Miracle-dz/UniFAR-FTUs huggingface dataset:
Overview on MTD technology based on game theory

Mobile target defense (MTD) is a research hotspot in the field of network security. The decision method of network defense based on game theory is an important technique to guide MTD to make the optimal defense behavior in different network environments (GT-MTD). A lot of related work has been put forward in this field. In this paper, we focus on the scope and field of GT-MTD, systematically introduce the application scenarios of MTD in combination with four different game theory models of classical games (static games, signal games), Markov games, differential games or evolutionary games, and put forward the future development direction. There are some new views and explanations on the research of GT-MTD.
DEVELOPING TISSUE ENGINEERING AND GENE THERAPY APPROACHES INVOLVING THE USE OF NERVE GROWTH FACTOR AND MUSCLE-DERIVED STEM CELLS TO IMPROVE THE REGENERATION OF DYSTROPHIC MUSCLE

In recent years, researchers have attempted to use gene- and cell-based therapies to restore dystrophin and alleviate the muscle weakness that results from Duchenne muscular dystrophy (DMD). Our research group has isolated a population of muscle-derived stem cells (MDSCs) from the postnatal skeletal muscle of mice. In comparison with satellite cells, MDSCs display an improved transplantation capacity in dystrophic mdx muscle that can be attributed to their ability to undergo long-term proliferation, self-renewal, and multipotent differentiation, including differentiation toward endothelial and neuronal lineages. The overall goal of this study was to investigate whether the use of nerve growth factor (NGF) improves the transplantation efficiency of MDSCs. Two methods of in vitro NGF stimulation were used: retroviral transduction of MDSCs with a CLNGF vector to constitutively express NGF and direct stimulation of MDSCs with NGF protein. Neither method of NGF treatment changed the marker profile or proliferation behavior of the MDSCs, but direct stimulation with NGF protein significantly delayed cells' in vitro differentiation ability. Stimulation with NGF also significantly enhanced the engraftment efficiency of MDSCs transplanted within the dystrophic muscle of mdx mice, resulting in better muscle regeneration. These findings highlight the importance of NGF as a modulatory molecule, the study of which will broaden our understanding of its biological role in the regeneration and repair of skeletal muscle by muscle-derived cells.
Breast cancer metastasis-associated genes: role in tumour progression to the metastatic state.

: Breast cancer patients usually do not die of their primary cancers; they die of metastatic disease. Thus understanding the progression of breast cancer to the metastatic state and the changes that take place in highly malignant breast cells are important goals that could eventually result in new therapeutic approaches to highly progressive breast disease. Changes in the expression of certain genes or alterations in gene structures and encoded products can result in benign tumour cells progressing to the metastatic state. Experimentally, this has been performed by transferring dominantly acting oncogenes into susceptible cells and then testing the malignant properties of these cells in suitable animal models, but such rapid qualitative changes occur in vivo only rarely, and the natural progression of mammary cells to the metastatic state is thought to occur through a slow stepwise process that can take several years. Some of the slow stepwise changes in mammary cancer progression can be reversible and need not involve dominantly acting oncogenes or tumour suppressor genes, consistent with clinical observations. An important element of the natural progression of mammary tumours to malignancy may be their ability to circumvent microenvironmental controls that regulate growth and cellular diversity, a process that appears to involve mainly quantitative changes in gene expression, resulting in loss of normal cellular regulation. One of the important mechanisms of cellular regulation in epithelial tissues, such as those found in the breast, is mediated by intercellular junctional communication. Alterations in gene expression can result in loss of gap-junctional communication, concomitant with cellular diversification and progression. It is thought that the highly malignant cancer cells that have slowly evolved in vivo with only a few qualitative changes in gene structure have undergone extensive cycles of diversification and the accumulation of several quantitative changes in the expression of various genes that encode products related to malignancy. We have identified some of the genes that are related to progression and metastasis in breast cancer. For example, one of these genes, a novel gene called mta1 (in rodents) or MTA1 (in humans) appears to be involved in mammary cell motility and growth regulation. Thus highly malignant cellular phenotypes can arise rapidly due to specific qualitative changes in critical controlling genes, or more slowly via less critical qualitative genetic changes coupled with other cellular changes, such as loss of intercellular communication, and changes in gene expression, such as in the MTA1 gene, resulting in cellular diversification and ultimately tumour progression to the metastatic state.
Evolutionary Ecology of Parasites: From individuals to communities

Preface. Introduction. Origins of parasitism and complex life cycles. Host specificity. Evolution of parasite life history strategies. Strategies of host exploitation. Parasite aggregation: causes and consequences. Parasite population dynamics. Interactions between parasite species. Parasite infracommunity structure. Component communities and parasite faunas. Conclusion. Reference. Index.
Performance Analysis and Enhancement of Beamforming Training in 802.11ad

Beamforming (BF) training is crucial to establishing reliable millimeter-wave communication connections between stations (STAs) and an access point. In IEEE 802.11ad BF training protocol, all STAs contend for limited BF training opportunities, i.e., associated BF training (A-BFT) slots, which results in severe collisions and significant BF training latency, especially in dense user scenarios. In this paper, we first develop an analytical model to evaluate the BF training protocol performance. Our analytical model accounts for various protocol components, including user density, the number of A-BFT slots, and protocol parameters, i.e., retry limit and contention window size. We then derive the average successful BF training probability, the BF training efficiency and latency. Since the derived BF training efficiency is an implicit function, to reveal the relationship between system parameters and BF training performance, we also derive an approximate expression of BF training efficiency. Theoretical analysis indicates that the BF training efficiency degrades drastically in dense user scenarios. To address this issue, we propose an enhancement scheme which adaptively adjusts the protocol parameters in tune with user density, to improve the BF training performance in dense user scenarios. Extensive simulations are carried out to validate the accuracy of the developed analytical model. In addition, simulation results show that the proposed enhancement scheme can improve the BF training efficiency by 35% in dense user scenarios.
Automatic Image Recognition of Rapid Malaria Emergency Diagnosis: A Deep Neural Network Approach

Deep learning is the state-of-the-art artificial intelligence (AI) method for visual pattern detection and automated diagnosis. This paper describes the application of convolutional neural network (CNN), the deep learning model for visual recognition, to automatic detection of plasmodium parasitized red blood cells for malaria field screening and rapid diagnosis. The malaria thin blood smears are from Bangladesh and initially labeled by a specialist. 27,578 red blood cell images are segmented (raw set). The images are rotated clockwise three times to generate an augmented dataset with 110,312 red blood cell images. A 12-layer and an 18-layer CNN-based Malaria Net models are applied to classify both the raw data set and the augmented dataset. The performance is evaluated by ten-fold cross-validation and compared to a transfer learning model. In the ten-fold cross-validation test for Malaria Net, the average accuracy is 97.37% (18-layer) and 96.09% (12-layer) with the raw set, and is 97.93% and 96.75% with the augmented set, in comparison to 91.99% with the raw set and 94.26% with the augmented set in transfer learning. In addition, the two CNN models show superiority over transfer learning in all performance indicators such as sensitivity, specificity, precision, F1 score, and Matthews correlation coefficient. The Malaria Net can accurately detect malaria-infected red blood cells. A CNN model trained by domain-specific data shows superior performance over the transfer-learning method. Automatic image classification powered by deep learning offers not only an accurate method for the malaria field screening and rapid diagnosis but also a new solution for malaria control especially in resource-poor regions.
A Review Paper on Intrusion Detection System in MANETs

The MANETs is one type of Ad Hoc network that is used for mobile communication. The Mobile Ad Hoc NETworks (MANETS) are used when the user is moving. Because MANET does not depend on fixed infrastructure. In MANETs Wireless networks are used to connect with different networks. surrounded by all the contemporary wireless networks, Mobile Ad hoc NETwork (MANET) is one of the most important and unique applications. The self-configuring ability of nodes in MANET absolutes it admired among essential mission applications like military use or emergency recovery. The mobility and scalability carried by wireless network ended it possible in many applications. Surrounded by all the contemporary wireless networks Mobile Ad hoc NETwork (MANET) is one of the most significant and exclusive applications. In this review paper a qualified learns of Secure Intrusion-Detection Systems for determining malicious nodes and attacks on MANETs are presented. One of the main advantages of wireless networks is its capability to permit data communication between different parties and still maintain their mobility.