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  1. Article: Towards a fundamental safe theory of composite Higgs and dark matter.

    Cacciapaglia, Giacomo / Ma, Teng / Vatani, Shahram / Wu, Yongcheng

    The European physical journal. C, Particles and fields

    2020  Volume 80, Issue 11, Page(s) 1088

    Abstract: We present a novel paradigm that allows to define a composite theory at the electroweak scale that is well defined all the way up to any energy by means of safety in the UV. The theory flows from a complete UV fixed point to an IR fixed point for the ... ...

    Abstract We present a novel paradigm that allows to define a composite theory at the electroweak scale that is well defined all the way up to any energy by means of safety in the UV. The theory flows from a complete UV fixed point to an IR fixed point for the strong dynamics (which gives the desired walking) before generating a mass gap at the TeV scale. We discuss two models featuring a composite Higgs, Dark Matter and partial compositeness for all SM fermions. The UV theories can also be embedded in a Pati-Salam partial unification, thus removing the instability generated by the
    Language English
    Publishing date 2020-11-24
    Publishing country France
    Document type Journal Article
    ZDB-ID 1459069-4
    ISSN 1434-6052 ; 1434-6044
    ISSN (online) 1434-6052
    ISSN 1434-6044
    DOI 10.1140/epjc/s10052-020-08648-7
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Epidemiological theory of virus variants.

    Cacciapaglia, Giacomo / Cot, Corentin / de Hoffer, Adele / Hohenegger, Stefan / Sannino, Francesco / Vatani, Shahram

    Physica A

    2022  Volume 596, Page(s) 127071

    Abstract: We propose a physics-inspired mathematical model underlying the temporal evolution of competing virus variants that relies on the existence of (quasi) fixed points capturing the large time scale invariance of the dynamics. To motivate our result we first ...

    Abstract We propose a physics-inspired mathematical model underlying the temporal evolution of competing virus variants that relies on the existence of (quasi) fixed points capturing the large time scale invariance of the dynamics. To motivate our result we first modify the time-honoured compartmental models of the SIR type to account for the existence of competing variants and then show how their evolution can be naturally re-phrased in terms of flow equations ending at quasi fixed points. As the natural next step we employ (near) scale invariance to organise the time evolution of the competing variants within the effective description of the
    Language English
    Publishing date 2022-02-16
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1466577-3
    ISSN 1873-2119 ; 0378-4371
    ISSN (online) 1873-2119
    ISSN 0378-4371
    DOI 10.1016/j.physa.2022.127071
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  3. Book ; Online: The field theoretical ABC of epidemic dynamics

    Cacciapaglia, Giacomo / Cot, Corentin / Della Morte, Michele / Hohenegger, Stefan / Sannino, Francesco / Vatani, Shahram

    2021  

    Abstract: Infectious diseases are a threat for human health with tremendous impact on our society at large. The recent COVID-19 pandemic, caused by the SARS-CoV-2, is the latest example of a highly infectious disease ravaging the world, since late 2019. It is ... ...

    Abstract Infectious diseases are a threat for human health with tremendous impact on our society at large. The recent COVID-19 pandemic, caused by the SARS-CoV-2, is the latest example of a highly infectious disease ravaging the world, since late 2019. It is therefore imperative to develop efficient mathematical models, able to substantially curb the damages of a pandemic by unveiling disease spreading dynamics and symmetries. This will help inform (non)-pharmaceutical prevention strategies. For the reasons above we wrote this report that goes at the heart of mathematical modelling of infectious disease diffusion by simultaneously investigating the underlying microscopic dynamics in terms of percolation models, effective description via compartmental models and the employment of temporal symmetries naturally encoded in the mathematical language of critical phenomena. Our report reviews these approaches and determines their common denominators, relevant for theoretical epidemiology and its link to important concepts in theoretical physics. We show that the different frameworks exhibit common features such as criticality and self-similarity under time rescaling. These features are naturally encoded within the unifying field theoretical approach. The latter leads to an efficient description of the time evolution of the disease via a framework in which (near) time-dilation invariance is explicitly realised. As important test of the relevance of symmetries we show how to mathematically account for observed phenomena such as multi-wave dynamics. The models presented here are of immediate relevance for different realms of scientific enquiry from medical applications to the understanding of human behaviour. Our review offers novel perspectives on how to model, capture, organise and understand epidemiological data and disease dynamics for modelling real-world phenomena.

    Comment: 57 pages, 40 figures. Article expanded into a review. Prepared for submission to Physics Reports
    Keywords Quantitative Biology - Populations and Evolution ; High Energy Physics - Lattice ; High Energy Physics - Theory ; Physics - Physics and Society
    Subject code 190
    Publishing date 2021-01-25
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Variant-driven early warning via unsupervised machine learning analysis of spike protein mutations for COVID-19.

    de Hoffer, Adele / Vatani, Shahram / Cot, Corentin / Cacciapaglia, Giacomo / Chiusano, Maria Luisa / Cimarelli, Andrea / Conventi, Francesco / Giannini, Antonio / Hohenegger, Stefan / Sannino, Francesco

    Scientific reports

    2022  Volume 12, Issue 1, Page(s) 9275

    Abstract: Never before such a vast amount of data, including genome sequencing, has been collected for any viral pandemic than for the current case of COVID-19. This offers the possibility to trace the virus evolution and to assess the role mutations play in its ... ...

    Abstract Never before such a vast amount of data, including genome sequencing, has been collected for any viral pandemic than for the current case of COVID-19. This offers the possibility to trace the virus evolution and to assess the role mutations play in its spread within the population, in real time. To this end, we focused on the Spike protein for its central role in mediating viral outbreak and replication in host cells. Employing the Levenshtein distance on the Spike protein sequences, we designed a machine learning algorithm yielding a temporal clustering of the available dataset. From this, we were able to identify and define emerging persistent variants that are in agreement with known evidences. Our novel algorithm allowed us to define persistent variants as chains that remain stable over time and to highlight emerging variants of epidemiological interest as branching events that occur over time. Hence, we determined the relationship and temporal connection between variants of interest and the ensuing passage to dominance of the current variants of concern. Remarkably, the analysis and the relevant tools introduced in our work serve as an early warning for the emergence of new persistent variants once the associated cluster reaches 1% of the time-binned sequence data. We validated our approach and its effectiveness on the onset of the Alpha variant of concern. We further predict that the recently identified lineage AY.4.2 ('Delta plus') is causing a new emerging variant. Comparing our findings with the epidemiological data we demonstrated that each new wave is dominated by a new emerging variant, thus confirming the hypothesis of the existence of a strong correlation between the birth of variants and the pandemic multi-wave temporal pattern. The above allows us to introduce the epidemiology of variants that we described via the Mutation epidemiological Renormalisation Group framework.
    MeSH term(s) COVID-19/epidemiology ; COVID-19/genetics ; Humans ; Mutation ; SARS-CoV-2/genetics ; Spike Glycoprotein, Coronavirus/genetics ; Unsupervised Machine Learning
    Chemical Substances Spike Glycoprotein, Coronavirus ; spike protein, SARS-CoV-2
    Language English
    Publishing date 2022-06-03
    Publishing country England
    Document type Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-022-12442-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Variant-driven multi-wave pattern of COVID-19 via Machine Learning clustering of spike protein mutations

    de Hoffer, Adele / Vatani, Shahram / Cot, Corentin / Cacciapaglia, Giacomo / Conventi, Francesco / Giannini, Antonio / Hohenegger, Stefan / Sannino, Francesco

    medRxiv

    Abstract: Never before such a vast amount of data has been collected for any viral pandemic than for the current case of COVID-19. This offers the possibility to answer a number of highly relevant questions, regarding the evolution of the virus and the role ... ...

    Abstract Never before such a vast amount of data has been collected for any viral pandemic than for the current case of COVID-19. This offers the possibility to answer a number of highly relevant questions, regarding the evolution of the virus and the role mutations play in its spread among the population. We focus on spike proteins, as they bear the main responsibility for the effectiveness of the virus diffusion by controlling the interactions with the host cells. Using the available temporal structure of the sequencing data for the SARS-CoV-2 spike protein in the UK, we demonstrate that every wave of the pandemic is dominated by a different variant. Consequently, the time evolution of each variant follows a temporal structure encoded in the epidemiological Renormalisation Group approach to compartmental models. Machine learning is the tool of choice to determine the variants at play, independent of (but complementary to) the virological classification. Our Machine Learning algorithm on spike protein sequencing provides a simple and unbiased way to identify, classify and track relevant virus variants without any prior knowledge of their characteristics. Hence, we propose a new tool that can help preventing and forecasting the emergence of new waves, and that can be used by decision makers to define short and long term strategies to curb the current COVID-19 pandemic or future ones.
    Keywords covid19
    Language English
    Publishing date 2021-07-24
    Publisher Cold Spring Harbor Laboratory Press
    Document type Article ; Online
    DOI 10.1101/2021.07.22.21260952
    Database COVID19

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  6. Book ; Online: Variant-driven multi-wave pattern of COVID-19 via a Machine Learning analysis of spike protein mutations

    de Hoffer, Adele / Vatani, Shahram / Cot, Corentin / Cacciapaglia, Giacomo / Chiusano, Maria Luisa / Cimarelli, Andrea / Conventi, Francesco / Giannini, Antonio / Hohenegger, Stefan / Sannino, Francesco

    2021  

    Abstract: Applying a ML approach to the temporal variability of the Spike protein sequence enables us to identify, classify and track emerging virus variants. Our analysis is unbiased, in the sense that it does not require any prior knowledge of the variant ... ...

    Abstract Applying a ML approach to the temporal variability of the Spike protein sequence enables us to identify, classify and track emerging virus variants. Our analysis is unbiased, in the sense that it does not require any prior knowledge of the variant characteristics, and our results are validated by other informed methods that define variants based on the complete genome. Furthermore, correlating persistent variants of our approach to epidemiological data, we discover that each new wave of the COVID-19 pandemic is driven and dominated by a new emerging variant. Our results are therefore indispensable for further studies on the evolution of SARS-CoV-2 and the prediction of evolutionary patterns that determine current and future mutations of the Spike proteins, as well as their diversification and persistence during the viral spread. Moreover, our ML algorithm works as an efficient early warning system for the emergence of new persistent variants that may pose a threat of triggering a new wave of COVID-19. Capable of a timely identification of potential new epidemiological threats when the variant only represents 1% of the new sequences, our ML strategy is a crucial tool for decision makers to define short and long term strategies to curb future outbreaks. The same methodology can be applied to other viral diseases, influenza included, if sufficient sequencing data is available.

    Comment: 16 pages, 6 figures, supplementary material in a separate file. Analysis extended with early warning performance and spike protein diversification
    Keywords Quantitative Biology - Genomics
    Subject code 006
    Publishing date 2021-07-21
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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