Care of terminally ill / Palliative care

E2EDA: Protein Domain Assembly Based on End-to-End Deep Learning.

With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and further improving the accuracy of the full-chain modeling by accurately predicting inter-domain orientation while improving the assembly efficiency will ...

FDNet: An end-to-end fusion decomposition network for infrared and visible images.

Infrared and visible image fusion can generate a fusion image with clear texture and prominent goals under extreme conditions. This capability is important for all-day climate detection and other tasks. However, most existing fusion methods for extracting features from infrared and visible images are based on convolutional neural networks (CNNs). These methods often fail to make full use of the sa...

Integrating deep learning, threading alignments, and a multi-MSA strategy for high-quality protein monomer and complex structure prediction in CASP15.

We report the results of the "UM-TBM" and "Zheng" groups in CASP15 for protein monomer and complex structure prediction. These prediction sets were ob...

A narrative review of recent tools and innovations toward automating living systematic reviews and evidence syntheses.

Living reviews are an increasingly popular research paradigm. The purpose of a 'living' approach is to allow rapid collation, appraisal and synthesis ...

AEAU-Net: an unsupervised end-to-end registration network by combining affine transformation and deformable medical image registration.

Deformable medical image registration plays an essential role in clinical diagnosis and treatment. However, due to the large difference in image defor...

Comparing the Lower-Limb Muscle Activation Patterns of Simulated Walking Using an End-Effector-Type Robot with Real Level and Stair Walking in Children with Spastic Bilateral Cerebral Palsy.

Cerebral palsy is a neurologic disorder caused by lesions on an immature brain, often resulting in spasticity and gait abnormality. This study aimed t...

An End-to-End Dynamic Posture Perception Method for Soft Actuators Based on Distributed Thin Flexible Porous Piezoresistive Sensors.

This paper proposes a method for accurate 3D posture sensing of the soft actuators, which could be applied to the closed-loop control of soft robots. ...

Neural-Network-Based Adaptive Control of Uncertain MIMO Singularly Perturbed Systems With Full-State Constraints.

This article investigates the tracking control problem for a class of nonlinear multi-input-multi-output (MIMO) uncertain singularly perturbed systems...

An End-to-End Framework for Joint Denoising and Classification of Hyperspectral Images.

Image denoising and classification are typically conducted separately and sequentially according to their respective objectives. In such a setup, wher...

High Throughput Read-Across for Screening a Large Inventory of Related Structures by Balancing Artificial Intelligence/Machine Learning and Human Knowledge.

Read-across is an in silico method applied in chemical risk assessment for data-poor chemicals. The read-across outcomes for repeated-dose toxicity en...

An end-end arrhythmia diagnosis model based on deep learning neural network with multi-scale feature extraction.

This study presents an innovative end-to-end deep learning arrhythmia diagnosis model that aims to address the problems in arrhythmia diagnosis. The m...

Optimizing Two-Way Partial AUC With an End-to-End Framework.

The Area Under the ROC Curve (AUC) is a crucial metric for machine learning, which evaluates the average performance over all possible True Positive R...

Fully Convolutional Change Detection Framework With Generative Adversarial Network for Unsupervised, Weakly Supervised and Regional Supervised Change Detection.

Deep learning for change detection is one of the current hot topics in the field of remote sensing. However, most end-to-end networks are proposed for...

System Based on Artificial Intelligence Edge Computing for Detecting Bedside Falls and Sleep Posture.

Bedside falls and pressure ulcers are crucial issues in geriatric care. Although many bedside monitoring systems have been proposed, they are limited ...

Gigapixel end-to-end training using streaming and attention.

Current hardware limitations make it impossible to train convolutional neural networks on gigapixel image inputs directly. Recent developments in weak...

Evolving a Pipeline Approach for Abstract Meaning Representation Parsing Towards Dynamic Neural Networks.

Meaning Representation parsing aims to represent a sentence as a structured, Directed, Acyclic Graph (DAG), in an attempt to extract meaning from text...

Prediction of Human Organ Toxicity via Artificial Intelligence Methods.

Unpredicted human organ level toxicity remains one of the major reasons for drug clinical failure. There is a critical need for cost-efficient strateg...

An end-to-end convolutional neural network for automated failure localisation and characterisation of 3D interconnects.

The advancement in the field of 3D integration circuit technology leads to new challenges for quality assessment of interconnects such as through sili...

Comparing end-effector position and joint angle feedback for online robotic limb tracking.

Somatosensation greatly increases the ability to control our natural body. This suggests that supplementing vision with haptic sensory feedback would ...

Automatic brain MRI motion artifact detection based on end-to-end deep learning is similarly effective as traditional machine learning trained on image quality metrics.

Head motion artifacts in magnetic resonance imaging (MRI) are an important confounding factor concerning brain research as well as clinical practice. ...