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Diluvian or: How We Learned to Stop Worrying and Love the Bomb Cyclone
No full textFrom the Fiction section of the from: SUMMER 2024 Swimming Lessons: Staying afloat in our flooded futur
Making Plans Findable, Accessible, Interoperable, and Reusable with Data Infrastructure: A Search Engine for Constructing, Analyzing, and Visualizing Planning Documents
Get PDFLocal land-use plans help guide future development, but it is often difficult to compare content across jurisdictions, making regional coordination and plan evaluation challenging. This research reviews federal, state, and local data infrastructure guidance for land-use plans and compares such guidance to compliance with a California use-case. Findings indicate a number of obstacles to fostering data sharing and comparative analysis of plans: there is currently no central repository of land-use plans; plans are not uniform in format and are often out of date; many plans are not machine-readable thereby inhibiting text extraction, and planning language varies so greatly that there are numerous synonyms for terms of interest. Nonetheless, we demonstrate that the creation of digital platforms for archiving and searching across plans is currently feasible and enables large-scale quantitative analysis. Based on currently available metadata in existing land-use plans, we designed and piloted a structured database to enable users to search for terms and phrases across over 500 land-use plans. To center issues of social equity, the open access platform was developed in collaboration with state agencies and community organizations focused on environmental justice. Based on the pilot, we conclude with a framework for both developing plan data infrastructure given current constraints in standardized plan metadata and availability as well as guidance for plan formatting using FAIR standards (Findable, Accessible, Interoperable, and Reusable)
Adapting to Survive: The Influence of Environmental Conditions on Uropathogenic E. coli Biofilm Formation
No full textUropathogenic Escherichia coli (UPEC) is the leading cause of urinary tract infections (Eberly et al., 2017), which can be challenging to treat due to antibiotic resistance. This is due to UPEC forming robust biofilms that colonize bladder epithelial cells and catheters (Eberly et al., 2017). Bacteria have developed complex mechanisms to sense environmental changes and use these to respond and adapt to different niches. This study examines how changes in temperature, oxygen levels, incubation method, and environmental cues, which might change in the bladder, impact biofilm formation. I utilized the UPEC strain CFT073 and, as a comparison, the probiotic strain EcN and the biofilm-former non-pathogenic E.coli strain AR3110, at two different temperatures (23°C and 37°C). Because urine is high in amino acids, yeast extract and casamino acids (YESCA) were used as the growth medium. A key finding shows UPEC\u27s temperature-dependent biofilm formation at 37°C under both anaerobic and aerobic conditions, particularly on specific substrates, with UPEC displaying enhanced biofilm formation at anaerobic compared to aerobic conditions. The findings also note EcN\u27s capacity to form biofilms under a broader range of conditions. The findings emphasize the importance of environmental stability for bacterial survival, revealing that UPEC and EcN form more biofilm in static conditions. The study identifies crucial roles for cellular structures like Type 1 fimbriae and P-pili in UPEC\u27s attachment and invasion processes. It highlights the modulation of surface structures, such as curli and Ag43, that contribute to biofilm stability at elevated temperatures. Additionally, the gene papA demonstrates a higher expression under body temperature and low oxygen conditions. This study aims to deepen our understanding of UPEC\u27s adaptive mechanisms, which may help us develop more effective treatment strategies for urinary tract infections
Antarctica Foraminifera Biodiversity in an Evolving Climate
No full textForaminifera are microbial amoeboid protists that are versatile, diverse, and ecologically significant. Despite their importance, there are likely many undiscovered species and we lack a full understanding of their diversity or ecological roles especially in less explored habitats such as the poles. Advances in molecular techniques have allowed for the presence and relative abundance of foraminifera species to better be characterized. These developments have additionally allowed for the discovery of novel species of forams. In this study, we aimed to examine the biodiversity of foraminifera across multiple areas in Antarctica using molecular tools. Our results demonstrated a large amount of diversity among foraminifera species, with a range in species relative abundance and presence. Further, about half of the foraminifera in this study indicated endemic tendencies, appearing exclusively in one location. We were additionally able to identify a potential novel clade of Antarctic foraminifera. These insights allowed for the further characterization of forams, along with their existence in Antarctica to be better understood. A deeper understanding of Antarctic foraminifera relative abundance, presence, and habitat tendencies aids in knowledge on foraminifera in regards to climate change and our changing planet
Generative Artificial Intelligence Consensus in a Trustless Network
Get PDFWe performed a billion locality sensitive hash comparisons between artificially generated data samples to answer the critical question - can we reproduce the results of generative AI models? Reproducibility is one of the pillars of scientific research for verifiability, bench- marking, trust, and transparency. Futhermore, we take this research to the next level by verifying the “correctness” of generative AI output in a non-deterministic, trustless, decentralized network. We generate millions of data samples from a variety of open source diffusion and large language models and describe the procedures and trade-offs between generating more verses less deterministic output. Additionally, we analyze the outputs to provide empirical evidence of different parameterizations of tolerance and error bounds for verification. For our results, we show that with a majority vote between three independent verifiers, we can detect image generated perceptual collisions in generated AI with over 99.89% probability and less than 0.0267% chance of intra-class collision. For large language models (LLMs), we are able to gain 100% consen- sus using greedy methods or n-way beam searches to generate consensus demonstrated on different LLMs. In the context of generative AI train- ing, we pinpoint and minimize the major sources of stochasticity and present gossip and synchronization training techniques for verifiability. Thus, this work provides a practical, solid foundation for AI verification, reproducibility, and consensus for generative AI applications
Generalized Splines on Graphs with Two Labels and Polynomial Splines on Cycles
Get PDFGeneralized splines are an algebraic combinatorial framework that generalizes and unifies various established concepts across different fields, most notably the classical notion of splines and the topological notion of GKM theory. The for- mer consists of piecewise polynomials on a combinatorial geometric object like a polytope, whose polynomial pieces agree to a specified degree of differentiability. The latter is a graph-theoretic construction of torus-equivariant cohomology that Shareshian and Wachs used to reformulate the well-known Stanley–Stembridge con- jecture, a reformulation that was recently proven to hold by Brosnan and Chow and independently Guay-Paquet. This paper focuses on the theory of generalized splines. A generalized spline on a graph G with each edge labeled by an ideal in a ring R consists of a vertex-labeling by elements of R so that the labels on adjacent vertices u, v differ by an element of the ideal associated to the edge uv. We study the R-module of generalized splines and produce minimum generating sets for several families of graphs and edge-labelings: 1) for all graphs when the set of possible edge-labelings consists of at most two finitely-generated ideals, and 2) for cycles when the set of possible edge-labelings consists of principal ideals generated by elements of the form (ax + by)2 in the polynomial ring C[x, y]. We obtain the generators using a constructive algorithm that is suitable for computer implementation and give several applications, including contextualizing several results in the theory of classical (analytic) splines
Optimal Policing with (and without) Criminal Search
Get PDFWe develop a search-theoretic model, in which a police agency allocates scarce resources across neighborhoods—heterogeneous in “vigilance” and valuables—to minimize crime, while potential criminals decide whether, and if so, when and where to commit a crime. When criminals sequentially search for the best target, the optimal police allocation depends on the difference in vigilance levels across neighborhoods, prioritizing neighborhoods with low vigilance. However, in the absence of criminal search, the optimal allocation depends on the degree of rent inequality among neighborhoods, with a priority placed on neighborhoods with higher rents. We also identify conditions under which policing all neighborhoods equally is optimal. Our findings underscore that an optimal policing design must not only consider neighborhood characteristics but also other factors that may impact criminals’ decision-making, including whether they engage in active search
The JWST Early Release Science Program for Direct Observations of Exoplanetary Systems. V. Do Self-consistent Atmospheric Models Represent JWST Spectra? A Showcase with VHS 1256-1257 b
Get PDFThe unprecedented medium-resolution (R λ ∼ 1500-3500) near- and mid-infrared (1-18 μm) spectrum provided by JWST for the young (140 ± 20 Myr) low-mass (12-20 M Jup) L-T transition (L7) companion VHS 1256 b gives access to a catalog of molecular absorptions. In this study, we present a comprehensive analysis of this data set utilizing a forward-modeling approach applying our Bayesian framework, ForMoSA. We explore five distinct atmospheric models to assess their performance in estimating key atmospheric parameters: T eff, log(g), [M/H], C/O, γ, f sed, and R. Our findings reveal that each parameter’s estimate is significantly influenced by factors such as the wavelength range considered and the model chosen for the fit. This is attributed to systematic errors in the models and their challenges in accurately replicating the complex atmospheric structure of VHS 1256 b, notably the complexity of its clouds and dust distribution. To propagate the impact of these systematic uncertainties on our atmospheric property estimates, we introduce innovative fitting methodologies based on independent fits performed on different spectral windows. We finally derived a T eff consistent with the spectral type of the target, considering its young age, which is confirmed by our estimate of log(g). Despite the exceptional data quality, attaining robust estimates for chemical abundances [M/H] and C/O, often employed as indicators of formation history, remains challenging. Nevertheless, the pioneering case of JWST’s data for VHS 1256 b has paved the way for future acquisitions of substellar spectra that will be systematically analyzed to directly compare the properties of these objects and correct the systematics in the models
