COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis

At the time of writing, the world population is suffering from more than 10,000 registered COVID-19 disease epidemic induced deaths since the outbreak of the Corona virus more than three months ago now officially known as SARS-CoV-2. Since, tremendous efforts have been made worldwide to counter-steer and control the epidemic by now labelled as pandemic. In this contribution, we provide an overview on the potential for computer audition (CA), i.e., the usage of speech and sound analysis by artificial intelligence to help in this scenario. We first survey which types of related or contextually significant phenomena can be automatically assessed from speech or sound. These include the automatic recognition and monitoring of breathing, dry and wet coughing or sneezing sounds, speech under cold, eating behaviour, sleepiness, or pain to name but a few. Then, we consider potential use-cases for exploitation. These include risk assessment and diagnosis based on symptom histograms and their development over time, as well as monitoring of spread, social distancing and its effects, treatment and recovery, and patient wellbeing. We quickly guide further through challenges that need to be faced for real-life usage. We come to the conclusion that CA appears ready for implementation of (pre-)diagnosis and monitoring tools, and more generally provides rich and significant, yet so far untapped potential in the fight against COVID-19 spread

Association between genetically predicted rheumatoid arthritis and alopecia areata: a two-sample Mendelian randomization study

BackgroundAlthough numerous observational studies have indicated a potential association between autoimmune diseases, such as rheumatoid arthritis (RA) and alopecia areata (AA), the research reports lack a clear causal relationship. In this study, our objective is to utilize the Mendelian randomization (MR) design to examine the potential causal association between RA and AA.MethodsTo investigate the causal relationship between RA and AA, we utilized large-scale gene aggregation data from genome-wide association studies (GWAS), including RA (n=58,284) and AA (n=361,822) based on previous observational studies. In our analysis, we mainly employed the inverse variance-weighted (IVW) method of the random effects model, supplemented by the weighted median (WM) method and the MR Egger method.ResultsThe findings from the IVW methods revealed a significant association between genetically predicted RA and an increased likelihood of AA, as evidenced by an odds ratio of 1.21 (95%CI = 1.11-1.32; P < 0.001. Both the WM method and MR-Egger regression consistently showed significant directional outcomes (Both P < 0.05), indicating a robust association between RA and AA. Additionally, both the funnel plot and the MR-Egger intercepts provided evidence of the absence of directional pleiotropy, suggesting that the observed association is not influenced by other common genetic factors.ConclusionsThe results of the study suggest a possible link between genetically predicted RA and AA. This finding highlights the importance for individuals diagnosed with RA to remain vigilant and aware of the potential development of AA. Regular monitoring and early detection can be crucial in managing and addressing this potential complication

An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety

The COVID-19 outbreak was announced as a global pandemic by the World Health Organisation in March 2020 and has affected a growing number of people in the past few weeks. In this context, advanced artificial intelligence techniques are brought to the fore in responding to fight against and reduce the impact of this global health crisis. In this study, we focus on developing some potential use-cases of intelligent speech analysis for COVID-19 diagnosed patients. In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety. For this purpose, two established acoustic feature sets and support vector machines are utilised. Our experiments show that an average accuracy of .69 obtained estimating the severity of illness, which is derived from the number of days in hospitalisation. We hope that this study can foster an extremely fast, low-cost, and convenient way to automatically detect the COVID-19 disease

Twist morphing of a composite rotor blade using a novel metamaterial

A novel meta-material has been designed and implemented into a rotor blade to enhance aerodynamic efficiency by achieving a passive twist during rotation. The twist is induced by bend-twist coupling exhibited in the meta-material, which is created to possess anisotropic elastic properties at the bulk level. A concept design of a rectangular blade spar is proposed where the metamaterial is used as the core material to induce twist. Using finite element analysis(FEA) we demonstrate how the bend-twist property of the blade spar is governed by cell geometries of the core material. The twist is activated by a lagwise bending moment generated from a movable mass at the blade tip due to off-centre centrifugal forces. The relationship between the twist, mass location and rotational speed has been explored. Moreover, it was found that the bend-twist property achieved by the proposed blade spar is more effective compared to that of an anisotropic thin-walled composite beam

A scenario for high-temperature excitonic insulators

While excitonic insulators (EIs) have been intensively studied, proper platforms of them with stable lattice and non-cryogenic T _C are rare. By analysing their Bardeen–Cooper–Schieffer-like gap equation, we propose that high T _C EIs can exist in small indirect band gap 2D materials. After screening 2D transition-metal dichalcogenides from existing computational works, we select 2 H -TiTe _2 and 1 T -PdTe _2 , and show that their T _C can be as high as 150 to 200 K under strains. A transition of their condensate EI state from that composed by Wannier excitons to that composed by plasmonic ones exists, even if negligible changes are reflected by the EI band structures, demonstrating the rich quantum feature of these systems. The high T _C also implies that they are ideal platforms for the demonstration and applications of EIs and their related quantum states in non-cryogenic environments

Hydrogels for Engineering of Perfusable Vascular Networks

Hydrogels are commonly used biomaterials for tissue engineering. With their high-water content, good biocompatibility and biodegradability they resemble the natural extracellular environment and have been widely used as scaffolds for 3D cell culture and studies of cell biology. The possible size of such hydrogel constructs with embedded cells is limited by the cellular demand for oxygen and nutrients. For the fabrication of large and complex tissue constructs, vascular structures become necessary within the hydrogels to supply the encapsulated cells. In this review, we discuss the types of hydrogels that are currently used for the fabrication of constructs with embedded vascular networks, the key properties of hydrogels needed for this purpose and current techniques to engineer perfusable vascular structures into these hydrogels. We then discuss directions for future research aimed at engineering of vascularized tissue for implantation