Mathematical model reveals that heterogeneity in the number of ion transporters regulates the fraction of mouse sperm capacitation

Capacitation is a complex maturation process mammalian sperm must undergo in the female genital tract to be able to fertilize an egg. This process involves, amongst others, physiological changes in flagellar beating pattern, membrane potential, intracellular ion concentrations and protein phosphorylation. Typically, in a capacitation medium, only a fraction of sperm achieve this state. The cause for this heterogeneous response is still not well understood and remains an open question. Here, one of our principal results is to develop a discrete regulatory network, with mostly deterministic dynamics in conjunction with some stochastic elements, for the main biochemical and biophysical processes involved in the early events of capacitation. The model criterion for capacitation requires the convergence of specific levels of a select set of nodes. Besides reproducing several experimental results and providing some insight on the network interrelations, the main contribution of the model is the suggestion that the degree of variability in the total amount and individual number of ion transporters among spermatozoa regulates the fraction of capacitated spermatozoa. This conclusion is consistent with recently reported experimental results. Based on this mathematical analysis, experimental clues are proposed for the control of capacitation levels. Furthermore, cooperative and interference traits that become apparent in the modelling among some components also call for future theoretical and experimental studies.

Reaction Rate Theory-Based Mathematical Approximation For The Amount Of Time It Takes For Cellular Respiration To Occur

The venerable process of cellular respiration is essential for cells to produce energy from glucose molecules, in order to carry out cellular work. The process is responsible for producing molecules of ATP, a molecule which is thermodynamically coupled with other biochemical and biophysical processes in order to provide energy for such processes to occur. While the process of cellular respiration is essential to biology, one cycle of the process occurs only in a matter of milliseconds, and so, it would be impractical to measure the time it takes for the process to occur through conventional means. Therefore, using concepts from reaction rate theory, particularly Marcus Theory of electron transfer, Michaelis-Menten kinetics for enzymatic catalysis, and the hard-sphere model of collision theory, I formulate and propose a mathematical approximation for the amount of time it takes forcellular respiration to occur. Through this heuristic approach, quantitatively knowing the amount of time it takes for one cycle of cellular respiration to occur could potentially have future applications in biological research.

From Millimeters to Micrometers; Re-introducing Myocytes in Models of Cardiac Electrophysiology

Computational modeling has contributed significantly to present understanding of cardiac electrophysiology including cardiac conduction, excitation-contraction coupling, and the effects and side-effects of drugs. However, the accuracy of in silico analysis of electrochemical wave dynamics in cardiac tissue is limited by the homogenization procedure (spatial averaging) intrinsic to standard continuum models of conduction. Averaged models cannot resolve the intricate dynamics in the vicinity of individual cardiomyocytes simply because the myocytes are not present in these models. Here we demonstrate how recently developed mathematical models based on representing every myocyte can significantly increase the accuracy, and thus the utility of modeling electrophysiological function and dysfunction in collections of coupled cardiomyocytes. The present gold standard of numerical simulation for cardiac electrophysiology is based on the bidomain model. In the bidomain model, the extracellular (E) space, the cell membrane (M) and the intracellular (I) space are all assumed to be present everywhere in the tissue. Consequently, it is impossible to study biophysical processes taking place close to individual myocytes. The bidomain model represents the tissue by averaging over several hundred myocytes and this inherently limits the accuracy of the model. In our alternative approach both E, M, and I are represented in the model which is therefore referred to as the EMI model. The EMI model approach allows for detailed analysis of the biophysical processes going on in functionally important spaces very close to individual myocytes, although at the cost of significantly increased CPU-requirements.

A biophysical approach to assess weather impacts on honey bee colony winter mortality

The western honey bee ( Apis mellifera ) is one of the most important insects kept by humans, but high colony losses are reported around the world. While the effects of general climatic conditions on colony winter mortality were already demonstrated, no study has investigated specific weather conditions linked to biophysical processes governing colony vitality. Here, we quantify the comparative relevance of four such processes that co-determine the colonies' fitness for wintering during the annual hive management cycle, using a 10-year dataset of winter colony mortality in Austria that includes 266 378 bee colonies. We formulate four process-based hypotheses for wintering success and operationalize them with weather indicators. The empirical data is used to fit simple and multiple linear regression models on different geographical scales. The results show that approximately 20% of winter mortality variability can be explained by the analysed weather conditions, and that it is most sensitive to the duration of extreme cold spells in mid and late winter. Our approach shows the potential of developing weather indicators based on biophysical processes and discusses the way forward for applying them in climate change studies.

Metode za merjenje vsebnosti vode v tleh

Water has a significant influence on fundamental biophysical processes in the soil. It is one of the limiting factors for plant growth, which is why monitoring the water content in the field is particularly important in agriculture. In this article we present the methods currently used to measure the soil water content. We have described their functional principles, advantages, disadvantages and possible applications. Due to their widespread use in agriculture, we have focused on dielectric sensors, which are classified as electromagnetic methods. We have investigated the influence of soil properties on measurements with dielectric sensors and described possible methods for soil-specific calibration. In agriculture and environmental sciences, measurements of soil water content are particularly important for irrigation management. Irrigation based on measurements enables us to optimize the use of water resources and reduce the negative impact on the environment. For the correct functioning of such sensors it is necessary to check the suitability of the factory calibration function. Special attention is required when installing the sensors, as the presence of air gaps causes errors in the measurements.

A geospatial mapping pipeline for ecologists

Geospatial modelling can give fundamental insights in the biogeography of life, providing key information about the living world in current and future climate scenarios. Emerging statistical and machine learning approaches can help us to generate new levels of predictive accuracy in exploring the spatial patterns in ecological and biophysical processes. Although these statistical models cannot necessarily represent the essential mechanistic insights that are needed to understand global biogeochemical processes under ever-changing environmental conditions, they can provide unparalleled predictive insights that can be useful for exploring the variation in biophysical processes across space. As such, these emerging tools can be a valuable approach to complement existing mechanistic approaches as we aim to understand the biogeography of Earth's ecosystems. Here, we present a comprehensive methodology that efficiently handles large datasets to produce global predictions. This mapping pipeline can be used to generate quantitative, spatially explicit predictions, with a particular emphasis on spatially-explicit insights into the evaluation of model uncertainties and inaccuracies.

Bacteria Single-Cell and Photosensitizer Interaction Revealed by Quantitative Phase Imaging

Quantifying changes in bacteria cells in the presence of antibacterial treatment is one of the main challenges facing contemporary medicine; it is a challenge that is relevant for tackling issues pertaining to bacterial biofilm formation that substantially decreases susceptibility to biocidal agents. Three-dimensional label-free imaging and quantitative analysis of bacteria–photosensitizer interactions, crucial for antimicrobial photodynamic therapy, is still limited due to the use of conventional imaging techniques. We present a new method for investigating the alterations in living cells and quantitatively analyzing the process of bacteria photodynamic inactivation. Digital holographic tomography (DHT) was used for in situ examination of the response of Escherichia coli and Staphylococcus aureus to the accumulation of the photosensitizers immobilized in the copolymer revealed by the changes in the 3D refractive index distributions of single cells. Obtained results were confirmed by confocal microscopy and statistical analysis. We demonstrated that DHT enables real-time characterization of the subcellular structures, the biophysical processes, and the induced local changes of the intracellular density in a label-free manner and at sub-micrometer spatial resolution.

Divergent climate feedbacks on winter wheat growing and dormancy periods as affected by sowing date in the North China Plain

Crop phenology exerts measurable impacts on soil surface properties, biophysical processes and climate feedbacks, particularly at local or regional scales. Nevertheless, the response of surface biophysical processes to climate feedbacks as affected by sowing date in winter wheat croplands has been overlooked, especially during winter dormancy. The dynamics of leaf area index (LAI), surface energy balance and canopy temperature (Tc) were simulated by a modified SiBcrop (Simple Biosphere) model under two sowing date scenarios (early sowing, EP; late sowing, LP) at 10 stations in the North China Plain. The results showed that the SiBcrop model with a modified crop phenology scheme well simulated the seasonal dynamic of LAI, Tc, phenology and surface heat fluxes. An earlier sowing date had a higher LAI with earlier development than a later sowing date. But the response of Tc to the sowing date exhibited opposite patterns during the dormancy and active-growth periods: EP led to higher Tc (0.05 K) than LP in the dormancy period and lower Tc (−0.2 K) in the growth period. The highest difference (0.6 K) between EP and LP happened at the time when wheat was sown in EP but was not in LP. The higher LAI captured more net radiation with a warming effect but partitioned more energy into latent heat flux with cooling. The climate feedback of the sowing date, which was more obvious in winter in the northern areas and in the growing period in the southern areas, was determined by the relative contributions of the albedo radiative process and partitioning non-radiative process. The study highlights the surface biophysical process of land management in modulating climate.