Sparks

Atmospheric oxidation enhancement (AOE) is a proposed category of methane removal that involves dispersing oxidizing agents to accelerate the natural oxidation of atmospheric methane. There is currently a lack of standardized metrics for assessing and comparing proposed AOE approaches. We present a quantitative assessment framework centered on conversion efficiency—the ratio of oxidized methane mass to dispersed material mass—to evaluate the feasibility of AOE approaches.We propose two necessary but insufficient criteria: (1) climate beneficial (net carbon negative in CO₂e, accounting for methane oxidation and emissions from material production/transport), and (2) cost-effectiveness (cheaper than the social cost of methane, currently estimated to be ~$2000/tCH₄). These thresholds define minimum conversion efficiency requirements for each approach. We apply this framework to two case studies: (1) iron salt aerosols (ISA), 40% FeCl₃ dispersed from marine vessels, and (2) hydrogen peroxide, 50% H₂O₂ dispersed from land-based towers. Drawing on published atmospheric modeling results and assumptions for material production costs and carbon intensities, we evaluate the conversion efficiencies that would be required to meet the climate beneficial and cost-effective criteria.Conversion efficiency varies by location for both ISA and H₂O₂, with some deployment scenarios being climate detrimental. Critical uncertainties include regional variations in OH and Cl recycling rates and poorly constrained atmospheric loss pathways—uncertainties that could shift the conversion efficiency by orders of magnitude.This proposed framework enables standardized comparisons and identifies priority research questions. Approaches failing to meet these criteria under optimistic assumptions suggest resources may be better allocated elsewhere, while those showing potential under plausible conditions merit deeper investigation. The goal is to provide a shared analytical foundation to help the community efficiently navigate the expanding solution space for atmospheric oxidation enhancement.

Hospital readmission among diabetic patients remains a critical challenge in modern healthcare, reflecting both disease complexity and the quality of inpatient and post-discharge care. This study investigates the use of machine learning (ML) technique

The SPARQL query language is the standard method to access knowledge graphs (KGs). However, formulating SPARQL queries is a significant challenge for non-expert users, and remains time-consuming for the experienced ones. Best practices recommend to document KGs with competency questions and example queries to contextualise the knowledge they contain and illustrate their potential applications. In practice, however, this is either not the case or the examples are provided in limited numbers. Large Language Models (LLMs) are being used in conversational agents and are proving to be an attractive solution with a wide range of applications, from simple question-answering about common knowledge to generating code in a targeted programming language. However, training and testing these models to produce high quality SPARQL queries from natural language questions requires substantial datasets of question-query pairs. In this paper, we present Q${}^2$Forge that addresses the challenge of generating new competency questions for a KG and corresponding SPARQL queries. It iteratively validates those queries with human feedback and LLM as a judge. Q${}^2$Forge is open source, generic, extensible and modular, meaning that the different modules of the application (CQ generation, query generation and query refinement) can be used separately, as an integrated pipeline, or replaced by alternative services. The result is a complete pipeline from competency question formulation to query evaluation, supporting the creation of reference query sets for any target KG.

We explore the use of Chain-of-Thought (CoT) prompting with large language models (LLMs) to improve the accuracy of granular sentiment categorization in app store reviews. Traditional numeric and polarity-based ratings often fail to capture the nuanced sentiment embedded in user feedback. We evaluated the effectiveness of CoT prompting versus simple prompting on 2000 Amazon app reviews by comparing each method's predictions to human judgements. CoT prompting improved classification accuracy from 84% to 93% highlighting the benefit of explicit reasoning in enhancing sentiment analysis performance.

Abstract Temperature influences nucleosome dynamics, and thus chromatin, to regulate gene expression. Such mechanisms underlie the epigenetic silencing of ArabidopsisFLOWERING LOCUS C(FLC) by prolonged cold. Here, we show a temperature-dependent transition in local chromatin structure at the H3K27me3 nucleation region, from a modality active for transcription to a state that can be Polycomb silenced.In vivochromatin measurements and coarse-grained simulations at near-atomistic resolution show the active transcription state is characterized by a highly dynamic nucleosome arrangement that exposes theFLCTSS. Cold exposure then changes the chromatin structure by reducing nucleosome dynamics and re-positioning the +1 nucleosome, leading to transcriptional repression. This local chromatin transition partially depends on VRN1, a non-sequence-specific DNA-binding protein. Loss of VRN1 results in hyperaccumulation of H2A.Z, more dynamic nucleosomes and an inability to accumulate H2Aub and H3K27me3. Our work highlights how local nucleosome dynamics link to chromatin structure transitions to integrate temperature inputs into epigenetic switching mechanisms in plants.