COVID-19 Detection from Respiratory Sounds with Hierarchical Spectrogram Transformers

Monitoring of prevalent airborne diseases such as COVID-19 characteristically involves respiratory assessments. While auscultation is a mainstream method for preliminary screening of disease symptoms, its utility is hampered by the need for dedicated hospital visits. Remote monitoring based on recordings of respiratory sounds on portable devices is a promising alternative, which can assist in early assessment of COVID-19 that primarily affects the lower respiratory tract. In this study, we introduce a novel deep learning approach to distinguish patients with COVID-19 from healthy controls given audio recordings of cough or breathing sounds. The proposed approach leverages a novel hierarchical spectrogram transformer (HST) on spectrogram representations of respiratory sounds. HST embodies self-attention mechanisms over local windows in spectrograms, and window size is progressively grown over model stages to capture local to global context. HST is compared against state-of-the-art conventional and deep-learning baselines. Demonstrations on crowd-sourced multi-national datasets indicate that HST outperforms competing methods, achieving over 83% area under the receiver operating characteristic curve (AUC) in detecting COVID-19 cases

Parameter estimation for biochemical reaction networks using Wasserstein distances

We present a method for estimating parameters in stochastic models of biochemical reaction networks by fitting steady-state distributions using Wasserstein distances. We simulate a reaction network at different parameter settings and train a Gaussian process to learn the Wasserstein distance between observations and the simulator output for all parameters. We then use Bayesian optimization to find parameters minimizing this distance based on the trained Gaussian process. The effectiveness of our method is demonstrated on the three-stage model of gene expression and a genetic feedback loop for which moment-based methods are known to perform poorly. Our method is applicable to any simulator model of stochastic reaction networks, including Brownian Dynamics.Comment: 22 pages, 8 figures. Slight modifications/additions to the text; added new section (Section 4.4) and Appendi

Self-consistent local-equilibrium model for density profile and distribution of dissipative currents in a Hall bar under strong magnetic fields

Recent spatially resolved measurements of the electrostatic-potential variation across a Hall bar in strong magnetic fields, which revealed a clear correlation between current-carrying strips and incompressible strips expected near the edges of the Hall bar, cannot be understood on the basis of existing equilibrium theories. To explain these experiments, we generalize the Thomas-Fermi--Poisson approach for the self-consistent calculation of electrostatic potential and electron density in {\em total} thermal equilibrium to a {\em local equilibrium} theory that allows to treat finite gradients of the electrochemical potential as driving forces of currents in the presence of dissipation. A conventional conductivity model with small values of the longitudinal conductivity for integer values of the (local) Landau-level filling factor shows that, in apparent agreement with experiment, the current density is localized near incompressible strips, whose location and width in turn depend on the applied current.Comment: 9 pages, 7 figure

Cross-sectional evaluation of the periapical status as related to quality of root canal fillings and coronal restorations in a rural adult male population of Turkey

<p>Abstract</p> <p>Background</p> <p>To determine the prevalence of periapical lesions in root canal-treated teeth in a rural, male adult, Turkish population and to investigate the influence of the quality of root canal fillings on prevalence of periapical lesions.</p> <p>Methods</p> <p>The sample for this cross-sectional study consisted of 552 adult male patients, 18-32 years of age, presenting consecutively as new patients seeking routine dental care at the Dental Sciences of Gulhane Military Medicine, Ankara. The radiographs of the 1014 root canal-treated teeth were evaluated. The teeth were grouped according to the radiographic quality of the root canal filling and the coronal restoration. The criteria used for the examination were slightly modified from those described by De Moor. Periapical status was assessed by the Periapical Index scores (PAI) proposed by Orstavik.</p> <p>Results</p> <p>The overall success rate of root canal treatment was 32.1%. The success rates of adequately root canal treatment were significantly higher than inadequately root canal treatment, regardless of the quality or presence of the coronal restoration (P < .001). In addition, the success rate of inadequate root canal treatment was also significantly affected by the quality of coronal restorations.</p> <p>Conclusions</p> <p>Our results revealed a high prevalence of periapical lesions in root canal treatment, which is comparable to that reported in other methodologically compatible studies from diverse geographical locations. In addition, the results from the present study confirm the findings of other studies that found the quality of the root canal treatment to be a key factor for prognosis with or without adequate coronal restoration.</p

Multilevel factors are associated with immunosuppressant nonadherence in heart transplant recipients: The international BRIGHT study

Factors at the level of family/healthcare worker, organization, and system are neglected in medication nonadherence research in heart transplantation (HTx). The 4-continent, 11-country cross-sectional Building Research Initiative Group: Chronic Illness Management and Adherence in Transplantation (BRIGHT) study used multistaged sampling to examine 36 HTx centers, including 36 HTx directors, 100 clinicians, and 1397 patients. Nonadherence to immunosuppressants\u2014defined as any deviation in taking or timing adherence and/or dose reduction\u2014was assessed using the Basel Assessment of Adherence to Immunosuppressive Medications Scale \ua9 (BAASIS \ua9 ) interview. Guided by the Integrative Model of Behavioral Prediction and Bronfenbrenner's ecological model, we analyzed factors at these multiple levels using sequential logistic regression analysis (6 blocks). The nonadherence prevalence was 34.1%. Six multilevel factors were associated independently (either positively or negatively) with nonadherence: patient level: barriers to taking immunosuppressants (odds ratio [OR]: 11.48); smoking (OR: 2.19); family/healthcare provider level: frequency of having someone to help patients read health-related materials (OR: 0.85); organization level: clinicians reporting nonadherent patients were targeted with adherence interventions (OR: 0.66); pickup of medications at physician's office (OR: 2.31); and policy level: monthly out-of-pocket costs for medication (OR: 1.16). Factors associated with nonadherence are evident at multiple levels. Improving medication nonadherence requires addressing not only the patient, but also family/healthcare provider, organization, and policy levels

The Relative Contribution of High-Gamma Linguistic Processing Stages of Word Production, and Motor Imagery of Articulation in Class Separability of Covert Speech Tasks in EEG Data

Word production begins with high-Gamma automatic linguistic processing functions followed by speech motor planning and articulation. Phonetic properties are processed in both linguistic and motor stages of word production. Four phonetically dissimilar phonemic structures “BA”, “FO”, “LE”, and “RY” were chosen as covert speech tasks. Ten neurologically healthy volunteers with the age range of 21–33 participated in this experiment. Participants were asked to covertly speak a phonemic structure when they heard an auditory cue. EEG was recorded with 64 electrodes at 2048 samples/s. Initially, one-second trials were used, which contained linguistic and motor imagery activities. The four-class true positive rate was calculated. In the next stage, 312 ms trials were used to exclude covert articulation from analysis. By eliminating the covert articulation stage, the four-class grand average classification accuracy dropped from 96.4% to 94.5%. The most valuable features emerge after Auditory cue recognition (~100 ms post onset), and within the 70–128 Hz frequency range. The most significant identified brain regions were the Prefrontal Cortex (linked to stimulus driven executive control), Wernicke’s area (linked to Phonological code retrieval), the right IFG, and Broca’s area (linked to syllabification). Alpha and Beta band oscillations associated with motor imagery do not contain enough information to fully reflect the complexity of speech movements. Over 90% of the most class-dependent features were in the 30-128 Hz range, even during the covert articulation stage. As a result, compared to linguistic functions, the contribution of motor imagery of articulation in class separability of covert speech tasks from EEG data is negligible

Validation of the patient assessment of chronic illness care (PACIC) short form scale in heart transplant recipients: The international cross-sectional bright study

Background: Transplant recipients are chronically ill patients, who require lifelong follow-up to manage co-morbidities and prevent graft loss. This necessitates a system of care that is congruent with the Chronic Care Model. The eleven-item self-report Patient Assessment of Chronic Illness Care (PACIC) scale assesses whether chronic care is congruent with the Chronic Care Model, yet its validity for heart transplant patients has not been tested. Methods: We tested the validity of the English version of the PACIC, and compared the similarity of the internal structure of the PACIC across English-speaking countries (USA, Canada, Australia and United Kingdom) and across six languages (French, German, Dutch, Spanish, Italian and Portuguese). This was done using data from the cross-sectional international BRIGHT study that included 1378 heart transplant patients from eleven countries across 4 continents. To test the validity of the instrument, confirmatory factor analyses to check the expected unidimensional internal structure, and relations to other variables, were performed. Results: Main analyses confirmed the validity of the English PACIC version for heart transplant patients. Exploratory analyses across English-speaking countries and languages also confirmed the single factorial dimension, except in Italian and Spanish. Conclusion: This scale could help healthcare providers monitor level of chronic illness management and improve transplantation care. Trial registration: Clinicaltrials.gov ID: NCT01608477, first patient enrolled in March 2012, registered retrospectively: May 30, 2012