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Oxygen (O2)-sensing matrices are promising tools for the live monitoring of extracellular O2 consumption levels in long-term cell cultures. In this study, ratiometric O2-sensing membranes were prepared by electrospinning, an easy, low-cost, scalable, and robust method for fabricating nanofibers. Poly({\epsilon}-caprolactone) and poly(dimethyl)siloxane polymers were blended with tris(4,7-diphenyl-1,10-phenanthroline) ruthenium(II) dichloride, which was used as the O2-sensing probe, and rhodamine B isothiocyanate, which was used as the reference dye. The functionalized scaffolds were morphologically characterized by scanning electron microscopy, and their physicochemical profiles were obtained by Fourier transform infrared spectroscopy, thermogravimetric analysis, and water contact angle measurement. The sensing capabilities were investigated by confocal laser scanning microscopy, performing photobleaching, reversibility, and calibration curve studies toward different dissolved O2 (DO) concentrations. Electrospun sensing nanofibers showed a high response to changes in DO concentrations in the physiological-pathological range from 0.5 to 20% and good stability under ratiometric imaging. In addition, the sensing systems were highly biocompatible for cell growth promoting adhesiveness and growth of three cancer cell lines, namely metastatic melanoma cell line SK-MEL2, breast cancer cell line MCF-7, and pancreatic ductal adenocarcinoma cell line Panc-1, thus recreating a suitable biological environment in vitro. These O2-sensing biomaterials can potentially measure alterations in cell metabolism caused by changes in ambient O2 content during drug testing/validation and tissue regeneration processes., Comment: 15 pages, 4 figures

The formation of protein precursors, due to the condensation of atomic carbon under the low-temperature conditions of the molecular phases of the interstellar medium, opens alternative pathways for the origin of life. We perform peptide synthesis under conditions prevailing in space and provide a comprehensive analytic characterization of its products. The application of 13C allowed us to confirm the suggested pathway of peptide formation that proceeds due to the polymerization of aminoketene molecules that are formed in the C + CO + NH3 reaction. Here, we address the question of how the efficiency of peptide production is modified by the presence of water molecules. We demonstrate that although water slightly reduces the efficiency of polymerization of aminoketene, it does not prevent the formation of peptides.

The outstanding lubrication of articular cartilage in the major synovial joints such as hips and knees, essential for the joint well-being, has been attributed to boundary layers of lipids at the outer cartilage surfaces, which have very low friction mediated by the hydration lubrication mechanism at their highly hydrated exposed headgroups. However, the role of spontaneously present lipid splays, lipids with an acyl tail in each of the opposing bilayers, in modulating the frictional force between lipid bilayers has not, to date, been considered. In this study, we perform all-atom molecular dynamics simulations to quantitatively assess the significance of splayed molecules within the framework of lubricating lipid bilayers. We demonstrate that, although transient, splayed molecules significantly increase the inter-membrane friction until their retraction back into the lamellar phase, with this effect more steadily occurring at lower sliding velocities that are comparable to the physiological velocities of sliding articular cartilage.

Active systems -- including sperm cells, living organisms like bacteria, fish, birds, or active soft matter systems like synthetic ''microswimmers'' -- are characterized by motility, i.e., the ability to propel using their own ''engine''. Motility is the key feature that distinguishes active systems from passive or externally driven systems. In a large ensemble, motility of individual species can vary in a wide range. Selecting active species according to their motility represents an exciting and challenging problem. We propose a new method for selecting active species based on their motility using an acoustofluidic setup where highly motile species escape from the acoustic trap. This is demonstrated in simulations and in experiments with self-propelled Janus particles and human sperm. The immediate application of this method is selecting highly motile sperm for medically assisted reproduction (MAR). Due to the tunable acoustic trap, the proposed method is more flexible than the existing passive microfluidic methods. The proposed selection method based on motility can also be applied to other active systems that require selecting highly motile species or removing immotile species., Comment: 18 pages, 4 figures (preliminary version)

Nonreciprocal interaction crowd systems, such as human-human, human-vehicle, and human-robot systems, often have serious impacts on pedestrian safety and social order. A more comprehensive understanding of these systems is needed to optimize system stability and efficiency. Despite the importance of these interactions, empirical research in this area remains limited. Thus, in our study we explore this underresearched area, focusing on scenarios where nonreciprocity plays a critical role, such as mass stabbings, which pose a substantial risk to public safety. We conducted the first experiments on this system and analysed high-accuracy data obtained from these experiments. The extent of the direct threat zone is determined by the speed of the moving threat and the radius of danger occurrence. We further categorize potential threats into direct, adjacent, and rear-view zones, quantifying the level of threat for pedestrians. Our study revealed that a pedestrian's desired velocity correlated positively with potential threat intensity, increasing until near the direct threat zone. An emerging steady state is observed when escape routes are blocked by moving threats. This deviation affects the density-velocity relationship, making it distinct from the general relationship. This deviation signifies unique pedestrian behaviour in the presence of moving threats. Additionally, the rate of change in the angle for pedestrian motion in various desired directions is synchronized. This indicates the emergence of collective intelligence in nonreciprocal interaction crowd systems. As a result, our study may constitute a pioneering step towards understanding nonreciprocal interactions in crowd systems through laboratory experiments. These findings may enhance pedestrian safety and inform not only government crowd management strategies but also individual self-protection measures.

Cellular microrheology has shown that cancer cells with high metastatic potential are softer compared to non-tumorigenic normal cells. These findings rely on measuring the apparent Young modulus of whole cells using primarily atomic force microscopy. This study aims to explore whether alternative mechanical parameters have discriminating features with regard to metastatic potential. Magnetic rotational spectroscopy (MRS) is employed in the examination of mammary epithelial cell lines: MCF-7 and MDA-MB-231, representing low and high meta-static potential, alongside normal-like MCF-10A cells. MRS utilizes active micron-sized magnetic wires in a rotating magnetic field to measure the viscosity and elastic modulus of the cy-toplasm. All three cell lines display viscoelastic behavior, with cytoplasmic viscosities ranging from 10-70 Pa s and elastic moduli from 30-80 Pa. It is found that the tumorigenic MCF-7 and MDA-MB-231 cells are softer than the MCF-10A cells, with a twofold decrease in elastic modulus. To differentiate cells with low and high malignancy however, viscosity emerges as the more discriminating parameter, as MCF-7 exhibits a 5 times higher viscosity as compared to MDA-MB-231. These findings highlight the sensitivity of cytoplasmic viscosity to metastatic potential, suggesting its potential utility as a mechanical marker for malignant cancer cells.

Emergence of regular spatial patterns is a hallmark in living matter ranging from subcellular organelles to developing embryos and to ecosystems. Mechanisms for the formation of ordered spatial patterns in biology often require chemical signaling that coordinates cellular behavior and differentiation. Here we discovered a novel route to large-scale regular pattern formation in living matter mediated by purely physical interactions. We found that dense bacterial living matter spontaneously developed an ordered lattice of mesoscale, fast-spinning vortices each consisting of ~10^4-10^5 motile cells; these mesoscale vortices were arranged in space over centimeter scale with apparent hexagonal order, while individual cells in the vortices moved in coordinated directions with strong polar and vortical order. Single-cell tracking and numerical simulations suggest that the phenomenon is enabled by self-enhanced mobility of individual cells in the system. Our findings demonstrate a simple physical mechanism for self-organized pattern formation in living systems and more generally, in other active matter systems near the boundary of fluidlike and solidlike behaviors.

On-demand controlled drug delivery is essential for the treatment of a wide range of chronic diseases. As the drug is released at the time when required, its efficacy is boosted and the side effects are minimized. However, so far, drug delivery devices often rely on the passive diffusion process for a sustained release, which is slow and uncontrollable. Here, we present a miniaturized microfluidic device for wirelessly controlled ultrafast active drug delivery, driven by an oscillating solid-liquid interface. The oscillation generates acoustic streaming in the drug reservoir, which opens an elastic valve to deliver the drug. High-speed microscopy reveals the fast response of the valve on the order of 1 ms, which is more than three orders of magnitude faster than the start-of-the-art. The amount of the released drug exhibits a linear relationship with the working time and the electric power applied to the ultrasonic resonator. The trigger of the release is wirelessly controlled via a magnetic field, and the system shows stable output in a continuous experiment for two weeks. The integrated system shows great promise as a long-term controlled drug delivery implant for chronic diseases., Comment: 19 pages, 6 figures, 1 table

The detection of extracellular pH at single cell resolution is challenging and requires advanced sensibility. Sensing pH at a high spatial and temporal resolution might provide crucial information in understanding the role of pH and its fluctuations in a wide range of physio-pathological cellular processes, including cancer. Here, a method to embed silica-based fluorescent pH sensors into alginate-based three-dimensional (3D) microgels tumour models, coupled with a computational method for fine data analysis, is presented. By means of confocal laser scanning microscopy, live-cell time-lapse imaging of 3D alginate microgels was performed and the extracellular pH metabolic variations were monitored in both in vitro 3D mono- and 3D co-cultures of tumour and stromal pancreatic cells. The results show that the extracellular pH is cell line-specific and time-dependent. Moreover, differences in pH were also detected between 3D monocultures versus 3D co-cultures, thus suggesting the existence of a metabolic crosstalk between tumour and stromal cells. In conclusion, the system has the potential to image multiple live cells types in a 3D environment and to decipher in real-time their pH metabolic interplay under controlled experimental conditions, thus being also a suitable platform for drug screening and personalized medicine.

The tumor microenvironment (TME) features distinct hallmarks, including acidosis, hypoxia, reactive oxygen species (ROS) generation, and altered ion fluxes, which are crucial targets for early cancer biomarker detection, tumor diagnosis, and therapeutic strategies. A variety of imaging and sensing techniques have been developed and employed in both research and clinical settings to visualize and monitor cellular and TME dynamics. Among these, ratiometric fluorescence-based sensors have emerged as powerful analytical tools, providing precise and sensitive insights into the TME and enabling real-time detection and tracking of dynamic changes. In this comprehensive review, we discuss the latest advancements in ratiometric fluorescent probes designed for optical mapping of pH, oxygen, ROS, ions, and biomarkers within the TME. We elucidate their structural designs and sensing mechanisms, as well as their applications in in vitro and in vivo detection. Furthermore, we explore integrated sensing platforms that reveal the spatiotemporal behavior of complex tumor cultures, highlighting the potential of high-resolution imaging techniques combined with computational methods. This review aims to provide a solid foundation for understanding the current state of the art and the future potential of fluorescent nano- and microparticles in the field of cellular microenvironment sensing.

Cell sorting, the segregation of cells with different properties into distinct domains, is a key phenomenon in biological processes such as embryogenesis. We use a phase-field model of a confluent cell layer to study the role of activity in cell sorting. We find that a mixture of cells with extensile or contractile dipolar activity, and which are identical apart from their activity, quickly sort into small, elongated patches which then grow slowly in time. We interpret the sorting as driven by the different diffusivity of the extensile and contractile cells, mirroring the ordering of Brownian particles connected to different hot and cold thermostats. We check that the free energy is not changed by either partial or complete sorting, thus confirming that activity can be responsible for the ordering even in the absence of thermodynamic mechanisms., Comment: 6 pages, 3 figures

The description of neuronal activity has been of great importance in neuroscience. In this field, mathematical models are useful to describe the electrophysical behaviour of neurons. One successful model used for this purpose is the Adaptive Exponential Integrate-and-Fire (Adex), which is composed of two ordinary differential equations. Usually, this model is considered in his standard formulation, i.e., with integer order derivatives. In this work, we propose and study the fractal extension of Adex model, which in simple terms corresponds to replacing the integer derivative by non-integer. As non-integer operators, we choose the fractal derivatives. We explore the effects of equal and different orders of fractal derivatives in the firing patterns and mean frequency of the neuron described by the Adex model. Previous results suggest that fractal derivatives can provide a more realistic representation due to the fact that the standard operators are generalized. Our findings show that the fractal order influences the inter-spike intervals and changes the mean firing frequency. In addition, the firing patterns depend not only on the neuronal parameters but also on the order of respective fractal operators. As our main conclusion, the fractal order below the unit value increases the influence of the adaptation mechanism in the spike firing patterns.

The ability to distinguish between stochastic systems based on their trajectories is crucial in thermodynamics, chemistry, and biophysics. The Kullback-Leibler (KL) divergence, $D_{\text{KL}}^{AB}(0,\tau)$, quantifies the distinguishability between the two ensembles of length-$\tau$ trajectories from Markov processes A and B. However, evaluating $D_{\text{KL}}^{AB}(0,\tau)$ from histograms of trajectories faces sufficient sampling difficulties, and no theory explicitly reveals what dynamical features contribute to the distinguishability. This letter provides a general formula that decomposes $D_{\text{KL}}^{AB}(0,\tau)$ in space and time for any Markov processes, arbitrarily far from equilibrium or steady state. It circumvents the sampling difficulty of evaluating $D_{\text{KL}}^{AB}(0,\tau)$. Furthermore, it explicitly connects trajectory KL divergence with individual transition events and their waiting time statistics. The results provide insights into understanding distinguishability between Markov processes, leading to new theoretical frameworks for designing biological sensors and optimizing signal transduction.

The latest trends in cancer research and nanomedicine focus on using nanocarriers to target cancer stem cells (CSCs). Specifically, lipid liquid nanocapsules are usually developed as nanocarriers for lipophilic drug delivery. Here, we developed olive oil liquid NCs (O2LNCs) functionalized by covalent coupling of an anti-CD44-fluorescein isothiocyanate antibody ({\alpha}CD44). First, O2LNCs are formed by a core of olive oil surrounded by a shell containing phospholipids, a nonionic surfactant, and deoxycholic acid molecules. Then, O2LNCs were coated with an {\alpha}CD44 antibody ({\alpha}CD44-O2LNC). The optimization of an {\alpha}CD44 coating procedure, a complete physicochemical characterization, as well as clear evidence of their efficacy in vitro and in vivo were demonstrated. Our results indicate the high targeted uptake of these {\alpha}CD44-O2LNCs, and the increased antitumor efficacy (up to four times) of paclitaxel-loaded-{\alpha}CD44-O2LNC compared to free paclitaxel in pancreatic CSCs (PCSCs). Also, {\alpha}CD44-O2LNCs were able to selectively target PCSCs in an orthotopic xenotransplant in vivo model., Comment: 16 pages, 4 figures, 1 table

Water molecules confined between biological membranes exhibit a distinctive non-Gaussian displacement distribution, far different from bulk water. Here, we introduce a new transport equation for water molecules in the intermembrane space, quantitatively explaining molecular dynamics simulation results. We find the unique transport dynamics of water molecules stems from the lateral diffusion coefficient fluctuation caused by their longitudinal motion. We also identify an interfacial region where water possesses distinct physical properties, unaffected by changes in the intermembrane separation., Comment: 4 figures in main text, 4 figures in Supplemental Material, 1 Supplemental Video

Nanoparticles (NPs) based on the polymer poly (lactide-co-glycolide) acid (PLGA) have been widely studied in developing delivery systems for drugs and therapeutic biomolecules, due to the biocompatible and biodegradable properties of the PLGA. In this work, a synthesis method for bone morphogenetic protein (BMP-2)-loaded PLGA NPs was developed and optimized, in order to carry out and control the release of BMP-2, based on the double-emulsion (water/oil/water,W/O/W) solvent evaporation technique. The polymeric surfactant Pluronic F68 was used in the synthesis procedure, as it is known to have an effect on the reduction of the size of the NPs, the enhancement of their stability, and the protection of the encapsulated biomolecule. Spherical solid polymeric NPs were synthesized, showing a reproducible multimodal size distribution, with diameters between 100 and 500 nm. This size range appears to allow the protein to act on the cell surface and at the cytoplasm level. The effect of carrying BMP-2 co-adsorbed with bovine serum albumin on the NP surface was analyzed. The colloidal properties of these systems (morphology by SEM, hydrodynamic size, electrophoretic mobility, temporal stability, protein encapsulation, and short-term release profile) were studied. The effect of both BMP2-loaded NPs on the proliferation, migration, and osteogenic differentiation of mesenchymal stromal cells from human alveolar bone (ABSC) was also analyzed in vitro., Comment: 18 pages, 8 figures

The main aim of this study is to report basic knowledge on how a protein corona (PC) could affect or modify the way in which multifunctionalized nanoparticles interact with cells. With this purpose, we have firstly optimized the development of a target-specific nanocarrier by coupling a specific fluorescent antibody on the surface of functionalized lipid liquid nanocapsules (LLNCs). Thus, an anti-HER2-FITC antibody ({\alpha}HER2) has been used, HER2 being a surface receptor that is overexpressed in several tumor cells. Subsequently, the in vitro formation of a PC has been developed using fetal bovine serum supplemented with human fibrinogen. Dynamic Light Scattering (DLS), Nanoparticle Tracking Analysis (NTA), Laser Doppler Electrophoresis (LDE), and Gel Chromatography techniques have been used to assure a complete physico-chemical characterization of the nano-complexes with (LLNCs-{\alpha}HER2-PC) and without (LLNCs-{\alpha}HER2) the surrounding PC. In addition, cellular assays were performed to study the cellular uptake and the specific cellular-nanocarrier interactions using the SKBR3 (high expression of HER2) breast cancer cell line and human dermal fibroblasts (HDFa) (healthy cell line without expression of HER2 receptors as control), showing that the SKBR3 cell line had a higher transport rate (50-fold) than HDFa at 60 min with LLNCs-{\alpha}HER2. Moreover, the SKBR3 cell line incubated with LLNCs-{\alpha}HER2-PC suffered a significant reduction (40%) in the uptake. These results suggest that the formation of a PC onto LLNCs does not prevent specific cell targeting, although it does have an important influence on cell uptake., Comment: 19 pages, 7 figures

Langmuir monolayers are advantageous systems used to investigate how lipid membranes get involved in the physiology of many living structures, such as collapse phenomena in alveolar structures. Much work focuses on characterizing the pressure-bearing capacity of Langmuir films, expressed in the form of isotherm curves. These show that monolayers experience different phases during compression with an according evolution of their mechanical response, incurring into instability events when a critical stress threshold is overcome. Although well-known state equations, which establish an inverse relationship between surface pressure and area change, are able to describe monolayer behavior during liquid expanded phase, the modelling of their nonlinear behavior in the subsequent condensed region is still an open issue. In this regard, efforts are addressed to explain out-of-plane collapse by modeling buckling and wrinkling mainly resorting to linearly elastic plate theory. However, some experiments on Langmuir monolayers also show in-plane instability phenomena leading to the formation of the so-called shear bands and, to date, no theoretical description of the onset of shear banding bifurcation in monolayers has been yet provided. For this reason, by adopting a macroscopic description, we here study material stability of the monolayers and exploit an incremental approach to find the conditions that kindle shear bands. By starting from the widely assumed hypothesis that monolayers behave elastically in the solid-like region, a hyperfoam hyperelastic potential is introduced as a new constitutive strategy to trace back the nonlinear response of monolayer response during densification. In this way, the obtained mechanical properties together with the adopted strain energy are successfully employed to reproduce the onset of shear banding exhibited by some lipid systems under different conditions.

Equilibrium bifurcation in natural systems can sometimes be explained as a route to stress shielding for preventing failure. Although compressive buckling has been known for a long time, its less-intuitive tensile counterpart was only recently discovered and yet never identified in living structures or organisms. Through the analysis of an unprecedented all-in-one paradigm of elastic instability, it is theoretically and experimentally shown that coexistence of two curvatures in human finger joints is the result of an optimal design by nature that exploits both compressive and tensile buckling for inducing luxation in case of traumas, so realizing a unique mechanism for protecting tissues and preventing more severe damage under extreme loads. Our findings might pave the way to conceive complex architectured and bio-inspired materials, as well as next generation artificial joint prostheses and robotic arms for bio-engineering and healthcare applications.

Future brain-machine interfaces, prosthetics, and intelligent soft robotics will require integrating artificial neuromorphic devices with biological systems. Due to their poor biocompatibility, circuit complexity, low energy efficiency, and operating principles fundamentally different from the ion signal modulation of biology, traditional Silicon-based neuromorphic implementations have limited bio-integration potential. Here, we report the first organic electrochemical neurons (OECNs) with ion-modulated spiking, based on allprinted complementary organic electrochemical transistors. We demonstrate facile biointegration of OECNs with Venus Flytrap (Dionaea muscipula) to induce lobe closure upon input stimuli. The OECNs can also be integrated with all-printed organic electrochemical synapses (OECSs), exhibiting short-term plasticity with paired-pulse facilitation and longterm plasticity with retention >1000 s, facilitating Hebbian learning. These soft and flexible OECNs operate below 0.6 V and respond to multiple stimuli, defining a new vista for localized artificial neuronal systems possible to integrate with bio-signaling systems of plants, invertebrates, and vertebrates.

Optical microscopy is an indispensable tool in life sciences research, but conventional techniques require compromises between imaging parameters like speed, resolution, field-of-view, and phototoxicity. To overcome these limitations, data-driven microscopes incorporate feedback loops between data acquisition and analysis. This review overviews how machine learning enables automated image analysis to optimise microscopy in real-time. We first introduce key data-driven microscopy concepts and machine learning methods relevant to microscopy image analysis. Subsequently, we highlight pioneering works and recent advances in integrating machine learning into microscopy acquisition workflows, including optimising illumination, switching modalities and acquisition rates, and triggering targeted experiments. We then discuss the remaining challenges and future outlook. Overall, intelligent microscopes that can sense, analyse, and adapt promise to transform optical imaging by opening new experimental possibilities., Comment: 7 pages, 4 figures, review

Controlling the degradation rates of polymers is crucial for their application in tissue engineering or to achieve degradation of the polymers in the wastewater purification. As hydrophobic polyesters often exhibit very slow degradation rates, we report here increased biodegradation rates of poly(globalide-co-{\epsilon}-caprolactone) copolymers (PGlCL) produced by enzymatic ring-opening copolymerization and post-functionalized with N-acetylcysteine by thiol-ene reaction. The degradation rates of the PGlCL and post-modified PGlCL-NAC films were determined by weight-loss experiments. The polymer films were immersed in phosphate-buffered saline (PBS) solution, and PBS containing lipase from Pseudomonas cepacia. The degree of functionalization affected the degradation behavior, and samples with a higher degree of functionalization presented higher weight loss. Finally, a degradation assay was performed in activated sludge, and PGlCL-NAC presented high degradability, having a degradation behavior similar to starch. Density Functional Theory (DFT) calculations were used to assess the changes in chemical properties and electronic charge distribution of PGlCL after its functionalization with NAC, helping to understand its influence in their degradability. The results obtained confirm the possibility to increase the degradation rates of copolyesters based on caprolactone and globalide by thiol-ene post-functionalization, being a promising alternative for applications in biomedicine or the packaging sector., Comment: 24 pages, 5 figures, 1 table

Biodegradability is a key feature for the application of polymeric devices in medicine. This study reports an experimental and theoretical study of the degradation of poly(thioether-ester) (PTEe) nanoparticles in aqueous media. The $\alpha$,$\omega$-diene diester derived from vegetable oil, 1,3-propylene diundeca-10-polenoate (Pd10e) was used as monomer in the solvent-free synthesis of Pd10e-based nanoparticles (A-PTEe nanoparticles) via thiol-ene miniemulsion polymerization. The theoretical partition coefficients of A-PTEe and a PTEe based on dianhydro-D-glucityl diundec-10-enoate (DGU) (B-PTEe nanoparticles) were calculated using density functional theory (DFT), in order to compare their degradation behavior. The results showed that A-PTEe is more hydrophilic than B-PTEe, thus indicating the possible faster degradation of the former. The experimental degradation studies showed that, in fact, A-PTEe nanoparticles are faster degraded than B-PTEe, presenting substantial molecular weight decrease, which confirms the theoretical results. The effects of degradation could be observed in the chemical composition and thermal properties of the polymer. Considering its applicability potential as a biomaterial due to its fast degradation behavior, the cytotoxicity of A-PTEe nanoparticles and its degradation products were evaluated. In vitro assays confirmed the biocompatibility of A-PTEe nanoparticles and its degradation products when exposed on fibroblasts and red blood cells. These results suggest A-PTEe nanoparticles can be promising candidates as biobased nanocarriers for biomedical applications., Comment: 27 pages, 7 figures, 1 table

Comparison of click chemistry and sortagging grafting strategies for functionalizing AGuIX nanoparticles with nanobodies to develop a tri-functional technology combining MRI imaging, radiotherapy, and immunotherapy by inhibiting immune checkpoints., Comment: Nanoscale, 2024

MicroGraphited-Diamond-Multi Electrode Arrays ({\mu}G-D-MEAs) can be successfully used to reveal, in real time, quantal exocytotic events occurring from many individual neurosecretory cells and/or from many neurons within a network. As {\mu}G-D-MEAs arrays are patterned with up to 16 sensing microelectrodes, each of them recording large amounts of data revealing the exocytotic activity, the aim of this work was to support an adequate analysis code to speed up the signal detection. The cutting-edge technology of microGraphited-Diamond-Multi Electrode Arrays ({\mu}G-D-MEAs) has been implemented with an automated analysis code (APE, Amperometric Peak Analysis) developed using Matlab R2022a software to provide easy and accurate detection of amperometric spike parameters, including the analysis of the pre-spike foot that sometimes precedes the complete fusion pore dilatation. Data have been acquired from cultured PC12 cells, either collecting events during spontaneous exocytosis or after L-DOPA incubation. Validation of the APE code was performed by comparing the acquired spike parameters with those obtained using Quanta Analysis (Igor macro) by Mosharov et al.

Since the lipid raft model was developed at the end of the last century, it became clear that the specific molecular arrangements of phospholipid assemblies within a membrane have profound implications in a vast range of physiological functions. Studies of such condensed lipid islands in model systems using fluorescence and Brewster angle microscopies have shown a wide range of sizes and morphologies, with suggestions of substantial in-plane molecular anisotropy and mesoscopic structural chirality. Whilst these variations can significantly alter many membrane properties including its fluidity, permeability, and molecular recognition, the details of the in-plane molecular orientations underlying these traits remain largely unknown. Here, we use phase-resolved sum-frequency generation microscopy on model membranes of phospholipid monolayers with mixed molecular chirality, which form micron-scale circular domains of condensed lipids, to fully determine their three-dimensional molecular structure. We find that the domains possess curved molecular directionality with spiralling mesoscopic packing. By comparing different enantiomeric mixtures, both the molecular and spiral turning directions are shown to depend on the lipid chirality, but with a clear deviation from mirror symmetry in the formed structures. This demonstrates strong enantioselectivity in the domain growth process, which has potential connections to the evolution of homochirality in all living organisms as well as implications for enantioselective drug design., Comment: Main Text: 14 pages, 4 figures. Supplementary Information: 10 pages, 6 figures

Mechanical stresses are increasingly found to be associated with various biological functionalities. At the same time, topological defects are being identified across a diverse range of biological systems and are points of localized mechanical stress. It is therefore important to ask how mechanical stress localization around topological defects is controlled. Here, we use continuum simulations of nonequilibrium, fluctuating and active nematics to explore the patterns of stress localization, as well as their extent and intensity around topological defects. We find that by increasing the orientational elasticity of the material, the isotropic stress pattern around topological defects is changed substantially from a stress dipole characterized by symmetric compression-tension regions around the core of the defect to a localized stress monopole at the defect position. Moreover, we show that elastic anisotropy alters the extent and intensity of the stresses, and can result in the dominance of tension or compression around defects. Finally, including both nonequilibrium fluctuations and active stress generation, we find that the elastic constant tunes the relative effect of each, leading to the flipping of tension and compression regions around topological defects. This flipping of the tension-compression regions only by changing the elastic constant presents an interesting, simple, way of switching the dynamic behavior in active matter by changing a passive material property. We expect these findings to motivate further exploration tuning stresses in active biological materials by varying material properties of the constituent units., Comment: Accepted in Soft Matter

Intrinsically disordered proteins (IDPs) are a subset of proteins that lack stable secondary structure. Given their polymeric nature, previous mean-field approximations have been used to describe the statistical structure of IDPs. However, the amino-acid sequence heterogeneity and complex intermolecular interaction network have significantly impeded the ability to get proper approximations. One such case is the intrinsically disordered tail domain of Neurofilament low (NFLt), which comprises a 50 residue-long uncharged domain followed by a 96 residue-long negatively charged domain. Here, we measure two NFLt variants to identify the impact of the NFLt two main subdomains on its complex interactions and statistical structure. Using synchrotron small-angle x-ray scattering, we find that the uncharged domain of the NFLt induces attractive interactions that cause it to self-assemble into star-like polymer brushes. On the other hand, when the uncharged domain is truncated, the remaining charged N-terminal domains remain isolated in solution with typical polyelectrolyte characteristics. We further discuss how competing long- and short-ranged interactions within the polymer brushes dominate their ensemble structure, and, in turn, their implications on previously observed phenomena in NFL native and diseased states.

Finding the best strategy to minimize the time needed to find a given target is a crucial task both in nature and in reaching decisive technological advances. By considering learning agents able to switch their dynamics between standard and active Brownian motion, here we focus on developing effective target-search behavioral policies for microswimmers navigating a homogeneous environment and searching for targets of unknown position. We exploit Projective Simulation, a reinforcement learning algorithm, to acquire an efficient stochastic policy represented by the probability of switching the phase, i.e. the navigation mode, in response to the type and the duration of the current phase. Our findings reveal that the target-search efficiency increases with the particle's self-propulsion during the active phase and that, while the optimal duration of the passive case decreases monotonically with the activity, the optimal duration of the active phase displays a non-monotonic behavior., Comment: 10 pages, 3 figures

Modeling the behavior of biological tissues and organs often necessitates the knowledge of their shape in the absence of external loads. However, when their geometry is acquired in-vivo through imaging techniques, bodies are typically subject to mechanical deformation due to the presence of external forces, and the load-free configuration needs to be reconstructed. This paper addresses this crucial and frequently overlooked topic, known as the inverse elasticity problem (IEP), by delving into both theoretical and numerical aspects, with a particular focus on cardiac mechanics. In this work, we extend Shield's seminal work to determine the structure of the IEP with arbitrary material inhomogeneities and in the presence of both body and active forces. These aspects are fundamental in computational cardiology, and we show that they may break the variational structure of the inverse problem. In addition, we show that the inverse problem might have no solution even in the presence of constant Neumann boundary conditions and a polyconvex strain energy functional. We then present the results of extensive numerical tests to validate our theoretical framework, and to characterize the computational challenges associated with a direct numerical approximation of the IEP. Specifically, we show that this framework outperforms existing approaches both in terms of robustness and optimality, such as Sellier's iterative procedure, even when the latter is improved with acceleration techniques. A notable discovery is that multigrid preconditioners are, in contrast to standard elasticity, not efficient, where a one-level additive Schwarz and generalized Dryja-Smith-Widlund provide a much more reliable alternative. Finally, we successfully address the IEP for a full-heart geometry, demonstrating that the IEP formulation can compute the stress-free configuration in real-life scenarios.

A novel method combining the ensemble refinement by maximum entropy principle and the force field fitting approach is presented. Its formulation allows to continuously interpolate in between these two methods, which can thus be interpreted as two limiting cases. A cross-validation procedure enables to correctly assess the relative weight of both of them, distinguishing scenarios where the combined approach is meaningful from those in which either ensemble refinement or force field fitting separately prevails. The efficacy of their combination is examined for a realistic case study of RNA oligomers. Within the new scheme, molecular dynamics simulations are integrated with experimental data provided by nuclear-magnetic-resonance measures. We show that force field corrections are in general superior when applied to the appropriate force field terms, but are automatically discarded by the method when applied to inappropriate force field terms.

Microbes thrive in diverse porous environments -- from soil and riverbeds to human lungs and cancer tissues -- spanning multiple scales and conditions. Short- to long-term fluctuations in local factors induce spatio-temporal heterogeneities, often leading to physiologically stressful settings. How microbes respond and adapt to such biophysical constraints is an active field of research where considerable insight has been gained over the last decade and a half. With a focus on bacteria, here we review recent advances in microbial self-organization and dispersal in inorganic and organic porous settings, highlighting the role of active interactions and feedback which mediate their survival and fitness. We conclude by discussing open questions and opportunities for leveraging integrative cross-disciplinary approaches to advance our understanding of the biophysical strategies that microbes employ -- at both species and community scales -- to make porous settings habitable. Active and responsive behaviour is key to microbial survival in porous environments, with far-reaching ramifications for developing strategies to mitigate anthropogenic impacts, innovate subsurface storage solutions, and predict future ecological scenarios imposed by current climatic changes., Comment: Literature review, 41 pages, 6 figures

Canonical analysis has long been the primary analysis method for studies of phase transitions. However, this approach is not sensitive enough if transition signals are too close in temperature space. The recently introduced generalized microcanonical inflection-point analysis method not only enables the systematic identification and classification of transitions in systems of any size, but it can also distinguish transitions that standard canonical analysis cannot resolve. By applying this method to a generic coarse-grained model for semiflexible polymers, we identify a mixed structural phase dominated by secondary structures such as hairpins and loops that originates from a bifurcation in the hyperspace spanned by inverse temperature and bending stiffness. This intermediate phase, which is embraced by the well-known random-coil and toroidal phases, is testimony to the necessity of balancing entropic variability and energetic stability in functional macromolecules under physiological conditions.

Animal motion and flocking are ubiquitous nonequilibrium phenomena that are often studied within active matter. In examples such as insect swarms, macroscopic quantities exhibit power laws with measurable critical exponents and ideas from phase transitions and statistical mechanics have been explored to explain them. The widely used Vicsek model with periodic boundary conditions has an ordering phase transition but the corresponding homogeneous ordered or disordered phases are different from observations of natural swarms. If a harmonic potential (instead of a periodic box) is used to confine particles, the numerical simulations of the Vicsek model display periodic, quasiperiodic and chaotic attractors. The latter are scale free on critical curves that produce power laws and critical exponents. Here we investigate the scale-free-chaos phase transition in two space dimensions. We show that the shape of the chaotic swarm on the critical curve reflects the split between core and vapor of insects observed in midge swarms and that the dynamic correlation function collapses only for a finite interval of small scaled times, as also observed. We explain the algorithms used to calculate the largest Lyapunov exponents, the static and dynamic critical exponents and compare them to those of the three-dimensional model., Comment: 26 pages, 13 figures, revtex

We introduce oxNA, a new model for the simulation of DNA-RNA hybrids which is based on two previously developed coarse-grained models$\unicode{x2014}$oxDNA and oxRNA. The model naturally reproduces the physical properties of hybrid duplexes including their structure, persistence length and force-extension characteristics. By parameterising the DNA-RNA hydrogen bonding interaction we fit the model's thermodynamic properties to experimental data using both average-sequence and sequence-dependent parameters. To demonstrate the model's applicability we provide three examples of its use$\unicode{x2014}$calculating the free energy profiles of hybrid strand displacement reactions, studying the resolution of a short R-loop and simulating RNA-scaffolded wireframe origami., Comment: 15 pages, 10 figures

The genomic DNA of bacteria occupies only a fraction of the cell called the nucleoid, although it is not bounded by any membrane and would occupy a volume hundreds of times larger than the cell in the absence of constraints. The two most important contributions to the compaction of the DNA coil are the cross-linking of the DNA by nucleoid proteins (like H-NS and StpA) and the demixing of DNA and other abundant globular macromolecules which do not bind to the DNA (like ribosomes). The present work deals with the interplay of DNA-bridging proteins and globular macromolecular crowders, with the goal of determining the extent to which they collaborate in organizing the nucleoid. In order to answer this question, a coarse-grained model was developed and its properties were investigated through Brownian dynamics simulations. These simulations reveal that the radius of gyration of the DNA coil decreases linearly with the effective volume ratio of globular crowders and the number of DNA bridges formed by nucleoid proteins in the whole range of physiological values. Moreover, simulations highlight the fact that the number of DNA bridges formed by nucleoid proteins depends crucially on their ability to self-associate (oligomerize). An explanation for this result is proposed in terms of the mean distance between DNA segments and the capacity of proteins to maintain DNA--bridging in spite of the thermal fluctuations of the DNA network. Finally, simulations indicate that non-associating proteins preserve a high mobility inside the nucleoid while contributing to its compaction, leading to a DNA/protein complex which looks like a liquid droplet. In contrast, self-associating proteins form a little deformable network which cross-links the DNA chain, with the consequence that the DNA/protein complex looks more like a gel.

Many imaging techniques for biological systems -- like fixation of cells coupled with fluorescence microscopy -- provide sharp spatial resolution in reporting locations of individuals at a single moment in time but also destroy the dynamics they intend to capture. These snapshot observations contain no information about individual trajectories, but still encode information about movement and demographic dynamics, especially when combined with a well-motivated biophysical model. The relationship between spatially evolving populations and single-moment representations of their collective locations is well-established with partial differential equations (PDEs) and their inverse problems. However, experimental data is commonly a set of locations whose number is insufficient to approximate a continuous-in-space PDE solution. Here, motivated by popular subcellular imaging data of gene expression, we embrace the stochastic nature of the data and investigate the mathematical foundations of parametrically inferring demographic rates from snapshots of particles undergoing birth, diffusion, and death in a nuclear or cellular domain. Toward inference, we rigorously derive a connection between individual particle paths and their presentation as a Poisson spatial process. Using this framework, we investigate the properties of the resulting inverse problem and study factors that affect quality of inference. One pervasive feature of this experimental regime is the presence of cell-to-cell heterogeneity. Rather than being a hindrance, we show that cell-to-cell geometric heterogeneity can increase the quality of inference on dynamics for certain parameter regimes. Altogether, the results serve as a basis for more detailed investigations of subcellular spatial patterns of RNA molecules and other stochastically evolving populations that can only be observed for single instants in their time evolution., Comment: 33 pages, 6 figures

Structural fluctuations and dynamic cross-correlations in the mouse eugenol olfactory receptor (Olfr73) were studied by molecular dynamics (MD) simulation to characterize the dynamic response of the protein upon ligand binding. The initial structure was generated by the artificial intelligence tool AlfaFold2 due to the current lack of experimental data. We focused on the hydrogen (H) bond of the odorant eugenol to Ser113, Asn207, and Tyr260 of the receptor protein, the importance of which has been suggested by previous experimental studies. The H-bond was not observed in docking simulations, but in subsequent MD simulations the H-bond to Ser113 was formed in 2--4 ns. The lifetime of the H-bond was in the range of 1--20 ns. On the trajectory with the most stable (20 ns) H-bond, the structural fluctuation of the $\alpha$-carbon atoms of the receptor main chain was studied by calculating the root mean square fluctuations, the dynamic cross-correlation map, and the time-dependent dynamic cross-correlation. The analysis suggested a correlation transfer pathway Ser113 $\to$ Phe182 $\to$ (Leu259 or Tyr260) $\to$ Tyr291 induced by the ligand binding with a time scale of 4--6 ns., Comment: 6 pages, 8 figures

Chemotactic active particles, such as bacteria and cells, exhibit an adaptive run-and-tumble motion, giving rise to complex emergent behaviors in response to external chemical fields. This motion is generated by the conversion of internal chemical energy into self-propulsion, allowing each agent to sustain a steady-state far from thermal equilibrium and perform works. The rate of entropy production serves as an indicates of how extensive these agents operate away from thermal equilibrium, providing a measure for estimating maximum obtainable power. Here we present the general framework for calculating the entropy production rate created by such population of agents from the first principle, using the minimal model of bacterial adaptive chemotaxis, as they execute the most basic collective action -- the mass transport.

The weak correlation between spatiotemporal fluctuations in nonequilibrium complex systems is shown to govern the fluctuation distribution, maximizing the conditional entropy associated with such fluctuations. The result is illustrated in diffusion phenomena observed in living cells. A generic feature of the weak correlation is briefly mentioned., Comment: 17 pages, no figures. Some comments made and references added. Published version

A novel characterization method to measure the pulse duration of ultrafast near-IR pulses is introduced, which uses simple tabletop optics, is relatively inexpensive, and is expected to work in a broad wavelength range. Our diagnostic tool quantitatively characterizes the laser pulse duration of any near-IR wavelength assuming a Gaussian pulse shape with a linear chirp. We negatively prechirp near-IR pulses with a home-built broadband pulse compressor (BPC) and send this prechirped beam through a cell filled with a low-molar solution of a fluorescent dye in a liquid. After two-photon absorption, this dye fluoresces in the visible, and we record this visible signal as a function of the propagation distance in the liquid cell. We calibrate the group velocity dispersion (GVD) of our home-built BPC device against the known GVD of the compressor of our 800 nm laser and confirm this value using geometric considerations. Now knowing the GVD of BPC and the recorded visible signal for various amounts of negative chirp, let us extract the smallest pulse duration of the near-IR pulse from this visible signal. As a useful corollary, our analysis also enables the direct measurement of the GVD for liquids and the indirect measurement of the absorption coefficient for liquids in the near-IR range, in contrast to indirect GVD measurements that rely on methods such as the double derivative of the refractive index., Comment: 12 pages, 9 figures

Electronically conductive protein-based materials could enable the creation of bioelectronic components and devices from sustainable and nontoxic materials, while also being well-suited to interface with biological systems, such as living cells, for biosensor applications. In addition, protein materials have other desirable properties such as inherent self-assembly and molecular recognition capabilities. However, as proteins are generally electrical insulators, the ability to render protein assemblies electronically conductive in a tailorable manner could usher in a plethora of useful materials. Here, we present an approach to fabricate electronically conductive protein nanowires by incorporating and aligning heme molecules in proximity along an ultrastable protein filament. The heme-incorporated protein nanowires demonstrated electron transfer over micrometer distances, with conductive atomic force microscopy showing individual nanowires having comparable conductance to naturally occurring bacterial nanowires. The heme-incorporated nanowires were also capable of harvesting energy from ambient humidity when deposited as multilayer films. Exposure of films to humidity produced electrical current, presumably through water molecules ionizing carboxy groups in the protein filament and creating an unbalanced total charge distribution that is enhanced by the presence of heme. A wide variety of other porphyrin molecules exist with varying electrochemical behaviors that could enable the electrical properties of protein assemblies to be tailored, paving the way to structurally- and electrically-defined protein-based bioelectronic devices., Comment: 39 pages, 5 main figures, 12 SI figures

Homogeneous suspensions of red blood cells (RBCs or erythrocytes) in blood plasma are unstable in the absence of driving forces and form elongated stacks, called rouleau. These erythrocyte aggregates are often branched porous networks -- a feature that existing red blood cell aggregation models and simulations fail to predict exactly. Here we establish that alignment-dependent attractive forces in a system of dimers can precisely generate branched structures similar to RBC aggregates observed under a microscope. Our simulations consistently predict that the growth rate of typical mean rouleau size remains sub-linear -- a hallmark from past studies -- which we also confirm by deriving a reaction kernel taking into account appropriate collision cross-section, approach velocities, and an area-dependent sticking probability. The system exhibits unique features such as the existence of percolated and/or single giant cluster states, multiple coexisting mass-size scalings, and transition to a branched phase upon fine-tuning of model parameters. Upon decreasing the depletion thickness we find that the percolation threshold increases and the morphology of the structures opens up towards an increased degree of branching. Remarkably the system self-organizes to produce a universal power-law size distribution scaling irrespective of the model parameters.

Linguistic laws, the common statistical patterns of human language, have been investigated by quantitative linguists for nearly a century. Recently, biologists from a range of disciplines have started to explore the prevalence of these laws beyond language, finding patterns consistent with linguistic laws across multiple levels of biological organisation, from molecular (genomes, genes, and proteins) to organismal (animal behaviour) to ecological (populations and ecosystems). We propose a new conceptual framework for the study of linguistic laws in biology, comprising and integrating distinct levels of analysis, from description to prediction to theory building. Adopting this framework will provide critical new insights into the fundamental rules of organisation underpinning natural systems, unifying linguistic laws and core theory in biology.

Self-propelled micron-size particles suspended in a fluid, like bacteria or synthetic microswimmers, are strongly non-equilibrium systems where particle motility breaks the microscopic detailed balance, often resulting in large-scale collective motion. Previous theoretical work has identified long-range hydrodynamic interactions as the main driver of collective motion in unbounded dilute suspension of rear-actuated ("pusher") microswimmers. In contrast, most experimental studies of collective motion in microswimmer suspensions have been carried out in quasi-2-dimensional geometries such as in thin films or near solid or fluid interfaces, where both the swimmers' motion and their long-range flow fields become altered due to the proximity of a boundary. Here, we study numerically a minimal model of microswimmers in such a restricted geometry, where the particles move in the midplane between two no-slip walls. For pushers, we demonstrate collective motion with only short-ranged order, in contrast with the long-ranged flows observed in unbounded systems. For front-actuated ("puller") microswimmers, we discover a long-wavelength density instability resulting in the formation of dense microswimmer clusters. Both types of collective motion are fundamentally different from their previously studied counterparts in unbounded domains. Our results illustrate that hydrodynamic screening due to the presence of a wall is subdominant in determining the collective state of the suspension, which is instead dictated by the geometrical restriction of the swimmers' motion.

We study a solution of interacting semiflexible polymers with curvature energy in poor-solvent conditions on the d-dimensional cubic lattice using mean-field theory and Monte Carlo computer simulations. Building upon past studies on a single chain, we construct a field-theory representation of the system and solve it within a mean-field approximation supported by Monte Carlo simulations in d=3. A gas-liquid transition is found in the temperature-density plane that is then interpreted in terms of real systems. Interestingly, we find this transition to be independent of the bending rigidity. Past classical Flory-Huggins and Flory mean-field results are shown to be particular cases of this more general framework. Perspectives in terms of guiding experimental results towards optimal conditions are also proposed., Comment: 19 pages, 10 figures, 3 appendices, accepted for publication

The term biosurfactants refers to a complex mixture of metabolites with surface-active properties produced by specific microorganisms. However, nowadays trends moves towards isolation, screening and purifying single biocompatible, biodegradable biosurfactants with high commercialization potential. Current legislation limiting petrochemicals combined with environmentally concerned consumers did not only stimulate research and development but it also promoted large-scale production of this class of molecules. However, recent data recorded on single congeners question the actual pertinence of using the word ''biosurfactant'' associated to these molecules. By evaluating the accepted characteristics of surfactants and comparing them to the actual self-assembly and bulk properties in water of molecules traditionally called ''biosurfactants'', this opinion paper aims at showing that the term ''biosurfactant'' can be somewhat reductive when applied to specific individual compounds produced by fermentation. The use of a more generic term, like bioamphiphile could probably be more pertinent and appropriate for consideration in the future., Comment: Current Opinion in Colloid & Interface Science, In press

Social insects have evolved a variety of architectural formations. Bees and wasps are well known for their ability to achieve compact structures by building hexagonal cells. Polistes wattii, an open nesting paper wasp species, builds planar hexagonal structures. Here, using the pair correlation function approach, we show that their nests exhibit short-range hexagonal order no long-range order akin to amorphous materials. Hexagonal orientational order was well preserved globally. We also show the presence of topological defects such as dislocations (pentagon-heptagon disclination pairs) and Stone-Wales quadrupoles, and discuss how these defects were organized in the nest, thereby restoring order. Furthermore, we suggest the possible role of such defects in shaping nesting architectures of other social insect species., Comment: 21 pages,12 figues

Neural interactions occur on different levels and scales. It is of particular importance to understand how they are distributed among different neuroanatomical and physiological relevant brain regions. We investigated neural cross-frequency couplings between different brain regions according to the Desikan-Killiany brain parcellation. The adaptive dynamic Bayesian inference method was applied to EEG measurements of healthy resting subjects in order to reconstruct the coupling functions. It was found that even after averaging over all subjects, the mean coupling function showed a characteristic waveform, confirming the direct influence of the delta-phase on the alpha-phase dynamics in certain brain regions and that the shape of the coupling function changes for different regions. While the averaged coupling function within a region was of similar form, the region-averaged coupling function was averaged out, which implies that there is a common dependence within separate regions across the subjects. It was also found that for certain regions the influence of delta on alpha oscillations is more pronounced and that oscillations that influence other are more evenly distributed across brain regions than the influenced oscillations. When presenting the information on brain lobes, it was shown that the influence of delta emanating from the brain as a whole is greatest on the alpha oscillations of the cingulate frontal lobe, and at the same time the influence of delta from the cingulate parietal brain lobe is greatest on the alpha oscillations of the whole brain., Comment: Preprint - accepted in 'Chaos: An Interdisciplinary Journal of Nonlinear Science'

An analytic equation for electrochemical impedance of a single-cell measured with a microelectrode is presented. A previously reported equation had a practical problem that it is valid only when the microelectrode resides at the center of the cell under test. In this work, we propose a new analytic equation incorporating dependence on cell position, and confirmed its effectiveness by numerical simulation. Comparisons show that our proposed equation gives an excellent agreement with simulated impedance values. Discrepancies between results from our equation and numerical simulation is suppressed within 13%, which is a dramatic reduction from the previously-reported equation as large as 58%. The proposed analytic equation is expected to enable more accurate analysis in actual cell experiments., Comment: 31 pages, 8 figures, journal manuscript