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A . baumannii can rapidly acquire new resistance mechanisms and persist on abiotic surface, enabling the colonization of asymptomatic human host. In Acinetobacter the type VI secretion system (T6SS) is involved in twitching, surface motility and is used for interbacterial competition allowing the bacteria to uptake DNA. A . baumannii possesses a T6SS that has been well studied for its regulation and specific activity, but little is known concerning its assembly and architecture. The T6SS nanomachine is built from three architectural sub-complexes. Unlike the baseplate (BP) and the tail-tube complex (TTC), which are inherited from bacteriophages, the membrane complex (MC) originates from bacteria. The MC is the most external part of the T6SS and, as such, is subjected to evolution and adaptation. One unanswered question on the MC is how such a gigantesque molecular edifice is inserted and crosses the bacterial cell envelope. The A . baumannii MC lacks an essential component, the TssJ lipoprotein, which anchors the MC to the outer membrane. In this work, we studied how A . baumannii compensates the absence of a TssJ. We have characterized for the first time the A . baumannii’ s specific T6SS MC, its unique characteristic, its membrane localization, and assembly dynamics. We also defined its composition, demonstrating that its biogenesis employs three Acinetobacter -specific envelope-associated proteins that define an intricate network leading to the assembly of a five-proteins membrane super-complex. Our data suggest that A . baumannii has divided the function of TssJ by (1) co-opting a new protein TsmK that stabilizes the MC and by (2) evolving a new domain in TssM for homo-oligomerization, a prerequisite to build the T6SS channel. We believe that the atypical species-specific features we report in this study will have profound implication in our understanding of the assembly and evolutionary diversity of different T6SSs, that warrants future investigation.

This study provides a comprehensive assessment of NO2 changes across the main European urban areas induced by COVID-19 lockdowns using satellite retrievals from the Tropospheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5p satellite, surface site measurements, and simulations from the Copernicus Atmosphere Monitoring Service (CAMS) regional ensemble of air quality models. Some recent TROPOMI-based estimates of changes in atmospheric NO2 concentrations have neglected the influence of weather variability between the reference and lockdown periods. Here we provide weather-normalized estimates based on a machine learning method (gradient boosting) along with an assessment of the biases that can be expected from methods that omit the influence of weather. We also compare the weather-normalized satellite-estimated NO2 column changes with weather-normalized surface NO2 concentration changes and the CAMS regional ensemble, composed of 11 models, using recently published estimates of emission reductions induced by the lockdown. All estimates show similar NO2 reductions. Locations where the lockdown measures were stricter show stronger reductions, and, conversely, locations where softer measures were implemented show milder reductions in NO2 pollution levels. Average reduction estimates based on either satellite observations (−23 %), surface stations (−43 %), or models (−32 %) are presented, showing the importance of vertical sampling but also the horizontal representativeness. Surface station estimates are significantly changed when sampled to the TROPOMI overpasses (−37 %), pointing out the importance of the variability in time of such estimates. Observation-based machine learning estimates show a stronger temporal variability than model-based estimates.

Abstract Lytic polysaccharide monooxygenases (LPMOs) are taxonomically widespread copper-enzymes boosting biopolymers conversion (e.g. cellulose, chitin) in Nature. White-rot Polyporales, which are major fungal wood decayers, may possess up to 60 LPMO-encoding genes belonging to the auxiliary activities family 9 (AA9). Yet, the functional relevance of such multiplicity remains to be uncovered. Previous comparative transcriptomic studies of six Polyporales fungi grown on cellulosic substrates had shown the overexpression of numerous AA9-encoding genes, including some holding a C-terminal domain of unknown function (“X282”). Here, after carrying out structural predictions and phylogenetic analyses, we selected and characterized six AA9-X282s with different C-term modularities and atypical features hitherto unreported. Unexpectedly, after screening a large array of conditions, these AA9-X282s showed only weak binding properties to cellulose, and low to no cellulolytic oxidative activity. Strikingly, proteomic analysis revealed the presence of multiple phosphorylated residues at the surface of these AA9-X282s, including a conserved residue next to the copper site. Further analyses focusing on a 9 residues glycine-rich C-term extension suggested that it could hold phosphate-binding properties. Our results question the involvement of these AA9 proteins in the degradation of plant cell wall and open new avenues as to the divergence of function of some AA9 members.

This research was funded by Grant IJC2020-045140-I from MCIN/AEI/10.13039/501100011 033 and “European Union NextGenerationEU/PRTR”, and by the European Union’s Horizon 2020 Programme under the CoE RAISE project grant agreement Id 951733.

There are personal and institutional decisions that can increase the seismic resilience of the buildings in a city. However, some of these decisions are possible if we have basic knowledge of buildings’ seismic risk. The present document describes the main results of a detailed study of seismic vulnerability and seismic risk of residential buildings of Ciutat Vella (the ancient district of Barcelona) and Nou Barris (one of the newest districts of Barcelona). In this study, we assessed seismic risk according to the Vulnerability Index Method-Probabilistic named as VIM_P. Moreover, we analyzed the influence of basic buildings’ features in the final vulnerability and seismic risk values. For instance, we assessed the seismic vulnerability and the seismic risk of groups of buildings defined according to the number of stories of the buildings. Findings of this research reveal that the annual frequency of exceedance of the collapse damage state in Ciutat Vella buildings is, on average, 4.7 times higher than for the buildings in Nou Barris. Moreover, according to the Best vulnerability curve, 70.31% and 2.81% of Ciutat Vella and Nou Barris buildings, respectively, have an annual frequency of exceedance of the collapse damage state greater than 1 × 10–5.

The necessity of dealing with uncertainties is growing in many different fields of science and engineering. Due to the constant development of computational capabilities, current solvers must satisfy both statistical accuracy and computational efficiency. The aim of this work is to introduce an asynchronous framework for Monte Carlo and Multilevel Monte Carlo methods to achieve such a result. The proposed approach presents the same reliability of state of the art techniques, and aims at improving the computational efficiency by adding a new level of parallelism with respect to existing algorithms: between batches, where each batch owns its hierarchy and is independent from the others. Two different numerical problems are considered and solved in a supercomputer to show the behavior of the proposed approach.

We present a stable high-order hybridizable discontinuous Galerkin (HDG) formulation coupled with high-order diagonal implicit Runge–Kuta (DIRK) schemes to simulate slightly compressible one-phase flow through porous media. The HDG stability depends on the selection of a single parameter and its definition is crucial to ensure the stability and to achieve the high-order properties of the method. Thus, we extend the work of Nguyen et al. in J Comput Phys 228, 8841–8855, 2009 to deduce an analytical expression for the stabilization parameter using the material parameters of the problem and the Engquist-Osher monotone flux scheme. The formulation is high-order accurate for the pressure, the flux and the velocity with the same convergence rate of P+1, being P the polynomial degree of the approximation. This is important because high-order methods have the potential to reduce the computational cost while obtaining more accurate solutions with less dissipation and dispersion errors than low order methods. The formulation can use unstructured meshes to capture the heterogeneous properties of the reservoir. In addition, it is conservative at the element level, which is important when solving PDE’s in conservative form. Moreover, a hybridization procedure can be applied to reduce the size of the global linear system. To keep these advantages, we use DIRK schemes to perform the time integration. DIRK schemes are high-order accurate and have a low memory footprint. We show numerical evidence of the optimal convergence rates obtained with the proposed formulation. Finally, we present several examples to illustrate the capabilities of the formulation.

There is an increasing demand to enhance infrastructure asset management within the drinking water sector. A key factor for achieving this is improving the accuracy of pipe failure prediction models. Machine learning-based models have emerged as a powerful tool in enhancing the predictive capabilities of water distribution network models. Extensive research has been conducted to explore the role of explanatory variables in optimizing model outputs. However, the underlying mechanisms of incorporating explanatory variable data into the models still need to be better understood. This review aims to expand our understanding of explanatory variables and their relationship with existing models through a comprehensive investigation of the explanatory variables employed in models over the past 15 years. The review underscores the importance of obtaining a substantial and reliable dataset directly from Water Utilities databases. Only with a sizeable dataset containing high-quality data can we better understand how all the variables interact, a crucial prerequisite before assessing the performance of pipe failure rate prediction models.

Shared memory programming models usually provide worksharing and task constructs. The former relies on the efficient fork-join execution model to exploit structured parallelism; while the latter relies on fine-grained synchronization among tasks and a flexible data-flow execution model to exploit dynamic, irregular, and nested parallelism. On applications that show both structured and unstructured parallelism, both worksharing and task constructs can be combined. However, it is difficult to mix both execution models without penalizing the data-flow execution model. Hence, on many applications structured parallelism is also exploited using tasks to leverage the full benefits of a pure data-flow execution model. However, task creation and management might introduce a non-negligible overhead that prevents the efficient exploitation of fine-grained structured parallelism, especially on many-core processors. In this work, we propose worksharing tasks. These are tasks that internally leverage worksharing techniques to exploit fine-grained structured loop-based parallelism. The evaluation shows promising results on several benchmarks and platforms.

DNA transposable elements and transposase-derived genes are present in most living organisms, including vertebrates, but their function is largely unknown. PiggyBac Transposable Element Derived 5 (PGBD5) is an evolutionarily conserved vertebrate DNA transposase-derived gene with retained nuclease activity in human cells. Vertebrate brain development is known to be associated with prominent neuronal cell death and DNA breaks, but their causes and functions are not well understood. Here, we show that PGBD5 contributes to normal brain development in mice and humans, where its deficiency causes disorder of intellectual disability, movement, and seizures. In mice, Pgbd5 is required for the developmental induction of post-mitotic DNA breaks and recurrent somatic genome rearrangements. In the brain cortex, loss of Pgbd5 leads to aberrant differentiation and gene expression of distinct neuronal populations, including specific types of glutamatergic neurons, which explains the features of PGBD5 deficiency in humans. Thus, PGBD5 might be a transposase-derived enzyme required for brain development in mammals.