Showing papers by "Teesside University published in 2020"

Laparoscopic surgery for endometriosis

Abstract: Background Endometriosis is the presence in inappropriate sites of tissue that normally lines the uterus It can cause pain and subfertility Different treatments for endometriosis are available, one of which is laparoscopic ('key hole') surgery, performed to remove visible areas of endometriosis Cochrane review authors assessed the evidence on the use of laparoscopic surgery to treat pain and fertility problems in women with endometriosis Laparoscopic surgical techniques include ablation, which means destruction of a lesion (for example by burning), and excision, which means cutting a lesion out Study characteristics We included 10 randomised controlled trials (involving 973 participants) They were conducted in Australia, Canada, Egypt, Iran and the United Kingdom Most compared laparoscopic ablation or excision versus diagnostic laparoscopy only Four of the 10 studies reported their source of funding The evidence was current to July 2013 Key results We found that laparoscopic surgery may be of benefit in treating overall pain and subfertility associated with mild to moderate endometriosis Laparoscopic excision and ablation were similarly effective in relieving pain, although this result came from a single study There was insufficient evidence on adverse events to allow any conclusions to be drawn regarding safety

HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images.

Abstract: Recently, a novel virus called COVID-19 has pervasive worldwide, starting from China and moving to all the world to eliminate a lot of persons. Many attempts have been experimented to identify the infection with COVID-19. The X-ray images were one of the attempts to detect the influence of COVID-19 on the infected persons from involving those experiments. According to the X-ray analysis, bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities can be caused by COVID-19 - sometimes with a rounded morphology and a peripheral lung distribution. But unfortunately, the specification or if the person infected with COVID-19 or not is so hard under the X-ray images. X-ray images could be classified using the machine learning techniques to specify if the person infected severely, mild, or not infected. To improve the classification accuracy of the machine learning, the region of interest within the image that contains the features of COVID-19 must be extracted. This problem is called the image segmentation problem (ISP). Many techniques have been proposed to overcome ISP. The most commonly used technique due to its simplicity, speed, and accuracy are threshold-based segmentation. This paper proposes a new hybrid approach based on the thresholding technique to overcome ISP for COVID-19 chest X-ray images by integrating a novel meta-heuristic algorithm known as a slime mold algorithm (SMA) with the whale optimization algorithm to maximize the Kapur's entropy. The performance of integrated SMA has been evaluated on 12 chest X-ray images with threshold levels up to 30 and compared with five algorithms: Lshade algorithm, whale optimization algorithm (WOA), FireFly algorithm (FFA), Harris-hawks algorithm (HHA), salp swarm algorithms (SSA), and the standard SMA. The experimental results demonstrate that the proposed algorithm outperforms SMA under Kapur's entropy for all the metrics used and the standard SMA could perform better than the other algorithms in the comparison under all the metrics.

Teaching in the Age of Covid-19

Abstract: A collection of 84 author's testimonies and workspace photographs between 18 March and 5 May 2020

Midkine (MDK) growth factor: a key player in cancer progression and a promising therapeutic target.

Abstract: Midkine is a heparin-binding growth factor, originally reported as the product of a retinoic acid-responsive gene during embryogenesis, but currently viewed as a multifaceted factor contributing to both normal tissue homeostasis and disease development. Midkine is abnormally expressed at high levels in various human malignancies and acts as a mediator for the acquisition of critical hallmarks of cancer, including cell growth, survival, metastasis, migration, and angiogenesis. Several studies have investigated the role of midkine as a cancer biomarker for the detection, prognosis, and management of cancer, as well as for monitoring the response to cancer treatment. Moreover, several efforts are also being made to elucidate its underlying mechanisms in therapeutic resistance and immunomodulation within the tumor microenvironment. We hereby summarize the current knowledge on midkine expression and function in cancer development and progression, and highlight its promising potential as a cancer biomarker and as a future therapeutic target in personalized cancer medicine.

Piezoelectric and pyroelectric effects induced by interface polar symmetry

Abstract: Interfaces in heterostructures have been a key point of interest in condensed-matter physics for decades owing to a plethora of distinctive phenomena-such as rectification1, the photovoltaic effect2, the quantum Hall effect3 and high-temperature superconductivity4-and their critical roles in present-day technical devices However, the symmetry modulation at interfaces and the resultant effects have been largely overlooked Here we show that a built-in electric field that originates from band bending at heterostructure interfaces induces polar symmetry therein that results in emergent functionalities, including piezoelectricity and pyroelectricity, even though the component materials are centrosymmetric We study classic interfaces-namely, Schottky junctions-formed by noble metal and centrosymmetric semiconductors, including niobium-doped strontium titanium oxide crystals, niobium-doped titanium dioxide crystals, niobium-doped barium strontium titanium oxide ceramics, and silicon The built-in electric field in the depletion region induces polar structures in the semiconductors and generates substantial piezoelectric and pyroelectric effects In particular, the pyroelectric coefficient and figure of merit of the interface are over one order of magnitude larger than those of conventional bulk polar materials Our study enriches the functionalities of heterostructure interfaces, offering a distinctive approach to realizing energy transduction beyond the conventional limitation imposed by intrinsic symmetry

Antiviral Functional Foods and Exercise Lifestyle Prevention of Coronavirus.

Abstract: Novel coronavirus (COVID-19) is causing global mortality and lockdown burdens. A compromised immune system is a known risk factor for all viral influenza infections. Functional foods optimize the immune system capacity to prevent and control pathogenic viral infections, while physical activity augments such protective benefits. Exercise enhances innate and adaptive immune systems through acute, transient, and long-term adaptations to physical activity in a dose-response relationship. Functional foods prevention of non-communicable disease can be translated into protecting against respiratory viral infections and COVID-19. Functional foods and nutraceuticals within popular diets contain immune-boosting nutraceuticals, polyphenols, terpenoids, flavonoids, alkaloids, sterols, pigments, unsaturated fatty-acids, micronutrient vitamins and minerals, including vitamin A, B6, B12, C, D, E, and folate, and trace elements, including zinc, iron, selenium, magnesium, and copper. Foods with antiviral properties include fruits, vegetables, fermented foods and probiotics, olive oil, fish, nuts and seeds, herbs, roots, fungi, amino acids, peptides, and cyclotides. Regular moderate exercise may contribute to reduce viral risk and enhance sleep quality during quarantine, in combination with appropriate dietary habits and functional foods. Lifestyle and appropriate nutrition with functional compounds may offer further antiviral approaches for public health.

A novel equilibrium optimization algorithm for multi-thresholding image segmentation problems

Abstract: Image segmentation is considered a crucial step required for image analysis and research. Many techniques have been proposed to resolve the existing problems and improve the quality of research, such as region-based, threshold-based, edge-based, and feature-based clustering in the literature. The researchers have moved toward using the threshold technique due to the ease of use for image segmentation. To find the optimal threshold value for a grayscale image, we improved and used a novel meta-heuristic equilibrium algorithm to resolve this scientific problem. Additionally, our improved algorithm has the ability to enhance the accuracy of the segmented image for research analysis with a significant threshold level. The performance of our algorithm is compared with seven other algorithms like whale optimization algorithm, bat algorithm, sine–cosine algorithm, salp swarm algorithm, Harris hawks algorithm, crow search algorithm, and particle swarm optimization. Based on a set of well-known test images taken from Berkeley Segmentation Dataset, the performance evaluation of our algorithm and well-known algorithms described above has been conducted and compared. According to the independent results and analysis of each algorithm, our algorithm can outperform all other algorithms in fitness values, peak signal-to-noise ratio metric, structured similarity index metric, maximum absolute error, and signal-to-noise ratio. However, our algorithm cannot outperform some algorithms in standard deviation values and central processing unit time with the large threshold levels observed.

First principles study of electronic and nonlinear optical properties of A–D–π–A and D–A–D–π–A configured compounds containing novel quinoline–carbazole derivatives

Abstract: Materials with nonlinear optical (NLO) properties have significant applications in different fields, including nuclear science, biophysics, medicine, chemical dynamics, solid physics, materials science and surface interface applications. Quinoline and carbazole, owing to their electron-deficient and electron-rich character respectively, play a role in charge transfer applications in optoelectronics. Therefore, an attempt has been made herein to explore quinoline–carbazole based novel materials with highly nonlinear optical properties. Structural tailoring has been made at the donor and acceptor units of two recently synthesized quinoline–carbazole molecules (Q1, Q2) and acceptor–donor–π–acceptor (A–D–π–A) and donor–acceptor–donor–π–acceptor (D–A–D–π–A) type novel molecules Q1D1–Q1D3 and Q2D2–Q2D3 have been quantum chemically designed, respectively. Density functional theory (DFT) and time-dependent density functional theory (TDDFT) computations are performed to process the impact of acceptor and donor units on photophysical, electronic and NLO properties of selected molecules. The λmax values (321 and 319 nm) for Q1 and Q2 in DSMO were in good agreement with the experimental values (326 and 323 nm). The largest shift in absorption maximum is displayed by Q1D2 (436 nm). The designed compounds (Q1D3–Q2D3) express absorption spectra with an increased border and with a reduced band gap compared to the parent compounds (Q1 and Q2). Natural bond orbital (NBO) investigations showed that the extended hyper conjugation and strong intramolecular interaction play significant roles in stabilising these systems. All molecules expressed significant NLO responses. A large value of βtot was elevated in Q1D2 (23 885.90 a.u.). This theoretical framework reveals the NLO response properties of novel quinoline–carbazole derivatives that can be significant for their use in advanced applications.

Multi-class multi-level classification algorithm for skin lesions classification using machine learning techniques

Abstract: Skin diseases remain a major cause of disability worldwide and contribute approximately 1.79% of the global burden of disease measured in disability-adjusted life years. In the United Kingdom alone, 60% of the population suffer from skin diseases during their lifetime. In this paper, we propose an intelligent digital diagnosis scheme to improve the classification accuracy of multiple diseases. A Multi-Class Multi-Level (MCML) classification algorithm inspired by the “divide and conquer” rule is explored to address the research challenges. The MCML classification algorithm is implemented using traditional machine learning and advanced deep learning approaches. Improved techniques are proposed for noise removal in the traditional machine learning approach. The proposed algorithm is evaluated on 3672 classified images, collected from different sources and the diagnostic accuracy of 96.47% is achieved. To verify the performance of the proposed algorithm, its metrics are compared with the Multi-Class Single-Level classification algorithm which is the main algorithm used in most of the existing literature. The results also indicate that the MCML classification algorithm is capable of enhancing the classification performance of multiple skin lesions.

A Method of State-of-Charge Estimation for EV Power Lithium-Ion Battery Using a Novel Adaptive Extended Kalman Filter

Abstract: Battery management system (BMS) is one of the key subsystems of electric vehicle, and the battery state-of -charge (SOC) is a crucial input for the calculations of energy and power. Therefore, SOC estimation is a significant task for BMS. In this paper, a new method for online estimating SOC is proposed, which combines a novel adaptive extended Kalman filter (AEKF) and a parameter identification algorithm based on adaptive recursive least squares (RLS). Specifically, according to the first order R-C network equivalent circuit model, the battery model parameters are identified online using the RLS with multiple forgetting factors. Based on the identified parameters, the novel AEKF is used to accurately estimate the battery SOC. The online identification of parameter tracks the varying model. At the same time, due to the novel AEKF algorithm to dynamically adjust the system noise parameter, excellent accuracy of the SOC real-time estimation is obtained. Experiments are set to evaluate the accuracy and robustness of the proposed SOC estimation method. The simulation test results indicate that under DST and UDDS conditions, the maximum absolute errors are less than 0.015 after filtering convergence. In addition, the maximum absolute error is less than 0.02 in the simulation of DST with current and voltage measurement noise, so is in DST with current offset sensor error. The tests indicate that the proposed method can accurately estimate battery SOC and has strong robustness.

Typestate-guided fuzzer for discovering use-after-free vulnerabilities

Abstract: Existing coverage-based fuzzers usually use the individual control flow graph (CFG) edge coverage to guide the fuzzing process, which has shown great potential in finding vulnerabilities. However, CFG edge coverage is not effective in discovering vulnerabilities such as use-after-free (UaF). This is because, to trigger UaF vulnerabilities, one needs not only to cover individual edges, but also to traverse some (long) sequence of edges in a particular order, which is challenging for existing fuzzers. To this end, we propose to model UaF vulnerabilities as typestate properties, and develop a typestate-guided fuzzer, named UAFL, for discovering vulnerabilities violating typestate properties. Given a typestate property, we first perform a static typestate analysis to find operation sequences potentially violating the property. Our fuzzing process is then guided by the operation sequences in order to progressively generate test cases triggering property violations. In addition, we also employ an information flow analysis to improve the efficiency of the fuzzing process. We have performed a thorough evaluation of UAFL on 14 widely-used real-world programs. The experiment results show that UAFL substantially outperforms the state-of-the-art fuzzers, including AFL, AFLFast, FairFuzz, MOpt, Angora and QSYM, in terms of the time taken to discover vulnerabilities. We have discovered 10 previously unknown vulnerabilities, and received 5 new CVEs.

MemLock: memory usage guided fuzzing

Abstract: Uncontrolled memory consumption is a kind of critical software security weaknesses. It can also become a security-critical vulnerability when attackers can take control of the input to consume a large amount of memory and launch a Denial-of-Service attack. However, detecting such vulnerability is challenging, as the state-of-the-art fuzzing techniques focus on the code coverage but not memory consumption. To this end, we propose a memory usage guided fuzzing technique, named MemLock, to generate the excessive memory consumption inputs and trigger uncontrolled memory consumption bugs. The fuzzing process is guided with memory consumption information so that our approach is general and does not require any domain knowledge. We perform a thorough evaluation for MemLock on 14 widely-used real-world programs. Our experiment results show that MemLock substantially outperforms the state-of-the-art fuzzing techniques, including AFL, AFLfast, PerfFuzz, FairFuzz, Angora and QSYM, in discovering memory consumption bugs. During the experiments, we discovered many previously unknown memory consumption bugs and received 15 new CVEs.

Application of deep reinforcement learning in stock trading strategies and stock forecasting

Abstract: The role of the stock market across the overall financial market is indispensable. The way to acquire practical trading signals in the transaction process to maximize the benefits is a problem that has been studied for a long time. This paper put forward a theory of deep reinforcement learning in the stock trading decisions and stock price prediction, the reliability and availability of the model are proved by experimental data, and the model is compared with the traditional model to prove its advantages. From the point of view of stock market forecasting and intelligent decision-making mechanism, this paper proves the feasibility of deep reinforcement learning in financial markets and the credibility and advantages of strategic decision-making.

Reflection for all healthcare staff: A national evaluation of Schwartz Rounds.

Abstract: The emotional toll of working in healthcare is widely recognised, but staff rarely have time to reflect on their experiences. Schwartz Rounds provide an opportunity for all staff in a healthcare organisation to meet regularly and reflect on the human connections made with patients and the emotional impact of their work. They are now running in over 200 organisations across the UK & Ireland. In the first evaluation of a national sample in the UK, we review feedback received from a large sample of 402 Schwartz Rounds in a total of 47 organisations, including acute and non-acute NHS trusts and hospices. Analyses were undertaken to explore self-reported experiences of the Rounds, and differences between the proportions of professional staff groups attending. The overall experience of Schwartz Rounds was very positive across all settings. In particular, staff reported that Rounds helped them to gain insight into the working lives of their colleagues. There were no differences between the responses of clinical and non-clinical staff, indicating that all staff value a reflective space regardless of background. Healthcare staff value an opportunity to reflect on the emotional impact of their work. In increasingly overstretched and hurried services, it is a priority to provide this.

Low-Latency and Resource-Efficient Service Function Chaining Orchestration in Network Function Virtualization

Abstract: Recently, network function virtualization (NFV) has been proposed to solve the dilemma faced by traditional networks and to improve network performance through hardware and software decoupling. The deployment of the service function chain (SFC) is a key technology that affects the performance of virtual network function (VNF). The key issue in the deployment of SFCs is proposing effective algorithms to achieve efficient use of resources. In this article, we propose an SFC deployment optimization (SFCDO) algorithm based on a breadth-first search (BFS). The algorithm first uses a BFS-based algorithm to find the shortest path between the source node and the destination node. Then, based on the shortest path, the path with the fewest hops is preferentially chosen to implement the SFC deployment. Finally, we compare the performances with the greedy and simulated annealing (G-SA) algorithm. The experiment results show that the proposed algorithm is optimized in terms of end-to-end delay and bandwidth resource consumption. In addition, we also consider the load rate of the nodes to achieve network load balancing.

COVID-19 pandemic in the United Kingdom.

Abstract: Objectives To describe epidemiological data on cases of COVID-19 and the spread of Severe Acute Respiratory Syndrome Coronavirus 2 in the United Kingdom (UK), and the subsequent policy and technological response to the pandemic, including impact on healthcare, business and the economy. Methods Epidemiological, business and economic data were extracted from official government sources covering the period 31st January to 13th August 2020; healthcare system data up to end of June 2019. Results UK-wide COVID-19 cases and deaths were 313,798 and 46,706 respectively (472 cases and 70 deaths per 100,000 population) by 12th August. There were regional variations in England, with London and North West (756 and 666 cases per 100,000 population respectively) disproportionately affected compared with other regions. As of 11th August, 13,618,470 tests had been conducted in the UK. Increased risk of mortality was associated with age (≥60 years), gender (male) and BAME groups. Since onset of the pandemic, emergency department attendance, primary care utilisation and cancer referrals and inpatient/outpatient referrals have declined; emergency ambulance and NHS111 calls increased. Business sectors most impacted are the arts, entertainment and recreation, followed by accommodation and food services. Government interventions aimed at curtailing the business and economic impact have been implemented, but applications for state benefits have increased. Conclusions The impact of COVID-19 on the UK population, health system and economy has been profound. More data are needed to implement the optimal policy and technological responses to preventing further spikes in COVID-19 cases, and to inform strategic planning to manage future pandemics.

A mechanism-aware and multiomic machine-learning pipeline characterizes yeast cell growth.

Abstract: Metabolic modeling and machine learning are key components in the emerging next generation of systems and synthetic biology tools, targeting the genotype–phenotype–environment relationship. Rather than being used in isolation, it is becoming clear that their value is maximized when they are combined. However, the potential of integrating these two frameworks for omic data augmentation and integration is largely unexplored. We propose, rigorously assess, and compare machine-learning–based data integration techniques, combining gene expression profiles with computationally generated metabolic flux data to predict yeast cell growth. To this end, we create strain-specific metabolic models for 1,143 Saccharomyces cerevisiae mutants and we test 27 machine-learning methods, incorporating state-of-the-art feature selection and multiview learning approaches. We propose a multiview neural network using fluxomic and transcriptomic data, showing that the former increases the predictive accuracy of the latter and reveals functional patterns that are not directly deducible from gene expression alone. We test the proposed neural network on a further 86 strains generated in a different experiment, therefore verifying its robustness to an additional independent dataset. Finally, we show that introducing mechanistic flux features improves the predictions also for knockout strains whose genes were not modeled in the metabolic reconstruction. Our results thus demonstrate that fusing experimental cues with in silico models, based on known biochemistry, can contribute with disjoint information toward biologically informed and interpretable machine learning. Overall, this study provides tools for understanding and manipulating complex phenotypes, increasing both the prediction accuracy and the extent of discernible mechanistic biological insights.

Quantification of training and match-load distribution across a season in elite English Premier League soccer players

Abstract: Objective: To examine training and match loads undertaken by soccer players competing in the English Premier League.Methods: Using a retrospective design, external (GPS) and internal training loads...