Magnitude In Space Research Articles

Exploring the structure, metabolism, and biochemistry of the neuronal microenvironment label-free using fast simultaneous multimodal optical microscopy

The technologies to examine the neuronal microenvironment label free remain critically underexplored. There is a gap in our knowledge of underlying metabolic, biochemical, and electrophysiological mechanisms behind several neurological processes at a cellular level, which can be traced to the lack of versatile and high-throughput tools to investigate neural networks. In this paper, four label-free contrasts were explored as mechanisms to study neuronal activity, namely, scattering, birefringence, autofluorescence from metabolic cofactors and molecules, and local biochemistry. To overcome challenges of observing neuronal activity spanning three orders of magnitude in space and time, microscopes had to be developed to simultaneously capture these contrasts quickly, with high resolution, and over a large FOV. We developed versatile autofluorescence lifetime, multiharmonic generation, polarization-sensitive interferometry, and Raman imaging in epi-detection (VAMPIRE) microscopy to simultaneously observe multiple facets of neuronal structure and dynamics. The accelerated computational-imaging-driven acquisition speeds, the utilization of a single light source to evoke all contrasts, the simultaneous acquisition that provides an otherwise impossible multimodal dynamic imaging capability, and the real-time processing of the data enable VAMPIRE microscopy as a powerful imaging platform for neurophotonics and beyond.

Validation of ultrasound velocimetry and computational fluid dynamics for flow assessment in femoral artery stenotic disease.

To investigate the accuracy of high-framerate echo particle image velocimetry (ePIV) and computational fluid dynamics (CFD) for determining velocity vectors in femoral bifurcation models through comparison with optical particle image velocimetry (oPIV). Separate femoral bifurcation models were built for oPIV and ePIV measurements of a non-stenosed (control) and a 75%-area stenosed common femoral artery. A flow loop was used to create triphasic pulsatile flow. In-plane velocity vectors were measured with oPIV and ePIV. Flow was simulated with CFD using boundary conditions from ePIV and additional duplex-ultrasound (DUS) measurements. Mean differences and 95%-limits of agreement (1.96*SD) of the velocity magnitudes in space and time were compared, and the similarity of vector complexity (VC) and time-averaged wall shear stress (TAWSS) was assessed. Similar flow features were observed between modalities with velocities up to 110 and in the control and the stenosed model, respectively. Relative to oPIV, ePIV and CFD-ePIV showed negligible mean differences in velocity (), with limits of agreement of (control) and (stenosed). CFD-DUS overestimated velocities with limits of agreements of and for the control and stenosed model, respectively. VC showed good agreement, whereas TAWSS showed similar trends but with higher values for ePIV, CFD-DUS, and CFD-ePIV compared to oPIV. EPIV and CFD-ePIV can accurately measure complex flow features in the femoral bifurcation and around a stenosis. CFD-DUS showed larger deviations in velocities making it a less robust technique for hemodynamical assessment. The applied ePIV and CFD techniques enable two- and three-dimensional assessment of local hemodynamics with high spatiotemporal resolution and thereby overcome key limitations of current clinical modalities making them an attractive and cost-effective alternative for hemodynamical assessment in clinical practice.

Validation of assessments to accurately analyze the body composition of highly trained sitting volleyball players: A pilot study

Body composition is a fundamental component of physical fitness related to the performance of Sitting volleyball (SV) players. Also, establishing the best method for evaluating the body composition of these para-athletes would be highly necessary for this field. The purpose of this study was (1) to describe the body composition of male and female highly trained SV players, (2) to compare the values obtained from this population by two different methods and (3) to establish validity on one of these methods. Thirteen Brazilian SV national team players (five males and eight females) participated in this study. The air-displacement plethysmography (ADP) method as the criterion assessment and the skinfolds (SF) method were conducted for each player. Results showed that there were no significant differences between the values of all players, which ADP and SF measured for body fat percentage (BF%) and body density (BD) (p > 0.05). We found significantly different values between male and female players for BF% by SF (p = 0.04) and BD by SF (p = 0.04). A high degree of reliability was found between ADP and SF measures for BF% and BD. There were statistically significant positive correlations between BF% and BD in all values for both methods (p < 0.01). This pilot study suggests that considering the magnitude of space, expense, and other limitations related to the ADP method against the SF method, we recommend using the SF method, which is a valid, viable and reliable method for measuring body composition in elite SV players.

Incorporating non-linear effects in fast semi-analytical thermal modelling of powder bed fusion

The usefulness of semi-analytical thermal models for predicting the connection between process, microstructure and properties in powder bed fusion has been well illustrated in recent years. Such an approach provides predictions that are orders of magnitude more computationally efficient than numerical approaches. However, the opportunity to make predictions that span several orders of magnitude in space and time comes at the cost of significant simplifications, limiting fully quantitative predictions without empirical calibration. This approach relies on the solution to a linear problem meaning that first order non-linear effects induced by e.g. the temperature dependence of material properties and surface boundary conditions, are not incorporated. Here, we revisit these limitations and highlight ways that temperature-varying material properties and radiative heat loss from the melt pool can be systematically accounted for without prior calibration. These corrections, made with an eye to minimizing additional computational overhead, bring the technique’s predictive capability much closer to that of higher fidelity thermal simulations without a significant computational cost. Quantitative comparisons to experiments are used to illustrate the important impact of including such corrections.

A Massively Parallel Spatially Resolved Stochastic Cluster Dynamics Method for Simulations of Irradiated Materials

The spatially resolved stochastic cluster dynamics (SRSCD) method is one of the most important methods for simulating the time-evolution of spatially correlated microstructures in irradiated materials. It is a kinetic Monte Carlo-based method for stochastically evolving integer-valued populations of defects within multi finite volume elements according to reaction rates. However, the increasing spatial scale and complexity of the simulated systems have exceeded the capabilities of serial SRSCD. To extend SRSCD to simulate large-scale complex systems, we propose a massively parallel SRSCD method in this paper and implement the program named MISA-SLSCD. It contains four contributions: (1) a dynamic Defect-Reaction Tree data structure to efficiently store and update millions of defect species and reactions; (2) a double-grouping search strategy to speed up the search for defects and reactions; (3) an adaptive synchronous algorithm to balance the accuracy and efficiency of simulations; and (4) an on-demand communication strategy to eliminate communication redundancy. A series of numerical simulation results show that our method has high accuracy in simulating damage accumulation in irradiated materials and obtains a good performance of over 90% parallel efficiency on 32,000 CPU cores with a 32 million-volume-element system. Program summaryProgram Title: MISA-SLSCDCPC Library link to program files:https://doi.org/10.17632/ctmn5f5bzk.1Code Ocean capsule:https://codeocean.com/capsule/8351487Licensing provisions: BSD 3-clauseProgramming language: Fortran90Nature of problem: Behaviours of defects in irradiated materials are typically space-dependent long-term dynamical processes, and spanning multiple orders of magnitude in space (∼nm to m) and time (∼ μs to years). The SRSCD method is an important method for simulating the time-evolution of spatially correlated microstructures in irradiated materials. However, the increasing spatial scale and complexity of the simulated systems have exceeded the capabilities of serial and existing parallel versions. It is necessary to develop new massively parallel method and program to extend SRSCD to simulate large-scale complex systems.Solution method: To extend SRSCD to simulate large-scale complex systems, we propose a massively parallel SRSCD method in this paper and implement the program named MISA-SLSCD. It contains: (1) a dynamic Defect-Reaction Tree data structure to efficiently store and update millions of defect species and reactions; (2) a double-grouping search strategy to speed up the search for defects and reactions; (3) an adaptive synchronous algorithm to balance the accuracy and efficiency of simulations; and (4) an on-demand communication strategy to eliminate communication redundancy.

Nanoscale Prediction of the Thermal, Mechanical, and Transport Properties of Hydrated Clay on 106- and 1015-Fold Larger Length and Time Scales.

Coupled thermal, hydraulic, mechanical, and chemical (THMC) processes, such as desiccation-driven cracking or chemically driven fluid flow, significantly impact the performance of composite materials formed by fluid-mediated nanoparticle assembly, including energy storage materials, ordinary Portland cement, bioinorganic nanocomposites, liquid crystals, and engineered clay barriers used in the isolation of hazardous wastes. These couplings are particularly important in the isolation of high-level radioactive waste (HLRW), where heat generated by radioactive decay can drive the temperature up to at least 373 K in the engineered barrier. Here, we use large-scale all-atom molecular dynamics simulations of hydrated smectite clay nanoparticle assemblages to predict the fundamental THMC properties of hydrated compacted clay over a wide range of temperatures (up to 373 K) and dry densities relevant to HLRW management. Equilibrium simulations of clay-water mixtures at different hydration levels are analyzed to quantify material properties, including thermal conductivity, heat capacity, thermal expansion, suction, water and ion self-diffusivity, and hydraulic conductivity. Predictions are validated against experimental results for the properties of compacted bentonite clay. Our results demonstrate the feasibility of using atomistic-level simulations of assemblages of clay nanoparticles on scales of tens of nanometers and nanoseconds to infer the properties of compacted bentonite on scales of centimeters and days, a direct upscaling over 6 orders of magnitude in space and 15 orders of magnitude in time.

Highly accurate and numerical tractable coupling of nanoparticle nucleation, growth and fluid flow

Supersaturation as the thermodynamic driving force of particulate precipitation couples the mixing of solute compounds with the solid formation consisting of nucleation and molecular growth. Because the process spans many orders of magnitude in space and time, gathering spatio-temporal information by experiments is hardly achievable. Numerical simulation of the nonlinear, non-local balance equations for mass, momentum and dispersed phase provide detailed solution information in space and time. We here introduce a novel, highly accurate and very efficient numerical formulation of the population balance equations for the dispersed phase coupled to the concentration and velocity field. We reformulate the problem in terms of a fixed-point equation solving the population balance equation along Lagrangian paths with the recently developed exact Method of Moments (eMoM, Pflug et al., 2020) coupled to the concentration field (Eulerian frame). The analytical reformulation provides the information about the full particle size distribution while the computational effort remains in the order of classical moment methods and is much less than numerical schemes approximating the full particle size distribution such as finite volume methods. Furthermore, we explore the difference between an Euler-Euler and Euler-Lagrange coupling considering moderate to large P é clet numbers for the solutes and particles. It turns out that the mean particle size as a solution of the population balance is much more sensitive to the numerical discretization of the advection term than the approximation of the number density function. Based on this finding, we eventually explore the impact of particle diffusion on the solid formation process and give guidelines, when particle diffusion should be taken into account.

Modeling Nearby Low-Luminosity Active-Galactic-Nucleus Jet Images at All VLBI Scales

Relativistic jets from nearby low-luminosity active-galactic-nuclei (LLAGN) were observed by Very-Long Baseline Interferometry (VLBI) across many orders of magnitude in space, from milliparsec to sub-parsec scales, and from the jet base in the vicinity of black holes to the jet collimation and acceleration regions. With the improved resolution for VLBI observations, resolved VLBI jet morphologies provide valuable opportunities for testing and constraining black hole jet physics. In this review, we summarize and discuss the current progress of modeling nearby LLAGN jet images from horizon scales to large scales, including the construction of jet models and the assumed emission details. Illustrative examples for jet image modeling are also given to demonstrate how jet image features may vary with the underlying physics.

Impact of Physical Heterogeneity and Transport Conditions on Effective Reaction Rates in Dissolution

A continuous-time random walk (CTRW) reactive transport model is used to study the impact of physical heterogeneity on the effective reaction rates in porous media in a sample of length 15 cm over timescales up to 10^8 s (3 years). The model has previously been validated using nuclear magnetic resonance (NMR) measurements during dissolution of a limestone. The model assumes first-order reaction. We construct three domains with increasing physical heterogeneity and study dissolution at four Péclet numbers, Pe = 0.0542, 0.542, 5.42 and 54.2. We characterize signatures of physical heterogeneity in the three porous media using velocity distributions and show how these imprint on the signatures of particle displacement, namely particle propagator distributions. In addition, we demonstrate the ability of our CTRW model to capture the impact of physical heterogeneity on the longitudinal dispersion coefficient over several orders of magnitude in space and time. Reactive transport simulations show that the effective reaction rates depend on (i) initial physical heterogeneity and (ii) transport conditions. For all heterogeneities and Pe, the late-time reaction rate exhibits a time dependence t^{-a} with a ne 0.5 that indicates the persistence of incomplete mixing. We show that the higher the initial heterogeneity, the lower the late-time reaction rate. A decrease in Pe promotes mixing by diffusion over advection, resulting in higher reaction rates. The post-dissolution propagators indicate an increase in the degree of non-Fickian transport. Overall, we establish a framework to demonstrate and quantify the impact of physical heterogeneity on transport and effective reaction rates in porous media.

Black Hole to Photosphere: 3D GRMHD Simulations of Collapsars Reveal Wobbling and Hybrid Composition Jets

Long-duration γ-ray bursts (GRBs) accompany the collapse of massive stars and carry information about the central engine. However, no 3D models have been able to follow these jets from their birth via black hole (BH) to the photosphere. We present the first such 3D general-relativity magnetohydrodynamic simulations, which span over six orders of magnitude in space and time. The collapsing stellar envelope forms an accretion disk, which drags inwardly the magnetic flux that accumulates around the BH, becomes dynamically important, and launches bipolar jets. The jets reach the photosphere at ∼1012 cm with an opening angle θ j ∼ 6° and a Lorentz factor Γ j ≲ 30, unbinding ≳90% of the star. We find that (i) the disk–jet system spontaneously develops misalignment relative to the BH rotational axis. As a result, the jet wobbles with an angle θ t ∼ 12°, which can naturally explain quiescent times in GRB lightcurves. The effective opening angle for detection θ j + θ t suggests that the intrinsic GRB rate is lower by an order of magnitude than standard estimates. This suggests that successful GRBs are rarer than currently thought and emerge in only ∼0.1% of supernovae Ib/c, implying that jets are either not launched or choked inside most supernova Ib/c progenitors. (ii) The magnetic energy in the jet decreases due to mixing with the star, resulting in jets with a hybrid composition of magnetic and thermal components at the photosphere, where ∼10% of the gas maintains magnetization σ ≳ 0.1. This indicates that both a photospheric component and reconnection may play a role in the prompt emission.

Evaluation of aerosol number concentrations from CALIPSO with ATom airborne in situ measurements

Abstract. The present study aims to evaluate the available aerosol number concentration (ANC) retrieval algorithms for spaceborne lidar CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) aboard the CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) satellite with the airborne in situ measurements from the ATom (Atmospheric Tomography Mission) campaign. We used HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory model) to match both the measurements in space and identified 53 cases that were suitable for comparison. Since the ATom data include the dry aerosol extinction coefficient, we used kappa parameterization to adjust the ambient measurements from CALIOP to dry conditions. As both the datasets have a different vertical resolution, we regrid them to uniform height bins of 240 m from the surface to a height of 5 km. On comparing the dry extinction coefficients, we found a reasonable agreement between the CALIOP and ATom measurements with Spearman's correlation coefficient of 0.715. Disagreement was found mostly for retrievals above 3 km altitude. Thus, to compare the ANC, which may vary by orders of magnitude in space and time, we further limit the datasets and only select those height bins for which the CALIOP-derived dry extinction coefficient is within ±50 % of the ATom measurements. This additional filter further increases the probability of comparing the same air parcel. The altitude bins which qualify the extinction coefficient constraint are used to estimate ANC with a dry radius &gt;50 nm (n50,dry) and &gt;250 nm (n250,dry). The POLIPHON (Polarization Lidar Photometer Networking) and OMCAM (Optical Modelling of CALIPSO Aerosol Microphysics) algorithms were used to estimate the n50,dry and n250,dry. The POLIPHON estimates of n50,dry and n250,dry were found to be in good agreement with the in situ measurements, with a correlation coefficient of 0.829 and 0.47, root mean square error (RMSE) of 234 and 13 cm−3, and bias of −97 and 4 cm−3, respectively. The OMCAM estimates of n50,dry and n250,dry were also in reasonable agreement with the in situ measurements, with a correlation coefficient of 0.823 and 0.463, RMSE of 247 and 13 cm−3, and bias of 44 and 4 cm−3, respectively. However, we found that the OMCAM-estimated n50,dry were about an order of magnitude less than the in situ measurements for marine-dominated cases. We propose a modification to the OMCAM algorithm by using an AERONET-based marine model. With the updated OMCAM algorithm, the n50,dry agrees well with the ATom measurements. Such concurrence between the satellite-derived ANC and the independent ATom in situ measurements emboldens the use of CALIOP in studying the aerosol–cloud interactions.

Particle Post – Letters from the Universe: A sound installation through cosmic muons

We humans are all in a constant correspondence with the universe. It speaks to us ever so subtly, showering us with invisible remnants of our mutual distant past, across the magnitude of space and time. Yet, in the infinitely grand scale of all things universal, one tends to neglect the infinitely small. It is this omnipresent chronicle of the universe that is humanity's most intimate connection to the everywhere and always, spoken to us through invisible particles and waves of energy resembling the smallest of postcards and parcels. Within the journey of each traveling particle there lies a piece of a common history, a memoir of a voyage spanning billions of years, connecting humans, in this very point in space and time, to the dawn of the universe. We propose that one way for humans to appreciate the wonders of the universe is through the poetics of sound, connecting humans to the universe by designing a sound installation. Receiving cosmic muons through a scintillation detector, a postbox subtly emits sound and light as a direct consequence to each particle it detects. This unheard sound of the universe is the result of a poetic and artistic transformation. It is therefore not a product of mere sonification, but rather of a meaningful translation of what humans do not, and cannot directly perceive. It is through this process that the implied aesthetics of the unobservable are being explored, as are the means by which it could be indirectly appreciated in different ways through the bodies and minds of humans. In this paper, the background, concepts, systems, and the collaborative process of creating this installation, Particle Post – Letters from the Universe are discussed. With the team consisting of sound designers, artists, and an experimental physicist, it was presented at the Ars Electronica Festival 2019 in Linz, Austria. Considering these processes and the result, future directions of this project are also suggested.

Spectral Ranking of Causal Influence in Complex Systems.

Similar to natural complex systems, such as the Earth’s climate or a living cell, semiconductor lithography systems are characterized by nonlinear dynamics across more than a dozen orders of magnitude in space and time. Thousands of sensors measure relevant process variables at appropriate sampling rates, to provide time series as primary sources for system diagnostics. However, high-dimensionality, non-linearity and non-stationarity of the data are major challenges to efficiently, yet accurately, diagnose rare or new system issues by merely using model-based approaches. To reliably narrow down the causal search space, we validate a ranking algorithm that applies transfer entropy for bivariate interaction analysis of a system’s multivariate time series to obtain a weighted directed graph, and graph eigenvector centrality to identify the system’s most important sources of original information or causal influence. The results suggest that this approach robustly identifies the true drivers or causes of a complex system’s deviant behavior, even when its reconstructed information transfer network includes redundant edges.

An index for moving objects with constant-time access to their compressed trajectories

ABSTRACT As the number of vehicles and devices equipped with GPS technology has grown explosively, an urgent need has arisen for time- and space-efficient data structures to represent their trajectories. The most commonly desired queries are the following: queries about an object’s trajectory, range queries, and nearest neighbor queries. In this paper, we consider that the objects can move freely and we present a new compressed data structure for storing their trajectories, based on a combination of logs and snapshots, with the logs storing sequences of the objects’ relative movements and the snapshots storing their absolute positions sampled at regular time intervals. We call our data structure ContaCT because it provides Constant- time access to Compressed Trajectories. Its logs are based on a compact partial-sums data structure that returns cumulative displacement in constant time, and allows us to compute in constant time any object’s position at any instant, enabling a speedup when processing several other queries. We have compared ContaCT experimentally with another compact data structure for trajectories, called GraCT, and with a classic spatio-temporal index, the MVR-tree. Our results show that ContaCT outperforms the MVR-tree by orders of magnitude in space and also outperforms the compressed representation in time performance.

Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning

All medications have adverse effects. Among the most serious of these are cardiac arrhythmias. Current paradigms for drug safety evaluation are costly, lengthy, conservative, and impede efficient drug development. Here, we combine multiscale experiment and simulation, high-performance computing, and machine learning to create a risk estimator to stratify new and existing drugs according to their proarrhythmic potential. We capitalize on recent developments in machine learning and integrate information across 10 orders of magnitude in space and time to provide a holistic picture of the effects of drugs, either individually or in combination with other drugs. We show, both experimentally and computationally, that drug-induced arrhythmias are dominated by the interplay between two currents with opposing effects: the rapid delayed rectifier potassium current and the L-type calcium current. Using Gaussian process classification, we create a classifier that stratifies drugs into safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 22 common drugs exclusively on the basis of their concentrations at 50% current block. Our new risk assessment tool explains under which conditions blocking the L-type calcium current can delay or even entirely suppress arrhythmogenic events. Using machine learning in drug safety evaluation can provide a more accurate and comprehensive mechanistic assessment of the proarrhythmic potential of new drugs. Our study paves the way toward establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders.

Fully Functional Suffix Trees and Optimal Text Searching in BWT-Runs Bounded Space

Indexing highly repetitive texts—such as genomic databases, software repositories and versioned text collections—has become an important problem since the turn of the millennium. A relevant compressibility measure for repetitive texts is r , the number of runs in their Burrows-Wheeler Transforms (BWTs). One of the earliest indexes for repetitive collections, the Run-Length FM-index, used O ( r ) space and was able to efficiently count the number of occurrences of a pattern of length m in a text of length n (in O ( m log log n ) time, with current techniques). However, it was unable to locate the positions of those occurrences efficiently within a space bounded in terms of r . In this article, we close this long-standing problem, showing how to extend the Run-Length FM-index so that it can locate the occ occurrences efficiently (in O ( occ log log n ) time) within O ( r ) space. By raising the space to O ( r log log n ), our index counts the occurrences in optimal time, O ( m ), and locates them in optimal time as well, O ( m + occ ). By further raising the space by an O ( w / log σ) factor, where σ is the alphabet size and w = Ω (log n ) is the RAM machine size in bits, we support count and locate in O (⌈ m log (σ)/ w ⌉) and O (⌈ m log (σ)/ w ⌉ + occ ) time, which is optimal in the packed setting and had not been obtained before in compressed space. We also describe a structure using O ( r log ( n / r )) space that replaces the text and extracts any text substring of length ℓ in the almost-optimal time O (log ( n / r )+ℓ log (σ)/ w ). Within that space, we similarly provide access to arbitrary suffix array, inverse suffix array, and longest common prefix array cells in time O (log ( n / r )), and extend these capabilities to full suffix tree functionality, typically in O (log ( n / r )) time per operation. Our experiments show that our O ( r )-space index outperforms the space-competitive alternatives by 1--2 orders of magnitude in time. Competitive implementations of the original FM-index are outperformed by 1--2 orders of magnitude in space and/or 2--3 in time.

Soil hydraulic properties: A simple and practical approach to estimate the number of samples

There have been a number of studies dealing with soil hydraulic properties. Yet, there is a poor discussion on the number of samples necessary to represent such variables that usually vary orders of magnitude in space. In the present paper, we examine the adequate number of samples for two soil saturated hydraulic conductivity (Ksat) data sets: (1) normal distribution (a 40 year-old pasture) and (2) non-normal distribution (primary forest). To assess the adequate number of samples in each case, we used for normal distribution, an statistical criterion of standard deviation lower than 5% compared to a high sampling effort (n = 25) as an indicative of a proper representation of Ksat variability. In the case of non-normal distribution, we used the same criterion but using median absolute deviation (a non-parametric statistics). Both data sets were available in Salemi et al. (2013) and were Ksat measured at 0.15 m soil depth for medium-textured inceptisols in São Paulo State, Brazil. For each data set, we simulated 10 ‘new’ samplings in which we calculated mean and standard deviation from sample 1 to 25 (for normal data) and median and median absolute deviation (for non-normal data). We found that, on average, at least 17 to 22 samples had to be collected to meet the adopted criterion for normal data whereas 20 to 25 had to be collected for non-normal data. Such numbers of samples exceed those used in a number of papers. Additional examples of this method with a light modification are given to establish number of samples in new study areas as well as to estimate number of samples when comparing two (or more) land-uses. Simple and practical procedures like those presented here could estimate the number of samples that adequately represents soil hydraulic properties variability.

Learning the space-time phase diagram of bacterial swarm expansion

Coordinated dynamics of individual components in active matter are an essential aspect of life on all scales. Establishing a comprehensive, causal connection between intracellular, intercellular, and macroscopic behaviors has remained a major challenge due to limitations in data acquisition and analysis techniques suitable for multiscale dynamics. Here, we combine a high-throughput adaptive microscopy approach with machine learning, to identify key biological and physical mechanisms that determine distinct microscopic and macroscopic collective behavior phases which develop as Bacillus subtilis swarms expand over five orders of magnitude in space. Our experiments, continuum modeling, and particle-based simulations reveal that macroscopic swarm expansion is primarily driven by cellular growth kinetics, whereas the microscopic swarming motility phases are dominated by physical cell-cell interactions. These results provide a unified understanding of bacterial multiscale behavioral complexity in swarms.

Latent viral reactivation is associated with changes in plasma antimicrobial protein concentrations during long-duration spaceflight

Long duration spaceflights are associated with profound dysregulation of the immune system and latent viral reactivations. However, little is known on the impact of long duration spaceflight on innate immunity which raises concerns on crewmembers' ability to fight infections during a mission. The aim of this study was to determine the effects of spaceflight on plasma antimicrobial proteins (AMPs) and how these changes impact latent herpesvirus reactivations. Plasma, saliva and urine samples were obtained from 23 crewmembers before, during and after a 6-month mission on the International Space Station (ISS). Plasma AMP concentrations were determined by ELISA, and saliva Epstein-Barr virus (EBV) and varicella zoster virus (VZV) and urine cytomegalovirus (CMV) DNA levels were quantified by Real-Time PCR. There was a non-significant increase in plasma HNP1-3 and LL-37 during the early and middle stages of the missions, which was significantly associated with changes in viral DNA during and after spaceflight. Plasma HNP1-3 and Lysozyme increased at the late mission stages in astronauts who had exhibited EBV and VZV reactivations during the early flight stages. Following return to Earth and during recovery, HNP1-3 and lysozyme concentrations were associated with EBV and VZV viral DNA levels, reducing the magnitude of viral reactivation. Reductions in plasma LL-37 upon return were associated with greater CMV reactivation. This study shows that biomarkers of innate immunity appeared to be partially restored after 6-months in space and suggests that following adaptation to the space environment, plasma HNP1-3 and lysozyme facilitate the control of EBV and VZV reactivation rate and magnitude in space and upon return on earth. However, the landing-associated decline in plasma LL-37 may enhance the rate of CMV reactivation in astronauts following spaceflight, potentially compromising crewmember health after landing.

Parametric Analysis of the Factors Influencing Airborne Infection

This study investigated the influence of the various factors such as airborne pathogen type, space magnitude, ventilation rate and exposure time on the airborne infection risk. The parametric study is performed by airflow network simulation tool, CONTAMW 3.2. In addition, Wells-Riley equation is adopted to calculate airborne infection probability in a controlled volume. The results indicate occupancy time of infector significantly influence the airborne infection probability. In addition, smaller room size also affected to the increased infection risk. Especially, in the case of infector reside in a small room (30 m2) for 4 hours with 6 ACH of ventialtin rate, Tuberculosis infection risk of inpatient around infector increased to 21.0% (Influenza: 90.6%). Even though ventilation rate of 6 ACH looks appropriate in general condition to prevent airborne infection, increased ventilation over 6 ACH must be implemented to these critical environmental conditions involving smaller room size and long occupancy time.