Search

We present ab initio simulations of a large system of 2400 particles of molten NaCl to investigate the behavior of collective mode dispersion beyond the hydrodynamic regime. In particular, we aim to explain the unusually strong increase in the apparent speed of sound with wave number, which significantly exceeds the typical positive sound dispersion of 10%–25% observed in simple liquids. We compare dispersions of "bare" acoustic and optic modes in NaCl with ab initio simulations of other ionic melts such as CuCl and LiBr, metallic liquid alloys such as Pb44Bi56 and Li4Tl, and the regular Lennard-Jones KrAr liquid simulated by classical molecular dynamics. Analytical expressions for the "bare" acoustic and optic branches of collective excitations help us to identify the impact of the high-frequency optic branch on the emergence of "fast sound" in binary melts. Our findings show that in ionic melts, the high-frequency speed of sound is much larger than in the simple Lennard-Jones liquids and metallic melts, leading to an observed strong viscoelastic increase in the apparent speed of sound—more than double its adiabatic value. [ABSTRACT FROM AUTHOR]

Amphid wing “C” (AWC) neurons are among the most important and studied neurons of the nematode Caenorhabditis elegans. In this work, we unify the existing electrical and intracellular calcium dynamics descriptions to obtain a biophysically accurate model of olfactory transduction in AWCON neurons. We study the membrane voltage and the intracellular calcium dynamics at different exposure times and odorant concentrations to grasp a complete picture of AWCON functioning. Moreover, we investigate the complex cascade of biochemical processes that allow AWC activation upon odor removal. We analyze the behavior of the different components of the models and, by suppressing them selectively, we extrapolate their contribution to the overall neuron response and study the resilience of the dynamical system. Our results are all in agreement with the available experimental data. Therefore, we provide an accurate mathematical and biophysical model for studying olfactory signal processing in C. elegans.

Investigating the binding between proteins and aptamers, such as peptides or RNA molecules, is of crucial importance both for understanding the molecular mechanisms that regulate cellular activities and for therapeutic applications in several pathologies. Here, a new computational procedure, employing mainly docking, clustering analysis, and molecular dynamics simulations, was designed to estimate the binding affinities between a protein and some RNA aptamers, through the investigation of the dynamical behavior of the predicted molecular complex. Using the state‐of‐the‐art software catRAPID, we computationally designed a set of RNA aptamers interacting with the TAR DNA‐binding protein 43 (TDP‐43), a protein associated with several neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). We thus devised a computational protocol to predict the RNA–protein molecular complex, so that the structural and dynamical behavior of such a complex can be investigated through extensive molecular dynamics simulation. We hypothesized that the coordinated and synchronized motion of the protein‐binding residues, when in contact with RNA molecule, is a critical requisite in order to have a stable binding. Indeed, we calculated the motion covariance exhibited by the interface residues during molecular dynamics simulation: we tested the results against experimental measurements of binding affinity (in this case, the dissociation constant) for six RNA molecules, resulting in a linear correlation of about 0.9. Our findings suggest that the synchronized movement of interface residues plays a pivotal role in ensuring the stability within RNA–protein complexes, moreover providing insights into the contribution of each interface residue. This promising pipeline could thus contribute to the design of RNA aptamers interacting with proteins. [ABSTRACT FROM AUTHOR]

In this study, we introduce a novel optical setup tailored for measuring scattering spectra of small biological aggregates with minimal sample volumes. Calibration was achieved using Polystyrene beads (PS beads) based on Mie scattering principles, enabling accurate measurements of scattering intensities. Bovine serum albumin (BSA) served as a model for studying protein aggregation due to its predictable aggregation behaviour at elevated temperatures. Analysis of non-aggregated and aggregated BSA solutions revealed significant differences in particle size, underscoring the setup's capability to detect variations in aggregation states. Key findings demonstrate the system's efficacy in monitoring protein aggregation processes, which is critical for biochemical and pharmaceutical research. The precise calibration method and the use of BSA as a validation tool highlight the setup's sensitivity and accuracy in quantifying changes in particle concentration and size due to aggregation. This study provides a framework for analysing protein aggregation and offers insights into the aggregation's impact on scattering properties.

Single-cell analysis without immune-specific labelling is essential across research fields, but conventional flow cytometers (FCMs) struggle with label-free analysis. We introduce a novel microfluidic scanning flow cytometer (μSFC) designed for label-free analysis within a simple microfluidic chip. Our system outperforms traditional FCMs for label-free analysis but its signal-to-noise ratio (SNR) limits the minimum detectable size. We present three modifications to enhance SNR and improve the smallest detectable particle size: additional neutral optical density filtering, a lower noise-equivalent-power photoreceiver, and laser spot size reduction. These improvements enable reliable characterization of particles as small as 3 μm. Experimental results validate the correlation between angular profile oscillations and particle size. While reliable detection down to 1 μm is achieved, further refinement is needed. The simplicity and low setup of the μSFC make it promising for integration into multi-parametric single-cell analysis systems, facilitating comprehensive cellular characterization for diagnostic and point-of-care applications.

Disordered, self-assembled media, contain a large amount of information, which can be seen as a huge set of random and uncontrolled memory patterns. In the framework of optics, in which an opaque medium may modelled with the transmission matrix approach, each transmitted mode “contains” a memory element which is embodied by the correspondent transmission vector. Even if the stored amount of information in this system is huge, these random memories cannot be tailored easily. Here we present a new approach to write, read, and classify memory patterns: the photonic emergent learning. The writing paradigm is borrowed form a-physical- mathematical model for the biological memory, the emergent archetype, which we translated to photonics. In our approach the random patterns enclosed in the transmitted electromagnetic modes, are used as prototypes which are summed in constructive fashion in order write our target archetype-memory into our disordered optical memory (DOM). The DOM can work as a content addressable memory, retrieving at the lightning speed which memory in the library is the closest to an optically proposed query pattern. Moreover, the optical memories can be organized into super structures containing memories of the same thus efficiently delivering a classification task.

Heterogeneity in the size distribution of cancer cell populations is linked to drug resistance and invasiveness. However, understanding how such heterogeneity arises is still damped by the difficulties of monitoring the proliferation at the typical timescales of mammalian cells. Here, we show how to infer the growth regime and division strategy of leukemia cell populations using live cell fluorescence labeling and flow cytometry in combination with an analytical model where cell growth and division rates depend on powers of the size. We found that the dynamics of the size distribution of Jurkat T-cells is reproduced by (i) a sizer-like division strategy, with (ii) division times following an Erlang distribution and (iii) fluctuations up to ten percent of the inherited fraction of size at division. Overall, our apparatus can be extended to other cell types and environmental conditions allowing for a comprehensive characterization of the growth and division model different cells adopt.Understanding how cancer cells regulate their size is still largely an open question. The authors propose a method to infer the division strategy of leukemia cells via live cell fluorescence labeling and flow cytometry measurements combined with a mathematical model based on size-dependent growth and division rates. [ABSTRACT FROM AUTHOR]

Antibody light chain amyloidosis is a disorder in which protein aggregates, mainly composed of immunoglobulin light chains, deposit in diverse tissues impairing the correct functioning of organs. Interestingly, due to the high susceptibility of antibodies to mutations, AL amyloidosis appears to be strongly patient‐specific. Indeed, every patient will display their own mutations that will make the proteins involved prone to aggregation thus hindering the study of this disease on a wide scale. In this framework, determining the molecular mechanisms that drive the aggregation could pave the way to the development of patient‐specific therapeutics. Here, we focus on a particular patient‐derived light chain, which has been experimentally characterized. We investigated the early phases of the aggregation pathway through extensive full‐atom molecular dynamics simulations, highlighting a structural rearrangement and the exposure of two hydrophobic regions in the aggregation‐prone species. Next, we moved to consider the pathological dimerization process through docking and molecular dynamics simulations, proposing a dimeric structure as a candidate pathological first assembly. Overall, our results shed light on the first phases of the aggregation pathway for a light chain at an atomic level detail, offering new structural insights into the corresponding aggregation process. [ABSTRACT FROM AUTHOR]