John R. Cohn

Quanfu Fan, Yilai Li, Yuguang Yao et al.

Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream structural biology techniques because of its ability to determine high-resolution structures of dynamic bio-molecules. However, cryo-EM data acquisition remains expensive and labor-intensive, requiring substantial expertise. Structural biologists need a more efficient and objective method to collect the best data in a limited time frame. We formulate the cryo-EM data collection task as an optimization problem in this work. The goal is to maximize the total number of good images taken within a specified period. We show that reinforcement learning offers an effective way to plan cryo-EM data collection, successfully navigating heterogenous cryo-EM grids. The approach we developed, cryoRL, demonstrates better performance than average users for data collection under similar settings.

John Meklenburg, Michael Specter, Michael Wentz et al.

Throughout the course of the COVID-19 pandemic, several countries have developed and released contact tracing and exposure notification smartphone applications (apps) to help slow the spread of the disease. To support such apps, Apple and Google have released Exposure Notification Application Programming Interfaces (APIs) to infer device (user) proximity using Bluetooth Low Energy (BLE) beacons. The Private Automated Contact Tracing (PACT) team has shown that accurately estimating the distance between devices using only BLE radio signals is challenging. This paper describes the design and implementation of the SonicPACT protocol to use near-ultrasonic signals on commodity iOS and Android smartphones to estimate distances using time-of-flight measurements. The protocol allows Android and iOS devices to interoperate, augmenting and improving the current exposure notification APIs. Our initial experimental results are promising, suggesting that SonicPACT should be considered for implementation by Apple and Google.