Trends in Research and Development for CO Capture and Sequestration.

Technological and medical advances over the past few decades epitomize human capabilities. However, the increased life expectancies and concomitant land-use changes have significantly contributed to the release of ∼830 gigatons of CO into the atmosphere over the last three decades, an amount comparable to the prior two and a half centuries of CO emissions. The United Nations has adopted a pledge to achieve "net zero", i.

Meta-Learning Initializations for Interactive Medical Image Registration.

We present a meta-learning framework for interactive medical image registration. Our proposed framework comprises three components: a learning-based medical image registration algorithm, a form of user interaction that refines registration at inference, and a meta-learning protocol that learns a rapidly adaptable network initialization. This paper describes a specific algorithm that implements the registration, interaction and meta-learning protocol for our exemplar clinical application: registration of magnetic resonance (MR) imaging to interactively acquired, sparsely-sampled transrectal ultrasound (TRUS) images.

Materials Research Directions Toward a Green Hydrogen Economy: A Review.

A constellation of technologies has been researched with an eye toward enabling a hydrogen economy. Within the research fields of hydrogen production, storage, and utilization in fuel cells, various classes of materials have been developed that target higher efficiencies and utility. This Review examines recent progress in these research fields from the years 2011-2021, exploring the most commonly occurring concepts and the materials directions important to each field.

Image quality assessment for machine learning tasks using meta-reinforcement learning.

In this paper, we consider image quality assessment (IQA) as a measure of how images are amenable with respect to a given downstream task, or task amenability. When the task is performed using machine learning algorithms, such as a neural-network-based task predictor for image classification or segmentation, the performance of the task predictor provides an objective estimate of task amenability. In this work, we use an IQA controller to predict the task amenability which, itself being parameterised by neural networks, can be trained simultaneously with the task predictor.

Real-time multimodal image registration with partial intraoperative point-set data.

We present Free Point Transformer (FPT) - a deep neural network architecture for non-rigid point-set registration. Consisting of two modules, a global feature extraction module and a point transformation module, FPT does not assume explicit constraints based on point vicinity, thereby overcoming a common requirement of previous learning-based point-set registration methods. FPT is designed to accept unordered and unstructured point-sets with a variable number of points and uses a "model-free" approach without heuristic constraints.

Artificial Intelligence in Chemistry: Current Trends and Future Directions.

The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015.

Automatic Spine Ultrasound Segmentation for Scoliosis Visualization and Measurement.

Objective: Integrate tracked ultrasound and AI methods to provide a safer and more accessible alternative to X-ray for scoliosis measurement. We propose automatic ultrasound segmentation for 3-dimensional spine visualization and scoliosis measurement to address difficulties in using ultrasound for spine imaging.

Methods: We trained a convolutional neural network for spine segmentation on ultrasound scans using data from eight healthy adult volunteers.

Patient-specific pediatric silicone heart valve models based on 3D ultrasound.

Purpose: Patient-specific heart and valve models have shown promise as training and planning tools for heart surgery, but physically realistic valve models remain elusive. Available proprietary, simulation-focused heart valve models are generic adult mitral valves and do not allow for patient-specific modeling as may be needed for rare diseases such as congenitally abnormal valves. We propose creating silicone valve models from a 3D-printed plastic mold as a solution that can be adapted to any individual patient and heart valve at a fraction of the cost of direct 3D-printing using soft materials.

Potential impact of co-payment at point of care to influence emergency department utilization.

Background. Many proponents for healthcare reform suggest increased cost-sharing by patients as a method to reduce overall expenditures. Prior studies on the effects of co-payments for ED visits have generally not been directed toward understanding patient attitudes/behavior at point of care.