A suspension of nanoparticles with very low volume fraction is found to assemble into a macroscopic cellular phase that is composed of particle-rich walls and particle-free voids under the collective influence of AC and DC voltages. Systematic study of this phase transition shows that it was the result of electrophoretic assembly into a two-dimensional configuration followed by spinodal decomposition into particle-rich walls and particle-poor cells mediated principally by electrohydrodynamic flow. This mechanistic understanding reveals two characteristics needed for a cellular phase to form, namely (1) a system that is considered two dimensional and (2) short-range attractive, long-range repulsive interparticle interactions. In addition to determining the mechanism underpinning the formation of the cellular phase, this work presents a method to reversibly assemble microscale continuous structures out of nanoscale particles in a manner that may enable the creation of materials that impact diverse fields including energy storage and filtration.

We report new dynamical modes in confined soft granular flows, such as stochastic jetting and dripping, with no counterpart in continuum viscous fluids. The new modes emerge as a result of the propagation of the chaotic behavior of individual grains - here, monodisperse emulsion droplets - to the level of the entire system as the emulsion is focused into a narrow orifice by an external viscous flow. We observe avalanching dynamics and the formation of remarkably stable jets - single-file granular chains - which occasionally break, resulting in a non-Gaussian distribution of cluster sizes. We find that the sequences of droplet rearrangements that lead to the formation of such chains resemble unfolding of cancer cell clusters in narrow capillaries, overall demonstrating that microfluidic emulsion systems could serve to model various aspects of soft granular flows, including also tissue dynamics at the mesoscale.

We have investigated systematically and statistically methanol-concentration effects on methane-hydrate nucleation using both experiment and restrained molecular-dynamics simulation, employing simple observables to achieve an initially homogeneous methane-supersaturated solution particularly favorable for nucleation realization in reasonable simulation times. We observe the pronounced "bifurcated" character of the nucleation rate upon methanol concentration in both experiments and simulation, with promotion at low concentrations and switching to industrially familiar inhibition at higher concentrations. Higher methanol concentrations suppress hydrate growth by in-lattice methanol incorporation, resulting in the formation of "defects", increasing the energy of the nucleus. At low concentrations, on the contrary, the detrimental effect of defects is more than compensated for by the beneficial contribution of CHin easing methane incorporation in the cages or replacing it altogether.

In this perspective we take stock of the current state of the art of computational models for droplets microfluidics and we suggest some strategies which may open the way to the full-scale simulation of microfluidic phenomena with interfaces, from near-contact interactions to the device operational lengths.

We present LBcuda, a GPU accelerated version of LBsoft, our open-source MPI-based software for the simulation of multi-component colloidal flows. We describe the design principles, the optimization and the resulting performance as compared to the CPU version, using both an average cost GPU and high-end NVidia GPU cards (V100 and the latest A100). The results show a substantial acceleration for the fluid solver reaching up to 200 GLUPS (Giga Lattice Updates Per Second) on a cluster made of 512 A100 NVIDIA cards simulating a grid of eight billion lattice points. These results open attractive prospects for the computational design of new materials based on colloidal particles. Program summary: Program Title: LBcuda CPC Library link to program files: https://doi.org/10.17632/v6fvmzpcrn.1 Developer's repository link: https://github.com/copmat/LBcuda Licensing provisions: 3-Clause BSD License Programming language: CUDA Fortran Nature of problem: Hydro-dynamics of colloidal multi-component systems and Pickering emulsions. Solution method: Lattice-Boltzmann method solving the Navier-Stokes equations for the fluid dynamics within an Eulerian description. Particle solver describing colloidal particles within a Lagrangian representation coupled to the fluid solver. The numerical solution of the coupling algorithm includes the back reaction effects for each force terms according to a fluid-particle multi-scale paradigm.

Deep neural networks are rapidly emerging as data analysis tools, often outperforming the conventional techniques used in complex microfluidic systems. One fundamental analysis frequently desired in microfluidic experiments is counting and tracking the droplets. Specifically, droplet tracking in dense emulsions is challenging due to inherently small droplets moving in tightly packed configurations. Sometimes, the individual droplets in these dense clusters are hard to resolve, even for a human observer. Here, two deep learning-based cutting-edge algorithms for object detection [you only look once (YOLO)] and object tracking (DeepSORT) are combined into a single image analysis tool, DropTrack, to track droplets in the microfluidic experiments. DropTrack analyzes input microfluidic experimental videos, extracts droplets' trajectories, and infers other observables of interest, such as droplet numbers. Training an object detector network for droplet recognition with manually annotated images is a labor-intensive task and a persistent bottleneck. In this work, this problem is partly resolved by training many object detector networks (YOLOv5) with several hybrid datasets containing real and synthetic images. We present an analysis of a double emulsion experiment as a case study to measure DropTrack's performance. For our test case, the YOLO network trained by combining 40% real images and 60% synthetic images yields the best accuracy in droplet detection and droplet counting in real experimental videos. Also, this strategy reduces labor-intensive image annotation work by 60%. DropTrack's performance is measured in terms of mean average precision of droplet detection, mean squared error in counting the droplets, and image analysis speed for inferring droplets' trajectories. The fastest configuration of DropTrack can detect and track the droplets at approximately 30 frames per second, well within the standards for a real-time image analysis.

Background COVID-19 lockdown caused sudden changes in people's lifestyle, as a consequence of the forced lockdown imposed by governments all over the world. We aimed to evaluate the impact of lockdown on body mass index (BMI) in a cohort of allergic children and adolescents. Methods From the first of June until the end of October 2020, we submitted a written questionnaire to all the patients who, after lockdown, carried out a visit at the Pediatric Allergy Unit of the Department of Mother-Child, Urological Science, Sapienza University of Rome. The questionnaire was composed by 10 questions, referring to the changes in their daily activities. Data were extrapolated from the questionnaire and then analyzed considering six variables: BMI before and BMI after lockdown, sugar intake, sport, screens, sleep, and anxiety. Results One hundred fifty-three patients agreed to answer our questionnaire. Results showed a statistically significant increase in the BMI after lockdown (20.97 kg/m2 ± 2.63) with respect to the BMI before lockdown (19.18 kg/m2 ± 2.70). A multivariate regression analysis showed that the two variables that mostly influenced the increase in BMI were sleep and anxiety. Conclusions For the analyzed cohort of allergic children and adolescents we obtained significant gain in BMI as consequences of lockdown, which can be explained by many factors: high consumption of consolatory food, less sport activities, more time spent in front of screens, sleep alteration associated with increased anxiety. All these factors acted together, although sleep alteration and increased anxiety were the most influential factors that led to the worsening or the onset of weight gain, creating the basis for future health problems.

AI-enabled bot and social media: A survey of tools, techniques, and platforms for the arms race

Graphstructuresnowadays pervasiveBigData.It is oftenusefulto regroupsuchclustersdata incanclusters,accordingdistinctivenodefeatures,and use area representativeelementinforeachcluster.In manyreal-worldcases,be identifiedby toa setof connectedfeatures,and shareuse a representativeelementfor eachfunction,cluster. Ini.e.manyreal-worldcases,clustersbe identifiedbyrepresentationa set of connectedvertices thatthe result of somecategoricala mappingof theverticesintocansomecategoricalthatverticesthat insharethe setresultof somecategoricalfunction,a mappingterrainsof the withverticesinto somecategoricalthattakes valuesa finiteC. Asan example,we canidentifyi.e.contiguousthe samediscretepropertyrepresentationon a geographicaltakesvaluesinafinitesetC.Asanexample,wecanidentifycontiguousterrainswiththesamediscretepropertyonageographicalmap, leveraging Space Syntax. In this case, thematic areas within cities are labelled with different colors and color zones aremap,leveragingSpaceSyntax.In thisareas withinContractedcities are labelledwithdifferentzones areanalysedby meansof theirstructureandcase,theirthematicmutual interactions.graphs canhelpidentifycolorsissuesandandcolorcharacteristicsanalysedbymeansoftheirstructureandtheirmutualinteractions.Contractedgraphscanhelpidentifyissuesandcharacteristicsof the original structures that were not visible before.of Thisthe originalstructures andthatdiscusseswere not visiblebefore.paper introducesthe problemof contracting possibly large colored graphs into much smaller representatives.Thisprovidespaper introducesand discussesthe problemof contractinggraphs into muchrepresentatives.It alsoa novel serialbut parallelizablealgorithmto tackle possiblythis task.largeSomecoloredinitial performanceplots smallerare givenand discussedItalsoprovidesanovelserialbutparallelizablealgorithmtotacklethistask.Someinitialperformanceplotsaregivenand discussedtogether with hints for future development.together with hints for future development.

Objective The immune response arises from a fne balance of mechanisms that provide for surveillance, tolerance, and elimination of dangers. Sulfavant A (SULF A) is a sulfolipid with a promising adjuvant activity. Here we studied the mechanismof action of SULF A and addressed the identifcation of its molecular target in human dendritic cells (hDCs).Methods Adjuvant efect and immunological response to SULF A were assessed on DCs derived from human donors. Inaddition to testing various reporter cells, target identifcation and downstream signalling was supported by a reverse pharmacology approach based on antibody blocking and gene silencing, crosstalk with TLR pathways, use of human allogeneicmixed lymphocyte reaction.Results SULF A binds to the Triggering Receptor Expressed on Myeloid cells-2 (TREM2) and initiates an unconventionalmaturation of hDCs leading to enhanced migration activity and up-regulation of MHC and co-stimulatory molecules without release of conventional cytokines. This response involves the SYK-NFAT axis and is compromised by blockade orgene silencing of TREM2. Activation by SULF A preserved the DC functions to excite the allogeneic T cell response, andincreased interleukin-10 release after lipopolysaccharide stimulation.Conclusion SULF A is the frst synthetic small molecule that binds to TREM2. The receptor engagement drives diferentiation of an unprecedented DC phenotype (homeDCs) that contributes to immune homeostasis without compromising lymphocyte activation and immunogenic response. This mechanism fully supports the adjuvant and immunoregulatory activityof SULF A. We also propose that the biological p

Summary: ADViSELipidomics is a novel Shiny app for preprocessing, analyzing and visualizing lipidomics data. Ithandles the outputs from LipidSearch and LIQUID for lipid identification and quantification and the data fromthe Metabolomics Workbench. ADViSELipidomics extracts information by parsing lipid species (using LIPID MAPSclassification) and, together with information available on the samples, performs several exploratory and statisticalanalyses. When the experiment includes internal lipid standards, ADViSELipidomics can normalize the data matrix,providing normalized concentration values per lipids and samples. Moreover, it identifies differentially abundantlipids in simple and complex experimental designs, dealing with batch effect correction. Finally, ADViSELipidomicshas a user-friendly graphical user interface and supports an extensive series of interactive graphics.

The molecular programs involved in regulatory T (Treg) cell activation and homeostasis remain incompletely understood. Here, we show that T cell receptor (TCR) signaling in Treg cells induces the nuclear translocation of serine/threonine kinase 4 (Stk4), leading to the formation of an Stk4-NF-?B p65-Foxp3 complex that regulates Foxp3- and p65-dependent transcriptional programs. This complex was stabilized by Stk4-dependent phosphorylation of Foxp3 on serine-418. Stk4 deficiency in Treg cells, either alone or in combination with its homolog Stk3, precipitated a fatal autoimmune lymphoproliferative disease in mice characterized by decreased Treg cell p65 expression and nuclear translocation, impaired NF-?B p65-Foxp3 complex formation, and defective Treg cell activation. In an adoptive immunotherapy model, overexpression of p65 or the phosphomimetic Foxp3S418E in Stk3/4-deficient Treg cells ameliorated their immune regulatory defects. Our studies identify Stk4 as an essential TCR-responsive regulator of p65-Foxp3-dependent transcription that promotes Treg cell-mediated immune tolerance.

The dynamic behavior of a self-propelled semiflexible filament of length L is con- sidered under the action of a linear shear flow. The system is studied by using Brownian multi-particle collision dynamics. The system can be characterized in terms of the persistence length Lp of the chain, of the Peclet number, and of the Weissenberg number. The quantity Lp/L measures the bending rigidity of the polymer, the Peclet number Pe is the ratio of active force times L to thermal energy, and the Weissenberg number Wi characterizes the flow strength over thermal effects. In this presentation we will focus our attention to intermediate values of Pe corresponding to the weak spiral regime when no external flow is applied. The numerical results allow us to outline the main features of the physics underlying the considered system: o At low values of Wi, polymer is stretched by activity and aligned by shear along the flow direction. This effect is more marked in the case of more flexible chains. o At the intermediate values of Wi, polymer is prone to tumble due to shear and this promotes a contraction of the chain. o At very high values of Wi, activity sums up to shear enhancing polymer stretching and deformation.

The possibility to computationally prioritize candi- date disease genes capitalizing on existing information has led to a speedup in the discovery of new methods. Many gene discovery techniques exploit network data, like protein-protein interactions (PPIs), in order to extract knowledge from the network structure relying on several network metrics. We here present PROCONSUL, a method that builds on top of the concept of connectivity significance (CS) and exploits the idea of probabilistic exploration of the space of putative disease genes. We show that our methodology is able to outperform the state-of- the-art tool based on CS in several settings, and propose different, effective gene discovery strategies according to specific disease network properties.

The importance of faster drug development has never been more evident than in present time when the whole world is struggling to cope up with the COVID-19 pandemic. At times when timely development of effective drugs and treatment plans could potentially save millions of lives, drug repurposing is one area of medicine that has garnered much of research interest. Apart from experimental drug repurposing studies that happen within wet labs, lot many new quantitative methods have been proposed in the literature. In this paper, one such quantitative methods for drug repurposing is implemented and evaluated. DruSiLa (DRUg in-SIlico LAboratory) is an in-silico drug re- purposing method that leverages disease similarity measures to quantitatively rank existing drugs for their potential therapeutic efficacy against novel diseases. The proposed method makes use of available, manually curated, and open datasets on diseases, their genetic origins, and disease-related patho-phenotypes. DruSiLa evaluates pairwise disease similarity scores of any given target disease to each known disease in our dataset. Such similarity scores are then propagated through disease-drug associations, and aggregated at drug nodes to rank them for their predicted effectiveness against the target disease.

A classical Lotka-Volterra model with the logistical growth of prey-and-hunting coopera-tion in the functional response of predators to prey was extended by introducing advection terms,which included the velocities of animals. The effect of velocity on the kinetics of the problem wasanalyzed. In order to examine the band behavior of species over time, traveling wave solutions wereintroduced, and conditions for the coexistence of both populations and/or extinction were found.Numerical simulations illustrating the obtained results were performe

The dynamic behavior of a self-propelled semiflexible filament of length L is considered under the action of an external unbounded shear flow. The system is studied by using Brownian multi-particle collision dynamics. The system can be characterized in terms of the persistence length Lp of the chain, of the Peclet number, and of the Weissenberg number. The quantity Lp/L measures the bending rigidity of the polymer, the Peclet number Pe is the ratio of active force to thermal energy, and the Weissenberg number Wi characterizes the flow strength over thermal effects. In this presentation we will focus our attention to intermediate values of Pe corresponding to the weak spiral regime when no external flow is applied. The numerical results allow us to outline the main features of the physics underlying the considered system: o At low values of Wi, polymers are stretched by activity and aligned by shear along the flow direction. This effect is more marked in the case of more flexible chains. o At the intermediate values of Wi, polymers are prone to tumble due to shear and this promotes a contraction of the chain. o At very high values of Wi, activity sums up to shear enhancing polymer stretching and deformation.

In this paper we devise a microscopic (agent-based) mathematical model for reproducing crowd behavior in a specific scenario: a number of pedestrians, consisting of numerous social groups, flow along a corridor until a gate located at the end of the corridor closes. People are not informed about the closure of the gate and perceive the blockage observing dynamically the local crowd conditions. Once people become aware of the new conditions, they stop and then decide either to stay, waiting for reopening, or to go back and leave the corridor forever. People going back hit against newly incoming people creating a dangerous counter-flow. We run several numerical simulations varying parameters which control the crowd behavior, in order to understand the factors which have the greatest impact on the system dynamics. We also study the optimal way to inform people about the blockage in order to prevent the counter-flow. We conclude with some useful suggestions directed to the organizers of mass events.

In this paper, we describe some work aimed at upgrading the Alya code with up-to-date parallel linear solvers capable of achieving reliability, efficiency, and scalability in the computation of the pressure field at each time step of the numerical procedure for solving an LES formulation of the incompressible Navier-Stokes equations. We developed a software module in Alya's kernel to interface the libraries included in the current version of PSCToolkit, a framework for the iterative solution of sparse linear systems on parallel distributed-memory computers by Krylov methods coupled to Algebraic MultiGrid preconditioners. The Toolkit has undergone some extensions within the EoCoE-II project with the primary goal to face the exascale challenge. Results on a realistic benchmark for airflow simulations in wind farm applications show that the PSCToolkit solvers significantly outperform the original versions of the Conjugate Gradient method available in the Alya kernel in terms of scalability and parallel efficiency and represent a very promising software layer to move the Alya code towards exascale.

Models of networks play a major role in explaining and reproducing empirically observed patterns. Suitable models can be used to randomize an observed network while preserving some of its features, or to generate synthetic graphs whose properties may be tuned upon the characteristics of a given population. In the present paper, we introduce the Fitness-Corrected Block Model, an adjustable-density variation of the well-known Degree-Corrected Block Model, and we show that the proposed construction yields a maximum entropy model. When the network is sparse, we derive an analytical expression for the degree distribution of the model that depends on just the constraints and the chosen fitness-distribution. Our model is perfectly suited to define maximum-entropy data-driven spatial social networks, where each block identifies vertices having similar position (e.g., residence) and age, and where the expected block-to-block adjacency matrix can be inferred from the available data. In this case, the sparse-regime approximation coincides with a phenomenological model where the probability of a link binding two individuals is directly proportional to their sociability and to the typical cohesion of their age-groups, whereas it decays as an inverse-power of their geographic distance. We support our analytical findings through simulations of a stylized urban area.