCG analysis, detection and classification of heart murmurs; and the automatic generation of murmur reports. For future work, we intend to make a comparative study, concerning the segmentation and classification of heart sound signals, in the CirCor DigiScope dataset.
SUMMARY OF THE CURRENTLY AVAILABLE OPEN ACCESS DATASETS. NPCA : NORMAL AND PATHOLOGICAL CHILDREN AND ADULTS; NPC: NORMAL AND PATHOLOGICAL CHILDREN; FET : FETUS AND PREGNANT; NA : NORMAL ADULTS. FH: FUNDAMENTAL HEART SOUNDS; NPFM: NORMAL AND PATHOLOGICAL FETAL AND MATERNAL. Oliveira et al. released a pediatric dataset composed of 29 heart sounds from 29 patients, ranging in age from six months to 17 years old. The recordings have a minimum, average, and maximum duration of approximately 2, 8, and 20 seconds, respectively. Heart sounds have been collected in Real Hospital Português (Recife, Brasil) using a Littmann 3200 stethoscope embedded with the DigiScope Collector.
VIII. ACKNOWLEDGMENT This work is a result of the Project  DigiScope2  ( POCI-01-0145-FEDER-029200-PTDC/CCI-COM/29200/2017 ) funded by  Fundo Europeu de Desenvolvimento Regional (FEDER) , through  Programa Operacional Competitividade e Internacionalização (POCI) , and by national funds, through  Fundação para a Ciência e Tecnologia (FCT). This work is also financed by  National Funds through the Portuguese funding agency, FCT -Fundação para a Ciência e a Tecnologia , within project  UIDB/50014/2020. F. Renna also acknowledges national funds through  FCT -Fundação para a Ciência e a Tecnologia, I. P. , under the  Scientific Employment Stimulus-Individual Call -CEECIND/01970/2017. A. Elola  receives financial support by the  Spanish Ministerio de Ciencia, Innovación y Universidades  through grant  RTI2018-101475-BI00 , jointly with the  Fondo Europeo de Desarrollo Regional (FEDER) , and by the  Basque Government  through grant  IT1229-19.
Acknowledgements We thank members of  RL lab  for critical reading of the manuscript. AV is supported by a PhD grant from the  Ecole Doctorale Complexité du Vivant  ( Sorbonne University ) and from an extension grant of  La Ligue contre le Cancer  ( IP/SC-17130 ), work in RL lab is supported by the  Institut Pasteur  ( G5  starting package), the  ERC starting grant CoSpaDD (Competition for Space in Development and Disease , grant number  758457 ), the  Cercle FSER  and the  CNRS  ( UMR 3738 ).
• There are new data on people's behaviour coming from their searches on search engines, online purchasing activities, and reading and entertainment habits  (Bello-Orgaz et al. , 2016; Renner et al. , 2020). Moreover, the format of data collected is often unconventional. While statistics has been developed to deal with figures, most of the data available today record texts, images, and videos, only eventually transformed into binary figures by the process of digitization. These types of data convey new content of a paramount importance for the social scientist since it allows to analyse information about ideas, opinions, and feelings  (Ambrosino et al. , 2018; Fontana et al. , 2019). Thus, different from statistics, which evolved over the last century in time of data scarcity, the present state of the art in the use of data leverages precisely on the vast size of datasets in terms of number of observations, of their attributes, and of different data formats  (Nuccio et al.
, 2020). As a consequence, newly collected data is increasingly stored in the same location, and there is a constant effort to link and merge existing datasets in data warehouses or data lakes. The traditional solution in data science for data storage is a data warehouse, in which data is extracted, transformed, and loaded, while more recently many organizations are opting for a data lake solution, which stores heterogeneously structured raw data from various sources  (Ravat & Zhao, 2019). A concurrent and partly connected phenomenon is the widespread adoption of big data analytics as a service (BDaaS), that is, when firms and institutions rely on cloud services on online platforms for the storage and analysis of data  (Aldinucci et al. , 2018). As a result, there is an increasing presence of very large online databases.
This present situation raises the following challenges to CSS. • The collection, storage, and maintenance of vast datasets create competitive advantages for the private sectors, but it is a very costly process. For these reasons, firms are not always willing to share their data with third parties involved in research-or data-driven policy making. Moreover, even in the presence of an open approach to data sharing, privacy laws do not always allow it without the explicit authorization of subjects providing the data, as in the case of the European Union GDPR 2   (Peloquin et al. , 2020; Suman & Pierce, 2018). Public organizations are increasingly digitized and becoming an important hub of data collection of fine-grained data. However, even in the presence of large investments, they often lack adequate human resource and organization capabilities for both the deployment of data warehouse and data lakes and their accessibility for research purposes.
• The capacity for investing in data structure and the ability to collect data are very skewed, with few large players owning a tremendous amount of data. Alphabet's yearly investment in production equipment, facilities, and data centres has been around 10 billion dollars for the last 5 years for the maintenance of about 15 exabytes of data  (Nuccio & Guerzoni, 2019). This high concentration allows a small handful of players to exploit data at a scale incomparable even with the scientific community. As a result, scientific institutions need to rely on partnership with these private players to effectively conduct research. • The storage of vast amounts also raises issues in cybersecurity since dataset can become a target for unlawful activities due to the monetary value of detailed and sensitive information.
In the words of most philosophers of science, classification is a mean to "bring related items together"  (Wynar et al. , 1985, p. 317) , "putting together like things"  (Richardson, 1935) ;  (Svenonius, 2000, p. 10) , and "putting together things that are alike"  (Vickery, 1975, p. 1) (see  Mai, 2011  for a review). Of course, the antecedent of this approach dates back in the Aristotelian positive approach to science, which describes and compares vis-á-vis Plato's normative approach  (Reale, 1985). More recently, the availability of large dataset made a qualitative approach to the creation of taxonomic possible only at the expense of a sharp a priori reduction of the information in data. However, at the same time, algorithms and computational power allow for an automatic elaboration of the information with the purpose of creating a taxonomy.
This approach is known as pattern recognition, unsupervised machine learning, or clustering and has been introduced in science by the anthropologists  Driver and Kroeber (1932)  and the psychologists  Zubin (1938)  and  Tyron (1939). Typically, unsupervised algorithms are fed by rich datasets in terms of both variables and observations and require as main output the number of groups to be identified from the researcher. On this basis, as output, they provide a classification which minimize within-group variation and maximize between-groups variation, usually captured by some measures of distance in the n-dimensional space of the n variables. Although among these methods in social science the use of K-means algorithm  MacQueen et al. (1967)  is the most widespread, it has some weaknesses such as a possible dependence by initial condition and the risk of lock-in in local optima.
The Way Forward Data and algorithms applied to CSS can heavily impact upon the way we conceive the process of policy generation. However, the adoption of such tools needs preceding efforts by the policy itself, mainly in the areas of data as an input. The ability of the public infrastructure to gather and store data for many sources calls for investment in technology, human capital, and a legislation that find a fine balance between citizens' right for privacy and a flexible use of the data. The storage and the computational power of large amount of data should not rely on foreign service providers since data should be subject to European regulation. Therefore, policies within the European Data Infrastructure such as the European Open Science Cloud are welcome as well as the high prioritization of technological infrastructure in the European Regional Development Fund.
58 However, rather than blaming political correctness, underlining the role that apartheid and its socioeconomic consequences played in the differential spread of HIV virus could have explained more adequately the approach undertaken. 58 Lastly and briefly, other researchers are unfortunately reluctant to follow recommendations for the simple reason that it would require a revision of previous results, or a renunciation of the use of old databases built on OMB categories or on vague continental designations. 113  conclusion, practices regarding the labeling of subpopulations do not exhibit a level of rigor one would expect in biomedical research. Attempts of resolving these issues are not facilitated by the wide range of opinions among researchers, and, most of all, the quandary surrounding this topic.
In the same manner, the harmonization of practices between different biobanks will facilitate the sharing and comparative analyses of such samples and foster regional and international collaborations between researchers. This harmonization includes the data aspects, as associated data would also need to be shared and/or be amenable to comparisons. In addressing the need of biobank harmonization, international agencies (e. g. , the   [4] , the OECD Guidance for Human Genetic Research Databases (2006)  [5] , the IARC Common Minimum Technical Standards and Protocols for Biobanks Dedicated to Cancer Research (2017) (IARC CMTS)  [6] , and the ISO standard 20387:2018-Biotechnology-Biobanking-Gen eral Requirements for Biobanking (ISO20387)  [7]. While these documents do not specifically focus on infectious disease, rather the general practice of biobanking, their implementation anticipates increasing the level of professionalism in biobanking  [8].
A major leap forward can be achieved if challenges relating to staff training and retention, funding and scale (that were temporarily addressed for the needs of the COVID-19 pandemic response) can be re-considered post-pandemic, as part of the building back better healthcare agenda  [21]. However, this would necessitate the synergistic efforts of multiple stakeholders-as has been demonstrated during the mobilization of resources and efforts for vertical programmes relating to Human Immunodeficiency Virus (HIV), Tuberculosis (TB), and malaria, as well as regional translational infrastructures such as the European Research Infrastructure on Highly Pathogenic Agents (ERINHA), the European Virus Archive -Global (EVAg), the European COVID-19 Data Platform, and others. Such plans require a good understanding of the healthcare challenges, as well as the harmonization of biobanking practices, so that any contribution to downstream research can be inclusive of multiple facets (from diagnostic microbiology to business models).
Different regulatory levels are accessed by large-scale data on mRNA concentrations (the transcriptome), protein concentrations (the proteome), metabolite pools (the metabolome) and dynamic processes such as metabolite fluxes. Very few metabolic models include information on mRNA degradation rates. The fact that mRNA degradation is rarely considered in systems biology impedes rationally designed engineering strategies based on regulating mRNA stability. This is at least partially explained by the scarcity of large-scale mRNA stability datasets in the literature. Rates of mRNA degradation are generally determined by modelling the exponential decay of the mRNA concentration after transcription is arrested by adding rifampicin  (Laguerre et al. , 2018). Even for E. coli, which is one of the best studied model bacteria, large-scale mRNA stability data are only available for a very limited number of growth conditions  (Bernstein et al.
Integration of mRNA degradation into systems biology approaches Systems biology is a powerful means to address the complex crosstalk between mRNA decay and other cellular processes. In this context, mathematical modelling has long been a companion to experimental studies of mRNA degradation, whether to extract decay rates from data Fig. 2. Genes in the central carbon metabolism whose expression is regulated by mRNA degradation. The 51 genes shown in the figure have their expression regulated by mRNA degradation; they represent around 20% of all the genes involved in carbohydrate transport and metabolism in E. coli (according to the database of Clusters of Orthologous Groups of proteins). The functions of the genes are described in the text. or to help integrate and analyze new experimental findings.
, 2012; O'Brien et al. , 2013; Lloyd et al. , 2018). These so-called ME-Models, already available for E. coli  (O'Brien et al. , 2013; O'Brien and Palsson, 2015; Salvy and Hatzimanikatis, 2020) , have increased predictive capacity and accuracy. Although they come at a higher price in terms of simulation difficulty and conceptual understanding  (O'Brien and Palsson, 2015) , the possibility of integrating omics datasetsincluding mRNA degradation rate constants [e. g. (Salvy and Hatzimanikatis, 2020) ] -should facilitate exploration of the crosstalk between mRNA decay, metabolism and the other gene expression processes. The tight coupling of mRNA degradation to cell processes thus poses a challenge to modellers in systems biology. There is no ideal model but clearly, it should be tailored to best exploit the available experimental data and provide information on the crosstalk studied.
Funding: A. V. V. received support from the  National Institutes of Allergy and Infectious Diseases  project number  AI136799 ; I. B. and G. P. received support from the  French national research agency ANR  (Mistec  ANR-17CE11-0028 ). H. V. received support from the  National Health and Medical Research Council of Australia  (grant number  GN 1147538 ).
The utility of establishing national and regional genomics networks has been demonstrated over the course of the COVID-19 pandemic through partnerships established between in-country laboratories and their respective Ministries of Health and policy makers, and regional genomics reference laboratories. For example, through partnerships between in country laboratories and the Microbiological Diagnostic Unit (MDU) public health and genomics reference laboratory at the Doherty Institute, SARS-CoV-2 sequence data from Timor Leste, Papua New Guinea and Fiji has been generated, reported and uploaded to a global, open access database for SARS-CoV-2 genomic surveillance, GISAID. 11 This work has also contributed to the development of an established framework for access to WGS and planning for genomics implementation across Fiji, Timor-Leste and Papua New Guinea.
In recent years, an increasing number of studies have focused on immunotherapy. The basis of immunotherapy is to overcome the mechanisms involved in immune tolerance to tumor self-antigens and to block the immunosuppressive response that occurs in the tumor microenvironment. This process is primarily driven by the inactivation and depletion of T cells via the activation of immune checkpoint inhibitors (ICIs) on the surface of T cells, which prevent them from recognizing tumor neoantigens. Current therapies target the PD-1 receptor (Programmed cell Death protein 1) and its ligand PD-L1 (Programmed cell Death protein Ligand 1), and CTLA-4 (Cytotoxic T lymphocyte Antigen 4). In the exploratory NICHE study (ClinicalTrials. gov: NCT03026140), patients with early-stage MSS or MSI CRC and neoadjuvant treatment with a single dose of anti-CTLA-4 (ipilimumab) and two doses of anti-PD1 (nivolumab) led to 100% and 27% response in MSI and MMS tumors, respectively  [27].
CXCL12 as a Prognostic Factor Clinically, there are divergent viewpoints on the prognostic value of CXCL12 expression level. High expression is significantly associated with high tumor stage, lymphatic invasion, venous invasion, lymph node and distant metastases, and decreased survival  [93, 96, 97]. Likewise, other studies suggest an association between CXCL12/CXCR4 expression and the induction of adenomas, carcinomas, and the development of metastases  [94]. Transcriptomic analysis of a cohort of 49 CRCs and RNA-Seq data from TCGA for 375 CRCs indicate that increased CXCR4/CXCR7+CXCL12 signature expression is the only independent prognostic marker for the presence/occurrence of metastasis and decreased overall survival in both datasets  [119]. In contrast, in two cohorts of 290 and 306 patients with stage III CRC, high cytoplasmic expression of CXCL12, assessed by in situ hybridization and immunohistochemistry, is associated with a better 5-year event-free survival  [120].
CXCR4 as a Prognostic Factor Regarding the prognostic implication of CXCR4, the literature agrees that high CXCR4 expression in CRC patients is unfavorable, as it correlates with advanced tumor stage and increased risk of recurrence and distant metastasis  [96, 121, 122]. Several meta-analyses conclude that there is a significant association between high CXCR4 expression and poor overall survival  [80, 121, [129] [130] [131]. In a similar way, a recent study indicated a particularly poor prognosis for patients having CRCs jointly and strongly express CXCR4 and VEGF (Vascular Endothelial Growth Factor) in more than 50% of cells, and this combination of high expression is a strong and independent predictor of early distant relapse  [132]. In another cohort, the CXCR4+CXCR7+CXCL12-β+ signature stratifies patients with risk of metastasis and in a TCGA dataset (n = 375), this signature predicts the presence of metastasis and overall survival  [119].
Regulation of CXCR7 Expression While the literature provides numerous studies regarding the mechanisms of regulation of CXCR4 expression in CRC, much less data are available for CXCR7. Evidence for an impact of hypoxia and the transcription factors HIF-1 and -2 exists in other cell types, such as in bone marrow-derived mesenchymal stem cells where the PI3K/Akt-HIF-1α-CXCR4/CXCR7 pathway is essential for cell migration, adhesion, and survival  [168]  or in glioblastoma cells  [191]. The only study published to date in CRC is a work by our team that showed that in human colonic cells, hypoxia or HIF-1α silencing does not alter the expression level of CXCR7  [82]. Gene expression can be regulated by transcription factors such as the HIC1 (Hypermethylated in Cancer 1), which is hypermethylated in many tumors including CRC  [192, 193] , and inactivation following hypermethylation is thought to be a tumorigenesis-triggering event  [194].