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Abstract Background and AimsPlant elicitor peptides (Peps), which originate from their precursor proteins known as PROPEPs, play essential roles as signaling molecules that modulate both plant defense responses and developmental processes. MethodsIn this study, we investigated the role of PROPEP2 and its derived peptide Pep2, in regulating root hair development in Arabidopsis thaliana. Root hairs at different growth stages along the primary root and in distinct zones of the root tip were analyzed. We further examined the relationship between Pep2 and auxin signaling in regulating root hair growth. Key resultsOur findings indicate that loss of PROPEP2 function results in markedly decreased root hair number and elongation during the primary root development phase, whereas root hair formation during the embryonic root stage remains unaffected. Notably, exogenous Pep2 application rescued these defects and even induced root hair formation in non-hair-forming regions. We further demonstrated that Pep2 enhances root hair growth by stimulating localized auxin production in the root tip region. The mutants defective in auxin production (yuc1 yuc4 and wei8 tar1 tar2) failed to respond to Pep2. Furthermore, we identified the receptor-like kinases FERONIA (FER) and [Ca2+]cyt-associated protein kinase 1 (CAP1) as critical components of the Pep2 signaling pathway, with mutations in these genes impairing root hair growth and rendering plants unresponsive to Pep2. ConclusionsThese results uncover a previously uncharacterized interaction between Pep2 and auxin signaling components in the regulation of root hair development, offering additional insights into the molecular control of root morphology. 

Precision medicine that enables personalized treatment decision support has become an increasingly important research topic in chronic disease care. The main challenges in designing a treatment algorithm include modeling individual disease progression dynamics and designing adaptive treatment selection strategy. This study aims to develop an adaptive treatment selection framework tailored to an individual patient’s disease progression pattern and treatment response. We propose a Partially Observable Collaborative Model (POCM) to capture the individual variations in a heterogeneous population and optimize treatment outcomes in three stages. The POCM first infers the disease progression models by subgroup patterns using population data in stage one and then fine-tunes the models for individual patients with a small number of treatment trials in stage two. In stage three, we show how the treatment policies based on the Partially Observable Markov Decision Process (POMDP) can be tailored to individual patients by utilizing the disease models learned from the POCM. Using a simulated population of chronic depression patients, we show that the POCM can more accurately estimate the personal disease progression than the traditional method of solving a hidden Markov model. We also compare the POMDP treatment policies with other heuristic policies and demonstrate that the POCM-based policies give the highest net monetary benefits in majority of parameter settings. To conclude, the POCM method is a promising approach to model the chronic disease progression process and recommend a personalized treatment plan for individual patients in a heterogeneous population. 

The objective is to understand the role of emerging variants, vaccination, and NPI policies on COVID-19 infections and deaths. We aim to identify scenarios in which COVID-19 can be managed such that the death rate from COVID-19 becomes comparable with the combined annual mortality rate from influenza and pneumonia. As a case study for a large urban area, we simulate COVID-19 transmission in King County, Washington, (greater Seattle) using an agent- based simulation model. Calibrated to local epidemiological data, our study uses detailed synthetic population data and includes interactions between vaccination and specific NPIs while considering waning immunity and emergence of variants. Virus mutation scenarios include 12 combinations of infectivity, disease severity, and immune evasiveness. A highly effective pancoronavirus vaccine that works against all strains is considered an optimistic scenario. Our findings highlight the potential benefits of pancoronavirus vaccines that offer enhanced and longer-lasting immunity. We emphasize the crucial role of nonpharmaceutical interventions in reducing COVID-19 deaths regardless of virus mutation scenarios. Owing to highly immune evasive and contagious SARS-CoV-2 variants, most scenarios in this study fail to reduce the mortality of COVID-19 to the level of influenza and pneumonia. However, our findings indicate that periodic vaccinations and a threshold nonpharmaceutical intervention policy may succeed in achieving this goal. This indicates the need for caution and vigilance in managing a continuing COVID-19 epidemic. 

The 45Sc nuclear transition from the ground to the isomeric state at 12.389~keV, with a lifetime of 0.46~s, exhibits an extraordinarily narrow natural width of 1.4~feV and a quality factor ≃1019 -- surpassing those of the most precise atomic clocks -- making 45Sc a compelling platform for advanced metrology and nuclear clocks. Here we investigate how closely the spectral width and quality factor of the solid-state 45Sc resonance can approach these natural limits. Using the European X-ray Free-Electron Laser, we confirm the isomer's lifetime via time-delayed incoherent Kα,β fluorescence and observe previously unreported elastic fluorescence, yielding a partial internal conversion coefficient of 390(60). The absence of a clear nuclear forward scattering signal beyond a 2-ms delay implies environmental broadening of at least 500 Γ0 under experimental conditions, placing bounds on solid-state decoherence mechanisms. These findings set new experimental benchmarks for solid-state nuclear clock development. https://arxiv.org/abs/2508.17538; submitted to Nature Communications 

In this study, we investigate three different polymeric networks in terms of their tensile strength as a function of stretching rate, or temperature, or medium viscosity. 

The outbreak of seasonal flu costs billions of dollars in health care utilization and lost productivity. Despite the effectiveness of vaccination and antiviral medications to prevent serious flu-related complications and slow down the spread of an influenza epidemic, only 52% of the U.S. population aged 6 months and older received flu vaccines in the 2019-20 flu season. In addition, a costly out-of-pocket expense results in fewer patients seeking treatment, leading to potential hospitalizations and even flu-related deaths. In this study, we develop an integrated healthcare insurance mechanism that optimizes two incentive policies, vaccination reward and cost-sharing, to alleviate the medical cost and disease burden while preventing the outbreak of seasonal influenza. We model the dynamic interaction between a single insurer and multiple insureds as a Stackelberg vaccination game; we then embed the game into an agent-based simulation to model the spread of flu in a population under different policies. Finally, we apply machine learning and simulation optimization to optimize healthcare incentive policies in a large-scale flu transmission simulation. Simulation results indicate that the proposed methodology efficiently identifies a set of good incentive policies under different scenarios of flu vaccine efficacy and reproduction numbers.