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CAMx-LPiG (Comprehensive Air Quality Model with Extensions—Linear Plume in Grid) is an online hybrid model based on the Chemistry and Transport Model (CTM) CAMx, which includes a sub-grid scale module to simulate the dispersion of linear road traffic emissions called LPiG. LPiG is a plume in grid module specifically developed by extending the capabilities of the Lagrangian puff sub-grid model available in CAMx. The online integration of the local scale model within the Eulerian CTM allows for a multiscale simulation of air quality from the regional scale to the urban scale, preserving a coherent description of the chemical state of the atmosphere at all spatial scales and avoiding any double counting of the emissions simulated by the sub-grid module. In this work, the model is presented and evaluated against measured NO2 concentrations for the city of Milan for the month of January 2017. The model can introduce road traffic-induced gradient in NO2 concentration at sub-grid resolution. Moreover, CAMx-LPiG has been shown to reduce bias compared to CAMx stand-alone simulations.

Recent observation and modeling-based studies have shown how air quality has been positively affected by the containment measures enforced due to the COVID-19 outbreak. This work aims to analyze Lombardy’s NO2 atmospheric concentration during the spring lockdown. The region of Lombardy is known for having the largest number of residents in Italy and high levels of pollution. It is also the region where the first European confinement measures were imposed by the Italian government. The modeling suite composed of CAMx (Comprehensive Air Quality Model with Extensions) and WRF (Weather Research and Forecasting model) provides the setting to compare the atmospheric NO2 concentration from mid-February to the end of March with a business as usual situation. The main interest in this work is to investigate the response of NO2 atmospheric concentration to increasingly reduced road traffic. We can simulate, for the first time, a real circumstance of progressively reduced mobility, as well as validating it with measured air quality data. Focusing on the city of Milan, we found that the decrease in NO2 concentration reflects progressively reduced traffic contraction. In the case of a large traffic abatement (71%), the concentration level is reduced by one third. We also find that industrial activities have a relevant impact on NO2 atmospheric concentration, especially in the provinces of Brescia and Bergamo. This study provides an overview of how incisive policies must be implemented to achieve the set environmental targets and protect human health.

Following the advent of the Sars – Cov 2 pandemic, HVAC systems have been affected by different interventions both in the emergency and in the medium term. These interventions were dictated by the need to maximise the risk reduction within air-conditioned environments and among these interventions one of the most applied has been the elimination of recirculation in favor of a full-out-door- air operation, specifically in multi-zone systems. Some plant, such as those served by roof-top units, however, provide a minimum share of recirculation that is necessary for the proper functioning of the integrated refrigeration unit. For these units, the elimination of recirculation has led to a significant increase in blocking situations. The research provides the basis for the application of integrated solutions designed to prevent blocking situations for new and existing systems through the insertion of a modulated bypass between the supply air and the external air intake so as to allow temporarily the recirculation function without, however, creating a situation of risk propagation. The proposed solutions have been applied to a real system, the regulation has been carried out in integration with the management logic of the unit and has been tested to be effective in cooling mode.

This paper introduces a new model of pedestrian flow, formulated within a measure-theoretic framework. It consists of a macroscopic representation of the system via a family of measures which, pushed forward by some flow maps, provide an estimate of the space occupancy by pedestrians at successive times. From the modeling point of view, this setting is particularly suitable for treating nonlocal interactions among pedestrians, obstacles, and wall boundary conditions. In addition, the analysis and numerical approximation of the resulting mathematical structures, which are the principal objectives of this work, follow more easily than for models based on standard hyperbolic conservation laws.

Phonons (quanta of collective vibrations) are a major source of energy dissipation and drive some of the most relevant properties of materials. In nanotechnology, phonons severely affect light emission and charge transport of nanodevices. While the phonon response is conventionally considered an inherent property of a nanomaterial, here we show that the dipole-active phonon resonance of semiconducting (CdS) nanocrystals can be drastically reshaped inside a terahertz plasmonic nanocavity, via the phonon strong coupling with the cavity vacuum electric field. Such quantum zero-point field can indeed reach extreme values in a plasmonic nanocavity, thanks to a mode volume well below λ 3 /10 7 . Through Raman measurements, we find that the nanocrystals within a nanocavity exhibit two new “hybridized” phonon peaks, whose spectral separation increases with the number of nanocrystals. Our findings open exciting perspectives for engineering the optical phonon response of functional nanomaterials and for implementing a novel platform for nanoscale quantum optomechanics. Here the authors show that the dipole-active phonon resonance of semiconducting nanocrystals can be hybridized by a strongly concentrated terahertz vacuum field of a plasmonic nanocavity, thus achieving strong plasmon–phonon coupling even in the absence of direct terahertz illumination.

Oral-vaccine-induced IgA cross-links growing bacteria into clonal aggregates, inhibiting pathogenesis, adaption and the spread of antimicrobial resistance genes. Clumping antibody protects gut Immunoglobulin A (IgA) is a key component in the body's first line of defence against many infections, but the physical processes that drive its protective function in the gut are poorly defined. Kathrin Moor et al . show that IgA protects against Salmonella infection in the intestines of mice by enchaining the progeny of dividing bacteria into clonal or oligoclonal clumps. This clumping mechanism enables IgA to directly disarm potentially invasive species and prevent bacterial invasion, while avoiding immune processes that could cause damage to the host. Vaccine-induced high-avidity IgA can protect against bacterial enteropathogens by directly neutralizing virulence factors or by poorly defined mechanisms that physically impede bacterial interactions with the gut tissues (‘immune exclusion’) 1 , 2 , 3 . IgA-mediated cross-linking clumps bacteria in the gut lumen and is critical for protection against infection by non-typhoidal Salmonella enterica subspecies enterica serovar Typhimurium ( S. Typhimurium). However, classical agglutination, which was thought to drive this process, is efficient only at high pathogen densities (≥10 8 non-motile bacteria per gram). In typical infections, much lower densities 4 , 5 (10 0 –10 7 colony-forming units per gram) of rapidly dividing bacteria are present in the gut lumen. Here we show that a different physical process drives formation of clumps in vivo : IgA-mediated cross-linking enchains daughter cells, preventing their separation after division, and clumping is therefore dependent on growth. Enchained growth is effective at all realistic pathogen densities, and accelerates pathogen clearance from the gut lumen. Furthermore, IgA enchains plasmid-donor and -recipient clones into separate clumps, impeding conjugative plasmid transfer in vivo . Enchained growth is therefore a mechanism by which IgA can disarm and clear potentially invasive species from the intestinal lumen without requiring high pathogen densities, inflammation or bacterial killing. Furthermore, our results reveal an untapped potential for oral vaccines in combating the spread of antimicrobial resistance.

The potentiality of cv. Malbec grape pomace (GP) as a functional ingredient in the formulation of bakery foods (muffins, biscuits and cereal bars) was studied. The effect of GP addition on the phenolic compounds (PCs) composition, nutritional and sensory properties were evaluated. The addition of GP increased the content of dietary fiber, proteins, ash, total phenolic content (TPC), antiradical capacity (AC), anthocyanins and non-anthocyanin PCs while decreasing the carbohydrates content. The main PCs given by the GP to supplemented foods were quercetin-3-O-glucoside, rutin, caffeic acid, syringic acid and (+)-catechin. For anthocyanins, the acylated derivatives were more stable to heat treatment (baking) in food processing which was evidenced by a higher proportion of these PCs compounds when compared to the same derivatives quantified in GP. In general, when the TPC or individual concentrations of PCs were analyzed in a nutritional or functional context, one portion of the supplemented foods showed levels high enough to satisfy the recommended dose per day of these bioactive compounds. Additionally, the foods were well received by consumers during the sensory evaluation and supplemented biscuits received the highest acceptability. This study demonstrated that GP could be a viable functional ingredient in bakery foods to incorporate components like PCs and dietary fiber into traditional consumers’ diets.

In this paper a new multiscale modeling technique is proposed. It relies on a recently introduced measure-theoretic approach, which allows one to manage the microscopic and the macroscopic scale under a unique framework. In the resulting coupled model the two scales coexist and share information. This way it is possible to perform numerical simulations in which the trajectories and the density of the particles affect each other. Crowd dynamics is the motivating application throughout the paper. [PUBLICATION ABSTRACT]

In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LC) form toxic aggregates and amyloid fibrils in target organs. Prompt diagnosis is crucial to avoid permanent organ damage, but delayed diagnosis is common because symptoms usually appear only after strong organ involvement. Here we present LICTOR, a machine learning approach predicting LC toxicity in AL, based on the distribution of somatic mutations acquired during clonal selection. LICTOR achieves a specificity and a sensitivity of 0.82 and 0.76, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.87. Tested on an independent set of 12 LCs sequences with known clinical phenotypes, LICTOR achieves a prediction accuracy of 83%. Furthermore, we are able to abolish the toxic phenotype of an LC by in silico reverting two germline-specific somatic mutations identified by LICTOR, and by experimentally assessing the loss of in vivo toxicity in a Caenorhabditis elegans model. Therefore, LICTOR represents a promising strategy for AL diagnosis and reducing high mortality rates in AL. Systemic light chain amyloidosis (AL) is caused by the production of toxic light chains and can be fatal, yet effective treatments are often not possible due to delayed diagnosis. Here the authors show that a machine learning platform analyzing light chain somatic mutations allows the prediction of light chain toxicity to serve as a possible tool for early diagnosis of AL.

In this paper, we systematically apply the mathematical structures by time-evolving measures developed in a previous work to the macroscopic modeling of pedestrian flows. We propose a discrete-time Eulerian model, in which the space occupancy by pedestrians is described via a sequence of Radon-positive measures generated by a push-forward recursive relation. We assume that two fundamental aspects of pedestrian behavior rule the dynamics of the system: on the one hand, the will to reach specific targets, which determines the main direction of motion of the walkers; on the other hand, the tendency to avoid crowding, which introduces interactions among the individuals. The resulting model is able to reproduce several experimental evidences of pedestrian flows pointed out in the specialized literature, being at the same time much easier to handle, from both the analytical and the numerical point of view, than other models relying on nonlinear hyperbolic conservation laws. This makes it suitable to address two-dimensional applications of practical interest, chiefly the motion of pedestrians in complex domains scattered with obstacles.