A Novel Artificially Humanized Anti-Cripto-1 Antibody Suppressing Cancer Cell Growth

Cripto-1 is a member of the EGF-CFC/FRL1/Cryptic family and is involved in embryonic development and carcinogenesis. We designed a novel anti-Cripto-1 artificial antibody and assessed the recognition to the antigen and the potential to suppress the growth of cancer stem cells. First, single chain antibody clones were isolated by bio-panning with the affinity to recombinant Cripto-1 protein from our original phage-display library. Then, the variable regions of heavy chain VH and light chain VL in each clone were fused to constant regions of heavy chain CH and light chain CL regions respectively. These fused genes were expressed in ExpiCHO-S cells to produce artificial humanized antibodies against Cripto-1. After evaluation of the expression levels, one clone was selected and the anti-Cripto-1 antibody was produced and purified. The purified antibody showed affinity to recombinant Cripto-1 at 1.1 pmol and immunoreactivity to cancer tissues and cell lines. The antibody was available to detect the immunoreactivity in tissue microarrays of malignant tumors as well as in Cripto-1 overexpressing cells. Simultaneously, the antibody exhibited the potential to suppress the growth of human colon cancer derived GEO cells overexpressing Cripto-1 with IC₅₀ at approximately 110 nM. The artificially humanized antibody is proposed to be a good candidate to target cancer cells overexpressing Cripto-1.     

Index-Based Cache Coherence Protocol

A multiprocessor envirorLment may encounter many problems such as deadlock, load balancing and cache coherence. However, the latter is considered the most dangerous if not properly designed, the system works naturally but generates inaccurate results. This occurs if obsolete versions of a memory block are used. Users may not be aware of the presence of such problem. Two main approaches are known to maintain data consistency： namely, snoopy and directory-based protocols. Each approach has its advantages and limitations. This paper proposes a new technique that considers both previously mentioned approaches. The network architecture is slightly updated by adding an index table to each processor. The proposed protocol is expected to reduce the access time, decrease the number of accesses to main memory, maintain data consistency, and assure the usage of the most recent value of a shared variable.     

A performance evaluation of a fault-tolerant path recommendation protocol for smart transportation system

Recommending traveling vehicles to take a certain path towards their targeted destinations have received great interest recently. At the downtown area several paths can lead to the same located destination, this is due to the grid-layout architecture of modern downtowns. Drivers always wish to reach their destinations as fast as possible and without traveling drastically long distance or without consuming extra fuel. The best path towards any destination is determined based on the relative location of the vehicle from its destination and based on other vehicles traffic distribution on the road network. Although numerous studies have investigated this issue over the road network, the communication failures and their effects on the obtained path have been neglected in those previous studies. In this paper, we investigate these potential faults and their effects on the correctness of the selected paths. We then proposed a new protocol to tackle these potential failures while selecting the best path towards each destination over the road network, fault tolerant path recommendation protocol (FT-PR). From the experimental results, we can see that the FT-PR protocol has a higher success ratio than previous path recommendation protocols, such as ICOD. This is demonstrated by obtaining paths with shorter traveling time and shorter traveling distance. The FT-PR protocol also eliminates extra loops over the road network in each selected path.     

Induction of telomerase activity by UV-irradiation in Chinese hamster cells

Telomerase is a ribonucleoprotein whose activity has been detected in germline cells and in neoplastic and immortal cells. Telomerase compensates the telomere loss arising by the end replication problem by synthesizing telomeric repeats at the 3' end of the eukaryotic chromosomes. Telomerase is reactivated during cancer progression in human and mice. In order to determine whether the telomerase activity can be upregulated in vitro in response to DNA damaging agents, we examined the telomerase activity in five Chinese hamster cell lines following exposure to 5 J/m2 or 40 J/m2 UV-C radiation. All the cell lines tested showed an increase in telomerase activity in the PCR-based telomeric repeat amplification protocol (TRAP) in a dose dependent manner. This increase in telomerase activity correlated well with the number of cells being in the S and G2/M phase after UV exposure. However, in unirradiated control cells, similar levels of telomerase activity were observed in different phases of the cell cycle. Furthermore, telomeric signals were clustered in one or more parts of the disintegrating nuclear particles of the apoptotic cell as detected by fluorescence in situ hybridization (FISH). This is the first study to demonstrate the induction of telomerase activity following exposure to DNA-damaging agents like UV radiation in Chinese hamster cells in vitro.     

Determination of higher order coefficients and zones of dominance using a singular integral equation approach

A method to determine higher order coefficients from the solution of a singular integral equation is presented. The coefficients are defined by , which gives the radial stress at a distance, r, in front of the crack tip. In this asymptotic series the stress intensity factor, k₀, is the first coefficient, and the T-stress, T₀, is the second coefficient. For the example of an edge crack in a half space, converged values of the first 12 mode I coefficients (k_n and T_n, n = 0, … , 5) have been determined, and for an edge crack in a finite width strip, the first six coefficients are presented. Coefficients for an internal crack in a half space are also presented. Results for an edge crack in a finite width strip are used to quantify the size of the k-dominant zone, the kT-dominant zone and the zones associated with three and four terms, taking into account the entire region around the crack tip.     

Deep Learning Based Sentiment Analysis of COVID-19 Tweets via Resampling and Label Analysis

Twitter has emerged as a platform that produces new data every day through its users which can be utilized for various purposes. People express their unique ideas and views on multiple topics thus providing vast knowledge. Sentiment analysis is critical from the corporate and political perspectives as it can impact decision-making. Since the proliferation of COVID-19, it has become an important challenge to detect the sentiment of COVID-19-related tweets so that people’s opinions can be tracked. The purpose of this research is to detect the sentiment of people regarding this problem with limited data as it can be challenging considering the various textual characteristics that must be analyzed. Hence, this research presents a deep learning-based model that utilizes the positives of random minority oversampling combined with class label analysis to achieve the best results for sentiment analysis. This research specifically focuses on utilizing class label analysis to deal with the multiclass problem by combining the class labels with a similar overall sentiment. This can be particularly helpful when dealing with smaller datasets. Furthermore, our proposed model integrates various preprocessing steps with random minority oversampling and various deep learning algorithms including standard deep learning and bi-directional deep learning algorithms. This research explores several algorithms and their impact on sentiment analysis tasks and concludes that bidirectional neural networks do not provide any advantage over standard neural networks as standard Neural Networks provide slightly better results than their bidirectional counterparts. The experimental results validate that our model offers excellent results with a validation accuracy of 92.5% and an F1 measure of 0.92.     

An efficient cooperative technique for power-constrained multiuser wireless network

Cooperative communication plays an important role in wireless networks by improving network connectivity, spectrum efficiency, power, and communication reliability. Moreover, cooperative communication also facilitates the development of a well-organized approach in order to improve the quality of wireless terminals. Besides, it enables the utilisation of communication resources by allowing the nodes and pathways in a network to cooperate with one another via data transmissions. To control a wireless network, cooperative communication must manage its power to improve a network’s energy efficiency, capacity and reliability. When information is transmitted at a higher power, this decreases the lifespans of both the nodes and the network itself. Thus, controlling over the transmission of power is essential to obtain a sufficient level of bit-error-rate (BER) performance at the receiver. Relay nodes can improve system performance by reducing power consumption. Moreover, the decode-and-forward method is one of the best cooperative relay protocols that can be used to achieve better system performance in power constraints and BERs. In the present paper, system model containing source, destination and relay node is analysed. One cooperative scheme which including decode and forward is employed and investigated. At the experimental and simulation levels, the present paper showed that the power in the transmitters was observed and calculated in order to show the savings which are resulting from the use of relay nodes.     

An efficient dynamic traffic light scheduling algorithm considering emergency vehicles for intelligent transportation systems

Traffic lights have been installed throughout road networks to control competing traffic flows at road intersections. These traffic lights are primarily intended to enhance vehicle safety while crossing road intersections, by scheduling conflicting traffic flows. However, traffic lights decrease vehicles’ efficiency over road networks. This reduction occurs because vehicles must wait for the green phase of the traffic light to pass through the intersection. The reduction in traffic efficiency becomes more severe in the presence of emergency vehicles. Emergency vehicles always take priority over all other vehicles when proceeding through any signalized road intersection, even during the red phase of the traffic light. Inexperienced or careless drivers may cause an accident if they take inappropriate action during these scenarios. In this paper, we aim to design a dynamic and efficient traffic light scheduling algorithm that adjusts the best green phase time of each traffic flow, based on the real-time traffic distribution around the signalized road intersection. This proposed algorithm has also considered the presence of emergency vehicles, allowing them to pass through the signalized intersection as soon as possible. The phases of each traffic light are set to allow any emergency vehicle approaching the signalized intersection to pass smoothly. Furthermore, scenarios in which multiple emergency vehicles approach the signalized intersection have been investigated to select the most efficient and suitable schedule. Finally, an extensive set of experiments have been utilized to evaluate the performance of the proposed algorithm.     

A novel optimized neutrosophic <Emphasis Type="Italic">k</Emphasis>-means using genetic algorithm for skin lesion detection in dermoscopy images

This paper implemented a new skin lesion detection method based on the genetic algorithm (GA) for optimizing the neutrosophic set (NS) operation to reduce the indeterminacy on the dermoscopy images. Then, k-means clustering is applied to segment the skin lesion regions. Therefore, the proposed method is called optimized neutrosophic k-means (ONKM). On the training images set, an initial value of \(\alpha \) in the \(\alpha \)-mean operation of the NS is used with the GA to determine the optimized \(\alpha \) value. The Jaccard index is used as the fitness function during the optimization process. The GA found the optimal \(\alpha \) in the \(\alpha \)-mean operation as \(\alpha _{\mathrm{optimal}} =0.0014\) in the NS, which achieved the best performance using five fold cross-validation. Afterward, the dermoscopy images are transformed into the neutrosophic domain via three memberships, namely true, indeterminate, and false, using \(\alpha _{\mathrm{optimal}}\). The proposed ONKM method is carried out to segment the dermoscopy images. Different random subsets of 50 images from the ISIC 2016 challenge dataset are used from the training dataset during the fivefold cross-validation to train the proposed system and determine \(\alpha _{\mathrm{optimal}}\). Several evaluation metrics, namely the Dice coefficient, specificity, sensitivity, and accuracy, are measured for performance evaluation of the test images using the proposed ONKM method with \(\alpha _{\mathrm{optimal}} =0.0014\) compared to the k-means, and the \(\gamma \)–k-means methods. The results depicted the dominance of the ONKM method with \(99.29\pm 1.61\%\) average accuracy compared with k-means and \(\gamma \)–k-means methods.