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This article features an interview with Jingjing Sun and Angelike Stathopoulos, who conducted a study on the collective migration of caudal visceral mesoderm (CVM) cells in Drosophila embryos. They discovered that cell division coordinates two gene expression programs in migrating CVM cells. The researchers discuss their scientific backgrounds and the questions they are trying to answer in their lab. They also highlight the importance of the Drosophila system for their research. The study's key findings include the identification of two groups of genes that regulate the behaviors of CVM cells during migration, and the role of cell division in controlling gene expression programs. The researchers also discuss their motivations and future directions for their research. [Extracted from the article]

This document is a compilation of various scientific papers and preprints covering a wide range of topics, including protein folding, cell migration, machine learning, deep neural networks, neural scaling laws, representations and generalization in artificial and brain neural networks, and the neuron as a direct data-driven controller. The papers discuss different aspects of these subjects, providing a comprehensive overview of the current research in these fields. This document can be a valuable resource for library patrons conducting research on these topics, particularly in the fields of neuroscience and artificial intelligence. [Extracted from the article]

Mathematical models are indispensable for disentangling the interactions through which biological components work together to generate the forces and flows that position, mix, and distribute proteins, nutrients, and organelles within the cell. To illuminate the ever more specific questions studied at the edge of biological inquiry, such models inevitably become more complex. Solving, simulating, and learning from these more realistic models requires the development of new analytic techniques, numerical methods, and scalable software. In this review, we discuss some recent developments in tools for understanding how large numbers of cytoskeletal filaments, driven by molecular motors and interacting with the cytoplasm and other structures in their environment, generate fluid flows, instabilities, and material deformations which help drive crucial cellular processes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Definition: This entry paper is an attempt to explain how the discrete nature of light (energy discreteness in the form of photons) constrains the light detection process all along the evolutionary path, in the not-fully-understood photoreceptive systems of unicellular microorganisms (nonimaging systems) and in the complex and well-known visual system of higher organisms (imaging systems). All these systems are perfect examples of the interplay between physics and biology, i.e., they are the perfect topic of research for biophysicists. The paper describes how photoreceptive and visual systems achieve the goal of photon counting, which information is conveyed by a finite number of photons, and which noise factors limit light-detecting processes. [ABSTRACT FROM AUTHOR]

Josep Rizo is a Professor of Biophysics, Biochemistry and Pharmacology at the University of Texas Southwestern Medical Center, where he is Virginia Lazenby O'Hara Chair in Biochemistry. He is particularly interested in the study of the mechanisms of neurotransmitter release and intracellular membrane fusion using structural biology, a variety of biophysical techniques and reconstitution approaches. Jose has been a part of the FEBS Open Bio Editorial Board since 2021. In this interview, he shares his insights into developments in the field of neurotransmitter release, describes his move from Spain to the United States, and discusses how sometimes you need to use both logic and scientific hunches. [ABSTRACT FROM AUTHOR]

Understanding active matter has led to new perspectives on biophysics and non-equilibrium dynamics. However, the development of numerical tools for simulating active fluids capable of incorporating non-trivial boundaries or inclusions has lagged behind. Active particle-based methods, which typically excel at this, suffer from large density fluctuations that affect the dynamics of inclusions. To this end, we advance the Active-Nematic Multi-Particle Collision Dynamics algorithm, a particle-based method for simulating active nematics, by addressing the large density fluctuations that arise from activity. This paper introduces three activity formulations that mitigate the coupling between activity and local density. Local density fluctuations are decreased to a level comparable to the passive limit while retaining active nematic phenomenology and increasing the active turbulence regime four-fold in two dimensions. These developments extend the technique into a flexible tool for modeling active systems, including solutes and inclusions, with broad applications for the study of biophysical systems. Particle-based models of soft active matter are known to exhibit large density fluctuations, which may be problematic for considering homogeneous systems. Through considering the coupling of activity to local density, we reduce density fluctuations while retaining active turbulent dynamics. [ABSTRACT FROM AUTHOR]

The degree of DNA compaction in various regions of the cell nucleus determines whether the corresponding genes should be expressed and other crucial cellular processes, such as DNA replication and repair, should occur. Therefore, the development of approaches to the experimental assessment of DNA compactness in cell nuclei, as well as its indicator, dynamic disorder which determines the degree of thermal fluctuation in the position and mutual orientation of molecular fragments, is highly relevant. In this paper, using single-stranded guanine and cytosine oligonucleotides, as well as their double-stranded combination as an example, it is shown that dynamic disorder in DNA can be assessed based on low-frequency Raman scattering (RS) spectroscopy data. For the first time, Raman spectra of oligonucleotides have been measured over a wide frequency range, including the low-frequency (10–200 cm ) and high-frequency (200–2000 cm ) regions. It was found that the low-frequency Raman intensity is maximal in single-stranded oligocytosine and minimal in the double-stranded oligonucleotide, which is in full agreement with the magnitude of dynamic disorder estimated from molecular dynamics simulations. The obtained results indicate the promising application of low-frequency Raman spectroscopy for assessing dynamic disorder and DNA compactness. The use of such a technique is expected to contribute to the understanding of key cellular processes and the physical mechanisms that underlie them, which is necessary for the development of advanced methods in molecular biophysics and cell biology. [ABSTRACT FROM AUTHOR]

In the past year, biophysics obtained the first impact factor of 1.5. Before starting the new work, we will review and summarize the work in 2023 and make a simple plan for the work in 2024. [ABSTRACT FROM AUTHOR]

We examine the use of time series data, derived from Electric Cell-substrate Impedance Sensing (ECIS), to differentiate between standard mammalian cell cultures and those infected with a mycoplasma organism. With the goal of easy visualization and interpretation, we perform low-dimensional feature-based classification, extracting application-relevant features from the ECIS time courses. We can achieve very high classification accuracy using only two features, which depend on the cell line under examination. Initial results also show the existence of experimental variation between plates and suggest types of features that may prove more robust to such variation. Our paper is the first to perform a broad examination of ECIS time course features in the context of detecting contamination; to combine different types of features to achieve classification accuracy while preserving interpretability; and to describe and suggest possibilities for ameliorating plate-to-plate variation. [ABSTRACT FROM AUTHOR]

This research delves into a comprehensive exploration of exact solutions for the time-space fractional phi-four equation through the implementation of both the unified method and the modified Khater method. The Phi-four equation, rooted in the Klein-Gordon model, holds significance in diverse scientific realms, such as biological and nuclear physics, making it imperative to unravel its solutions. Leveraging a well-suited wave transformation, the governing equation is transformed into an ordinary differential equation, laying the groundwork for an in-depth analysis. The proposed methodologies yield a rich spectrum of solutions, encompassing trigonometric, hyperbolic, and rational forms. These solutions manifest as Kink, bright singular, dark, combo dark bright, periodic and singular periodic solutions, each characterized by explicit conditions defining their validity. The versatility and computational robustness of these methods establish a robust framework for soliton research. To provide a comprehensive assessment, a comparative analysis is conducted, contrasting the outcomes obtained through the proposed methodologies with those derived from earlier approaches. A key fact of this study is the visual representation of a subset of these solutions through carefully crafted graphical illustrations. Parameter values are judiciously selected to elucidate the nuanced characteristics of the solutions, enhancing our understanding of the model's physical behavior. It is noteworthy that all computations are meticulously executed with Mathematica software, underscoring the precision and computational efficiency integral to this research. The novelty of this paper lies in the identification of new solutions through the application of the unified and modified Khater methods. These solutions are not previously documented in existing literature. The visual depictions of dynamic behaviors through two and three-dimensional figures further contribute to the originality of our findings. [ABSTRACT FROM AUTHOR]

A key biophysical process in biological systems, liquid–liquid phase separation (LLPS) is responsible for constructing membrane-less organelles and modulating cellular activities. The biophysical mechanisms underlying LLPS are explored in this paper, with an emphasis on multivalent interactions, entropy-driven processes, and how they affect cellular organization. We investigate the impact of external stimuli on phase behavior and clarify the physiological roles of biomolecular condensates, from stress response modulation to gene expression regulation. In addition, we study dysregulated LLPS in human disorders, providing information about possible therapeutic targets and approaches to innovative therapeutics. Through this interdisciplinary approach, we aim to improve our knowledge of LLPS and its effects on health and illness, opening the door to new therapeutic approaches that focus on the behavior of proteins and the structure of cells. [ABSTRACT FROM AUTHOR]

The anterolateral ligament (ALL) was first described in 1879 in the context of Segond fractures, which correlate with a 75–100% chance of an anterior cruciate ligament (ACL) tear or a 66–75% chance of a meniscal tear. The purpose of this paper is to provide an updated comprehensive review on the anterolateral ligament complex of the knee focusing on the: (1) anatomy of the ALL/ALC; (2) associated biomechanics/function; and (3) important surgical considerations in contemporary anterior cruciate ligament (ACL) reconstruction and total knee arthroplasty (TKA). A systematic review of studies on ALL was conducted on Pubmed/MEDLINE and Cochrane databases (May 7th, 2020 to February 1st, 2022), with 20 studies meeting inclusion/exclusion criteria. Studies meeting inclusion criteria were anatomical/biomechanical studies assessing ALL function, cadaveric and computer simulations, and comparative studies on surgical outcomes of ALLR (concomitant with ACL reconstruction). Eight studies were included and graded by MINOR and Newcastle–Ottawa scale to identify potential biases. The anatomy of the ALL is part of the anterolateral ligament complex (ALC), which includes the superficial/deep iliotibial band (including the Kaplan fiber system), iliopatellar band, ALL, and anterolateral capsule. Multiple biomechanical studies have characterized the ALC as a secondary passive stabilizer in resisting tibial internal rotation. Given the role of the ALC in resisting internal tibial rotation, lateral extra-articular procedures including ALL augmentation may be considered for chronic ACL tears, ACL revisions, and a high-grade pivot shift test. In the context of TKA, in the event of injury to the ALC, a more constrained implant or soft-tissue reconstruction may be necessary to restore appropriate knee stability. [ABSTRACT FROM AUTHOR]

This document discusses recent advancements in molecular biophysics research in Russia. The focus has shifted from studying individual molecules to analyzing protein complexes and understanding their regulation and structure. The document also highlights the development of technology for studying the functional activity of single molecules of enzymes using nanopores. Additionally, it explores research on the SARS-CoV-2 virus, including efforts to develop a vaccine, understand the body's response to different antibody vaccines, and investigate the inflammatory response caused by the virus. The document also mentions the identification of peptides from proteins that can regulate receptor activity and be used in the treatment of autoimmune and cancer diseases. Furthermore, it provides information on the SARS-CoV-2 proteome, different variants of the virus, and the molecular basis of their differences. The document concludes by discussing the importance of reviewing existing medications and the role of protein S-acylation in virus infectivity. Overall, this document provides valuable insights into the current state of molecular biophysics research in Russia and its implications for various fields, including drug discovery and virus understanding. [Extracted from the article]

Cutting-edge X-ray sources have enabled the structural dynamics of proteins to be tracked during biochemical processes, but the findings have been questioned. Two experts discuss the implications of a study that digs into this issue.Absorption of multiple photons alters structural dynamics of proteins. [ABSTRACT FROM AUTHOR]

Ervin Bauer (1890–1938) outlined the paradigm of theoretical biology from the perspective of biophysics and bioenergetics. His molecular-based biological theory is centered on the principle of sustainable non-equilibrium, which is continuously produced and maintained by all biological systems throughout their life. Ervin Bauer became the victim of Stalin's Great Terror. Here we present two of the fundamental works of Ervin Bauer in English translation: the paper "The definition of living beings on the basis of their thermodynamic properties, and the fundamental biological principles that follow from it" published in Naturwissenschaften (1920) and the excerpts from his magnum opus "Theoretical Biology" (1935). These works became a bibliographical rarity. A complete English translation of "Theoretical Biology" is an important task for the future. [ABSTRACT FROM AUTHOR]

Large cells often rely on cytoplasmic flows for intracellular transport, maintaining homeostasis, and positioning cellular components. Understanding the mechanisms of these flows is essential for gaining insights into cell function, developmental processes, and evolutionary adaptability. Here, we focus on a class of self-organized cytoplasmic stirring mechanisms that result from fluid--structure interactions between cytoskeletal elements at the cell cortex. Drawing inspiration from streaming flows in late-stage fruit fly oocytes, we propose an analytically tractable active carpet theory. This model deciphers the origins and three-dimensional spatiotemporal organization of such flows. Through a combination of simulations and weakly nonlinear theory, we establish the pathway of the streaming flow to its global attractor: a cell-spanning vortical twister. Our study reveals the inherent symmetries of this emergent flow, its low-dimensional structure, and illustrates how complex fluid--structure interaction aligns with classical solutions in Stokes flow. This framework can be easily adapted to elucidate a broad spectrum of self-organized, cortex-driven intracellular flows. [ABSTRACT FROM AUTHOR]

The outer membrane (OM) of gram-negative bacteria serves as a vital organelle that is densely populated with OM proteins (OMPs) and plays pivotal roles in cellular functions and virulence. The assembly and insertion of these OMPs into the OM represent a fundamental process requiring specialized molecular chaperones. One example is the translocation and assembly module (TAM), which functions as a transenvelope chaperone promoting the folding of specific autotransporters, adhesins, and secretion systems. The catalytic unit of TAM, TamA, comprises a catalytic β-barrel domain anchored within the OM and three periplasmic polypeptide-transport-associated (POTRA) domains that recruit the TamB subunit. The latter acts as a periplasmic ladder that facilitates the transport of unfolded OMPs across the periplasm. In addition to their role in recruiting the auxiliary protein TamB, our data demonstrate that the POTRA domains mediate interactions with the inner surface of the OM, ultimately modulating the membrane properties. Through the integration of X-ray crystallography, molecular dynamic simulations, and biomolecular interaction methodologies, we located the membrane-binding site on the first and second POTRA domains. Our data highlight a binding preference for phosphatidylglycerol, a minor lipid constituent present in the OM, which has been previously reported to facilitate OMP assembly. In the context of the densely OMP-populated membrane, this association may serve as a mechanism to secure lipid accessibility for nascent OMPs through steric interactions with existing OMPs, in addition to creating favorable conditions for OMP biogenesis. [ABSTRACT FROM AUTHOR]

Organisms often swim through density-stratified fluids. Here, we investigate the dynamics of active particles swimming in fluid density gradients and report theoretical evidence of taxis as a result of these gradients (densitaxis). Specifically, we calculate the effect of density stratification on the dynamics of a force- and torque-free spherical squirmer and show that density gradients induce reorientation that tends to align swimming either parallel or normal to the gradient depending on the swimming gait. In particular, swimmers that propel by generating thrust in the front (pullers) rotate to swim parallel to gradients and hence display (positive or negative) densitaxis, while swimmers that propel by generating thrust in the back (pushers) rotate to swim normal to the gradients. This work could be useful to understand the motion of marine organisms in ocean or be leveraged to sort or organize a suspension of active particles by modulating density gradients. [ABSTRACT FROM AUTHOR]

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery. [ABSTRACT FROM AUTHOR]

Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodologies. We discuss how ideas coming from physical modeling neuronal processing led to early formulations of computational neural networks, e.g., Hopfield networks. We then show how modern learning approaches like Potts models, Boltzmann machines, and the transformer architecture are related to each other, specifically, through a shared energy representation. We summarize recent efforts to establish these connections and provide examples on how each of these formulations integrating physical modeling and machine learning have been successful in tackling recent problems in biomolecular structure, dynamics, function, evolution, and design. Instances include protein structure prediction; improvement in computational complexity and accuracy of molecular dynamics simulations; better inference of the effects of mutations in proteins leading to improved evolutionary modeling and finally how machine learning is revolutionizing protein engineering and design. Going beyond naturally existing protein sequences, a connection to protein design is discussed where synthetic sequences are able to fold to naturally occurring motifs driven by a model rooted in physical principles. We show that this model is "learnable" and propose its future use in the generation of unique sequences that can fold into a target structure. [ABSTRACT FROM AUTHOR]

Background and Objectives: Effective diagnostics of depression state using instrumental methods of biopotentials measurement is promising both as a tool for increasing the efficiency of work of a diagnostician and for application in automated hardware and software therapy complexes, including those with biofeedback, allowing to create neuroadaptive systems for correction of psychoemotional and cognitive problems of patients. The purpose of this paper is to analyze the requirements to the methodology of biophysical data collection, hardware and software for their primary processing on the basis of open datasets of psychoemotional state determination, to formulate a methodology for the formation of multimodal datasets for the study of mental states, their changes, suitable for use in machine learning algorithms, to describe a possible method of realization of these requirements in hardware and software complexes. Materials and Methods: Open datasets of depressed patients were selected to analyze the main characteristics of datasets characterizing mental states. To formulate the main requirements, publications on the features of multimodal data application for the diagnosis of depressive disorders were reviewed. Results: The result of the work are the requirements to multimodal biopotential data for the study of psychoemotional states, the methodology and functional concept of hardware-software complex for their registration, the synchronization and recording in annotated form. Conclusion: The example of depressive disorder shows the usefulness and possibility of recording multimodal, synchronized annotated data on the psycho-emotional state of the subject for research, diagnostic purposes and application as a training sample in machine learning algorithms. The proposed methodology and the concept of the hardware-software complex allow to level out the main disadvantages of multimodal systems of biopotentials registration realized in the form of separate blocks and to supplement the instrumental data with annotation by state labels and time labels.

The topological properties of DNA have long been a focal point in biophysics. In the 1970s, White proposed that the topology of closed DNA double helix follows White's formula: L k = W r + T w. However, there has been controversy in the calculation of DNA twisting number, partly due to discrepancies in the definition of torsion in differential geometry. In this paper, we delved into a detailed study of torsion, revealing that the calculation of DNA twisting number should use the curve's geodesic torsion. Furthermore, we found that the discrepancy in DNA twisting numbers calculated using different torsion is N. This study elucidated the impact of torsion on the calculation of DNA twisting numbers, aiming to resolve controversies in the calculation of DNA topology and provided accurate computational methods and theoretical foundations for related research. • Both as a spatial curve and as a curve lying on a surface, the DNA helix axis is taken into account during the calculation of the twisting number. The torsion derived from each perspective varies. • We have clarified that the torsion mentioned in Fuller's works should refer to the geodesic torsion of the surface where the DNA helix axis resides. • The twisting number computed from the torsion of a surface curve differs from that of a spatial curve by a specific numerical factor. [ABSTRACT FROM AUTHOR]

The neurophysiological effects of spinal cord stimulation (SCS) for chronic pain are poorly understood, resulting in inefficient failure-prone programming protocols and inadequate pain relief. Nonetheless, novel stimulation patterns are regularly introduced and adopted clinically. Traditionally, paresthetic sensation is considered necessary for pain relief, although novel paradigms provide analgesia without paresthesia. However, like pain relief, the neurophysiological underpinnings of SCS-induced paresthesia are unknown. Here, we paired biophysical modeling with clinical paresthesia thresholds (of both sexes) to investigate how stimulation frequency affects the neural response to SCS relevant to paresthesia and analgesia. Specifically, we modeled the dorsal column (DC) axonal response, dorsal column nucleus (DCN) synaptic transmission, conduction failure within DC fiber collaterals, and dorsal horn network output. Importantly, we found that high-frequency stimulation reduces DC fiber activation thresholds, which in turn accurately predicts clinical paresthesia perception thresholds. Furthermore, we show that high-frequency SCS produces asynchronous DC fiber spiking and ultimately asynchronous DCN output, offering a plausible biophysical basis for why high-frequency SCS is less comfortable and produces qualitatively different sensation than low-frequency stimulation. Finally, we demonstrate that the model dorsal horn network output is sensitive to SCS-inherent variations in spike timing, which could contribute to heterogeneous pain relief across patients. Importantly, we show that model DC fiber collaterals cannot reliably follow high-frequency stimulation, strongly affecting the network output and typically producing antinociceptive effects at high frequencies. Altogether, these findings clarify how SCS affects the nervous system and provide insight into the biophysics of paresthesia generation and pain relief. [ABSTRACT FROM AUTHOR]

The study of the phenomena occurring in the plantar region is remarkably intriguing, especially when performing a normal gait cycle where the foot is under loading conditions. The effects presented in the foot while walking provide relevant indicators regarding clinical means for enhancing regular performance or rehabilitation therapies. Nevertheless, more than traditional methods are needed to biomechanically evaluate foot structural conditions, leading to an incomplete database for determining the patient's needs so that advanced methodologies provide detailed medical assessment. Therefore, it is necessary to employ technological engineering tools to optimize biomechanical plantar pressure evaluations to reach suitable personalized treatments. This research initially evaluated numerically the pressure points in the foot sole region in each one of the five stance phases in a normal gait cycle. Medical imaging techniques were utilized to construct an anatomically accurate biomodel of the soft tissues of the right foot. The Finite Element Method was employed to predict peak plantar pressure in barefoot conditions for all stance phases; results from this case study presented a close alignment with gait experimental testing implemented to analyze the feasibility and validation of all mechanical considerations for the numerical analyses. Hence, having a solid foundation in the biomechanical behavior from the first case study close estimates, a 3D-printable patient-specific insole was designed and numerically analyzed to observe the mechanical response in the plantar critical zones utilizing a personalized orthotic device. Results from the second case study notably demonstrated a crucial decrement in excessive pressure values. Employing morphological customization orthopedics modeling combined with 3D-printable materials is revolutionizing assistive device design and fabrication techniques. The fundamental contribution of this research relies on deepening the knowledge of foot biomechanics from an interdisciplinary approach by numerically analyzing pressure distribution in critical regions for all five stances phases; thus, based on the methods employed, the results obtained contribute to the advances of patient-specific foot orthopedics. [ABSTRACT FROM AUTHOR]

The vagina is an essential component of the female reproductive system and is responsible for providing female sexual satisfaction. Vaginal smooth muscle contraction plays a crucial role in various physiological processes, including sexual arousal, childbirth, and urinary continence. In pathophysiological conditions, such as pelvic floor disorders, aberrations in vaginal smooth muscle function can lead to urinary incontinence and pelvic organ prolapse. A set of cellular and sub-cellular physiological mechanisms regulates the contractile properties of the vaginal smooth muscle cells. Calcium influx is a crucial determinant of smooth muscle contraction, facilitated through voltage-dependent calcium channels and calcium release from intracellular stores. Comprehensive reviews on smooth muscle biophysics are relatively scarce within the scientific literature, likely due to the complexity and specialized nature of the topic. The objective of this review is to provide a comprehensive description of alterations in the cellular physiology of vaginal smooth muscle contraction. The benefit associated with this particular approach is that conducting a comprehensive examination of the cellular mechanisms underlying contractile activation will enable the creation of more targeted therapeutic agents to control vaginal contraction disorders. [ABSTRACT FROM AUTHOR]

High‐resolution spatio‐temporal monitoring of the cell membrane lipid order provides visual insights into the complex and sophisticated systems that control cellular physiological functions. Solvatochromic fluorescent probes are highly promising noninvasive visualization tools for identifying the ordering of the microenvironment of plasma membrane microdomains. However, conventional probes, although capable of structural analysis, lack the necessary long‐term photostability required for live imaging at the cellular level. Here, an ultra‐high‐light‐resistant solvatochromic fluorescence probe, 2‐N,N‐diethylamino‐7‐(4‐methoxycarbonylphenyl)‐9,9‐dimethylfluorene (FπCM) is reported, which enables live lipid order imaging of cell division. This probe and its derivatives exhibit sufficient fluorescence wavelengths, brightness, polarity responsiveness, low phototoxicity, and remarkable photostability under physiological conditions compared to conventional solvatochromic probes. Therefore, these probes have the potential to overcome the limitations of fluorescence microscopy, particularly those associated with photobleaching. FπCM probes can serve as valuable tools for elucidating mechanisms of cellular processes at the bio‐membrane level. [ABSTRACT FROM AUTHOR]

Metal ions can perform multiple roles ranging from regulatory to structural and are crucial for cell function. While some metal ions like Na+ are ubiquitously present at high concentrations, other ions, especially Ca2+ and transition metals, such as Zn2+ or Cu+/2+ are regulated. The concentrations above or below the physiological range cause severe changes in the behavior of biomolecules that bind them and subsequently affect the cell wellbeing. This has led to the development of specialized protocols to study metal ion binding biomolecules in bulk conditions that mimic the cell environment. Recently, there is growing evidence of influence of post-transcriptional and post-translational modifications on the affinity of the metal ion binding sites. However, such targets are difficult to obtain in amounts required for classical biophysical experiments. Single molecule techniques have revolutionized the field of biophysics, molecular and structural biology. Their biggest advantage is the ability to observe each molecule's interaction independently, without the need for synchronization. An additional benefit is its extremely low sample consumption. This feature allows characterization of designer biomolecules or targets obtained coming from natural sources. All types of biomolecules, including proteins, DNA and RNA were characterized using single molecule methods. However, one group is underrepresented in those studies. These are the metal ion binding biomolecules. Single molecule experiments often require separate optimization, due to extremely different concentrations used during the experiments. In this review we focus on single molecule methods, such as single molecule FRET, nanopores and optical tweezers that are used to study metal ion binding biomolecules. We summarize various examples of recently characterized targets and reported experimental conditions. Finally, we discuss the potential promises and pitfalls of single molecule characterization on metal ion binding biomolecules. [ABSTRACT FROM AUTHOR]

In this study, a novel, high content technique using a cylindrical acoustic transducer, stroboscopic fast imaging, and homodyne detection to recover the mechanical properties (dynamic shear modulus) of living adherent cells at low ultrasonic frequencies is presented. By analyzing the micro‐oscillations of cells, whole populations are simultaneously mechanotyped with sub‐cellular resolution. The technique can be combined with standard fluorescence imaging allowing to further cross‐correlate biological and mechanical information. The potential of the technique is demonstrated by mechanotyping co‐cultures of different cell types with significantly different mechanical properties. [ABSTRACT FROM AUTHOR]

Smart polymer materials that are nonliving yet exhibit complex "life-like" or biomimetic behaviors have been the focus of intensive research over the past decades, in the quest to broaden our understanding of how living systems function under nonequilibrium conditions. Identification of how chemical and mechanical coupling can generate resonance and entrainment with other cells or external environment is an important research question. We prepared Belousov-Zhabotinsky (BZ) selfoscillating hydrogels which convert chemical energy to mechanical oscillation. By cyclically applying external mechanical stimulation to the BZ hydrogels, we found that when the oscillation of a gel sample entered into harmonic resonance with the applied oscillation during stimulation, the system kept a "memory" of the resonant oscillation period and maintained it post stimulation, demonstrating an entrainment effect. More surprisingly, by systematically varying the cycle length of the external stimulation, we revealed the discrete nature of the stimulation-induced resonance and entrainment behaviors in chemical oscillations of BZ hydrogels, i.e., the hydrogels slow down their oscillation periods to the harmonics of the cycle length of the external mechanical stimulation. Our theoretical model calculations suggest the important roles of the delayed mechanical response caused by reactant diffusion and solvent migration in affecting the chemomechanical coupling in active hydrogels and consequently synchronizing their chemical oscillations with external mechanical oscillations. [ABSTRACT FROM AUTHOR]

The modeling of biological systems has recently gained much attention considering the possibility to describe the time evolution of a biological system by employing differential equations. Different frameworks have been proposed depending on the number of dynamic variables. Ordinary differential equations (ODE) are employed if time is the only dynamic variable; partial differential equations (PDE) are proposed when, in addition to the time variable, space and/or velocity variables are considered. In the context of differential equation models, new frameworks have been proposed where stochastic terms are added to classical deterministic terms. A specific model is proposed when the differential equations are coupled to initial and/or boundary conditions. This editorial article deals with the topic of this special issue, which is devoted to the new developments in the multiscale modeling of complex biological systems with special attention to the interplay between different scholars. [ABSTRACT FROM AUTHOR]

Protein dynamics form a critical bridge between protein structure and function, yet the impact of evolution on ultrafast processes inside proteins remains enigmatic. This study delves deep into nanosecond-scale protein dynamics of a structurally and functionally conserved protein across species separated by almost a billion years, investigating ten homologs in complex with their ligand. By inducing a photo-triggered destabilization of the ligand inside the binding pocket, we resolved distinct kinetic footprints for each homolog via transient infrared spectroscopy. Strikingly, we found a cascade of rearrangements within the protein complex which manifest in time points of increased dynamic activity conserved over hundreds of millions of years within a narrow window. Among these processes, one displays a subtle temporal shift correlating with evolutionary divergence, suggesting reduced selective pressure in the past. Our study not only uncovers the impact of evolution on molecular processes in a specific case, but has also the potential to initiate a field of scientific inquiry within molecular paleontology, where species are compared and classified based on the rapid pace of protein dynamic processes; a field which connects the shortest conceivable time scale in living matter (10-9 s) with the largest ones (1016 s). [ABSTRACT FROM AUTHOR]

Bioelectronic Medicine stands as an emerging field that rapidly evolves and offers distinctive clinical benefits, alongside unique challenges. It consists of the modulation of the nervous system by precise delivery of electrical current for the treatment of clinical conditions, such as post-stroke movement recovery or drug-resistant disorders. The unquestionable clinical impact of Bioelectronic Medicine is underscored by the successful translation to humans in the last decades, and the long list of preclinical studies. Given the emergency of accelerating the progress in new neuromodulation treatments (i.e., drug-resistant hypertension, autoimmune and degenerative diseases), collaboration between multiple fields is imperative. This work intends to foster multidisciplinary work and bring together different fields to provide the fundamental basis underlying Bioelectronic Medicine. In this review we will go from the biophysics of the cell membrane, which we consider the inner core of neuromodulation, to patient care. We will discuss the recently discovered mechanism of neurotransmission switching and how it will impact neuromodulation design, and we will provide an update on neuronal and glial basis in health and disease. The advances in biomedical technology have facilitated the collection of large amounts of data, thereby introducing new challenges in data analysis. We will discuss the current approaches and challenges in high throughput data analysis, encompassing big data, networks, artificial intelligence, and internet of things. Emphasis will be placed on understanding the electrochemical properties of neural interfaces, along with the integration of biocompatible and reliable materials and compliance with biomedical regulations for translational applications. Preclinical validation is foundational to the translational process, and we will discuss the critical aspects of such animal studies. Finally, we will focus on the patient point-of-care and challenges in neuromodulation as the ultimate goal of bioelectronic medicine. This review is a call to scientists from different fields to work together with a common endeavor: accelerate the decoding and modulation of the nervous system in a new era of therapeutic possibilities. [ABSTRACT FROM AUTHOR]

The biophysical characteristics of the areas that can be seen while travelling on motorways have an impact on the perception of the landscape. Highways provide diverse landscape experiences to travellers according to their natural and cultural qualities. Especially coastal ways that combine with nature and the sea have a high potential for scenic attractiveness. This study aims to analyse the scenic attractiveness of coastal ways using GIS and RS techniques. Persembe-Bolaman coastal way in the Black Sea Region of Turkey was selected as a case study. Three road features and seven viewshed features that are assumed to affect landscape attractiveness on the Persembe-Bolaman coastal road were selected. The data set of these features was categorised into three clusters by k-means clustering, one of the unsupervised learning algorithms. The most attractive cluster in terms of scenic attractiveness was selected by determining the characteristics of the clusters. In conclusion, it was found that the scenic attractiveness was the highest in Cluster-1, which corresponds to 46.3% of the selected route. [ABSTRACT FROM AUTHOR]

In the context of the 21st century and the fourth industrial revolution, the substantial proliferation of data has established it as a valuable resource, fostering enhanced computational capabilities across scientific disciplines, including physics. The integration of Machine Learning stands as a prominent solution to unravel the intricacies inherent to scientific data. While diverse machine learning algorithms find utility in various branches of physics, there exists a need for a systematic framework for the application of Machine Learning to the field. This review offers a comprehensive exploration of the fundamental principles and algorithms of Machine Learning, with a focus on their implementation within distinct domains of physics. The review delves into the contemporary trends of Machine Learning application in condensed matter physics, biophysics, astrophysics, material science, and addresses emerging challenges. The potential for Machine Learning to revolutionize the comprehension of intricate physical phenomena is underscored. Nevertheless, persisting challenges in the form of more efficient and precise algorithm development are acknowledged within this review. [ABSTRACT FROM AUTHOR]

Many of the real-life problems including dynamical structures can be modeled in the shape of differential equations. A number of analytic and numerical methods are being proposed for the solution of these differential equations but for some instances, we may come across a few limitations attached to them. However, theoretically strong and computationally favorable tools such as artificial neural networks can be utilized to approximate the solutions of these differential equations. In this work, we developed mathematical models for some biophysics systems on the basis of their dynamical behavior and opted a neural network having single hidden layer of 50 neurons and Broyden–Fletcher–Goldfarb–Shanno algorithm as an optimizer to simulate the results for population of micro-organisms. The graphical representations of the results obtained both from the neural network and analytic methods are compared for different parameters and we got almost the same results. [ABSTRACT FROM AUTHOR]

(1) Background: Skin barrier function resides mostly in the stratum corneum, which consists of a protein component, the corneocyte (bricks), which provides a scaffold for the second component, the extracellular matrix, consisting of multilayers of lipids (mortar). These two components closely interact and this could be the basis for the differences in the biophysical properties of the skin between anatomical regions. So, the aim of this study was to compare skin microstructural properties between body sites. (2) Methods: A comparative study was conducted that included healthy individuals without previous skin diseases. Skin barrier function parameters and microtopography parameters (smoothness, roughness, desquamation, wrinkles, surface, volume, contrast, variance, homogeneity, anisotropy, total cell count, flaking index, skin surface hardness, brightness, deformability and friction) were measured on the forearm, cheek and palm. (3) Results: 44 participants were included in this study, with a mean age of 38.8 ± 15.0 years. Significant differences were found between body sites for 14 of the 15 parameters evaluated. Smoothness was higher on the forearm than on the cheek and palm (240.02 Sems vs. 348.16 vs. 408.19 Sems, p < 0.05). Hardness was higher on the palm than on the forearm and cheek (13.22 AU vs. 9.44 AU vs. 7.94 AU, p < 0.05). Moreover, we observed that sociodemographic characteristics such as age, sex, tobacco and/or alcohol use, influenced the parameters evaluated. (4) Conclusions: The differences in skin barrier function and microtopography between anatomical regions reflects the different structure of skin in each body part and could help to understand the influence of the sociodemographic characteristics on theses parameters. This information could be useful for comparison with pathological skin characteristics and for targeting new treatments. [ABSTRACT FROM AUTHOR]