dataesr/scientific-paragraphs-categorization huggingface dataset:
CPS Leak Monitoring Applications Nasir and Soong  [105]  proposed an RFID-based architecture for pipe leak monitoring. The application of the proposed system was demonstrated on a small laboratory test involving simple piping runs, however, the feasibility of the system on a real water distribution system is a major concern. Most water piping runs are installed underground, however, RFID system suffers from poor signal strength in underground environments. Kartakis et al. [106]  proposed a test for leak monitoring in water networks using integrated flow and pressure sensors. In Lang et al. [15] , a CPS framework for leak localization in multibranch pipelines was proposed. Several sensing devices arranged in a network mode were utilized to acquire data related to the pipe internal pressure, flow rate, and temperature.
Also, communication between the sensor network and a base station was provided via Wi-Fi, ZigBee, and 3G communication technologies. Due to the fact that the pipe internal pressure varies and is nonstationary, during data processing, a wavelet packet was employed to decompose and analyze the pressure signal for leak localization. The framework was simulated in MATLAB and Flowmaster software environments with some level of success regarding leak localization. However, a real-time application is a major concern. Elsewhere, several computational tools have been used to analyze the nonstationary pressure wave signals. Zhang et al. [80]  proposed a hybrid method employing independent component analysis and the support vector machine for analyzing pressure traces with relatively good accuracy. Also, in  [107] , an interactive self-organizing data analysis algorithm was utilized for the pressure traces analysis in the pipes.
Advanced and Smart Data Transmission and Analytical Solutions The sensing devices in CPSs for various water supply system applications generate huge continuous and heterogeneous datasets, which must be processed and managed efficiently. Most of the current data processing methodologies are computationally complex and not power intensive. Advanced and robust data analytics solutions should further be investigated. The data analytical solutions should also consider advance lossless data compression methods. These compression methods will reduce the size of huge datasets before transmission and allow them to be retrieved without loss of information.
From single targets to validated, causal networks In the diseasome, disease clusters are mechanistically defined by several genes and proteins forming a signaling network. 18 This has been most extensively hypothesized for three distinct networks for macular degeneration  18, 19  and cancer. 20 The validity of these networks is essential, because both the diagnostic and therapeutic strategies reside on it. Defining the causal signaling network is not trivial and not at all obvious. It is state-of-the-art to rely on highly curated signaling pathway databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) or WikiPathways, a collection of manually drawn pathway maps representing our apparent knowledge on molecular interactions, reactions and relation networks, or review articles. KEGG, however, shows 29 cyclic guanosine monophosphate (GMP) and 12 reactive oxygen pathways, none of which is comprehensive and all of which fail to cover a recently discovered functional and molecular link between the two,  21  uniting both, in fact, to one network.
In this context, the present review on systems medicine first introduces the intended outcomes and definitions. It discusses in the ''Basic science and data for network and systems medicine'' section the required technologies for multidimensional data generation, the current data availability, as well as the computa-tional tools for data integration and interpretation. In the ''Clinical Applications'' section, it illustrates its potentiality through some clinical applications. Finally, it discusses the current remaining issues and prospects of this large domain.
Big data availability and information systems The emergence of powerful approaches allowed large datasets to be produced and analyzed, in the perspective of developing decision-making tools for health management. One of the challenges is the security of personal and private health data. 55 Moreover, the generation of high volumes of big omics data, combined with the health care provider's high rate of data generation (also known as data velocity) constitute a critical challenge for supporting research and practical implementation of system medicine and tools. Securing personal and private health data is an additional crucial challenge for managing systems. Therefore, the future development of systems medicine requires advanced informatics tools for merging the different kinds of data to be shared among different communities.
64 ne potential way out of this dilemma is federated ML. However, many challenges are to be overcome until client-sided ML becomes ubiquitous  65  ; however, recent experiments on deep learning demonstrated that it can be made practical and that there are many intriguing opportunities. 66 Europe has dedicated research projects to this task, for example, FeatureCloud, but no applications for federated systems medicine (e. g. , for federated network enrichment, federated composite biomarker extraction, or federated mechanotyping) exist yet; however, it would be necessary to make systems medicine big-data-ready. From a Health care Services perspective, data availability is a sensitive matter. For example, in the United Kingdom, the National Health Services is financed by the government but health care customers' data are not centralized.
In contrast, in some countries, such as Denmark  67, 68  and Israel although the health care system is also financed and regulated by the government, health care customers' data are centralized. In Israel, these data are centralized by the Healthcare Management Organizations (HMO) whereas some of the data reside at hospitals and the Health care ministry. The data available to the HMO include, among others, socio-demographic data, and information on biological tests, clinical examinations, pharmacological treatments, and communication channels. These data have been continuously collected and stored at the health care customer level for the past 25 years. 69 In Israel, the health care data de facto fit the FAIR standards. 70, 71 andardization Large datasets are often generated at great cost, consuming significant time and resources.
Critical to realizing their full value is that we can quickly and easily deploy a diverse and well-developed set of software tools for analysis. This is best achieved when the datasets are made available following a common set of data standards used by a wide range of software tools. Conversely, the incentive for developing software tools is invariably stronger when a rich landscape of suitable data already exists that is easily accessible via data standards. Hence, data standards are vital to realizing the potential in large datasets. The adoption of Binary Alignment Map and Variant Call Format file formats, for example, has underpinned the explosive growth in the availability of genome data and the software tools for analysis. As we move into the era of systems medicine, data standards will be central to maximizing the value we derive from systems-level datasets.
Larger and more complex datasets require more sophisticated analysis and, as analysis grows in sophistication, it becomes increasingly challenging to reproduce. This is in part due to the network of software dependencies associated with the analytical tools and in part due to the array of design choices that form part of the analytical workflow. Hence, we not only need standards to optimize data availability but also need standards that make analyses reproducible and verifiable. Among the key current standards that will support the growth of systems medicine are the Systems Biology Graphical Notation (SBGN), a set of symbols and usage rules that have been developed through open community action, as tools for mapping out the network of molecular interactions between genes, proteins, and small molecules.
72 Although many mapping systems exist, SBGN's strength lies in its lack of ambiguity and its machine-parsable structure, which means that maps can be translated straight to mathematical models. Three different flavors of SBGN have been created with the Process Diagram, providing the most details and the highest level of parsability whereas the activity Flow and Entity Relationship provide greater levels of abstraction and lower levels of parsability. * Such models typically require great effort to develop  73, 74  and to facilitate their reuse, expansion, and refinement, they can be made available by using the SBGN-ML file format that captures the structure of the maps and encodes the biological meaning of the symbols. 75 It is based on coded use of plain text and therefore it can be edited not only in SBGN-ML compliant software but also manually in any text editor.
The mathematical model that facilitates simulation of the pathways can be encoded and disseminated by using the Systems Biology Markup Language (SBML). Similar to SBGN-ML, it makes use of a coded plain text file format that can be edited either by SBML compliant software or manually in any text editor. 76 However, SBML captures the system of Ordinary Differential Equations that describe the kinetics of all the interactions between genes, proteins, and small molecules in a machine-parsable form along with their structure and meaning. { Ensuring the reproducibility of simulations and computational experiments requires the adoption of the standards for the maps and models mentioned earlier as well as a further set of standards to describe how they were used. In particular, the minimum information about a simulation experiment (MIASE) standard requires users to (i) specify and make available the exact model used, (ii) specify exactly how the model is simulated, and (iii) specify how the outputs are calculated from the model.
Traditionally, integrative analysis techniques have focused on combining evidence derived from real data combined with public database knowledge. 90, 91 he field then moved on, from exploiting the combination of private and publicly available knowledge to accelerate drug discovery,  92  to combining multidimensional views in, for instance, gene mapping. 93 Method developers are only gradually pacing up with the vast amount of heterogeneous data sources that become available and with introducing the necessary complexities into the models. 94, 95 With omics data increasingly being collected on the same set of individuals, it becomes theoretically possible to connect different layers of cellular or molecular information (for instance in causal models  96  ), while combining analytics to available expert knowledge.
Integrative tools for Big Data ideally combine kernel theory (to bring in notions of nonlinearity), components theory (to reduce dimensionality), and graph theory (to handle dependencies and interactions in systems). From a practical point of view, it remains essen-tial to understand the minimum requirements that each analytic tool for omics integration should have for it to be able to distinguish ''noise'' from ''signal'' and to compensate for the intrinsic power deficits resulting from having relatively small numbers of individuals with huge numbers of omics measurements. The metabolomics community has recently published viewpoints and recommendations for the development of multi-omics integration in the context of systems biology. 97 ''Special Issues'' on omics integration (e. g. , Genin and Devoto)  94  highlight remaining challenges, including the integration of dependent and independent omics datasets in meta-analyses,  98  and the integration of omics with non-omics data.
99 nce omics data have been curated, Hamid et al. have identified three general roads to travel. 84 Either the data are fused before modeling (Fig. 2A ) or the representation of each omics data sources is altered to make it more digestible before deriving an integrative solution to the problem of interest (Fig. 2B ), or each omics dataset is modeled separately and results are integrated (Fig. 2C ). Although these stages of integration are often discussed in the framework of association analyses, they also apply to prediction and profiling (pattern recognition) contexts. Notably, most of the novel analytic approaches to integrate multiple omics dimensions do not concern analysis on fused data (Fig. 2A ). The main reason is that such analyses imply quite many information technology-infrastructure and computational challenges as well as analytic challenges in that any model is believed to be a too severe oversimplification of the rich information that the interrelated data potentially entails.
104, 105 lidation One critical step of modeling approaches is validation, as fitting a model to data does not prove that it will accurately capture the clinical outcome. In particular, the high dimensionality of datasets is a major challenge in data analysis, especially for model reliability, as it is prone to overfitting. Therefore, there is a special need to develop dedicated protocols for validation of integrative (systemic) analyses. This effort requires tools that enable simulating realistic and sufficiently complex data. Consequently, simulation-oriented approaches have been increasingly applied over the past 5 years. Different alternatives exist in terms of validation strategy: 1. In silico data generation: As an example, a multiomics data simulator for complex disease studies was developed and applied to evaluate multi-omics data analysis methods for disease classification.
Cancer pathways and personalized therapy Recent personalized therapeutic approaches in oncology target multiple pathways within a mechanistically defined cancer type by combining several drugs with the aim of curing or at least significantly improving survival and quality of life beyond current symptomatic or cytotoxic approaches. In this context, the increasing availability of pathway knowledge that is relevant for human systems modeling, for instance from databases including Signor  108  and Reactome,  109  provides quite extensive information for building cellular signaling networks that allow the analysis of cancer cell function. The conversion of such a cell fate decision Prior Knowledge Network to a Boolean model is in practice a relatively trivial task, starting with the use of the causal interaction information to generate the logical rules that mathematically define the interactions of the network as a whole.
An accurately designed logical model of a cell will follow these rules to arrive at a stable state in which the activities of the model components will quite accurately represent the activities of their biological counterparts in the cell that is represented. Logical models built using Signor data, complemented with some additional ad hoc literature curation, have allowed, for instance, the assembly of several versions of a colorectal cancer model that have quite significant predictive power in assessing the effect of combinations of targeted drugs 109,110,{ on cellular states, and they can be used to identify potential syner-gistic drugs that together are more effective for inhibiting cell proliferation than separately. The procedure to do this is as follows: A general logical model is configured to represent a specific cancer cell line using baseline biomarker data that inform the logical model about the activity states of Boolean network nodes (Active = 1, Non-Active = 0), and the resulting cell line-specific model can be used to filter out in silico the combinations that are least likely to display synergy.
This approach has been implemented, in 2015, on 309,460 patients with diabetes and 7 dominant profiles have been discovered and characterized to help health care decision makers to improve follow-up policies and tools. Personalized services focusing on patients' needs and preferences were implemented based on this analysis. 140, 141 ltogether, to increase the frequency of successful translational stories, the research enterprise needs to re-design research studies by considering the complexity and variability of human physiology, and by collecting high-dimensional datasets that will allow researchers to identify confounding variables and to stratify populations at early phases of biomarker discovery. As the ''omics'' term is expanding to wider systems, all of these have to be interrelated.
Along with this, there is also the interrelated difficulty to scale up to the whole organism tier, clearly necessary in the clinical practice, due to the intrinsic limits of the conclusions related to the narrow experimental/biological context (e. g. , gene regulatory networks acting in cellular processes, signaling pathways analysis, etc. , whose analyses often provide views that are too limited to be relevant in the clinic). Several issues occur when collecting supporting, comprehensible, and secured (another buzzword itself, nowadays) datasets in clinical settings. Indeed, the type, arrangement, and nature of medical and clinical data have their peculiar production methods, schemas, ontologies, standards, complexity, and access limits, which often conflict with the requirement and the complications to tie unambiguously such clinical data with the clinical samples.
This peculiarity of AI methods can often hamper or delay the deployment of predictive models, because humans simply do not understand, and thus trust them. 145 Relevant efforts are being made to overcome this issue by providing interpretable ML methodologies that are capable of balancing accuracy, human interpretability, and, last but not least, computational viability. 146, 147 s a final consideration, it is now clear that the structure of the health care system will have to adjust radically to be able to run with highly interdisciplinary crews, operating daily with multi-omics, multisource data, large-scale databases and storage facilities, complex analytical processes, and-clearly-effective managerial and organizational frameworks. Such practices call for tailored education programs and continuous, complimentary training for hospital personnel as well as for systemic scientists.
36, 148, 149 pplications: Improving patient engagement and treatment adherence. '' Å. F. wrote part of the article, specifically the ''Cancer pathways and personalized therapy. '' C. F. wrote part of the article, specifically the ''Why we need new in medicine'' and ''2D-, 3D drug printing. '' F. H. wrote part of the article, specifically the ''Definition and goals of network and systems medicine. '' N. H. wrote part of the article, specifically the ''Multidimensional omics data generation,'' ''Big data, data analytics, and information systems,'' and ''Improving patient engagement and treatment adherence. '' M. K. wrote part of the article, specifically the pathways and personalized therapy. '' E. P. -G. wrote part of the article, specifically the ''Abstract,'' ''Introduction,'' ''Multidimensional omics,'' ''FAIR,'' ''Embracing the challenge of data integration and validation,'' and ''SWOT Analysis.
'' T. R. wrote part of the article, specifically the ''Enlarging network and systems medicine applications: training. '' D. R. wrote and revised a part of the article, particularly the ''Enlarging network and systems medicine applications: training. '' J. S. wrote part of the article, specifically the ''SWOT Analysis. '' J. A. S. wrote part of the article, specifically the ''Definition and goals of network and systems medicine'' and ''Enlarging network and systems medicine applications: training. '' P. T. wrote part of the article, specifically the ''Concluding remarks and outlook. '' K. V. S. wrote part of the article, specifically the ''Embracing the challenge of data integration,'' ''Out-box of thinking,'' and ''Validation''. S. V. wrote part of the article, specifically the ''Enlarging network and systems medicine applications: training.
Funding Information This article is based on work from  Collaboration on Science and Technology Action OpenMultiMed  ( CA15120 ), supported by  Collaboration on Science and Technology (European Cooperation in Science and Technology). This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 777111  ( REPO-TRIAL ). This reflects only the author's view and the  European Commission  is not responsible for any use that may be made of the information it contains.
06 at 41,458,260 bp and the other at 47,397,550 bp with a maximum LRT value of 60. 9 (Fig. 3A ). Phased SNP data of BTA2 of FV-Polled1, its parents and its offspring were inspected and the parental and maternal haplotypes deduced. The region was inspected for recombination events and reduced to a confidence interval (CI) from 40,021,512 to 53,369,279 bp, which is secured by one recombinant polled offspring on the distal and two recombinant polled offspring on the proximal end of the CI. For this region on BTA2, all polled offspring carried the common segment of the paternal haplotype of FV-Polled1, while all horned offspring carried the maternal FV-Polled1 haplotype (Fig. 2 ). Therefore, the de novo mutation must be located on the paternal haplotype of FV-Polled1. It must be taken into account that high quality of haplotypes is secured for all polled offspring by genotyping of trios, i.
Identification of candidate de novo variants associated with polledness. To identify the de novo variant in the candidate region identified in the previous step, we sequenced the genome of FV-Polled1 with two different high throughput sequencing technologies: Illumina HiSeq paired-end sequencing and Prome-thION. Illumina paired-end sequencing generated about 397 million reads, while PromethION sequencing generated about 7. 6 million reads with a mean read length of about 7 kbp. Both sequencing approaches generated sequenced data with an average depth of about 15 ×. In the first step, variants were identified from Illumina sequences because PromethION long-read sequencing has a high error rate (~ 10%). In total, 27,453 variants were identified in the candidate region of ~ 13 Mbp on BTA2, using the algorithm implemented in samtools mpileup.
Del11 in ZEB2 induces a frameshift mutation. Bovine ZEB2 (ENSBTAT00000061551. 2) comprises 10 exons that encode a protein sequence (ENSBTAP00000053646. 2) of 1209 amino acids. Del11, which was identified as de novo and compatible with the inheritance of the polled condition, is located in exon 2 (not counting 5′ UTR). Annotation by VEP predicted that Del11 induces a translational frameshift (on the transcript predicted by Ensembl pipeline), which would alter the protein sequence from amino acid position 99 onwards. To better annotate ZEB2 gene in UMD3. 1 assembly and understand the precise consequences of the mutations, an independent annotation analysis was carried out. For this purpose, ZEB2 (ENSBTAT00000061551. 2) wild type cDNA was downloaded from Ensembl and translated into the expected protein using ExPASy.