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Aluminium-lithium (Al-Li) 2060 alloy, a 3rd generation Al-Li alloy, is considered a structural material for aircraft components. This study employs the Friction Stir Welding (FSW) process with a kinematic 5-axis robotic arm to weld 4-mm-thick plates of 2060-T8E30 Al-Li alloy. The focus is on the impact of tool axial force and speeds on the microstructural evolution, mechanical properties, and surface integrity of the welded joints. The applied process parameters included rotational speeds ranging from 800 to 1600 rpm, traverse speeds from 2 to 4 mm/s, and axial forces from 4 to 6 kN. We utilise the Taguchi L9 orthogonal array to optimise the process parameters. The results revealed that rotational speed is paramount for affecting the welds' quality, followed by axial force and then traverse. Defect-free samples exhibited a fine surface finish, with average roughness values of 3.05 μm and 3.536 μm. The study also showed that 5 kN of axial force, 1200 rpm of rotational speed, and 3 mm/s of traverse speed were the best FSW conditions for getting a maximum stir zone microhardness value of 128.77 HV. This study also shows how to improve the FSW parameters for Al-Li alloys, showing how important precise parameter control is for improving joint strength and weld quality in high-tech aerospace and automotive applications., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

The electrical activity of the neural processes involved in cognitive functions is captured in EEG signals, allowing the exploration of the integration and coordination of neuronal oscillations across multiple spatiotemporal scales. We have proposed a novel approach that combines the transformation of EEG signal into image sequences, considering cross-frequency phase synchronisation (CFS) dynamics involved in low-level auditory processing, with the development of a two-stage deep learning model for the detection of developmental dyslexia (DD). This deep learning model exploits spatial and temporal information preserved in the image sequences to find discriminative patterns of phase synchronisation over time achieving a balanced accuracy of up to 83%. This result supports the existence of differential brain synchronisation dynamics between typical and dyslexic seven-year-old readers. Furthermore, we have obtained interpretable representations using a novel feature mask to link the most relevant regions during classification with the cognitive processes attributed to normal reading and those corresponding to compensatory mechanisms found in dyslexia., (© 2024. The Author(s).)

Optical microscopy is widely regarded to be an indispensable tool in healthcare and manufacturing quality control processes, although its inability to resolve structures separated by a lateral distance under ~200 nm has culminated in the emergence of a new field named fluorescence nanoscopy , while this too is prone to several caveats (namely phototoxicity, interference caused by exogenous probes and cost). In this regard, we present a triplet string of concatenated O-Net ('bead') architectures (termed 'Θ-Net' in the present study) as a cost-efficient and non-invasive approach to enhancing the resolution of non-fluorescent phase-modulated optical microscopical images in silico . The quality of the afore-mentioned enhanced resolution (ER) images was compared with that obtained via other popular frameworks (such as ANNA-PALM, BSRGAN and 3D RCAN), with the Θ-Net-generated ER images depicting an increased level of detail (unlike previous DNNs). In addition, the use of cross-domain (transfer) learning to enhance the capabilities of models trained on differential interference contrast (DIC) datasets [where phasic variations are not as prominently manifested as amplitude/intensity differences in the individual pixels unlike phase-contrast microscopy (PCM)] has resulted in the Θ-Net-generated images closely approximating that of the expected (ground truth) images for both the DIC and PCM datasets. This thus demonstrates the viability of our current Θ-Net architecture in attaining highly resolved images under poor signal-to-noise ratios while eliminating the need for a priori PSF and OTF information, thereby potentially impacting several engineering fronts (particularly biomedical imaging and sensing, precision engineering and optical metrology).

Motivation: There exists an unexplained diverse variation within the predefined colon cancer stages using only features from either genomics or histopathological whole slide images as prognostic factors. Unraveling this variation will bring about improved staging and treatment outcomes. Hence, motivated by the advancement of Deep Neural Network (DNN) libraries and complementary factors within some genomics datasets, we aggregate atypia patterns in histopathological images with diverse carcinogenic expression from mRNA, miRNA and DNA methylation as an integrative input source into a deep neural network for colon cancer stages classification, and samples stratification into low or high-risk survival groups., Results: The genomics-only and integrated input features return Area Under Curve-Receiver Operating Characteristic curve (AUC-ROC) of 0.97 compared with AUC-ROC of 0.78 obtained when only image features are used for the stage's classification. A further analysis of prediction accuracy using the confusion matrix shows that the integrated features have a weakly improved accuracy of 0.08% more than the accuracy obtained with genomics features. Also, the extracted features were used to split the patients into low or high-risk survival groups. Among the 2,700 fused features, 1,836 (68%) features showed statistically significant survival probability differences in aggregating samples into either low or high between the two risk survival groups. Availability and Implementation: https://github.com/Ogundipe-L/EDCNN., Competing Interests: NO authors have competing interests, (Copyright: © 2024 Ogundipe et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The tensor nuclear norm (TNN), defined as the sum of nuclear norms of frontal slices of the tensor in a frequency domain, has been found useful in solving low-rank tensor recovery problems. Existing TNN-based methods use either fixed or data-independent transformations, which may not be the optimal choices for the given tensors. As the consequence, these methods cannot exploit the potential low-rank structure of tensor data adaptively. In this article, we propose a framework called self-adaptive learnable transform (SALT) to learn a transformation matrix from the given tensor. Specifically, SALT aims to learn a lossless transformation that induces a lower average-rank tensor, where the Schatten- p quasi-norm is used as the rank proxy. Then, because SALT is less sensitive to the orientation, we generalize SALT to other dimensions of tensor (SALTS), namely, learning three self-adaptive transformation matrices simultaneously from given tensor. SALTS is able to adaptively exploit the potential low-rank structures in all directions. We provide a unified optimization framework based on alternating direction multiplier method for SALTS model and theoretically prove the weak convergence property of the proposed algorithm. Experimental results in hyperspectral image (HSI), color video, magnetic resonance imaging (MRI), and COIL-20 datasets show that SALTS is much more accurate in tensor completion than existing methods. The demo code can be found at https://faculty.uestc.edu.cn/gaobin/zh_CN/lwcg/153392/list/index.htm.

Background: Cirrhosis is associated with sarcopaenia and fat wasting, which drive decompensation and mortality. Currently, nutritional status, through body composition assessment, is not routinely monitored in outpatients. Given the deleterious outcomes associated with poor nutrition in decompensated cirrhosis, there is a need for remotely monitoring this to optimise community care., Methods: A retrospective analysis was conducted on patients monitored remotely with digital sensors post hospital discharge, to assess outcomes and indicators of new cirrhosis complications. 15 patients had daily fat mass measurements as part of monitoring over a median 10 weeks, using a Withing's bioimpedance scale. The Clinical Frailty Score (CFS) was used to assess frailty and several liver disease severity scores were assessed., Results: 73.3% (11/15) patients were male with a median age of 63 (52-68). There was a trend towards more severe liver disease based on CLIF-Consortium Acute Decompensation (CLIF-C AD) scores in frail patients vs. those not frail (53 vs 46, p = 0.072). When the cohort was split into patients who gained fat mass over 8 weeks vs. those that lost fat mass, the baseline CLIF-C AD scores and WBC were significantly higher in those that lost fat (58 vs 48, p = 0.048 and 11.2 × 10 9 vs 4.7 × 10 9 , p = 0.031)., Conclusions: This proof-of-principle study shows feasibility for remote monitoring of fat mass and nutritional reserve in decompensated cirrhosis. Our results suggest fat mass is associated with greater severity of acute decompensation and may serve as an indicator of systemic inflammatory response. Further prospective studies are required to validate this digital biomarker., (© 2023. BioMed Central Ltd., part of Springer Nature.)

While the brain connectivity network can inform the understanding and diagnosis of developmental dyslexia, its cause-effect relationships have not yet enough been examined. Employing electroencephalography signals and band-limited white noise stimulus at 4.8 Hz (prosodic-syllabic frequency), we measure the phase Granger causalities among channels to identify differences between dyslexic learners and controls, thereby proposing a method to calculate directional connectivity. As causal relationships run in both directions, we explore three scenarios, namely channels' activity as sources, as sinks, and in total. Our proposed method can be used for both classification and exploratory analysis. In all scenarios, we find confirmation of the established right-lateralized Theta sampling network anomaly, in line with the assumption of the temporal sampling framework of oscillatory differences in the Theta and Gamma bands. Further, we show that this anomaly primarily occurs in the causal relationships of channels acting as sinks, where it is significantly more pronounced than when only total activity is observed. In the sink scenario, our classifier obtains 0.84 and 0.88 accuracy and 0.87 and 0.93 AUC for the Theta and Gamma bands, respectively.

Surgery is a high-risk procedure of therapy and is associated to post trauma complications of longer hospital stay, estimated blood loss and long duration of surgeries. Reports have suggested that over 2.5% patients die during and post operation. This paper is aimed at systematic review of previous research on artificial intelligence (AI) in surgery, analyzing their results with suitable software to validate their research by obtaining same or contrary results. Six published research articles have been reviewed across three continents. These articles have been re-validated using software including SPSS and MedCalc to obtain the statistical features such as the mean, standard deviation, significant level, and standard error. From the significant values, the experiments are then classified according to the null (p < 0.05) or alternative (p>0.05) hypotheses. The results obtained from the analysis have suggested significant difference in operating time, docking time, staging time, and estimated blood loss but show no significant difference in length of hospital stay, recovery time and lymph nodes harvested between robotic assisted surgery using AI and normal conventional surgery. From the evaluations, this research suggests that AI-assisted surgery improves over the conventional surgery as safer and more efficient system of surgery with minimal or no complications.

Inertial sensor-based human activity recognition (HAR) has a range of healthcare applications as it can indicate the overall health status or functional capabilities of people with impaired mobility. Typically, artificial intelligence models achieve high recognition accuracies when trained with rich and diverse inertial datasets. However, obtaining such datasets may not be feasible in neurological populations due to, e.g., impaired patient mobility to perform many daily activities. This study proposes a novel framework to overcome the challenge of creating rich and diverse datasets for HAR in neurological populations. The framework produces images from numerical inertial time-series data (initial state) and then artificially augments the number of produced images (enhanced state) to achieve a larger dataset. Here, we used convolutional neural network (CNN) architectures by utilizing image input. In addition, CNN enables transfer learning which enables limited datasets to benefit from models that are trained with big data. Initially, two benchmarked public datasets were used to verify the framework. Afterward, the approach was tested in limited local datasets of healthy subjects (HS), Parkinson's disease (PD) population, and stroke survivors (SS) to further investigate validity. The experimental results show that when data augmentation is applied, recognition accuracies have been increased in HS, SS, and PD by 25.6%, 21.4%, and 5.8%, respectively, compared to the no data augmentation state. In addition, data augmentation contributes to better detection of stair ascent and stair descent by 39.1% and 18.0%, respectively, in limited local datasets. Findings also suggest that CNN architectures that have a small number of deep layers can achieve high accuracy. The implication of this study has the potential to reduce the burden on participants and researchers where limited datasets are accrued., Competing Interests: The authors declare no conflict of interest.

With the global issue of marine debris ever expanding, it is imperative that the technology industry steps in. The aim is to find if deep learning can successfully distinguish between marine life and synthetic debris underwater. This study assesses whether we could safely clean up our oceans with Artificial Intelligence without disrupting the delicate balance of aquatic ecosystems. Our research compares a simple convolutional neural network with a VGG-16 model using an original database of 1644 underwater images and a binary classification to sort synthetic material from aquatic life. Our results show first insights to safely distinguishing between debris and life., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Underwater fish monitoring is the one of the most challenging problems for efficiently feeding and harvesting fish, while still being environmentally friendly. The proposed 2D computer vision method is aimed at non-intrusively estimating the weight of Tilapia fish in turbid water environments. Additionally, the proposed method avoids the issue of using high-cost stereo cameras and instead uses only a low-cost video camera to observe the underwater life through a single channel recording. An in-house curated Tilapia-image dataset and Tilapia-file dataset with various ages of Tilapia are used. The proposed method consists of a Tilapia detection step and Tilapia weight-estimation step. A Mask Recurrent-Convolutional Neural Network model is first trained for detecting and extracting the image dimensions (i.e., in terms of image pixels) of the fish. Secondly, is the Tilapia weight-estimation step, wherein the proposed method estimates the depth of the fish in the tanks and then converts the Tilapia's extracted image dimensions from pixels to centimeters. Subsequently, the Tilapia's weight is estimated by a trained model based on regression learning. Linear regression, random forest regression, and support vector regression have been developed to determine the best models for weight estimation. The achieved experimental results have demonstrated that the proposed method yields a Mean Absolute Error of 42.54 g, R2 of 0.70, and an average weight error of 30.30 (±23.09) grams in a turbid water environment, respectively, which show the practicality of the proposed framework.

Wearables are objective tools for human activity recognition (HAR). Advances in wearables enable synchronized multi-sensing within a single device. This has resulted in studies investigating the use of single or multiple wearable sensor modalities for HAR. Some studies use inertial data, others use surface electromyography (sEMG) from multiple muscles and different post-processing approaches. Yet, questions remain about accuracies relating to e.g., multi-modal approaches, and sEMG post-processing. Here, we explored how inertial and sEMG could be efficiently combined with machine learning and used with post-processing methods for better HAR. This study aims recognition of four basic daily life activities; walking, standing, stair ascent and descent. Firstly, we created a new feature vector based on the domain knowledge gained from previous mobility studies. Then, a feature level data fusion approach was used to combine inertial and sEMG data. Finally, two supervised learning classifiers (Support Vector Machine, SVM, and the k-Nearest Neighbors, kNN) were tested with 5-fold cross-validation. Results show the use of inertial data with sEMG increased overall accuracy by 3.5% (SVM) and 6.3% (kNN). Extracting features from linear envelopes instead of bandpass filtered sEMG improves overall HAR accuracy in both classifiers. Clinical Relevance- Post-processing on sEMG signals can improve the performance of multimodal HAR.

We present a fast and precise deep-learning architecture, which we term O-Net, for obtaining super-resolved images from conventional phase-modulated optical microscopical techniques, such as phase-contrast microscopy and differential interference contrast microscopy. O-Net represents a novel deep convolutional neural network that can be trained on both simulated and experimental data, the latter of which is being demonstrated in the present context. The present study demonstrates the ability of the proposed method to achieve super-resolved images even under poor signal-to-noise ratios and does not require prior information on the point spread function or optical character of the system. Moreover, unlike previous state-of-the-art deep neural networks (such as U-Nets), the O-Net architecture seemingly demonstrates an immunity to network hallucination, a commonly cited issue caused by network overfitting when U-Nets are employed. Models derived from the proposed O-Net architecture are validated through empirical comparison with a similar sample imaged via scanning electron microscopy (SEM) and are found to generate ultra-resolved images which came close to that of the actual SEM micrograph.

The early diagnosis of cerebral palsy is an area which has recently seen significant multi-disciplinary research. Diagnostic tools such as the General Movements Assessment (GMA), have produced some very promising results. However, the prospect of automating these processes may improve accessibility of the assessment and also enhance the understanding of movement development of infants. Previous works have established the viability of using pose-based features extracted from RGB video sequences to undertake classification of infant body movements based upon the GMA. In this paper, we propose a series of new and improved features, and a feature fusion pipeline for this classification task. We also introduce the RVI-38 dataset, a series of videos captured as part of routine clinical care. By utilising this challenging dataset we establish the robustness of several motion features for classification, subsequently informing the design of our proposed feature fusion framework based upon the GMA. We evaluate our proposed framework's classification performance using both the RVI-38 dataset and the publicly available MINI-RGBD dataset. We also implement several other methods from the literature for direct comparison using these two independent datasets. Our experimental results and feature analysis show that our proposed pose-based method performs well across both datasets. The proposed features afford us the opportunity to include finer detail than previous methods, and further model GMA specific body movements. These new features also allow us to take advantage of additional body-part specific information as a means of improving the overall classification performance, whilst retaining GMA relevant, interpretable, and shareable features.

Gait assessment is emerging as a prominent way to understand impaired mobility and underlying neurological deficits. Various technologies have been used to assess gait inside and outside of laboratory settings, but wearables are the preferred option due to their cost-effective and practical use in both. There are robust conceptual gait models developed to ease the interpretation of gait parameters during indoor and outdoor environments. However, these models examine uni-modal gait characteristics (e.g., spatio-temporal parameters) only. Previous studies reported that understanding the underlying reason for impaired gait requires multi-modal gait assessment. Therefore, this study aims to develop a multi-modal approach using a synchronized inertial and electromyography (EMG) signals. Firstly, initial contact (IC), final contact (FC) moments and corresponding time stamps were identified from inertial data, producing temporal outcomes e.g., step time. Secondly, IC/FC time stamps were used to segment EMG data and define onset and offset times of muscle activities within the gait cycle and its subphases. For investigation purposes, we observed notable differences in temporal characteristics as well as muscle onset/offset timings and amplitudes between indoor and outdoor walking of three stroke survivors. Our preliminary analysis suggests a multi-modal approach may be important to augment and improve current inertial conceptual gait models by providing additional quantitative EMG data.

This article proposes a fuzzy logic-based energy-management system (FEMS) for a grid-connected microgrid with renewable energy sources (RESs) and energy storage system (ESS). The objectives of the FEMS are reducing the average peak load (APL) and operating cost through arbitrage operation of the ESS. These objectives are achieved by controlling the charge and discharge rate of the ESS based on the state of charge of ESS, the power difference between load and RES, and electricity market price. The effectiveness of the fuzzy logic greatly depends on the membership functions (MFs). The fuzzy MFs of the FEMS are optimized offline using a Pareto-based multiobjective evolutionary algorithm, nondominated sorting genetic algorithm (NSGA-II). The best compromise solution is selected as the final solution and implemented in the fuzzy-logic controller. A comparison with other control strategies with similar objectives is carried out at a simulation level. The proposed FEMS is experimentally validated on a real microgrid in the energy storage test bed at Newcastle University, U.K.

Wearable inertial measurement units (IMUs) are used in gait analysis due to their discrete wearable attachment and long data recording possibilities within indoor and outdoor environments. Previously, lower back and shin/shank-based IMU algorithms detecting initial and final contact events (ICs-FCs) were developed and validated on a limited number of healthy young adults (YA), reporting that both IMU wear locations are suitable to use during indoor and outdoor gait analysis. However, the impact of age (e.g., older adults, OA), pathology (e.g., Parkinson's Disease, PD) and/or environment (e.g., indoor vs. outdoor) on algorithm accuracy have not been fully investigated. Here, we examined IMU gait data from 128 participants (72-YA, 20-OA, and 36-PD) to thoroughly investigate the suitability of ICs-FCs detection algorithms (1 × lower back and 1 × shin/shank-based) for quantifying temporal gait characteristics depending on IMU wear location and walking environment. The level of agreement between algorithms was investigated for different cohorts and walking environments. Although mean temporal characteristics from both algorithms were significantly correlated for all groups and environments, subtle but characteristically nuanced differences were observed between cohorts and environments. The lowest absolute agreement level was observed in PD (ICC 2,1 = 0.979, 0.806, 0.730, 0.980) whereas highest in YA (ICC 2,1 = 0.987, 0.936, 0.909, 0.989) for mean stride, stance, swing, and step times, respectively. Absolute agreement during treadmill walking (ICC 2,1 = 0.975, 0.914, 0.684, 0.945), indoor walking (ICC 2,1 = 0.987, 0.936, 0.909, 0.989) and outdoor walking (ICC 2,1 = 0.998, 0.940, 0.856, 0.998) was found for mean stride, stance, swing, and step times, respectively. Findings of this study suggest that agreements between algorithms are sensitive to the target cohort and environment. Therefore, researchers/clinicians should be cautious while interpreting temporal parameters that are extracted from inertial sensors-based algorithms especially for those with a neurological condition.

Spatial susceptible landslide prediction is the one of the most challenging research areas which essentially concerns the safety of inhabitants. The novel geographic information web (GIW) application is proposed for dynamically predicting landslide risk in Chiang Rai, Thailand. The automated GIW system is coordinated between machine learning technologies, web technologies, and application programming interfaces (APIs). The new bidirectional long short-term memory (Bi-LSTM) algorithm is presented to forecast landslides. The proposed algorithm consists of 3 major steps, the first of which is the construction of a landslide dataset by using Quantum GIS (QGIS). The second step is to generate the landslide-risk model based on machine learning approaches. Finally, the automated landslide-risk visualization illustrates the likelihood of landslide via Google Maps on the website. Four static factors are considered for landslide-risk prediction, namely, land cover, soil properties, elevation and slope, and a single dynamic factor i.e., precipitation. Data are collected to construct a geospatial landslide database which comprises three historical landslide locations-Phu Chifa at Thoeng District, Ban Pha Duea at Mae Salong Nai, and Mai Salong Nok in Mae Fa Luang District, Chiang Rai, Thailand. Data collection is achieved using QGIS software to interpolate contour, elevation, slope degree and land cover from the Google satellite images, aerial and site survey photographs while the physiographic and rock type are on-site surveyed by experts. The state-of-the-art machine learning models have been trained i.e., linear regression (LR), artificial neural network (ANN), LSTM, and Bi-LSTM. Ablation studies have been conducted to determine the optimal parameters setting for each model. An enhancement method based on two-stage classifications has been presented to improve the landslide prediction of LSTM and Bi-LSTM models. The landslide-risk prediction performances of these models are subsequently evaluated using real-time dataset and it is shown that Bi-LSTM with Random Forest (Bi-LSTM-RF) yields the best prediction performance. Bi-LSTM-RF model has improved the landslide-risk predicting performance over LR, ANNs, LSTM, and Bi-LSTM in terms of the area under the receiver characteristic operator (AUC) scores by 0.42, 0.27, 0.46, and 0.47, respectively. Finally, an automated web GIS has been developed and it consists of software components including the trained models, rainfall API, Google API, and geodatabase. All components have been interfaced together via JavaScript and Node.js tool.

Background & Aims: Liver tight junctions (TJs) establish tissue barriers that isolate bile from the blood circulation. TJP2/ZO-2-inactivating mutations cause progressive cholestatic liver disease in humans. Because the underlying mechanisms remain elusive, we characterized mice with liver-specific inactivation of Tjp2., Methods: Tjp2 was deleted in hepatocytes, cholangiocytes, or both. Effects on the liver were assessed by biochemical analyses of plasma, liver, and bile and by electron microscopy, histology, and immunostaining. TJ barrier permeability was evaluated using fluorescein isothiocyanate-dextran (4 kDa). Cholic acid (CA) diet was used to assess susceptibility to liver injury., Results: Liver-specific deletion of Tjp2 resulted in lower Cldn1 protein levels, minor changes to the TJ, dilated canaliculi, lower microvilli density, and aberrant radixin and bile salt export pump (BSEP) distribution, without an overt increase in TJ permeability. Hepatic Tjp2-defcient mice presented with mild progressive cholestasis with lower expression levels of bile acid transporter Abcb11/Bsep and detoxification enzyme Cyp2b10. A CA diet tolerated by control mice caused severe cholestasis and liver necrosis in Tjp2-deficient animals. 1,4-Bis[2-(3,5-dichloropyridyloxy)]benzene ameliorated CA-induced injury by enhancing Cyp2b10 expression, and ursodeoxycholic acid provided partial improvement. Inactivating Tjp2 separately in hepatocytes or cholangiocytes showed only mild CA-induced liver injury., Conclusion: Tjp2 is required for normal cortical distribution of radixin, canalicular volume regulation, and microvilli density. Its inactivation deregulated expression of Cldn1 and key bile acid transporters and detoxification enzymes. The mice provide a novel animal model for cholestatic liver disease caused by TJP2-inactivating mutations in humans., (Copyright © 2021 AGA Institute. Published by Elsevier Inc. All rights reserved.)

A neural network that matches with a complex data function is likely to boost the classification performance as it is able to learn the useful aspect of the highly varying data. In this work, the temporal context of the time series data is chosen as the useful aspect of the data that is passed through the network for learning. By exploiting the compositional locality of the time series data at each level of the network, shift-invariant features can be extracted layer by layer at different time scales. The temporal context is made available to the deeper layers of the network by a set of data processing operations based on the concatenation operation. A matching learning algorithm for the revised network is described in this paper. It uses gradient routing in the backpropagation path. The framework as proposed in this work attains better generalization without overfitting the network to the data, as the weights can be pretrained appropriately. It can be used end-to-end with multivariate time series data in their raw form, without the need for manual feature crafting or data transformation. Data experiments with electroencephalogram signals and human activity signals show that with the right amount of concatenation in the deeper layers of the proposed network, it can improve the performance in signal classification.

Gait assessment is an essential tool for clinical applications not only to diagnose different neurological conditions but also to monitor disease progression as it contributes to the understanding of underlying deficits. There are established methods and models for data collection and interpretation of gait assessment within different pathologies. This narrative review aims to depict the evolution of gait assessment from observation and rating scales to wearable sensors and laboratory technologies and provide limitations and possible future directions in the field of gait assessment. In this context, we first present an extensive review of current clinical outcomes and gait models. Then, we demonstrate commercially available wearable technologies with their technical capabilities along with their use in gait assessment studies for various neurological conditions. In the next sections, a descriptive knowledge for existing inertial and EMG based algorithms and a sign based guide that shows the outcomes of previous neurological gait assessment studies are presented. Finally, we state a discussion for the use of wearables in gait assessment and speculate the possible research directions by revealing the limitations and knowledge gaps in the literature., Competing Interests: Declaration of Competing Interest None., (Copyright © 2020 IPEM. Published by Elsevier Ltd. All rights reserved.)

This article proposes a hybrid multi-dimensional features fusion structure of spatial and temporal segmentation model for automated thermography defects detection. In addition, the newly designed attention block encourages local interaction among the neighboring pixels to recalibrate the feature maps adaptively. A Sequence-PCA layer is embedded in the network to provide enhanced semantic information. The final model results in a lightweight structure with smaller number of parameters and yet yields uncompromising performance after model compression. The proposed model allows better capture of the semantic information to improve the detection rate in an end-to-end procedure. Compared with current state-of-the-art deep semantic segmentation algorithms, the proposed model presents more accurate and robust results. In addition, the proposed attention module has led to improved performance on two classification tasks compared with other prevalent attention blocks. In order to verify the effectiveness and robustness of the proposed model, experimental studies have been carried out for defects detection on four different datasets. The demo code of the proposed method can be linked soon: http://faculty.uestc.edu.cn/gaobin/zh&#95;CN/lwcg/153392/list/index.htm.

Pulmonary artery pseudoaneurysms are uncommon and can cause severe, life-threatening haemoptysis. We present a case of a 74-year-old gentleman who was being treated for COVID-19 pneumonitis and a concomitant segmental pulmonary artery thrombus with conventional treatment and anticoagulation. The patient developed significant haemoptysis during admission. A repeat computed tomography pulmonary angiogram revealed an 8 mm left upper lobe pulmonary artery pseudoaneurysm. Anticoagulation was withheld and the pseudoaneurysm was successfully treated with endovascular embolisation with an Amplatzer® IV plug, leading to resolution of the haemoptysis. To our knowledge this is the first case of a pulmonary artery pseudoaneurysm secondary to COVID-19., Competing Interests: The authors report no declarations of interest., (Crown Copyright © 2021 Published by Elsevier Ltd.)

With the advancement of electromagnetic induction thermography and imaging technology in non-destructive testing field, this system has significantly benefitted modern industries in fast and contactless defects detection. However, due to the limitations of front-end hardware experimental equipment and the complicated test pieces, these have brought forth new challenges to the detection process. Making use of the spatio-temporal video data captured by the thermal imaging device and linking it with advanced video processing algorithm to defects detection has become a necessary alternative way to solve these detection challenges. The extremely weak and sparse defect signal is buried in complex background with the presence of strong noise in the real experimental scene has prevented progress to be made in defects detection. In this paper, we propose a novel hierarchical low-rank and sparse tensor decomposition method to mine anomalous patterns in the induction thermography stream for defects detection. The proposed algorithm offers advantages not only in suppressing the interference of strong background and sharpens the visual features of defects, but also overcoming the problems of over- and under-sparseness suffered by similar state-of-the-art algorithms. Real-time natural defect detection experiments have been conducted to verify that the proposed algorithm is more efficient and accurate than existing algorithms in terms of visual presentations and evaluation criteria. This article is part of the theme issue 'Advanced electromagnetic non-destructive evaluation and smart monitoring'.

This paper proposes a solution for events classification from a sole noisy mixture that consist of two major steps: a sound-event separation and a sound-event classification. The traditional complex nonnegative matrix factorization (CMF) is extended by cooperation with the optimal adaptive L 1 sparsity to decompose a noisy single-channel mixture. The proposed adaptive L 1 sparsity CMF algorithm encodes the spectra pattern and estimates the phase of the original signals in time-frequency representation. Their features enhance the temporal decomposition process efficiently. The support vector machine (SVM) based one versus one (OvsO) strategy was applied with a mean supervector to categorize the demixed sound into the matching sound-event class. The first step of the multi-class MSVM method is to segment the separated signal into blocks by sliding demixed signals, then encoding the three features of each block. Mel frequency cepstral coefficients, short-time energy, and short-time zero-crossing rate are learned with multi sound-event classes by the SVM based OvsO method. The mean supervector is encoded from the obtained features. The proposed method has been evaluated with both separation and classification scenarios using real-world single recorded signals and compared with the state-of-the-art separation method. Experimental results confirmed that the proposed method outperformed the state-of-the-art methods., Competing Interests: The authors declare no conflicts of interest.

The Sports Concussion Assessment Tool (SCAT) is a pen and paper-based evaluation tool for use by healthcare professionals in the acute evaluation of suspected concussion. Here we present a feasibility study towards instrumented SCAT (iSCAT). Traditionally, a healthcare professional subjectively counts errors according to SCAT marking criteria matrix. It is hypothesized that an instrumented version of the test will be more accurate while providing additional digital-based parameters to better inform player management. The feasibility study focuses on the SCAT physical functioning tasks only: double leg stance, single-leg stance, tandem stance and tandem gait. Amateur university rugby players underwent iSCAT testing and data were recorded with 8 inertial units attached at different anatomical locations. Video data were gathered simultaneously as reference. An iSCAT algorithm was used to detect errors and quantify additional concussion-based time and frequency domain parameters to assess participant stability during balance and gait tasks. Future work aims to instrument other SCAT features such as hand-eye coordination while deploying methods within a large concussion project.

Epstein-Barr virus (EBV) is associated with a variety of tumors and nonmalignant conditions. Latent EBV genomes in cells, including tumor cells, are often CpG methylated, whereas virion DNA is not CpG methylated. We demonstrate that methyl CpG binding magnetic beads can be used to fractionate among sources of EBV DNA (DNA extracted from laboratory-purified virions vs DNA extracted from latently infected cell lines). We then applied the technique to plasma specimens and showed that this technique can distinguish EBV DNA from patients with EBV-associated tumors (nasopharyngeal carcinoma, Hodgkin lymphoma) and viral DNA from patients without EBV-associated tumors, including immunocompromised patients and patients with EBV(-) Hodgkin lymphoma.

This paper designs an accurate and low-cost phishing detection sensor by exploring deep learning techniques. Phishing is a very common social engineering technique. The attackers try to deceive online users by mimicking a uniform resource locator (URL) and a webpage. Traditionally, phishing detection is largely based on manual reports from users. Machine learning techniques have recently been introduced for phishing detection. With the recent rapid development of deep learning techniques, many deep-learning-based recognition methods have also been explored to improve classification performance. This paper proposes a light-weight deep learning algorithm to detect the malicious URLs and enable a real-time and energy-saving phishing detection sensor. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. According to the experiments, the true detection rate has been improved. This paper has also verified that the proposed method can run in an energy-saving embedded single board computer in real-time.

Artificial intelligence was recognised many years ago as a potential and powerful tool to predict disease outcome in many clinical situations. The conventional approaches using statistical methods have provided much information, but are subject to limitations imposed by the complexity of medical data. The structures of the important variants of the machine learning system artificial neural networks (ANN) are discussed and emphasis is given to the powerful analytical support that could be provided by ANN for the prediction of cancer progression and prognosis. The predictive ability of the cellular markers, DNA ploidy and cell-cycle profiles, and molecular markers, such as tumour promoter and suppressor gene, and growth factor and steroid hormone receptors in breast cancer management were also analysed. ANN systems have been successfully deployed to evaluate microRNA profiles of tumours which saliently sway cancer progression and prognosis of the disease, thus counteracting the negative implications of their numerical abundance. Finally, in this setting, the prospective technical improvements in artificial neural networks, as hybrid systems in combination with fuzzy logic and artificial immune networks were also addressed., (Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Thermographic inspection has been widely applied to non-destructive testing and evaluation with the capabilities of rapid, contactless, and large surface area detection. Image segmentation is considered essential for identifying and sizing defects. To attain a high-level performance, specific physics-based models that describe defects generation and enable the precise extraction of target region are of crucial importance. In this paper, an effective genetic first-order statistical image segmentation algorithm is proposed for quantitative crack detection. The proposed method automatically extracts valuable spatial-temporal patterns from unsupervised feature extraction algorithm and avoids a range of issues associated with human intervention in laborious manual selection of specific thermal video frames for processing. An internal genetic functionality is built into the proposed algorithm to automatically control the segmentation threshold to render enhanced accuracy in sizing the cracks. Eddy current pulsed thermography will be implemented as a platform to demonstrate surface crack detection. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. In addition, a global quantitative assessment index F-score has been adopted to objectively evaluate the performance of different segmentation algorithms.

This paper presents an unsupervised learning algorithm for sparse nonnegative matrix factor time⁻frequency deconvolution with optimized fractional β-divergence. The β-divergence is a group of cost functions parametrized by a single parameter β. The Itakura⁻Saito divergence, Kullback⁻Leibler divergence and Least Square distance are special cases that correspond to β=0, 1, 2, respectively. This paper presents a generalized algorithm that uses a flexible range of β that includes fractional values. It describes a maximization⁻minimization (MM) algorithm leading to the development of a fast convergence multiplicative update algorithm with guaranteed convergence. The proposed model operates in the time⁻frequency domain and decomposes an information-bearing matrix into two-dimensional deconvolution of factor matrices that represent the spectral dictionary and temporal codes. The deconvolution process has been optimized to yield sparse temporal codes through maximizing the likelihood of the observations. The paper also presents a method to estimate the fractional β value. The method is demonstrated on separating audio mixtures recorded from a single channel. The paper shows that the extraction of the spectral dictionary and temporal codes is significantly more efficient by using the proposed algorithm and subsequently leads to better source separation performance. Experimental tests and comparisons with other factorization methods have been conducted to verify its efficacy., Competing Interests: The authors declare no conflict of interest.

Aims: Autoimmune conditions such as rheumatoid arthritis-related interstitial lung disease (RA-ILD) have been linked to the existence of emphysema in never-smokers. We aimed to quantify emphysema prevalence in RA-ILD never-smokers and investigate whether combined pulmonary fibrosis and emphysema (CPFE) results in a worsened prognosis independent of baseline disease extent., Methods: RA-ILD patients presenting to the Royal Brompton Hospital (n=90) and Asan Medical Center (n=155) had CT's evaluated for a definite usual interstitial pneumonia (UIP) pattern, and visual extents of emphysema and ILD., Results: Emphysema, identified in 31/116 (27%) RA-ILD never-smokers, was associated with obstructive functional indices and conformed to a CPFE phenotype: disproportionate reduction in gas transfer (DLco), relative preservation of lung volumes. Using multivariate logistic regression, adjusted for patient age, gender and ILD extent, emphysema presence independently associated with a CT-UIP pattern in never-smokers (0.009) and smokers (0.02). On multivariate Cox analysis, following adjustment for patient age, gender, DLco, and a CT-UIP pattern, emphysema presence (representing the CPFE phenotype) independently associated with mortality in never-smokers (p=0.04) and smokers (p<0.05)., Conclusion: 27% of RA-ILD never-smokers demonstrate emphysema on CT. Emphysema presence in never-smokers independently associates with a definite CT-UIP pattern and a worsened outcome following adjustment for baseline disease severity., (Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.)

Competing Interests: Competing interests: None declared.

Emerging integrated sensing and monitoring of material degradation and cracks are increasingly required for characterizing the structural integrity and safety of infrastructure. However, most conventional nondestructive evaluation (NDE) methods are based on single modality sensing which is not adequate to evaluate structural integrity and natural cracks. This paper proposed electromagnetic pulsed thermography for fast and comprehensive defect characterization. It hybrids multiple physical phenomena i.e. magnetic flux leakage, induced eddy current and induction heating linking to physics as well as signal processing algorithms to provide abundant information of material properties and defects. New features are proposed using 1st derivation that reflects multiphysics spatial and temporal behaviors to enhance the detection of cracks with different orientations. Promising results that robust to lift-off changes and invariant features for artificial and natural cracks detection have been demonstrated that the proposed method significantly improves defect detectability. It opens up multiphysics sensing and integrated NDE with potential impact for natural understanding and better quantitative evaluation of natural cracks including stress corrosion crack (SCC) and rolling contact fatigue (RCF)., Competing Interests: The authors declare no competing financial interests.

Electromagnetic mechanism of Joule heating and thermal conduction on conductive material characterization broadens their scope for implementation in real thermography based Nondestructive testing and evaluation (NDT&E) systems by imparting sensitivity, conformability and allowing fast and imaging detection, which is necessary for efficiency. The issue of automatic material evaluation has not been fully addressed by researchers and it marks a crucial first step to analyzing the structural health of the material, which in turn sheds light on understanding the production of the defects mechanisms. In this study, we bridge the gap between the physics world and mathematical modeling world. We generate physics-mathematical modeling and mining route in the spatial-, time-, frequency-, and sparse-pattern domains. This is a significant step towards realizing the deeper insight in electromagnetic thermography (EMT) and automatic defect identification. This renders the EMT a promising candidate for the highly efficient and yet flexible NDT&E.

In this paper, a fusion of K models of full-rank weighted nonnegative tensor factor two-dimensional deconvolution (K-wNTF2D) is proposed to separate the acoustic sources that have been mixed in an underdetermined reverberant environment. The model is adapted in an unsupervised manner under the hybrid framework of the generalized expectation maximization and multiplicative update algorithms. The derivation of the algorithm and the development of proposed full-rank K-wNTF2D will be shown. The algorithm also encodes a set of variable sparsity parameters derived from Gibbs distribution into the K-wNTF2D model. This optimizes each sub-model in K-wNTF2D with the required sparsity to model the time-varying variances of the sources in the spectrogram. In addition, an initialization method is proposed to initialize the parameters in the K-wNTF2D. Experimental results on the underdetermined reverberant mixing environment have shown that the proposed algorithm is effective at separating the mixture with an average signal-to-distortion ratio of 3 dB.

Inflammatory myofibroblastic tumour (IMT) of the lung represents an extremely rare type of inflammatory pseudo tumor that appears most commonly in children and young individuals. There has been an ongoing controversy whether an IMT is a reactive lesion or a true neoplasm making the further management extremely challenging. Purpose of the paper is through a literature review to highlight the existence of this rare tumour along with its key features and the management options available.

An innovative method of single-channel blind source separation is proposed. The proposed method is a complex-valued non-negative matrix factorization with probabilistically optimal L1-norm sparsity. This preserves the phase information of the source signals and enforces the inherent structures of the temporal codes to be optimally sparse, thus resulting in more meaningful parts factorization. An efficient algorithm with closed-form expression to compute the parameters of the model including the sparsity has been developed. Real-time acoustic mixtures recorded from a single-channel are used to verify the effectiveness of the proposed method.

Primary pulmonary synovial sarcomas represent a rare clinical entity and account for approximately 0.5% of lung malignancies. We report the case of a 30-year-old male who presented clinically with haemothorax. Imaging revealed a complex collection obscuring a multi-lobulated mass in the right lower lobe of the lung. He underwent a right thoracotomy for evacuation of collection and surgical resection of his pulmonary mass. Histological analysis confirmed a grade 3 monophasic fibrous synovial sarcoma of the lung with infiltration to adjacent pleura, causing his initial haemothorax. Postoperative period was uneventful and patient was referred to the oncology team for further management. Primary pulmonary synovial sarcoma, though rare, should remain an important differential when considering lung malignancies, as complete surgical resection is the mainstay of treatment.

The purpose of this paper is to highlight the existence and the management of lung choriocarcinoma (CCA), a rare category of lung tumors. We present a 42-year-old female that presented to our department with a PET positive lesion in the left upper lobe and a history of pregnancy 6 months prior to onset of symptoms. CT guided biopsy was inconclusive for diagnosis and the patient underwent a left thoracotomy and lingula sparing upper lobectomy. Histology revealed CCA of the lung and subsequently blood results confirmed the elevated b-HCG. CCA of the lung is a clinical entity that should be considered in the differential diagnosis of lung lesions in women after pregnancy.

Mucoepidermoid carcinoma (MEC) of the thymus is a rare malignant neoplasm of the anterior mediastinum. There are less than 30 cases described in the English literature. We report a case of a 47-year-old lady who presented with myasthenia gravis and was found to have a well-circumscribed anterior mediastinal mass in her medical work-up. This mass was surgically resected and subsequently found to be a primary MEC of the thymus. This is the first reported case of thymic MEC with concurrent myasthenia gravis. Her myasthenia symptoms have persisted following complete surgical resection of her tumour.

A novel unsupervised machine learning algorithm for single channel source separation is presented. The proposed method is based on nonnegative matrix factorization, which is optimized under the framework of maximum a posteriori probability and Itakura-Saito divergence. The method enables a generalized criterion for variable sparseness to be imposed onto the solution and prior information to be explicitly incorporated through the basis vectors. In addition, the method is scale invariant where both low and high energy components of a signal are treated with equal importance. The proposed algorithm is a more complete and efficient approach for matrix factorization of signals that exhibit temporal dependency of the frequency patterns. Experimental tests have been conducted and compared with other algorithms to verify the efficiency of the proposed method.

An unsupervised single channel audio separation method from pattern recognition viewpoint is presented. The proposed method does not require training knowledge and the separation system is based on non-uniform time-frequency (TF) analysis and feature extraction. Unlike conventional research that concentrates on the use of spectrogram or its variants, the proposed separation algorithm uses an alternative TF representation based on the gammatone filterbank. In particular, the monaural mixed audio signal is shown to be considerably more separable in this non-uniform TF domain. The analysis of signal separability to verify this finding is provided. In addition, a variational Bayesian approach is derived to learn the sparsity parameters for optimizing the matrix factorization. Experimental tests have been conducted, which show that the extraction of the spectral dictionary and temporal codes is more efficient using sparsity learning and subsequently leads to better separation performance.

Yeast and mycobacteria can cause infections in immunocompromised patients and normal hosts. The rapid identification of these organisms can significantly improve patient care. There has been an increasing number of studies on using matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) for rapid yeast and mycobacterial identifications. However, studies on direct comparisons between the Bruker Biotyper and bioMérieux Vitek MS systems for the identification of yeast and mycobacteria have been limited. This study compared the performance of the two systems in their identification of 98 yeast and 102 mycobacteria isolates. Among the 98 yeast isolates, both systems generated species-level identifications in >70% of the specimens, of which Candida albicans was the most commonly cultured species. At a genus-level identification, the Biotyper system identified more isolates than the Vitek MS system for Candida (75/78 [96.2%]versus 68/78 [87.2%], respectively; P = 0.0426) and non-Candida yeasts (18/20 [90.0%]versus 7/20 [35.0%], respectively; P = 0.0008). For mycobacterial identification, the Biotyper system generated reliable identifications for 89 (87.3%) and 64 (62.8%) clinical isolates at the genus and species levels, respectively, from solid culture media, whereas the Vitek MS system did not generate any reliable identification. The MS method differentiated 12/21 clinical species, despite the fact that no differentiation between Mycobacterium abscessus and Mycobacterium chelonae was found by using 16S rRNA gene sequencing. In summary, the MALDI-TOF MS method provides short turnaround times and a standardized working protocol for the identification of yeast and mycobacteria. Our study demonstrates that MALDI-TOF MS is suitable as a first-line test for the identification of yeast and mycobacteria in clinical laboratories.