article implements a mathematical procedure so that no training is required at all, and the compositional structure is evident from the procedure. We will disclose the extension of the SSO method in Sections II and III and explain the construction of the deep network in Section IV.Cells, in order to regulate their activities, process transcripts by controlling which genes to transcribe and by what amount. The transcription level of genes often change over time. Rate of change of gene transcription varies between genes. It can even change for the same gene across different members of a population. Thus, for a given gene, it is important to study the transcription level not only at a single time point, but across multiple time points to capture changes in patterns of gene expression which underlies several phenotypic or exiernal factors. In such a dataset perturbation can happen due to which it may have missing transcription values for different samples at different time points. In this paper, we define three data perturbation models that are significant with respect to random deletion. We also define a recovery method that recovers data loss in the perturbed dataset such that the error is minimized. Our experimental results show that the recovery method compensates for the loss made by perturbation models. We show by means of two measures, namely, normalized distance and Pearson’s correlation coefficient that the distance between the original and perturbed dataset is more than the distance between original and recovered dataset.Energy-based modelling brings engineering insight to the understanding of biomolecular systems. It is shown how well-established control engineering concepts, such as loop-gain, arise from energy feedback loops and are therefore amenable to control engineering insight. In particular, a novel method is introduced to allow the transfer function based approach of classical linear control to be utilised in the analysis of feedback systems modelled by network thermodynamics and thus amalgamate energy-based modelling with control systems analysis. The approach is illustrated using a class of metabolic cycles with activation and inhibition leading to the concept of Cyclic Flow Modulation.In this study, we report the fabrication of poly-L-lysine (PLL) coated large surface TiO2 and SnO2 based biosensing devices to analyze the influence of the functional behaviour of primary cortical neuronal cells. Through frequency-dependent impedance study, we observed an increase in the impedance values initially most likely due to cell adhesion, proliferation and differentiation processes leading to an increase in both the single-cell mass as well as overall cellular mass; however, it got decreased eventually with the progression of various other cellular functions including neural activity, synapse formation and neuron-neuron communication. Typically, formation and regulation of the neuronal junction i.e., synapses noticeably affected the functional behaviour of the fabricated biosensing device by increasing the neuronal communication and thereby improving the flow of current by altering the thin film resistance and capacitance. Further, the neuro-electrical phenomenon is validated by fitting the experimental impedance data to an equivalent electrical circuit model. A significant shift in the Nyquist plot was also observed visually, which indicates that this alternation is primarily due to change in characteristic behaviour of the fabricated biosensing device. Hence, we anticipate that the fabricated PLL coated large surface TiO2 and SnO2 based biosensing device can serve as a promising tool to monitor the influence of the functional behaviour of neuronal cells.Lane detection on road segments with complex topologies such as lane merge/split and highway ramps is not yet a solved problem. This paper presents a novel graph-embedded solution. It consists of two key parts, a learning-based low-level lane feature extraction algorithm, and a graph-embedded lane inference algorithm. I-BET151 The former reduces the over-reliance on customized annotated/labeled lane data. We leveraged several open-source semantic segmentation datasets (e.g., Cityscape, Vistas, and Apollo) and designed a dedicated network that can be trained across these heterogeneous datasets to extract lane attributes. The latter algorithm constructs a graph to represent the lane geometry and topology. It does not rely on strong geometric assumptions such as lane lines are a set of parallel polynomials. Instead, it constructs a graph based on detected lane nodes. The lane parameters in the world coordinate are inferred by efficient graph-based searching and calculation. The performance of the proposed method is verified on both open source and our own collected data. On-vehicle experiments were also conducted and the comparison with Mobileye EyeQ2 shows favorable results.Lanthanum-gallium tantalate (LGT) is a member of the LGX crystal family (langasite, langanite, and langatate) known for high-quality factor and stability at higher temperatures. These characteristics enable filters and sensors for use in harsh environments. Accurate values for the second-order material constants are required for electromechanical modeling of such devices. A sufficient set of bulk acoustic wave (BAW) propagation velocities have been measured in cube-shaped samples to obtain the full set of second-order elastic and piezoelectric material constants along with the experimental uncertainties. The cross correlation method was used to accurately measure the time-of-flight (TOF) values between the first two back-wall echoes under a constant room-temperature environment. The calculated BAW velocities and extracted material constants show good agreement with earlier work.Optical diffraction tomography measures the three-dimensional refractive index map of a specimen and visualizes biochemical phenomena at the nanoscale in a non-destructive manner. One major drawback of optical diffraction tomography is poor axial resolution due to limited access to the three-dimensional optical transfer function. This missing cone problem has been addressed through regularization algorithms that use a priori information, such as non-negativity and sample smoothness. However, the iterative nature of these algorithms and their parameter dependency make real-time visualization impossible. In this article, we propose and experimentally demonstrate a deep neural network, which we term DeepRegularizer, that rapidly improves the resolution of a three-dimensional refractive index map. Trained with pairs of datasets (a raw refractive index tomogram and a resolution-enhanced refractive index tomogram via the iterative total variation algorithm), the three-dimensional U-net-based convolutional neural network learns a transformation between the two tomogram domains.