Identification of replicative senescence-associated genes in human umbilical vein endothelial cells by an annealing control primer system

Cellular senescence is regulated by specific genes in many organisms. The identification and functional analysis of senescence-associated genes could provide valuable insights into the senescence process. Here, we employed a new and improved differential display reverse transcription-polymerase chain reaction (DDRT-PCR) method that involves annealing control primers (ACPs) to identify genes that are differentially expressed in human umbilical endothelial cells during replicative senescence. Using 120 ACPs, we identified 31 differentially expressed genes (DEGs). Basic local alignment search tool (BLAST) search revealed 29 known genes and two unknown genes. Expression levels of the 29 known genes were confirmed by real-time quantitative RT-RCR and by Western blotting for eight of these genes. CD9 antigen, MHC class I chain-related sequence A (MICA) and cell division cycle 37 homolog (CDC37) were up-regulated, and bone morphogenetic protein 4 (BMP4), dickkopf-1 (DKK1), and transcription factor 7-like 1 (TCF7L1) were down-regulated in old cells. Treatment with recombinant human MICA caused a decrease in cell proliferation and an increase in senescence-associated beta-galactosidase staining. Further analysis of differentially expressed genes may provide insights into the molecular basis of replicative senescence and vascular diseases associated with cellular senescence

An InGaAs/InP p-i-n-JFET OEIC with a wing-shaped p+-InP layer

A new receiver OEIC structure with an InGaAs p-i-n photodiode, InGaAs self-aligned junction FETs and a bias resistor has been fabricated on a semi-insulating InP substrate. The fabrication processes are highly compatible between the photodiode and the JFET, and reduction in FET gate length is achieved using anisotropic selective etching and a two-step OMVPE growth schedule. The 80 pm diameter p-i-n detector exhibits a leakage current of 2 nA and a capacitance of about 0.35 pF at -5 V bias voltage. Extrinsic transconductance and a gate-source capacitance of the JFET are typically 45 mS/mm and 4.0 pF/mm at OV, respectively. The maximum voltage gain of the pre-amplifier is 12.5 and the bandwidth of the p-i-n amplifier OEIC is expected to be about 1.2 GHz

The Antibacterial Assay of Tectorigenin with Detergents or ATPase Inhibitors against Methicillin-Resistant Staphylococcus aureus

Tectorigenin (TTR) is an O-methylated isoflavone derived from the rhizome of Belamacanda chinensis (L.) DC. It is known to perform a wide spectrum of biological activities such as antioxidant, anti-inflammatory, anti-tumor. The aim of this study is to examine the mechanism of antibacterial activity of TTR against methicillin-resistant Staphylococcus aureus (MRSA). The anti-MRSA activity of TTR was analyzed in combination assays with detergent, ATPase inhibitors, and peptidoglycan (PGN) derived from S. aureus. Transmission electron microscopy (TEM) was used to monitor survival characteristics and changes in S. aureus morphology. The MIC values of TTR against all the tested strains were 125 μg/mL. The OD(600) of each suspension treated with a combination of Triton X-100, DCCD, and NaN3 with TTR (1/10 × MIC) had been reduced from 68% to 80%, compared to the TTR alone. At a concentration of 125 μg/mL, PGN blocked antibacterial activity of TTR. This study indicates that anti-MRSA action of TTR is closely related to cytoplasmic membrane permeability and ABC transporter, and PGN at 125 μg/mL directly bind to and inhibit TTR at 62.5 μg/mL. These results can be important indication in study on antimicrobial activity mechanism against multidrug resistant strains

A Classification Method for the Cellular Images Based on Active Learning and Cross-Modal Transfer Learning

In computer-aided diagnosis (CAD) systems, the automatic classification of the different types of the human epithelial type 2 (HEp-2) cells represents one of the critical steps in the diagnosis procedure of autoimmune diseases. Most of the methods prefer to tackle this task using the supervised learning paradigm. However, the necessity of having thousands of manually annotated examples constitutes a serious concern for the state-of-the-art HEp-2 cells classification methods. We present in this work a method that uses active learning in order to minimize the necessity of annotating the majority of the examples in the dataset. For this purpose, we use cross-modal transfer learning coupled with parallel deep residual networks. First, the parallel networks, which take simultaneously different wavelet coefficients as inputs, are trained in a fully supervised way by using a very small and already annotated dataset. Then, the trained networks are utilized on the targeted dataset, which is quite larger compared to the first one, using active learning techniques in order to only select the images that really need to be annotated among all the examples. The obtained results show that active learning, when mixed with an efficient transfer learning technique, can allow one to achieve a quite pleasant discrimination performance with only a few annotated examples in hands. This will help in building CAD systems by simplifying the burdensome task of labeling images while maintaining a similar performance with the state-of-the-art methods

An Efficient SC-FDM Modulation Technique for a UAV Communication Link

Since the communication link of an unmanned aerial vehicle (UAV) and its reliability evaluation represent an arduous field, we have concentrated our work on this topic. The demand regarding the validity and reliability of the communication and data link of UAV is much higher since the environment of the modern battlefield is becoming more and more complex. Therefore, the communication channel between the vehicle and ground control station (GCS) should be secure and provide an efficient data link. In addition, similar to other types of communications, the data link of a UAV has several requirements such as long-range operation, high efficiency, reliability, and low latency. In order to achieve an efficient data link, we need to adopt a highly efficient modulation technique, which leads to an increase in the flight time of the UAV, data transmission rate, and the reliability of the communication link. For this purpose, we have investigated the single-carrier frequency division multiplexing (SC-FDM) modulation technique for a UAV communication system. The results obtained from the comparative study demonstrate that SC-FDM has better performance than the currently used modulation technique for a UAV communication link. We expect that our proposed approach can be a remarkable framework that will help drone manufacturers to establish an efficient UAV communication link and extend the flight duration of drones, especially those being used for search and rescue operations, military tasks, and delivery services

A Dynamic Learning Method for the Classification of the HEp-2 Cell Images

The complete analysis of the images representing the human epithelial cells of type 2, commonly referred to as HEp-2 cells, is one of the most important tasks in the diagnosis procedure of various autoimmune diseases. The problem of the automatic classification of these images has been widely discussed since the unfolding of deep learning-based methods. Certain datasets of the HEp-2 cell images exhibit an extreme complexity due to their significant heterogeneity. We propose in this work a method that tackles specifically the problem related to this disparity. A dynamic learning process is conducted with different networks taking different input variations in parallel. In order to emphasize the localized changes in intensity, the discrete wavelet transform is used to produce different versions of the input image. The approximation and detail coefficients are fed to four different deep networks in a parallel learning paradigm in order to efficiently homogenize the features extracted from the images that have different intensity levels. The feature maps from these different networks are then concatenated and passed to the classification layers to produce the final type of the cellular image. The proposed method was tested on a public dataset that comprises images from two intensity levels. The significant heterogeneity of this dataset limits the discrimination results of some of the state-of-the-art deep learning-based methods. We have conducted a comparative study with these methods in order to demonstrate how the dynamic learning proposed in this work manages to significantly minimize this heterogeneity related problem, thus boosting the discrimination results

A Deep Learning Method for 3D Object Classification Using the Wave Kernel Signature and A Center Point of the 3D-Triangle Mesh

Computer vision recently has many applications such as smart cars, robot navigation, and computer-aided manufacturing. Object classification, in particular 3D classification, is a major part of computer vision. In this paper, we propose a novel method, wave kernel signature (WKS) and a center point (CP) method, which extracts color and distance features from a 3D model to tackle 3D object classification. The motivation of this idea is from the nature of human vision, which we tend to classify an object based on its color and size. Firstly, we find a center point of the mesh to define distance feature. Secondly, we calculate eigenvalues from the 3D mesh, and WKS values, respectively, to capture color feature. These features will be an input of a 2D convolution neural network (CNN) architecture. We use two large-scale 3D model datasets: ModelNet10 and ModelNet40 to evaluate the proposed method. Our experimental results show more accuracy and efficiency than other methods. The proposed method could apply for actual-world problems like autonomous driving and augmented/virtual reality

Interpolating Spline Curve-Based Perceptual Encryption for 3D Printing Models

With the development of 3D printing technology, 3D printing has recently been applied to many areas of life including healthcare and the automotive industry. Due to the benefit of 3D printing, 3D printing models are often attacked by hackers and distributed without agreement from the original providers. Furthermore, certain special models and anti-weapon models in 3D printing must be protected against unauthorized users. Therefore, in order to prevent attacks and illegal copying and to ensure that all access is authorized, 3D printing models should be encrypted before being transmitted and stored. A novel perceptual encryption algorithm for 3D printing models for secure storage and transmission is presented in this paper. A facet of 3D printing model is extracted to interpolate a spline curve of degree 2 in three-dimensional space that is determined by three control points, the curvature coefficients of degree 2, and an interpolating vector. Three control points, the curvature coefficients, and interpolating vector of the spline curve of degree 2 are encrypted by a secret key. The encrypted features of the spline curve are then used to obtain the encrypted 3D printing model by inverse interpolation and geometric distortion. The results of experiments and evaluations prove that the entire 3D triangle model is altered and deformed after the perceptual encryption process. The proposed algorithm is responsive to the various formats of 3D printing models. The results of the perceptual encryption process is superior to those of previous methods. The proposed algorithm also provides a better method and more security than previous methods