Simple Cell Balance Circuit

A method has been developed for continuous cell voltage balancing for rechargeable batteries (e.g. lithium ion batteries). A resistor divider chain is provided that generates a set of voltages representing the ideal cell voltage (the voltage of each cell should be as if the cells were perfectly balanced). An operational amplifier circuit with an added current buffer stage generates the ideal voltage with a very high degree of accuracy, using the concept of negative feedback. The ideal voltages are each connected to the corresponding cell through a current- limiting resistance. Over time, having the cell connected to the ideal voltage provides a balancing current that moves the cell voltage very close to that ideal level. In effect, it adjusts the current of each cell during charging, discharging, and standby periods to force the cell voltages to be equal to the ideal voltages generated by the resistor divider. The device also includes solid-state switches that disconnect the circuit from the battery so that it will not discharge the battery during storage. This solution requires relatively few parts and is, therefore, of lower cost and of increased reliability due to the fewer failure modes. Additionally, this design uses very little power. A preliminary model predicts a power usage of 0.18 W for an 8-cell battery. This approach is applicable to a wide range of battery capacities and voltages

A Simple Cell Model with Multiple Spatial Frequency Selectivity and Linear/Non-Linear Response Properties

A model is described for cortical simple cells. Simple cells are selective for local contrast polarity, signaling light-dark and dark-light transitions. The proposed new architecture exhibits both linear and non-linear properties of simple cells. Linear responses are obtained by integration of the input stimulus within subfields of the cells, and by combinations of them. Non-linear behavior can be seen in the selectivity for certain features that can be characterized by the spatial arrangement of activations generated by initial on- and off-cells (center-surround). The new model also exhibits spatial frequency selectivity with the generation of multi-scale properties being based on a single-scale band-pass input that is generated by the initial (retinal) center-surround processing stage.German BMFT grant (413-5839-01 IN 101 C/1); CNPq and NUTES/UFRJ, Brazi

Tracking bifurcating solutions of a model biological pattern generator

We study heterogeneous steady-state solutions of a cell-chemotaxis model for generating biological spatial patterns in two-dimensional domains with zero flux boundary conditions. We use the finite-element package ENTWIFE to investigate bifurcation from the uniform solution as the chemotactic parameter varies and as the domain scale and geometry change. We show that this simple cell-chemotaxis model can produce a remarkably wide and surprising range of complex spatial patterns

Are v1 simple cells optimized for visual occlusions? : A comparative study

Abstract: Simple cells in primary visual cortex were famously found to respond to low-level image components such as edges. Sparse coding and independent component analysis (ICA) emerged as the standard computational models for simple cell coding because they linked their receptive fields to the statistics of visual stimuli. However, a salient feature of image statistics, occlusions of image components, is not considered by these models. Here we ask if occlusions have an effect on the predicted shapes of simple cell receptive fields. We use a comparative approach to answer this question and investigate two models for simple cells: a standard linear model and an occlusive model. For both models we simultaneously estimate optimal receptive fields, sparsity and stimulus noise. The two models are identical except for their component superposition assumption. We find the image encoding and receptive fields predicted by the models to differ significantly. While both models predict many Gabor-like fields, the occlusive model predicts a much sparser encoding and high percentages of ‘globular’ receptive fields. This relatively new center-surround type of simple cell response is observed since reverse correlation is used in experimental studies. While high percentages of ‘globular’ fields can be obtained using specific choices of sparsity and overcompleteness in linear sparse coding, no or only low proportions are reported in the vast majority of studies on linear models (including all ICA models). Likewise, for the here investigated linear model and optimal sparsity, only low proportions of ‘globular’ fields are observed. In comparison, the occlusive model robustly infers high proportions and can match the experimentally observed high proportions of ‘globular’ fields well. Our computational study, therefore, suggests that ‘globular’ fields may be evidence for an optimal encoding of visual occlusions in primary visual cortex. Author Summary: The statistics of our visual world is dominated by occlusions. Almost every image processed by our brain consists of mutually occluding objects, animals and plants. Our visual cortex is optimized through evolution and throughout our lifespan for such stimuli. Yet, the standard computational models of primary visual processing do not consider occlusions. In this study, we ask what effects visual occlusions may have on predicted response properties of simple cells which are the first cortical processing units for images. Our results suggest that recently observed differences between experiments and predictions of the standard simple cell models can be attributed to occlusions. The most significant consequence of occlusions is the prediction of many cells sensitive to center-surround stimuli. Experimentally, large quantities of such cells are observed since new techniques (reverse correlation) are used. Without occlusions, they are only obtained for specific settings and none of the seminal studies (sparse coding, ICA) predicted such fields. In contrast, the new type of response naturally emerges as soon as occlusions are considered. In comparison with recent in vivo experiments we find that occlusive models are consistent with the high percentages of center-surround simple cells observed in macaque monkeys, ferrets and mice

Origin of complexity in multicellular organisms

Through extensive studies of dynamical system modeling cellular growth and reproduction, we find evidence that complexity arises in multicellular organisms naturally through evolution. Without any elaborate control mechanism, these systems can exhibit complex pattern formation with spontaneous cell differentiation. Such systems employ a `cooperative' use of resources and maintain a larger growth speed than simple cell systems, which exist in a homogeneous state and behave 'selfishly'. The relevance of the diversity of chemicals and reaction dynamics to the growth of a multicellular organism is demonstrated. Chaotic biochemical dynamics are found to provide the multi-potency of stem cells.Comment: 6 pages, 2 figures, Physical Review Letters, 84, 6130, (2000

A Monolithically Fabricated Combinatorial Mixer for Microchip-Based High-Throughput Cell Culturing Assays

We present an integrated method to fabricate 3- D microfluidic networks and fabricated the first on-chip cell culture device with an integrated combinatorial mixer. The combinatorial mixer is designed for screening the combinatorial effects of different compounds on cells. The monolithic fabrication method with parylene C as the basic structural material allows us to avoid wafer bonding and achieves precise alignment between microfluidic channels. As a proof-of-concept, we fabricated a device with a three-input combinatorial mixer and demonstrated that the mixer can produce all the possible combinations. Also, we demonstrated the ability to culture cells on-chip and performed a simple cell assay on-chip using trypan blue to stain dead cells

Exercise-Derived Microvesicles: A Review of the Literature

Initially suggested as simple cell debris, cell-derived microvesicles (MVs) have now gained acceptance as recognized players in cellular communication and physiology. Shed by most, and perhaps all, human cells, these tiny lipid-membrane vesicles carry bioactive agents, such as proteins, lipids and microRNA from their cell source, and are produced under orchestrated events in response to a myriad of stimuli. Physical exercise introduces systemic physiological challenges capable of acutely disrupting cell homeostasis and stimulating the release of MVs into the circulation. The novel and promising field of exercise-derived MVs is expanding quickly, and the following work provides a review of the influence of exercise on circulating MVs, considering both acute and chronic aspects of exercise and training. Potential effects of the MV response to exercise are highlighted and future directions suggested as exercise and sports sciences extend the realm of extracellular vesicles

Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions.

Cell-to-cell communication networks have critical roles in coordinating diverse organismal processes, such as tissue development or immune cell response. However, compared with intracellular signal transduction networks, the function and engineering principles of cell-to-cell communication networks are far less understood. Major complications include: cells are themselves regulated by complex intracellular signaling networks; individual cells are heterogeneous; and output of any one cell can recursively become an additional input signal to other cells. Here, we make use of a framework that treats intracellular signal transduction networks as "black boxes" with characterized input-to-output response relationships. We study simple cell-to-cell communication circuit motifs and find conditions that generate bimodal responses in time, as well as mechanisms for independently controlling synchronization and delay of cell-population responses. We apply our modeling approach to explain otherwise puzzling data on cytokine secretion onset times in T cells. Our approach can be used to predict communication network structure using experimentally accessible input-to-output measurements and without detailed knowledge of intermediate steps