This work reports the design of and experimentation with a topographically patterned cell culture substrate of variable local density and anisotropy as a facile and efficient platform to guide the organization and migration of cells in spatially desirable patterns. Using UV-assisted capillary force lithography, an optically transparent microstructured layer of a UV curable poly(urethane acrylate) resin is fabricated and employed as a cell-culture substrate after coating with fibronectin. With variable local pattern density and anisotropy present in a single cell-culture substrate, the differential polarization of cell morphology and movement in a single experiment is quantitatively characterized. It is found that cell shape and velocity are exquisitely sensitive to variation in the local anisotropy of the two-dimensional rectangular lattice arrays, with cell elongation and speed decreasing on symmetric lattice patterns. It is also found that cells could integrate orthogonal spatial cues when determining the direction of cell orientation and movement. Furthermore, cells preferentially migrate toward the topographically denser areas from sparser ones. Consistent with these results, it is demonstrated that systematic variation of local densities of rectangular lattice arrays enable a planar assembly of cells into a specified location. It is envisioned that lithographically defined substrates of variable local density and anisotropy not only provide a new route to tailoring the cell-material interface but could serve as a template for advanced tissue engineering.

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Due to recent developments in highway research and increased utilization of vehicles, there has been significant interest paid on latest, effective, and precise Intelligent Transportation System (ITS). The process of identifying particular objects in an image plays a crucial part in the fields of computer vision or digital image processing. Vehicle License Plate Recognition (VLPR) process is a challenging process because of variations in viewpoint, shape, color, multiple formats and non-uniform illumination conditions at the time of image acquisition. This paper presents an effective deep learning-based VLPR model using optimal K-means (OKM) clustering-based segmentation and Convolutional Neural Network (CNN) based recognition called OKM-CNN model. The proposed OKM-CNN model operates on three main stages namely License Plate (LP) detection, segmentation using OKM clustering technique and license plate number recognition using CNN model. During first stage, LP localization and detection process take place using Improved Bernsen Algorithm (IBA) and Connected Component Analysis (CCA) models. Then, OKM clustering with Krill Herd (KH) algorithm get executed to segment the LP image. Finally, the characters in LP get recognized with the help of CNN model. An extensive experimental investigation was conducted using three datasets namely Stanford Cars, FZU Cars and HumAIn 2019 Challenge dataset. The attained simulation outcome ensured effective performance of the OKM-CNN model over other compared methods in a considerable way.

A blockchain as a trustworthy and secure decentralized and distributed network has been emerged for many applications such as in banking, finance, insurance, healthcare and business. Recently, many communities in blockchain networks want to deploy machine learning models to get meaningful knowledge from geographically distributed large-scale data owned by each participant. To run a learning model without data centralization, distributed machine learning (DML) for blockchain networks has been studied. While several works have been proposed, privacy and security have not been sufficiently addressed, and as we show later, there are vulnerabilities in the architecture and limitations in terms of efficiency. In this paper, we propose a privacy-preserving DML model for a permissioned blockchain to resolve the privacy, security, and performance issues in a systematic way. We develop a differentially private stochastic gradient descent method and an error-based aggregation rule as core primitives. Our model can treat any type of differentially private learning algorithm where non-deterministic functions should be defined. The proposed error-based aggregation rule is effective to prevent attacks by an adversarial node that tries to deteriorate the accuracy of DML models. Our experiment results show that our proposed model provides stronger resilience against adversarial attacks than other aggregation rules under a differentially private scenario. Finally, we show that our proposed model has high usability because it has low computational complexity and low transaction latency.

The rapid increase in the emergence of antifungal-resistant Candida albicans strains is becoming a serious health concern. Because antimicrobial peptides (AMPs) may provide a potential alternative to conventional antifungal agents, we have synthesized a series of peptides with a varying number of lysine and tryptophan repeats (KWn-NH2). The antifungal activity of these peptides increased with peptide length, but only the longest KW5 peptide displayed cytotoxicity towards a human keratinocyte cell line. The KW4 and KW5 peptides exhibited strong antifungal activity against C. albicans, even under conditions of high-salt and acidic pH, or the addition of fungal cell wall components. Moreover, KW4 inhibited biofilm formation by a fluconazole-resistant C. albicans strain. Circular dichroism and fluorescence spectroscopy indicated that fungal liposomes could interact with the longer peptides but that they did not release the fluorescent dye calcein. Subsequently, fluorescence assays with different dyes revealed that KW4 did not disrupt the membrane integrity of intact fungal cells. Scanning electron microscopy showed no changes in fungal morphology, while laser-scanning confocal microscopy indicated that KW4 can localize into the cytosol of C. albicans. Gel retardation assays revealed that KW4 can bind to fungal RNA as a potential intracellular target. Taken together, our data indicate that KW4 can inhibit cellular functions by binding to RNA and DNA after it has been translocated into the cell, resulting in the eradication of C. albicans.

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Traditional public key exchange protocols are based on algebraic number theory. In another perspective, neural cryptography, which is based on neural networks, has been emerging. It has been reported that two parties can exchange secret key pairs with the synchronization phenomenon in neural networks. Although there are various models of neural cryptography, called Tree Parity Machine (TPM), many of them are not suitable for practical use, considering efficiency and security. In this paper, we propose a Vector-Valued Tree Parity Machine (VVTPM), which is a generalized architecture of TPM models and can be more efficient and secure for real-life systems. In terms of efficiency and security, we show that the synchronization time of the VVTPM has the same order as the basic TPM model, and it can be more secure than previous results with the same synaptic depth.

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