bia-binder: A web-native cloud compute service for the bioimage analysis community.

We introduce bia-binder (BioImage Archive Binder), an open-source, cloud-architectured, and web-based coding environment tailored to bioimage analysis that is freely accessible to all researchers. The service generates easy-to-use Jupyter Notebook coding environments hosted on EMBL-EBI's Embassy Cloud, which provides significant computational resources. The bia-binder architecture is free, open-source and publicly available for deployment.

Direct dimethyl carbonate synthesis from CO and methanol over a flower-like CeO catalyst with 2-cyanopyridine as a dehydrating agent in continuous packed-bed reactor.

A flower-like CeO catalyst was successfully synthesized using an acrylamide graft copolymerized on glucose under hydrothermal conditions and used for the direct synthesis of dimethyl carbonate (DMC) from CO and CHOH in a packed-bed reactor with 2-cyanopyridine as a dehydrating agent. The synthesized flower-like CeO exhibited both basicity and acidity properties with values of 300 μmol g and 80 μmol g, respectively, according to CO-TPD and NH-TPD results. The effect of reaction parameters such as reaction temperature, feed ratio, catalyst quantity, and operating pressure on the DMC production over the flower-like CeO catalyst was investigated.

Making the most of bioimaging data through interdisciplinary interactions.

The increasing technical complexity of all aspects involving bioimages, ranging from their acquisition to their analysis, has led to a diversification in the expertise of scientists engaged at the different stages of the discovery process. Although this diversity of profiles comes with the major challenge of establishing fruitful interdisciplinary collaboration, such collaboration also offers a superb opportunity for scientific discovery. In this Perspective, we review the different actors within the bioimaging research universe and identify the primary obstacles that hinder their interactions.

MyThisYourThat for interpretable identification of systematic bias in federated learning for biomedical images.

Distributed collaborative learning is a promising approach for building predictive models for privacy-sensitive biomedical images. Here, several data owners (clients) train a joint model without sharing their original data. However, concealed systematic biases can compromise model performance and fairness.