State Aggregations in Markov Chains and Block Models of Networks.

We consider state-aggregation schemes for Markov chains from an information-theoretic perspective. Specifically, we consider aggregating the states of a Markov chain such that the mutual information of the aggregated states separated by T time steps is maximized. We show that for T=1 this recovers the maximum-likelihood estimator of the degree-corrected stochastic block model as a particular case, which enables us to explain certain features of the likelihood landscape of this generative network model from a dynamical lens. We further highlight how we can uncover coherent, long-range dynamical modules for which considering a timescale T≫1 is essential. We demonstrate our results using synthetic flows and real-world ocean currents, where we are able to recover the fundamental features of the surface currents of the oceans.

Mapping the Topography of a Protein Energy Landscape.

Protein energy landscapes are highly complex, yet the vast majority of states within them tend to be invisible to experimentalists. Here, using site-directed mutagenesis and exploiting the simplicity of tandem-repeat protein structures, we delineate a network of these states and the routes between them. We show that our target, gankyrin, a 226-residue 7-ankyrin-repeat protein, can access two alternative (un)folding pathways. We resolve intermediates as well as transition states, constituting a comprehensive series of snapshots that map early and late stages of the two pathways and show both to be polarized such that the repeat array progressively unravels from one end of the molecule or the other. Strikingly, we find that the protein folds via one pathway but unfolds via a different one. The origins of this behavior can be rationalized using the numerical results of a simple statistical mechanics model that allows us to visualize the equilibrium behavior as well as single-molecule folding/unfolding trajectories, thereby filling in the gaps that are not accessible to direct experimental observation. Our study highlights the complexity of repeat-protein folding arising from their symmetrical structures; at the same time, however, this structural simplicity enables us to dissect the complexity and thereby map the precise topography of the energy landscape in full breadth and remarkable detail. That we can recapitulate the key features of the folding mechanism by computational analysis of the native structure alone will help toward the ultimate goal of designed amino-acid sequences with made-to-measure folding mechanisms-the Holy Grail of protein folding.

MS/MS spectra interpretation as a statistical-mechanics problem.

We describe a new method for peptide sequencing based on the mapping of the interpretation of tandem mass spectra onto the analysis of the equilibrium distribution of a suitably defined physical model, whose variables describe the positions of the fragmentation sites along a discrete mass index. The model is governed by a potential energy function that, at present, we derive ad hoc from the distribution of peaks in a data set of experimental spectra. The statistical-physics perspective prompts for a consistent and unified approach to de novo and database-search methods, which is a distinctive feature of this approach over alternative ones: the characterization of the ground state of the model allows the de novo identification of the precursor peptide; the study of the thermodynamic variables as a function of the (fictitious) temperature gives insight on the quality of the prediction, while the probability profiles at nonzero temperature reveal, on one hand, which fragments are more reliably predicted. On the other hand, they can be used as a spectrum-adapted, a posteriori score for database search. Results obtained with two different test data sets reveal a performance similar to that of other de novo and database-search methods, which is reasonable, given the lack of an aggressive optimization of the energy function at this stage. An important feature of the method is that it is quite general and can be applied with different choices of the energy function: we discuss its possible improvements and generalizations.

Measuring dynamical systems on directed hypergraphs.

Networks and graphs provide a simple but effective model to a vast set of systems in which building blocks interact throughout pairwise interactions. Unfortunately, such models fail to describe all those systems in which building blocks interact at a higher order. Higher-order graphs provide us the right tools for the task, but introduce a higher computing complexity due to the interaction order. In this paper we analyze the interplay between the structure of a directed hypergraph and a linear dynamical system, a random walk, defined on it. How can one extend network measures, such as centrality or modularity, to this framework? Instead of redefining network measures through the hypergraph framework, with the consequent complexity boost, we will measure the dynamical system associated to it. This approach let us apply known measures to pairwise structures, such as the transition matrix, and determine a family of measures that are amenable to such a procedure.

Assessing the influence of French vaccine critics during the two first years of the COVID-19 pandemic.

When the threat of COVID-19 became widely acknowledged, many hoped that this pandemic would squash "the anti-vaccine movement". However, when vaccines started arriving in rich countries at the end of 2020, it appeared that vaccine hesitancy might be an issue even in the context of this major pandemic. Does it mean that the mobilization of vaccine-critical activists on social media is one of the main causes of this reticence to vaccinate against COVID-19? In this paper, we wish to contribute to current work on vaccine hesitancy during the COVID-19 pandemic by looking at one of the many mechanisms which can cause reticence towards vaccines: the capacity of vaccine-critical activists to influence a wider public on social media. We analyze the evolution of debates over the COVID-19 vaccine on the French Twittosphere, during two first years of the pandemic, with a particular attention to the spreading capacity of vaccine-critical websites. We address two main questions: 1) Did vaccine-critical contents gain ground during this period? 2) Who were the main actors in the diffusion of these contents? While debates over vaccines experienced a tremendous surge during this period, the share of vaccine-critical contents in these debates remains stable except for a limited number of short periods associated with specific events. Secondly, analyzing the community structure of the re-tweets hyper-graph, we reconstruct the mesoscale structure of the information flows, identifying and characterizing the major communities of users. We analyze their role in the information ecosystem: the largest right-wing community has a typical echo-chamber behavior collecting all the vaccine-critical tweets from outside and recirculating it inside the community. The smaller left-wing community is less permeable to vaccine-critical contents but, has a large capacity to spread it once adopted.

Data-driven identification of communities with high levels of tuberculosis infection in the Democratic Republic of Congo.

When access to diagnosis and treatment of tuberculosis is disrupted by poverty or unequal access to health services, marginalized communities not only endorse the burden of preventable deaths, but also suffer from the dramatic consequences of a disease which impacts one's ability to access education and minimal financial incomes. Unfortunately, these pockets are often left unrecognized in the flow of data collected in national tuberculosis reports, as localized hotspots are diluted in aggregated reports focusing on notified cases. Such system is therefore profoundly inadequate to identify these marginalized groups, which urgently require adapted interventions. We computed an estimated incidence-rate map for the South-Kivu province of the Democratic Republic of Congo, a province of 5.8 million inhabitants, leveraging available data including notified incidence, level of access to health care and exposition to identifiable risk factors. These estimations were validated in a prospective multi-centric study. We could demonstrate that combining different sources of openly-available data allows to precisely identify pockets of the population which endorses the biggest part of the burden of disease. We could precisely identify areas with a predicted annual incidence higher than 1%, a value three times higher than the national estimates. While hosting only 2.5% of the total population, we estimated that these areas were responsible for 23.5% of the actual tuberculosis cases of the province. The bacteriological results obtained from systematic screenings strongly correlated with the estimated incidence (r = 0.86), and much less with the incidence reported by epidemiological reports (r = 0.77), highlighting the inadequacy of these reports when used alone to guide disease control programs.

Analysis of the equilibrium and kinetics of the ankyrin repeat protein myotrophin.

We apply the Wako-Saito-Muñoz-Eaton model to the study of myotrophin, a small ankyrin repeat protein, whose folding equilibrium and kinetics have been recently characterized experimentally. The model, which is a native-centric with binary variables, provides a finer microscopic detail than the Ising model that has been recently applied to some different repeat proteins, while being still amenable for an exact solution. In partial agreement with the experiments, our results reveal a weakly three-state equilibrium and a two-state-like kinetics of the wild-type protein despite the presence of a nontrivial free-energy profile. These features appear to be related to a careful "design" of the free-energy landscape, so that mutations can alter this picture, stabilizing some intermediates and changing the position of the rate-limiting step. Also, the experimental findings of two alternative pathways, an N-terminal and a C-terminal one, are qualitatively confirmed, even if the variations in the rates upon the experimental mutations cannot be quantitatively reproduced. Interestingly, the folding and unfolding pathways appear to be different, even if closely related: a property that is not generally considered in the phenomenological interpretation of the experimental data.

Outbreak of multidrug-resistant tuberculosis in South Africa undetected by WHO-endorsed commercial tests: an observational study.

BACKGROUND: Global roll-out of rapid molecular assays is revolutionising the diagnosis of rifampicin resistance, predictive of multidrug-resistance, in tuberculosis. However, 30% of the multidrug-resistant (MDR) strains in an eSwatini study harboured the Ile491Phe mutation in the rpoB gene, which is associated with poor rifampicin-based treatment outcomes but is missed by commercial molecular assays or scored as susceptible by phenotypic drug-susceptibility testing deployed in South Africa. We evaluated the presence of Ile491Phe among South African tuberculosis isolates reported as isoniazid-monoresistant according to current national testing algorithms. METHODS: We screened records of 37 644 Mycobacterium tuberculosis positive cultures from four South African provinces, diagnosed at the National Health Laboratory Service-Dr George Mukhari Tertiary Laboratory, to identify isolates with rifampicin sensitivity and isoniazid resistance according to Xpert MTB/RIF, GenoType MTBDRplus, and BACTEC MGIT 960. Of 1823 isolates that met these criteria, 277 were randomly selected and screened for Ile491Phe with multiplex allele-specific PCR and Sanger sequencing of rpoB. Ile491Phe-positive strains (as well as 17 Ile491Phe-bearing isolates from the eSwatini study) were then tested by Deeplex-MycTB deep sequencing and whole-genome sequencing to evaluate their patterns of extensive resistance, transmission, and evolution. FINDINGS: Ile491Phe was identified in 37 (15%) of 249 samples with valid multiplex allele-specific PCR and sequencing results, thus reclassifying them as MDR. All 37 isolates were additionally identified as genotypically resistant to all first-line drugs by Deeplex-MycTB. Six of the South African isolates harboured four distinct mutations potentially associated with decreased bedaquiline sensitivity. Consistent with Deeplex-MycTB genotypic profiles, whole-genome sequencing revealed concurrent silent spread in South Africa of a MDR tuberculosis strain lineage extending from the eSwatini outbreak and at least another independently emerged Ile491Phe-bearing lineage. Whole-genome sequencing further suggested acquisition of mechanisms compensating for the Ile491Phe fitness cost, and of additional bedaquiline resistance following the introduction of this drug in South Africa. INTERPRETATION: A substantial number of MDR tuberculosis cases harbouring the Ile491Phe mutation in the rpoB gene in South Africa are missed by current diagnostic strategies, resulting in ineffective first-line treatment, continued amplification of drug resistance, and concurrent silent spread in the community. FUNDING: VLIR-UOS, National Research Foundation (South Africa), and INNOVIRIS.

Quantum transport enhancement by time-reversal symmetry breaking.

Quantum mechanics still provides new unexpected effects when considering the transport of energy and information. Models of continuous time quantum walks, which implicitly use time-reversal symmetric Hamiltonians, have been intensely used to investigate the effectiveness of transport. Here we show how breaking time-reversal symmetry of the unitary dynamics in this model can enable directional control, enhancement, and suppression of quantum transport. Examples ranging from exciton transport to complex networks are presented. This opens new prospects for more efficient methods to transport energy and information.