53,914 research outputs found

    Moment-based analysis of biochemical networks in a heterogeneous population of communicating cells

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    Cells can utilize chemical communication to exchange information and coordinate their behavior in the presence of noise. Communication can reduce noise to shape a collective response, or amplify noise to generate distinct phenotypic subpopulations. Here we discuss a moment-based approach to study how cell-cell communication affects noise in biochemical networks that arises from both intrinsic and extrinsic sources. We derive a system of approximate differential equations that captures lower-order moments of a population of cells, which communicate by secreting and sensing a diffusing molecule. Since the number of obtained equations grows combinatorially with number of considered cells, we employ a previously proposed model reduction technique, which exploits symmetries in the underlying moment dynamics. Importantly, the number of equations obtained in this way is independent of the number of considered cells such that the method scales to arbitrary population sizes. Based on this approach, we study how cell-cell communication affects population variability in several biochemical networks. Moreover, we analyze the accuracy and computational efficiency of the moment-based approximation by comparing it with moments obtained from stochastic simulations.Comment: 6 pages, 5 Figure

    Optical properties of Si/Si0.87Ge0.13 multiple quantum well wires

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    Nanometer-scale wires cut into a Si/Si0.87Ge0.13 multiple quantum well structure were fabricated and characterized by using photoluminescence and photoreflectance at temperatures between 4 and 20 K. It was found that, in addition to a low-energy broadband emission at around 0.8 eV and other features normally observable in photoluminescence measurements, fabrication process induced strain relaxation and enhanced electron-hole droplets emission together with a new feature at 1.131 eV at 4 K were observed. The latter was further identified as a transition related to impurities located at the Si/Si0.87Ge0.13 heterointerfaces

    To synchronize or not to synchronize, that is the question: finite-size scaling and fluctuation effects in the Kuramoto model

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    The entrainment transition of coupled random frequency oscillators presents a long-standing problem in nonlinear physics. The onset of entrainment in populations of large but finite size exhibits strong sensitivity to fluctuations in the oscillator density at the synchronizing frequency. This is the source for the unusual values assumed by the correlation size exponent ν′\nu'. Locally coupled oscillators on a dd-dimensional lattice exhibit two types of frequency entrainment: symmetry-breaking at d>4d > 4, and aggregation of compact synchronized domains in three and four dimensions. Various critical properties of the transition are well captured by finite-size scaling relations with simple yet unconventional exponent values.Comment: 9 pages, 1 figure, to appear in a special issue of JSTAT dedicated to Statphys2

    Measuring and analysing vibration motors in insoles via accelerometers

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    Purpose: Falling is a major public health concern among elderly people, and they often cause serious injuries1,2. They most frequently occur during walking and are associated with the chronic deterioration in the neuromuscular and sensory systems, as well as with ankle muscle weakness and lower endurance of these muscles to fatigue1,3. Vibrating insoles, providing a subsensory mechanical noise signal to the plantar side of the feet, may improve balance in healthy young and older people and in patients with stroke or diabetic neuropathy4. The object of this study is to find the most suitable vibrator to put into the insole which can effectively improve the balance control of the elderlies. Method: We choose three different vibration actuators (micro vibration motor, brushless motor and eccentric motor) with two different weights on the insole. First, we put three same motors and two accelerometers on the insole, as shown in Figure1, then attach another layer on both side of the insole. Second, connect the motors to the power supply and the accelerometer to NI PXI-1033 spectrum analyzer which is used to collect the accelerometers' data. At last, using Fast Fourier Transform (FFT) to analyze and compare the results to see which motor is the most stable and suitable to put into the insole. Results & Discussion: The results showed that the most stable one is the brushless motor. The reason why the frequency is stable is that the relationship between voltage and frequency is linear, and the error is small through continuous measurements. On the other hand, when a person weight 55 kg stands on the insole, the frequency isn't affected by the weight. These two results appear very similar to each other, as shown in Figure 2. According to the result, we use the brushless motor to be our vibrator in the insole, and hope this will help the elderlies improve their balance control ability more efficiency

    COCO_TS Dataset: Pixel-level Annotations Based on Weak Supervision for Scene Text Segmentation

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    The absence of large scale datasets with pixel-level supervisions is a significant obstacle for the training of deep convolutional networks for scene text segmentation. For this reason, synthetic data generation is normally employed to enlarge the training dataset. Nonetheless, synthetic data cannot reproduce the complexity and variability of natural images. In this paper, a weakly supervised learning approach is used to reduce the shift between training on real and synthetic data. Pixel-level supervisions for a text detection dataset (i.e. where only bounding-box annotations are available) are generated. In particular, the COCO-Text-Segmentation (COCO_TS) dataset, which provides pixel-level supervisions for the COCO-Text dataset, is created and released. The generated annotations are used to train a deep convolutional neural network for semantic segmentation. Experiments show that the proposed dataset can be used instead of synthetic data, allowing us to use only a fraction of the training samples and significantly improving the performances
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