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Scanning Ka-band Doppler radar observations reveal the development and intensification of a counter-rotating vortex pair (CVP) embedded in an advancing fire-front during California’s Dixie Fire in August 2021. The observations show that an initially isolated plume associated with a new spot fire develops flow splitting and a fire-generated inflow wind on the plume’s lee side. This inflow retards the fire progression and enhances the lateral wind shear along the plume flanks. The lateral shear evolves into quasi-symmetric cyclonic and anticyclonic vortices with winds >40 m s⁻¹. This counter rotating vortex pair (CVP) spreads perpendicular to the wind direction, yielding a “y-shaped” fire perimeter, with fire intensity and direction of spread strongly linked to the vortices. Detailed snapshots of the vortices reveal associated radar hook echoes and orbiting sub-vortices of tornado-like intensity. Some vortices remain attached to the fire, while others shed downstream. Additional lidar observations show the structure and development of the fire’s inflow. We discuss the observed vortex evolution in the context of existing conceptualizations for CVPs in wildland fire, including their preferential occurrence on lee slopes and their role in generating lateral fire spread. 

Abstract Hybrid perovskites are interesting optoelectronic materials. The perovskite ABX₃structure offers a vast compositional space, and we have identified over 300 perovskite ions. This flexibility enables tuneable properties and has significantly contributed to the success of perovskite optoelectronics. However, this diversity also leads to confusion, ambiguity, and inconsistencies causing challenges for data mining and machine learning applications. To address this issue, we propose guidelines and a JSON schema to standardize the reporting of perovskite compositions. The schema adheres to IUPAC recommendations and is designed to make data both human- and machine-readable. It captures key descriptors such as perovskite composition, molecular formula, SMILES representation, IUPAC name, and CAS number for each ion. To facilitate adoption, we have developed utilities to automatically generate comprehensive and standardized perovskite descriptions from standard ion abbreviations and stoichiometric coefficients. Additionally, we provide a curated database of all identified perovskite ions with associated descriptive data. 

Abstract A major goal of cosmology is to understand the nature of the field(s) which drove primordial Inflation. Through future observations, the statistics of large-scale structure will allow us to probe primordial non-Gaussianity of thecurvature perturbation at the end of Inflation. We show how a new correlation statistic can significantly improve these constraints over conventional methods. Next-generation radio telescope arrays are under construction which will map the density field of neutral hydrogen to high redshifts. These telescopes can operate as an interferometer, able to probe small scales, or as a collection of single dishes, combining signals to map the large scales. We show how to fuse these operating modes in order to measure the squeezed bispectrum with higher precision and greater economy. This leads to constraints on primordial non-Gaussianity that will improve on measurements by Planck,and out-perform other surveys such as Euclid. We forecast that σ(f_NL^loc)∼ 3, achieved by using a small subset, 𝒪(10²- 10³), of the total number of accessible triangles. The proposed method identifies a low instrumental noise, systematic-free scale regime, enabling clean squeezed bispectrum measurements. This provides a pristine window into local primordial non-Gaussianity, allowing tight constraints not only on primordial non-Gaussianity, but on any observable that peaks in squeezed configurations. 

Drought is a significant environmental stressor that severely impairs plant growth and agricultural productivity. Unraveling the molecular mechanisms underlying plant responses to drought is crucial for developing crops with enhanced resilience. In this study, we investigated the transcriptomic responses of cultivated tomato (Solanum lycopersicum) and its drought-tolerant wild relative,Solanum pennellii, to identify “stress-ready” gene expression patterns associated with pre-adaptation to arid environments. Through RNA-seq analysis, we identified orthologous genes between the two species and compared their transcriptomic profiles under both control and drought conditions. Approximately 43% of the orthologous genes exhibited species-specific expression patterns, while nearly 20% were classified as stress-ready. These stress-ready genes were significantly enriched for functions related to nucleosome assembly, RNA metabolism, and transcriptional regulation. Furthermore, transcription factor binding motif analysis revealed a marked enrichment of ERF family motifs, emphasizing their role in both stress-ready and species-specific responses. Our findings indicate that regulatory mechanisms, particularly those mediated by ERF transcription factors, are pivotal to the drought resilience ofS. pennellii, providing a foundation for future crop improvement strategies. 

Foot and Mouth Disease (FMD) affects cloven-hoofed animals globally and has become a major economic burden for many countries around the world. Countries that have had recent FMD outbreaks are prohibited from exporting most meat products; this has major economic consequences for farmers in those countries, particularly farmers that experience outbreaks or are near outbreaks. Reducing the number of FMD outbreaks in countries where the disease is endemic is an important challenge that could drastically improve the livelihoods of millions of people. As a result, significant effort is expended on surveillance; but there is a concern that uninformative surveillance strategies may waste resources that could be better used on control management. Rapid detection through sentinel surveillance may be a useful tool to reduce the scale and burden of outbreaks. In this study, we use an extensive outbreak and cattle shipment network dataset from the Republic of Türkiye to retrospectively test three possible strategies for sentinel surveillance allocation in countries with endemic FMD and minimal existing FMD surveillance infrastructure that differ in their data requirements: ranging from low to high data needs, we allocate limited surveillance to [1] farms that frequently send and receive shipments of animals (Network Connectivity), [2] farms near other farms with past outbreaks (Spatial Proximity) and [3] farms that receive many shipments from other farms with past outbreaks (Network Proximity). We determine that all of these surveillance methods find a similar number of outbreaks – 2-4.5 times more outbreaks than were detected by surveying farms at random. On average across surveillance efforts, the Network Proximity and Network Connectivity methods each find a similar number of outbreaks and the Spatial Proximity method always finds the fewest outbreaks. Since the Network Proximity method does not outperform the other methods, these results indicate that incorporating both cattle shipment data and outbreak data provides only marginal benefit over the less data-intensive surveillance allocation methods for this objective. We also find that these methods all find more outbreaks when outbreaks are rare. This is encouraging, as early detection is critical for outbreak management. Overall, since the Spatial Proximity and Network Connectivity methods find a similar proportion of outbreaks, and are less data-intensive than the Network Proximity method, countries with endemic FMD whose resources are constrained could prioritize allocating sentinels based on whichever of those two methods requires less additional data collection.