Modeling Cell Biological Features of Meiotic Chromosome Pairing

During meiosis, homologous chromosomes become associated side by side in a process known as homologous chromosome pairing. Pairing requires long range chromosome motion through a nucleus that is full of other chromosomes. It remains unclear how the cell manages to align each pair of chromosomes quickly while mitigating and resolving interlocks. Here, we use a coarse-grained molecular dynamics model to investigate how specific features of meiosis, including motor-driven telomere motion, nuclear envelope interactions, and increased nuclear size, affect the rate of pairing and the mitigation/resolution of interlocks. By creating in silico versions of three yeast strains and comparing the results of our model to experimental data, we find that a more distributed placement of pairing sites along the chromosome is necessary to replicate experimental findings. Active motion of the telomeric ends speeds up pairing only if binding sites are spread along the chromosome length. Adding a meiotic bouquet significantly speeds up pairing but does not significantly change the number of interlocks. An increase in nuclear size slows down pairing while greatly reducing the number of interlocks. Interestingly, active forces increase the number of interlocks, which raises the question: How do these interlocks resolve? Our model gives us detailed movies of interlock resolution events which we then analyze to build a step-by-step recipe for interlock resolution. In our model, interlocks must first translocate to the ends, where they are held in a quasi-stable state by a large number of paired sites on one side. To completely resolve an interlock, the telomeres of the involved chromosomes must come in close proximity so that the cooperativity of pairing coupled with random motion causes the telomeres to unwind. Together our results indicate that computational modeling of homolog pairing provides insight into the specific cell biological changes that occur during meiosis.

Quantifying the Morphology and Mechanisms of Cancer Progression in 3D in-vitro environments: Integrating Experiments, Multiscale Models, and Spatial Validation

Throughout the years, mathematical models of cancer growth have become increasingly more accurate in terms of the description of cancer growth in both space and time. However, the limited amount of data typically available has resulted in a larger number of qualitative rather than quantitative studies. In this study, we provide an integrated experimental-computational framework for the quantification of the morphological characteristics and the mechanistic modelling of cancer progression in 3D environments. The proposed framework allows the calibration of multiscale-spatiotemporal models of cancer growth using 3D cell culture data, and their validation based on the morphological patterns. The implementation of this framework enables us to pursue two goals; first, the quantitative description of the morphology of cancer progression in 3D cultures, and second, the relation of tumour morphology with underlying biophysical mechanisms that govern cancer growth. We apply this framework to the study of the spatiotemporal progression of Triple Negative Breast Cancer (TNBC) cells cultured in 3D Matrigel scaffolds, under the hypothesis of chemotactic migration using a multiscale Keller-Segel model. The results reveal transient, non-random spatial distributions of cancer cells that consist of clustered patterns across a wide range of neighbourhood distances, as well as dispersion for larger distances. Overall, the proposed model was able to describe the general characteristics of the experimental observations and suggests that cancer cells exhibited chemotactic migration and cell accumulation, as well as random motion throughout the period of development. To our knowledge, this is the first time a framework attempts to quantify the relationship of the spatial patterns and the underlying mechanisms of cancer growth in 3D environments.

In-House Software Development for Data Visualization of Random Motion Configuration of Particles in Fluid

This study aims to develop in-house software for data visualization program using Python programming language. Implement a simple algorithm and use the matplotlib library for 2D plotting. The results of development of this program were tested using a dataset from random motion simulation results of many particles modeled by two-dimensional circle shape and the diameter size as D. Data visualization in the form of particles configuration then confirmed with the particles configuration of the simulation results. Based on the test, it is found that the particles configuration results of the visualization are same as the configuration of the simulated particles. This shows that the data visualization program that has been developed can be used to process other data stored in a predetermined data format.

In-House Software Development for Data Visualization of Random Motion Configuration of Particles in Fluid

This study aims to develop in-house software for data visualization program using Python programming language. Implement a simple algorithm and use the matplotlib library for 2D plotting. The results of development of this program were tested using a dataset from random motion simulation results of many particles modeled by two-dimensional circle shape and the diameter size as D. Data visualization in the form of particles configuration then confirmed with the particles configuration of the simulation results. Based on the test, it is found that the particles configuration results of the visualization are same as the configuration of the simulated particles. This shows that the data visualization program that has been developed can be used to process other data stored in a predetermined data format.

Centralized Algorithms Based on Clustering with Self-tuning of Parameters for Cooperative Target Observation

Clustering on target positions is a class of centralized algorithms used to calculate the surveillance robots' displacements in the Cooperative Target Observation (CTO) problem. This work proposes and evaluates Fuzzy C-means (FCM) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with K-means (DBSk) based self-tuning clustering centralized algorithms for the CTO problem and compares its performances with that of K-means. Two random motion patterns are adopted for the targets: in free space or on a grid. As a contribution, the work allows identifying ranges of problem configuration parameters in which each algorithm shows the highest average performance. As a first conclusion, in the challenging situation in which the relative speed of the targets is high, and the relative sensor range of the surveillance is low, for which the existing algorithms present a substantial drop in performance, the FCM algorithm proposed outperforms the others. Finally, the DBSk algorithm adapts very well in low execution frequency, showing promising results in this challenging situation.

Effects of caffeine ingestion on dynamic visual acuity: a placebo-controlled, double-blind, balanced-crossover study in low caffeine consumers

Background Acute caffeine ingestion has been associated with improvements in cognitive performance and visual functioning. The main objective of this study was to determine the effects of caffeine intake on dynamic visual acuity (DVA). Methods Twenty-one low caffeine consumers (22.5 ± 1.6 years) took part in this placebo-controlled, double-blind, and balanced crossover study. In two different days and following a random order, participants ingested either caffeine (4 mg/kg) or placebo, and DVA was measured after 60 min of ingesting the corresponding capsule. A recently developed and validated software (moV& test, V&mp Vision Suite, Waterloo, Canada) was used to assess DVA. Results We found a greater accuracy for both the horizontal and random motion paths of DVA after caffeine ingestion (p < 0.001 and p = 0.002, respectively). In regard to the speed of the response, our data revealed that caffeine intake was associated with a faster reaction time for horizontally (p = 0.012) but not for randomly (p = 0.846) moving targets. Also, participants reported higher levels of perceived activation after consuming caffeine in comparison to placebo (p < 0.001). Conclusions Our data suggest that caffeine intake (i.e., a capsule containing 4 mg/kg) has an ergogenic effect on DVA, which may be of special relevance in real-word contexts that require to accurately and rapidly detect moving targets (e.g., sports, driving, or piloting).

Restless motion

Atoms in solids are in constant random motion. Their kinetic energy is heat. Heat associated with local regions may fluctuate. The size of the fluctuations increases with decreasing size of the region. Such fluctuations enable thermally activated processes to occur. At equilibrium interstitials and vacancies undergo random walks in solids, which gives rise to diffusion in crystals and reptation in polymers. The activation energy is the free energy barrier these defects have to overcome to jump between sites. Diffusion is biased by driving forces resulting from gradients of chemical potential. The mobility relates the drift velocity of defects to the driving force on them. The Einstein relation relates the mobility to the diffusivity. It is an example of the fluctuation-dissipation theorem. Atomic motion enables diffusion and limits mobility. Thermal expansion is also a consequence of atomic motion, resulting from a fundamental asymmetry in all interatomic forces.