Relevant Time Scales Research Articles

Comprehensive Euler/Lagrange modelling including particle erosion for confined gas-solid flows

The present research aims to assess the capability of a comprehensive Euler/Lagrange approach for predicting gas-solid flows and the associated solid particle erosion. The open-source code OpenFOAM® 4.1 was used to carry out the numerical simulations, where the standard Lagrangian libraries were substantially extended to account for all necessary models. Particles are tracked considering both translational and rotational motion as well as all relevant forces, such as gravity/buoyancy, drag and transverse lift due to shear and particle rotation. The tracking time step was dynamically adapted according to the locally relevant time scales, which drastically reduces computational times. Stochastic approaches are adopted to model particle turbulent dispersion, particle collisions with rough walls and particle-particle interactions. Five solid particle erosion models, available in the literature, were considered to estimate pipe bend erosion. Three study cases are provided to validate the adopted numerical approach and erosion models extensively. The first case intends to evaluate the ability of the extended CFD code to predict the behaviour of gas-solid flows in pneumatic conveying systems. This goal is achieved by comparing the numerical results with the experimental data obtained by Huber (1997) and Huber and Sommerfeld (1994, 1998) in a pneumatic conveying system. Here, the importance of considering inter-particle collisions and surface roughness for predicting particle velocity, mass flux and mean diameter distributions in gas-solid flows is highlighted. The second and third case intend to evaluate the ability of the erosion models in estimating bend erosion in diluted gas-solid flows. The erosion data obtained experimentally by Mazumder et al. (2008) and Solnordal et al. (2015) in very dilut pneumatic conveying systems is used for validating the numerical results, neglecting now inter-particle collisions and two-way coupling. Besides a comprehensive analysis of the different influential properties on erosion, the innovation of the present study is as follows. For the first time also a temporal modification of the surface roughness due to the erosion was considered in the simulations obtained from previous measurements (Novelletto Ricardo & Sommerfeld, 2020). As the surface roughness is increased due to erosion, eventually erosion rate becomes lower. This is the result of diminishing wall collision frequency. Simulations for several degrees of surface roughness showed that larger roughness is coupled with a drastic reduction of erosion. Hence, numerical simulations neglecting wall surface roughness are not realistic. The consideration of a particle size distribution instead of mono-sized computations showed a possible reduction of erosion rate. The detailed analysis of the different single-particle erosion models revealed that the model proposed by Oka et al. (2005) and Oka and Yoshida (2005) yields the best agreement with the measurements, however particle and wall properties are needed.

Helium bubble size effects on the surface morphological response of plasma-facing tungsten

Abstract We report a simulation study on the effects of helium (He) bubble size on the morphological evolution and pattern formation on the surface of tungsten used as a plasma-facing component (PFC) in nuclear fusion devices. We have carried out a systematic investigation based on self-consistent dynamical simulations of surface morphological evolution according to an atomistically-informed, 3D continuum-scale model that captures well the relevant length and time scales of surface nanostructure formation in PFC tungsten. The model accounts for PFC surface diffusion, driven by the biaxial compressive stress originating from the over-pressurized He bubbles in the near-surface region of PFC tungsten as a result of He plasma exposure, combined with the formation of self-interstitial atoms in tungsten that diffuse toward the PFC surface and the flux of surface adatoms generated as a result of surface vacancy-adatom pair formation upon He implantation; this transport of surface adatoms contributes to the anisotropic growth of surface nanostructural features due to the different rates of adatom diffusion along and across step edges of islands on the tungsten surface. Our detailed analysis reveals that varying the average He bubble size plays an important role in the PFC surface growth kinetics as well as the resulting surface topography. Specifically, we find that an increase in the He bubble size leads to a deceleration in the growth rate of the tungsten nanotendrils that emanate from the PFC surface. We also find that the separation distance between the resulting surface features increases with increasing He bubble size, as well as over time. This coarsening effect is a thermally activated process resulting in an accurate description of the temperature dependence of the average surface feature separation by an Arrhenius relation.

Temporal control by cofactors prevents kinetic trapping in retroviral Gag lattice assembly

For retroviruses like HIV to proliferate, they must form virions shaped by the self-assembly of Gag polyproteins into a rigid lattice. This immature Gag lattice has been structurally characterized and reconstituted in vitro, revealing the sensitivity of lattice assembly to multiple cofactors. Due to this sensitivity, the energetic criterion for forming stable lattices is unknown, as are their corresponding rates. Here, we use a reaction-diffusion model designed from the cryo-ET structure of the immature Gag lattice to map a phase diagram of assembly outcomes controlled by experimentally constrained rates and free energies, over experimentally relevant timescales. We find that productive assembly of complete lattices in bulk solution is extraordinarily difficult due to the large size of this ∼3700 monomer complex. Multiple Gag lattices nucleate before growth can complete, resulting in loss of free monomers and frequent kinetic trapping. We therefore derive a time-dependent protocol to titrate or “activate” the Gag monomers slowly within the solution volume, mimicking the biological roles of cofactors. This general strategy works remarkably well, yielding productive growth of self-assembled lattices for multiple interaction strengths and binding rates. By comparing to the in vitro assembly kinetics, we can estimate bounds on rates of Gag binding to Gag and the cellular cofactor IP6. Our results show that Gag binding to IP6 can provide the additional time delay necessary to support smooth growth of the immature lattice with relatively fast assembly kinetics, mostly avoiding kinetic traps. Our work provides a foundation for predicting and disrupting formation of the immature Gag lattice via targeting specific protein-protein binding interactions.

Dynamic Effects on Hole Transport in Amorphous Organic Semiconductors: a Combined QM/MM and kMC Study.

Small-molecule-based amorphous organic semiconductors (OSCs) are essential components of organic photovoltaics and organic light-emitting diodes. The charge carrier mobility of these materials is an integral and limiting factor in regard to their performance. Integrated computational models for the hole mobility, taking into account structural disorder in systems of several thousand molecules, have been the object of research in the past. Due to static and dynamic contributions to the total structural disorder, efficient strategies to sample the charge transfer parameters become necessary. In this paper, we investigate the impact of structural disorder in amorphous OSCs on the transfer parameters and charge mobilities in different materials. We present a sampling strategy for incorporating static and dynamic structural disorder which are based on QM/MM methods using semiempirical Hamiltonians and extensive MD sampling. We show how the disorder affects the distributions of HOMO energies and intermolecular couplings and validate the results using kinetic Monte Carlo simulations of the mobility. We find that dynamic disorder causes an order of magnitude difference in the calculated mobility between morphologies of the same material. Our method allows the sampling of disorder in HOMO energies and couplings, and the statistical analysis enables us to characterize the relevant time scales on which charge transfer occurs in these complex materials. The findings presented here shed light on the interplay of the fluctuating amorphous matrix with charge carrier transport and aid in the development of a better understanding of these complex processes.

Realistic scheme for quantum simulation of {{mathbb{Z}}}_{2} lattice gauge theories with dynamical matter in (2 + 1)D

Gauge fields coupled to dynamical matter are ubiquitous in many disciplines of physics, ranging from particle to condensed matter physics, but their implementation in large-scale quantum simulators remains challenging. Here we propose a realistic scheme for Rydberg atom array experiments in which a {{mathbb{Z}}}_{2} gauge structure with dynamical charges emerges on experimentally relevant timescales from only local two-body interactions and one-body terms in two spatial dimensions. The scheme enables the experimental study of a variety of models, including (2 + 1)D {{mathbb{Z}}}_{2} lattice gauge theories coupled to different types of dynamical matter and quantum dimer models on the honeycomb lattice, for which we derive effective Hamiltonians. We discuss ground-state phase diagrams of the experimentally most relevant effective {{mathbb{Z}}}_{2} lattice gauge theories with dynamical matter featuring various confined and deconfined, quantum spin liquid phases. Further, we present selected probes with immediate experimental relevance, including signatures of disorder-free localization and a thermal deconfinement transition of two charges.

A mechanistic pharmacokinetic model for intrathecal administration of antisense oligonucleotides.

Intrathecal administration is an important mode for delivering biological agents targeting central nervous system (CNS) diseases. However, current clinical practices lack a sound theorical basis for a quantitative understanding of the variables and conditions that govern the delivery efficiency and specific tissue targeting especially in the brain. This work presents a distributed mechanistic pharmacokinetic model (DMPK) for predictive analysis of intrathecal drug delivery to CNS. The proposed DMPK model captures the spatiotemporal dispersion of antisense oligonucleotides (ASO) along the neuraxis over clinically relevant time scales of days and weeks as a function of infusion, physiological and molecular properties. We demonstrate its prediction capability using biodistribution data of antisense oligonucleotide (ASO) administration in non-human primates. The results are in close agreement with the observed ASO pharmacokinetics in all key compartments of the central nervous system. The model enables determination of optimal injection parameters such as intrathecal infusion volume and duration for maximum ASO delivery to the brain. Our quantitative model-guided analysis is suitable for identifying optimal parameter settings to target specific brain regions with therapeutic drugs such as ASOs.

Emergent anomalous transport and non-Gaussianity in a simple mobile–immobile model: the role of advection

We analyse the transport of diffusive particles that switch between mobile and immobile states with finite rates. We focus on the effect of advection on the density functions and mean squared displacements (MSDs). At relevant intermediate time scales we find strong anomalous diffusion with cubic scaling in time of the MSD for high Péclet numbers. The cubic scaling exists for short and long mean residence times in the immobile state . For long the plateau in the MSD at intermediate times, previously found in the absence of advection, also exists for high Péclet numbers. Initially immobile tracers are subject to the newly observed regime of advection induced subdiffusion for short immobilisations and high Péclet numbers. In the long-time limit the effective advection velocity is reduced compared to advection in the mobile phase. In contrast, the MSD is enhanced by advection. We explore physical mechanisms behind the emerging non-Gaussian density functions and the features of the MSD.

Lipid packing in biological membranes governs protein localization and membrane permeability

Plasma membrane (PM) heterogeneity has long been implicated in various cellular functions. However, mechanistic principles governing functional regulations of lipid environment are not well understood due to the inherent complexities associated with the relevant length and timescales that limit both direct experimental measurements and their interpretation. In this context, computer simulations hold immense potential to investigate molecular-level interactions and mechanisms that lead to PM heterogeneity and its functions. Herein, we investigate spatial and dynamic heterogeneity in model membranes with coexisting liquid ordered and liquid disordered phases and characterize the membrane order in terms of the local topological changes in lipid environment using the nonaffine deformation framework. Furthermore, we probe the packing defects in these membranes, which can be considered as the conjugate of membrane order assessed in terms of the nonaffine parameter. In doing so, we formalize the connection between membrane packing and local membrane order and use that to explore the mechanistic principles behind their functions. Our observations suggest that heterogeneity in mixed phase membranes is a consequence of local lipid topology and its temporal evolution, which give rise to disparate lipid packing in ordered and disordered domains. This in turn governs the distinct nature of packing defects in these domains that can play a crucial role in preferential localization of proteins in mixed phase membranes. Furthermore, we observe that lipid packing also leads to contrasting distribution of free volume in the membrane core region in ordered and disordered membranes, which can lead to distinctive membrane permeability of small molecules. Our results, thus, indicate that heterogeneity in mixed phase membranes closely governs the membrane functions that may emerge from packing-related basic design principles.

Applications of organoid technology to brain tumors.

Lacking appropriate model impedes basic and preclinical researches of brain tumors. Organoids technology applying on brain tumors enables great recapitulation of the original tumors. Here, we compared brain tumor organoids (BTOs) with common models including cell lines, tumor spheroids, and patient-derived xenografts. Different BTOs can be customized to research objectives and particular brain tumor features. We systematically introduce the establishments and strengths of four different BTOs. BTOs derived from patient somatic cells are suitable for mimicking brain tumors caused by germline mutations and abnormal neurodevelopment, such as the tuberous sclerosis complex. BTOs derived from human pluripotent stem cells with genetic manipulations endow for identifying and understanding the roles of oncogenes and processes of oncogenesis. Brain tumoroids are the most clinically applicable BTOs, which could be generated within clinically relevant timescale and applied for drug screening, immunotherapy testing, biobanking, and investigating brain tumor mechanisms, such as cancer stem cells and therapy resistance. Brain organoids co-cultured with brain tumors (BO-BTs) own the greatest recapitulation of brain tumors. Tumor invasion and interactions between tumor cells and brain components could be greatly explored in this model. BO-BTs also offer a humanized platform for testing the therapeutic efficacy and side effects on neurons in preclinical trials. We also introduce the BTOs establishment fused with other advanced techniques, such as 3D bioprinting. So far, over 11 brain tumor types of BTOs have been established, especially for glioblastoma. We conclude BTOs could be a reliable model to understand brain tumors and develop targeted therapies.

Monte Carlo study of the free radical yields in minibeam radiation therapy.

Minibeam radiation therapy (MBRT) is a novel technique which has been shown to widen the therapeutic window through significant normal tissue sparing. Despite the heterogeneous dose distributions, tumor control is still ensured. Nevertheless the exact radiobiological mechanisms responsible for MBRT efficacy are not fullyunderstood. Reactive oxygen species (ROS) resulting from water radiolysis were investigated given their implications not only on targeted DNA damage, but also for their role in the immune response and non-targeted cell signalling effects: two potential drivers of MBRTefficacy. Monte Carlo simulations were performed using TOPAS-nBio to carry out the irradiation of a water phantom with beams of protons (pMBRT), photons (xMBRT), 4 He ions (HeMBRT), and 12 C ions (CMBRT). Primary yields at the end of the chemical stage were calculated in spheres of 20 μm diameter, located in the peaks and valleys at various depths up to the Bragg peak. The chemical stage was limited to 1 ns to approximate biological scavenging, and the yield of · OH, H2 O2 , and wasrecorded. Beyond 10 mm, there were no substantial differences in the primary yields between peaks and valleys of the pMBRT and HeMBRT modalities. For xMBRT, there was a lower primary yield of the radical species · OHand at all depths in the valleys compared to the peaks, and a higher primary yield of H2 O2 . Compared to the peaks, the valleys of the CMBRT modality were subject to a higher · OHand yield, and lower H2 O2 yield. This difference between peaks and valleys became more severe in depth. Near the Bragg peak, the increase in the primary yield of the valleys over the peaks was 6% and 4% for · OH and respectively, while there was a decrease in the yield of H2 O2 by 16%. Given the similar ROS primary yields in the peaks and valleys of pMBRT and HeMBRT, the level of indirect DNA damage is expected to be directly proportional to the peak to valley dose ratio (PVDR). The difference in the primary yields implicates a lower level of indirect DNA damage in the valleys compared to the peaks than what would be suggested by the PVDR for xMBRT, and a higher level for CMBRT. These results highlight the notion that depending on the particle chosen, one can expect different levels of ROS in the peaks and valley that goes beyond what would be expected by the macroscopic PVDR. The combination of MBRT with heavier ions is shown to be particularly interesting as the primary yield in the valleys progressively diverges from the level observed in the peaks as the LET increases. While differences in the reported · OH yields of this work implicated the indirect DNA damage, H2 O2 yields particularly implicate non-targeted cell signalling effects, and therefore this work provides a point of reference for future simulations in which the distribution of this species at more biologically relevant timescales could beinvestigated.

Phase-Space Geometry and Optimal State Preparation in Quantum Metrology with Collective Spins

We revisit well-known protocols in quantum metrology using collective spins and propose a unifying picture for optimal state preparation based on a semiclassical description in phase space. We show how this framework allows for quantitative predictions of the timescales required to prepare various metrologically useful states, and that these predictions remain accurate even for moderate system sizes, surprisingly far from the classical limit. Furthermore, this framework allows us to build a geometric picture that relates optimal (exponentially fast) entangled probe preparation to the existence of separatrices connecting saddle points in phase space. We illustrate our results with the paradigmatic examples of the two-axis countertwisting and twisting-and-turning Hamiltonians, where we provide analytical expressions for all the relevant optimal timescales. Finally, we propose a generalization of these models to include p-body collective interaction (or p-order twisting), beyond the usual case of p=2. Using our geometric framework, we prove a no-go theorem for the local optimality of these models for p>2.Received 8 November 2022Revised 1 February 2023Accepted 28 March 2023DOI:https://doi.org/10.1103/PRXQuantum.4.020314Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum metrologyQuantum sensingTechniquesPhase space dynamicsQuantum InformationNonlinear DynamicsAtomic, Molecular & Optical

Simple master equations for describing driven systems subject to classical non-Markovian noise

Driven quantum systems subject to non-Markovian noise are typically difficult to model even if the noise is classical. We present a systematic method based on generalized cumulant expansions for deriving a time-local master equation for such systems. This master equation has an intuitive form that directly parallels a standard Lindblad equation, but contains several surprising features: the combination of driving and non-Markovianity results in effective time-dependent dephasing rates that can be negative, and the noise can generate Hamiltonian renormalizations even though it is classical. We analyze in detail the highly relevant case of a Rabi-driven qubit subject to various kinds of non-Markovian noise including 1/f fluctuations, finding an excellent agreement between our master equation and numerically-exact simulations over relevant timescales. The approach outlined here is more accurate than commonly employed phenomenological master equations which ignore the interplay between driving and noise.

Abstract 192: A novel and rapid patient-derived organoid breast cancer platform for precision medicine

Abstract An increasing number of studies performing correlative drug screens on patient-derived organoids (PDO) are revealing enormous potential for these models in predicting patient response to therapy. Despite this, their future use in a clinical setting is hindered by intrinsic limitations of PDO models, namely low success rates in establishing growing cell cultures from tumor tissue samples and long return times for drug response data that fall outside timescales of clinical actionability. To address these limitations, we leveraged technological advances in emulsion-based microfluidics and droplet generators to develop MicroOrganoSpheres (MOS) - microscale environments containing tumorspheres that retain structural, cellular, and genetic properties of an individual patient’s diseased tissue. Here, we focused on generating MOS from breast cancer tissue biopsies and resections across different subtypes of breast cancer. We established over 30 unique patients’ breast MOS samples at an average of 80% success, with the majority of these samples being hormone receptor positive (ER+/PR+) - the most clinically lethal and challenging subtype to establish. We evaluated MOS dose-responses across several first line standard of care chemotherapies for breast cancer. For this, we created a semi-automated workflow paired with high-content imaging for quantifying dose responses and tracking longitudinal cell dynamics during dosing. We generated dose response data for MOS samples, being able to move from primary tissue samples to MOS to drug dosing data within a clinically relevant timescale of 2-3 weeks. We conclude that our platform demonstrates the feasibility of 1) efficiently establishing MOS from breast cancer patient tumor samples from different subtypes and 2) performing drug dosing studies on these samples with improved turnaround times that enable clinical actionability for the patient. Together, these findings provide a foundation for evaluating this technology as a diagnostic tool in future clinical settings. Citation Format: David M. Graham, Gabrielle Rupprecht, Eric D. Bankaitis, Jeremy M. Force, Wylie Watlington, Steven W. Metzger, Xiling Shen, David Hsu. A novel and rapid patient-derived organoid breast cancer platform for precision medicine [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 192.

Constraints on the Timing and Extent of Deglacial Grounding Line Retreat in West Antarctica

AbstractProjections of Antarctica's contribution to future sea level rise are associated with significant uncertainty, in part because the observational record is too short to capture long‐term processes necessary to estimate ice mass changes over societally relevant timescales. Records of grounding line retreat from the geologic past offer an opportunity to extend our observations of these processes beyond the modern record and to gain a more comprehensive understanding of ice‐sheet change. Here, we present constraints on the timing and inland extent of deglacial grounding line retreat in the southern Ross Sea, Antarctica, obtained via direct sampling of a subglacial lake located 150 km inland from the modern grounding line and beneath >1 km of ice. Isotopic measurements of water and sediment from the lake enabled us to evaluate how the subglacial microbial community accessed radiocarbon‐bearing organic carbon for energy, as well as where it transferred carbon metabolically. Using radiocarbon as a natural tracer, we found that sedimentary organic carbon was microbially translocated to dissolved carbon pools in the subglacial hydrologic system during the 4.5‐year period of water accumulation prior to our sampling. This finding indicates that the grounding line along the Siple Coast of West Antarctica retreated more than 250 km inland during the mid‐Holocene (6.3 ± 1.0 ka), prior to re‐advancing to its modern position.

Diet changes thermal acclimation capacity, but not acclimation rate, in a marine ectotherm (Girella nigricans) during warming.

Global climate change is increasing thermal variability in coastal marine environments and the frequency, intensity and duration of marine heatwaves. At the same time, food availability and quality are being altered by anthropogenic environmental changes. Marine ectotherms often cope with changes in temperature through physiological acclimation, which can take several weeks and is a nutritionally demanding process. Here, we tested the hypothesis that different ecologically relevant diets (omnivorous, herbivorous, carnivorous) impact thermal acclimation rate and capacity, using a temperate omnivorous fish as a model (opaleye, Girella nigricans). We measured acute thermal performance curves for maximum heart rate because cardiac function has been observed to set upper thermal limits in ectotherms. Opaleye acclimated rapidly after raising water temperatures, but their thermal limits and acclimation rate were not affected by their diet. However, the fish's acclimation capacity for maximum heart rate was sensitive to diet, with fish in the herbivorous treatment displaying the smallest change in heart rate throughout acclimation. Mechanistically, ventricle fatty acid composition differed with diet treatment and was related to cardiac performance in ways consistent with homoviscous adaptation. Our results suggest that diet is an important, but often overlooked, determinant of thermal performance in ectotherms on environmentally relevant time scales.

High-speed low-light in vivo two-photon voltage imaging of large neuronal populations.

Monitoring spiking activity across large neuronal populations at behaviorally relevant timescales is critical for understanding neural circuit function. Unlike calcium imaging, voltage imaging requires kilohertz sampling rates that reduce fluorescence detection to near shot-noise levels. High-photon flux excitation can overcome photon-limited shot noise, but photobleaching and photodamage restrict the number and duration of simultaneously imaged neurons. We investigated an alternative approach aimed at low two-photon flux, which is voltage imaging below the shot-noise limit. This framework involved developing positive-going voltage indicators with improved spike detection (SpikeyGi and SpikeyGi2); a two-photon microscope ('SMURF') for kilohertz frame rate imaging across a 0.4 mm × 0.4 mm field of view; and a self-supervised denoising algorithm (DeepVID) for inferring fluorescence from shot-noise-limited signals. Through these combined advances, we achieved simultaneous high-speed deep-tissue imaging of more than 100 densely labeled neurons over 1 hour in awake behaving mice. This demonstrates a scalable approach for voltage imaging across increasing neuronal populations.

Physics-separating artificial neural networks for predicting sputtering and thin film deposition of AlN in Ar/N2 discharges on experimental timescales

Understanding and modeling plasma–surface interactions frame a multi-scale as well as multi-physics problem. Scale-bridging machine learning surface surrogate models have been demonstrated to perceive the fundamental atomic fidelity for the physical vapor deposition of pure metals. However, the immense computational cost of the data-generating simulations render a practical application with predictions on relevant timescales impracticable. This issue is resolved in this work for the sputter deposition of AlN in Ar/N2 discharges by developing a scheme that populates the parameter spaces effectively. Hybrid reactive molecular dynamics/time-stamped force-bias Monte Carlo simulations of randomized plasma-surface interactions/diffusion processes are used to setup a physics-separating artificial neural network. The application of this generic machine learning model to a specific experimental reference case study enables the systematic analysis of the particle flux emission as well as underlying system state (e.g. composition, density, point defect structure) evolution within process times of up to 45 min.

Lipid exchange among electroneutral Sulfo-DIBMA nanodiscs is independent of ion concentration.

Polymer-encapsulated nanodiscs enable membrane proteins to be investigated within a native-like lipid-bilayer environment. Unlike other bilayer-based membrane mimetics, these nanodiscs are equilibrium structures that permit lipid exchange on experimentally relevant timescales. Therefore, examining the kinetics and mechanisms of lipid exchange is of great interest. Since the high charge densities of existing anionic polymers can interfere with protein-protein and protein-lipid interactions as well as charge-sensitive analysis techniques, electroneutral nanodisc-forming polymers have been recently introduced. However, it has remained unclear how the electroneutrality of these polymers affects the lipid-exchange behavior of the nanodiscs. Here, we use time-resolved Förster resonance energy transfer to study the kinetics and the mechanisms of lipid exchange among nanodiscs formed by the electroneutral polymer Sulfo-DIBMA. We also examine the role of coulombic repulsion and specific counterion association in lipid exchange. Our results show that Sulfo-DIBMA nanodiscs exchange lipids on a similar timescale as DIBMA nanodiscs. In contrast with nanodiscs made from polyanionic DIBMA, however, the presence of mono- and divalent cations does not influence lipid exchange among Sulfo-DIBMA nanodiscs, as expected from their electroneutrality. The robustness of Sulfo-DIBMA nanodiscs against varying ion concentrations opens new possibilities for investigating charge-sensitive processes involving membrane proteins.

Long-term exposure to a pharmaceutical pollutant affects geotaxic behaviour in the adult but not juvenile life stage of killifish

Ecosystems around the world are increasingly polluted with pharmaceutical compounds that may perturb wildlife behaviour. Because many pharmaceuticals are continuously present in the aquatic environment, animals are often exposed to them across several life stages or even their entire life. Despite a large body of literature showing various impacts of exposure to pharmaceuticals on fish, hardly any long-term studies across different life stages have been conducted which makes it hard to accurately estimate the ecological outcomes of pharmaceutical pollution. Here, we performed a laboratory experiment in which we exposed hatchlings of the fish model Nothobranchius furzeri to an environmentally relevant concentration (0.5 μg/L) of the antidepressant fluoxetine until well into adulthood. We monitored total body length and geotaxic behaviour (i.e. gravity-mediated activity) of each fish as two traits that are ecologically relevant and naturally differ between juvenile and adult killifish. Fish exposed to fluoxetine were smaller compared to control fish, an effect that became more apparent as fish aged. Even though fluoxetine did not affect average swimming depth of either juveniles or adults, nor the time spent at the surface or bottom of the water column, exposed fish changed their position in the water column (depth) more frequently in the adult but not juvenile phase. These results suggest that important morphological and behavioural responses to pharmaceutical exposure–and their potential ecological consequences–may only emerge later in time and/or during specific life stages. Therefore, our results highlight the importance of considering ecologically relevant timescales across developmental stages when studying the ecotoxicology of pharmaceuticals.

Vertical growth dynamics of biofilms

During the biofilm life cycle, bacteria attach to a surface and then reproduce, forming crowded, growing communities. Many theoretical models of biofilm growth dynamics have been proposed; however, difficulties in accurately measuring biofilm height across relevant time and length scales have prevented testing these models, or their biophysical underpinnings, empirically. Using white light interferometry, we measure the heights of microbial colonies with nanometer precision from inoculation to their final equilibrium height, producing a detailed empirical characterization of vertical growth dynamics. We propose a heuristic model for vertical growth dynamics based on basic biophysical processes inside a biofilm: diffusion and consumption of nutrients and growth and decay of the colony. This model captures the vertical growth dynamics from short to long time scales (10 min to 14 d) of diverse microorganisms, including bacteria and fungi.