Physics

Super-resolution imaging techniques have largely improved our capabilities to visualize nanometric structures in biological systems. Their application further enables one to potentially quantitate relevant parameters to determine the molecular organization and stoichiometry in cells. However, the inherently stochastic nature of the fluorescence emission and labeling strategies imposes the use of dedicated methods to accurately measure these parameters. Here, we describe a Bayesian approach to precisely quantitate the relative abundance of molecular oligomers from segmented images. The distribution of proxies for the number of molecules in a cluster -- such as the number of localizations or the fluorescence intensity -- is fitted via a nested sampling algorithm to compare mixture models of increasing complexity and determine the optimal number of mixture components and their weights. We test the performance of the algorithm on {\it in silico} data as a function of the number of data points, threshold, and distribution shape. We compare these results to those obtained with other statistical methods, showing the improved performance of our approach. Our method provides a robust tool for model selection in fitting data extracted from fluorescence imaging, thus improving the precision of parameter determination. Importantly, the largest benefit of this method occurs for small-statistics or incomplete datasets, enabling accurate analysis at the single image level. We further present the results of its application to experimental data obtained from the super-resolution imaging of dynein in HeLa cells, confirming the presence of a mixed population of cytoplasmatic single motors and higher-order structures.

Biological cells estimate concentration gradients of signaling molecules with a precision that is limited not only by sensing noise, but additionally by the cell's own stochastic motion. We ask for the theoretical limits of gradient estimation in the presence of both motility and sensing noise. We introduce a minimal model of a stationary chemotactic agent in the plane subject to rotational diffusion, which uses Bayesian estimation to optimally infer a gradient direction from noisy concentration measurements. Contrary to the known case of gradient sensing by temporal comparison, we show that for spatial comparison, the ultimate precision of gradient sensing scales not with the rotational diffusion time, but with its square-root. To achieve this precision, an individual agent needs to know its own rotational diffusion coefficient. This agent can accurately estimate the expected variability within an ensemble of agents. If an agent, however, does not account for its own motility noise, Bayesian estimation fails in a characteristic manner.

Extrinsic noise-induced transitions to bimodal dynamics have been largely investigated in a variety of chemical, physical, and biological systems. In the standard approach in physical and chemical systems, the key properties that make these systems mathematically tractable are that the noise appears linearly in the dynamical equations, and it is assumed Gaussian and white. In biology, the Gaussian approximation has been successful in specific systems, but the relevant noise being usually non-Gaussian, non-white, and nonlinear poses serious limitations to its general applicability. Here we revisit the fundamental features of linear Gaussian noise, pinpoint its limitations, and review recent new approaches based on nonlinear bounded noises, which highlight novel mechanisms to account for transitions to bimodal behaviour. We do this by considering a simple but fundamental gene expression model, the repressed gene, which is characterized by linear and nonlinear dependencies on external parameters. We then review a general methodology introduced recently, so-called nonlinear noise filtering, which allows the investigation of linear, nonlinear, Gaussian and non-Gaussian noises. We also present a derivation of it, which highlights its dynamical origin. Testing the methodology on the repressed gene confirms that the emergence of noise-induced transitions appears to be strongly dependent on the type of noise adopted, and on the degree of nonlinearity present in the system.

Binding of anthracycline antibiotic carminomycin (CM) to synthetic polyribonucleotides poly(A) and poly(U) was studied in solution of low ionic strength in a wide phosphate-to-dye (P/D) range using absorption and polarized fluorescence spectroscopy. Two different modes of CM binding to the ss-polynucleotides have been identified. The first of them dominating at low phosphate-to-dye (P/D) ratios is self-assembly of the heterocyclic dye on the polymer surface driven by to cooperative electrostatic binding of amino group of CM sugar moiety to negatively charged polynucleotide phosphate groups with the chromophores self-stacking. At high P/D values, the stacking-associates disintegrate and monomeric binding of ligand to nucleic bases become prevalent. Thermodynamic parameters of binding were estimated for the both cases.

In this review, we aim to introduce the reader to the technique of electrical impedance spectroscopy (EIS) with a focus on its biological and medical applications. We explain the theoretical and experimental aspects of the EIS with the details essential for biological studies, i.e interaction of metal electrodes with biological matter and liquids, strategies of increasing measurement rate and noise reduction in bio-EIS experiments, etc. We give various examples of successful bio-EIS practical implementations in science and technology: from the whole-body health monitoring and sensors for vision prosthetic care to single living cell examination platforms and virus diseases research. Present review can be used as a bio-EIS tutorial for students as well as a handbook for scientists and engineers due to extensive references covering the contemporary research papers in the field.

Bacterial colonization ofimplanted biomedical devicesis themain cause of healthcare-associated infections, estimated to be 8.8 million per year in Europe. Many infections originate from damaged skin, which lets microorganisms exploit injuries and surgical accesses as passageways to reach the implant site and inner organs. Therefore, an effective treatment of skin damage is highly desirable for the success of many biomaterial-related surgical procedures. Due to gained resistance to antibiotics, new antibacterial treatments are becoming vital to control nosocomial infections arising as surgical and post-surgical complications. Surface coatings can avoid biofouling and bacterial colonization thanks to biomaterial inherent properties (e.g., super hydrophobicity), specifically without using drugs, which may cause bacterial resistance. The focus of this review is to highlight the emerging role of degradable polymeric micro- and nano-structures that show intrinsic antifouling and antimicrobial properties, with a special outlook towards biomedical applications dealing with skin and skin damage. The intrinsic properties owned by the biomaterials encompass three main categories: (1) physical-mechanical, (2) chemical, and (3) electrostatic. Clinical relevance in ear prostheses and breast implants is reported. Collecting and discussing the updated outcomes in this field would help the development of better performing biomaterial-based antimicrobial strategies, which are useful to prevent infections.

Background and controlled electromagnetic radiation (EMR) on biological cells and tissues induces thermal, non-thermal, and dielectric property change. After EMR interaction with cells/tissues the resulting signal is used for imaging, bio-molecular response, and photo-biomodulation studies at infrared regime, and for therapeutic use. We attempt to present a review of current literature with a focus to present compilation of published experimental results for each regime viz. microwave (extremely low frequency, ELF to 3 GHz), to cellular communication frequencies (100 KHz to 300 GHz), millimeter wave (300 GHz- 1 THz), and the infra-red band extending up to 461 THz. A unique graphical representation of frequency effects and their relevant significance in detection of direct biological effects, therapeutic applications and biophysical interpretation is presented. A total of seventy research papers from peer-reviewed journals were used to compile a mixture of useful information, all presented in a narrative style. Out of the Journal articles used for this paper, 63 journal articles were published between 2000 to 2020. Physical, biological, and therapeutic mechanisms of thermal, non-thermal and complex dielectric effects of EMR on cells are all explained in relevant sections of this paper. A broad up to date review for the EMR range KHz-NIR (kilohertz to near infra-red) is prepared. Published reports indicate that number of biological cell irradiation impact studies fall off rapidly beyond a few THz EMR, leading to relatively a smaller number of studies in FIR and NIR bands covering most of the thermal effects and microthermal effects, and rotation-vibration effects.

Brillouin imaging is increasingly recognized to be a powerful technique that enables non-invasive measurement of the mechanical properties of cells and tissues on a microscopic scale. This provides an unprecedented tool for investigating cell mechanobiology, cell-matrix interactions, tissue biomechanics in healthy and disease conditions and other fundamental biological questions. Recent advances in optical hardware have particularly accelerated the development of the technique, with increasingly finer spectral resolution and more powerful system capabilities. We envision that further developments will enable translation of Brillouin imaging to assess clinical specimens and samples for disease screening and monitoring. The purpose of this review is to summarize the state-of-the-art in Brillouin microscopy and imaging with a specific focus on biological tissue and cell measurements. Key system and operational requirements will be discussed to facilitate wider application of Brillouin imaging along with current challenges for translation of the technology for clinical and medical applications.

Living systems break detailed balance at small scales, consuming energy and producing entropy in the environment in order to perform molecular and cellular functions. However, it remains unclear how broken detailed balance manifests at macroscopic scales, and how such dynamics support higher-order biological functions. Here we present a framework to quantify broken detailed balance by measuring entropy production in macroscopic systems. We apply our method to the human brain, an organ whose immense metabolic consumption drives a diverse range of cognitive functions. Using whole-brain imaging data, we demonstrate that the brain nearly obeys detailed balance when at rest, but strongly breaks detailed balance when performing physically and cognitively demanding tasks. Using a dynamic Ising model, we show that these large-scale violations of detailed balance can emerge from fine-scale asymmetries in the interactions between elements, a known feature of neural systems. Together, these results suggest that violations of detailed balance are vital for cognition, and provide a general tool for quantifying entropy production in macroscopic systems.

In 2018, West Nile Virus (WNV) was detected for the first time in Germany. Since the first detection, 36 human cases and 175 cases in horses and birds are detected. The transmission cycle of West Nile Virus includes birds and mosquitoes and -- as dead-end hosts -- people and horses. Spatial dissemination of the disease is caused by the movements of birds and mosquitoes. While the activity and movement of mosquitoes are depending mainly on temperature, in the birds there is a complex movement pattern caused by local birds and long range dispersal birds. To this end, we have developed a metapopulation network model framework to delineate the potential spatial distribution and spread of WNV across Germany as well as to evaluate the risk throughout our proposed network model. Our model facilitates the interconnection amongst the vector, local birds and long range dispersal birds contact networks. We have assumed different distance dispersal kernels models for the vector and avian populations with the intention to include short and long range dispersal. The model includes spatial variation of mosquito abundance and the movements to resemble the reality.