Search for: All records

The colony-forming cyanobacteria Trichodesmium spp. are considered one of the most important nitrogen-fixing genera in the warm, low nutrient ocean. Despite this central biogeochemical role, many questions about their evolution, physiology, and trophic interactions remain unanswered. To address these questions, we describe Trichodesmium pangenomic potential via significantly improved genomic assemblies from two isolates and 15 new >50% complete Trichodesmium metagenome-assembled genomes from hand-picked, Trichodesmium colonies spanning the Atlantic Ocean. Phylogenomics identified ~four N2 fixing clades of Trichodesmium across the transect, with T. thiebautii dominating the colony-specific reads. Pangenomic analyses showed that all T. thiebautii MAGs are enriched in COG defense mechanisms and encode a vertically inherited Type III-B Clustered Regularly Interspaced Short Palindromic Repeats and associated protein-based immunity system (CRISPR-Cas). Surprisingly, this CRISPR-Cas system was absent in all T. erythraeum genomes, vertically inherited by T. thiebautii, and correlated with increased signatures of horizontal gene transfer. Additionally, the system was expressed in metaproteomic and transcriptomic datasets and CRISPR spacer sequences with 100% identical hits to field-assembled, putative phage genome fragments were identified. While the currently CO2-limited T. erythraeum is expected to be a ‘winner’ of anthropogenic climate change, their genomic dearth of known phage resistance mechanisms, compared to T. thiebautii, could put this outcome in question. Thus, the clear demarcation of T. thiebautii maintaining CRISPR-Cas systems, while T. erythraeum does not, identifies Trichodesmium as an ecologically important CRISPR-Cas model system, and highlights the need for more research on phage-Trichodesmium interactions.

 

In December, 2020, Apple began requiring developers to disclose their data collection and use practices to generate a “privacy label” for their application. The use of mobile application Software Development Kits (SDKs) and third-party libraries, coupled with a typical lack of expertise in privacy, makes it challenging for developers to accurately report their data collection and use practices. In this work we discuss the design and evaluation of a tool to help iOS developers generate privacy labels. The tool combines static code analysis to identify likely data collection and use practices with interactive functionality designed to prompt developers to elucidate analysis results and carefully reflect on their applications’ data practices. We conducted semi-structured interviews with iOS developers as they used an initial version of the tool. We discuss how these results motivated us to develop an enhanced software tool, Privacy Label Wiz, that more closely resembles interactions developers reported to be most useful in our semi-structured interviews. We present findings from our interviews and the enhanced tool motivated by our study. We also outline future directions for software tools to better assist developers communicating their mobile app’s data practices to different audiences. 

We consider the Kuramoto–Sivashinsky equation (KSE) on the two-dimensional torus in the presence of advection by a given background shear flow. Under the assumption that the shear has a finite number of critical points and there are linearly growing modes only in the direction of the shear, we prove global existence of solutions with data in$$L^2$$$L^2$, using a bootstrap argument. The initial data can be taken arbitrarily large.

 

“Notice and choice” is the predominant approach for data privacy protection today. There is considerable user-centered research on providing effective privacy notices but not enough guidance on designing privacy choices. Recent data privacy regulations worldwide established new requirements for privacy choices, but system practitioners struggle to implement legally compliant privacy choices that also provide users meaningful privacy control. We construct a design space for privacy choices based on a user-centered analysis of how people exercise privacy choices in real-world systems. This work contributes a conceptual framework that considers privacy choice as a user-centered process as well as a taxonomy for practitioners to design meaningful privacy choices in their systems. We also present a use case of how we leverage the design space to finalize the design decisions for a real-world privacy choice platform, the Internet of Things (IoT) Assistant, to provide meaningful privacy control in the IoT.