Whole-brain serial-section electron microscopy in larval zebrafish.

High-resolution serial-section electron microscopy (ssEM) makes it possible to investigate the dense meshwork of axons, dendrites, and synapses that form neuronal circuits. However, the imaging scale required to comprehensively reconstruct these structures is more than ten orders of magnitude smaller than the spatial extents occupied by networks of interconnected neurons, some of which span nearly the entire brain. Difficulties in generating and handling data for large volumes at nanoscale resolution have thus restricted vertebrate studies to fragments of circuits. These efforts were recently transformed by advances in computing, sample handling, and imaging techniques, but high-resolution examination of entire brains remains a challenge. Here, we present ssEM data for the complete brain of a larval zebrafish (Danio rerio) at 5.5 days post-fertilization. Our approach utilizes multiple rounds of targeted imaging at different scales to reduce acquisition time and data management requirements. The resulting dataset can be analysed to reconstruct neuronal processes, permitting us to survey all myelinated axons (the projectome). These reconstructions enable precise investigations of neuronal morphology, which reveal remarkable bilateral symmetry in myelinated reticulospinal and lateral line afferent axons. We further set the stage for whole-brain structure-function comparisons by co-registering functional reference atlases and in vivo two-photon fluorescence microscopy data from the same specimen. All obtained images and reconstructions are provided as an open-access resource.

Age-seroprevalence curves for the multi-strain structure of influenza A virus.

The relationship between age and seroprevalence can be used to estimate the annual attack rate of an infectious disease. For pathogens with multiple serologically distinct strains, there is a need to describe composite exposure to an antigenically variable group of pathogens. In this study, we assay 24,402 general-population serum samples, collected in Vietnam between 2009 to 2015, for antibodies to eleven human influenza A strains. We report that a principal components decomposition of antibody titer data gives the first principal component as an appropriate surrogate for seroprevalence; this results in annual attack rate estimates of 25.6% (95% CI: 24.1% - 27.1%) for subtype H3 and 16.0% (95% CI: 14.7% - 17.3%) for subtype H1. The remaining principal components separate the strains by serological similarity and associate birth cohorts with their particular influenza histories. Our work shows that dimensionality reduction can be used on human antibody profiles to construct an age-seroprevalence relationship for antigenically variable pathogens.

Estimates of the global burden of Japanese encephalitis and the impact of vaccination from 2000-2015.

Japanese encephalitis (JE) is a mosquito-borne disease, known for its high mortality and disability rate among symptomatic cases. Many effective vaccines are available for JE, and the use of a recently developed and inexpensive vaccine, SA 14-14-2, has been increasing over the recent years particularly with Gavi support. Estimates of the local burden and the past impact of vaccination are therefore increasingly needed, but difficult due to the limitations of JE surveillance. In this study, we implemented a mathematical modelling method (catalytic model) combined with age-stratifed case data from our systematic review which can overcome some of these limitations. We estimate in 2015 JEV infections caused 100,308 JE cases (95% CI: 61,720-157,522) and 25,125 deaths (95% CI: 14,550-46,031) globally, and that between 2000 and 2015 307,774 JE cases (95% CI: 167,442-509,583) were averted due to vaccination globally. Our results highlight areas that could have the greatest benefit from starting vaccination or from scaling up existing programs and will be of use to support local and international policymakers in making vaccine allocation decisions.

Frequency-splitting dynamic MRI reconstruction using multi-scale 3D convolutional sparse coding and automatic parameter selection.

In this paper, we propose a novel image reconstruction algorithm using multi-scale 3D convolutional sparse coding and a spectral decomposition technique for highly undersampled dynamic Magnetic Resonance Imaging (MRI) data. The proposed method recovers high-frequency information using a shared 3D convolution-based dictionary built progressively during the reconstruction process in an unsupervised manner, while low-frequency information is recovered using a total variation-based energy minimization method that leverages temporal coherence in dynamic MRI. Additionally, the proposed 3D dictionary is built across three different scales to more efficiently adapt to various feature sizes, and elastic net regularization is employed to promote a better approximation to the sparse input data. We also propose an automatic parameter selection technique based on a genetic algorithm to find optimal parameters for our numerical solver which is a variant of the alternating direction method of multipliers (ADMM). We demonstrate the performance of our method by comparing it with state-of-the-art methods on 15 single-coil cardiac, 7 single-coil DCE, and a multi-coil brain MRI datasets at different sampling rates (12.5%, 25% and 50%). The results show that our method significantly outperforms the other state-of-the-art methods in reconstruction quality with a comparable running time and is resilient to noise.

Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss.

Compressed sensing magnetic resonance imaging (CS-MRI) has provided theoretical foundations upon which the time-consuming MRI acquisition process can be accelerated. However, it primarily relies on iterative numerical solvers, which still hinders their adaptation in time-critical applications. In addition, recent advances in deep neural networks have shown their potential in computer vision and image processing, but their adaptation to MRI reconstruction is still in an early stage. In this paper, we propose a novel deep learning-based generative adversarial model, RefineGAN, for fast and accurate CS-MRI reconstruction. The proposed model is a variant of fully-residual convolutional autoencoder and generative adversarial networks (GANs), specifically designed for CS-MRI formulation; it employs deeper generator and discriminator networks with cyclic data consistency loss for faithful interpolation in the given under-sampled -space data. In addition, our solution leverages a chained network to further enhance the reconstruction quality. RefineGAN is fast and accurate-the reconstruction process is extremely rapid, as low as tens of milliseconds for reconstruction of a image, because it is one-way deployment on a feed-forward network, and the image quality is superior even for extremely low sampling rate (as low as 10%) due to the data-driven nature of the method. We demonstrate that RefineGAN outperforms the state-of-the-art CS-MRI methods by a large margin in terms of both running time and image quality via evaluation using several open-source MRI databases.

An Intelligent System Approach for Probabilistic Volume Rendering Using Hierarchical 3D Convolutional Sparse Coding.

In this paper, we propose a novel machine learning-based voxel classification method for highly-accurate volume rendering. Unlike conventional voxel classification methods that incorporate intensity-based features, the proposed method employs dictionary based features learned directly from the input data using hierarchical multi-scale 3D convolutional sparse coding, a novel extension of the state-of-the-art learning-based sparse feature representation method. The proposed approach automatically generates high-dimensional feature vectors in up to 75 dimensions, which are then fed into an intelligent system built on a random forest classifier for accurately classifying voxels from only a handful of selection scribbles made directly on the input data by the user. We apply the probabilistic transfer function to further customize and refine the rendered result. The proposed method is more intuitive to use and more robust to noise in comparison with conventional intensity-based classification methods. We evaluate the proposed method using several synthetic and real-world volume datasets, and demonstrate the methods usability through a user study.

Evidence of previous but not current transmission of chikungunya virus in southern and central Vietnam: Results from a systematic review and a seroprevalence study in four locations.

BACKGROUND: Arbovirus infections are a serious concern in tropical countries due to their high levels of transmission and morbidity. With the outbreaks of chikungunya (CHIKV) in surrounding regions in recent years and the fact that the environment in Vietnam is suitable for the vectors of CHIKV, the possibility of transmission of CHIKV in Vietnam is of great interest. However, information about CHIKV activity in Vietnam remains limited. METHODOLOGY: In order to address this question, we performed a systematic review of CHIKV in Vietnam and a CHIKV seroprevalence survey. The seroprevalence survey tested for CHIKV IgG in population serum samples from individuals of all ages in 2015 from four locations in Vietnam. PRINCIPAL FINDINGS: The four locations were An Giang province (n = 137), Ho Chi Minh City (n = 136), Dak Lak province (n = 137), and Hue City (n = 136). The findings give us evidence of some CHIKV activity: 73/546 of overall samples were seropositive (13.4%). The age-adjusted seroprevalences were 12.30% (6.58-18.02), 13.42% (7.16-19.68), 7.97% (3.56-12.38), and 3.72% (1.75-5.69) in An Giang province, Ho Chi Minh City, Dak Lak province, and Hue City respectively. However, the age-stratified seroprevalence suggests that the last transmission ended around 30 years ago, consistent with results from the systematic review. We see no evidence for on-going transmission in three of the locations, though with some evidence of recent exposure in Dak Lak, most likely due to transmission in neighbouring countries. Before the 1980s, when transmission was occurring, we estimate on average 2-4% of the population were infected each year in HCMC and An Giang and Hue (though transmision ended earlier in Hue). We estimate lower transmission in Dak Lak, with around 1% of the population infected each year. CONCLUSION: In conclusion, we find evidence of past CHIKV transmission in central and southern Vietnam, but no evidence of recent sustained transmission. When transmission of CHIKV did occur, it appeared to be widespread and affect a geographically diverse population. The estimated susceptibility of the population to chikungunya is continually increasing, therefore the possibility of future CHIKV transmission in Vietnam remains.

Structure of general-population antibody titer distributions to influenza A virus.

Seroepidemiological studies aim to understand population-level exposure and immunity to infectious diseases. Their results are normally presented as binary outcomes describing the presence or absence of pathogen-specific antibody, despite the fact that many assays measure continuous quantities. A population's natural distribution of antibody titers to an endemic infectious disease may include information on multiple serological states - naiveté, recent infection, non-recent infection, childhood infection - depending on the disease in question and the acquisition and waning patterns of immunity. In this study, we investigate 20,152 general-population serum samples from southern Vietnam collected between 2009 and 2013 from which we report antibody titers to the influenza virus HA1 protein using a continuous titer measurement from a protein microarray assay. We describe the distributions of antibody titers to subtypes 2009 H1N1 and H3N2. Using a model selection approach to fit mixture distributions, we show that 2009 H1N1 antibody titers fall into four titer subgroups and that H3N2 titers fall into three subgroups. For H1N1, our interpretation is that the two highest-titer subgroups correspond to recent and historical infection, which is consistent with 2009 pandemic attack rates. Similar interpretations are available for H3N2, but right-censoring of titers makes these interpretations difficult to validate.

Vivaldi: A Domain-Specific Language for Volume Processing and Visualization on Distributed Heterogeneous Systems.

As the size of image data from microscopes and telescopes increases, the need for high-throughput processing and visualization of large volumetric data has become more pressing. At the same time, many-core processors and GPU accelerators are commonplace, making high-performance distributed heterogeneous computing systems affordable. However, effectively utilizing GPU clusters is difficult for novice programmers, and even experienced programmers often fail to fully leverage the computing power of new parallel architectures due to their steep learning curve and programming complexity. In this paper, we propose Vivaldi, a new domain-specific language for volume processing and visualization on distributed heterogeneous computing systems. Vivaldi's Python-like grammar and parallel processing abstractions provide flexible programming tools for non-experts to easily write high-performance parallel computing code. Vivaldi provides commonly used functions and numerical operators for customized visualization and high-throughput image processing applications. We demonstrate the performance and usability of Vivaldi on several examples ranging from volume rendering to image segmentation.