Engineering and Information Technology Collected Works - Research Publications
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ItemReducing the Erodibility of Sandy Soils Engineered by Cyanobacteria Inoculation: A Laboratory Investigation(MDPI AG, 2023-02)Windblown and water-induced erosion cause substantial soil losses worldwide, especially for drylands. Any sustainable management program that increases soil organic matter and improves the stability of the crustal layer could considerably enhance soil productivity and the preservation of erosion-prone land. This paper presents a laboratory investigation of cyanobacteria-inoculated medium sand and fine sand soils studied for severe runoff conditions that were simulated using an erosion function apparatus (EFA). Loosely deposited sand specimens prepared by air-pluviation were inoculated with a single native filamentous-cyanobacterium strain (investigating both Nostoc sp. and Calothrix sp.) and then incubated under high exposure to white light for 32- or 48-day periods. Well-developed bio-crusts were produced on the specimens’ top surface that achieved substantial improvements in erosion resistance, as was demonstrated for a wide range of hydraulic shear stress investigated using EFA experiments. Relative improvements in hydraulic erosion resistance were explained in terms of the nature of the cyanobacteria-developed microstructures (cyanobacteria filament infiltration of pore-void spaces and exopolysaccharide excretion), as were observed by scanning electron microscope examinations. The developed microstructure depended on the cyanobacterium strain employed and the nominal pore-void sizes that are related to the sand gradation and density state. The encouraging findings of this experimental investigation suggest a tailored approach (i.e., employing a suitable native cyanobacterium strain chosen for its compatibility with the soil’s physical properties) could lay the basis for developing a novel technology for soil protection.
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ItemPerformance of a right-triangle stilling basin: a laboratory investigation(IWA Publishing, 2023-09)One of the most used hydraulic structures for energy dissipation of supercritical flow is the hydraulic jump stilling basin. From dimensional analysis, the sequent flow depth ratio of a hydraulic jump over the right-triangle basin is derived as a function of the inflow Froude number and relative length of the basin front. The proposed structure stabilized the hydraulic jump at the toe of the chute spillway and hydraulic jump characteristics were investigated for the Froude number ranging from 4.4 < F1 < 7. The results obtained from both numerical and experimental simulations yielded increased efficiency in the energy dissipation performance of this novel design. The modeling showed the formation of two large recirculation regions at the jump roller and jump bed at the beginning of the downstream channel, which resulted in intense energy dissipation in the right-triangle basin. The relative energy loss is approximately 37% higher for relative basin front lengths larger than three compared to the classic jump. Practitioners and academia on the usefulness of a right-triangle basin for hydraulic purposes and further experimental tests are needed to estimate the scalability and cost–benefit of this modified system for implementation.
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ItemTissue-associated and vertically transmitted bacterial symbiont in the coral Pocillopora acuta(OXFORD UNIV PRESS, 2024-01-08)Coral microhabitats are colonized by a myriad of microorganisms, including diverse bacteria which are essential for host functioning and survival. However, the location, transmission, and functions of individual bacterial species living inside the coral tissues remain poorly studied. Here, we show that a previously undescribed bacterial symbiont of the coral Pocillopora acuta forms cell-associated microbial aggregates (CAMAs) within the mesenterial filaments. CAMAs were found in both adults and larval offspring, suggesting vertical transmission. In situ laser capture microdissection of CAMAs followed by 16S rRNA gene amplicon sequencing and shotgun metagenomics produced a near complete metagenome-assembled genome. We subsequently cultured the CAMA bacteria from Pocillopora acuta colonies, and sequenced and assembled their genomes. Phylogenetic analyses showed that the CAMA bacteria belong to an undescribed Endozoicomonadaceae genus and species, which we propose to name Candidatus Sororendozoicomonas aggregata gen. nov sp. nov. Metabolic pathway reconstruction from its genome sequence suggests this species can synthesize most amino acids, several B vitamins, and antioxidants, and participate in carbon cycling and prey digestion, which may be beneficial to its coral hosts. This study provides detailed insights into a new member of the widespread Endozoicomonadaceae family, thereby improving our understanding of coral holobiont functioning. Vertically transmitted, tissue-associated bacteria, such as Sororendozoicomonas aggregata may be key candidates for the development of microbiome manipulation approaches with long-term positive effects on the coral host.
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ItemInoculation with Roseovarius increases thermal tolerance of the coral photosymbiont, Breviolum minutum(FRONTIERS MEDIA SA, 2023-08-10)Coral reefs are diverse marine ecosystems that have tremendous ecological and cultural value and support more than 25% of eukaryote marine biodiversity. Increased ocean temperatures and light intensity trigger coral bleaching, the breakdown of the relationship between corals and their photosymbionts, dinoflagellates of the family Symbiodiniaceae. This leaves corals without their primary energy source, thereby leading to starvation and, often, death. Coral bleaching is hypothesized to occur due to an overproduction of reactive oxygen species (ROS) by Symbiodiniaceae, which subsequently accumulate in coral tissues. Bacterial probiotics have been proposed as an approach to mitigate coral bleaching, by reducing ROS levels in the coral holobiont through bacterial antioxidant production. Both corals and Symbiodiniaceae are known to associate with bacteria. However, the Symbiodiniaceae-bacteria relationship, and its impact on Symbiodiniaceae thermal tolerance, remains a poorly studied area. In this study, cultured Symbiodiniaceae of the species Breviolum minutum were treated with antibiotics to reduce their bacterial load. The cultures were subsequently inoculated with bacterial isolates from the genus Roseovarius that were isolated from the same B. minutum culture and showed either high or low ROS-scavenging abilities. The B. minutum cultures were then exposed to experimental heat stress for 16 days, and their health was monitored through measurements of cell density and photochemical efficiency of photosystem II. It was found that B. minutum inoculated with Roseovarius with higher ROS-scavenging abilities showed greater cell growth at elevated temperatures, compared to cultures inoculated with a Roseovarius strain with lower ROS-scavenging abilities. This suggests that Roseovarius may play a role in Symbiodiniaceae fitness at elevated temperatures. Analysis of Symbiodiniaceae-associated bacterial communities through 16S rRNA gene metabarcoding revealed that Roseovarius relative abundance increased in B. minutum cultures following inoculation and with elevated temperature exposure, highlighting the contribution they may have in shielding B. minutum from thermal stress, although other bacterial community changes may have also contributed to these observations. This study begins to unpick the relationship between Symbiodiniaceae and their bacteria and opens the door for the use of Symbiodiniaceae-associated bacteria in coral reef conservation approaches.
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ItemOptimised deep k-nearest neighbour's based diabetic retinopathy diagnosis(ODeep-NN) using retinal images(SPRINGER, 2024-03-09)Diabetes mellitus has been regarded as one of the prime health issues in present days, which can often lead to diabetic retinopathy, a complication of the disease that affects the eyes, causing loss of vision. For precisely detecting the condition's existence, clinicians are required to recognise the presence of lesions in colour fundus images, making it an arduous and time-consuming task. To deal with this problem, a lot of work has been undertaken to develop deep learning-based computer-aided diagnosis systems that assist clinicians in making accurate diagnoses of the diseases in medical images. Contrariwise, the basic operations involved in deep learning models lead to the extraction of a bulky set of features, further taking a long period of training to predict the existence of the disease. For effective execution of these models, feature selection becomes an important task that aids in selecting the most appropriate features, with an aim to increase the classification accuracy. This research presents an optimised deep k-nearest neighbours'-based pipeline model in a bid to amalgamate the feature extraction capability of deep learning models with nature-inspired metaheuristic algorithms, further using k-nearest neighbour algorithm for classification. The proposed model attains an accuracy of 97.67 and 98.05% on two different datasets considered, outperforming Resnet50 and AlexNet deep learning models. Additionally, the experimental results also portray an analysis of five different nature-inspired metaheuristic algorithms, considered for feature selection on the basis of various evaluation parameters.
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ItemNo Preview AvailableSeizure occurrence is linked to multiday cycles in diverse physiological signals(WILEY, 2023-06)OBJECTIVE: The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing. METHODS: In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist-worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter-Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology. RESULTS: Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal. SIGNIFICANCE: Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time-varying approaches to epilepsy care.
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ItemNo Preview AvailableDeveloping a deep learning natural language processing algorithm for automated reporting of adverse drug reactions(ACADEMIC PRESS INC ELSEVIER SCIENCE, 2023-01)The detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, ADRs are a significant source of patient morbidity, responsible for 5%-10% of acute care hospital admissions worldwide. Spontaneous reporting of ADRs has long been the standard method of reporting, however this approach is known to have high rates of under-reporting, a problem that limits pharmacovigilance efforts. Automated ADR reporting presents an alternative pathway to increase reporting rates, although this may be limited by over-reporting of other drug-related adverse events. We developed a deep learning natural language processing algorithm to identify ADRs in discharge summaries at a single academic hospital centre. Our model was developed in two stages: first, a pre-trained model (DeBERTa) was further pre-trained on 1.1 million unlabelled clinical documents; secondly, this model was fine-tuned to detect ADR mentions in a corpus of 861 annotated discharge summaries. This model was compared to a version without the pre-training step, and a previously published RoBERTa model pretrained on MIMIC III, which has demonstrated strong performance on other pharmacovigilance tasks. To ensure that our algorithm could differentiate ADRs from other drug-related adverse events, the annotated corpus was enriched for both validated ADR reports and confounding drug-related adverse events using. The final model demonstrated good performance with a ROC-AUC of 0.955 (95% CI 0.933 - 0.978) for the task of identifying discharge summaries containing ADR mentions, significantly outperforming the two comparator models.
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ItemThe Challenge of Cartilage Integration: Understanding a Major Barrier to Chondral Repair(MARY ANN LIEBERT, INC, 2022-02-01)Articular cartilage defects caused by injury frequently lead to osteoarthritis, a painful and costly disease. Despite widely used surgical methods to treat articular cartilage defects and a plethora of research into regenerative strategies as treatments, long-term clinical outcomes are not satisfactory. Failure to integrate repair tissue with native cartilage is a recurring issue in surgical and tissue-engineered strategies, seeing eventual degradation of the regenerated or surrounding tissue. This review delves into the current understanding of why continuous and robust integration with native cartilage is so difficult to achieve. Both the intrinsic limitations of chondrocytes to remodel injured cartilage, and the significant challenges posed by a compromised biomechanical environment are described. Recent scaffold and cell-based techniques to repair cartilage are also discussed, and limitations of existing methods to evaluate integrative repair. In particular, the importance of evaluating the mechanical integrity of the interface between native and repair tissue is highlighted as a meaningful assessment of any strategy to repair this load-bearing tissue. Impact statement The failure to integrate grafts or biomaterials with native cartilage is a major barrier to cartilage repair. An in-depth understanding of the reasons cartilage integration remains a challenge is required to inform cartilage repair strategies. In particular, this review highlights that integration of cartilage repair strategies is frequently assessed in terms of the continuity of tissue, but not the mechanical integrity. Given the load-bearing nature of cartilage, evaluating integration in terms of interfacial strength is essential to assessing the potential success of cartilage repair methods.
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ItemDiagnostic Clinical Decision Support based on Deep Learning and Knowledge-based Systems for Psoriasis: From Diagnosis to Treatment Options(Elsevier, 2023-11)Psoriasis is an acute immuno-dermatological disease, affecting people of all ages, which significantly decreases quality of life. While the standard approach to identification and diagnosis of psoriasis is based on dermatologist decisions, various Deep Learning (DL) methods have been utilized to create Computer-Aided Diagnosis (CAD) systems to detect and classify psoriasis cases. In response to the knowledge gap of an existing practical and functional DL-based solution to psoriasis diagnosis, this study proposed an ensemble Convolutional Neural Network (CNN) model using Residual Network 50 Version 2 (ResNet50V2), ResNet101V2, and ResNet152V2 networks to create a CAD system for detecting and classifying psoriatic images. This ensemble model determines whether an input image is psoriatic using a binary classification procedure in the initial stage and classifies the psoriatic images into seven variants utilizing a multi-class classification. Furthermore, a treatment suggestion system was embedded within the diagnostic algorithm to suggest the best treatment options for psoriasis variants using a Multi-Criteria Decision Making (MCDM) method with the aim of reducing the disease symptoms in patients. A web-based Decision and Diagnostic Support System (D&DSS) is constructed to determine whether an input image is psoriatic, classify the psoriatic images into different variants, and accordingly recommend the best treatment options based on the observed disease symptoms in a patient. Nevertheless, the functionality and reliability of the proposed D&DSS are validated with high accuracy rates in both diagnostic and identification stages of the approach, which ratifies the practicality of this proposition.
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ItemAn operational planning for emergency medical services considering the application of IoT(Springer, 2023)In the last two years, the worldwide outbreak of the COVID-19 pandemic and the resulting heavy casualties have highlighted the importance of further research in healthcare. In addition, the advent of new technologies such as the Internet of Things (IoT) and their applications in preventing and detecting casualty cases has attracted a lot of attention. The IoT is able to help organize medical services by collecting significant amounts of data and information. This paper proposes a novel mathematical model for Emergency Medical Services (EMS) using the IoT. The proposed model is designed in two phases. In the first phase, the data is collected by the IoT, and the demands for ambulances are categorized and prioritized. Then in the second phase, ambulances are allocated to demand areas (patients). Two main objectives of the proposed model are reducing total costs and the mortality risk due to lack of timely service. In addition, demand uncertainty for ambulances is considered with various scenarios at demand levels. Numerical experiments have been conducted on actual data from a case study in Kermanshah, Iran. Due to the NP-hard nature of the mathematical model, three meta-heuristic algorithms Multi-Objective Simulated Annealing (MOSA) algorithm and Multi-Objective Particle Swarm Optimization (MOPSO) algorithm, and L-MOPSO have been used to solve the proposed model on medium and large scales in addition to the exact solution method. The results show that the proposed model significantly reduces mortality risk, in addition to reducing total cost. Data analysis also led to useful managerial insights.