Learning to process with spikes and to localise pulses

In the last few decades, deep learning with artificial neural networks (ANNs) has emerged as one of the most widely used techniques in tasks such as classification and regression, achieving competitive results and in some cases even surpassing human-level performance. Nonetheless, as ANN architectures are optimised towards empirical results and departed from their biological precursors, how exactly human brains process information using these short electrical pulses called spikes remains a mystery. Hence, in this thesis, we explore the problem of learning to process with spikes and to localise pulses. We first consider spiking neural networks (SNNs), a type of ANN that more closely mimic biological neural networks in that neurons communicate with one another using spikes. This unique architecture allows us to look into the role of heterogeneity in learning. Since it is conjectured that the information is encoded by the timing of spikes, we are particularly interested in the heterogeneity of time constants of neurons. We then trained SNNs for classification tasks on a range of visual and auditory neuromorphic datasets, which contain streams of events (spike times) instead of the conventional frame-based data, and show that the overall performance is improved by allowing the neurons to have different time constants, especially on tasks with richer temporal structure. We also find that the learned time constants are distributed similarly to those experimentally observed in some mammalian cells. Besides, we demonstrate that learning with heterogeneity improves robustness against hyperparameter mistuning. These results suggest that heterogeneity may be more than the byproduct of noisy processes and perhaps serves a key role in learning in changing environments, yet heterogeneity has been overlooked in basic artificial models. While neuromorphic datasets, which are often captured by neuromorphic devices that closely model the corresponding biological systems, have enabled us to explore the more biologically plausible SNNs, there still exists a gap in understanding how spike times encode information in actual biological neural networks like human brains, as such data is difficult to acquire due to the trade-off between the timing precision and the number of cells simultaneously recorded electrically. Instead, what we usually obtain is the low-rate discrete samples of trains of filtered spikes. Hence, in the second part of the thesis, we focus on a different type of problem involving pulses, that is to retrieve the precise pulse locations from these low-rate samples. We make use of the finite rate of innovation (FRI) sampling theory, which states that perfect reconstruction is possible for classes of continuous non-bandlimited signals that have a small number of free parameters. However, existing FRI methods break down under very noisy conditions due to the so-called subspace swap event. Thus, we present two novel model-based learning architectures: Deep Unfolded Projected Wirtinger Gradient Descent (Deep Unfolded PWGD) and FRI Encoder-Decoder Network (FRIED-Net). The former is based on the existing iterative denoising algorithm for subspace-based methods, while the latter models directly the relationship between the samples and the locations of the pulses using an autoencoder-like network. Using a stream of K Diracs as an example, we show that both algorithms are able to overcome the breakdown inherent in the existing subspace-based methods. Moreover, we extend our FRIED-Net framework beyond conventional FRI methods by considering when the shape is unknown. We show that the pulse shape can be learned using backpropagation. This coincides with the application of spike detection from real-world calcium imaging data, where we achieve competitive results. Finally, we explore beyond canonical FRI signals and demonstrate that FRIED-Net is able to reconstruct streams of pulses with different shapes.Open Acces

Fine Mapping of the NRG1 Hirschsprung's Disease Locus

The primary pathology of Hirschsprung's disease (HSCR, colon aganglionosis) is the absence of ganglia in variable lengths of the hindgut, resulting in functional obstruction. HSCR is attributed to a failure of migration of the enteric ganglion precursors along the developing gut. RET is a key regulator of the development of the enteric nervous system (ENS) and the major HSCR-causing gene. Yet the reduced penetrance of RET DNA HSCR-associated variants together with the phenotypic variability suggest the involvement of additional genes in the disease. Through a genome-wide association study, we uncovered a ∼350 kb HSCR-associated region encompassing part of the neuregulin-1 gene (NRG1). To identify the causal NRG1 variants contributing to HSCR, we genotyped 243 SNPs variants on 343 ethnic Chinese HSCR patients and 359 controls. Genotype analysis coupled with imputation narrowed down the HSCR-associated region to 21 kb, with four of the most associated SNPs (rs10088313, rs10094655, rs4624987, and rs3884552) mapping to the NRG1 promoter. We investigated whether there was correlation between the genotype at the rs10088313 locus and the amount of NRG1 expressed in human gut tissues (40 patients and 21 controls) and found differences in expression as a function of genotype. We also found significant differences in NRG1 expression levels between diseased and control individuals bearing the same rs10088313 risk genotype. This indicates that the effects of NRG1 common variants are likely to depend on other alleles or epigenetic factors present in the patients and would account for the variability in the genetic predisposition to HSCR

Consultation pattern of non-urgent patients of Accident & Emergency Department

published_or_final_versionMedical SciencesMasterMaster of Medical Science

Correlations of Myeloperoxidase (MPO), Adenosine deaminase (ADA), C–C motif chemokine 22 (CCL22), Tumour necrosis factor alpha (TNFα) and Interleukin-6 (IL-6) mRNA expression in the nasopharyngeal specimens with the diagnosis and severity of SARS-CoV-2 infections

ABSTRACTCytokine dynamics in patients with coronavirus disease 2019 (COVID-19) have been studied in blood but seldomly in respiratory specimens. We studied different cell markers and cytokines in fresh nasopharyngeal swab specimens for the diagnosis and for stratifying the severity of COVID-19. This was a retrospective case-control study comparing Myeloperoxidase (MPO), Adenosine deaminase (ADA), C–C motif chemokine ligand 22 (CCL22), Tumour necrosis factor alpha (TNFα) and Interleukin-6 (IL-6) mRNA expression in 490 (327 patients and 163 control) nasopharyngeal specimens from 317 (154 COVID-19 and 163 control) hospitalized patients. Of the 154 COVID-19 cases, 46 died. Both total and normalized MPO, ADA, CCL22, TNFα, and IL-6 mRNA expression levels were significantly higher in the nasopharyngeal specimens of infected patients when compared with controls, with ADA showing better performance (OR 5.703, 95% CI 3.424–9.500, p < 0.001). Receiver operating characteristics (ROC) curve showed that the cut-off value of normalized ADA mRNA level at 2.37 × 10–3 had a sensitivity of 81.8% and specificity of 83.4%. While patients with severe COVID-19 had more respiratory symptoms, and elevated lactate dehydrogenase, multivariate analysis showed that severe COVID-19 patients had lower CCL22 mRNA (OR 0.211, 95% CI 0.060–0.746, p = 0.016) in nasopharyngeal specimens, while lymphocyte count, C-reactive protein, and viral load in nasopharyngeal specimens did not correlate with disease severity. In summary, ADA appears to be a better biomarker to differentiate between infected and uninfected patients, while CCL22 has the potential in stratifying the severity of COVID-19

Minimal Residual Disease-Based Risk Stratification in Chinese Childhood Acute Lymphoblastic Leukemia by Flow Cytometry and Plasma DNA Quantitative Polymerase Chain Reaction

<div><p>Minimal residual disease, or MRD, is an important prognostic indicator in childhood acute lymphoblastic leukemia. In ALL-IC-BFM 2002 study, we employed a standardized method of flow cytometry MRD monitoring for multiple centers internationally using uniformed gating, and determined the relevant MRD-based risk stratification strategies in our local patient cohort. We also evaluated a novel method of PCR MRD quantitation using peripheral blood plasma. For the bone marrow flow MRD study, patients could be stratified into 3 risk groups according to MRD level using a single time-point at day-15 (Model I) (I-A: <0.1%, I-B: 0.1–10%, I-C: >10%), or using two time-points at day-15 and day-33 (Model II) (II-A: day-15<10% and day-33<0.01%, II-B: day-15≥10% or day-33≥0.01% but not both, II-C: day-15≥10% and day-33≥0.01%), which showed significantly superior prediction of relapse (p = .00047 and <0.0001 respectively). Importantly, patients with good outcome (frequency: 56.0%, event-free survival: 90.1%) could be more accurately predicted by Model II. In peripheral blood plasma PCR MRD investigation, patients with day-15-MRD≥10⁻⁴ were at a significantly higher risk of relapse (p = 0.0117). By multivariate analysis, MRD results from both methods could independently predict patients’ prognosis, with 20–35-fold increase in risk of relapse for flow MRD I-C and II-C respectively, and 5.8-fold for patients having plasma MRD of ≥10⁻⁴. We confirmed that MRD detection by flow cytometry is useful for prognostic evaluation in our Chinese cohort of childhood ALL after treatment. Moreover, peripheral blood plasma DNA MRD can be an alternative where bone marrow specimen is unavailable and as a less invasive method, which allows close monitoring.</p></div

Development of a Novel, Genome Subtraction-Derived, SARS-CoV-2-Specific COVID-19-nsp2 Real-Time RT-PCR Assay and Its Evaluation Using Clinical Specimens

The pandemic novel coronavirus infection, Coronavirus Disease 2019 (COVID-19), has affected at least 190 countries or territories, with 465,915 confirmed cases and 21,031 deaths. In a containment-based strategy, rapid, sensitive and specific testing is important in epidemiological control and clinical management. Using 96 SARS-CoV-2 and 104 non-SARS-CoV-2 coronavirus genomes and our in-house program, GolayMetaMiner, four specific regions longer than 50 nucleotides in the SARS-CoV-2 genome were identified. Primers were designed to target the longest and previously untargeted nsp2 region and optimized as a probe-free real-time reverse transcription-polymerase chain reaction (RT-PCR) assay. The new COVID-19-nsp2 assay had a limit of detection (LOD) of 1.8 TCID50/mL and did not amplify other human-pathogenic coronaviruses and respiratory viruses. Assay reproducibility in terms of cycle threshold (Cp) values was satisfactory, with the total imprecision (% CV) values well below 5%. Evaluation of the new assay using 59 clinical specimens from 14 confirmed cases showed 100% concordance with our previously developed COVID-19-RdRp/Hel reference assay. A rapid, sensitive, SARS-CoV-2-specific real-time RT-PCR assay, COVID-19-nsp2, was developed