Efficient multi-phenotype genome-wide analysis identifies genetic associations for unsupervised deep-learning-derived high-dimensional brain imaging phenotypes.

Brain imaging is a high-content modality that offers dense insights into the structure and pathology of the brain. Existing genetic association studies of brain imaging, typically focusing on a number of individual image-derived phenotypes (IDPs), have successfully identified many genetic loci. Previously, we have created a 128-dimensional Unsupervised Deep learning derived Imaging Phenotypes (UDIPs), and identified multiple loci from single-phenotype genome-wide association studies (GWAS) for individual UDIP dimensions, using data from the UK Biobank (UKB).

iGWAS: Image-based genome-wide association of self-supervised deep phenotyping of retina fundus images.

Existing imaging genetics studies have been mostly limited in scope by using imaging-derived phenotypes defined by human experts. Here, leveraging new breakthroughs in self-supervised deep representation learning, we propose a new approach, image-based genome-wide association study (iGWAS), for identifying genetic factors associated with phenotypes discovered from medical images using contrastive learning. Using retinal fundus photos, our model extracts a 128-dimensional vector representing features of the retina as phenotypes.

Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging.

Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning.

Deep learning model for personalized prediction of positive MRSA culture using time-series electronic health records.

Methicillin-resistant Staphylococcus aureus (MRSA) poses significant morbidity and mortality in hospitals. Rapid, accurate risk stratification of MRSA is crucial for optimizing antibiotic therapy. Our study introduced a deep learning model, PyTorch_EHR, which leverages electronic health record (EHR) time-series data, including wide-variety patient specific data, to predict MRSA culture positivity within two weeks.

Genetic InfoMax: Exploring Mutual Information Maximization in High-Dimensional Imaging Genetics Studies.

Genome-wide association studies (GWAS) are used to identify relationships between genetic variations and specific traits. When applied to high-dimensional medical imaging data, a key step is to extract lower-dimensional, yet informative representations of the data as traits. Representation learning for imaging genetics is largely under-explored due to the unique challenges posed by GWAS in comparison to typical visual representation learning.

Issues in Melanoma Detection: Semisupervised Deep Learning Algorithm Development via a Combination of Human and Artificial Intelligence.

Background: Automatic skin lesion recognition has shown to be effective in increasing access to reliable dermatology evaluation; however, most existing algorithms rely solely on images. Many diagnostic rules, including the 3-point checklist, are not considered by artificial intelligence algorithms, which comprise human knowledge and reflect the diagnosis process of human experts.

Objective: In this paper, we aimed to develop a semisupervised model that can not only integrate the dermoscopic features and scoring rule from the 3-point checklist but also automate the feature-annotation process.

PK-RNN-V E: A deep learning model approach to vancomycin therapeutic drug monitoring using electronic health record data.

Vancomycin is a commonly used antimicrobial in hospitals, and therapeutic drug monitoring (TDM) is required to optimize its efficacy and avoid toxicities. Bayesian models are currently recommended to predict the antibiotic levels. These models, however, although using carefully designed lab observations, were often developed in limited patient populations.

Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data.

Background: Predicting outcomes of patients with COVID-19 at an early stage is crucial for optimised clinical care and resource management, especially during a pandemic. Although multiple machine learning models have been proposed to address this issue, because of their requirements for extensive data preprocessing and feature engineering, they have not been validated or implemented outside of their original study site. Therefore, we aimed to develop accurate and transferrable predictive models of outcomes on hospital admission for patients with COVID-19.

Med-BERT: pretrained contextualized embeddings on large-scale structured electronic health records for disease prediction.

Deep learning (DL)-based predictive models from electronic health records (EHRs) deliver impressive performance in many clinical tasks. Large training cohorts, however, are often required by these models to achieve high accuracy, hindering the adoption of DL-based models in scenarios with limited training data. Recently, bidirectional encoder representations from transformers (BERT) and related models have achieved tremendous successes in the natural language processing domain.

Synthesis of Ultrathin Graphdiyne Film Using a Surface Template.

Graphdiyne is predicted to have a natural band gap and simultaneously possesses superior carrier mobility, which makes it potential for electronic devices. Synthesis of ultrathin graphdiyne film is highly demanded. In this work, we proposed an approach for synthesis of ultrathin graphdiyne film using graphene as a surface template, which can induce confined reaction on substrate.

Diatomite-Templated Synthesis of Freestanding 3D Graphdiyne for Energy Storage and Catalysis Application.

Graphdiyne (GDY), a new kind of two-dimensional (2D) carbon allotropes, has extraordinary electrical, mechanical, and optical properties, leading to advanced applications in the fields of energy storage, photocatalysis, electrochemical catalysis, and sensors. However, almost all reported methods require metallic copper as a substrate, which severely limits their large-scale application because of the high cost and low specific surface area (SSA) of copper substrate. Here, freestanding three-dimensional GDY (3DGDY) is successfully prepared using naturally abundant and inexpensive diatomite as template.

Template Synthesis of an Ultrathin β-Graphdiyne-Like Film Using the Eglinton Coupling Reaction.

β-Graphdiyne (β-GDY) is a two-dimensional carbon material with zero band gap and highly intrinsic carrier mobility and a promising material with potential applications in electronic devices. However, the synthesis of continuous single or ultrathin β-GDY has not been realized yet. Here, we proposed an approach for ultrathin β-GDY-like film synthesis using graphene as a template because of the strong π-π interaction between β-GDY and graphene.

Decoding of finger trajectory from ECoG using deep learning.

Objective: Conventional decoding pipeline for brain-machine interfaces (BMIs) consists of chained different stages of feature extraction, time-frequency analysis and statistical learning models. Each of these stages uses a different algorithm trained in a sequential manner, which makes it difficult to make the whole system adaptive. The goal was to create an adaptive online system with a single objective function and a single learning algorithm so that the whole system can be trained in parallel to increase the decoding performance.

Architecture of β-Graphdiyne-Containing Thin Film Using Modified Glaser-Hay Coupling Reaction for Enhanced Photocatalytic Property of TiO.

β-Graphdiyne (β-GDY) is a member of 2D graphyne family with zero band gap, and is a promising material with potential applications in energy storage, organic electronics, etc. However, the synthesis of β-GDY has not been realized yet, and the measurement of its intrinsic properties remains elusive. In this work, β-GDY-containing thin film is successfully synthesized on copper foil using modified Glaser-Hay coupling reaction with tetraethynylethene as precursor.

Chemical Vapor Deposition Growth of Linked Carbon Monolayers with Acetylenic Scaffoldings on Silver Foil.

Graphdiyne analogs, linked carbon monolayers with acetylenic scaffoldings, are fabricated by adopting low-temperature chemical vapor deposition which provides a route for the synthesis of two-dimensional carbon materials via molecular building blocks. The electrical conductivity of the as-grown films can reach up to 6.72 S cm .

Direct Synthesis of Graphdiyne Nanowalls on Arbitrary Substrates and Its Application for Photoelectrochemical Water Splitting Cell.

A general and simple route to fabricate graphdiyne nanowalls on arbitrary substrates is developed by using a copper envelope catalysis strategy. The GDY/BiVO system is but one example of combing the unique properites of GDY with those target substrates where GDY improves the photoelectrochemical performance dramatically.

Robust Superhydrophobic Foam: A Graphdiyne-Based Hierarchical Architecture for Oil/Water Separation.

Robust superhydrophobic foam is fabricated by combining an ordered graphdiyne-based hierarchical structure with a low-surface-energy coating. This foam shows not only superhydrophobicity both in air (≈160.1°) and in oil (≈171.

Synthesis of Graphdiyne Nanowalls Using Acetylenic Coupling Reaction.

Synthesizing graphdiyne with a well-defined structure is a great challenge. We reported herein a rational approach to synthesize graphdiyne nanowalls using a modified Glaser-Hay coupling reaction. Hexaethynylbenzene and copper plate were selected as monomer and substrate, respectively.