Cellular decision making is accomplished by complex networks, the structure of which has traditionally been inferred from mean gene expression data. In addition to mean data, quantitative measures of distributions across a population can be obtained using techniques such as flow cytometry that measure expression in single cells. The resulting distributions, which reflect a population's variability or noise, constitute a potentially rich source of information for network reconstruction. A significant portion of molecular noise in a biological process is propagated from the upstream regulators. This propagated component provides additional information about causal network connections. Here, we devise a procedure in which we exploit equations for dynamic noise propagation in a network under nonsteady state conditions to distinguish between alternate gene regulatory relationships. We test our approach in silico using data obtained from stochastic simulations as well as in vivo using experimental data collected from synthetic circuits constructed in yeast.
Objective: Electroencephalography (EEG) and Magnetoencephalography (MEG) are widely used non-invasive techniques in clinical and cognitive neuroscience. However, low spatial resolution measurements, partial brain coverage by some sensor arrays, as well as noisy sensors could result in distorted sensor topographies resulting in inaccurate reconstructions of underlying brain dynamics. Solving these problems has been a challenging task, This paper proposes a robust framework based on electromagnetic source imaging for interpolation of unknown or poor quality EEG/MEG measurements.
Thermal-induced transitions between multistable states hold significant interest in stochastic thermodynamics and dynamical control with nanomechanical systems. Here, we study kinetic-energy-dependent over-barrier behaviors in the rotational degree of freedom of silica nanodumbells in tilted periodic potentials. In the rotational degree of freedom, nanodumbbells can undergo critical transitions between librations and rotations as the ellipticity of the trapping laser field changes.
AbstractTheoretical studies from diverse areas of population biology have shown that demographic stochasticity can substantially impact evolutionary dynamics in finite populations, including scenarios where traits that are disfavored by natural selection can nevertheless increase in frequency through the course of evolution. Here, we analytically describe the eco-evolutionary dynamics of finite populations from demographic first principles. We investigate how noise-induced effects can alter the evolutionary fate of populations in which total population size may vary stochastically over time.
A compact and robust optical excitation photoacoustic sensor with a self-integrated laser module excitation and an optimized differential resonator was developed to achieve high sensitivity and full linear range detection of carbon dioxide (CO) based on dual modes of wavelength modulated photoacoustic spectroscopy (WMPAS) and resonant frequency tracking (RFT). The integrated laser module equipped with three lasers (a quantum cascade laser (QCL), a distributed feedback laser (DFB) and a He-Ne laser) working in a time-division multiplexing mode was used as an integrated set of spectroscopic sources for detection of the designated concentration levels of CO. With the absorption photoacoustic mode, the WMPAS detection with the QCL and DFB sources was capable of CO detection at concentrations below 20 %, yielding a noise equivalent concentration (NEC) as low as 240 ppt and a normalized noise equivalent absorption coefficient (NNEA) of 4.
Purpose: To quantitatively evaluate the performance of two types of recurrent neural networks (RNNs), long short-term memory (LSTM) and gated recurrent units (GRU), using Monte Carlo dropout (MCD) to predict pharmacokinetic (PK) parameters from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) data.
Methods: DCE-MRI data for simulation studies were synthesized using the extended Tofts model and a population-averaged arterial input function (AIF). The ranges of PK parameters for training the RNNs were determined from data of patients with brain tumors.
