Resolving the contrasting leaf hydraulic adaptation of C and C grasses.

Grasses are exceptionally productive, yet their hydraulic adaptation is paradoxical. Among C grasses, a high photosynthetic rate (A) may depend on higher vein density (D) and hydraulic conductance (K). However, the higher D of C grasses suggests a hydraulic surplus, given their reduced need for high K resulting from lower stomatal conductance (g).

In situ observations confirm that the wrought-iron butterflyfish Chaetodon daedalma is a facultative corallivore.

Here, we provide the first in situ observations of foraging habitats of Chaetodon daedalma, which is endemic to the subtropical north-west Pacific. Overall, 62.4% of bites were from the substratum, 30.

Ambulatory care of patients with arteriopathies: Overview for vascular medicine advanced practice providers.

Arteriopathies are a group of vascular disorders that encompass arterial dissection, aneurysm, and tortuosity that may or may not have an identifiable cause. Given the varied clinical presentations and underlying disorders of patients with arteriopathies, clinicians must develop a wide range of tools to care for these patients, including a focused history, physical examination, diagnostic imaging, medical and surgical therapies, genetic testing, and education. The vascular medicine clinic is one setting that can provide comprehensive care for this patient population, and vascular medicine advanced practice providers (APPs) are essential in this setting.

A genomic approach to Porites (Anthozoa: Scleractinia) megadiversity from the Indo-Pacific.

Porites corals are vital components of tropical reef ecosystems worldwide, serving as ecosystem engineers and hubs of biodiversity in shallow water coral reefs. Despite their ecological significance and the widespread use of Porites spp. as models for research, the richness and evolutionary relationships of species within the genus remain elusive.

Deep feature batch correction using ComBat for machine learning applications in computational pathology.

Background: Developing artificial intelligence (AI) models for digital pathology requires large datasets from multiple sources. However, without careful implementation, AI models risk learning confounding site-specific features in datasets instead of clinically relevant information, leading to overestimated performance, poor generalizability to real-world data, and potential misdiagnosis.

Methods: Whole-slide images (WSIs) from The Cancer Genome Atlas (TCGA) colon (COAD), and stomach adenocarcinoma datasets were selected for inclusion in this study.