A novel stochastic simulation approach enables exploration of mechanisms for regulating polarity site movement

Cells polarize their movement or growth toward external directional cues in many different contexts. For example, budding yeast cells grow toward potential mating partners in response to pheromone gradients. Directed growth is controlled by polarity factors that assemble into clusters at the cell membrane. The clusters assemble, disassemble, and move between different regions of the membrane before eventually forming a stable polarity site directed toward the pheromone source. Pathways that regulate clustering have been identified but the molecular mechanisms that regulate cluster mobility are not well understood. To gain insight into the contribution of chemical noise to cluster behavior we simulated clustering within the reaction-diffusion master equation (RDME) framework to account for molecular-level fluctuations. RDME simulations are a computationally efficient approximation, but their results can diverge from the underlying microscopic dynamics. We implemented novel concentration-dependent rate constants that improved the accuracy of RDME-based simulations of cluster behavior, allowing us to efficiently investigate how cluster dynamics might be regulated. Molecular noise was effective in relocating clusters when the clusters contained low numbers of limiting polarity factors, and when Cdc42, the central polarity regulator, exhibited short dwell times at the polarity site. Cluster stabilization occurred when abundances or binding rates were altered to either lengthen dwell times or increase the number of polarity molecules in the cluster. We validated key results using full 3D particle-based simulations. Understanding the mechanisms cells use to regulate the dynamics of polarity clusters should provide insights into how cells dynamically track external directional cues.

Analysis of Cluster and Unrest Behaviors of Laying Hens Housed under Different Thermal Conditions and Light Wave Length

Laying hens are affected by the intensity, wavelength, and duration of light, and the behavioral patterns of these animals are important indicators of stress. The objective of the present study was to evaluate cluster and unrest behaviors of lying hens submitted to three environments with different treatments of monochromatic lighting (blue, green, and red). For 29 weeks, 60 laying hens from the Lohmann variety were divided into three groups and monitored by surveillance cameras installed on each shed ceiling and directed to the floor. Each group was housed in a small-scale shed and maintained under a monochromatic lighting treatment. The recordings were made at two times of the day, 15 min in the morning and 15 min in the afternoon, and the videos were processed, segmented, and analyzed computationally. From the analysis of the images, the cluster and unrest indexes were calculated. The results showed the influence of lighting on these behaviors, displaying that the birds were more agitated in the treatments with shorter wavelengths. Cluster behavior was higher in birds housed under red light. There was an interaction between the lighting treatments and the thermal environment, indicating that more studies should be carried out in this area to better understand these behavioral changes.

A novel stochastic simulation approach enables exploration of mechanisms for regulating polarity site movement

Cells polarize their movement or growth toward external directional cues in many different contexts. For example, budding yeast cells grow toward potential mating partners in response to pheromone gradients. Directed growth is controlled by polarity factors that assemble into clusters at the cell membrane. The clusters assemble, disassemble, and move between different regions of the membrane before eventually forming a stable polarity site directed toward the pheromone source. Pathways that regulate clustering have been identified but the molecular mechanisms that regulate cluster mobility are not well understood. To gain insight into the contribution of chemical noise to cluster behavior we simulated clustering within the reaction-diffusion master equation (RDME) framework to account for molecular-level fluctuations. RDME simulations are a computationally efficient approximation, but their results can diverge from the underlying microscopic dynamics. We implemented novel concentration-dependent rate constants that improved the accuracy of RDME-based simulations of cluster behavior, allowing us to efficiently investigate how cluster dynamics might be regulated. Molecular noise was effective in relocating clusters when the clusters contained low numbers of limiting polarity factors, and when Cdc42, the central polarity regulator, exhibited short dwell times at the polarity site. Cluster stabilization occurred when abundances or binding rates were altered to either lengthen dwell times or increase the number of polarity molecules in the cluster. We validated key results using full 3D particle-based simulations. Understanding the mechanisms cells use to regulate the dynamics of polarity clusters should provide insights into how cells dynamically track external directional cues.Author summaryCells localize polarity molecules in a small region of the plasma membrane forming a polarity cluster that directs functions such as migration, reproduction, and growth. Guided by external signals, these clusters move across the membrane allowing cells to reorient growth or motion. The polarity molecules continuously and randomly shuttle between the cluster and the cell cytosol and, as a result, the number and distribution of molecules at the cluster constantly changes. Here we present an improved stochastic simulation algorithm to investigate how such molecular-scale fluctuations induce cluster movement across the cell membrane. Unexpectedly, cluster mobility does not correlate with variations in total molecule abundance within the cluster, but rather with changes in the spatial distribution of molecules that form the cluster. Cluster motion is faster when polarity molecules are scarce and when they shuttle rapidly between the cluster and the cytosol. Our results suggest that cells control cluster mobility by regulating the abundance of polarity molecules and biochemical reactions that affect the time molecules spend at the cluster. We provide insights into how cells harness random molecular behavior to perform functions important for survival, such as detecting the direction of external signals.

3D CPFD Simulation of Circulating Fluidized Bed Downer and Riser: Comparisons of Flow Structure and Solids Back-Mixing Behavior

The difference of gas-solids flow between a circulating fluidized bed (CFB) downer and riser was compared by computational particle fluid dynamics (CPFD) approach. The comparison was conducted under the same operating conditions. Simulation results demonstrated that the downer showed much more uniform solids holdup and solids velocity distribution compared with the riser. The radial non-uniformity index of the solids holdup in the riser was over 10 times than that in the downer. In addition, small clusters tended to be present in the whole downer, large clusters tended to be present near the wall in riser. It was found that the different cluster behavior is important in determining the different flow behaviors of solids in the downer and riser. While the particle residence time increased evenly along the downward direction in the downer, particles with both shorter and longer residence time were predicted in the whole riser. The nearly vertical cumulative residence time distribution (RTD) curve in the downer further demonstrated that the solids back-mixing in the downer is limited while that in the riser is severe. Solids turbulence in the downer was much weaker compared with the riser, while the large clusters formation near the wall in the riser would hinder solids transportation ability.

Análise e reconhecimento de padrões cognitivos em escutas musicais e sonoros em áudios

We are involved in an environment full of sounds around us. Studying and analyzing the impacts that musical practice causes and showing mathematically that this practice provides significant cognitive effects on the human brain are the main motivations of this thesis. In more detail, the aim of this thesis was to develop a methodology capable of characterizing the cortical activation patterns generated during the register of Electroencephalogram (EEG) signals through pattern recognition techniques in statistics, in addition to analyzing the acoustic features commonly employed in this context, in order to reveal whether they are statistically relevant. A computational framework was initially developed to address a 2 group classification problem based on data from EEG signals extracted from volunteer musicians and non-musicians during an auditory task, to predict whether a particular person is a musician or not. The results showed that it is possible to classify the sampled groups with accuracy ranging from 69.2% to 93.8%, allowing not only a better description of the neural activation patterns that characterize the musician and non-musician volunteers, but also highlighting how these patterns they change in the transition regions and decision boundaries that separate the sampled groups, indicating a plausible linear separation between these groups. Additionally, as another original contribution of this thesis, the audio signals from a public and internationally referenced database containing 1000 musical excerpts with 10 different genres were analyzed to investigate numerical similarities between the short-term acoustic features extracted from the audios and commonly explored in related literature. The results obtained show a similar cluster behavior among these features for all analyzed music, regardless of the musical genre. It was then possible to discuss in an unprecedented way the relationship between the way the acoustic features of songs are described in the literature and how they are grouped statistically, revealing that the information we use to cognitively process these sound features is implicitly statistical. Although all the methods described and implemented in this thesis are based on EEG signals, it is believed that they can be extended to other types of multivariate cognitive signals, such as, for example, functional Magnetic Resonance Imaging (fMRI), allowing a greater cortical and sub-cortical understanding of the functioning of our brain during listening

Implementation of Total Productive Maintenance for Overall Equipment Effectiveness Improvement in Machine Shop

In Total Productive Maintenance tiny cluster behavior are interweave. Independent protection is one of the most vital pillars of TPM. Self-directed preservation aims to instruct the accomplice in the principle and philosophy of self-directed maintenance and to furnish them chance to extend their skills & assurance. TPM lend a hand to elevate the value of OEE by providing a structure to make easy the judgment of the losses. TPM look for to cheer the setting of striving, but reasonable goals for raising the value of the OEE. In the present work, machines were suspiciously considered and this revision discover the ways in which auto constituent business be able to execute TPM as a policy and society for civilizing its presentation. In this study, it is proposed to initiate a innovative term “give up” in the OEE computation. This helps in giving a better account of the material utilization.

Cluster Behavior of Barbituric Acid Based on Hartree-Fock (HF) Theoretic Calculations

Barbiturates, a class of aromatic hydrocarbons, were discovered during the first decade of the twentieth century. They act as central nervous system depressants. This article discuss the cluster behavior of barbituric acid based on Hartree-Fock (HF) theoretic calculations for (BA)n, n =1-7

G1/S transcription factors assemble in discrete clusters that increase in number as cells grow

The spatio-temporal organization of transcription factor (TF)-promoter interactions is critical for the coordination of transcriptional programs. In budding yeast, the main G1/S transcription factors, SBF and MBF, are limiting with respect to target promoters in small G1 phase cells and accumulate as cells grow, raising the question of how SBF/MBF are dynamically distributed across the G1/S regulon. Super-resolution Photo-Activatable Localization Microscopy (PALM) mapping of the static positions of SBF/MBF subunits revealed that 85% were organized into discrete clusters containing ∼8 copies regardless of cell size, while the number of clusters increased with growth. Stochastic simulations with a mathematical model based on co-localization of promoters in clusters recapitulated observed cluster behavior. A prediction of the model that SBF/MBF should exhibit both fast and slow dynamics was confirmed in PALM experiments on live cells. This spatio-temporal organization of the TFs that activate the G1/S regulon may help coordinate commitment to division.