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Given that online higher education shows no sign of abating during the COVID-19 pandemic in Australia, understanding the nature of e-learning and e-learners in this particular setting is much needed. However, little is known about the e-learning approaches that international students apply, or about how they experience the process of e-learning. This article is a critical reflection on the misalignment between an international student’s (Author 1) e-learning behaviours and the expectation of online education. It outlines the autoethnographic method and employs self-study to explore why Author 1 behaves in a certain way with particular reference made to the Biggs’ 3P model. In so doing, the study attempts to shed light on the values and interests of international students that have been silenced in the discourse of e-learning design in Australian universities. With the aim of obtaining a profound insight into the effectiveness of e-learning, the present study challenges the notion that the virtual university is a means of achieving educational equality; it suggests the potential of online education in undermining the social inclusion agenda of internationalised universities. The findings show that while the participant could engage with the curriculum to some extent, there are signs of disconnection, isolation and emotional instability associated with the establishment and development of the e-learning environment. Illustrations of these emerging issues could help educators better understand the downside of e-learning and e-practice by identifying various influential elements, including individuals’ socioeconomic status, cultural heritage and environmental learning settings. The study points out that international students’ education outcomes could be compromised, and expectations could be unfulfilled via e-learning. Thus, there is a further need to prepare learners for e-learning environments.

Background COVID-19 patients develop hypolipidemia. However, it is unknown whether lipid levels have improved and there are potential sequlae in recovered patients. Objective In this follow-up study, we evaluated serum lipidemia and various physiopathological laboratory values in recovered patients. Methods A 3–6 month follow-up study was performed between June 15 and September 3, 2020, to examine serum levels of laboratory values in 107 discharged COVID-19 patients (mild = 59; severe/critical = 48; diagnoses on admission). Sixty-one patients had a revisit chest CT scan. A Wilcoxon signed-rank test was used to analyze changes in laboratory values at admission and follow-up. Results LDL-c and HDL-c levels were significantly higher at follow-up than at admission in severe/critical cases (p p p p p Conclusions Improvements of LDL-c, HDL-c, liver functions, and incomplete resolution of lung lesions were observed at 3–6 month follow-up for recovered patients, indicating that a long-term recovery process could be required and the development of sequelae such as pulmonary fibrosis could be expected in some patients.

Anthocyans, containing anthocyanins and anthocyanidins, play a crucial role in preventing and treating inflammatory bowel disease (IBD). Most anthocyanins and their basic elements, namely anthocyanidins have been recognized for the effective treatment of IBD, but the key biomarkers of anthocyan-treated IBD remain unclear. In this study, a bioinformatics analysis based on network pharmacology was performed to demonstrate the core-targets, biological functions, and signaling pathways of most common anthocyanidins that existed in anthocyans to reveal their potential or major mechanisms. The network pharmacology of the multi-target drug molecular design with specific signal nodes was selected, which was used to analyse core targets and complete the bioinformatics analysis of core targets. The network assays indicated 44 common targeted genes, 5 of which were core targets of both six most common anthocyanidins and IBD. These 44 common targets related to major signaling mechanisms of the six most common anthocyanidins in IBD may involve following processes: promotion of intracellular metabolism and proliferation, inhibition of cell necrosis, anti-inflammation and regulation of intestinal epithelial survival mainly via pathways such as, the EGFR tyrosine kinase inhibitor resistance pathway, platelet activation, microRNAs in cancer, arachidonic acid metabolism and the cGMP-PKG signaling pathway. Thus, our findings may provide other molecular details about anthocyans in the treatment of IBD and contribute towards the use of anthocyanidins, which will be meaningful shedding light on the action mechanisms of anthocyanidins in treating IBD.

Covert channels have been an effective means for leaking confidential information across security domains and numerous studies are available on typical covert channels attacks and defenses. Existing covert channel threat restriction solutions are based on the threat estimation criteria of covert channels such as capacity, accuracy, and short messages which are effective in evaluating the information transmission ability of a covert (storage) channel. However, these criteria cannot comprehensively reflect the key factors in the communication process such as shared resources and synchronization and therefore are unable to evaluate covertness and complexity of increasingly upgraded covert storage channels. As a solution, the anti-detection criterion was introduced to eliminate these limitations of cover channels. Though effective, most threat restriction techniques inevitably incur high performance overhead and hence become impractical. In this work, we avoid such overheads and present a restriction algorithm based on the anti-detection criterion to restrict threats that are associated with covert storage channels in virtual machines while maintaining the resource efficiency of the systems. Experimental evaluation shows that our proposed solution is able to counter covert storage channel attacks in an effective manner. Compared with Pump, a well-known traditional restriction algorithm used in practical systems, our solution significantly reduces the system overhead.

The tumor microenvironment (TME) is a complex ecosystem, which includes many different types of cells, abnormal vascular systems, and immunosuppressive cytokines. TME serves an important function in tumor tolerance and escapes from immune surveillance leading to tumor progression. Indeed, there is increasing evidence that gut microbiome is associated with cancer in a variety of ways, as specific microbial signatures are known to promote cancer development and influence safety, tolerability, and efficacy of therapies. Studies over the past five years have shown that the composition of the intestinal microbiota has a significant impact on the efficacy of anticancer immunosurveillance, which contribute to the therapeutic activity of cancer immunotherapies based on targeting cytotoxic T lymphocyte protein 4 (CTLA-4) or programmed cell death protein 1 (PD-1)–programmed cell death 1 ligand 1 (PD-L1) axis. In this review, we mainly discuss the impact of TME on cancer and immunotherapy through immune-related mechanisms. We subsequently discuss the influence of gut microbiota and its metabolites on the host immune system and the formation of TME. In addition, this review also summarizes the latest research on the role of gut microbiota in cancer immunotherapy.

Background: COVID-19 patients develop hypolipidemia. However, it is unknown whether lipid levels have improved in recovered patients.Objective: In this follow-up study, we evaluated serum lipidemia and other physiopathological laboratory values in recovered patients.Methods: A 3–6 month follow-up study was performed between June 15 and September 3, 2020, to examine serum levels of laboratory values in 107 discharged COVID-19 patients (mild = 59; severe/critical = 48; diagnoses on admission). 61 patients had a revisit chest CT scan. A Wilcoxon signed-rank test was used to analyze changes in laboratory values at admission and follow-up.Results: LDL-c and HDL-c levels were significantly higher at follow-up than at admission in severe/critical cases (p < 0.05). LDL-c levels were significantly higher at follow-up than at admission in mild cases (p < 0.05). With adjustment of the factor of traditional Chinese medicine, LDL-c and HDL-c levels were significantly improved at follow-up than at admission in severe/critical cases (p < 0.05). Coagulation and liver laboratory values were significantly lower at follow-up than at admission for patients (p < 0.05). Increases in HDL-c significantly correlated with increases in numbers of white blood cells (pp < 0.05) during patients’ recovery. Residue lesions were observed in CT images in 69% (42 of 61) of follow-up patients.Conclusions: Improvements of LDL-c, HDL-c and incomplete absorption of lung lesions were observed at 3–6 month follow-up for recovered patients, indicating that a long-term recovery process could be required.

Background Autism spectrum disorder (ASD) is characterised by many of features including problem in social interactions, different ways of learning, some children showing a keen interest in specific subjects, inclination to routines, challenges in typical communication, and particular ways of processing sensory information. Early intervention and suitable supports for these children may make a significant contribution to their development. However, considerable difficulties have been encountered in the screening and diagnosis of ASD. The literature has indicated that certain retinal features are significantly associated with ASD. In this study, we investigated the use of machine learning approaches on retinal images to further enhance the classification accuracy. Methods Forty-six ASD participants were recruited from three special needs schools and 24 normal control were recruited from the community. Among them, 23 age-gender matched ASD and normal control participant-pairs were constructed for the primary analysis. All retinal images were captured using a nonmydriatic fundus camera. Automatic retinal image analysis (ARIA) methodology applying machine-learning technology was used to optimise the information of the retina to develop a classification model for ASD. The model's validity was then assessed using a 10-fold cross-validation approach to assess its validity. Findings The sensitivity and specificity were 95.7% (95% CI 76.0%, 99.8%) and 91.3% (95% CI 70.5%, 98.5%) respectively. The area under the ROC curve was 0.974 (95% CI 0.934, 1.000); however, it was noted that the specificity for female participants might not be as high as that for male participants. Interpretation Because ARIA is a fully automatic cloud-based algorithm and relies only on retinal images, it can be used as a risk assessment tool for ASD screening. Further diagnosis and confirmation can then be made by professionals, and potential treatment may be provided at a relatively early stage.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) is the causative organism for the pandemic of coronavirus disease 2019 (COVID-19) that

With the development of urbanization and information technology, the efficiency and comfort of the office environment have become the two major concerns of the office market and users. This article analyzes the psychological characteristics and emotional needs of young women, studies the user’s sitting posture, determines the structure and size of the chair based on ergonomics, improves the chair structure adjustment form, and approaches the aesthetic requirements of young women through design methods such as symbolic metaphors on the appearance. The design of female office chairs based on ergonomics and emotional design is conducive to improving people’s work efficiency and work comfort, thereby enhancing users’ happiness in life.

Object detectors based on CNN are now able to achieve satisfactory accuracy, but their ability to deal with some targets with geometric deformation or occlusion is often poor. This is largely due to the fixed geometric structure of the convolution kernel and the single inflexible network structure. In our work, we use dual branch parallel processing to extract the different features of the target area to coordinate the prediction. To further enhance the performance of the network, this study rebuilds the feature extraction module. Finally, our detector learns to adapt to a variety of different shapes and sizes. The proposed method achieves up to 81.76% mAP on the Pascal VOC2007 dataset and 79.6% mAP on the Pascal VOC2012 dataset.

Covert channels are widely considered as a major risk of information leakage in various operating systems, such as desktop, cloud, and mobile systems. The existing works of modeling covert channels have mainly focused on using finite state machines (FSMs) and their transforms to describe the process of covert channel transmission. However, a FSM is rather an abstract model, where information about the shared resource, synchronization, and encoding/decoding cannot be presented in the model, making it difficult for researchers to realize and analyze the covert channels. In this paper, we use the high-level Petri Nets (HLPN) to model the structural and behavioral properties of covert channels. We use the HLPN to model the classic covert channel protocol. Moreover, the results from the analysis of the HLPN model are used to highlight the major shortcomings and interferences in the protocol. Furthermore, we propose two new covert channel models, namely: (a) two channel transmission protocol (TCTP) model and (b) self-adaptive protocol (SAP) model. The TCTP model circumvents the mutual inferences in encoding and synchronization operations; whereas the SAP model uses sleeping time and redundancy check to ensure correct transmission in an environment with strong noise. To demonstrate the correctness and usability of our proposed models in heterogeneous environments, we implement the TCTP and SAP in three different systems: (a) Linux, (b) Xen, and (c) Fiasco.OC. Our implementation also indicates the practicability of the models in heterogeneous, scalable and flexible environments.

Although Indoleamine 2,3-dioxygenase (IDO), tryptophan-2,3-dioxygenase (TDO), and aryl hydrocarbon receptor (AHR) are involved in cancer immune escape, their prognostic impact on diffuse large B-cell lymphoma (DLBCL) is unknown. To examine the prognostic impact of IDO, TDO, and AHR on patients with DLBCL. This was a retrospective study on treatment-naïve patients with newly diagnosed DLBCL at the Henan Province People's Hospital between 01/2012 and 06/2015. Patients with inflammatory reactive lymph nodes were included as controls. All cases were reviewed by 2 pathologists. IDO, TDO, and AHR positivity was determined through immunochemistry. Survival was examined using the Kaplan-Meier method and multivariable Cox analyses. The positive expression of TDO (50.0% vs 16.7%, P = .005) and AHR (60.0% vs 8.3%, P

Data centers provides both the applications, systems software and the hardware as services over the Internet, which is named cloud computing [1–3]. It is core infrastructure of cloud computing, supporting dynamic deployment and elastic resource management. With the powerful computing and storing capabilities, cloud computing has become increasingly popular [4, 5]. The fundamental mechanism of cloud is virtualization which allows virtual machines (VM) instantiate stand-alone operating systems on demand based on a software layer called virtual machine monitor (VMM) or hypervisor [6]. Although the virtualization technology provides strong isolation for the cloud, security and privacy are always the open problems [7]. Some of the problems are essentially traditional web application and data-hosting ones, e.g., phishing, downtime, data loss, and password weakness. One of the new problems introduced by the shared environment to cloud computing is the covert channel attack [8]. By this way, information is leaked from the data centers and meanwhile the security provided by isolation is breaken down [9, 10].

Continuing bug reports and exploits in hyper visors indicate that hyper visors face similar integrity threats as tradition software. Previous approaches to protect a hyper visor that utilize hardware features are not easy to be extended. Besides, they mainly focus on code or control data integrity, without pay much attention to protecting non-control data. In this paper, we present Hyper Verify, a novel architecture to monitor hyper visor non-control data using a trusted VM. Since a VM cannot directly access a hyper visor's memory, Hyper Verify programs a popular device driver to read the hyper visor's hardware state in the trusted VM. Then a memory analysis library is used to translate the low-level hardware state into the high level hyper visor context. Several monitoring processes use such context to monitor hyper visor non-control data integrity. Each of the processes is responsible for monitoring one kind of non-control data. It is flexible for Hyper Verify to support monitoring new kinds of data structure. The experimental evaluation of our prototype shows that Hyper Verify incurs at most 4% performance overhead to end users.

Cloud computing security has become the focus in information security, where much attention has been drawn to the user privacy leakage. Although isolation and some other security policies have been provided to protect the security of cloud computing, confidential information can be still stolen by timing channels without being detected. In this paper, a new method named XenPump is presented aiming to mitigate the threat of the timing channels by adding latency. XenPump is designed as a module located in hypervisor, monitoring the hypercalls used by the timing channels and adding latencies to lower the threat into an acceptable level. The prototype of XenPump has been implemented in Xen virtualization platform, and the performance is evaluated by the shared memory based timing channel. The experiment results show that XenPump can mitigate the threat of the timing channel by interrupting both the capacity and transmission accuracy. It is believed that after small extension, XenPump can mitigate the incoming timing channels.

In this paper, I develop a new approach to improve the Contradiction Detection (CD) incorporating the additional information from the context, which aimed at improving the CD precision. My work focuses on using additional context from the sentences or pages to disambiguate the event/entity. I give a new method but the probability to do the ambiguity examination. What’s more, I develop a CD system which inspired by AUCONTRAIRE CD system and give the concrete steps to achieve the goal.