Mosquito detection with low-cost smartphones: data acquisition for malaria research

Mosquitoes are a major vector for malaria, causing hundreds of thousands of deaths in the developing world each year. Not only is the prevention of mosquito bites of paramount importance to the reduction of malaria transmission cases, but understanding in more forensic detail the interplay between malaria, mosquito vectors, vegetation, standing water and human populations is crucial to the deployment of more effective interventions. Typically the presence and detection of malaria-vectoring mosquitoes is only quantified by hand-operated insect traps or signified by the diagnosis of malaria. If we are to gather timely, large-scale data to improve this situation, we need to automate the process of mosquito detection and classification as much as possible. In this paper, we present a candidate mobile sensing system that acts as both a portable early warning device and an automatic acoustic data acquisition pipeline to help fuel scientific inquiry and policy. The machine learning algorithm that powers the mobile system achieves excellent off-line multi-species detection performance while remaining computationally efficient. Further, we have conducted preliminary live mosquito detection tests using low-cost mobile phones and achieved promising results. The deployment of this system for field usage in Southeast Asia and Africa is planned in the near future. In order to accelerate processing of field recordings and labelling of collected data, we employ a citizen science platform in conjunction with automated methods, the former implemented using the Zooniverse platform, allowing crowdsourcing on a grand scale.Comment: Presented at NIPS 2017 Workshop on Machine Learning for the Developing Worl

Mosquito Detection with Neural Networks: The Buzz of Deep Learning

Many real-world time-series analysis problems are characterised by scarce data. Solutions typically rely on hand-crafted features extracted from the time or frequency domain allied with classification or regression engines which condition on this (often low-dimensional) feature vector. The huge advances enjoyed by many application domains in recent years have been fuelled by the use of deep learning architectures trained on large data sets. This paper presents an application of deep learning for acoustic event detection in a challenging, data-scarce, real-world problem. Our candidate challenge is to accurately detect the presence of a mosquito from its acoustic signature. We develop convolutional neural networks (CNNs) operating on wavelet transformations of audio recordings. Furthermore, we interrogate the network's predictive power by visualising statistics of network-excitatory samples. These visualisations offer a deep insight into the relative informativeness of components in the detection problem. We include comparisons with conventional classifiers, conditioned on both hand-tuned and generic features, to stress the strength of automatic deep feature learning. Detection is achieved with performance metrics significantly surpassing those of existing algorithmic methods, as well as marginally exceeding those attained by individual human experts.Comment: For data and software related to this paper, see http://humbug.ac.uk/kiskin2017/. Submitted as a conference paper to ECML 201

Study of Solid Propellant Combustion under External Radiation

The influence of constant and transient radiant flux on the burning rate of solid propellants is considered. The validity of the equivalence principle for the radiant flux and increase in initial temperature and also the problem of possible photochemical effect of thermal radiation are discussed. Experimental data on burning rate response to periodical perturbations of radiant flux for different types of solid propellants are reported. The problem of correlation between burning rate response to perturbations of pressure and external radiation is considered. Formulation of the problem on transient combustion in terms of the Zeldovich- Novozhilov phenomenological approach is described and the results of numerical integration are presented

Developing an induced pluripotent stem cell model of pulmonary arterial hypertension to understand the contribution of BMPR2 mutations to disease-associated phenotypes in smooth muscle cells

Mutations in the gene encoding the bone morphogenetic protein type 2 receptor (BMPR2) are the most common genetic cause of heritable pulmonary arterial hypertension (PAH). However, given the reduced penetrance of BMPR2 mutations in affected families, a major outstanding question is the identity of additional factors or pathways that are responsible for the manifestation of clinical disease. Furthermore, limited human tissue is available for study and usually only from patients with end-stage disease, making it difficult to understand how PAH is established and progresses. Alternative human models of PAH are therefore required. This thesis describes the characterisation of the first human iPSC-derived smooth muscle cell (iPSC-SMC) model of PAH and elucidates the role of BMPR2 deficiency in establishing PAH-associated phenotypes in iPSC-derived SMCs. To achieve this, I used CRISPR-Cas9 gene editing to generate wild-type and BMPR2+/- iPSC lines with isogenic backgrounds which were subsequently differentiated into lineage-specific iPSC-SMCs that displayed a gene expression profile and responses to BMP signalling akin to those present in distal pulmonary artery smooth muscle cells (PASMCs). Using these cells, I found that the introduction of a single BMPR2 mutation in iPSC-SMCs was sufficient to recapitulate the pro-proliferative and anti-apoptotic phenotype of patient-derived BMPR2+/- PASMCs. However, acquisition of the mitochondrial hyperpolarisation phenotype was enhanced by inflammatory signalling and required an interaction between BMPR2 mutations and environmental stimuli provided by exposure to serum over time. Furthermore, I showed that BMPR2+/- iPSC-SMCs had an altered differentiation state and were less contractile compared to wild-type iPSC-SMCs, phenotypes which have not been observed previously in PAH-derived PASMCs. Finally, RNA sequencing analysis identified genes that were differentially expressed between wild-type and BMPR2+/- iPSC-SMCs and may hence provide further insights into PAH pathobiology. The iPSC-SMC model described in this study will be useful for identifying additional factors involved in disease penetrance and for validating therapeutic approaches that target BMPR2.This thesis project was supported by British Heart Foundation (BHF) PhD Studentship grant FS/13/51/30636, with work in the lab also being funded by BHF project grant PG/14/31/30786 and programme grant RG/13/4/30107, the Dinosaur Trust, Fondation Leducq, the Medical Research Council, Pulmonary Hypertension Association UK and Fight for Sight and the Robert McAlpine Foundation

Learning Outcomes for Large Versus Small Grain Orthographic Instruction in Adult L2 Learners of Russian Cyrillic

The process of learning a second language includes the acquisition of literacy skills. Currently, there is no standardized practice for teaching a new orthography, though research suggests that there are clear differences between languages with different orthographic depth, and the corresponding grain sizes that evolve as cognitive strategies. This study investigated the effects of two different instruction methods on reading outcomes for adult L2 learners of Russian. This study extends research done by Brennan & Booth (2015), which trained adults to read an artificial orthography. Here, we trained 34 literate English-speaking adults on Russian Cyrillic orthography with initial instruction that directed attention either to large or small grain size units (i.e., words or letters). After controlling for overall phonological skills, we found that small grain instruction resulted in higher accuracy for letter-phoneme matching, while large grain instruction led to greater accuracy with reading whole words in rime-rhyme foil trials. Additionally, differences among individual learners affected outcomes, as those in the large grain group who displayed greater phonemic skills also had slower reaction times. This same effect was not found for the small grain group, suggesting that these particular learners continued to apply small grain analysis even when large grains would have resulted in faster times. Overall, these results show that when adults are learning to read a second orthography, both large and small grain instruction can be beneficial in facilitating the development of accurate and efficient reading ability, thereby allowing the learner to use literacy as a scaffold for oral language development including vocabulary growth and increased grammar knowledge in order to improve L2 proficiency

Dual Bayesian ResNet: a deep learning approach to heart murmur detection

This study presents our team PathToMyHeart’s contribution to the George B. Moody PhysioNet Challenge 2022. Two models are implemented. The first model is a Dual Bayesian ResNet (DBRes), where each patient’s recording is segmented into overlapping log mel spectrograms. These undergo two binary classifications: present versus unknown or absent, and unknown versus present or absent. The classifications are aggregated to give a patient’s final classification. The second model is the output of DBRes integrated with demographic data and signal features using XGBoost. DBRes achieved our best weighted accuracy of 0.771 on the hidden test set for murmur classification, which placed us fourth for the murmur task. (On the clinical outcome task, which we neglected, we scored 17th with costs of 12637.) On our held-out subset of the training set, integrating the demographic data and signal features improved DBRes’s accuracy from 0.762 to 0.820. However, this decreased DBRes’s weighted accuracy from 0.780 to 0.749. Our results demonstrate that log mel spectrograms are an effective representation of heart sound recordings, Bayesian networks provide strong supervised classification performance, and treating the ternary classification as two binary classifications increases performance on the weighted accuracy