Using Animal Welfare Framing as an Effective Approach to Communicating Climate Change-A Review.

Introduction: Climate change and its effects continue to threaten the stability of environments worldwide, impacting nearly every species. Although framing is an established technique in climate communication science, little has been done in communicating the climate change effects from an animal welfare perspective.

Objectives: The primary objective of this article is to present the synthesis of evidence in existing literature on the effects of communicating climate change as an animal welfare issue.

A Prospective, Randomized, Noninferiority Study to Evaluate the Safety and Effectiveness of Steerable Ureteroscopic Renal Evacuation Compared with Standard Ureteroscopy: 30-Day Results of the ASPIRE Study.

We report the first multicenter, prospective, randomized noninferiority controlled trial of steerable ureteroscopic renal evacuation (SURE) for nephrolithiasis treatment. Candidates for laser lithotripsy ≥18 years with ≥1 renal stone ≥7 mm and 7-20 mm stone burden were randomized 1:1 SURE ureteroscopy (URS). SURE was performed using the CVAC Aspiration System, a novel steerable irrigation-aspiration catheter.

Reconfigurable unitary transformations of optical beam arrays.

Spatial transformations of light are ubiquitous in optics, with examples ranging from simple imaging with a lens to quantum and classical information processing in waveguide meshes. Multi-plane light converter (MPLC) systems have emerged as a platform that promises completely general spatial transformations, i.e.

A Computational Protocol for the Knowledge-Based Assessment and Capture of Pathologies.

We propose that one of the main hurdles in delivering comprehensively informed care results from the challenges surrounding the extraction, representation, and retention of prior clinical experience and basic medical knowledge, as well as its translation into time- and context-informed actionable interventions. While emerging applications in artificial intelligence-based techniques, for example, large language models, offer impressive pattern association capabilities, they often fall short in producing human-readable explanations crucial to their integration into clinical care. Moreover, they require large well-defined and well-integrated data sets that typically conflict with the availability of such data in all but a few areas of medicine, for example, medical imaging and neuroimaging, noninvasive monitoring of bio-electrical activity, etc.