HER2/neu-specific Breast Cancer Vaccine

Breast cancer is the most common cancer among women. Of all breast cancers cases, approximately 30 percent have amplification of the self-antigen HER2/neu. Later studies demonstrated that HER2/neu-specific antibody and T cell responses were found in HER2/neu-positive breast cancer patients, indicating HER2/neu is a good target for active immunotherapy. A humanized anti HER2/neu antibody trastuzumab, was developed and found to be an effective therapy for HER2/neu. However, acquired antibody resistance occurs sooner or later in antibody treated patients. Such limitations of antibody therapy provoked scientists into the search for other therapeutic strategies. HER2/neu-targeted immunotherapeutic strategies, including vaccines using tumor lysates, protein/peptide, DNA, adenoviral vectors (AdV) and dendritic cells (DCs) pulsed with the above reagents, have been shown to be effective in experimental models. However, they have also been proven to be incapable of breaking tolerance towards HER2/neu in clinical trials and eliciting then have not elicited adequate antitumor immunity in curing HER2/neu positive breast cancer in transgenic mice with HER2/neu-specific immune tolerance, although both humoral and cellular immune responses could be detected. CD4+ helper T (Th) cells play crucial roles in priming, expansion and memory of both humoral and CD8+ cytotoxic T lymphocyte (CTL) responses. Therefore, they are essential in antitumor immunity. The tetanus toxoid Th epitope 947-967 P30, FNNFTVSFWLRVPKVSASHLE, has been found to be a universal epitope in sensitizing and proliferating CD4+ T cells ex vivo. OVA-P30 peptide vaccine could break CD8+ and CD4+ T cell tolerances against the neo-self-antigen OVA; it was able to protect transgenic rat insulin promoter (RIP)-mOVA mice from tumor growth. Adenovirus-based vaccines are able to induce high frequencies of transgene product-specific CD8+ T cell responses. In this study, we immunized C57BL/6 mice with OVA-expressing AdVOVA. We found that AdVOVA induced sustained OVA-specific CTL responses, leading to preventive antitumor immunity against OVA-expressing BL6-10OVA tumor cell challenge in wild-type C57BL/6 mice. In addition, we also immunized transgenic FVBneuN mice with neu-expressing AdVneu. We found that AdVneu vaccination induced neu-specific CTL responses, leading to partial reduction of breast carcinogenesis in FVBneuN mice. To assess whether the foreign Th epitope P30 enhances CD4+ and CD8+ T cell responses, we constructed another two recombinant AdVs (AdVOVA-P30 and AdVneu-P30), expressing OVA-P30 and HER2/neu-P30 gene, respectively. We transfected C57BL/6 mouse bone marrow dendritic cells (DCs) with AdVOVA and AdVOVA-P30 for preparation of DCOVA and DCOVA-P30 vaccines. We immunized C57BL/6 mice with DCOVA and DCOVA-P30 and then assessed CD4+ and CD8+ T cell responses and antitumor immunity subsequent to immunization. We demonstrated that both DCOVA and DCOVA-P30 were capable of stimulating both enhanced CD4+ and CD8+ T cell responses, leading to preventive antitumor immunity against challenge of OVA-expressing BL6-10OVA tumor in 100% (8/8) of the immunized mice. However, DCOVA-P30 induced more efficient CD4+ and CD8+ T cell responses than DCOVA, leading to significant reduction of growth of 3 day-established lung tumor metastasis in C57BL/6 mice, indicating that the foreign CD4+ Th epitope P30 can enhance both CD4+ and CD8+ T cell responses. In this study, we also transfected transgenic FVBneuN mouse bone marrow DCs with AdVneu and AdVneu-P30 for preparation of DCneu and DCneu-P30 vaccines. We immunized transgenic FVBneuN mice with DCneu and DCneu-P30 and then assessed CD4+ and CD8+ T cell responses and antitumor immunity subsequent to immunization. We demonstrated that DCneu-P30 but not DCneu was capable of stimulating both enhanced CD4+ and CD8+ T cell responses, leading to preventive antitumor immunity against challenge with 0.3×106 neu-expressing Tg1-1 tumor cells in 100% (8/8) immunized transgenic FVBneuN mice; this significantly reduced lung metastasis tumor colonies in immunized transgenic FVBneuN mice challenged with 1×106 Tg1-1 tumor cells, confirming that incooperation of foreign CD4+ Th epitope P30 into DC-based vaccines can at least partially break self-immune tolerance, leading to enhanced CTL responses and antitumor immunity in transgenic FVBneuN mice. Taken together, our data demonstrate that the CD4+ Th epitope P30 can enhance both CD4+ and CD8+ T cell responses, leading to enhanced DC-stimulated antitumor immunity. This may have impact in designing new DC-based vaccines for treatment of breast cancer and other types of human malignancies

GAN-powered Deep Distributional Reinforcement Learning for Resource Management in Network Slicing

Network slicing is a key technology in 5G communications system. Its purpose is to dynamically and efficiently allocate resources for diversified services with distinct requirements over a common underlying physical infrastructure. Therein, demand-aware resource allocation is of significant importance to network slicing. In this paper, we consider a scenario that contains several slices in a radio access network with base stations that share the same physical resources (e.g., bandwidth or slots). We leverage deep reinforcement learning (DRL) to solve this problem by considering the varying service demands as the environment state and the allocated resources as the environment action. In order to reduce the effects of the annoying randomness and noise embedded in the received service level agreement (SLA) satisfaction ratio (SSR) and spectrum efficiency (SE), we primarily propose generative adversarial network-powered deep distributional Q network (GAN-DDQN) to learn the action-value distribution driven by minimizing the discrepancy between the estimated action-value distribution and the target action-value distribution. We put forward a reward-clipping mechanism to stabilize GAN-DDQN training against the effects of widely-spanning utility values. Moreover, we further develop Dueling GAN-DDQN, which uses a specially designed dueling generator, to learn the action-value distribution by estimating the state-value distribution and the action advantage function. Finally, we verify the performance of the proposed GAN-DDQN and Dueling GAN-DDQN algorithms through extensive simulations

Deep Learning with Long Short-Term Memory for Time Series Prediction

Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values. Learning long-range dependencies that are embedded in time series is often an obstacle for most algorithms, whereas Long Short-Term Memory (LSTM) solutions, as a specific kind of scheme in deep learning, promise to effectively overcome the problem. In this article, we first give a brief introduction to the structure and forward propagation mechanism of the LSTM model. Then, aiming at reducing the considerable computing cost of LSTM, we put forward the Random Connectivity LSTM (RCLSTM) model and test it by predicting traffic and user mobility in telecommunication networks. Compared to LSTM, RCLSTM is formed via stochastic connectivity between neurons, which achieves a significant breakthrough in the architecture formation of neural networks. In this way, the RCLSTM model exhibits a certain level of sparsity, which leads to an appealing decrease in the computational complexity and makes the RCLSTM model become more applicable in latency-stringent application scenarios. In the field of telecommunication networks, the prediction of traffic series and mobility traces could directly benefit from this improvement as we further demonstrate that the prediction accuracy of RCLSTM is comparable to that of the conventional LSTM no matter how we change the number of training samples or the length of input sequences.Comment: 9 pages, 5 figures, 14 reference

Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory

Traffic prediction plays an important role in evaluating the performance of telecommunication networks and attracts intense research interests. A significant number of algorithms and models have been put forward to analyse traffic data and make prediction. In the recent big data era, deep learning has been exploited to mine the profound information hidden in the data. In particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network (RNN) schemes, has attracted a lot of attentions due to its capability of processing the long-range dependency embedded in the sequential traffic data. However, LSTM has considerable computational cost, which can not be tolerated in tasks with stringent latency requirement. In this paper, we propose a deep learning model based on LSTM, called Random Connectivity LSTM (RCLSTM). Compared to the conventional LSTM, RCLSTM makes a notable breakthrough in the formation of neural network, which is that the neurons are connected in a stochastic manner rather than full connected. So, the RCLSTM, with certain intrinsic sparsity, have many neural connections absent (distinguished from the full connectivity) and which leads to the reduction of the parameters to be trained and the computational cost. We apply the RCLSTM to predict traffic and validate that the RCLSTM with even 35% neural connectivity still shows a satisfactory performance. When we gradually add training samples, the performance of RCLSTM becomes increasingly closer to the baseline LSTM. Moreover, for the input traffic sequences of enough length, the RCLSTM exhibits even superior prediction accuracy than the baseline LSTM.Comment: 6 pages, 9 figure

Manipulation and Imaging of Interactions Between Layer-by-layer Capsules and Live Cells Using Nanopipettes and SICM

PhDUsability of many chemical substances with significant potential for biomedical applications is limited by their poor solubility in water or limited stability in the physiological environment. One of promising strategies for therapeutic targeted delivery of these types of substances into cells and tissues is their encapsulation inside polyelectrolyte microcapsules (Volodkin et al., 2004b, Sukhorukov et al., 1998b). Successful internalisation of microcapsules loaded with various macromolecules have been observed in several types of living cells (Javier et al., 2008, Kastl et al., 2013), however the mechanisms of the uptake of capsules by living cells are not yet fully understood. Detailed understanding of physico-chemical and mechanical interactions between capsules and living cells is required for specific targeting, effective delivery, and elimination of any potential toxic side effects. This has been largely limited by capabilities of available imaging techniques and lack of specific fluorescent markers for certain types of cellular uptake. The rate of internalisation of microcapsules was primarily studied at the level of cell population using conventional optical/fluorescence microscopy, confocal microscopy, and flow cytometry (Gao et al., 2016, Ai et al., 2005, Sun et al., 2015). These conventional fluorescence methods are known to be prone to overestimating the number of internalised capsules due to their limited capability to exclude capsules which were not fully internalised and remained attached to the cell surface (Javier et al., 2006). Experimental evidence with resolution high enough to resolve the fine membrane processes interacting with microcapsules has been limited to fixed samples imaged by scanning electron microscopy and transmission electron microscopy (Kastl et al., 2013) capturing randomly timed “snapshots” of what is likely to be highly dynamic and complex interaction. Physical force interactions between cellular membrane and capsules during the internalisation were suggested to cause buckling of capsules based on indirect evidence obtained using fluorescence microscopy in live cells 15 (Palankar et al., 2013) and separate measurements of capsule deformation under colloidal probe atomic force microscopy (AFM) outside of the cellular environment (Delcea et al., 2010, Dubreuil et al., 2003). However, our knowledge of the mechanical properties of the fine membrane structures directly involved in the internalisation process or how these structures form during the internalisation is very limited, if non-existent. Here we employ a different approach based on a high-resolution scanning probe technique called scanning ion conductance microscopy (SICM). SICM uses reduction in ionic current through the probe represented by an electrolyte-filled glass nanopipette immersed in saline solution to detect proximity of sample surface (Hansma et al., 1989, Korchev et al., 1997a). The technique has been previously used for high-resolution scanning of biological samples of complexity similar to what can be expected in case of microcapsules interacting with cells (Novak et al., 2014, Novak et al., 2009), and also for mapping mechanical properties at high resolution (Ossola et al., 2015, Rheinlaender and Schaffer, 2013). It has been proved to be able to visualise internalisation process of 200 nm carboxy-modified latex nanoparticles (Novak et al., 2014), however it is not clear whether it would be suitable for visualising internalisation of substantially larger, microscale-sized particles. The aim of this research was to visualize the live internalisation process of microcapsules entering cells by using SICM. The first two chapters of this thesis are introduction and literature review, which summarise the current state of the art. Chapter 3 states the aim and objectives of this study. Chapter 4 introduces the materials and methods we used in our research. Chapter 5, 6, 7 present the main findings of our research. Chapter 5 states the challenges we met in visualising the live internalisation of microcapsule as well as our solution for overcoming those challenges. At the end of that chapter, we describe the detailed procedure we used for recording the live internalisation of microcapsules. The results we got using this procedure are presented in chapter 6 and 7. In chapter 8, we discuss the results we found by comparing them to the results of previous research. In chapter 9, we summarise our study and give some suggestions on future work

PO-224 Effect of high-fat diet on body weight and spontaneous physical activity of SD rats

Objective  Excessive intake of high-energy foods and insufficient levels of physical activity are important causes of obesity. In addition, inadequate physical activity is also a major cause of cardiovascular disease and type 2 diabetes. Relevant data suggests that most adults fail to achieve the level of physical activity needed to improve their health. Therefore, understanding the reasons for the lack of physical activity levels is essential for developing a reduction in sedentary and thus preventing chronic acute illnesses. It is well known that physical activity is good for health, but little is known about the genetic and biological factors that may affect this complex behavior. Some studies have shown that diet-induced obesity may alter dopaminergic activity and thus reduce physical activity levels, suggesting that obesity and diet may be inversely related to dopamine signaling. Therefore, it is necessary to further study the correlation between obesity, dopamine and physical activity levels, and to explore the relationship between high-fat diet and body weight changes and physical activity levels. Methods  Sixteen male Sprague-Dawley rats were randomly divided into two groups. The control group (n=8) was fed with basal diet for 8 weeks, and the high-fat group (n=8) was fed with high-fat diet for 8 weeks. To compare the difference in body weight and physical activity between SD rats fed with high-fat diet and normal diet, and the relationship between body weight and body activity level; in order to study the effect of obesity on exercise behavior, use the open field experimental recorder for each The movements of the rats in the group were recorded (autonomic activity for 30 min), and the correlation between the effects of high-fat diet on body weight and spontaneous activities of SD rats was analyzed. Results High-fat diet and normal-fed rats were in energy intake (high-fat group 4583.94±349.85; control group 3201±298.58), body weight (high-fat group 406.23±29.35; control group 306.66±31.44) and Lee's index (high-fat group 26.17 ± 0.57; control group 24.35 ± 0.97) were significantly different. There was a high correlation between energy intake and body weight in rats, correlation coefficient r=0.911 (p<0.01); correlation coefficient between body weight and physical activity level r = 0.576 (p < 0.05). In addition, by comparing the exercise time and average speed of rats in each group, the difference in exercise time between the two groups was not significant, and the average speed difference was significant (p<0.05); exercise time was significantly correlated with physical activity level, r= 0.734 (p<0.01); and the mean speed was also positively correlated with physical activity level, and the correlation coefficient was 0.660 (P<0.01). Conclusions Obesity is greatly affected by dietary factors, and long-term high-fat diets lead to a decline in physical activity, which in turn promotes further deterioration of obesity. This interaction can create a vicious circle between obesity and physical activity. Further research on the mechanisms of obesity, lack of physical activity and their interaction may provide a theoretical basis for increasing the level of physical activity in obese people

Nanoindentation induced anisotropy of deformation and damage behaviors of MgF2 crystals

The competition mechanism between the slip motions and cleavage fractures is related to the anisotropy of deformation behaviors, which is essential to manufacture complex optical components. To identify competition mechanism between the slip motions and cleavage fractures and reveal the anisotropy of deformation and damage behaviors of MgF2 crystals, the nanoindentation tests were systematically conducted on different crystal planes. In addition, the stress induced by the nanoindentation was developed and decomposed along the slip systems and cleavage planes, and cleavage factors and Schmid factors were calculated. The stress, cleavage factors and Schmid factors indicated that the activation degree of the slip motions and cleavage fractures determined the indentation morphologies. Under the same indentation conditions, the nanoindentation of the (001) crystal plane activated most slip motions, so the plastic deformation is most prone to occur on this crystal plane. The nanoindentation of the (010) crystal plane activated less slip motions and most cleavage fractures, resulting in the severest brittle fractures on the (010) crystal plane. The theoretical results consisted well with the experimental results, which provides the theoretical guidance to the low-damage manufacturing of MgF2 components