Hasil untuk "United States"

J. Kenny, N. L. Barber, S. Hutson et al.

Sunny H. Kim, B. Wise, Yuqing Zhang et al.

E. Carlson

Hyon B. Shin, R. Kominski

Luke M Gessel, S. Fields, C. Collins et al.

M. Alter, H. Margolis, K. Krawczyński et al.

K. Orloski, E. Hayes, G. Campbell et al.

A. Schuchat, K. Robinson, J. Wenger et al.

R. Pennak

Katharina Wolf-Maier, R. Cooper, H. Kramer et al.

H. Fowells

E. Han, Lisa M. Powell

M. Hurd, P. Martorell, Adeline Delavande et al.

Barbara R. Holloway

Alok Mahata, Nicolás. I. Neuman, Manuel Pech et al.

Stefano Bellucci, Stefania De Matteo

In quantum field theory, the algebraic existence of a field does not guarantee the existence of a corresponding localized asymptotic particle state. This distinction is well established in the presence of infrared effects, long-range correlations, and environmental interactions, and becomes particularly relevant in supersymmetric theories, where fermionic and bosonic degrees of freedom are constrained at the algebraic level but need not share identical asymptotic behavior. In this work we introduce a minimal and predynamical localization criterion that distinguishes algebraically allowed degrees of freedom from those capable of forming stable, phasecoherent asymptotic states. The criterion is formulated in terms of long-time stability under slow structural fluctuations of an effective background, without modifying the underlying field equations or introducing new physical interactions. We show that fermionic and scalar fields respond qualitatively differently to such structural effects. While fermionic modes may retain asymptotic stability, scalar modes generically exhibit decoherence and damping, preventing their interpretation as localized one-particle states. This provides a conservative and model-independent perspective on how supersymmetric algebraic structures may coexist with an asymmetric observable particle spectrum. The analysis is intentionally non-constructive and does not rely on specific supersymmetrybreaking mechanisms, cosmological assumptions, or new dynamical ingredients. Rather, it clarifies localization as an independent structural requirement for particle existence within standard quantum field theory.

Chuhan Cheng, Liyan Zhang

Background The neutrophil-to-lymphocyte ratio (NLR) is a marker of systemic inflammation associated with various diseases including respiratory conditions. However, the relationship between NLR and asthma in the pediatric population remains underexplored. Purpose This study aimed to explore the association between NLR and asthma in children and adolescents and assess its potential role as a predictive biomarker for pediatric asthma. Methods We retrospectively analyzed the medical records of 12,974 children and adolescents from the National Health and Nutrition Examination Survey in 2011–2020. NLR was defined as the ratio of NLR counts. Asthma was diagnosed using a structured questionnaire. Multivariate logistic regression models were used to evaluate the association between NLR and asthma. A restricted cubic spline was used to explore nonlinear relationships, and a threshold analysis was conducted to identify potential cutoff values for the NLR. Results A total of 12,974 children and adolescents were included (male: 6,686 [51.5%]; mean [interquartile range] age, 10 [5.0–14.0 years]). After the adjustment for confounders, participants with the highest versus lowest NLR exhibited a significantly elevated risk of asthma (odds ratio [OR], 1.39; 95% confidence interval [CI], 1.13–1.71). Additionally, a multivariate restricted cubic spline analysis revealed a nonlinear relationship between NLR and asthma (P=0.023). A threshold analysis revealed that an NLR<2.23 was significantly associated with an increased risk of asthma (OR, 1.23; 95% CI, 1.05–1.45), while an NLR≥2.23 showed no significant association. A subgroup analysis revealed no interactive role of NLR and asthma. Conclusion Our findings indicate a nonlinear saturation-effect relationship between NLR and asthma in children and adolescents.

Sandro Andric

Interpretability methods for large language models (LLMs) typically derive directions from textual supervision, which can lack external grounding. We propose using human brain activity not as a training signal but as a coordinate system for reading and steering LLM states. Using the SMN4Lang MEG dataset, we construct a word-level brain atlas of phase-locking value (PLV) patterns and extract latent axes via ICA. We validate axes with independent lexica and NER-based labels (POS/log-frequency used as sanity checks), then train lightweight adapters that map LLM hidden states to these brain axes without fine-tuning the LLM. Steering along the resulting brain-derived directions yields a robust lexical (frequency-linked) axis in a mid TinyLlama layer, surviving perplexity-matched controls, and a brain-vs-text probe comparison shows larger log-frequency shifts (relative to the text probe) with lower perplexity for the brain axis. A function/content axis (axis 13) shows consistent steering in TinyLlama, Qwen2-0.5B, and GPT-2, with PPL-matched text-level corroboration. Layer-4 effects in TinyLlama are large but inconsistent, so we treat them as secondary (Appendix). Axis structure is stable when the atlas is rebuilt without GPT embedding-change features or with word2vec embeddings (|r|=0.64-0.95 across matched axes), reducing circularity concerns. Exploratory fMRI anchoring suggests potential alignment for embedding change and log frequency, but effects are sensitive to hemodynamic modeling assumptions and are treated as population-level evidence only. These results support a new interface: neurophysiology-grounded axes provide interpretable and controllable handles for LLM behavior.

Stephanie Bond, Renaud Léguillette

Abstract Background Nebulized administration of dexamethasone on cytokine regulation in horses with moderate asthma has not been investigated. Objective To investigate the changes in expression of inflammatory cytokine mRNA after nebulized administration of dexamethasone treatment of horses with moderate asthma. Animals Horses with naturally occurring moderate asthma (n = 16) and healthy control horses (n = 4). All horses were kept in a dusty environment during the study. Methods Prospective, parallel, randomized, controlled, blinded clinical trial. Blood endogenous cortisol, tracheal mucus, and bronchoalveolar lavage (BAL) were sampled before and after 13 days treatment with either nebulized administration of dexamethasone (15 mg once daily) or 0.9% saline (3 mL). Treatment groups were randomly allocated via randomization function (Microsoft Excel). Amplification of target mRNA in BAL fluid (IL‐1β, IL‐4, IL‐5, IL‐6, IL‐8, IL‐10, IL‐12, IL‐17, IL‐23, IFN‐γ, Eotaxin‐2, and TNF‐α) was achieved by qPCR, and the relative expression software tool was used to analyze BAL inflammatory cytokine mRNA. Results Horses treated with nebulized administration of dexamethasone had increased relative expression of IL‐5 (1.70‐fold), IL‐6 (1.71‐fold), IL‐17 (3.25‐fold), IL‐12 (1.66‐fold), and TNF‐α (1.94‐fold), and decreased relative expression of IL‐23 (1.76‐fold; P = .04) in samples collected on Day 14, in comparison to samples collected on Day 0 (all P < .05). Horses treated with nebulized administration of saline had no significant difference in the relative expression of any gene (all P > .05). Conclusions and Clinical Importance Nebulized administration of dexamethasone was associated with increased expression of inflammatory cytokine mRNA. There was no improvement in inflammatory airway cytology associated with either dexamethasone or saline treatment.

Patricia Dao, Jashmitha Sappa, Saanvi Terala et al.

Traditional crime prediction techniques are slow and inefficient when generating predictions as crime increases rapidly \cite{r15}. To enhance traditional crime prediction methods, a Long Short-Term Memory and Gated Recurrent Unit model was constructed using datasets involving gender ratios, high school graduation rates, political status, unemployment rates, and median income by state over multiple years. While there may be other crime prediction tools, personalizing the model with hand picked factors allows a unique gap for the project. Producing an effective model would allow policymakers to strategically allocate specific resources and legislation in geographic areas that are impacted by crime, contributing to the criminal justice field of research \cite{r2A}. The model has an average total loss value of 70.792.30, and a average percent error of 9.74 percent, however both of these values are impacted by extreme outliers and with the correct optimization may be corrected.