Modeling the Role of the Cell Cycle in Regulating Proteus mirabilis Swarm-Colony Development

We present models and computational results which indicate that the spatial and temporal regularity seen in Proteus mirabilis swarm-colony development is largely an expression of a sharp age of dedifferentiation in the cell cycle from motile swarmer cells to immotile dividing cells (also called swimmer or vegetative cells.) This contrasts strongly with reaction-diffusion models of Proteus behavior that ignore or average out the age structure of the cell population and instead use only density-dependent mechanisms. We argue the necessity of retaining the explicit age structure, and suggest experiments that may help determine the underlying mechanisms empirically. Consequently, we advocate Proteus as a model organism for a multiscale understanding of how and to what extent the life cycle of individual cells affects the macroscopic behavior of a biological system

Models of Microbial Dormancy in Biofilms and Planktonic Cultures

We present models of dormancy in a planktonic culture and in biofilm, and examine the relative advantage of short dormancy versus long dormancy times in each case. Simulations and analyses indicate that in planktonic batch cultures and in chemostats, live biomass is maximized by the fastest possible exit from dormancy. The lower limit of time to reawakening is thus perhaps governed by physiological, biochemical or other constraints within the cells. In biofilm we see that the slower waker has a defensive advantage over the fast waker due to a larger amount of dormant biomass, without an appreciable difference in total live biomass. Thus it would seem that typical laboratory culture conditions can be unrepresentative of the natural state. We discuss the computational methods developed for this work

A methodology for long-range prediction of air transportation

A framework and methodology for long term projection of demand for aviation fuels is presented. The approach taken includes two basic components. The first was a new technique for establishing the socio-economic environment within which the future aviation industry is embedded. The concept utilized was a definition of an overall societal objective for the very long run future. Within a framework so defined, a set of scenarios by which the future will unfold are then written. These scenarios provide the determinants of the air transport industry operations and accordingly provide an assessment of future fuel requirements. The second part was the modeling of the industry in terms of an abstracted set of variables to represent the overall industry performance on a macro scale. The model was validated by testing the desired output variables from the model with historical data over the past decades

A Unified Term for Directed and Undirected Motility in Collective Cell Invasion

In this paper we develop mathematical models for collective cell motility. Initially we develop a model using a linear diffusion-advection type equation and fit the parameters to data from cell motility assays. This approach is helpful in classifying the results of cell motility assay experiments. In particular, this model can determine degrees of directed versus undirected collective cell motility. Next we develop a model using a nonlinear diffusion term that is able capture in a unified way directed and undirected collective cell motility. Finally we apply the nonlinear diffusion approach to a problem in tumor cell invasion, noting that neither chemotaxis or haptotaxis are present in the system under consideration in this article

A Multiscale Model of Biofilm as a Senescence-Structured Fluid

We derive a physiologically structured multiscale model for biofilm development. The model has components on two spatial scales, which induce different time scales into the problem. The macroscopic behavior of the system is modeled using growth-induced flow in a domain with a moving boundary. Cell-level processes are incorporated into the model using a so-called physiologically structured variable to represent cell senescence, which in turn affects cell division and mortality. We present computational results for our models which shed light on modeling the combined role senescence and the biofilm state play in the defense strategy of bacteria

PERANCANGAN KOMIK WEBTOON MELALUI KISAH FABEL AL-QURAN SEBAGAI MEDIA PEMBENTUKAN KARAKTER

AbstraKKetersediaan buku di Indonesia dari segi kualitas dan kuantitas masih sangat minim beredar. Padahal buku yang berkualitas merupakan salah satu sarana yang dapat membantu pembentukan karakter karena membangun nilai intelektual, emosional, sosial dan moral. Salah satu contohnya adalah buku bacaan yang dikreasikan melalui sastra berbentuk fabel dengan unsur edukasi dan moral yang terkandung didalamnya, yang umumnya berkaitan erat dengan ajaran agama seperti dalam agama Islam pada al-quran dalam surat An-Naml (semut). Namun jumlah pengkajian pembentukan karakter berbasis cerita fabel al-quran masih terbatas. Bertepatan dengan perkembangan zaman dan teknologi saat ini komik tidak hanya berbentuk media konvensional tetapi juga berbentuk digital contohnya platform komik digital yang dapat diakses dengan mudah dan gratis seperti Webtoon. Hal inilah yang melatarbelakangi perancangan komik webtoon melalui kisah fabel al-quran sebagai media pembentukan karakter. Penelitian yang digunakan menggunakan metode kualitatif dengan data yang diperoleh melalui studi pustaka dan wawancara serta menggunakan metode analisis matriks dan SWOT. Dengan adanya perancangan ini diharapkan dapat meningkatkan kualitas buku bahan bacaan yang mudah dijangkau oleh masyarakat Indonesia.Kata kunci: Fabel Al-Quran, pembentukan karakter, komik webtoon

Deep Learning Model With Adaptive Regularization for EEG-Based Emotion Recognition Using Temporal and Frequency Features

Since EEG signal acquisition is non-invasive and portable, it is convenient to be used for different applications. Recognizing emotions based on Brain-Computer Interface (BCI) is an important active BCI paradigm for recognizing the inner state of persons. There are extensive studies about emotion recognition, most of which heavily rely on staged complex handcrafted EEG feature extraction and classifier design. In this paper, we propose a hybrid multi-input deep model with convolution neural networks (CNNs) and bidirectional Long Short-term Memory (Bi-LSTM). CNNs extract time-invariant features from raw EEG data, and Bi-LSTM allows long-range lateral interactions between features. First, we propose a novel hybrid multi-input deep learning approach for emotion recognition from raw EEG signals. Second, in the first layers, we use two CNNs with small and large filter sizes to extract temporal and frequency features from each raw EEG epoch of 62-channel 2-s and merge with differential entropy of EEG band. Third, we apply the adaptive regularization method over each parallel CNN’s layer to consider the spatial information of EEG acquisition electrodes. The proposed method is evaluated on two public datasets, SEED and DEAP. Our results show that our technique can significantly improve the accuracy in comparison with the baseline where no adaptive regularization techniques are used

Modeling the Effects of Multiple Myeloma on Kidney Function

Multiple myeloma (MM), a plasma cell cancer, is associated with many health challenges, including damage to the kidney by tubulointerstitial fibrosis. We develop a mathematical model which captures the qualitative behavior of the cell and protein populations involved. Specifically, we model the interaction between cells in the proximal tubule of the kidney, free light chains, renal fibroblasts, and myeloma cells. We analyze the model for steady-state solutions to find a mathematically and biologically relevant stable steady-state solution. This foundational model provides a representation of dynamics between key populations in tubulointerstitial fibrosis that demonstrates how these populations interact to affect patient prognosis in patients with MM and renal impairment.Comment: Included version of model without tumor with steady-state analysis, corrected equations for free light chains and renal fibroblasts in model with tumor to reflect steady-state analysis, updated abstract, updated and added reference

Computational Methods and Results for Structured Multiscale Models of Tumor Invasion

We present multiscale models of cancer tumor invasion with components at the molecular, cellular, and tissue levels. We provide biological justifications for the model components, present computational results from the model, and discuss the scientific-computing methodology used to solve the model equations. The models and methodology presented in this paper form the basis for developing and treating increasingly complex, mechanistic models of tumor invasion that will be more predictive and less phenomenological. Because many of the features of the cancer models, such as taxis, aging and growth, are seen in other biological systems, the models and methods discussed here also provide a template for handling a broader range of biological problems