Device to dynamically stretch cells during microscopic visualization

Cells in physiological systems are constantly subjected to mechanical forces which affect myriad cellular functions and contribute to pathologies. Current devices to study cellular responses to strain in vitro have drawbacks including the inability to analyze cellular responses in “real-time”, non-uniform strains patterns, and limited operation time (\u3c6 hr). To overcome these limitations, a novel stretch device was developed using four linear motors and a novel silicone culture well. The device is capable of cyclic stretching of cells biaxially up to 30% strain in either or both of two orthogonal axes at 0.01 to 1 Hz frequency for a minimum of 6 hr during “real-time” analysis of cells under a standard inverted microscope. This new system will facilitate controlled mechanobiology studies

Carbon Reduction In Reigate & Banstead

This project used energy surveying protocols to identify opportunities for carbon reduction among small and medium enterprises in the borough of Reigate and Banstead. The team conducted energy surveys to discover the most common patterns of energy consumption, as well as areas for businesses to improve energy efficiency. Personalized reports were created for these companies, and separate recommendations were given to the Reigate and Banstead Borough Council regarding the replication of similar projects in the future. Additionally, the project team gained a valuable cross-cultural experience while learning project management and collaboration skills

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Correlation of Vortex Motion in High- Tc Superconductors

The magnetic flux noise generated by films and crystals of Bi2Sr2CaCu2 O8+y and YBa2Cu3 O7-x, up to 30 μm thick and cooled in nominally zero magnetic field, has been measured at opposing surfaces by two dc superconducting quantum interference devices. For both materials, the noise sources at the two surfaces were highly correlated at specific temperatures in a given cooldown. This result suggests that the observed vortices moved as rigid rods. At other temperatures, the noise was mostly uncorrelated, suggesting that the relevant vortices were pinned at more than one point along their length

Stephenson_PLOS ONE_R001

This excel file includes the safety data, immunologic endpoints, and virologic endpoints collected in a clinical trial testing an oral, live HIV vaccine candidate (NCT02366013). This was a phase 1 study conducted by Beth Israel Deaconess Medical Center and University of Rochester, NY