Automated Classification of Phonetic Segments in Child Speech Using Raw Ultrasound Imaging

Speech sound disorder (SSD) is defined as a persistent impairment in speech sound production leading to reduced speech intelligibility and hindered verbal communication. Early recognition and intervention of children with SSD and timely referral to speech and language therapists (SLTs) for treatment are crucial. Automated detection of speech impairment is regarded as an efficient method for examining and screening large populations. This study focuses on advancing the automatic diagnosis of SSD in early childhood by proposing a technical solution that integrates ultrasound tongue imaging (UTI) with deep-learning models. The introduced FusionNet model combines UTI data with the extracted texture features to classify UTI. The overarching aim is to elevate the accuracy and efficiency of UTI analysis, particularly for classifying speech sounds associated with SSD. This study compared the FusionNet approach with standard deep-learning methodologies, highlighting the excellent improvement results of the FusionNet model in UTI classification and the potential of multi-learning in improving UTI classification in speech therapy clinics

Automated classification of phonetic segments in child speech using raw ultrasound imaging

Speech sound disorder (SSD) is defined as a persistent impairment in speech sound production leading to reduced speech intelligibility and hindered verbal communication. Early recognition and intervention of children with SSD and timely referral to speech and language therapists (SLTs) for treatment are crucial. Automated detection of speech impairment is regarded as an efficient method for examining and screening large populations. This study focuses on advancing the automatic diagnosis of SSD in early childhood by proposing a technical solution that integrates ultrasound tongue imaging (UTI) with deep-learning models. The introduced FusionNet model combines UTI data with the extracted texture features to classify UTI. The overarching aim is to elevate the accuracy and efficiency of UTI analysis, particularly for classifying speech sounds associated with SSD. This study compared the FusionNet approach with standard deep-learning methodologies, highlighting the excellent improvement results of the FusionNet model in UTI classification and the potential of multi-learning in improving UTI classification in speech therapy clinics

Distributed Computing and Monitoring Technologies for Older Patients

This book summarizes various approaches for the automatic detection of health threats to older patients at home living alone. The text begins by briefly describing those who would most benefit from healthcare supervision. The book then summarizes possible scenarios for monitoring an older patient at home, deriving the common functional requirements for monitoring technology. Next, the work identifies the state of the art of technological monitoring approaches that are practically applicable to geriatric patients. A survey is presented on a range of such interdisciplinary fields as smart homes, telemonitoring, ambient intelligence, ambient assisted living, gerontechnology, and aging-in-place technology. The book discusses relevant experimental studies, highlighting the application of sensor fusion, signal processing and machine learning techniques. Finally, the text discusses future challenges, offering a number of suggestions for further research directions

Characterization of Urinary Microbiome and Their Association with Health and Disease

There has been a growing interest in human microbiome studies in the past decade, with the development of high-throughput sequencing techniques. These microorganisms interact and respond to the host as an entity, and are involved in various homeostatic functions including nutrition digestion, immune response, metabolism and endocrine regulation. The urinary microbiome, however, remains relatively under-investigated. One of the technical challenges of urinary microbiome studies is the samples usually contain a large number of host cells and low microbial biomass. These samples with the high host, low microbial abundance (“high-low” samples) are associated with increased risk of compromised quality of 16s rRNA gene sequencing results. An analysis with mock samples showed that mechanisms of host materials interfering with microbiome analysis includes reducing microbial DNA extract yield by competitively binding to the filter of DNA extraction column, inhibiting PCR amplification of 16S rRNA gene regions as non-target DNA, and consuming sequencing depth by unspecific amplification from PCR. To counter these issues, a refined processing protocol and a quality checking tool were developed for handling “high-low” samples. With these methods, a combination of sequencing-based methods and enhanced culture-based methods showed evidence of bacteria in renal tissue samples. On the other hand, the optimal urine sample collection and storage methods for microbiome study have not been reported. An optimisation experiment showed that urine samples with a volume higher than 20 mL and stored in centrifuged pellets generated the best sequencing results. The urinary microbiome of healthy subjects and urinary stone patients were characterised using 16s rRNA gene sequencing and enhanced quantitative urine culture (EQUC) techniques. Although no clear distinction was observed of urinary microbiome profiles between healthy subjects and urinary stone patients, male and female individuals do have their unique urinary microbiome profiles. The urinary microbiome profile of an individual remained stable throughout three months. Investigation of urine samples of metabolic stone patients before and after lithotripsy showed fluctuations in their urinary microbiome profiles, with newly-emerged microbes in sequencing results correlated with microbes cultured from stone samples. These results suggested bacteria liberated from metabolic stones during lithotripsy

Aerococcus Urinae: Establishing the Pathogenesis of an Emerging Uropathogen

Urinary tract infection (UTI) is the world\u27s most common bacterial infection. Much is known about the infectious process (pathogenesis) of a few of the bacteria that cause these infections, especially E. coli. Unfortunately, the pathogenesis of E. coli and other uropathogenic bacteria was explored almost exclusively in the belief that the bladder is supposed to be sterile. Our recent evidence, however, debunks this dogma. We used modern methods to reveal diverse bacterial communities in the bladders of adult women. These communities differ in women with and without lower urinary tract symptoms (LUTS), including UTI and urinary incontinence (UI). Many bacteria that we have detected in women with LUTS are understudied precisely because they were previously undetected or overlooked. Thus, very little is known about their pathogenesis. Aerococcus urinae is one of those understudied uropathogenic bacteria. It is associated with both UTI and UI. It is highly resistant to many antibiotics and, when undiagnosed, can cause invasive and life-threatening sepsis. Thus, I have begun a study of A. urinae\u27s pathogenesis. For well-studied uropathogens, the earliest stages of pathogenesis involve attachment to the cells that line the bladder wall (urothelium) and subsequent disruption of the host\u27s bladder immune system. I hypothesized that A. urinae also attaches to the urothelium and alters signaling to the host\u27s bladder immune system. To test my hypothesis, I first studied in vitro phenotypes of A. urinae related to attachment and colonization of the urothelium. Then, I studied the interaction between human urothelium and A. urinae strains isolated from womenwith LUTS. Results from this dissertation could be used to develop therapies that specifically target A. urinae

Self-supervised Learning for Electroencephalogram: A Systematic Survey

Electroencephalogram (EEG) is a non-invasive technique to record bioelectrical signals. Integrating supervised deep learning techniques with EEG signals has recently facilitated automatic analysis across diverse EEG-based tasks. However, the label issues of EEG signals have constrained the development of EEG-based deep models. Obtaining EEG annotations is difficult that requires domain experts to guide collection and labeling, and the variability of EEG signals among different subjects causes significant label shifts. To solve the above challenges, self-supervised learning (SSL) has been proposed to extract representations from unlabeled samples through well-designed pretext tasks. This paper concentrates on integrating SSL frameworks with temporal EEG signals to achieve efficient representation and proposes a systematic review of the SSL for EEG signals. In this paper, 1) we introduce the concept and theory of self-supervised learning and typical SSL frameworks. 2) We provide a comprehensive review of SSL for EEG analysis, including taxonomy, methodology, and technique details of the existing EEG-based SSL frameworks, and discuss the difference between these methods. 3) We investigate the adaptation of the SSL approach to various downstream tasks, including the task description and related benchmark datasets. 4) Finally, we discuss the potential directions for future SSL-EEG research.Comment: 35 pages, 12 figure

Evaluation of new diagnostic technologies for rapid detection of urinary pathogens and their antibiotic resistances

Background: Most urinary tract infections (UTIs) are trivial; but complicated UTIs are a growing reason for hospitalisation in the UK, and are among the commonest sources of sepsis. Increasing resistance among uropathogens complicates treatment and drives wider empirical use of previously-reserved antibiotics. Rapid precise detection of pathogens and resistances, without culture, might better guide early therapy in deteriorating UTI patients. Methods: Two approaches were evaluated: i) MALDI-TOF mass spectrometry for direct identification of pathogens from urine together with multiplex, tandem PCR (MT-PCR) for resistance gene profiling. MALDI-TOF was also explored for rapid detection of β-lactamase activity in bacteria harvested from urine; ii) MinION sequencing for bacterial and resistance gene identification, again directly from urine. As background, an epidemiological surveillance of uropathogens from the Norfolk and Norwich University Hospital in July and November 2014 was performed. Results: Direct MALDI-TOF on urines could achieve rapid bacterial identification within 1.5 h and also allowed direct detection of extended-spectrum β-lactamase (ESBL) activity. MT-PCR showed satisfactory results in detecting the commonest resistance genes in Enterobacteriaceae directly from urines and cultivated isolates within 3 h. Weaker association was found between streptomycin resistance and aadA1/A2/A3 genes. Fluoroquinolone-susceptible and -resistant Escherichia coli were distinguished by the melting temperatures of their gyrA product. MinION sequencing correctly identified uropathogens and their resistances in all urine samples within <5 h, without culture. Acquired resistance genes agreed with resistance phenotypes and closely matched Illumina sequencing, albeit with poor discrimination within some β-lactamase families (e.g. blaTEM). Epidemiological surveillance showed E. coli predominant in all age groups and location types, with high resistance rates to amoxicillin and trimethoprim. Conclusion: Either a MALDI-TOF plus PCR or a sequencing approach could significantly shorten the time required for microbiological investigation of urosepsis, allowing clinicians to adjust therapy before the second dose of a typical (i.e. q8h) antibiotic

A hybrid algorithm for Bayesian network structure learning with application to multi-label learning

We present a novel hybrid algorithm for Bayesian network structure learning, called H2PC. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. The algorithm is based on divide-and-conquer constraint-based subroutines to learn the local structure around a target variable. We conduct two series of experimental comparisons of H2PC against Max-Min Hill-Climbing (MMHC), which is currently the most powerful state-of-the-art algorithm for Bayesian network structure learning. First, we use eight well-known Bayesian network benchmarks with various data sizes to assess the quality of the learned structure returned by the algorithms. Our extensive experiments show that H2PC outperforms MMHC in terms of goodness of fit to new data and quality of the network structure with respect to the true dependence structure of the data. Second, we investigate H2PC's ability to solve the multi-label learning problem. We provide theoretical results to characterize and identify graphically the so-called minimal label powersets that appear as irreducible factors in the joint distribution under the faithfulness condition. The multi-label learning problem is then decomposed into a series of multi-class classification problems, where each multi-class variable encodes a label powerset. H2PC is shown to compare favorably to MMHC in terms of global classification accuracy over ten multi-label data sets covering different application domains. Overall, our experiments support the conclusions that local structural learning with H2PC in the form of local neighborhood induction is a theoretically well-motivated and empirically effective learning framework that is well suited to multi-label learning. The source code (in R) of H2PC as well as all data sets used for the empirical tests are publicly available.Comment: arXiv admin note: text overlap with arXiv:1101.5184 by other author