Aggregation Kinetics Research Articles

Distance-Dependent Tryptophan-Induced Quenching of Thioflavin T Defines the Amyloid Core Architecture.

Thioflavin T (ThT) is widely employed as a fluorogenic marker for amyloid formation. ThT fluorescence is utilized to detect amyloid fibrils as well as to follow aggregation kinetics. Here, we make a unique case to demonstrate that site-specific tryptophan-induced fluorescence quenching of ThT bound to the α-synuclein amyloid can define the central amyloid core. We show that distance-dependent quenching of amyloid-bound ThT by site-specifically incorporated tryptophan maps the proximal and distal locations of the polypeptide chain within amyloid fibrils. Our studies indicate that tryptophan-induced fluorescence quenching is dominated by the static quenching mechanism. Our findings underscore the utility of site-specific amino acid-based quenching of ThT fluorescence to characterize the core architecture of amyloid derived from a wide range of proteins.

RFCSC: Communication efficient reinforcement federated learning with dynamic client selection and adaptive gradient compression

In the field of public safety, high-quality data is often owned by governments, companies, and organizations, making it difficult to train effective models through centralized datasets. This paper leverages federated learning as a mechanism to address data privacy concerns. Within the federated learning framework, the data across different clients is typically Non-IID (non-Independently and Identically Distributed). Furthermore, The complexity of sensitive image recognition tasks in public safety, along with the large number of model parameters, can lead to communication congestion issues in federated learning. To solve these challenges, this paper proposes a Reinforcement Federated Client Selection and Gradient Compression Method(RFCSC). By integrating client data prototypes with accuracy metrics, we dynamically assess the contribution of each client in federated learning. Through an intelligent dynamic incentive mechanism based on reinforcement learning, high-quality client nodes are dynamically selected to participate in federated learning, achieving dynamic adaptive aggregation, reducing the influence of Non-IID data, reducing communication cost, enhancing model accuracy, and achieving a balance between quality and efficiency. To address the issue of high communication cost for parameter transmission, we propose an adaptive model compression strategy in reinforcement federated learning which make local clients find an appropriate compression rate. This approach not only reduces gradient communication overhead but also minimizes the impact of compression on model accuracy. The effectiveness of our proposed approach are corroborated through comprehensive experiments conducted on one private dataset and two public datasets.

Unravelling a diversity of cellular structures and aggregation dynamics during the early development of Myxococcus xanthus.

Aggregation underlies the collective dynamics of a diversity of organisms, enabling the formation of complex structures and emergent behaviours on interaction with the environment. Cellular aggregation constitutes one of the routes to collective motility and multicellular development. Myxococcus xanthus, a social bacterium, is a valuable model for studying the aggregative path to multicellularity, a major transition in the evolutionary history of life. While the collective developmental behaviour of M. xanthus has been largely studied in high cellular densities, there is a lack of understanding at low-density conditions that can be ecologically relevant. In this work, we study the early stages of emergent collective behaviour of M. xanthus under nutrient-poor and low-density conditions, uncovering the formation of diverse cellular structures with different shapes and sizes, ranging from individual cells to networks comprising thousands of cells. We study their motility patterns and their prevalence along development and discuss their cross-scale role on the population's exploratory dynamics. This work contributes to understanding key, yet largely understudied, aspects in the early stages of multicellular development in myxobacteria, shedding light on the dynamics underlying aggregative processes in this and other taxa and study systems.

Small Object Detection Method Based on Global Multi-Level Perception and Dynamic Region Aggregation

Small Object Detection Method Based on Global Multi-Level Perception and Dynamic Region Aggregation

Theoretical Analysis of Self-Shrinkage Spheroidization of Irregular Degradable Polymer Powder under Thermodynamic Nonequilibrium State

Laser three-dimensional (3D) printing offers significant advantages in integrating the shape and function of regenerative tissues through biomimetic manufacturing. However, its effectiveness is limited by the lack of specialized biopolymer powders—while solvent methods that use residual solvents produce powders with poor biocompatibility, mechanical methods result in irregularly shaped crystals. In this study, a biopolymer powder spheroidization and shaping technology, which utilizes the evolution of irregular powders into spheres with minimal surface free energy in the molten state, is proposed based on the thermodynamic principle of minimum energy. Initially, the motion trajectory and temperature field of the poly(L-lactic acid) (PLLA) powder during spheroidization were quantitatively assessed and optimized using Stokes’ law and Fourier's principle. Subsequently, the cohesive forces and aggregation kinetics of the polymer chains were calculated using molecular dynamics. Finally, based on these calculations, a phase-field model was constructed to simulate the evolution of the spheroidization rate and deduce the optimal parameters for the process. This precise approach enhances PLLA spheroidization control for laser 3D printing, improves part densification and surface quality, and offers a clean and efficient path for preparing high-quality PLLA spheroidized powder for laser 3D printing.

Dynamic protein hydration water mediates the aggregation kinetics of amyloid β peptides at interfaces

Protein hydration water is essential for protein misfolding and amyloid formation, but how it directs the course of amyloid formation has yet to be elucidated. Here, we experimentally demonstrated that femtosecond sum frequency generation vibrational spectroscopy (SFG–VS) and the femtosecond IR pump–SFG probe technique can serve as powerful tools for addressing this issue. Using amyloid β(1–42) peptide as a model, we determined the transient misfolding intermediates by probing the amide band spectral features and the local hydration water changes by measuring the ultrafast vibrational dynamics of the amide I band. For the first time, we established a correlation between the dynamic change in protein hydration water and aggregation propensity. The aggregation propensity depends on the dynamic change in the hydration water, rather than the static hydration water content of the initial protein state. Water expulsion enhances the aggregation propensity and promotes amyloid formation, while protein hydration attenuates the aggregation propensity and inhibits amyloid formation. The suppression of water expulsion and protein hydration can prevent protein aggregation and stabilize proteins. These findings contribute to a better understanding of the underlying effect of hydration water on amyloid formation and protein structural stability and provide a strategy for maintaining long–term stabilization of biomolecules.

Bisdemethoxycurcumin, a novel potent polyphenolic compound, effectively inhibits the formation of amyloid aggregates in ALS-associated hSOD1 mutant (L38R)

Protein misfolding is a biological process that leads to protein aggregation. Anomalous misfolding and aggregation of human superoxide dismutase (hSOD1) into amyloid aggregates is a characteristic feature of amyotrophic lateral sclerosis (ALS), a neurodegenerative illness. Thus, focusing on the L38R mutant may be a wise decision to comprehend the SOD1 disease process in ALS. We suggest that Bisdemethoxycurcumin (BDMC) may be a strong anti-amyloidogenic polyphenol against L38R mutant aggregation. Protein stability, hydrophobicity, and flexibility were altered when BDMC was bound to the L38R mutant, as shown by molecular dynamic (MD) simulations and molecular docking. FTIR data shows α-Helix dominance in BDMC-containing samples, with reduced β-sheet and disordered peaks, indicating the decrease of aggregate species. ThT aggregation kinetics curves show BDMC reduces L38R mutant aggregation dose-dependently, with higher BDMC concentrations yielding greater reductions. TEM images showed various quantities of amorphous aggregates, but notably, 60 μM BDMC markedly reduced aggregate density, underscoring BDMC's inhibitory effect. Hemolysis tests revealed aggregate species in BDMC-treated samples were less toxic than in L38R mutant samples alone at the same concentrations and exposure times. Overall, BDMC has substantial potential to develop highly effective inhibitors that mitigate the risk of fatal ALS.

Predictive Model Building for Aggregation Kinetics Based on Molecular Dynamics Simulations of an Antibody Fragment.

Computational methods including machine learning and molecular dynamics simulations have strong potential to characterize, understand, and ultimately predict the properties of proteins relevant to their stability and function as therapeutics. Such methods would streamline the development pathway by minimizing the current experimental testing required for many protein variants and formulations. The molecular understanding of thermostability and aggregation propensity has advanced significantly along with predictive algorithms based on the sequence-level or structural-level information on a protein. However, these approaches focus largely on a comparison of protein sequence variations to correlate the properties of proteins to their stability, solubility, and aggregation propensity. For therapeutic protein development, it is of equal importance to take into account the impact of the formulation conditions to elucidate and predict the stability of the antibody drugs. At the macroscopic level, changing temperature, pH, ionic strength, and the addition of excipients can significantly alter the kinetics of protein aggregation. The mechanisms controlling aggregation kinetics have been traced back to a combination of molecular features, including conformational stability, partial unfolding to aggregation-prone states, and the colloidal stability governed by surface charges and hydrophobicity. However, very little has been done to evaluate these features in the context of protein dynamics in different formulations. In this work, we have combined a range of molecular features calculated from the Fab A33 protein sequence and molecular dynamics simulations. Using the power of advanced, yet interpretable, statistical tools, it has been possible to uncover greater insights into the mechanisms behind protein stability, validating previous findings, and also develop models that can predict the aggregation kinetics within a range of 49 different solution conditions.

A simple model linking radiative–convective instability, convective aggregation and large-scale dynamics

Abstract. A simple model is presented which is designed to analyse the relation between the phenomenon of convective aggregation at small scales and larger-scale variability that results from coupling between dynamics and moisture in the tropical atmosphere. The model is based on single-layer dynamical equations coupled to a moisture equation to represent the dynamical effects of latent heating and radiative heating. The moisture variable q evolves through the effect of horizontal convergence, nonlinear horizontal advection and diffusion. Following previous work, the coupling between moisture and dynamics is included in such a way that a horizontally homogeneous state may be unstable to inhomogeneous disturbances, and, as a result, localised regions evolve towards either dry or moist states, with divergence or convergence respectively in the horizontal flow. The time evolution of the spatial structure of the dry and moist regions is investigated using a combination of theory and numerical simulation. One aspect of the evolution is a spatial coarsening that, if moist regions and dry regions are interpreted as convecting and non-convecting respectively, represents a form of convective aggregation. When the weak temperature gradient (WTG) approximation (i.e. a local balance between heating and convergence) applies, and horizontal advection is neglected, the system reduces to a nonlinear reaction–diffusion equation for q, and the coarsening is a well-known aspect of such systems. When nonlinear advection of moisture is included, the large-scale flow that arises from the spatial pattern of divergence and convergence leads to a distinctly different coarsening process. When thermal and frictional damping and f-plane rotation are included in the dynamics, there is a dynamical length scale Ldyn that sets an upper limit for the spatial coarsening of the moist and dry regions. The f-plane results provide a basis for interpreting the behaviour of the system on an equatorial β plane, where the dynamics implies a displacement in the zonal direction of the divergence relative to q and hence to coherent equatorially confined zonally propagating disturbances, comprising separate moist and dry regions. In many cases the propagation speed and direction depend on the equatorial wave response to the moist heating, with the relative strength of the Rossby wave response to the Kelvin wave response determining whether the propagation is eastward or westward. Within this model, the key overall properties of the propagating disturbances, the spatial scale and the phase speed, depend on nonlinearity in the coupling between moisture and dynamics, and any linear theory for such disturbances therefore has limited usefulness. The model described here, in which the moisture and dynamical fields vary in two spatial dimensions and important aspects of nonlinearity are captured, provides an intermediate model between theoretical models based on linearisation and one spatial dimension and general circulation models (GCMs) or convection-resolving models.

Benzofuran and Benzo[b]thiophene-2-Carboxamide Derivatives as Modulators of Amyloid Beta (Aβ42) Aggregation.

A group of N-phenylbenzofuran-2-carboxamide and N-phenylbenzo[b]thiophene-2-carboxamide derivatives were designed and synthesized as a novel class of Aβ42 aggregation modulators. In the thioflavin-T based fluorescence aggregation kinetics study, compounds 4 a, 4 b, 5 a and 5 b possessing a methoxyphenol pharmacophore were able to demonstrate concentration dependent inhibition of Aβ42 aggregation with maximum inhibition of 54 % observed for compound 4 b. In contrast, incorporation of a 4-methoxyphenyl ring in compounds 4 d and 5 d led to a significant increase in Aβ42 fibrillogenesis demonstrating their ability to accelerate Aβ42 aggregation. Compound 4 d exhibited 2.7-fold increase in Aβ42 fibrillogenesis when tested at the maximum concentration of 25 μM. These results were further confirmed by electron microscopy studies which demonstrates the ability of compounds 4 a, 4 b, 4 d, 5 a, 5 b and 5 d to modulate Aβ42 fibrillogenesis. Compounds 5 a and 5 b provided significant neuroprotection to mouse hippocampal neuronal HT22 cells against Aβ42-induced cytotoxicity. Molecular docking studies suggest that the orientation of the bicyclic aromatic rings (either benzofuran or benzo[b]thiophene) plays a major role in moderating their ability to either inhibit or accelerate Aβ42 aggregation. Our findings support the application of these novel derivatives as pharmacological tools to study the mechanisms of Aβ42 aggregation.

Mpsqd: A matrix product state based Python package to simulate closed and open system quantum dynamics.

We introduce a Python package based on matrix product states (MPS) to simulate both the time-dependent Schrödinger equation (TDSE) and the hierarchical equations of motion (HEOM). The wave function in the TDSE or the reduced density operator/auxiliary density operators in the HEOM are represented using MPS. A matrix product operator (MPO) is then constructed to represent the Hamiltonian in the TDSE or the generalized Liouvillian in the HEOM. The fourth-order Runge-Kutta method and the time-dependent variational principle are used to propagate the MPS. Several examples, including the nonadiabatic interconversion dynamics of the pyrazine molecule, excitation energy transfer dynamics in molecular aggregates and photosynthetic light-harvesting complexes, the spin-boson model, a laser driven two-state model, the Holstein model, and charge transport in the Anderson impurity model, are presented to demonstrate the capability of the package.

Trade liberalization and labor monopsony: Evidence from Chinese firms

We document that larger input tariff reductions were associated with lower labor markdowns in China, especially for skill-intensive firms. Guided by a stylized model of equilibrium labor market power, we leverage differences in the aggregate labor supply dynamics across labor markets—such as regional variations in China’s contemporaneous college expansion reforms—to that show trade-induced labor markdown decreased more in labor markets with more labor supply growth. Our estimates suggest that lower labor markdowns due to input trade liberalization offset China’s aggregate labor share decline by almost one-half percentage point in the early 2000s.

Analysis of the motion and deformation of water-in-oil emulsion droplets under the action of an electric field

Molecular dynamics simulations are used to study the aggregation dynamics of water-in-oil emulsion droplets under an electric field, and to analyze the effects of electric field strength, type, droplet position, and ionic concentration on their kinematic properties. The best droplet aggregation is observed at E=0.03V/Å for DC field condition, and E=0.045V/Å - 80ps for rectangular AC field condition. The rectangular alternating current (AC) field is able to merge the droplets farther away from the center of mass, but the two fields are not able to merge when the angle between the center of mass of the droplets and the electric field force is ≥35.54°. For different ion concentrations, t40N < t20N < t60N < t0N in the DC field and t20N < t40N < t0N < t60N in the rectangular AC field, and for the same ion concentration, the rectangular AC field takes longer time to merge.

Capital Investment, Equity Returns, and Aggregate Dynamics in Oligopolistic Production Economies

Abstract We analyze effects of tacit collusion in a dynamic general equilibrium model of oligopolistic sectors with capital investment and real frictions. Through their effects on equilibrium- and off- equilibrium stock prices, fundamental shocks affect incentives for defection from tacit collusion, amplifying the interaction between the real economy and financial markets as well as firms‘ risk exposure. The model implies ambiguous relationship between industry concentration and equity returns depending on operating leverage, which helps reconcile conflicting evidence in the literature. We find quantitative and empirical support for novel theoretical predictions regarding the effects of concentration on returns and investment. (JEL G12, G31, D43)

Dissipation Effects in the Tea Leaf Paradox

The Tea Leaf Paradox (TLP) describes unsteady fluid motions which help entrain and deposit suspended particles at the center of rotation. Various applications depend on the TLP for particle separations—spanning orders of magnitude in length scales—making it an important problem in fluid mechanics. Despite papers describing the phenomenon, the efficacy of particle separation using the TLP remains unclear as to the relative importance of, for example, hydrostatics, particle-fluid density ratio, wall friction, liquid bath aspect ratio and the rotation speed. The dynamics involved are notably complex and require a careful tuning of each variable. In this study, we have investigated the role of the limit of the aggregation dynamics in rotational flows within 3D-printed vessels of various sizes in tandem with particle imaging to probe the dissipation effects on the particle motions. We have found that the liquid bath aspect ratio limits how much aggregation may occur for a particle-fluid density ratio greater than unity (e.g., ρp/ρf&gt;1), where ρp is the density of the particle and ρf is the ambient fluid density.

Incubation of Amyloidogenic Peptides in Reverse Micelles Allow Active Control of Oligomer Size and Study of Protein-Protein Interactions.

Studies of the structure and dynamics of oligomeric aggregates of amyloidogenic peptides pose challenges due to their transient nature. This concept article provides a brief overview of various nucleation mechanisms with reference to the classical nucleation theory and illustrates the advantages of incubating amyloidogenic peptides in reverse micelles (RMs). The use of RMs not only facilitates size regulation of oligomeric aggregates but also provides an avenue to explore protein-protein interactions among the oligomeric aggregates of various amyloidogenic peptides. Additionally, we envision the feasibility of preparing brain tissue-derived oligomeric aggregates using RMs, potentially advancing the development of monoclonal antibodies with enhanced potency against these pathological species in vivo.

Biophysical Studies of Amyloid-Binding Fluorophores to Tau AD Core Fibrils Formed without Cofactors.

Tau is an intrinsically disordered protein involved in several neurodegenerative diseases where a common hallmark is the appearance of tau aggregates in the brain. One common approach to elucidate the mechanisms behind the aggregation of tau has been to recapitulate in vitro the self-assembly process in a fast and reproducible manner. While the seeding of tau aggregation is prompted by negatively charged cofactors, the obtained fibrils are morphologically distinct from those found in vivo. The Tau AD core fragment (TADC, tau 306-378) has emerged as a new model and potential solution for the cofactor-free in vitro aggregation of tau. Here, we use TADC to further study this process combining multiple amyloid-detecting fluorophores and fibril bioimaging. We confirmed by transmission electron microscopy that this fragment forms fibrils after quiescent incubation at 37 °C. We then employed a panel of eight amyloid-binding fluorophores to query the formed species by acquiring their emission spectra. The results obtained showed that nearly all dyes detect TADC self-assembled species. However, the successful monitoring of TADC aggregation kinetics was limited to three fluorophores (X-34, Bis-ANS, and pFTAA) which yielded sigmoidal curves but different aggregation half-times, hinting to different species being detected. Altogether, this study highlights the potential of using multiple extrinsic fluorescent probes, alone or in combination, as tools to further clarify mechanisms behind the aggregation of amyloidogenic proteins.

Ion Correlations Decrease Particle Aggregation Rate by Increasing Hydration Forces at Interfaces.

The connection between solution structure, particle forces, and emergent phenomena at solid-liquid interfaces remains ambiguous. In this case study on boehmite aggregation, we established a connection between interfacial solution structure, emerging hydration forces between two approaching particles, and the resulting structure and kinetics of particle aggregation. In contrast to expectations from continuum-based theories, we observed a nonmonotonic dependence of the aggregation rate on the concentration of sodium chloride, nitrate, or nitrite, decreasing by 15-fold in 4 molal compared to 1 molal solutions. These results are accompanied by an increase in repulsive hydration forces and interfacial oscillatory features from 0.27-0.31 nm in 0.01 molal to 0.38-0.52 nm in 2 molal. Moreover, molecular dynamics (MD) simulations indicated that these changes correspond to enhanced ion correlations near the interface and produced loosely bound aggregates that retain electrolyte between the particles. We anticipate that these results will enable the prediction of particle aggregation, attachment, and assembly, with broad relevance to interfacial phenomena.

Microbial community dynamics in different floc size aggregates during nitrogen removal process upgrading in a full-scale landfill leachate treatment plant

Upgrading processes to reduce biodegradable organic substance addition is crucial for treating landfill leachate with high pollutant concentrations, aiding carbon emission reduction. Aggregate size in activated sludge processes impacts pollutant removal and sludge/water separation. This study investigated microbial community succession and driving mechanisms in different floc-size aggregates during nitrogen removal progress upgrade from conventional to partial nitrification–denitrification in a full-scale landfill leachate treatment plant (LLTP) using 16S rRNA gene sequencing. The upgrade and floc sizes significantly influenced microbial diversity and composition. After upgrading, ammonia-oxidizing bacteria were enriched while nitrite-oxidizing bacteria suppressed in small flocs with homogeneity and high mass transfer efficiency. Larger flocs enriched Defluviicoccus, Thauera, and Truepera, while smaller flocs enriched Nitrosomonas, suggesting their potential as biomarkers. Multi-network analyses revealed microbial interactions. A deep learning model with convolutional neural networks predicted nitrogen removal efficiency. These findings guide optimizing LLTP processes and understanding microbial community dynamics based on floc size.

Behavioral Finance Insights into Land Management: Decision Aggregation and Real Estate Market Dynamics in China

Behavioral Finance Insights into Land Management: Decision Aggregation and Real Estate Market Dynamics in China