Comparative Evaluation of Bioefficiency and Photocatalytic Activity of Green Synthesized Bismuth Oxide Nanoparticles Using Three Different Leaf Extracts.

Nanotechnology has emerged as a promising method for wastewater recycling. In this line, the current study emphasizes the leaf-extract-mediated biosynthesis of bismuth oxide nanostructures (BiONPs) using three different plants, namely Coldenia procumbens Linn (Creeping Coldenia), Citrus limon (Lemon), and Murraya koenigii (Curry) through a greener approach and evaluates their biological properties as well as photocatalytic performance for the first time. As-synthesized BiONPs were physiochemically characterized using UV-visible spectroscopy, Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), and high-resolution transmission electron microscopy (HRTEM) with energy dispersive X-ray analysis (EDAX). Using the well diffusion method, research on the antibacterial efficiency of BiONPs against human pathogenic Gram-positive bacteria, such as Staphylococcus aureus and Enterococcus faecalis, and Gram-negative bacteria, including Escherichia coli and Klebsiella pneumonia, revealed that Gram-negative bacteria exhibited relatively strong activity. The larvicidal activity assessed against Aedes aegypti and Aedes albopictus mosquito larvae reveals promising larvicidal activity with a minimal dosage of BiONPs with LC50 values of 5.53 and 19.24 ppm, respectively, after 24 h of exposure. The excellent photocatalytic activity of as-synthesized BiONPs was demonstrated through the photodegradation of malachite green (MG) and methylene blue (MB) dyes with respective degradation performance parameters of 70 and 90%. The biogenic synthetic approach reported here enables the scalable commercial synthesis of bismuth nanostructures for their widespread use in catalysis for wastewater treatment and environmental cleanup.

Federated benchmarking of medical artificial intelligence with MedPerf.

Medical artificial intelligence (AI) has tremendous potential to advance healthcare by supporting and contributing to the evidence-based practice of medicine, personalizing patient treatment, reducing costs, and improving both healthcare provider and patient experience. Unlocking this potential requires systematic, quantitative evaluation of the performance of medical AI models on large-scale, heterogeneous data capturing diverse patient populations. Here, to meet this need, we introduce MedPerf, an open platform for benchmarking AI models in the medical domain. MedPerf focuses on enabling federated evaluation of AI models, by securely distributing them to different facilities, such as healthcare organizations. This process of bringing the model to the data empowers each facility to assess and verify the performance of AI models in an efficient and human-supervised process, while prioritizing privacy. We describe the current challenges healthcare and AI communities face, the need for an open platform, the design philosophy of MedPerf, its current implementation status and real-world deployment, our roadmap and, importantly, the use of MedPerf with multiple international institutions within cloud-based technology and on-premises scenarios. Finally, we welcome new contributions by researchers and organizations to further strengthen MedPerf as an open benchmarking platform.

OpenFL: the open federated learning library.

Objective.Federated learning (FL) is a computational paradigm that enables organizations to collaborate on machine learning (ML) and deep learning (DL) projects without sharing sensitive data, such as patient records, financial data, or classified secrets.Approach.Open federated learning (OpenFL) framework is an open-source python-based tool for training ML/DL algorithms using the data-private collaborative learning paradigm of FL, irrespective of the use case. OpenFL works with training pipelines built with both TensorFlow and PyTorch, and can be easily extended to other ML and DL frameworks.Main results.In this manuscript, we present OpenFL and summarize its motivation and development characteristics, with the intention of facilitating its application to existing ML/DL model training in a production environment. We further provide recommendations to secure a federation using trusted execution environments to ensure explicit model security and integrity, as well as maintain data confidentiality. Finally, we describe the first real-world healthcare federations that use the OpenFL library, and highlight how it can be applied to other non-healthcare use cases.Significance.The OpenFL library is designed for real world scalability, trusted execution, and also prioritizes easy migration of centralized ML models into a federated training pipeline. Although OpenFL's initial use case was in healthcare, it is applicable beyond this domain and is now reaching wider adoption both in research and production settings. The tool is open-sourced atgithub.com/intel/openfl.