Deep learning for optical coherence tomography angiography: Quantifying microvascular changes in diabetic retinopathy

Optical Coherence Tomography Angiography (OCT-A) permits visualization of the changes to the retinal circulation due to diabetic retinopathy (DR), a microvascular complication of diabetes. Machine learning applications have directly benefited ophthalmology, leveraging large amounts of data to create frameworks to aid clinical decision-making. In this thesis, several techniques to quantify the retinal microvasculature are explored. First, high-quality, averaged, 6x6mm OCT-A enface images are used to produce manual segmentations for the corresponding lower-quality, single-frame images to produce more training data. Using transfer learning, the resulting convolutional neural network (CNN) segmented the superficial capillary plexus and deep vascular complex with performance exceeding inter-rater comparisons. Next, a federated learning framework was designed to allow for collaborative training by multiple participants on a de-centralized data corpus. When trained for microvasculature segmentation, the framework achieved comparable performance to a CNN trained on a fully-centralized dataset

Frequent expansion of Plasmodium vivax Duffy Binding Protein in Ethiopia and its epidemiological significance.

Plasmodium vivax invasion of human erythrocytes depends on the Duffy Binding Protein (PvDBP) which interacts with the Duffy antigen. PvDBP copy number has been recently shown to vary between P. vivax isolates in Sub-Saharan Africa. However, the extent of PvDBP copy number variation, the type of PvDBP multiplications, as well as its significance across broad samples are still unclear. We determined the prevalence and type of PvDBP duplications, as well as PvDBP copy number variation among 178 Ethiopian P. vivax isolates using a PCR-based diagnostic method, a novel quantitative real-time PCR assay and whole genome sequencing. For the 145 symptomatic samples, PvDBP duplications were detected in 95 isolates, of which 81 had the Cambodian and 14 Malagasy-type PvDBP duplications. PvDBP varied from 1 to >4 copies. Isolates with multiple PvDBP copies were found to be higher in symptomatic than asymptomatic infections. For the 33 asymptomatic samples, PvDBP was detected with two copies in two of the isolates, and both were the Cambodian-type PvDBP duplication. PvDBP copy number in Duffy-negative heterozygotes was not significantly different from that in Duffy-positives, providing no support for the hypothesis that increased copy number is a specific association with Duffy-negativity, although the number of Duffy-negatives was small and further sampling is required to test this association thoroughly

Multi-Technology Cooperative Driving: An Analysis Based on PLEXE

Cooperative Driving requires ultra-reliable communications, and it is now clear that no single technology will ever be able to satisfy such stringent requirements, if only because active jamming can kill (almost) any wireless technology. Cooperative driving with multiple communication technologies which complement each other opens new spaces for research and development, but also poses several challenges. The work we present tackles the fallback and recovery mechanisms that the longitudinal controlling system of a platoon of vehicles can implement as a distributed system with multiple communication interfaces. We present a protocol and procedure to correctly compute the safe transition between different controlling algorithms, down to autonomous (or manual) driving when no communication is possible. To empower the study, we also develop a new version of PLEXE, which is an integral part of this contribution as the only Open Source, free simulation tool that enables the study of such systems with a modular approach, and that we deem offers the community the possibility of boosting research in this field. The results we present demonstrate the feasibility of safe fallback, but also highlight that such complex systems require careful design choices, as naive approaches can lead to instabilities or even collisions, and that such design can only be done with appropriate in-silico experiments

An Exploratory Study of Response Shift in Health-Related Quality of Life and Utility Assessment Among Patients with Osteoarthritis Undergoing Total Knee Replacement Surgery in a Tertiary Hospital in Singapore

AbstractObjectiveTo investigate the influence of response shift (RS) on health-related quality of life (HRQOL) and utility assessment among patients undergoing total knee replacement.MethodsConsenting patients undergoing total knee replacement were interviewed to determine their HRQOL by using the six-dimensional health state short form, derived from SF-36, and the EuroQol five-dimensional questionnaire at baseline (pretest 1) and the six-dimensional health state short form, derived from SF-36, at 6 (pretest 2) and 18 months after surgery (post-test). RS was studied by using a “then-test” approach by contacting participants 18 months after surgery and asking them to evaluate their HRQOL at baseline (then-test 1) and at 6 (then-test 2) and 18 months after surgery. RS was calculated as the score difference between pretest and then-test scores for a given time point. Relationships between RS and external variables were explored by using univariate and multiple liner regression analyses.ResultsIn 74 subjects (63% response rate, median age 68 years), median (interquantile range) six-dimensional health state short form, derived from SF-36, scores for then-tests at baseline (0.48 [0.42–0.49]) and at 6 months (0.72 [0.66–0.79]) after surgery were significantly different from respective pretest scores (0.61 [0.58–0.68] at baseline, P = 0.000; 0.69 [0.63–0.72] at 6 months, P = 0.000), showing RS at both time points. RS at baseline (0.14 [0.08–0.20]) was significantly larger than that at 6 months (−0.05 [0.14 to 0.00], P = 0.000). EuroQol five-dimensional questionnaire pretest and then-test scores at baseline also differed significantly (0.69 [0.17–0.73] vs. −0.18 [−0.23 to 0.00], P = 0.000). RS at baseline was not affected by assessed demographic or medical variables. RS at 6 months was greater in subjects with more years of education (16% of variance in multiple liner regression, P < 0.01).ConclusionRS was present and impacted HRQOL and utility assessment among patients undergoing total knee replacement before and 6 months after surgery

The relationship between psychological characteristics of patients and their utilization of psychiatric inpatient treatment: A cross-sectional study, using machine learning

High utilizers (HU) are patients with an above-average use of psychiatric inpatient treatment. A precise characterization of this patient group is important when tailoring specific treatment approaches for them. While the current literature reports evidence of sociodemographic, and socio-clinical characteristics of HU, knowledge regarding their psychological characteristics is sparse. This study aimed to investigate the association between patients' psychological characteristics and their utilization of psychiatric inpatient treatment. Patients from the University Psychiatric Clinics (UPK) Basel diagnosed with schizophrenia spectrum or bipolar affective disorders participated in a survey at the end of their inpatient treatment stay. The survey included assessments of psychological characteristics such as quality of life, self-esteem, self-stigma, subjective experience and meaning of psychoses, insight into the disease, and patients' utilization of psychiatric inpatient treatment in the last 30 months. The outcome variables were two indicators of utilization of psychiatric inpatient treatment, viz. "utilization pattern" (defined as HU vs. Non-HU [NHU]) and "length of stay" (number of inpatient treatment days in the last 30 months). Statistical analyses included multiple regression models, the least absolute shrinkage and selection operator (lasso) method, and the random forest model. We included 112 inpatients, of which 50 were classified as HU and 62 as NHU. The low performance of all statistical models used after cross-validation suggests that none of the estimated psychological variables showed predictive accuracy and hence clinical relevance regarding these two outcomes. Results indicate no link between psychological characteristics and inpatient treatment utilization in patients diagnosed with schizophrenia spectrum or bipolar affective disorders. Thus, in this study, the examined psychological variables do not seem to play an important role in patients' use of psychiatric inpatient treatment; this highlights the need for additional research to further examine underlying mechanisms of high utilization of psychiatric inpatient treatment

A simple, rapid typing method for Streptococcus agalactiae based on ribosomal subunit proteins by MALDI-TOF MS

Streptococcus agalactiae (Group B Streptococcus, GBS), is a frequent human colonizer and a leading cause of neonatal meningitis as well as an emerging pathogen in non-pregnant adults. GBS possesses a broad animal host spectrum, and recent studies proved atypical GBS genotypes can cause human invasive diseases through animal sources as food-borne zoonotic infections. We applied a MALDI-TOF MS typing method, based on molecular weight variations of predefined 28 ribosomal subunit proteins (rsp) to classify GBS strains of varying serotypes into major phylogenetic lineages. A total of 249 GBS isolates of representative and varying capsular serotypes from patients and animal food sources (fish and pig) collected during 2016-2018 in Hong Kong were analysed. Over 84% (143/171) noninvasive carriage GBS strains from patients were readily typed into 5 globally dominant rsp-profiles. Among GBS strains from food animals, over 90% (57/63) of fish and 13% (2/15) of pig GBS matched with existing rsp-profiles, while the remainder were classified into two novel rsp-profiles and we failed to assign a fish strain into any cluster. MALDI-TOF MS allowed for high-throughput screening and simultaneous detection of novel, so far not well described GBS genotypes. The method shown here is rapid, simple, readily transferable and adapted for use in a diagnostic microbiology laboratory with potential for the surveillance of emerging GBS genotypes with zoonotic potential

Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements

The immense and growing repositories of transcriptional data may contain critical insights for developing new therapies. Current approaches to mining these data largely rely on binary classifications of disease vs. control, and are not able to incorporate measures of disease severity. We report an analytical approach to integrate ordinal clinical information with transcriptomics. We apply this method to public data for a large cohort of Huntington's disease patients and controls, identifying and prioritizing phenotype-associated genes. We verify the role of a high-ranked gene in dysregulation of sphingolipid metabolism in the disease and demonstrate that inhibiting the enzyme, sphingosine-1-phosphate lyase 1 (SPL), has neuroprotective effects in Huntington's disease models. Finally, we show that one consequence of inhibiting SPL is intracellular inhibition of histone deacetylases, thus linking our observations in sphingolipid metabolism to a well-characterized Huntington's disease pathway. Our approach is easily applied to any data with ordinal clinical measurements, and may deepen our understanding of disease processes

Synthesizing Speech Test Cases with Text-to-Speech? An Empirical Study on the False Alarms in Automated Speech Recognition Testing

Recent studies have proposed the use of Text-To-Speech (TTS) systems to automatically synthesise speech test cases on a scale and uncover a large number of failures in ASR systems. However, the failures uncovered by synthetic test cases may not reflect the actual performance of an ASR system when it transcribes human audio, which we refer to as false alarms. Given a failed test case synthesised from TTS systems, which consists of TTS-generated audio and the corresponding ground truth text, we feed the human audio stating the same text to an ASR system. If human audio can be correctly transcribed, an instance of a false alarm is detected. In this study, we investigate false alarm occurrences in five popular ASR systems using synthetic audio generated from four TTS systems and human audio obtained from two commonly used datasets. Our results show that the least number of false alarms is identified when testing Deepspeech, and the number of false alarms is the highest when testing Wav2vec2. On average, false alarm rates range from 21% to 34% in all five ASR systems. Among the TTS systems used, Google TTS produces the least number of false alarms (17%), and Espeak TTS produces the highest number of false alarms (32%) among the four TTS systems. Additionally, we build a false alarm estimator that flags potential false alarms, which achieves promising results: a precision of 98.3%, a recall of 96.4%, an accuracy of 98.5%, and an F1 score of 97.3%. Our study provides insight into the appropriate selection of TTS systems to generate high-quality speech to test ASR systems. Additionally, a false alarm estimator can be a way to minimise the impact of false alarms and help developers choose suitable test inputs when evaluating ASR systems. The source code used in this paper is publicly available on GitHub at https://github.com/julianyonghao/FAinASRtest.Comment: 12 pages, Accepted at ISSTA202