Bonded Excimer in Stacked Cytosines: A Semiclassical Simulation Study

The formation of a covalent bond between two stacked cytosines, one of which is excited by an ultrafast laser pulse, was studied by semiclassical dynamics simulations. The results show that a bonded excimer is created, which sharply lowers the energy gap between the LUMO and HOMO and consequently facilitates the deactivation of the electronically excited molecule. This is different from the case of two stacked adenines, where the formation of a covalent bond alters the nonadiabatic deactivation mechanism in two opposite ways. It lowers the energy gap and consequently leads to the coupling between the HOMO and LUMO levels, thus enhancing the deactivation of the electronically excited molecule. On the other hand, it leads to restriction of the deformation vibration of the pyrimidine in the excited molecule, because of a steric effect, and this delays the deactivation process of the excited adenine molecule with return to the electronic ground state

ITPKC polymorphism (rs7251246 T > C), coronary artery aneurysms, and thrombosis in patients with Kawasaki disease in a Southern Han Chinese population

ObjectivesKawasaki disease (KD) is a commonly acquired pediatric systemic vasculitis disease resulting in coronary artery aneurysm (CAA). The relationship between the ITPKC polymorphism (rs7251246) and the severity and susceptibility to KD in the Han Chinese population in Southern China remains unclear.MethodsWe enrolled 262 children as controls and 221 children with KD (46 [20.8%] with intravenous immunoglobulin resistance and 82 [37.1%] with CAA). The relationship between the ITPKC rs7251246 polymorphism, KD susceptibility, and CAA formation was investigated.ResultsWhile the ITPKC rs7251246 T>C polymorphism was not significantly associated with KD susceptibility, it was significantly related to the CAA risk in children with KD [CC/CT vs. TT: adjusted odds ratio [OR] 2.089, 95% confidence interval [CI] 1.085–4.020]. Male children with the rs7251246 CT/TT genotype had a significantly lower risk of thrombosis [CT/TT vs. CC: adjusted OR 0.251, 95% CI 0.068–0.923]. Children with KD, especially those with CAA, had significantly downregulated ITPKC mRNA compared to healthy children. ITPKC mRNA levels were lower in children with CAA who developed thrombosis (P=0.039). In children with KD, the CC genotype showed lower mRNA levels of ITPKC (P=0.035).ConclusionThe ITPKC rs7251246 T>C polymorphism may be a risk factor for CAA and thrombosis in children with KD in the Han Chinese population, likely due to differences in mature mRNA levels caused by interference of RNA splicing. Dual antiplatelet therapy for thrombosis is recommended for male children with the rs7251246 CC genotype

Language-Specific Representation of Emotion-Concept Knowledge Causally Supports Emotion Inference

Understanding how language supports emotion inference remains a topic of debate in emotion science. The present study investigated whether language-derived emotion-concept knowledge would causally support emotion inference by manipulating the language-specific knowledge representations in large language models. Using the prompt technique, 14 attributes of emotion concepts were found to be represented by distinct artificial neuron populations. By manipulating these attribute-related neurons, the majority of the emotion inference tasks showed performance deterioration compared to random manipulations. The attribute-specific performance deterioration was related to the importance of different attributes in human mental space. Our findings provide causal evidence in support of a language-based mechanism for emotion inference and highlight the contributions of emotion-concept knowledge.Comment: 39 pages, 13 figures, 2 tables, fix formatting error

AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents

Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks. However, in real-world scenarios, cooperation among individuals is often required to enhance the efficiency and effectiveness of task accomplishment. Hence, inspired by human group dynamics, we propose a multi-agent framework \framework that can collaboratively and dynamically adjust its composition as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that \framework framework can effectively deploy multi-agent groups that outperform a single agent. Furthermore, we delve into the emergence of social behaviors among individual agents within a group during collaborative task accomplishment. In view of these behaviors, we discuss some possible strategies to leverage positive ones and mitigate negative ones for improving the collaborative potential of multi-agent groups. Our codes for \framework will soon be released at \url{https://github.com/OpenBMB/AgentVerse}.Comment: Work in progres

Ultraviolet Light Responsive N‐Nitroso Polymers for Antibacterial Nitric Oxide Delivery

AbstractThis study investigates the incorporation of active secondary amine moieties into the polymer backbone by co‐polymerizing 2,4,6‐tris(chloromethyl)‐mesitylene (TCM) with three diamines, namely 1,4‐diaminobutane (DAB), m‐phenylenediamine (MPD), and p‐phenylenediamine (PPD). This process results in the stabilisation of the amine moieties and the subsequently introduced nitroso groups. Charging bioactive nitric oxide (NO) into the polymers is accomplished by converting the amine moieties into N‐nitroso groups. The ability of the polymers to store and release NO depends on their structures, particularly the amounts of incorporated active secondary amines. With grafting photosensitive N‐nitroso groups into the polymers, the derived NO@polymers exhibit photoresponsivity. NO release is completely regulated by adjusting UV light irradiation. These resulting polymeric NO donors demonstrate remarkable bactericidal and bacteriostatic activity, effectively eradicating E. coli bacteria and inhibiting their growth. The findings from this study hold promising implications for combining NO delivery with phototherapy in various medical applications.This article is protected by copyright. All rights reserve

Effect of Initial Orientation on the Laser-Induced Cycloaddition Reaction of Benzene and Ethylene

The [2 + 2] photocycloaddition reaction of benzene and ethylene was investigated by semiclassical dynamics simulation and complete active space self-consistent field (CASSCF) ab initio calculations. Following laser excitation of the benzene molecule, two mechanisms were observed depending on the location of the second C of ethylene in relation to the hexagonal prism space defined by the first C and the plane of the benzene ring. Synchronous formation of two bonds was observed when the second C is outside the prism space; an asynchronous mechanism is observed otherwise. Charge transfer was observed only in the asynchronous mechanism; CASSCF calculations suggest that the asynchronous mechanism involves a barrierless path from the Frank-Condon point to a conical intersection, while the synchronous mechanism involves 0.8 eV barrier. These results are consistent with a higher quantum yield observed in the simulations for the asynchronous pathway

In Situ Study the Dynamics of Blade-Coated All-Polymer Bulk Heterojunction Formation and Impact on Photovoltaic Performance of Solar Cells

All-polymer solar cells (all-PSCs) have achieved impressive progress by employing acceptors polymerized from well performing small-molecule non-fullerene acceptors. Herein, the device performance and morphology evolution in blade-coated all-PSCs based on PBDBT:PF5–Y5 blends prepared from two different solvents, chlorobenzene (CB), and ortho-xylene (o-XY) are studied. The absorption spectra in CB solution indicate more ordered conformation for PF5–Y5. The drying process of PBDBT:PF5–Y5 blends is monitored by in situ multifunctional spectroscopy and the final film morphology is characterized with ex situ techniques. Finer-mixed donor/acceptor nanostructures are obtained in CB-cast film than that in o-XY-cast ones, corresponding to more efficient charge generation in the solar cells. More importantly, the conformation of polymers in solution determines the overall film morphology and the device performance. The relatively more ordered structure in CB-cast films is beneficial for charge transport and reduced non-radiative energy loss. Therefore, to achieve high-performance all-PSCs with small energy loss, it is crucial to gain favorable aggregation in the initial stage in solution

A Comprehensive Survey on Deep Graph Representation Learning

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future

Secondary Production of Gaseous Nitrated Phenols in Polluted Urban Environments

Nitrated phenols (NPs) are important atmospheric pollutants that affect air quality, radiation, and health. The recent development of the time-of-flight chemical ionization mass spectrometer (ToF-CIMS) allows quantitative online measurements of NPs for a better understanding of their sources and environmental impacts. Herein, we deployed nitrate ions as reagent ions in the ToF-CIMS and quantified six classes of gaseous NPs in Beijing. The concentrations of NPs are in the range of 1 to 520 ng m(-3). Nitrophenol (NPh) has the greatest mean concentration. Dinitrophenol (DNP) shows the greatest haze-to-clean concentration ratio, which may be associated with aqueous production. The high concentrations and distinct diurnal profiles of NPs indicate a strong secondary formation to overweigh losses, driven by high emissions of precursors, strong oxidative capacity, and high NOx levels. The budget analysis on the basis of our measurements and box-model calculations suggest a minor role of the photolysis of NPs (Peer reviewe