Implementation of Artificial Intelligence-Based Diabetic Retinopathy Screening in a Tertiary Care Hospital in Quebec: Prospective Validation Study.

Background: Diabetic retinopathy (DR) affects about 25% of people with diabetes in Canada. Early detection of DR is essential for preventing vision loss.

Objective: We evaluated the real-world performance of an artificial intelligence (AI) system that analyzes fundus images for DR screening in a Quebec tertiary care center.

Controlling chaos using edge computing hardware.

Machine learning provides a data-driven approach for creating a digital twin of a system - a digital model used to predict the system behavior. Having an accurate digital twin can drive many applications, such as controlling autonomous systems. Often, the size, weight, and power consumption of the digital twin or related controller must be minimized, ideally realized on embedded computing hardware that can operate without a cloud-computing connection.

A metal-catalysed functional group metathesis approach to the carbon isotope labelling of carboxylic acids.

The distribution, metabolism and ultimate fate of molecules within the body is central to the activity of pharmaceuticals. However, the introduction of radioisotopes into the metabolically stable carbon sites on drugs to probe these features typically requires toxic, radioactive gases such as [C]CO and [C]CO. Here we describe an approach to directly carbon-label carboxylic-acid-containing pharmaceuticals via a metal-catalysed functional group exchange reaction, forming C-labelled carboxylic-acid-containing drugs without radioactive gases, in one pot, using an easily available and handled carboxylic acid C source.

Controlling chaotic maps using next-generation reservoir computing.

In this work, we combine nonlinear system control techniques with next-generation reservoir computing, a best-in-class machine learning approach for predicting the behavior of dynamical systems. We demonstrate the performance of the controller in a series of control tasks for the chaotic Hénon map, including controlling the system between unstable fixed points, stabilizing the system to higher order periodic orbits, and to an arbitrary desired state. We show that our controller succeeds in these tasks, requires only ten data points for training, can control the system to a desired trajectory in a single iteration, and is robust to noise and modeling error.