Not seeing the forest for the trees: size of the minimum spanning trees (MSTs) forest and branch significance in MST-based phylogenetic analysis

Power-grid vulnerability and its relation with network structure

Interconnected systems with critical infrastructures can be affected by small failures that may trigger a large-scale cascade of failures, such as blackouts in power grids. Vulnerability indices provide quantitative measures of a network resilience to component failures, assessing the break of information or energy flow in a system. Here, we focus on a network vulnerability analysis, that is, indices based solely on the network structure and its static characteristics, which are reliably available for most complex networks. This work studies the structural connectivity of power grids, assessing the main centrality measures in network science to identify vulnerable components (transmission lines or edges) to attacks and failures. Specifically, we consider centrality measures that implicitly model the power flow distribution in power systems. This framework allow us to show that the efficiency of the power flow in a grid can be highly sensitive to attacks on specific (central) edges. Numerical results are presented for randomly generated power-grid models and established power-grid benchmarks, where we demonstrate that the system's energy efficiency is more vulnerable to attacks on edges that are central to the power flow distribution. We expect that the vulnerability indices investigated in our work can be used to guide the design of structurally resilient power grids.

Web tools to fight pandemics: the COVID-19 experience

The current outbreak of COVID-19 has generated an unprecedented scientific response worldwide, with the generation of vast amounts of publicly available epidemiological, biological and clinical data. Bioinformatics scientists have quickly produced online methods to provide non-computational users with the opportunity of analyzing such data. In this review, we report the results of this effort, by cataloguing the currently most popular web tools for COVID-19 research and analysis. Our focus was driven on tools drawing data from the fields of epidemiology, genomics, interactomics and pharmacology, in order to provide a meaningful depiction of the current state of the art of COVID-19 online resources.

In-Depth Longitudinal Study of Listeria monocytogenes ST9 Isolates from the Meat Processing Industry: Resolving Diversity and Transmission Patterns Using Whole-Genome Sequencing

Listeria monocytogenes is a deadly foodborne pathogen that is widespread in the environment, and certain types can be established in food factories. The sequence type ST9 dominates in meat processing environments, and this work was undertaken to obtain data needed for the tracking of this subtype. By using whole-genome sequencing (WGS), we revealed the presence of cross-contamination routes between meat factories as well as within a single factory, including the spread from different reservoirs within the same room. It was also possible to estimate the time frame of persistence in the factory, as well as when and how new clones had entered. The present work contributes valuable information about the diversity of ST9 and exemplifies the potential power of WGS in food safety management, allowing the determination of relationships between strains both in an international context and locally between and within factories.

Listeria monocytogenes is a pathogen mostly associated with the consumption of ready-to-eat foods and can cause severe disease and death. It can be introduced into food chains from raw materials, but often the contamination source is the food production environment, where certain clones can persist for years. In the meat chain, ST9 is one of the most commonly encountered L. monocytogenes sequence types, and for effective source tracking, the divergence and spread of ST9 must be understood. In this study, whole-genome sequencing (WGS) was used to characterize and track 252 L. monocytogenes ST9 isolates collected from four Norwegian meat processing plants between 2009 and 2017. The isolates formed distinct clusters relative to genomes found in public databases, and all but three isolates clustered into two major clonal populations. Different contamination patterns were revealed, e.g., evidence of contamination of two factories with a clone that diverged from its ancestor in the late 1990s through a common source of raw materials; breach of hygienic barriers within a factory, leading to repeated detection of two clones in the high-risk zone during a 4- to 6-year period; entry through the purchase and installation of second-hand equipment harboring a previously established clonal population; and spreading and diversification of two clones from two reservoirs within the same production room over a 9-year period. The present work provides data on the diversity of ST9, which is crucial for epidemiological investigations and highlights how WGS can be used for source tracking within food processing factories.

IMPORTANCEListeria monocytogenes is a deadly foodborne pathogen that is widespread in the environment, and certain types can be established in food factories. The sequence type ST9 dominates in meat processing environments, and this work was undertaken to obtain data needed for the tracking of this subtype. By using whole-genome sequencing (WGS), we revealed the presence of cross-contamination routes between meat factories as well as within a single factory, including the spread from different reservoirs within the same room. It was also possible to estimate the time frame of persistence in the factory, as well as when and how new clones had entered. The present work contributes valuable information about the diversity of ST9 and exemplifies the potential power of WGS in food safety management, allowing the determination of relationships between strains both in an international context and locally between and within factories.

Degree and centrality-based approaches in network-based variable selection: Insights from the Singapore Longitudinal Aging Study

We describe a network-based method to obtain a subset of representative variables from clinical data of subjects of the second Singapore Longitudinal Aging Study (SLAS-2), while preserving to a good extent the predictive performance of the full set with regards to a multi-faceted index of successful aging, SAGE. To examine differences in predictive performance of high-degree nodes (“hubs”) and high-centrality ones (“cores”), we implement four subsetting strategies (two degree-based, two centrality-based) and obtain four surrogate sets of variables, which we use as input features for machine learning models to predict the SAGE index of subjects. All four models have variables belonging to the physical, cardiovascular, cognitive and immunological domains among their fifteen most important predictors. A fifth domain (leisure-time activities, LTA) is also present in some form. From a comparison of the surrogate sets’ size and predictive performance, a centrality-based approach (selection of the most central variable-nodes within each cluster) yielded the smallest-sized surrogate set, while having high prediction accuracy (measured by its model’s area-under-curve, AUC) in comparison to its analogous degree-based strategy (selection of the highest-degree nodes per cluster). Inclusion of the next most-central variables yielded negligible changes in predictive performance while more than doubling the surrogate set size. The centrality-based approach thus yields a surrogate set which offers a good balance between number of variables and prediction performance, and can act as a representative subset of the SLAS-2 clinical dataset.

Evidence for Host-Genotype Associations of Borrelia burgdorferi Sensu Stricto

Different genotypes of the agent of Lyme disease in North America, Borrelia burgdorferi sensu stricto, show varying degrees of pathogenicity in humans. This variation in pathogenicity correlates with phylogeny and we have hypothesized that the different phylogenetic lineages in North America reflect adaptation to different host species. In this study, evidence for host species associations of B. burgdorferi genotypes was investigated using 41 B. burgdorferi-positive samples from five mammal species and 50 samples from host-seeking ticks collected during the course of field studies in four regions of Canada: Manitoba, northwestern Ontario, Quebec, and the Maritimes. The B. burgdorferi genotypes in the samples were characterized using three established molecular markers (multi-locus sequence typing [MLST], 16S-23S rrs-rrlA intergenic spacer, and outer surface protein C sequence [ospC] major groups). Correspondence analysis and generalized linear mixed effect models revealed significant associations between B. burgdorferi genotypes and host species (in particular chipmunks, and white-footed mice and deer mice), supporting the hypotheses that host adaptation contributes to the phylogenetic structure and possibly the observed variation in pathogenicity in humans.

Virtual ancestor reconstruction: Revealing the ancestor of modern humans and Neandertals

The timing and geographic origin of the common ancestor of modern humans and Neandertals remain controversial. A poor Pleistocene hominin fossil record and the evolutionary complexities introduced by dispersals and regionalisation of lineages have fuelled taxonomic uncertainty, while new ancient genomic data have raised completely new questions. Here, we use maximum likelihood and 3D geometric morphometric methods to predict possible morphologies of the last common ancestor of modern humans and Neandertals from a simplified, fully resolved phylogeny. We describe the fully rendered 3D shapes of the predicted ancestors of humans and Neandertals, and assess their similarity to individual fossils or populations of fossils of Pleistocene age. Our results support models of an Afro-European ancestral population in the Middle Pleistocene (Homo heidelbergensis sensu lato) and further predict an African origin for this ancestral population.