Gender-Based Deep Learning Firefly Optimization Method for Test Data Generation.

Software testing is a widespread validation means of software quality assurance in industry. Intelligent optimization algorithms have been proved to be an effective way of automatic test data generation. Firefly algorithm has received extensive attention and been widely used to solve optimization problems because of less parameters and simple implement. To overcome slow convergence rate and low accuracy of the firefly algorithm, a novel firefly algorithm with deep learning is proposed to generate structural test data. Initially, the population is divided into male subgroup and female subgroup. Following the randomly attracted model, each male firefly will be attracted by another randomly selected female firefly to focus on global search in whole space. Each female firefly implements local search under the leadership of the general center firefly, constructed based on historical experience with deep learning. At the final period of searching, chaos search is conducted near the best firefly to improve search accuracy. Simulation results show that the proposed algorithm can achieve better performance in terms of success coverage rate, coverage time, and diversity of solutions

A Novel Algorithm for Intrusion Detection Based on RASL Model Checking

The interval temporal logic (ITL) model checking (MC) technique enhances the power of intrusion detection systems (IDSs) to detect concurrent attacks due to the strong expressive power of ITL. However, an ITL formula suffers from difficulty in the description of the time constraints between different actions in the same attack. To address this problem, we formalize a novel real-time interval temporal logic—real-time attack signature logic (RASL). Based on such a new logic, we put forward a RASL model checking algorithm. Furthermore, we use RASL formulas to describe attack signatures and employ discrete timed automata to create an audit log. As a result, RASL model checking algorithm can be used to automatically verify whether the automata satisfy the formulas, that is, whether the audit log coincides with the attack signatures. The simulation experiments show that the new approach effectively enhances the detection power of the MC-based intrusion detection methods for a number of telnet attacks, p-trace attacks, and the other sixteen types of attacks. And these experiments indicate that the new algorithm can find several types of real-time attacks, whereas the existing MC-based intrusion detection approaches cannot do that

Mesenchymal stem cells as carriers and amplifiers in CRAd delivery to tumors

<p>Abstract</p> <p>Background</p> <p>Mesenchymal stem cells (MSCs) have been considered to be the attractive vehicles for delivering therapeutic agents toward various tumor diseases. This study was to explore the distribution pattern, kinetic delivery of adenovirus, and therapeutic efficacy of the MSC loading of E1A mutant conditionally replicative adenovirus Adv-Stat3(-) which selectively replicated and expressed high levels of anti-sense Stat3 complementary DNA in breast cancer and melanoma cells.</p> <p>Methods</p> <p>We assessed the release ability of conditionally replicative adenovirus (CRAd) from MSC using crystal violet staining, TCID₅₀assay, and quantitative PCR. In vitro killing competence of MSCs carrying Adv-Stat3(-) toward breast cancer and melanoma was performed using co-culture system of transwell plates. We examined tumor tropism of MSC by Prussian blue staining and immunofluorescence. In vivo killing competence of MSCs carrying Adv-Stat3(-) toward breast tumor was analyzed by comparison of tumor volumes and survival periods.</p> <p>Results</p> <p>Adv-Stat3(-) amplified in MSCs and were released 4 days after infection. MSCs carrying Adv-Stat3(-) caused viral amplification, depletion of Stat3 and its downstream proteins, and led to significant apoptosis in breast cancer and melanoma cell lines. In vivo experiments confirmed the preferential localization of MSCs in the tumor periphery 24 hours after tail vein injection, and this localization was mainly detected in the tumor parenchyma after 72 hours. Intravenous injection of MSCs carrying Adv-Stat3(-) suppressed the Stat3 pathway, down-regulated Ki67 expression, and recruited CD11b-positive cells in the local tumor, inhibiting tumor growth and increasing the survival of tumor-bearing mice.</p> <p>Conclusions</p> <p>These results indicate that MSCs migrate to the tumor site in a time-dependent manner and could be an effective platform for the targeted delivery of CRAd and the amplification of tumor killing effects.</p

Is there a correlation between sensory impairments and social isolation in middle-aged and older Chinese population? Cross-sectional and longitudinal evidence from a nationally representative survey

PurposeThe aim of this study is to investigate the cross-sectional and longitudinal associations between sensory impairments (SIs) including single vision impairment (SVI), single hearing impairment (SHI), and dual sensory impairments (DSI) with social isolation in the middle-aged and older Chinese population.MethodsData were obtained from the China Health and Retirement Longitudinal Survey (CHARLS). In total, 11,674 Chinese older adults aged over 45 were included at baseline 2011, and 6,859 participants who accomplished all four interviews from 2011 to 2018 were adapted for longitudinal analyses. Sensory status and social isolation measurements including social disconnectedness and self-perceived loneliness were collected. Assessment of social disconnectedness included the number of types of social activities in which they participated and the frequency of such participation. Loneliness referred to the subjective perception of loneliness. Other covariates included socio-demographic characteristics, medical conditions, and lifestyle-related factors. The impacts of baseline sensory status on social disconnectedness and loneliness were assessed using univariate and multivariate generalized linear models. A generalized linear model with generalized estimation equations (GEE) was used to assess the association between time-varying sensory statuses with social disconnectedness or loneliness over 8 years after being adjusted with multi-confounding factors.ResultsParticipants with SIs had significantly higher levels of social disconnectedness and self-perceived loneliness, compared to those who were free of SI. All kinds of SIs were significantly associated with loneliness according to both cross-sectional and longitudinal data. The correlations between DSI and social disconnectedness or loneliness at baseline and over 8 years were also noticed. SHI was found to be significantly associated with both frequency and types of social activities according to cross-sectional data and with the frequency of social activity participation in longitudinal analysis. SVI was only associated with the types of social activities at baseline (all p-values &lt; 0.05).ConclusionSensory impairments, especially dual sensory impairments, have explicitly detrimental effects on social isolation among the older Chinese population. Over time, single hearing impairment specifically jeopardizes their frequency rather than types of social activities participation

Durvalumab Plus Carboplatin/Paclitaxel Followed by Maintenance Durvalumab With or Without Olaparib as First-Line Treatment for Advanced Endometrial Cancer: The Phase III DUO-E Trial

PURPOSE Immunotherapy and chemotherapy combinations have shown activity in endometrial cancer, with greater benefit in mismatch repair (MMR)-deficient (dMMR) than MMR-proficient (pMMR) disease. Adding a poly(ADP-ribose) polymerase inhibitor may improve outcomes, especially in pMMR disease. METHODS This phase III, global, double-blind, placebo-controlled trial randomly assigned eligible patients with newly diagnosed advanced or recurrent endometrial cancer 1:1:1 to: carboplatin/paclitaxel plus durvalumab placebo followed by placebo maintenance (control arm); carboplatin/paclitaxel plus durvalumab followed by maintenance durvalumab plus olaparib placebo (durvalumab arm); or carboplatin/paclitaxel plus durvalumab followed by maintenance durvalumab plus olaparib (durvalumab + olaparib arm). The primary end points were progression-free survival (PFS) in the durvalumab arm versus control and the durvalumab + olaparib arm versus control. RESULTS Seven hundred eighteen patients were randomly assigned. In the intention-to-treat population, statistically significant PFS benefit was observed in the durvalumab (hazard ratio [HR], 0.71 [95% CI, 0.57 to 0.89]; P = .003) and durvalumab + olaparib arms (HR, 0.55 [95% CI, 0.43 to 0.69]; P < .0001) versus control. Prespecified, exploratory subgroup analyses showed PFS benefit in dMMR (HR [durvalumab v control], 0.42 [95% CI, 0.22 to 0.80]; HR [durvalumab + olaparib v control], 0.41 [95% CI, 0.21 to 0.75]) and pMMR subgroups (HR [durvalumab v control], 0.77 [95% CI, 0.60 to 0.97]; HR [durvalumab + olaparib v control] 0.57; [95% CI, 0.44 to 0.73]); and in PD-L1-positive subgroups (HR [durvalumab v control], 0.63 [95% CI, 0.48 to 0.83]; HR [durvalumab + olaparib v control], 0.42 [95% CI, 0.31 to 0.57]). Interim overall survival results (maturity approximately 28%) were supportive of the primary outcomes (durvalumab v control: HR, 0.77 [95% CI, 0.56 to 1.07]; P = .120; durvalumab + olaparib v control: HR, 0.59 [95% CI, 0.42 to 0.83]; P = .003). The safety profiles of the experimental arms were generally consistent with individual agents. CONCLUSION Carboplatin/paclitaxel plus durvalumab followed by maintenance durvalumab with or without olaparib demonstrated a statistically significant and clinically meaningful PFS benefit in patients with advanced or recurrent endometrial cancer

Modelling the Mimic Defence Technology for Multimedia Cloud Servers

A current research trend is to combine multimedia data with artificial intelligence and process them on cloud servers. In this context, ensuring the security of multimedia cloud servers is critical, and the cyber mimic defence (CMD) technology is a promising approach to this end. CMD, which is an innovative active defence technology developed in China, can be applied in many scenarios. However, although the mathematical model is a key component of CMD, a universally acceptable mathematical model for theoretical CMD has not been established yet. In this work, the attack problems and modelling difficulties were extensively examined, and a comprehensive modelling theory and concepts were clarified. By decoupling the model from the input and output of the specific system scene, the modelling difficulties were effectively avoided, and the mathematical expression of the CMD mechanism was enhanced. Furthermore, the process characteristics of the attack behaviour were identified by using a specific mathematical mapping method. Finally, based on the decomposition problem of large prime factors and convolution operations, an intuitive and exclusive CMD mathematical model was proposed. The proposed model could clearly express the CMD mechanism and transform the problems of attack and defence in the CMD domain into corresponding mathematical problems. These aspects were considered to qualitatively assess the CMD security, and it was noted that a high level of security can be realized. Furthermore, the overhead of CMD was analyzed. Moreover, the proposed model can be directly programmed

Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation

Emotion Recognition in Conversation (ERC) aims to recognize the emotion for each utterance in a conversation automatically. Due to the difficulty of collecting and labeling, this task lacks the dataset corpora available on a large scale. This increases the difficulty of finishing the supervised training required by large-scale neural networks. Introducing the large-scale generative conversational dataset can assist with modeling dialogue. However, the spatial distribution of feature vectors in the source and target domains is inconsistent after introducing the external dataset. To alleviate the problem, we propose a Domain Adversarial Network for Cross-Domain Emotion Recognition in Conversation (DAN-CDERC) model, consisting of domain adversarial and emotion recognition models. The domain adversarial model consists of the encoders, a generator and a domain discriminator. First, the encoders and generator learn contextual features from a large-scale source dataset. The discriminator performs domain adaptation by discriminating the domain to make the feature space of the source and target domain consistent, so as to obtain domain invariant features. Then DAN-CDERC transfers the learned domain invariant dialogue context knowledge from the domain adversarial model to the emotion recognition model to assist in modeling the dialogue context. Due to the use of a domain adversarial network, DAN-CDERC obtains dialogue-level contextual information that is domain invariant, thereby reducing the negative impact of inconsistency in domain space. Empirical studies illustrate that the proposed model outperforms the baseline models on three benchmark emotion recognition datasets