• Response of bEnd.3 cells to growing behavior on the graphene oxide film with 2-D grating structure by two-beam laser interference

      Yan, Jin; Cao, Liang; Wang, Lu; Xie, Chengcheng; Liu, Yan; Song, Zhengxun; Xu, Hongmei; Weng, Zhankun; Wang, Zuobin; Li, Li (Springer Science and Business Media Deutschland GmbH, 2021-02-22)
      Graphene (G) and its derivatives are important nanomaterials with potential medical applications for biosensors and implanting biomaterials. The hydrophobicity and surface microstructures of substrates have great influences on the biological and physical properties of the surface-bound cells. In this work, we used the two-beam laser interference (TBLI) technique to prepare a two-dimensional (2-D) grating structure on the surface of graphene oxide (GO) film. We investigated the effect of GO and the GO film with the 2-D grating structure substrates on the growth behavior of rat brain microvascular endothelial (bEnd.3) cells. The results demonstrated that the cell spreading area and the number of surface-bound cells were closely related to the hydrophobicity of the substrate and the presence of oxygen-containing functional groups (OCGs). Due to the interaction of laser and GO, the GO in the interference area was transformed into reduced graphene oxide (RGO). The grating-structured GO film significantly affected the direction of cell spreading and morphology. It has a good application prospect as a scaffold in tissue engineering, and promising applications in the fields that require highly directional growth of cells, such as nerve injury repair, tendon repair and regeneration.
    • A numerical study of the effects of oxy-fuel combustion under homogeneous charge compression ignition regime

      Mobasheri, Raouf; Aitouche, Abdel; Peng, Zhijun; Li, Xiang; Centre de Recherche en Informatique Signal et Automatique de Lille; Junia; University of Bedfordshire (SAGE Publications Ltd, 2021-02-16)
      The European Union (EU) has recently adopted new directives to reduce the level of pollutant emissions from non-road mobile machinery engines. The main scope of project RIVER for which this study is relating is to develop possible solutions to achieve nitrogen-free combustion and zero-carbon emissions in diesel engines. RIVER aims to apply oxy-fuel combustion with Carbon Capture and Storage (CCS) technology to eliminate NOx emissions and to capture and store carbon emissions. As part of this project, a computational fluid dynamic (CFD) analysis has been performed to investigate the effects of oxy-fuel combustion on combustion characteristics and engine operating conditions in a diesel engine under Homogenous Charge Compression Ignition (HCCI) mode. A reduced chemical n-heptane-n-butanol-PAH mechanism which consists of 76 species and 349 reactions has been applied for oxy-fuel HCCI combustion modeling. Different diluent strategies based on the volume fraction of oxygen and a diluent gas has been considered over a wide range of air-fuel equivalence ratios. Variation in the diluent ratio has been achieved by adding different percentages of carbon dioxide for a range from 77 to 83 vol.% in the intake charge. Results show that indicated thermal efficiency (ITE) has reduced from 32.7% to 20.9% as the CO2 concentration has increased from 77% to 83% at low engine loads while it doesn’t bring any remarkable change at high engine loads. It has also found that this technology has brought CO and PM emissions to a very ultra-low level (near zero) while NOx emissions have been completely eliminated.
    • Deep learning for early detection of pathological changes in X-ray bone microstructures: case of osteoarthritis

      Jakaite, Livija; Schetinin, Vitaly; Hladůvka, Jiří; Minaev, Sergey; Ambia, Aziz; Krzanowski, Wojtek; ; University of Bedfordshire; TU Wien; Stavropol State Medical University; et al. (Nature, 2021-01-27)
      Texture features are designed to quantitatively evaluate patterns of spatial distribution of image pixels for purposes of image analysis and interpretation. Unexplained variations in the texture patterns often lead to misinterpretation and undesirable consequences in medical image analysis. In this paper we explore the ability of machine learning (ML) methods to design a radiology test of Osteoarthritis (OA) at early stage when the number of patients’ cases is small. In our experiments we use high-resolution X-ray images of knees in patients which were identified with Kellgren–Lawrence scores progressing from 1. The existing ML methods have provided a limited diagnostic accuracy, whilst the proposed Group Method of Data Handling strategy of Deep Learning has significantly extended the diagnostic test. The comparative experiments demonstrate that the proposed framework using the Zernike-based texture features has significantly improved the diagnostic accuracy on average by 11%. This allows us to conclude that the designed model for early diagnostic of OA will provide more accurate radiology tests, although new study is required when a large number of patients’ cases will be available.
    • Sit-to-stand intention recognition

      Wang, Zuobin; Li, Dayou; Lu, Hang; Qiu, Renxi; Maple, Carsten; University of Bedfordshire; Changchun University of Science and Technology; Warwick University (Springer Science and Business Media Deutschland GmbH, 2021-01-23)
      Sit-to-stand (STS) difficulties are common among elderly because of the decline of their cognitive capabilities and motor functions. The way to help is to encourage them to practice their own functions and to assist only at the point where they need during STS processes. The provision of such support requires the elderly’s intention of standing up to be recognised and the amount of support as well as the moment when the support would be needed to be predicted. The research presented in this paper focuses on intention recognition as it is difficult due to uncertainties existing in STS processes and differences in individual’s biomechanical features. This paper presents fuzzy logic based self-adaptive approach to the recognition of standing up intention from sensor signals that contain the uncertainties.
    • Cross hashing: anonymizing encounters in decentralised contact tracing protocols

      Ali, Junade; Dyo, Vladimir; University of Bedfordshire (2021-01-16)
      During the COVID-19 (SARS-CoV-2) epidemic, Contact Tracing emerged as an essential tool for managing the epidemic. App-based solutions have emerged for Contact Tracing, including a protocol designed by Apple and Google (influenced by an open-source protocol known as DP3T). This protocol contains two well-documented de-anonymisation attacks. Firstly that when someone is marked as having tested positive and their keys are made public, they can be tracked over a large geographic area for 24 hours at a time. Secondly, whilst the app requires a minimum exposure duration to register a contact, there is no cryptographic guarantee for this property. This means an adversary can scan Bluetooth networks and retrospectively find who is infected. We propose a novel ”cross hashing” approach to cryptographically guarantee minimum exposure durations. We further mitigate the 24-hour data exposure of infected individuals and reduce computational time for identifying if a user has been exposed using k-Anonymous buckets of hashes and Private Set Intersection. We empirically demonstrate that this modified protocol can offer like-for-like efficacy to the existing protocol.
    • Editorial: Recent advances in 2020 2nd International Symposium on Big Data and Artificial Intelligence

      Crabbe, M. James C.; Li, Rita Yi Man; Dong, Rebecca Kechen; Manta, Otilia; Comite, Ubaldo; Oxford University; Hong Kong Shue Yan University; University of South Australia; Romanian-American University; University Giustino Fortunato (Association for Computing Machinery., 2021-01-16)
      The 2020 2nd International Symposium on Big Data and Artificial Intelligence was held in Johannesburg, South Africa, from October 15 - 16, 2020. It was organized by IETI, IDSAI, the University of Johannesburg (South Africa) and JRFM, with joint support from the Real Estate and Economics Research Lab of Hong Kong Shue Yan University, the Sustainable Real Estate Research Center of Hong Kong Shue Yan University, Shandong University of Finance and Economics (Mainland China), Guilin University of Technology (Mainland China), IAOE (Austria), the Department of Sport and Physical Education of Hong Kong Baptist University, Rattanakosin International College of Creative Entrepreneurship of Rajamangala University of Technology Rattanakosin (Thailand), Algebra University College (Croatia), and the Center for Financial and Monetary Research of Romanian Academy (Romania), University Giustino Fortunato (Italy). ISBDAI is there to discuss the challenges and possible solutions to these important issues. The conference focused on Artificial Intelligence, Computer Science, Cloud Computing, Big Data, the Internet of Things and the Mobile Web. The participants and speakers were from many countries and universities, including Mainland China, Hong Kong, Thailand, Romania, Italy, Singapore, Austria, Croatia, Australia, UK, Congo King, Portugal and Cyprus. The conference received a record 505 submissions, with 115 papers accepted for presentation. Positive recommendations of at least two reviewers were considered by the conference committees for acceptance of manuscripts. The Editors express a special gratitude to all the Committee Members and ACM-ICPS, who worked so speedily, efficiently, and professionally in support of the conference. Finally, on behalf of the Organizing Committee, we would like to thank all the authors, speakers, and participants for contributing to the success of ISBDAI 2020.
    • Personally identifiable information security in cloud computing

      Feng, Xiaohua; Zhang, Xiangrui (2020-12-18)
      A cyber security application in Personally identifiable information) PII is attracting more and more attention and related to majority people’s everyday activities. The paper is introduced the trends of cyber security in cloud computing and in particular, focus on the responsibility of data privacy, especially in European Union countries. As the impact is on data protection which includes organisation based in the union, or has branches in the union or provides services to the union residents. The paper is also introduced the updated recent development content in our society which caused the impact that we have to deliver ISO standards; for instance, ISO/IEC 27018 and so on. A consequence of the standard is that regular practices of risk assessment need to be carried out in a regular base; such as an annually assessment. Keywords- Data protection, personal privacy, cryptography, cloud computing, cyber security, security policy, Trustworthiness, data service, personally identifiable information (PII), and ISO 27018
    • A study on the effects of tumor-derived exosomes on hepatoma cells and hepatocytes by atomic force microscopy

      Ju, Tuoyu; Wang, Shuwei; Wang, Jiajia; Yang, Fan; Song, Zhengxun; Xu, Hongmei; Chen, Yujuan; Zhang, Jingran; Wang, Zuobin; Changchun University of Science and Technology; et al. (Royal Society of Chemistry, 2020-12-07)
      Tumor-derived exosomes (exos) are closely related to the occurrence, development and treatment of tumors. However, it is not clear how the exosomes affect the physical properties, which lead to the deterioration of the target cells. In this paper, atomic force microscopy (AFM) was used to study the effects of exosomes in HCC-LM3 cells and other cells (SMMC-7721 and HL-7702). The results showed that the HCC-LM3-exos (the exosomes secreted by HCC-LM3 cells, 50 μg mL-1) significantly promoted the proliferation and migration of HCC-LM3 cells. HCC-LM3-exos also promoted the proliferation and migration of SMMC-7721 and HL-7702 cells at 1000 and 1500 μg mL-1, respectively. With an increase in time and concentration, the proliferation effect was more significant. On comparing the mechanical properties of the three types of cells (HCC-LM3, SMMC-7721 and HL-7702 cells), the degradation degree and migration ability of the cells were from high to low in the above order. In turn, the surface roughness of the cells decreased, and adhesion and elastic modulus increased. With an increase in treatment time, surface roughness increased, while adhesion and elastic modulus decreased. These suggested that the HCC-LM3-exos could change the mechanical properties of cells, leading to their deterioration, and enhance their migration and invasion ability. In this paper, the effects of exosomes were analyzed from the perspective of the physical parameters of cells, which provide a new idea to study cancer metastasis and prognosis.
    • Selective anticancer effect of Phellinus linteus on epidermoid cell lines studied by atomic force microscopy: anticancer activity on A431 cancer cells and low toxicity on HaCat normal cells

      Gao, Mingyan; Huang, Yuxi; Hu, Cuihua; Hu, Jing; Wang, Ying; Chen, Yujuan; Song, Guicai; Song, Zhengxun; Wang, Zuobin; Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing; et al. (Institute of Electrical and Electronics Engineers Inc., 2020-12-02)
      The research on the morphological and mechanical properties of single cells has provided a crucial way of understanding the cellular physiology and metabolism. In this study, the selective anticancer effects of Phellinus linteus on A431 and HaCat cells and their morphological and mechanical properties were systematically investigated by atomic force microscopy (AFM). Notably, the cell morphology on the micronano scale was observed under both the physiological environment and immobilization conditions. The significant morphological changes of A431 cells from the flat to spherical shape, the increase of cell height, and the decrease of the particles on the cell membrane were confirmed to be related to the cell apoptosis under the treatment of the Phellinus linteus water extract (PLWE). Moreover, the small morphology variations of HaCat cells showed that the PLWE presented a high anticancer effect on A431 cells but low toxicity on HaCat cells, which indicated a potential cell selectivity between cancer and normal cells. This work proved that Phellinus linteus could be used as a potential candidate for selective anticancer treatments.
    • Entity-aware capsule network for multi-class classification of big data: a deep learning approach

      Jaiswal, Amit Kumar; Tiwari, Prayag; Garg, Sahil; Hossain, M. Shamim; University of Bedfordshire; University of Padova; École de technologie supérieure, Montréal; King Saud University (Elsevier B.V., 2020-11-20)
      Named entity recognition (NER) is one of the most challenging natural language processing (NLP) tasks, as its performance is related to constantly evolving languages and dependency on expert (human) annotation. The diverse and dynamic content on the web significantly raises the need for a more generalized approach—one that is capable of correctly classifying terms in a corpus and feeding subsequent NLP tasks, such as machine translation, query expansion, and many other applications. Although extensively researched in recent times, the variety of public corpora available nowadays provides room for new and more accurate methods to tackle the NER problem. This paper presents a novel method that uses deep learning techniques based on the capsule network architecture for predicting entities in a corpus. This type of network groups neurons into so-called capsules to detect specific features of an object without reducing the original input unlike convolutional neural networks and their ‘max-pooling’ strategy. Our extensive evaluation on several benchmarked datasets demonstrates how competitive our method is in comparison with state-of-the-art techniques and how the usage of the proposed architecture may represent a significant benefit to further NLP tasks, especially in cases where experts are needed. Also, we explore NER using a theoretical framework that leverages big data for security. For the sake of reproducibility, we make the codebase open-source.
    • Durotaxis behavior of bEnd.3 cells on soft substrate with patterned platinum nanoparticle array

      Wu, Xiaomin; Li, Li; Lei, Zecheng; Yang, Fan; Liu, Ri; Wang, Lu; Zhu, Xinyao; Wang, Zuobin; Changchun University of Science and Technology; University of Bedfordshire; et al. (Springer Science and Business Media, 2020-11-17)
      The directional arrangement of cells has crucial effect in tissue engineering fields such as wound healing and scar repair. Studies have shown that continuous nanostructures have directional regulatory effect on cells, but whether discontinuous nanostructures have the same regulatory effect on cells is also worthy of further study. Here, a series of discontinuous platinum nanoparticles (PtNPs) patterned on the surface of PDMS (PtNPs-PDMS&Glass) and glass (PtNPs-Glass) substrates were developed to investigate the effect on bEnd.3 cell durotaxis. The laser interference lithography and nanotransfer printing method were employed to fabricate the substrates. It was found that about 80% cells orderly arranged on the PtNPs-PDMS&Glass substrate, but only 20% cells orderly arrangement on the PtNPs-Glass substrate, and the number of cells on the PtNPs-PDMS&Glass substrate was five times more than that on the PDMS coated glass substrate (PDMS&Glass). The results suggested that patterning PtNPs on the PDMS substrate not only provided the topographical guidance for cells just like continuous nanostructures, but also promoted cell adhesion and growth. In addition, an improved whole cell coupling model was used to investigate and explain the cell durotaxis from the perspective of mechanism. These findings show the possibility of discontinuous nanostructures in regulating cell arrangement, and offer a useful method for the design of biological functional substrate, as well as help to understand the mechanism of cell durotaxis.
    • Counting calories without wearables: device-free human energy expenditure estimation

      Rahaman, Habibur; Dyo, Vladimir; University of Bedfordshire (2020-11-16)
      Maintaining certain physical activity levels is important to prevent or delay the onset of many medical conditions such as diabetes, or mental health disorders. Traditional calorie estimation methods require wearing devices, such as pedometers, smart watches or smart bracelets, which continuously monitor user activity and estimate the energy expenditure. However, wearable devices may not be suitable for some patients due to the need for periodic maintenance, frequent recharging and having to wear it all the time. In this paper we investigate a feasibility of a device- free human energy expenditure estimation based on RF-sensing, which recognises coarse-grained user activity, such as walking, standing, sitting or resting by monitoring the impact of a person’s activity on ambient wireless links. The calorie estimation is then based on Metabolic Equivalent concept that expresses the energy cost of an activity as a multiple of a person’s basal metabolic rate using Harrison-Benedict model. The experimental evaluation using low cost IEEE 802.15.4 transceivers demonstrated that the approach estimated energy expenditure within an indoor environment within 7.4% to 41.2% range when compared to a FitBit Blaze bracelet.
    • Artificial intelligence cyber security strategy

      Feng, Xiaohua; Feng, Yunzhong; Dawam, Edward Swarlat; University of Bedfordshire; Hebei Normal University (IEEE, 2020-11-11)
      Nowadays, STEM (science, technology, engineering and mathematics) have never been treated so seriously before. Artificial Intelligence (AI) has played an important role currently in STEM. Under the 2020 COVID-19 pandemic crisis, coronavirus disease across over the world we are living in. Every government seek advices from scientist before making their strategic plan. Most of countries collect data from hospitals (and care home and so on in the society), carried out data analysis, using formula to make some AI models, to predict the potential development patterns, in order to make their government strategy. AI security become essential. If a security attack make the pattern wrong, the model is not a true prediction, that could result in thousands life loss. The potential consequence of this non-accurate forecast would be even worse. Therefore, take security into account during the forecast AI modelling, step-by-step data governance, will be significant. Cyber security should be applied during this kind of prediction process using AI deep learning technology and so on. Some in-depth discussion will follow.AI security impact is a principle concern in the world. It is also significant for both nature science and social science researchers to consider in the future. In particular, because many services are running on online devices, security defenses are essential. The results should have properly data governance with security. AI security strategy should be up to the top priority to influence governments and their citizens in the world. AI security will help governments’ strategy makers to work reasonably balancing between technologies, socially and politics. In this paper, strategy related challenges of AI and Security will be discussed, along with suggestions AI cyber security and politics trade-off consideration from an initial planning stage to its near future further development.
    • Smart city lane detection for autonomous vehicle

      Dawam, Edward Swarlat; Feng, Xiaohua; University of Bedfordshire (IEEE, 2020-11-11)
      One of AI branch, Computer Vision-based recognition systems is necessary for security in Autonomous Vehicles (AVs). Traffic sign recognition systems are popularly used in AVs because it ensures driver safety and decrease vehicles accidents on roads. However, the inability of AVs to accurately detect road signs and pedestrian behaviour has led to road crashes and even death in recent times. Additionally, as cities become smarter, the traditional traffic signs dataset will change considerably, as theGoogle, 2020se vehicles and city infrastructure introduce modern facilities into their operation. In this paper, we introduce a computer vision based road surface marking recognition system to serve as an added layer of data source from which AVs will make decisions. We trained our detector using YOLOv3 running in the cloud to detect 25 classes of Road surface markings using over 25,000 images. The results of our experiment demonstrate a robust performance in terms of the accuracy and speed of detection. The results of which will consolidate the traffic sign recognition system, thereby ensuring more reliability and safety in AVs decision making. New algorithm using Deep Learning technology in Artificial intelligence (AI) application is implemented and tested successfully.
    • Battery-assisted electric vehicle charging: data driven performance analysis

      Ali, Junade; Dyo, Vladimir; Zhang, Sijing (2020-11-10)
      As the number of electric vehicles rapidly increases, their peak demand on the grid becomes one of the major challenges. A battery-assisted charging concept has emerged recently, which allows to accumulate energy during off-peak hours and in-between charging sessions to boost-charge the vehicle at a higher rate than available from the grid. While prior research focused on the design and implementation aspects of battery- assisted charging, its impact at large geographical scales remains largely unexplored. In this paper we analyse to which extent the battery-assisted charging can replace high-speed chargers using a dataset of over 3 million EV charging sessions in both domestic and public setting in the UK. We first develop a discrete-event EV charge model that takes into account battery capacity, grid supply capacity and power output among other parameters. We then run simulations to evaluate the battery-assisted charging performance in terms of delivered energy, charging time and parity with conventional high-speed chargers. The results indicate that in domestic settings battery-assisted charging provides 98% performance parity of high-speed chargers from a standard 3 kW grid connection with a single battery pack. For non-domestic settings, the battery-assisted chargers can provide 92% and 99% performance parity of high-speed chargers with 10 battery packs using 3kW and 7kW grid supply respectively.
    • Design optimization of resource allocation in OFDMA-based cognitive radio-enabled Internet of Vehicles (IoVs)

      Eze, Joy C.; Zhang, Sijing; Liu, Enjie; Eze, Elias Chinedum; ; University of Bedfordshire (MDPI, 2020-11-09)
      Joint optimal subcarrier and transmit power allocation with QoS guarantee for enhanced packet transmission over Cognitive Radio (CR)-Internet of Vehicles (IoVs) is a challenge. This open issue is considered in this paper. A novel SNBS-based wireless radio resource scheduling scheme in OFDMA CR-IoV network systems is proposed. This novel scheduler is termed the SNBS OFDMA-based overlay CR-Assisted Vehicular NETwork (SNO-CRAVNET) scheduling scheme. It is proposed for efficient joint transmit power and subcarrier allocation for dynamic spectral resource access in cellular OFDMA-based overlay CRAVNs in clusters. The objectives of the optimization model applied in this study include (1) maximization of the overall system throughput of the CR-IoV system, (2) avoiding harmful interference of transmissions of the shared channels’ licensed owners (or primary users (PUs)), (3) guaranteeing the proportional fairness and minimum data-rate requirement of each CR vehicular secondary user (CRV-SU), and (4) ensuring efficient transmit power allocation amongst CRV-SUs. Furthermore, a novel approach which uses Lambert-W function characteristics is introduced. Closed-form analytical solutions were obtained by applying time-sharing variable transformation. Finally, a low-complexity algorithm was developed. This algorithm overcame the iterative processes associated with searching for the optimal solution numerically through iterative programming methods. Theoretical analysis and simulation results demonstrated that, under similar conditions, the proposed solutions outperformed the reference scheduler schemes. In comparison to other scheduling schemes that are fairness-considerate, the SNO-CRAVNET scheme achieved a significantly higher overall average throughput gain. Similarly, the proposed time-sharing SNO-CRAVNET allocation based on the reformulated convex optimization problem is shown to be capable of achieving up to 99.987% for the average of the total theoretical capacity.
    • Dynamic causality knowledge graph generation for supporting the chatbot healthcare system

      Yu, Hong Qing; University of Bedfordshire (Springer Science and Business Media Deutschland GmbH, 2020-10-31)
      With recent viruses across the world affecting millions and millions of people, the self-healthcare information systems show an important role in helping individuals to understand the risks, self-assessment, and self-educating to avoid being affected. In addition, self-healthcare information systems can perform more interactive tasks to effectively assist the treatment process and health condition management. Currently, the technologies used in such kind of systems are mostly based on text crawling from website resources such as text-searching and blog-based crowdsourcing applications. In this research paper, we introduce a novel Artificial Intelligence (AI) framework to support interactive and causality reasoning for a Chatbot application. The Chatbot will interact with the user to provide self-healthcare education and self-assessment (condition prediction). The framework is a combination of Natural Language Processing (NLP) and Knowledge Graph (KG) technologies with added causality and probability (uncertainty) properties to original Description Logic. This novel framework can generate causal knowledge probability neural networks to perform question answering and condition prediction tasks. The experimental results from a prototype showed strong positive feedback. The paper also identified remaining limitations and future research directions.
    • Atomic force microscopy imaging of the G-banding process of chromosomes

      Wang, Bowei; Li, Jiani; Dong, Jianjun; Yang, Fan; Qu, Kaige; Wang, Ying; Zhang, Jingran; Song, Zhengxun; Hu, Hongmei; Wang, Zuobin; et al. (Springer Science and Business Media, 2020-10-24)
      The chromosome is an important genetic material carrier in living individuals and the spatial conformation (mainly referring to the chromosomal structure, quantity, centromere position and other morphological information) may be abnormal or mutated. Thus, it may generate a high possibility to cause diseases. Generally, the karyotype of chromosome G-bands is detected and analyzed using an optical microscope. However, it is difficult to detect the G-band structures for traditional optical microscopes on the nanometer scale. Herein, we have studied the detection method of chromosome G-band samples by atomic force microscopy (AFM) imaging. The structures of chromosome G-banding are studied with different trypsin treatment durations. The experiment result shows that the treatment duration of 20 s is the best time to form G-band structures. The AFM images show the structures of chromosome G-bands which cannot be observed under an optical microscope. This work provides a new way for the detection and diagnosis of chromosome diseases on the nanometer scale.
    • Dynamic mechanics of HK-2 cell reaction to HG stimulation studied by atomic force microscopy

      Yang, Fan; Wang, Jiajia; Qu, Kaige; Yang, Xue; Liu, Chuanzhi; Wang, Ying; Song, Zhengxun; Xu, Hongmei; Chen, Yujuan; Wang, Zuobin; et al. (Royal Society of Chemistry, 2020-10-02)
      Renal tubular cell injury by exposure to high glucose (HG) stimulation mainly accounts for diabetic nephropathy (DN). To understand the mechanism of injury by HG, quantitative characterization has commonly focused on the cells that are already impaired, which ignores the signals for the process of being injured. In this study, the architecture and morphology of HK-2 cells were observed dynamically by multiple imaging methods. AFM (atomic force microscopy)-based single-cell force spectroscopy was employed to investigate the dynamic mechanics quantitatively. The results showed that the Young's modulus increased continuously from 2.44 kPa up to 4.15 kPa for the whole period of injury by HG, while the surface adhesion decreased from 2.43 nN to 1.63 nN between 12 h and 72 h. In addition, the actin filaments of HK-2 cells exposed to HG depolymerized and then nucleated with increasing Young's modulus. The absence of cell pseudopodia coincided with the reduced cell adhesion, strongly suggesting close relationships between the cell architecture, morphology and mechanical properties. Furthermore, the stages of cell reactions were identified and assessed. Overall, the dynamic mechanics of the cells facilitate the identification of injured cells and the assessment of the degree of injury for accurate diagnoses and treatments.
    • Security challenges in cyber systems

      Safdar, Ghazanfar Ali; Kalsoom, Tahera; Ramzan, Naeem; University of Bedfordshire; University of the West of Scotland (Institute of Electrical and Electronics Engineers Inc., 2020-09-29)
      CPS (Cyber-Physical Systems) is proposed by the NSF (National Scientific Foundation) to describe a type of necessities which conglomerates hardware and software components and being the next step in development of embedded systems. CPS includes a wide range of research topics from signal processing to data analysis. This paper contains a brief review of the basic infrastructure for CPS including smart objects and network aspects in relation to TCP/IP stack. As CPS reflect the processes of the physical environment onto the cyber space, virtualisation as important tool for abstraction plays crucial role in CPS. In this context paper presents the challenges associated with mobility and vritualisation; accordingly, three main types of virtualisation, namely network, devices and applications virtualisation are presented in the paper. The main focus of the paper is made on security. Different threats, attack types and possible consequences are discussed as well as analysis of various approaches to cope with existing threats is introduced. Furthermore, needs and requirements for safety-critical CPS are reviewed.