Explore the vast range of titles available.

Within 1-2 decades, quantum computers are expected to obsolesce current public-key cryptography, driving authorities such as IETF and NIST to push for adopting quantum-resistant cryptography (QRC) in ecosystems like Internet Protocol Security (IPsec). However, IPsec struggles to adopt QRC, primarily due to the limited ability of Internet Key Exchange Protocol Version 2 (IKEv2), which establishes IPsec connections, to tolerate the large public keys and digital signatures of QRC. Many solutions (e.g., IETF RFCs) are proposed to integrate QRC into IKEv2, but remain largely untested in practice. In this paper, we measure the performance of these proposals over the Internet by designing and implementing a novel, scalable, and flexible testbed for quantum-resistant IPsec, and we expose the serious shortcomings of existing proposals for quantum-resistant IKEv2 when deployed in constrained (e.g., lossy, rate-limited) networks. Through experimental deployments ranging from cloud-based virtual networks to hardware-in-the-loop wireless links between software-defined radios, as well as deployment on the international FABRIC testbed for next-generation networks, we show that today's solutions for quantum-resistant IPsec are insufficient, necessitating development of better approaches.

Sunlight is the dominant control on phytoplankton biosynthetic activity, and darkness deprives them of their primary external energy source. Changes in the biochemical composition of phytoplankton communities over diel light cycles and attendant consequences for carbon and energy flux in environments remain poorly elucidated. Here we use lipidomic data from the North Pacific subtropical gyre to show that biosynthesis of energy-rich triacylglycerols (TAGs) by eukaryotic nanophytoplankton during the day and their subsequent consumption at night drives a large and previously uncharacterized daily carbon cycle. Diel oscillations in TAG concentration comprise 23 ± 11% of primary production by eukaryotic nanophytoplankton representing a global flux of about 2.4 Pg C yr −1 . Metatranscriptomic analyses of genes required for TAG biosynthesis indicate that haptophytes and dinoflagellates are active members in TAG production. Estimates suggest that these organisms could contain as much as 40% more calories at sunset than at sunrise due to TAG production. Day-night cycles in the biochemical composition of phytoplankton remain poorly understood. Here, Becker et al. use lipidomic and transcriptomic data from the North Pacific subtropical gyre to describe a daily cycle of production and consumption of energy-rich lipids by eukaryotic phytoplankton.

Significance Nonphosphorus lipids produced by heterotrophic bacteria have been measured in marine ecosystems without an understanding of their origins or role. This work shows SAR11 chemoheterotrophic bacteria synthesize multiple nonphosphorus lipids in response to phosphate depletion. Because this process results in a reduced cellular P:C ratio, it impacts our understanding of ocean processes related to cellular elemental stoichiometry by showing how different environmental parameters alter P:C ratios in heterotrophs. Also, SAR11 grown with excess organophosphonate synthesized phosphorus-free lipids. This finding contrasts the contemporary view of organophosphorus utilization because organophosphate-derived phosphorus did not equally substitute for inorganic phosphate in lipids. Considering lipid phosphorus content was lower in cells using organophosphonate, phosphorus-based productivity estimates may vary as a function of phosphorus source. Phytoplankton inhabiting oligotrophic ocean gyres actively reduce their phosphorus demand by replacing polar membrane phospholipids with those lacking phosphorus. Although the synthesis of nonphosphorus lipids is well documented in some heterotrophic bacterial lineages, phosphorus-free lipid synthesis in oligotrophic marine chemoheterotrophs has not been directly demonstrated, implying they are disadvantaged in phosphate-deplete ecosystems, relative to phytoplankton. Here, we show the SAR11 clade chemoheterotroph Pelagibacter sp. str. HTCC7211 renovates membrane lipids when phosphate starved by replacing a portion of its phospholipids with monoglucosyl- and glucuronosyl-diacylglycerols and by synthesizing new ornithine lipids. Lipid profiles of cells grown with excess phosphate consisted entirely of phospholipids. Conversely, up to 40% of the total lipids were converted to nonphosphorus lipids when cells were starved for phosphate, or when growing on methylphosphonate. Cells sequentially limited by phosphate and methylphosphonate transformed >75% of their lipids to phosphorus-free analogs. During phosphate starvation, a four-gene cluster was significantly up-regulated that likely encodes the enzymes responsible for lipid renovation. These genes were found in Pelagibacterales strains isolated from a phosphate-deficient ocean gyre, but not in other strains from coastal environments, suggesting alternate lipid synthesis is a specific adaptation to phosphate scarcity. Similar gene clusters are found in the genomes of other marine α-proteobacteria, implying lipid renovation is a common strategy used by heterotrophic cells to reduce their requirement for phosphorus in oligotrophic habitats.

Cutting edge detectors push sensing technology by further improving spatial and temporal resolution, increasing detector area and volume, and generally reducing backgrounds and noise. This has led to a explosion of more and more data being generated in next-generation experiments. Therefore, the need for near-sensor, at the data source, processing with more powerful algorithms is becoming increasingly important to more efficiently capture the right experimental data, reduce downstream system complexity, and enable faster and lower-power feedback loops. In this paper, we discuss the motivations and potential applications for on-detector AI. Furthermore, the unique requirements of particle physics can uniquely drive the development of novel AI hardware and design tools. We describe existing modern work for particle physics in this area. Finally, we outline a number of areas of opportunity where we can advance machine learning techniques, codesign workflows, and future microelectronics technologies which will accelerate design, performance, and implementations for next generation experiments.