Automatic Non-Invasive Cough Detection based on Accelerometer and Audio Signals.

We present an automatic non-invasive way of detecting cough events based on both accelerometer and audio signals. The acceleration signals are captured by a smartphone firmly attached to the patient's bed, using its integrated accelerometer. The audio signals are captured simultaneously by the same smartphone using an external microphone.

COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features.

We present an experimental investigation into the effectiveness of transfer learning and bottleneck feature extraction in detecting COVID-19 from audio recordings of cough, breath and speech. This type of screening is non-contact, does not require specialist medical expertise or laboratory facilities and can be deployed on inexpensive consumer hardware such as a smartphone. We use datasets that contain cough, sneeze, speech and other noises, but do not contain COVID-19 labels, to pre-train three deep neural networks: a CNN, an LSTM and a Resnet50.

Automatic cough classification for tuberculosis screening in a real-world environment.

The automatic discrimination between the coughing sounds produced by patients with tuberculosis (TB) and those produced by patients with other lung ailments.We present experiments based on a dataset of 1358 forced cough recordings obtained in a developing-world clinic from 16 patients with confirmed active pulmonary TB and 35 patients suffering from respiratory conditions suggestive of TB but confirmed to be TB negative. Using nested cross-validation, we have trained and evaluated five machine learning classifiers: logistic regression (LR), support vector machines, k-nearest neighbour, multilayer perceptrons and convolutional neural networks.

COVID-19 cough classification using machine learning and global smartphone recordings.

We present a machine learning based COVID-19 cough classifier which can discriminate COVID-19 positive coughs from both COVID-19 negative and healthy coughs recorded on a smartphone. This type of screening is non-contact, easy to apply, and can reduce the workload in testing centres as well as limit transmission by recommending early self-isolation to those who have a cough suggestive of COVID-19. The datasets used in this study include subjects from all six continents and contain both forced and natural coughs, indicating that the approach is widely applicable.

Mitochondrial event localiser (MEL) to quantitativelydescribe fission, fusion and depolarisation in the three-dimensional space.

Mitochondrial fission and fusion play an important role not only in maintaining mitochondrial homeostasis but also in preserving overall cellular viability. However, quantitative analysis based on the three-dimensional localisation of these highly dynamic mitochondrial events in the cellular context has not yet been accomplished. Moreover, it remains largely uncertain where in the mitochondrial network depolarisation is most likely to occur.

A Comparative Study of Features for Acoustic Cough Detection Using Deep Architectures.

Automatic cough detection is key to tracking the condition of patients suffering from tuberculosis. We evaluate various acoustic features for performing cough detection using deep architectures. As most previous studies have adopted features designed for speech recognition, we assess the suitability of these techniques as well as their respective extraction parameters.

Regression adjusted colocalisation colour mapping (RACC): A novel biological visual analysis method for qualitative colocalisation analysis of 3D fluorescence micrographs.

The qualitative analysis of colocalisation in fluorescence microscopy is of critical importance to the understanding of biological processes and cellular function. However, the degree of accuracy achieved may differ substantially when executing different yet commonly utilized colocalisation analyses. We propose a novel biological visual analysis method that determines the correlation within the fluorescence intensities and subsequently uses this correlation to assign a colourmap value to each voxel in a three-dimensional sample while also highlighting volumes with greater combined fluorescence intensity.

Computer simulation and physical phantom models for estimating the dielectric properties of rhinoceros tissue.

In vivo and ex vivo sensors have the potential to aid tracking and anti-poaching endeavours and provide new insights into rhinoceros physiology and environment. However, the propagation of electromagnetic signals in rhinoceros tissue is currently not known. We present simulation and agar models of the rhinoceros that allow the investigation of electromagnetic propagation by in vivo and ex vivo devices without the need for surgery.

Wild-type and mutant (G2019S) leucine-rich repeat kinase 2 (LRRK2) associate with subunits of the translocase of outer mitochondrial membrane (TOM) complex.

Leucine-rich repeat kinase 2 (LRRK2) is important in various cellular processes including mitochondrial homeostasis and mutations in this gene lead to Parkinson's disease (PD). However, the full spectrum of LRRK2's functions remain to be elucidated. The translocase of outer mitochondrial membrane (TOM) complex is essential for the import of almost all nuclear-encoded mitochondrial proteins and is fundamental for cellular survival.

Improved region of interest selection and colocalization analysis in three-dimensional fluorescence microscopy samples using virtual reality.

Although modern fluorescence microscopy produces detailed three-dimensional (3D) datasets, colocalization analysis and region of interest (ROI) selection is most commonly performed two-dimensionally (2D) using maximum intensity projections (MIP). However, these 2D projections exclude much of the available data. Furthermore, 2D ROI selections cannot adequately select complex 3D structures which may inadvertently lead to either the exclusion of relevant or the inclusion of irrelevant data points, consequently affecting the accuracy of the colocalization analysis.

Detection of tuberculosis by automatic cough sound analysis.

Objective: Globally, tuberculosis (TB) remains one of the most deadly diseases. Although several effective diagnosis methods exist, in lower income countries clinics may not be in a position to afford expensive equipment and employ the trained experts needed to interpret results. In these situations, symptoms including cough are commonly used to identify patients for testing.

Virtual reality assisted microscopy data visualization and colocalization analysis.

Background: Confocal microscopes deliver detailed three-dimensional data and are instrumental in biological analysis and research. Usually, this three-dimensional data is rendered as a projection onto a two-dimensional display. We describe a system for rendering such data using a modern virtual reality (VR) headset.

Clustering Acoustic Segments Using Multi-Stage Agglomerative Hierarchical Clustering.

Agglomerative hierarchical clustering becomes infeasible when applied to large datasets due to its O(N2) storage requirements. We present a multi-stage agglomerative hierarchical clustering (MAHC) approach aimed at large datasets of speech segments. The algorithm is based on an iterative divide-and-conquer strategy.

Automatic detection, segmentation and assessment of snoring from ambient acoustic data.

Snoring is a prevalent condition with a variety of negative social effects and associated health problems. Treatments, both surgical and therapeutic, have been developed, but the objective non-invasive monitoring of their success remains problematic. We present a method which allows the automatic monitoring of snoring characteristics, such as intensity and frequency, from audio data captured via a freestanding microphone.