Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

Neuron tracing and quantitative analyses of dendritic architecture reveal symmetrical three-way-junctions and phenotypes of git-1 in C. elegans.

In PLoS computational biology

Complex dendritic trees are a distinctive feature of neurons. Alterations to dendritic morphology are associated with developmental, behavioral and neurodegenerative changes. The highly-arborized PVD neuron of C. elegans serves as a model to study dendritic patterning; however, quantitative, objective and automated analyses of PVD morphology are missing. Here, we present a method for neuronal feature extraction, based on deep-learning and fitting algorithms. The extracted neuronal architecture is represented by a database of structural elements for abstracted analysis. We obtain excellent automatic tracing of PVD trees and uncover that dendritic junctions are unevenly distributed. Surprisingly, these junctions are three-way-symmetrical on average, while dendritic processes are arranged orthogonally. We quantify the effect of mutation in git-1, a regulator of dendritic spine formation, on PVD morphology and discover a localized reduction in junctions. Our findings shed new light on PVD architecture, demonstrating the effectiveness of our objective analyses of dendritic morphology and suggest molecular control mechanisms.

Yuval Omer, Iosilevskii Yael, Meledin Anna, Podbilewicz Benjamin, Shemesh Tom


General General

Identifying Communities at Risk for COVID-19-Related Burden Across 500 U.S. Cities and within New York City: Unsupervised Learning of Co-Prevalence of Health Indicators.

In JMIR public health and surveillance

BACKGROUND : While it is well-known that older individuals with certain comorbidities are at highest risk for complications related to COVID-19 including hospitalization and death, we lack tools to identify communities at highest risk with fine-grained spatial resolution. Information collected at a county level obscures local risk and complex interactions between clinical comorbidities, the built environment, population factors, and other social determinants of health.

OBJECTIVE : Development of a COVID-19 Community Risk Score that summarizes complex disease prevalence together with age and sex, and compare the score to different social determinants of health indicators and built environment measures derived from satellite images using deep-learning.

METHODS : We develop a robust COVID-19 Community Risk Score (COVID-19 Risk Score) that summarizes the complex disease co-occurrences (using data for 2019) for individual census tracts with unsupervised learning, selected on the basis of their association with risk for COVID-19 complications, such as death. We mapped the COVID-19 Risk Score to corresponding zip codes in New York City and associated the score with COVID-19 related death. We further model the variance of the COVID-19 Risk Score using satellite imagery and social determinants of health.

RESULTS : Using 2019 chronic disease data, the COVID-19 Risk Score describes 85% of variation in co-occurrence of 15 diseases and health behaviors that are risk factors for COVID-19 complications among ~28K census tract neighborhoods (median population size of tracts: 4,091). The COVID-19 Risk Score is associated with a 40% greater risk for COVID-19 related death across New York City (April and September 2020) for a 1 standard deviation (SD) change in the score (risk ratio for 1 SD change in COVID-19 Risk Score: 1.4, P < .001) at the zip code level. Satellite imagery coupled with social determinants of health explain nearly 90% of the variance in the COVID-19 Risk Score in the United States in census tracts (r2 = 0.87).

CONCLUSIONS : The COVID-19 Risk Score localizes risk at the census tract level and was able to predict COVID-19 related mortality in New York City. The built environment explained significant variations in the score, suggesting risk models could be enhanced with satellite imagery.


Deonarine Andrew, Lyons Genevieve, Lakhani Chirag


General General


In Journal of medical Internet research ; h5-index 88.0

BACKGROUND : Among successful measures to curb COVID-19 spread in large populations includes the implementation of a movement restriction order. Globally, it was observed that countries implementing strict movement control were more successful in controlling the spread of the virus as compared to countries with less stringent measures. Society's adherence to the movement control order has helped expedite the process to flatten the pandemic curve as seen in countries such as China and Malaysia. At the same time, there are countries facing challenges with society's nonconformity towards movement restriction orders due to various claims such as human rights violations as well as socio-cultural and economic issues. In Indonesia, society's adherence to its Large-Scale Social Restrictions (LSSR) order is also a challenge to achieve. Indonesia is regarded as among the worst in Southeast Asian countries in terms of managing the spread of COVID-19. It is proven by the significant number of daily confirmed cases and the total number of deaths which was more than 6% of total active cases as of May 2020.

OBJECTIVE : To explore public sentiments and emotions toward the LSSR and identify issues, fear and reluctance to observe this restriction among the Indonesian public.

METHODS : This study adopts sentiment analysis method with supervised machine learning approach on COVID-19 related posts on selected media platforms, which are Twitter, Facebook, Instagram, and YouTube. The analysis was also done on COVID-19 related news contained in more than 500 online news platforms recognized by the Indonesian Press Council. Social media posts and news originating from Indonesian online media between March 31 to May 31, 2020 were analyzed. Emotion analysis on Twitter platform was also performed to identify collective public emotions toward the LSSR.

RESULTS : The study found that positive sentiment surpasses other sentiment categories by 1,002,947 mentions (52%) of the total data collected via the search engine. Negative sentiment was recorded at 36%, and neutral sentiment at 13%. The analysis of Twitter posts also showed that the majority of public have the emotion of "trust" toward the LSSR.

CONCLUSIONS : Public sentiment toward the LSSR appeared to be positive despite doubts on government consistency in executing the LSSR. The emotion analysis also concluded that the majority of people believe in LSSR as the best method to break the chain of COVID-19 transmission. Overall, Indonesians showed trust and expressed hope towards the government's ability to manage this current global health crisis and win against COVID-19.


Tri Sakti Andi Muhammad, Mohamad Emma, Azlan Arina Anis


General General

Task-induced Pyramid and Attention GAN for Multimodal Brain Image Imputation and Classification in Alzheimers disease.

In IEEE journal of biomedical and health informatics

With the advance of medical imaging technologies, multimodal images such as magnetic resonance images (MRI) and positron emission tomography (PET) can capture subtle structural and functional changes of brain, facilating the diagnosis of brain diseases such as Alzheimers disease (AD). In practice, multimodal images may be incomplete since PET is often missing due to high financial cost or availability. Most of existing methods simply excluded subjects with missing data, which unfortunately reduced sample size. In addition, how to extract and combine multimodal features is still challenging. To address these problems, we propose a deep learning framework to integrate a task-induced pyramid and attention generative adversarial network (TPA-GAN) with a pathwise transfer dense convolution network (PT-DCN) for imputation and also classification of multimodal brain images. First, we propose a TPA-GAN to integrate pyramid convolution and attention module as well as disease classification task into GAN for generating the missing PET data with their MRI. Then, with the imputed multimodal brain images, we build a dense convolution network with pathwise transfer blocks to gradually learn and combine multimodal features for final disease classification. Experiments are performed on ADNI-1 and ADNI-2 datasets to evaluate our proposed method, achiving superior performance in image imputation and brain disease diagnosis compared to state-of-the-art methods.

Gao Xingyu, Shi Feng, Shen Dinggang, Liu Manhua


General General

A Supervised Learning Algorithm for Multilayer Spiking Neural Networks Based on Temporal Coding Toward Energy-Efficient VLSI Processor Design.

In IEEE transactions on neural networks and learning systems

Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machine-learning algorithms but also energy-efficient computational models when implemented in very-large-scale integration (VLSI) circuits. In this article, we propose a novel supervised learning algorithm for SNNs based on temporal coding. A spiking neuron in this algorithm is designed to facilitate analog VLSI implementations with analog resistive memory, by which ultrahigh energy efficiency can be achieved. We also propose several techniques to improve the performance on recognition tasks and show that the classification accuracy of the proposed algorithm is as high as that of the state-of-the-art temporal coding SNN algorithms on the MNIST and Fashion-MNIST datasets. Finally, we discuss the robustness of the proposed SNNs against variations that arise from the device manufacturing process and are unavoidable in analog VLSI implementation. We also propose a technique to suppress the effects of variations in the manufacturing process on the recognition performance.

Sakemi Yusuke, Morino Kai, Morie Takashi, Aihara Kazuyuki


General General

On sketch-based selections from scatterplots using KDE, compared to Mahalanobis and CNN brushing.

In IEEE computer graphics and applications

Fast and accurate brushing is crucial in visual data exploration and sketch-based solutions are successful methods. In this paper, we detail a solution, based on kernel density estimation (KDE), which computes a data subset selection in a scatterplot from a simple click-and-drag interaction. We explain, how this technique relates to two alternative approaches, i.e., Mahalanobis brushing and CNN brushing. To study this relation, we conducted two user studies and present both a quantitative three-fold comparison as well as additional details about the prevalence of all possible cases in that each technique succeeds/fails. With this, we also provide a comparison between empirical modeling and implicit modeling by deep learning in terms of accuracy, efficiency, generality and interpretability.

Fan Chaoran, Hauser Helwig