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The work engagement of foreign language teachers enhances both their performance and the quality of instruction. Recent studies have highlighted the pivotal roles of emotional intelligence, job burnout, and self-efficacy in fostering teachers' work engagement. Despite this, the complex interactions between these factors in the context of foreign language teaching remain underexplored. This study conducts a meta-analytic review to examine the connections among work engagement, emotional intelligence, burnout, and self-efficacy among foreign language educators. Utilizing a chain mediation model, it probes the mechanism through which emotional intelligence impacts work engagement, based on an analysis of 49 scholarly articles. Results from the meta-analysis robustly validate the hypothesized model. Specifically, emotional intelligence contributes to work engagement via four distinct routes: direct mediation through self-efficacy, direct mediation through job burnout, and chain mediation involving self-efficacy and job burnout. Of the evaluated factors, emotional intelligence had the most significant impact on enhancing work engagement. Implications of these findings may facilitate the training and ongoing professional development of foreign language teachers.

Super-resolution (SR) of remote sensing images is essential to compensate for missing information in the original high-resolution (HR) images. Single-image super-resolution (SISR) technique aims to recover high-resolution images from low-resolution (LR) images. However, traditional SISR methods often result in blurred and unclear images due to the loss of high-frequency details in LR images at high magnifications. In this paper, a super-segmental reconstruction model STGAN for remote sensing images is proposed, which fuses the Generative Adversarial Networks (GANs) and self-attention mechanism based on the Reference Super Resolution method (RefSR). The core module of the model consists of multiple CNN-Swin Transformer blocks (MCST), each of which consists of a CNN layer and a specific modified Swin Transformer, constituting the feature extraction channel. In image hypersegmentation reconstruction, the optimized and improved correlation attention block (RAM-V) uses feature maps and gradient maps to improve the robustness of the model under different scenarios (such as land cover change). The experimental results show that the STGAN model proposed in this paper exhibits the best image data perception quality results with the best performance of LPIPS and PI metrics in the test set under RRSSRD public datasets. In the experimental test set, the PSNR reaches 31.4151, the SSIM is 0.8408, and the performance on the RMSE and SAM metrics is excellent, which demonstrate the model’s superior image reconstruction details in super-resolution reconstruction and highlighting the great potential of RefSR’s application to the task of super-scalar processing of remotely sensed images.

Abstract Mutation-based greybox fuzzing has been one of the most prevalent techniques for security vulnerability discovery and a great deal of research work has been proposed to improve both its efficiency and effectiveness. Mutation-based greybox fuzzing generates input cases by mutating the input seed, i.e., applying a sequence of mutation operators to randomly selected mutation positions of the seed. However, existing fruitful research work focuses on scheduling mutation operators, leaving the schedule of mutation positions as an overlooked aspect of fuzzing efficiency. This paper proposes a novel greybox fuzzing method, PosFuzz, that statistically schedules mutation positions based on their historical performance. PosFuzz makes use of a concept of effective position distribution to represent the semantics of the input and to guide the mutations. PosFuzz first utilizes Good-Turing frequency estimation to calculate an effective position distribution for each mutation operator. It then leverages two sampling methods in different mutating stages to select the positions from the distribution. We have implemented PosFuzz on top of AFL, AFLFast and MOPT, called Pos-AFL, -AFLFast and -MOPT respectively, and evaluated them on the UNIFUZZ benchmark (20 widely used open source programs) and LAVA-M dataset. The result shows that, under the same testing time budget, the Pos-AFL, -AFLFast and -MOPT outperform their counterparts in code coverage and vulnerability discovery ability. Compared with AFL, AFLFast, and MOPT, PosFuzz gets 21% more edge coverage and finds 133% more paths on average. It also triggers 275% more unique bugs on average.

Abstract Introduction Stretch marks are common atrophic dermal scars with significant physical and psychological effects. Therefore, there is a need for effective cosmetics and procedures for stretch mark treatment. This study aimed to evaluate the efficacy and safety of a novel treatment for stretch marks that is made up of topical formulations containing beta-glucan combined with nanofractional radiofrequency. Methods This randomized, blinded control trial enrolled 64 Chinese women aged 20–45 years at > 6 months after delivery with obvious white or silver abdominal stretch marks. Participants were randomly allocated to group A (blank group), group B (topical product group), group C (product combined with nanofractional radiofrequency), and group D (vehicle combined with nanofractional radiofrequency). The stretch mark width, skin elasticity, skin color, skin thickness, and collagen density were noninvasively measured. Two trained assessors evaluated the severity, color, outline, and relaxation of the striae. Results Group C showed the best treatment efficacy, with no adverse effects observed during the study period. Conclusion Our findings indicate that stretch mark treatment using topical formulations containing beta-glucan, combined with nanofractional radiofrequency plus magnetic nanofractional radiofrequency, is tolerable and effective. Trial registration ChiCTR2200056725.

BackgroundThe developments in cosmetic sciences and technologies have generated a gap between the cosmetics and their users. Users including regular customers, clinicians, industry personnel, researchers, testing agencies, beauty salon workers, and mass media hardly possess the ability to distinguish truth from falsehood. The gap remained as one major reason for inappropriate cosmetics usage, insufficient efficacy, and even cosmetics adverse reactions (CARs).MethodsAiming at enhancing the relevant practitioners’ cosmetic and dermatologic sciences, we launched a cosmetic and dermatologic sciences continuing medical education (CME) since 2008. The objective of the current study was to evaluate the effectiveness of the CME. We summarized and analyzed the project for the last 15 years. Meanwhile, an online survey consisted of three parts was performed to evaluate the CME and to collect the trainees’ comments.ResultsA total of 3,923 trainees have participated in the CME project from 2008 to 2022. The trainees included clinicians, industry staffs, biomedical researchers, third-party cosmetics testing staffs, beauty salon staffs, students, and media staffs. The trainees had theory courses on cosmetic and dermatologic sciences, cosmetics DIY practice & video watching, and an optional guided tour during the 4.5-day CME. Eight hundred and twenty-three trainees and 586 control subjects responded to the online survey. The comprehensive test in the second part of the survey demonstrated that compared with the control group, the CME project significantly enhanced the trainees’ perception and knowledge regarding the cosmetics formula sciences, basic dermatologic sciences, cosmetics usage, noninvasive measurements, new advances, CARs, and laws (p = 0.000). Trainees of all occupations ranked “basic dermatologic sciences and skin diseases” as the most significant sections. Trainees of all occupations believed the CME has contributed most in “understand the function & efficacy of cosmetics.” We noticed the occupational variances. Over 97% of trainees were willing to recommend the CME to the others.ConclusionThe CME project significantly enhanced the trainees’ cosmetic and dermatologic sciences, which bridged the gap between cosmetics and public skin health. This multidisciplinary CME also contributed to establishing an interdisciplinary interaction and cooperation platform for the multiple occupations involved in the public skin health maintenance and promotion.

Abstract Network function virtualization provides programmable in-network middlewares by leveraging virtualization technologies and commodity hardware and has gained popularity among all mainstream network device manufacturers. Yet it is challenging to apply coverage-guided fuzzing, one of the state-of-the-art vulnerability discovery approaches, to those virtualized network devices, due to inevitable integrity protection adopted by those devices. In this paper, we propose a coverage-guided fuzzing framework NDFuzz for virtualized network devices with a novel integrity protection bypassing method, which is able to distinguish processes of virtualized network devices from hypervisors with a carefully designed non-intrusive page global directory inference technique. We implement NDFuzz atop of two black-box fuzzers and evaluate NDFuzz with three representative network protocols, SNMP , DHCP and NTP , on nine popular virtualized network devices. NDFuzz obtains an average 36% coverage improvement in comparison with its black-box counterparts. NDFuzz discovers 2 0-Day vulnerabilities and 1 1-Day vulnerability with coverage guidance while the black-box fuzzer can find only one of them. All discovered vulnerabilities are confirmed by corresponding vendors.

Abstract Grey-box fuzzing techniques have been widely used in software bug finding. In general, there are many decisions to make in the fuzzing process, including which code block in the target program should be explored first, which bytes of an input seed should be mutated to reach the target code block, and how to mutate the chosen input bytes. However, existing solutions usually rely on random exploration or certain heuristics to choose where and how to fuzz, which limits the efficiency of fuzzing. In this paper, we propose a novel solution CAMFuzz to guide the fuzzing process with explainable decisions in explainable artificial intelligence (XAI). First, we propose a dynamic weight adjustment algorithm, which considers both the difficulty of reaching a block and the number of unvisited blocks nearby, to find code blocks worthy to explore first. Second, we utilize a widely used local interpretation technique, i.e., class activation mapping (CAM), to recognize which part of an input seed should be mutated to reach a given target code block. Therefore, CAMFuzz can distinguish which part of code in the program is more important and which positions in the input file should be mutated first, in order to achieve a better code coverage and bug finding efficiency. Third, to further help the fuzzer increase fuzzing efficiency, we leverage a lightweight static program analysis to help the fuzzer identify magic values. We implement a prototype of CAMFuzz and evaluate it on 13 real-world programs (including 11 open source targets, 2 closed-source commercial products including a Microsoft component and Hancom Office) Results show that CAMFuzz outperforms state-of-the-art fuzzers in both code coverage and bug finding. To detail, CAMFuzz on average achieves 2.07 $$\times$$ × more bugs and 1.17 $$\times$$ × coverage improvements. In total, it found 19 previously unknown vulnerabilities, of which 6 have been assigned by CVE so far.

Background: Hemorrhagic transformation (HT) is a common complication of intravenous thrombolysis (IVT) in patients with acute ischemic stroke (AIS) and may lead to neurological deterioration. This article discusses whether monocyte count to high-density lipoprotein ratio (MHR) level is associated with HT in AIS patients. Materials and methods: The clinical data of AIS patients who underwent rt-PA IVT treatment were continuously collected. According to whether HT occurred, patients were divided into HT group and non-HT group. Potential association between MHR and HT in different subtypes AIS was explored by using logistic regression. Results: A total of 444 AIS patients were retrospective analyzed. The MHR level was lower in HT group compared with the non-HT group in all AIS patients (0.28 vs 0.36, P = .031) and in large-artery atherosclerosis (LAA) type AIS patients (0.31 vs 0.37, P = .032). Low MHR was independently related to HT (OR:0.035, 95%CI:0.003−0.390, P = .006). Among all TOAST subtypes, low MHR was only independently associated with HT in patients of LAA-type AIS after adjusting for confounding factors (OR:0.01, 95%CI:0.00−0.62, P = .031), with an optimal cut-off value of 0.41, sensitivity of 85.7%, and specificity of 43.1%. MHR was not correlated with SVO, VE, and CE subtype AIS. Conclusion: Low MHR may be an independent predictor of HT in patients with AIS and this conclusion only existed in LAA-type AIS.

Abstract Codes of Open Source Software (OSS) are widely reused during software development nowadays. However, reusing some specific versions of OSS introduces 1-day vulnerabilities of which details are publicly available, which may be exploited and lead to serious security issues. Existing state-of-the-art OSS reuse detection work can not identify the specific versions of reused OSS well. The features they selected are not distinguishable enough for version detection and the matching scores are only based on similarity.This paper presents B2SMatcher, a fine-grained version identification tool for OSS in commercial off-the-shelf (COTS) software. We first discuss five kinds of version-sensitive code features that are trackable in both binary and source code. We categorize these features into program-level features and function-level features and propose a two-stage version identification approach based on the two levels of code features. B2SMatcher also identifies different types of OSS version reuse based on matching scores and matched feature instances. In order to extract source code features as accurately as possible, B2SMatcher innovatively uses machine learning methods to obtain the source files involved in the compilation and uses function abstraction and normalization methods to eliminate the comparison costs on redundant functions across versions. We have evaluated B2SMatcher using 6351 candidate OSS versions and 585 binaries. The result shows that B2SMatcher achieves a high precision up to 89.2% and outperforms state-of-the-art tools. Finally, we show how B2SMatcher can be used to evaluate real-world software and find some security risks in practice.

Abstract SOHO (small office/home office) routers provide services for end devices to connect to the Internet, playing an important role in cyberspace. Unfortunately, security vulnerabilities pervasively exist in these routers, especially in the web server modules, greatly endangering end users. To discover these vulnerabilities, fuzzing web server modules of SOHO routers is the most popular solution. However, its effectiveness is limited due to the lack of input specification, lack of routers’ internal running states, and lack of testing environment recovery mechanisms. Moreover, existing works for device fuzzing are more likely to detect memory corruption vulnerabilities.In this paper, we propose a solution ESRFuzzer to address these issues. It is a fully automated fuzzing framework for testing physical SOHO devices. It continuously and effectively generates test cases by leveraging two input semantic models, i.e., KEY-VALUE data model and CONF-READ communication model, and automatically recovers the testing environment with power management. It also coordinates diversified mutation rules with multiple monitoring mechanisms to trigger multi-type vulnerabilities. With the guidance of the two semantic models, ESRFuzzer can work in two ways: general mode fuzzing and D-CONF mode fuzzing. General mode fuzzing can discover both issues which occur in the CONF and READ operation, while D-CONF mode fuzzing focus on the READ-op issues especially missed by general mode fuzzing.We ran ESRFuzzer on 10 popular routers across five vendors. In total, it discovered 136 unique issues, 120 of which have been confirmed as 0-day vulnerabilities we found. As an improvement of SRFuzzer, ESRFuzzer have discovered 35 previous undiscovered READ-op issues that belong to three vulnerability types, and 23 of them have been confirmed as 0-day vulnerabilities by vendors. The experimental results show that ESRFuzzer outperforms state-of-the-art solutions in terms of types and number of vulnerabilities found.

Abstract The Integer-Overflow-to-Buffer-Overflow (IO2BO) vulnerability has been widely exploited by attackers to cause severe damages to computer systems. Automatically identifying this kind of vulnerability is critical for software security. Despite many works have been done to mitigate integer overflow, existing tools either report large number of false positives or introduce unacceptable time consumption. To address this problem, in this article we present a static analysis framework. It first constructs an inter-procedural call graph and utilizes taint analysis to accurately identify potential IO2BO vulnerabilities. Then it uses a light-weight method to further filter out false positives. Specifically, it generates constraints representing the conditions under which a potential IO2BO vulnerability can be triggered, and feeds the constraints to SMT solver to decide their satisfiability. We have implemented a prototype system ELAID based on LLVM, and evaluated it on 228 programs of the NIST’s SAMATE Juliet test suite and 14 known IO2BO vulnerabilities in real world. The experiment results show that our system can effectively and efficiently detect all known IO2BO vulnerabilities.

Cryotherapy is one of the most common treatments for warts; however, pain during treatment and relatively high recurrence rates limit its use. Local hyperthermia has also been used successfully in the treatment of plantar warts. The aim of this study was to compare the clinical effectiveness of local hyperthermia vs cryotherapy for the treatment of plantar warts. This multi- centre, open, 2-arm, non-randomized concurrent controlled trial included 1,027 patients, who received either cryotherapy or local hyperthermia treatment. Three months after treatment, local hyperthermia and cryotherapy achieved complete clearance rates of 50.9% and 54.3%, respectively. Recurrence rates were 0.8% and 12%, respectively. Pain scores during local hyperthermia were significantly lower than for cryotherapy. Both local hyperthermia and cryotherapy demonstrated similar efficacy for clearance of plantar warts; while local hyperthermia had a lower recurrence rate and lower pain sensation during treatment.

Abstract Background Dysregulation of long non-coding RNAs (lncRNAs) is involved in development of prostate cancer. However, the molecular mechanisms of many lncRNAs in prostate cancer have not been studied yet. Methods The lncRNA Fer-1-like protein 4 (FER1L4) expression was explored in prostate tumors and normal prostate tissues by RT-qPCR and bioinformatic analysis. Overexpression of FER1L4 was performed to evaluate its role in prostate cancer cell proliferation and survival. The molecular mechanism of FER1L4 was investigated by dual luciferase reporter assay, RNA pull down assay, western blotting and RT-qPCR. Results It was found that FER1L4 was lower in prostate cancer tissues than normal tissues. Higher expression of FER1L4 was associated with prostate cancer tissues of early stage (AJCC stage I/II). Overexpression of FER1L4 inhibited cell proliferation and promoted cell apoptosis in prostate cancer cells. Bioinformatic analysis, RT-qPCR, RNA pull down assay and dual luciferase assay showed that FER1L4 upregulated F-box/WD repeat-containing protein 7 (FBXW7) tumor suppressor via sponging miR-92a-3p. Silencing of FBXW7 reversed the cell phenotypes caused by FER1L4 overexpression in prostate cancer cells. Conclusion The data demonstrated that FER1L4, a downregulated lncRNA in prostate cancer, was pivotal for cell proliferation and survival of prostate cancer. The study provided new sights into understanding of the signaling network in prostate cancer and implied that FER1L4 might be a biomarker for patients with prostate cancer.

Objective To explore the potential mechanism which miR-527 targeting the heparan sulfate 6-O-endosulfatase (SULF2) regulates TGF-β/SMAD signaling pathway induced epithelial-mesenchymal transition (EMT) in non-small-cell lung cancer (NSCLC). Methods 38 pairs of lung tumor biopsies and corresponding paracancerous biopsies were obtained from NSCLC patients with surgical resection, normal human bronchial epithelial BEAS-2B cells and five NSCLS cell lines were applied for our study. miR-527 and SULF2 expression were determined by qRT-PCR and immunohistochemistry. MiR-527 and SULF2 biological link were predicted by Targetscan.org and tested by dual luciferase. Cells proliferation and apoptosis were respectively detected by EDU staining and flow cytometry. Cells migration was examined by transwell and scratch-wound assay. Expression of proteins related to EMT and TGF-β/SMAD signaling pathway, such as E-cadherin, N-cadherin, p-Samd3 and p-Smad2, was detected by western blot. ResultsmiR-527 expression was decreased in lung tumor tissues and NSCLS cell lines, conversely, SULF2 expression was significantly increased. In addition, we found that miR-527 targeted 3'-untranslated regions (3'-UTR) of SULF2 and mediated its expression. Overexpression of miR-527 evidently suppressed NSCLC proliferation, invasion and EMT via TGF-β/SMAD signaling pathway. Moreover, the silence of SULF2 exhibited a similar effect. Conclusion miR-527 targeting SULF2 down-regulated SULF2 expression, concurrently, suppressed NSCLC epithelial-mesenchymal transition and invasion via inhibiting TGF-β/SMAD signaling pathway.

Aiming to improve the comprehensive aerodynamic performance of a high-speed train, a multi-objective shape optimization method for a streamlined train head is proposed in this work. The shape of the streamlined train head is parameterized with some spline curves. The optimization design variables are uniformly sampled using the optimal Latin hypercube design method. The aerodynamic resistance and dipole noise sources are chosen as the optimization objectives, which can be obtained through the computational fluid dynamics (CFD) method. An approximate calculation model is established by the radial basis function neural network so as to effectively predict the values of optimization objectives. The error between the predicted values and actual values of the aerodynamic resistance is less than 1%, and that of the dipole noise source is less than 3 dB, which demonstrate the validity of the approximate calculation model. In the optimization process, the algorithm NSGA-II is adopted to update the values of the optimization design variables, and the approximate calculation model is used to calculate the optimization objectives, which greatly reduces the optimization computation time of the streamlined head shape. Through iterative computation of the optimization algorithm in the design space, each optimized design variable shows a trend of convergence, and the aerodynamic resistance and dipole noise source generally show a decreasing trend. The Pareto front is corrected by the CFD method after optimization. The aerodynamic resistance can be reduced by up to 4.5%, and the dipole noise source can be reduced by up to 3.9 dB.

BackgroundMicroRNAs (miRNAs) have been discovered to dictate the development of various tumors. However, studies on the roles of miRNAs in the progression of gastric cancer (GC) are still lacking.MethodsHerein, by analyzing GC cell lines and patients samples, we observed that miR-148b-5p was significantly downregulated in GC. We also confirmed that miR-148b-5p overexpression significantly inhibited GC cell proliferation and invasion in vitro and in vivo.ResultsOverexpression of miR-148b-5p not only reprogrammed the metabolic properties of GC but also regulated the immune microenvironment by shifting lymphocyte and myeloid populations. Mechanistically, ATPIF1, an important glycolysis-associated gene, was identified as a direct target of miR-148b-5p and mediated the effect of miR-148b-5p. Notably, the low level of miR-148b-5p was significantly related with poor prognosis of GC patients (P < 0.001). Importantly, the levels of miR-148b-5p significantly changed the sensitivity of GC cells to several anti-cancer drugs (Doxorubicin, P < 0.05, Paclitaxel, P < 0.01, Docetaxel, P < 0.05).ConclusionsTargeting miR-148b-5p inhibits immunity microenvironment and gastric cancer progression.

A range-reduced static definition-use (def-use) fault detection framework is proposed to improve the scalability, but still retain its accuracy, when applied to large application programs. It casts common faults, such as null pointer dereferences, undefined references, buffer overflows, and memory leaks into a common def-use fault pattern, and uses a two-level path-insensitive approach to classify variable uses that can trigger faults into must-trigger, must-not-trigger, and may-trigger categories depending on whether the unsafe uses can actually be, never be, or may be executed. For those must-trigger unsafe uses, faults are immediately reported, and those must-not-trigger uses are dropped from further analysis. The already reduced program range that is relevant to the may-trigger unsafe uses is further reduced by using a binary decision diagram encoded path extraction scheme for more accurate, but more expensive, path-sensitive analysis. A prototype has been built using this approach, and a set of large realistic applications (a total of more than 4.8 MLOC) was tested for such common types of def-use faults. Compared with existing popular path-sensitive detection tools such as Clang Static Analyzer, we find our approach incurs less analysis time, but achieves good accuracy with a low false positive rate and no false negative.

Abstract Microglia play a pivotal role in clearance of Aβ by degrading them in lysosomes, countering amyloid plaque pathogenesis in Alzheimer’s disease (AD). Recent evidence suggests that lysosomal dysfunction leads to insufficient elimination of toxic protein aggregates. We tested whether enhancing lysosomal function with transcription factor EB (TFEB), an essential regulator modulating lysosomal pathways, would promote Aβ clearance in microglia. Here we show that microglial expression of TFEB facilitates fibrillar Aβ (fAβ) degradation and reduces deposited amyloid plaques, which are further enhanced by deacetylation of TFEB. Using mass spectrometry analysis, we firstly confirmed acetylation as a previously unreported modification of TFEB and found that SIRT1 directly interacted with and deacetylated TFEB at lysine residue 116. Subsequently, SIRT1 overexpression enhanced lysosomal function and fAβ degradation by upregulating transcriptional levels of TFEB downstream targets, which could be inhibited when TFEB was knocked down. Furthermore, overexpression of deacetylated TFEB at K116R mutant in microglia accelerated intracellular fAβ degradation by stimulating lysosomal biogenesis and greatly reduced the deposited amyloid plaques in the brain slices of APP/PS1 transgenic mice. Our findings reveal that deacetylation of TFEB could regulate lysosomal biogenesis and fAβ degradation, making microglial activation of TFEB a possible strategy for attenuating amyloid plaque deposition in AD.

Erectile dysfunction (ED) is a major sexual problem for men. Nogo-B receptor (NgBR) has been found to be involved in the regulation of vascular remodeling and angiogenesis. The present study explores the effects of NgBR in penile corpus cavernosum in rats with diabetic ED. Firstly, the ED model of Sprague Dawley rats was established. Hematoxylin-eosin staining and Masson staining were conducted to observe pathological morphology. Immunochemical assay was adopted to detect α-smooth muscle actin (α-SMA), NgBR and intercellular cell adhesion molecule-1 (ICAM-1) expression. Reverse transcription quantitative polymerase chain reaction assay and Western blot analysis were carried out for the assessment of NgBR, factors correlated to ICAM-1, including steroid receptor coactivator (SRC) and proline-rich tyrosine kinase2 (PYK2), and factors associated with apoptosis, including B-cell lymphoma-2 (Bcl-2), Bcl-2 associated protein X (Bax), caspase 3 and cleaved-caspase 3. The results found that capillaries and vascular smooth muscle cell content reduced, and NgBR and ICAM-1 were elevated in rats with diabetic ED. si-NgBR relieved ED by decreasing penile corpus cavernosum smooth muscle systolic percentage and increasing erectile time and rate, intracavernous pressure (ICP)/mean arterial pressure (MAP) and diastolic percentage, improving the pathological changes and inhibiting cavernosum cell apoptosis. si-NgBR also resulted in the down-regulation of ICAM-1 and downstream SRC and PYK2 and promoted α-SMA expression. In conclusion, si-NgBR can provide a potential therapy for diabetic ED in rats by down-regulating ICAM-1, SRC and PYK2, making it a potential therapeutic option for diabetic ED.

Web-based applications delivered using clouds are becoming increasingly popular due to less demand of client-side resources and easier maintenance than desktop counterparts. At the same time, larger attack surfaces and developers’ lack of security proficiency or awareness leave Web applications particularly vulnerable to security attacks. On the other hand, diversity has long been considered as a viable approach to detecting security attacks since functionally similar but internally different variants of an application will likely respond to the same attack in different ways. However, most diversity-by-design approaches have met difficulties in practice due to the prohibitive cost in terms of both development and maintenance. In this work, we propose to employ opportunistic diversity inherent to Web applications and their database backends to detect injection attacks. We first conduct a case study of common vulnerabilities to confirm the potential of opportunistic diversity for detecting potential attacks. We then devise a multi-stage approach to examine features extracted from the database queries, their effect on the database, the query results, as well as the user-end results. Next, we combine the partial results obtained from different stages using a learning-based approach to further improve the detection accuracy. Finally, we evaluate our approach using a real world Web application.

Background: Ischemic cerebral infarction is a severe clinical condition that can cause serious mortality. Artesunate, an anti-malarial drug that is widely used in cancer treatment, is known to facilitate accelerated cell apoptosis. The aim of this study is to explore the possible neuroprotective effects of artesunate on hypoxic-ischemic cells in rats.Methods: Middle cerebral artery occlusion (MCAO) rats were treated with artesunate in different doses to observe their survival rate. Primary hippocampal neurons were deprived of oxygen-glucose to induce ischemia symptoms. Western blot was performed to determine the protein expressions of p-mTOR, Beclin-1, and Mcl-1. A five-point scale was used to detect neurological deficit. Cell apoptosis was measured using a TUNEL assay.Results: Artesunate supplementation protected MCAO rats from death and ameliorated brain injury among them. Artesunate administration decreased the expression of p-mTOR, increased the expressions of Beclin-1 and Mcl-1, and decreased the activity of caspase-3 in both the rats' ischemia cerebral cortices and their primary ischemia hippocampal neurons when compared with artesunate-absent ischemic brains and cells. The neuroprotective effects of artesunate were abolished by either leucine (LEU) or 3-MA, while the effects of rapamycin were reversed by 3-MA. In vivo experiments verified the protective effects of artesunate on brain-infarct rats.Conclusion: The results indicate the protectiveness of artesunate against ischemic cerebral infarction, whereas the protectiveness might increase autophagy through regulating the activity of mTOR.

Background: Atopic dermatitis (AD) is characterized by defective skin barrier and imbalance in T helper 1/T helper 2 (Th1/Th2) cytokine expression. Filaggrin (FLG) is the key protein to maintaining skin barrier function. Recent studies indicated that Th1/Th2 cytokines influence FLG expression in keratinocytes. However, the role of Th1/Th2 cytokines on FLG processing is not substantially documented. Our aim was to investigate the impact of Th1/Th2 cytokines on FLG processing. Methods: HaCaT cells and normal human keratinocytes were cultured in low and high calcium media and stimulated by either interleukin (IL)-4, 13 or interferon-γ (IFN-γ). FLG, its major processing proteases and key protease inhibitor lymphoepithelial Kazal-type-related inhibitor (LEKTI) were measured by both real-time quantitative polymerase chain reaction and Western blotting. Their expression was also evaluated in acute and chronic AD lesions by immunohistochemistry. Results: IL-4/13 significantly reduced, while IFN-γ significantly up-regulated FLG expression. IL-4/13 significantly increased, whereas IFN-γ significantly decreased the expression of kallikreins 5 and 7, matriptase and channel-activating serine protease 1. On the contrary, IL-4/13 significantly decreased, while IFN-γ increased the expression of LEKTI and caspase-14. Similar trends were observed in AD lesions. Conclusions: Our results suggested that Th1/Th2 cytokines differentially regulated the expression of major FLG processing enzymes. The imbalance between Th1 and Th2 polarized immune response seems to extend to FLG homeostasis, through the network of FLG processing enzymes.

Here we present a method to estimate the total number of nodes of a network using locally observed response dynamics. The algorithm has the following advantages: (a) it is data-driven. Therefore it does not require any prior knowledge about the model; (b) it does not need to collect measurements from multiple stimulus; and (c) it is distributed as it uses local information only, without any prior information about the global network. Even if only a single node is measured, the exact network size can be correctly estimated using a single trajectory. The proposed algorithm has been applied to both linear and nonlinear networks in simulation, illustrating the applicability to real-world physical networks.

Increasingly serious traffic jams and traffic accidents pose threats to the social economy and human life. The lamp semantics of driving is a major way to transmit the driving behavior information between vehicles. The detection and recognition of the vehicle taillights can acquire and understand the taillight semantics, which is of great significance for realizing multi-vehicle behavior interaction and assists driving. It is a challenge to detect taillights and identify the taillight semantics on real traffic road during the day. The main research content of this paper is mainly to establish a neural network to detect vehicles and to complete recognition of the taillights of the preceding vehicle based on image processing. First, the outlines of the preceding vehicles are detected and extracted by using convolutional neural networks. Then, the taillight area in the Hue-Saturation-Value (HSV) color space are extracted and the taillight pairs are detected by correlations of histograms, color and positions. Then the taillight states are identified based on the histogram feature parameters of the taillight image. The detected taillight state of the preceding vehicle is prompted to the driver to reduce traffic accidents caused by the untimely judgement of the driving intention of the preceding vehicle. The experimental results show that this method can accurately identify taillight status during the daytime and can effectively reduce the occurrence of confused judgement caused by light interference.

A novel arginine-rich chitosan (CS) derivates mimicked cell penetration peptides; N-Arginine chitosan (N-Arg-CS) was prepared by two reaction methods involving activated L-arginine and the amine group on the chitosan. FTIR spectra showed that arginine was chemically coupled with CS. Elemental analysis estimated that the degrees of substitution (DS) of arginine in CS were 6%, 31.3% and 61.5%, respectively. The drug adefovir was chosen as model and its permeation flux across excised mice skin was investigated using a Franz diffusion cell. The results showed that the most effective enhancer was 2% (w/v) concentration of 10 kDa N-Arg-CS with 6% DS. At neutral pH, the cumulative amount of adefovir permeated after 12 hours was 2.63 ± 0.19 mg cm−2 which was 5.83-fold more than adefovir aqueous solution. Meanwhile N-Arg-CS was 1.83, 2.22, and 2.45 times more effective than Azone, eucalyptus and peppermint, respectively. The obtained results suggest that N-Arg-CS could be a promising transdermal enhancer.

In this work, char gasification of two coals (i.e., Shenfu bituminous coal and Zunyi anthracite) and a petroleum coke under a steam and CO2 mixture (steam/CO2 partial pressures, 0.025–0.075 MPa; total pressures, 0.100 MPa) and CO2/steam chemisorption of char samples were conducted in a Thermogravimetric Analyzer (TGA). Two conventional kinetic models exhibited difficulties in exactly fitting the experimental data of char–steam–CO2 gasification. Hence, a modified model based on Langmuir–Hinshelwood model and assuming that char–CO2 and char–steam reactions partially shared active sites was proposed and had indicated high accuracy for estimating the interactions in char–steam–CO2 reaction. Moreover, it was found that two new model parameters (respectively characterized as the amount ratio of shared active sites to total active sites in char–CO2 and char–steam reactions) in the modified model hardly varied with gasification conditions, and the results of chemisorption indicate that these two new model parameters mainly depended on the carbon active sites in char samples.

BackgroundAtopic dermatitis (AD) is the most prevalent chronic inflammatory skin disease in children characterized by dermatitis and pruritus. MicroRNAs (miRNAs) have been shown as great potential biomarkers for disease fingerprints to predict prognostics. We aimed to identify miRNA signature from serum and urine for the prognosis of AD patient by genome-wide miRNA profiling analysis.MethodsSerum and urine from 30 children with AD and 28 healthy children were collected and their genome-wide miRNA expression profiles were measured by TaqMan-based array and confirmed by quantitative real-time PCR. Inflammatory factors in serum were detected by Antibody Array System.ResultsmiR-203 and miR-483-5p were significantly up-regulated in serum of children with AD compared with healthy children. The level of miR-483-5p in serum was significantly associated with other atopic conditions, such as rhinitis and/or asthma. However, miR-203 was markedly decreased in urine of children with AD compared with healthy children. Down-regulated miR-203 in urine was significant associated with abnormal level of serum IgE in AD patients. 7 inflammatory factors in serum were altered in children with AD compared with healthy children. Up-regulated miR-203 in serum was significantly associated with increased sTNFRI and sTNFRII.ConclusionsUp-regulated miR-483-5p in serum may be indicative of other atopic conditions in children with AD. Down-regulated miR-203 in urine may serve as a biomarker for the severity of inflammation in children with AD.